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Objective: To assess the validity of the Fetal Medicine Foundation (FMF) chorionicity‐specific models for fetal growth in twin pregnancy. Methods: This was an external validation study of the FMF models using a nationwide Danish cohort of twin pregnancies. The cohort included all dichorionic (DC) and monochorionic diamniotic (MCDA) twin pregnancies with an estimated delivery date between 2008 and 2018, which satisfied the following inclusion criteria: two live fetuses at the first‐trimester ultrasound scan (11–14 weeks' gestation); biometric measurements available for the calculation of estimated fetal weight (EFW) using the Hadlock‐3 formula; and delivery of two liveborn infants. Validation involved assessing the distributional properties of the models and estimating the mean EFW Z‐score deviations. Additionally, the models were applied to pregnancies that delivered preterm and attended non‐scheduled visits (complicated pregnancies). Results: Overall, 8542 DC and 1675 MCDA twin pregnancies met the inclusion criteria. In DC twins, 17 084 fetuses were evaluated at a total of 95 346 ultrasound scans, of which 44.5% were performed at scheduled visits in pregnancies carried to 37 + 0 weeks or later. The median number of growth scans per DC twin fetus from 20 + 0 weeks onwards was four. The model showed good agreement with the validation cohort for scheduled visits in DC twins delivered at 37 + 0 weeks or later (mean ± SD EFW Z‐score, –0.14 ± 1.05). In MCDA twins, 3350 fetuses underwent 31 632 eligible ultrasound scans, of which 59.5% were performed at scheduled visits in pregnancies carried to 36 + 0 weeks or later. The median number of growth scans per MCDA twin fetus from 16 + 0 weeks onwards was 10. The model showed favorable agreement with the validation cohort for scheduled visits in MCDA twins delivered at 36 + 0 weeks or later (mean ± SD EFW Z‐score, –0.09 ± 1.01). Non‐scheduled visits and preterm delivery before 37 + 0 weeks for DC twins and before 36 + 0 weeks for MCDA twins corresponded with smaller weight estimates, which was consistent with the study's definition of complicated pregnancy. Conclusions: The FMF models provide a good fit for EFW measurements in our Danish national cohort of uncomplicated twin pregnancies assessed at routine scans. Therefore, the FMF models establish robust criteria for subsequent investigations and potential clinical applications. Future research should focus on exploring the consequences of clinical implementation, particularly regarding the identification of twins that are small‐for‐gestational age, as they are especially susceptible to adverse perinatal outcome. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

In the past, the use of remote sensing for winter wheat growth monitoring mainly relied on the relative growth assessment of a single vegetation index, such as normalized Vegetation index (NDVI). This study advanced the methodology by integrating field-measured data with Sentinel-2 data. In addition to NDVI, it innovatively incorporated two parameters, aboveground biomass (AGB) and leaf area index (LAI), for a more comprehensive relative growth assessment. Furthermore, the study employed the agricultural production systems simulator (APSIM) model to use LAI and AGB for absolute growth monitoring. The results showed that the simulated LAI and AGB closely match the field-measured values throughout the entire growth period of winter wheat under various conditions (R2 > 0.9). For relative growth monitoring, NDVI showed significant linear positive correlations (r > 0.74 and P< 0.05) with both LAI and AGB simulated by the APSIM model. Overall, this research shows that LAI and AGB obtained from the APSIM model provide a more detailed and accurate approach to monitoring of winter wheat growth. This improved monitoring capability can support effective land management arable and provide technical guidance to advance precision agriculture practices. [ABSTRACT FROM AUTHOR]

The balance between photosynthetic carbon accumulation and respiratory loss in plants varies depending on temperature. This leads to a situation where the increased need for carbon is not met when a certain temperature threshold is reached. Over the last two decades, temperature thresholds in carbon metabolism in autotrophic systems have been widely studied. However, it remains unclear how these thresholds manifest themselves in the natural growth of individual plant species. To address this issue, we used data from an extensive monitoring of the growth of peat moss Sphagnum riparium over 9 years in mires in Karelia (Russia). We measured the growth of shoots in sample plots and obtained 1609 estimates of growth rates during the monitoring period. Investigating the relationship between growth rate and temperature, we identified two distinct intervals in response to temperature. These two intervals are separated by the temperature threshold of 13.2 °C. The first interval, which covers 42% of the growing season, exhibits a strong exponential dependence of growth rate on temperature, with a coefficient Q10 = 4.01. This indicates that growth is most sensitive to changes in temperature within this range. In contrast, the second interval (58% of the growing season) shows a weaker dependence, with a Q10 coefficient of 1.21, suggesting that growth is less responsive to changes within this temperature range. The temperature threshold was found to be negatively related to May (r = −0.76; p = 0.018) and September (r = −0.78; p = 0.012) temperatures of the previous growing season, and together they best explain (r = −0.91; p = 0.0007) the temperature threshold. Overall, our findings suggest that the temperature threshold does exist in the growth of S. riparium and can be identified in different years. The negative correlation between temperature threshold and May and September temperatures from the previous year indicates that intervals in the growing season with temperatures near the temperature threshold have an impact on subsequent carbon balance and are particularly significant for the further growth and development of Sphagnum mosses. [ABSTRACT FROM AUTHOR]

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters' contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R2) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations. [ABSTRACT FROM AUTHOR]

The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology. [ABSTRACT FROM AUTHOR]

Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study's findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks. [ABSTRACT FROM AUTHOR]

Aims: The overarching aim of this study was to evaluate the Norwegian guidelines for growth monitoring using routinely collected data from healthy children up to five years of age. We analysed criteria for both status (size for age) and change (centile crossing) in growth. Methods: Longitudinal data were obtained from the electronic health record (EHR) at the well-baby clinic for 2130 children included in the Bergen growth study 1 (BGS1). Measurements of length, weight, weight-for-length, body mass index (BMI) and head circumference were converted to z -scores and compared with the World Health Organization (WHO) growth standards and the national growth reference. Results: Using the WHO growth standard, the proportion of children above +2SD was generally higher than the expected 2.3% for all traits at birth and for length at all ages. Crossing percentile channels was common during the first two years of life, particularly for length/height. By the age of five years, 37.9% of the children had been identified for follow-up regarding length/height, 33% for head circumference and 13.6% for high weight-for-length/BMI. Conclusions: The proportion of children beyond the normal limits of the charts is higher than expected, and a surprisingly large number of children were identified for rules concerning length or growth in head circumference. This suggests the need for a revision of the current guidelines for growth monitoring in Norway. [ABSTRACT FROM AUTHOR]

Cattle liveweight (LW) monitoring is essential for the effective management of animal productivity and welfare, particularly in decision-making on farms. Traditional static weigh (SW) systems require animals to be moved to fixed scales, posing challenges in extensive beef systems due to labour demands, costs, stress, and weight loss during muster. This study evaluated the relationship between LW measured by a SW system and a mobile in-field weighing system, Optiweigh (OW), in 65 weaners (Angus, Shorthorn, and Angus-Shorthorn cross) grazing on forage oats at a commercial beef property in north-west NSW, Australia. Over 22 weeks, cattle were weighed fortnightly using SW scales in the cattle yards while OW continuously monitored LW in the paddock. Lin's concordance correlation coefficient showed a strong association between OW and SW liveweight (CCC = 0.97; P < 0.001), with no influence from breed or sex. However, OW slightly over-predicted LW for lighter cattle (≤ 382 kg) and under-predicted for heavier cattle (> 382 kg), prompting the development of a correction. Further research is needed to understand the reasons for these discrepancies, potentially related to diet. Additionally, cattle attendance at OW was affected by size, season, and individual variation (P < 0.001). Overall, the OW system simply and accurately monitored the temporal changes in cattle LW through voluntary animal attendance in remote systems.

IntroductionWeekly head circumference (HC) measurements using a measuring tape is the current standard for longitudinal brain growth monitoring of preterm infants. The MONITOR3D (M3D) 3D scanner has been developed to measure both HC and cranial volume (CrV) of preterm infants within incubators. The M3D’s usability, accuracy and precision were validated in a simulated setting in a neonatal intensive care unit (NICU).Materials and methodsDuring a simulated routine care moment, NICU nurses conducted M3D scans of a preterm doll simulating an extreme low birthweight preterm (ELBW; BW < 1,000 g) infant, followed by manual HC measurements using a measuring tape. Usability was quantified by percentage of successful HC and CrV measurements from scans. HC and CrV were calculated by marking anatomical landmarks on the 3D image. Measurements were compared to the real, ground truth (GT) values of the doll’s head, defined by an accurate medical scanner. Measurement accuracy was assessed using mean or median absolute measurement error (ME), and precision by the spread of ME, represented by the 95% interval of the ME range. ME intervals were compared with preterm weekly growth increases to assess clinical usability.ResultsRegarding usability, 56 M3D scan sessions resulted in 25 successful (44.6%) HC and CrV measurements, with incomplete 3D data being the primary cause of unsuccessful scans. Accuracy of the measuring tape for HC was 0.2 cm (proportional 0.9% of GT), and precision was 1.6 cm (6.3%). M3D’s accuracy of HC was 0.4 cm (1.5%), and precision was 0.7 cm (2.9%). For CrV, M3D’s accuracy was 8.0 mL (3.8%) and precision 22.6 mL (10.8%).ConclusionThe M3D scanner is suitable for measuring HC and CrV in ELBW infants. However, current scan success rate is too low for practical usability. The M3D’s accuracy and precision are clinically sufficient, while the precision of the current measuring tape method is inadequate for preterm infants. This makes the M3D a promising alternative for HC, offering less disturbance to the infant. In the future, the M3D technique could facilitate the creation of CrV growth reference charts for ELBW infants, enhancing the accuracy of clinical growth monitoring for preterm infants.

In the past, the use of remote sensing for winter wheat growth monitoring mainly relied on the relative growth assessment of a single vegetation index, such as normalized Vegetation index (NDVI). This study advanced the methodology by integrating field-measured data with Sentinel-2 data. In addition to NDVI, it innovatively incorporated two parameters, aboveground biomass (AGB) and leaf area index (LAI), for a more comprehensive relative growth assessment. Furthermore, the study employed the agricultural production systems simulator (APSIM) model to use LAI and AGB for absolute growth monitoring. The results showed that the simulated LAI and AGB closely match the field-measured values throughout the entire growth period of winter wheat under various conditions (R2 > 0.9). For relative growth monitoring, NDVI showed significant linear positive correlations (r > 0.74 and P< 0.05) with both LAI and AGB simulated by the APSIM model. Overall, this research shows that LAI and AGB obtained from the APSIM model provide a more detailed and accurate approach to monitoring of winter wheat growth. This improved monitoring capability can support effective land management arable and provide technical guidance to advance precision agriculture practices.

Accurate and timely regional crop growth monitoring will be of great benefit to the establishment and adjustment of agricultural planning and policies. Remote sensing technology as an effective measure for collecting crop growth information across large areas has been receiving increasing attention nowadays. To enhance the accuracy and comprehensiveness of remote sensing monitoring winter wheat growth, a physico-chemical composite parameter (PCCP) was constructed using field measurements of aboveground fresh biomass (AFB), leaf area index (LAI), soil and plant analyzer development (SPAD) and leaf nitrogen content (LNC) of winter wheat at jointing stage. This construction was achieved through the utilization of the entropy weight method (EWM). Based on individual and community characteristics, winter wheat growth at jointing stage was divided into 3 levels in the study area, which were poor (Ⅰ), medium (Ⅱ) and well (Ⅲ). On this basis, the differences of each parameter under different growth levels were evaluated by Kruskal-Wallis test. To further validate the reliability of the composite parameter, linear regression models between grain yield of winter wheat and each parameter were constructed. Then, Sentinel-2A was used as the data source to analyze the correlation between different remote sensing indexes and LAI、SPAD、AFB、LNC、PCCP of winter wheat at jointing stage. Remote sensing indexes with high correlation were selected as inputs of back propagation (BP) artificial neural networks (ANN) to estimate PCCP. The 10-fold cross-validation method was used to obtain the optimal parameters of the BP-ANN model. The best model was selected to simulate values of the PCCP and to map the regional winter wheat growth conditions pixel by pixel at jointing stage. The weighting results showed that the weight of crop physical parameters was greater than biochemical parameters, among which LAI had the largest weight (0.387), followed by AFB and SPAD, and LNC had the least weight (0.105). The performance evaluation results of PCCP showed that the difference of PCCP under different growth levels was the most significant. The correlation between PCCP value and grain yield was closer than that between grain yield and LAI, SPAD, AFB, or LNC alone. The coefficient of determination was increased by 0.035 to 0.468, and the root-mean-square error is reduced by 46.2 kg/hm² to 520.0 kg/hm². During remote sensing monitoring, the correlation among the PCCP constructed by LAI, SPAD, AFB, and LNC and remote sensing indexes were all improved to different degrees compared with the single parameter. The accuracy of PCCP simulation by BP-ANN remote sensing monitoring model was high, which the coefficient of determination and the root mean square error were 0.830 and 0.080 in the test set, respectively. The overall growth of winter wheat at jointing stage in the study area was stable and concentrated, showing the spatial distribution characteristics of "the middle bad and the north-south well". Therefore, the construction of PCCP is an effective way to improve the reliability and accuracy of growth remote sensing monitoring, which can provide scientific basis for field management of winter wheat and serve the strategic needs of developing intelligent agriculture and building an agricultural power in China. [ABSTRACT FROM AUTHOR]

Background: Intergrowth-21st and Fenton 2013 growth charts are used for postnatal growth monitoring in preterms. There is no international consensus on which graph to refer to and why. This study is a local validation, of which graph would be plausible for the Indian population to detect small-for-gestational-age (SGA) and extra-uterine growth restriction (EUGR) babies, abetting in the settlement of this ambiguity. Objectives: The primary objective was to compare the Intergrowth-21st with Fenton 2013 growth charts for birth size classification and to detect the proportion of EUGR in preterms. The secondary objective was to assess the proportion of comorbidities in SGA babies by both these graphs. Methods: The design of the study was a prospective comparative observational study. All preterm newborns (24–<37 weeks of gestation) admitted to the neonatal intensive care unit of Noble Hospital and Research Center, Maharashtra, were the participants. Weight, length, and head circumference were plotted on Intergrowth 21 and Fenton growth charts at birth and at 4 weeks of age or at 36 weeks of postmenstrual age whichever is later. Corresponding Z-scores and percentiles were calculated electronically from their respective online software. Outcome: the reliability of Intergrowth-21st when compared to Fenton 2013 growth charts in assessing the birth size was better, and detection of the proportion of EUGR in preterm babies was better with Fenton charts. Results: A total of 429 preterm babies with a mean gestational age of 33.3 ± 2.4 weeks were included in the study. Fenton (67.1%) overestimated the proportion of EUGR when compared to Intergrowth-21st (18.6%) which was statistically significant (P < 0.001). On the contrary, although the proportion of SGA babies detected was higher with Intergrowth-21st (29.8%) when compared to Fenton (19.6%), there was moderate-to-high statistically significant agreement observed between the two growth charts in detecting SGA babies (Kappa = 0.716, P < 0.001). The proportion of comorbidities did not vary significantly among the SGA babies between the growth charts (P > 0.05). Conclusion: Fenton overestimates EUGR when compared to Intergrowth-21st, whereas both the growth charts are equally good in identifying SGA babies with no differences in the comorbidities detected. Intergrowth-21st standards look more pertinent for growth monitoring in the current study setting for Indian preterm babies. [ABSTRACT FROM AUTHOR]

In order to realize agricultural modernization, to promote the structural reform of the agricultural supply side, and to ensure highquality and efficient wheat production, we collected and analyzed the data of soil moisture, seedling growth, photosynthesis, insects and meteorology in wheat planting process, so as to provide data and planting options for decision making of agricultural technicians and producers. Taking wheat production of Ningjin County as an example, we used the Internet of Things, UAV remote sensing and big data platform processing to design a big data platform system for wheat growth monitoring. This system could be divided to 3 modules, including 1 big data platform and 5 field monitoring point. The system collected insect pest situation, soil moisture and meteorological data every 30 min, provided monitoring picture of wheat growth at the site all through 24 h. The data were transferred to the database of the big data platform through the internet. The platform summarized the data to form images and reports, so as to provide more suitable planting schemes for agricultural experts and agricultural producers and operators. [ABSTRACT FROM AUTHOR]

Childhood stunting is a significant public health concern in Bangladesh. This study analysed the data from the Healthy Village programme, which aims to address childhood stunting in southern coastal Bangladesh. The aim was to assess childhood stunting prevalence over time and explore the risk factors in the programme areas. A cross-sectional, secondary data analysis was conducted for point-prevalence estimates of stunting from 2018 to 2021, including 132,038 anthropometric measurements of under-five children. Multivariate logistic regression analyses were conducted for risk factor analysis (n = 20,174). Stunting prevalence decreased from 51% in 2018 to 25% in 2021. The risk of stunting increased in hardcore poor (aOR: 1.46, 95% CI: 1.27, 1.68) and poor (aOR: 1.50, 95% CI: 1.33, 1.70) versus rich households, children with mothers who were illiterate (aOR: 1.25, 95% CI: 1.09, 1.44) and could read and write (aOR: 1.35, 95% CI: 1.16, 1.56) versus mothers with higher education, and children aged 1–2 years compared with children under one year (aOR: 1.32, 95% CI: 1.20, 1.45). The stunting rate was halved over three years in programme areas, which is faster than the national trend. We recommend addressing socioeconomic inequalities when tackling stunting and providing targeted interventions to mothers during the early weaning period. [ABSTRACT FROM AUTHOR]

Canopy coverage-based crop growth monitoring is highly dependent on the performance of crop segmentation algorithms. Under field conditions, crop segmentation for unmanned aerial vehicle (UAV) imagery should be sophisticated considering geometric distortion of images by wind and illumination variations. Under Korean cultivation conditions, a plastic mulch used to restrict weeds and prevent cold weather damage increases the complexity of the image background. In particular, on-site monitoring of onion and garlic growth has been limited by their morphology because they have long narrow leaves. The ultimate goal of this study was to quantify the growth parameters of onion and garlic at multiple growth stages using red, green, and blue (RGB) imagery obtained with UAVs. Canopy coverage and plant height were used as predictor variables to develop mathematical models to estimate the fresh weights of onion and garlic. The use of a CIE L*a*b* color space and mean shift (MS) algorithm enhanced the extraction of the canopy coverage of onion and garlic from complex backgrounds, including plastic mulch, soil, and shadows under varying illumination conditions. Multiple linear regression models consisting of the a* band-based vegetation fraction (VF) and structure from motion (SfM)-based plant height (PH) fitted the fresh weight data of onion and garlic well with high coefficients of determination (R2) ranging from 0.82 to 0.92. The validation results showed an almost 1:1 slope with highly linear relationships (R2 > 0.82) between the onion and garlic fresh weights obtained with the UAV RGB imagery and actual fresh weights, confirming that the UAV-RGB imagery based on the use of the a*band and PH can be used to quantify the spatial and temporal variability of onion and garlic growth parameters during the growing season.

Abstract Introduction Early childhood development (ECD) centres are important community hubs in South Africa and act as sites for community detection of childhood nutrition problems. This study aimed to assess the ability of trained ECD practitioners with optimal support to correctly classify the nutritional status of infants and young children at ECD centres in the Nelson Mandela Bay. Methods A descriptive, cross‐sectional study was used to collect data from 1645 infants and children at 88 ECD centres. Anthropometric measurements were taken by trained fieldworkers and growth monitoring and promotion infrastructure was audited at ECD centres. Results Of the sample, 4.4% (n = 72) were underweight by weight for age Z‐score (WAZ

The balance between photosynthetic carbon accumulation and respiratory loss in plants varies depending on temperature. This leads to a situation where the increased need for carbon is not met when a certain temperature threshold is reached. Over the last two decades, temperature thresholds in carbon metabolism in autotrophic systems have been widely studied. However, it remains unclear how these thresholds manifest themselves in the natural growth of individual plant species. To address this issue, we used data from an extensive monitoring of the growth of peat moss Sphagnum riparium over 9 years in mires in Karelia (Russia). We measured the growth of shoots in sample plots and obtained 1609 estimates of growth rates during the monitoring period. Investigating the relationship between growth rate and temperature, we identified two distinct intervals in response to temperature. These two intervals are separated by the temperature threshold of 13.2 °C. The first interval, which covers 42% of the growing season, exhibits a strong exponential dependence of growth rate on temperature, with a coefficient Q10 = 4.01. This indicates that growth is most sensitive to changes in temperature within this range. In contrast, the second interval (58% of the growing season) shows a weaker dependence, with a Q10 coefficient of 1.21, suggesting that growth is less responsive to changes within this temperature range. The temperature threshold was found to be negatively related to May (r = −0.76; p = 0.018) and September (r = −0.78; p = 0.012) temperatures of the previous growing season, and together they best explain (r = −0.91; p = 0.0007) the temperature threshold. Overall, our findings suggest that the temperature threshold does exist in the growth of S. riparium and can be identified in different years. The negative correlation between temperature threshold and May and September temperatures from the previous year indicates that intervals in the growing season with temperatures near the temperature threshold have an impact on subsequent carbon balance and are particularly significant for the further growth and development of Sphagnum mosses.

Background/Aims: Mothers' knowledge of the mother and child health handbook positively affects their practice in child healthcare. However, there is evidence that their understanding of the handbook's contents and its purpose is limited. This study's aim was to assess the impact of a health education intervention designed to improve mothers' knowledge of the handbook's use and contents. Methods: In this quasi-experimental study, participants were recruited from four selected health facilities. Participants in the experimental arm were given health training on the mother and child health handbook while those in the control arm received usual care. Data on participants' understanding of the handbook's purpose and contents were collected via questionnaire at baseline and, for intervention participants, after 9 months. Interviews were conducted with four healthcare providers, to explore their perception of women's understanding of the handbook. Data were analysed using the Chi-squared test, Fisher's exact test and odds ratios. Results: Participants who received health education showed significant improvement in knowledge of the handbook's contents, with the exception of the HIV prophylaxis schedule. This included being more likely to know that weight-for-height monitoring (odds ratio=2.222; P<0.001), height-for-age monitoring (odds ratio=2.308; P<0.001) and a recommended vaccine schedule (odds ratio=3.000; P<0.001) were found in the handbook. The proportion of mothers who recognised the growth monitoring curve also improved greatly (16.7% pre-intervention, 100.0% post-intervention). Conclusions: Mothers' knowledge of the mother and child health handbook improved significantly after health education. Mothers should be given education to ensure that they can make full use of the handbook. [ABSTRACT FROM AUTHOR]

To determine the optimal method for monitoring the size distribution of cultivated yellowtail growth, we employed three different approaches: capture measurement, manual measurement using stereo cameras, and automatic measurement through stereo camera-based image recognition technology. Conventional capture measurements showed inadequate prediction interval owing to limited sample size, preventing accurate assessment of growth. Both manual and automatic camera measurements successfully conformed to a growth model exhibiting periodicity. The expected values derived from each model closely matched with the mean of landings conducted at the end of the study. However, the 95% prediction interval for manual measurement with cameras was comparable to that for the landing measurement, whereas the prediction interval for the automatic measurement with cameras was overestimated. Additionally, the growth rate of farmed yellowtail demonstrated seasonal fluctuations. Notably, the mean obtained from a single automatic measurement with cameras, prior to landing, significantly deviated from the overall mean of all measurements. This suggests a potential risk associated with relying on accidental outliers in a single measurement. Therefore, it is crucial to employ a growth model unaffected by outliers in continuous measurements to ensure reliable predictions. [ABSTRACT FROM AUTHOR]

Objective: To compare morbidity, as measured by length of stay in the neonatal intensive care unit (NICU), in twin and singleton gestations classified as small‐for‐gestational age (SGA) according to estimated fetal weight < 10th percentile on twin or singleton growth charts. Methods: NICU length of stay was compared in 1150 twins and 29 035 singletons that underwent ultrasound assessment between 35 + 0 and 36 + 6 weeks' gestation. Estimated fetal weight was obtained from measurements of head circumference, abdominal circumference and femur length using the Hadlock formula. Gestational age was derived from the first‐trimester crown–rump length measurement, using the larger of the two twins. Singletons and twins were compared in terms of NICU admission rate and length of stay according to classification as SGA by the Fetal Medicine Foundation singleton and twin reference distributions. Results: The overall proportions of twins and singletons admitted to NICU were similar (7.3% vs 7.4%), but twins tended to have longer lengths of stay in NICU (≥ 7 days: 2.4% vs 0.8%; relative risk (RR), 3.0 (95% CI, 1.6–4.4)). Using the singleton chart, a higher proportion of twins were classified as SGA compared with singletons (37.6% vs 7.0%). However, the proportion of SGA neonates entering NICU was similar (10.2% for twins and 10.1% for singletons) and the proportion of SGA neonates spending ≥ 7 days in NICU was substantially higher for twins compared with singletons (3.7% vs 1.4%; RR, 2.6 (95% CI, 1.4–4.7)). Conclusions: When singleton charts are used to define SGA in twins and in singletons, there is a greater degree of growth‐related neonatal morbidity amongst SGA twins compared with SGA singletons. Consequently, singleton charts do not inappropriately overdiagnose fetal growth restriction in twins and they should be used for monitoring fetal growth in both twins and singletons. © 2023 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Objective: To derive reference distributions of estimated fetal weight (EFW) in twins relative to singletons. Methods: Gestational‐age‐ and chorionicity‐specific reference distributions for singleton percentiles and EFW were fitted to data on 4391 twin pregnancies with two liveborn fetuses from four European centers, including 3323 dichorionic (DC) and 1068 monochorionic diamniotic (MCDA) twin pregnancies. Gestational age was derived using the larger of the two crown–rump length measurements obtained during the first trimester of pregnancy. EFW was obtained from ultrasound measurements of head circumference, abdominal circumference and femur length using the Hadlock formula. Singleton percentiles were obtained using the Fetal Medicine Foundation population weight charts for singleton pregnancies. Hierarchical models were fitted to singleton Z‐scores with autoregressive terms for serial correlations within the same fetus and between twins from the same pregnancy. Separate models were fitted for DC and MCDA twins. Results: Fetuses from twin pregnancies tended to be smaller than singletons at the earliest gestational ages (16 weeks for MCDA and 20 weeks for DC twins). This was followed by a period of catch‐up growth until around 24 weeks. After that, both DC and MCDA twins showed reduced growth. In DC twins, the EFW corresponding to the 50th percentile was at the 50th percentile of singleton pregnancies at 23 weeks, the 43rd percentile at 28 weeks, the 32nd percentile at 32 weeks and the 22nd percentile at 36 weeks. In MCDA twins, the EFW corresponding to the 50th percentile was at the 36th percentile of singleton pregnancies at 24 weeks, the 29th percentile at 28 weeks, the 19th percentile at 32 weeks and the 12th percentile at 36 weeks. Conclusions: In DC and, to a greater extent, MCDA twin pregnancies, fetal growth is reduced compared with that observed in singleton pregnancies. Furthermore, after 24 weeks, the divergence in growth trajectories between twin and singleton pregnancies becomes more pronounced as gestational age increases. © 2023 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Background: Growth monitoring plays an essential role in the development of young children. Anthropometric indices are of utmost importance for healthcare professionals to identify children at risk of inadequate growth and malnutrition. Aim: This study aimed to assess the capturing of the growth indices in the Road to Health Booklets (RTHB) in clinics. Setting: The study was carried out in Mangaung Metropolitan municipal clinics in the Free State province, South Africa. Methods: A descriptive quantitative study was conducted using a checklist to audit 264 RTHBs. Descriptive statistics were used to analyse data. Results: The findings showed that birth weight was recorded in most 99% (n = 262) of the RTHBs. The mid-upper arm circumference (MUAC) was not recorded in 58% (n = 153) of the cases during the last visit. Weight-for-Age (WfA) was routinely plotted in 91% (n = 241) of the RTHB. The length or Height-for-Age (LHfA) was plotted in 38% (n = 99) of the RTHB and Weight-for-Length or height (WfLH) was plotted in 31% (n = 81) of the RTHB. Conclusion: The results demonstrated that certain anthropometric measures including MUAC, length, or height were absent from the records of the RTHB. Consequently, RTHB may not be effectively used as a means of evaluating nutritional status, affecting early detection of malnutrition in children. Contribution: The research makes a valuable addition to the existing body of knowledge for monitoring growth and measurement of anthropometric indices in the RTHB, as well as the appropriate execution of these practices.

SignificanceCrop production is related to national food security, economic development and social stability, so timely information on the growth of major crops is of great significance for strengthening the crop production management and ensuring food security. The traditional crop growth monitoring mainly judges the growth of crops by manually observing the shape, color and other appearance characteristics of crops through the external industry, which has better reliability and authenticity, but it will consume a lot of manpower, is inefficient and difficult to carry out monitoring of a large area. With the development of space technology, satellite remote sensing technology provides an opportunity for large area crop growth monitoring. However, the acquisition of optical remote sensing data is often limited by the weather during the peak crop growth season when rain and heat coincide. Synthetic aperture radar (SAR) compensates well for the shortcomings of optical remote sensing, and has a wide demand and great potential for application in crop growth monitoring. However, the current research on crop growth monitoring using SAR data is still relatively small and lacks systematic sorting and summarization. In this paper, the research progress of SAR inversion of crop growth parameters were summarized through comprehensive analysis of existing literature, clarify the main technical methods and application of SAR monitoring of crop growth, and explore the existing problems and look forward to its future research direction.Progress]The current research status of SAR crop growth monitoring were reviewed, the application of SAR technology had gone through several development stages: from the early single-polarization, single-band stage, gradually evolving to the mid-term multi-polarization, multi-band stage, and then to the stage of joint application of tight polarization and optical remote sensing. Then, the research progress and milestone achievements of crop growth monitoring based on SAR data were summarized in three aspects, namely, crop growth SAR remote sensing monitoring indexes, crop growth SAR remote sensing monitoring data and crop growth SAR remote sensing monitoring methods. First, the key parameters of crop growth were summarized, and the crop growth monitoring indexes were divided into morphological indicators, physiological and biochemical indicators, yield indicators and stress indicators. Secondly, the core principle of SAR monitoring of crop growth parameters was introduced, which was based on the interaction between SAR signals and vegetation, and then the specific scattering model and inversion algorithm were used to estimate the crop growth parameters. Then, a detailed summary and analysis of the radar indicators mainly applied to crop growth monitoring were also presented. Finally, SAR remote sensing methods for crop growth monitoring, including mechanistic modeling, empirical modeling, semi-empirical modeling, direct monitoring, and assimilation monitoring of crop growth models, were described, and their applicability and applications in growth monitoring were analyzed.Conclusions and ProspectsFour challenges exist in SAR crop growth monitoring are proposed: 1) Compared with the methods of crop growth monitoring using optical remote sensing data, the methods of crop growth monitoring using SAR data are obviously relatively small. The reason may be that SAR remote sensing itself has some inherent shortcomings; 2) Insufficient mining of microwave scattering characteristics, at present, a large number of studies have applied the backward scattering intensity and polarization characteristics to crop growth monitoring, but few have applied the phase information to crop growth monitoring, especially the application study of polarization decomposition parameters to growth monitoring. The research on the application of polarization decomposition parameter to crop growth monitoring is still to be deepened; 3) Compared with the optical vegetation index, the radar vegetation index applied to crop growth monitoring is relatively less; 4 ) Crop growth monitoring based on SAR scattered intensity is mainly based on an empirical model, which is difficult to be extended to different regions and types of crops, and the existence of this limitation prevents the SAR scattering intensity-based technology from effectively realizing its potential in crop growth monitoring. Finally, future research should focus on mining microwave scattering features, utilizing SAR polarization decomposition parameters, developing and optimizing radar vegetation indices, and deepening scattering models for crop growth monitoring.

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R2) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.

Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.

The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology.

Abstract Background Regular growth monitoring can be used to evaluate young children’s nutritional and physical health. While adequate evaluation of the scope and quality of nutrition interventions is necessary to increase their effectiveness, there is little research on growth monitoring coverage measurement. The purpose of this study was to investigate socioeconomic disparities in under-5 Rwandan children who participate in growth monitoring and nutrition promotion. Methods We used data from the 2019–2020 Rwanda Demographic and Health Survey (RDHS), which included 8092under-5 children. Percentage was employed in univariate analysis. To examine the socioeconomic inequalities, concentration indices and Lorenz curves were used in growth monitoring and nutrition promotion among under-5 children. Results A weighted prevalence of 33.0% (95%CI: 30.6-35.6%) under-5 children growth monitoring and nutrition promotion was estimated. Growth monitoring and nutrition promotion among under-5 children had higher uptake in the most disadvantaged cohort, as the line of equality sags below the diagonal line in Lorenz curve. Overall, there was pro-poor growth monitoring and nutrition promotion among under-5 in Rwanda (Conc. Index = 0.0994; SE = 0.0111). Across the levels of child and mother’s characteristics, the results show higher coverage of under-5 growth monitoring and nutrition promotion in the most socioeconomic disadvantaged cohort. Conclusion The study found a pro-poor disparity in growth monitoring and nutrition promotion among under-5 children in Rwanda. By implication, the most disadvantaged children had a higher uptake of growth monitoring and nutrition promotion. The Rwanda government should develop policies and programmes to achieve the universal health coverage for the well-off and underserved population.

Abstract Objectives: Prenatal growth affects short- and long-term morbidity, mortality and growth, yet communication between prenatal and postnatal healthcare teams is often minimal. This paper aims to develop an integrated, interdisciplinary framework for foetal/infant growth assessment, contributing to the continuity of care across the first 1000 d of life. Design: A multidisciplinary think-tank met regularly over many months to share and debate their practice and research experience related to foetal/infant growth assessment. Participants’ personal practice and knowledge were verified against and supplemented by published research. Setting: Online and in-person brainstorming sessions of growth assessment practices that are feasible and valuable in resource-limited, low- and middle-income country (LMIC) settings. Participants: A group of obstetricians, paediatricians, dietitians/nutritionists and a statistician. Results: Numerous measurements, indices and indicators were identified for growth assessment in the first 1000 d. Relationships between foetal, neonatal and infant measurements were elucidated and integrated into an interdisciplinary framework. Practices relevant to LMIC were then highlighted: antenatal Doppler screening, comprehensive and accurate birth anthropometry (including proportionality of weight, length and head circumference), placenta weighing and incorporation of length-for-age, weight-for-length and mid-upper arm circumference in routine growth monitoring. The need for appropriate, standardised clinical records and corresponding policies to guide clinical practice and facilitate interdisciplinary communication over time became apparent. Conclusions: Clearer communication between prenatal, perinatal and postnatal health care providers, within the framework of a common understanding of growth assessment and a supportive policy environment, is a prerequisite to continuity of care and optimal health and development outcomes.

[Significance] Crop production is related to national food security, economic development and social stability, so timely information on the growth of major crops is of great significance for strengthening the crop production management and ensuring food security. The traditional crop growth monitoring mainly judges the growth of crops by manually observing the shape, color and other appearance characteristics of crops through the external industry, which has better reliability and authenticity, but it will consume a lot of manpow‐ er, is inefficient and difficult to carry out monitoring of a large area. With the development of space technology, satellite remote sens‐ ing technology provides an opportunity for large area crop growth monitoring. However, the acquisition of optical remote sensing data is often limited by the weather during the peak crop growth season when rain and heat coincide. Synthetic aperture radar (SAR) com‐ pensates well for the shortcomings of optical remote sensing, and has a wide demand and great potential for application in crop growth monitoring. However, the current research on crop growth monitoring using SAR data is still relatively small and lacks system‐ atic sorting and summarization. In this paper, the research progress of SAR inversion of crop growth parameters were summarized through comprehensive analysis of existing literature, clarify the main technical methods and application of SAR monitoring of crop growth, and explore the existing problems and look forward to its future research direction. [Progress] The current research status of SAR crop growth monitoring were reviewed, the application of SAR technology had gone through several development stages: from the early single-polarization, single-band stage, gradually evolving to the mid-term multipolarization, multi-band stage, and then to the stage of joint application of tight polarization and optical remote sensing. Then, the re‐ search progress and milestone achievements of crop growth monitoring based on SAR data were summarized in three aspects, namely, crop growth SAR remote sensing monitoring indexes, crop growth SAR remote sensing monitoring data and crop growth SAR remote sensing monitoring methods. First, the key parameters of crop growth were summarized, and the crop growth monitoring indexes were divided into morphological indicators, physiological and biochemical indicators, yield indicators and stress indicators. Secondly, the core principle of SAR monitoring of crop growth parameters was introduced, which was based on the interaction between SAR sig‐ nals and vegetation, and then the specific scattering model and inversion algorithm were used to estimate the crop growth parameters. Then, a detailed summary and analysis of the radar indicators mainly applied to crop growth monitoring were also presented. Finally, SAR remote sensing methods for crop growth monitoring, including mechanistic modeling, empirical modeling, semi-empirical modeling, direct monitoring, and assimilation monitoring of crop growth models, were described, and their applicability and applications in growth monitoring were analyzed. [Conclusions and Prospects] Four challenges exist in SAR crop growth monitoring are proposed: 1) Compared with the methods of crop growth monitoring using optical remote sensing data, the methods of crop growth monitoring using SAR data are obviously relatively small. The reason may be that SAR remote sensing itself has some inherent shortcomings; 2) Insufficient mining of microwave scattering characteristics, at present, a large number of studies have applied the backward scattering intensity and polarization characteristics to crop growth monitoring, but few have applied the phase information to crop growth monitoring, especially the application study of polarization decomposition parameters to growth monitoring. The research on the application of polarization decomposition parameter to crop growth monitoring is still to be deepened; 3) Compared with the optical vegetation index, the radar vegetation index applied to crop growth monitoring is relatively less; 4 ) Crop growth monitoring based on SAR scattered intensity is mainly based on an empirical model, which is difficult to be extended to different regions and types of crops, and the existence of this limitation pre‐ vents the SAR scattering intensity-based technology from effectively realizing its potential in crop growth monitoring. Finally, future research should focus on mining microwave scattering features, utilizing SAR polarization decomposition parameters, developing and optimizing radar vegetation indices, and deepening scattering models for crop growth monitoring. [ABSTRACT FROM AUTHOR]

Regular weight measurement is important in fattening geese to assess their health status. Failure to gain weight may indicate a potential illness. Standard weight gain analysis involves direct contact with the animal, which can cause stress to the animal, resulting in overall negative impacts on animal welfare. The focus of this study was to design a smart solution for monitoring weight changes in the breeding of farm animals. The proposed IoT system with a weighing device equipped with RFID technology for animal registration aimed to minimize the negative aspects associated with measuring in contact with humans. The proposed system aims to incorporate modern approaches in animal husbandry and use these obtained data for the potential development of husbandry approaches for different breeds of animals and enhanced managerial decision-making within husbandry. The system consisted of three main components: a data acquisition system, a weighing system with RFID, and an environmental monitoring system. In this study, the RFID system accuracy for detecting geese in the weighing system environment was assessed. The entire system evaluation yielded a sensitivity of 95.13%, specificity of 99.89%, accuracy of 99.78%, and precision of 95.01%. Regression analysis revealed a good correlation between observed feeding and RFID registrations with a determination coefficient of R2 = 0.9813. [ABSTRACT FROM AUTHOR]

Temperature plays an essential role in a plant's life. The current investigation reveals that photoreceptors, whose activity is affected by the geomagnetic field, are a critical element of its perception. This knowledge suggests that plants' responses to temperature could shift in different geomagnetic conditions. To test this hypothesis, we studied the change in the growth response of the peat moss Sphagnum riparium to temperature with a gradual increase in the geomagnetic Kp index. Growth data for this species were collected from Karelian mires by detailed monitoring over eight full growing seasons. The growth of 209,490 shoots was measured and 1439 growth rates were obtained for this period. The analysis showed a strong positive dependence of sphagnum growth on temperature (r = 0.58; n = 1439; P = 1.7 × 10−119), which is strongest in the Kp range from 0.87 to 1.61 (r = 0.65; n = 464; P = 4.5 × 10−58). This Kp interval is clearer after removing the seasonal contributions from the growth rate and temperature and is preserved when diurnal temperature is used. Our results are consistent with the hypothesis and show the unknown contribution of the geomagnetic field to the temperature responses of plants. [ABSTRACT FROM AUTHOR]

The high-flux acquisition of crop growth information can be realized using field monitoring robotic platforms. However, most of the existing agricultural monitoring robots have been converted from expensive commercial platforms, and they thus have a hard time adapting to the farmland working environment, let alone satisfying the basic requirements of sensor testing. To address these problems, a wheeled crop-growth-monitoring robot that features the accurate, nondestructive, and efficient acquisition of crop growth information was developed based on the cultivation characteristics of wheat, the obstacle characteristics of the wheat field, and the monitoring mechanism of spectral sensors. By analyzing the phenotypic structural change characteristics and the requirements for the row spacing of different wheat varieties throughout the growth period, a four-wheel mobile chassis was designed with an adjustable wheel track and a high-clearance body structure that can effectively eliminate the risk of the robot destroying the wheat during operation. Moreover, considering the requirements for wheeled robots to overcome obstacles in field operations, a three-dimensional (3D) model of the robot was created in Pro/E. Models of obstacles in the field (e.g., pits and bumps) were created in Adams to simulate the operational stability of the robot. The simulation results showed that the mass center displacement of the robot was smaller than 0.2 cm on flat pavement and the maximum mass center displacement was 1.78 cm during obstacle crossing (10 cm deep pits and 10 cm high bumps). The field test showed that the robot equipped with active-light-source crop growth sensors achieved stable, real-time, nondestructive, and accurate acquisition of the canopy vegetation parameters—NDVI (normalized difference vegetation index) and RVI (ratio vegetation index)—and the wheat growth parameters—LAI (leaf area index), LDW (leaf dry weight), LNA (leaf nitrogen accumulation), and LNC (leaf nitrogen content). [ABSTRACT FROM AUTHOR]

Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study's results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix's evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of staminate pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency. [ABSTRACT FROM AUTHOR]

Abstract Background The United Nations’ Sustainable Development Goal (SDG)-2 aims to eliminate child hunger or end all forms of child malnutrition by 2030. To achieve this goal the cost-effective method is the implementation of growth monitoring and promotion service with the contribution of Women Development Army (WDA) as community volunteers. However, According to the data, the program’s implementation varies throughout the country and lack of evidence on the practical contribution of the WDA to enhancing child nutritional care outcomes. Therefore this study aimed to determine practical contribution of WDA and associated factors on growth monitoring and promotion service in two rural districts of central Gondar zone, Northwest Ethiopia. Methods A community based mixed study was conducted from March 6 to April 7, 2022 among 615 Women Development Army. Multistage sampling technique was used to select study participants. A structured questionnaire was used to collect quantitative data and in-depth interview were used to generate qualitative information. Qualitative data were coded and grouped and discussed using identified themes. Binary logistic regression was fitted, odds ratio with 95% confidence interval was estimated to identify factors of practical contribution of WDA and qualitative data was analyzed using thematic analysis. Results In this study practical contribution of WDA on growth monitoring was 31.4% (95% CI: 28.0-35.3%). Having GMP training (AOR = 4.2, 95%CI: 1.63, 10.58), regular community conversation (AOR = 6.0, 95%CI: 3.12, 11.54), good knowledge about GMP (AOR = 2.1, 95%CI: 1.17, 3.83) and not having regular schedule of GMP service in the area (AOR = 0.04, 95%CI: 0.02, 0.09), were statistically significantly associated with practical contribution of growth monitoring. During in-depth interview, lack of training, low motivation or commitment among WDA and low communication between WDA and health extension workers were mentioned among the problems faced during growth monitoring service. Conclusion In this study, practical contribution of growth monitoring among WDA was low. GMP training regular community conversation, knowledge about GMP and regular schedule of GMP service in the local area were significantly associated for practical contribution of growth monitoring service. Lack of training, low motivation or commitment among WDA and low communication between WDA and health extension workers were reasons for did not contribute effectively for GMP service. Therefore, giving training for WDA and improving community conversation at kebeles level are important to improve GM service. .

Introduction: Low Birth Weight (LBW) is a commonly encountered problem in developing countries. Growth is the single most important predictor of morbidity and mortality in a LBW infant. LBW babies show a pattern of growth, different from normal weighing babies. Aim: To assess the growth pattern of LBW infants in first six months of life. Materials and Methods: This prospective cohort study was done in Department of Paediatrics, of Kilpauk Medical College and Hospital, a tertiary care hospital in Southern India, from September 2019 to August 2020. Total 310 LBW infants, of which 200 were term and rest preterm were included and followed-up at the ages of three and six months. Clinical details including gender, gestational age, order of birth, length of stay in nursery, maternal and neonatal morbidities, type of feeding and intercurrent illness were noted. The weight, length and head circumference of the infants were measured by standard methods and interpreted using appropriate charts. The anthropometric measures were converted into z-score and compared. The outcome variables were statistically analysed using Chi-square test. Results: Out of 310 babies, 172 (55.5%) were girls and 200 (64.5%) were term babies. Six month follow-up rate was 92%. A total of 228 (79%) infants were on exclusive breastfeeding while the remaining 21% were partially breastfed. The prevalence (95% confidence interval) of undernutrition, short stature and microcephaly at six months were 54.9% (48.93-60.76), 62.2% (56.34-67.88) and 30.7% (25.47-36.47), respectively. Fall in standard deviation score of length, weight and head circumference was observed which was more pronounced in preterm than in term infants (p

【Objective】 Winter wheat is the second-largest stable crop in China and comprehending its growth and the factors affecting it on a large scale is crucial for food security. This paper aims to investigate the feasibility of using satellite imagery to accomplish this objective. 【Method】 The study is based on Sentinel-2 images. The spatial distribution of winter wheat planted from 2018 to 2020 in the studied region was extracted using the random forest method, which were then used to analyze the changes in wheat growth in rejuvenation, jointing, pregnant ear pumping, and flowering stages in each year. For comparison, we divided the growth into health growth, normal growth and poor growth. Wheat growth was linked to 11 abiotic and geographic factors, including temperature, precipitation, slope of the lands, slope aspect, elevation, soil type, soil moisture, sunshine time, population density, rural labor resources and GDP. 【Result】 Compared with 2018—2019, wheat in 2020 grew better during the greening and jointing stages in more than 90% of the studied area, but worse in the pregnant ear pumping stage in more than 20% of the studied area. Wheat growth was normal during the flowering stage in 80% of the studied area. The factors which affect winter wheat growth were ranked in the following order based on their significance: rural labor resources> soil moisture> precipitation> temperature> sunshine time. It was also found that the interaction between different factors in their impact on wheat growth is manifested as a bifold or nonlinear enhancement. 【Conclusion】 The change in winter wheat growth in the studied region is due to the complex interplay of multiple factors.

Childhood stunting is a significant public health concern in Bangladesh. This study analysed the data from the Healthy Village programme, which aims to address childhood stunting in southern coastal Bangladesh. The aim was to assess childhood stunting prevalence over time and explore the risk factors in the programme areas. A cross-sectional, secondary data analysis was conducted for point-prevalence estimates of stunting from 2018 to 2021, including 132,038 anthropometric measurements of under-five children. Multivariate logistic regression analyses were conducted for risk factor analysis (n = 20,174). Stunting prevalence decreased from 51% in 2018 to 25% in 2021. The risk of stunting increased in hardcore poor (aOR: 1.46, 95% CI: 1.27, 1.68) and poor (aOR: 1.50, 95% CI: 1.33, 1.70) versus rich households, children with mothers who were illiterate (aOR: 1.25, 95% CI: 1.09, 1.44) and could read and write (aOR: 1.35, 95% CI: 1.16, 1.56) versus mothers with higher education, and children aged 1–2 years compared with children under one year (aOR: 1.32, 95% CI: 1.20, 1.45). The stunting rate was halved over three years in programme areas, which is faster than the national trend. We recommend addressing socioeconomic inequalities when tackling stunting and providing targeted interventions to mothers during the early weaning period.

In the growth process of crops, the growth information of crops is an important basis for judging the growth trend and yield of crops, and it is also important research for monitoring the changes in crop growth. In this study, we constructed a monitoring system based on improved Faster R-CNN, selected the situation of different rice varieties in three cities of Jiangxi Province, and used the data to analyze the relationship between the growth and development of early rice and late rice and climate in Jiangxi Province. Based on the data results, for the case of the correlation of rice growth to climate ability in Jiangxi Province, it was concluded that the total growing season temperatures of both early and late rice passed the significance test. By using the monitoring system to identify the growth trend of rice shoots, the image recognition of rice shoots was adopted, and after pre-processing the images, the length, width, area and number of rice shoots were finally analyzed with respect to the law of time, and the growth process of shoots was monitored. The period of rising rice growth area is in July-August, which is one of the months of rice shoot area growth. The monitoring system employed in this paper is capable of effectively monitoring the impact of climate on the growth cycle of crops.

Remote sensing technology has become an important tool for monitoring the growth status of potato crops by virtue of its ability to obtain information on the growth of potato crops in a rapid, real-time, accurate, and nondestructive manner. Potato crops have aspects unique to remote sensing monitoring studies because their stems and leaves and tubers grow above and below ground, respectively, and their spectral reflection mechanisms are all different from those of grass crops (such as rice, maize, and wheat). This study summarizes the characteristics of remote sensing technology, the types of remote sensing platforms and remote sensing monitoring methods, and the progress of domestic and foreign research in applying remote sensing technology in the fields of potato crop acreage extraction, disease management, growth monitoring and yield estimation, analyzes the shortcomings of remote sensing technology in monitoring growth information of potato crops, makes further relevant suggestions for this problem, and points out exploring information on spatial distribution of potato crops and changes of potato crops, efficient monitoring of potato crop growth, potato crop yield prediction model, and assimilation mechanism of remote sensing model and agronomic model of potato crops are the future development directions. [ABSTRACT FROM AUTHOR]

Background: Regular growth monitoring can be used to evaluate young children's nutritional and physical health. While adequate evaluation of the scope and quality of nutrition interventions is necessary to increase their effectiveness, there is little research on growth monitoring coverage measurement. The purpose of this study was to investigate socioeconomic disparities in under-5 Rwandan children who participate in growth monitoring and nutrition promotion. Methods: We used data from the 2019–2020 Rwanda Demographic and Health Survey (RDHS), which included 8092under-5 children. Percentage was employed in univariate analysis. To examine the socioeconomic inequalities, concentration indices and Lorenz curves were used in growth monitoring and nutrition promotion among under-5 children. Results: A weighted prevalence of 33.0% (95%CI: 30.6-35.6%) under-5 children growth monitoring and nutrition promotion was estimated. Growth monitoring and nutrition promotion among under-5 children had higher uptake in the most disadvantaged cohort, as the line of equality sags below the diagonal line in Lorenz curve. Overall, there was pro-poor growth monitoring and nutrition promotion among under-5 in Rwanda (Conc. Index = 0.0994; SE = 0.0111). Across the levels of child and mother's characteristics, the results show higher coverage of under-5 growth monitoring and nutrition promotion in the most socioeconomic disadvantaged cohort. Conclusion: The study found a pro-poor disparity in growth monitoring and nutrition promotion among under-5 children in Rwanda. By implication, the most disadvantaged children had a higher uptake of growth monitoring and nutrition promotion. The Rwanda government should develop policies and programmes to achieve the universal health coverage for the well-off and underserved population. [ABSTRACT FROM AUTHOR]

Introduction: Anthropometric parameters play vital role in monitoring growth in pediatrics. Many etiological factors lead to short stature. So, before assessing the etiological factors short stature needs to be addressed. This study aimed to screen short stature for age in school-going children aged 5 to 16 years in Uttarakhand. Material and Methods: In this cross-sectional observational study, the height (through stadiometer) and weight (through weight machine) of 4189 students of government and private school in Rishikesh (Uttarakhand) aged 5-16 years were measured after the verbal assent of the students and individual's height is in the 3rd percentile for the mean height of a given age, sex, and population group and was considered short stature. The data collection was performed from October 2019 to July 2021. The data were categorized according to different age groups to 5-8 years, 9-12 years, and 13-16 years. The data were recorded in Microsoft (MS) Excel spreadsheet program. Statistical Package for the Social Sciences (SPSS) v23 (IBM Corp.) was used for data analysis. Descriptive statistics were elaborated in the form of means or standard deviations and medians or Interquartile range IQRs for continuous variables and frequencies and percentages for categorical variables. The Chi-square test was used for group comparisons for categorical data. Results: 7.1% of children were short stature (height 143.16 ± 15.09 cm) in the Himalayan belt, and males were more prone to short stature at age of 9-12 years. Conclusion: In the growing phase of children, the etiology of short stature has to be rectified, so the children can achieve such proper growth. Parents and physicians have to assess and monitor the growth of children timely. This study can be a stepping stone for further epidemiological studies. [ABSTRACT FROM AUTHOR]

The Remote Malnutrition Application (R-MAPP) was developed during the COVID-19 pandemic to provide support for health care professionals (HCPs) working in the community to complete remote nutritional assessments and provide practical guidance for nutritional care. R-MAPP was adapted into Pediatric Remote Malnutrition Application (Pedi-R-MAPP) using a modified Delphi consensus, with the goal of providing a structured approach to completing a nutrition focused assessment as part of a technology enabled care service (TECS) consultation. The aim of this study was to develop and validate a digital version of Pedi-R-MAPP using the IDEAS framework (Integrate, Design, Assess and Share). A ten-step process was completed using the IDEAS framework. This involved the four concept processes; Stage-1, Integrate (Step 1–3) identify the problem, specify the goal, and use an evidence-based approach. Stage-2, (Step 4–7) design iteratively and rapidly with user feedback. Stage 3, (Step 8–9) Assess rigorously, and Stage 4 (Step 9–10) publish and launch of the tool. Stage 1:Evidence-based development, Pedi-R-MAPP was developed using Delphi consensus methodology. Stage 2:Iteration & design, HCPs (n = 22) from UK, Europe, South Africa, and North America were involved four workshops to further develop a paper prototype of the tool and complete small-scale testing of a beta version of the tool which resulted in eight iterations. Stage 3:Assess rigorously, Small scale retrospective testing of the tool on children with congenital heart disease (n = 80) was completed by a single researcher, with iterative changes made to improve agreement with summary advice. Large scale testing amongst (n = 745) children in different settings was completed by specialist paediatric dietitians (n = 15) advice who recorded agreement with the summary advice compared with their own clinical assessment. Paediatric dietitians were in overall agreement with the summary advice in the tool 86% (n = 640), compared to their own clinical practice. The main reasons for disagreement were i) frequency of planned review 57.1% (n = 60/105), ii) need for ongoing dietetic review due to chronic condition 20.0% (n = 21/105), iii) disagreement with recommendation for discharge 16.2% (n = 17/105) and iv) concerns with faltering growth and/or need for condition specific growth charts 6.7% (7/105). Iterative changes were made to the algorithm, leading to an improvement in agreement of the summary advice on re-evaluation to 98% (p=<0.0001). A digital version of the Pedi-R-MAPP nutrition awareness tool was developed using the IDEAS framework. The summary advice provided by the tool achieved a high level of agreement when compared to paediatric dietetic assessment, by providing a structured approach to completing a remote nutrition focused assessment, along with identifying the frequency of follow-up or an in-person assessment. [ABSTRACT FROM AUTHOR]

Aeroponics is a popular soilless crop cultivation technology that integrates plant nutrition, physiology, and ecological control. It offers automated monitoring, protected cultivation, improved growth mechanisms, better yield and requires less maintenance. Here, to predict the crop yield, two systems are available: manual and automated. Manual systems often fail to produce better prediction results, leading to substantial crop losses whereas, the automated systems use machine intelligence for growth monitoring. This article proposes a lettuce crop growth monitoring-boost (LCGM-Boost) regression model for lettuce yield forecasting in aeroponic vertical farming system. This model is highly robust to outliers, produces better prediction results of 95.86% and lower error rates of 0.36 (MAE), 0.40 (MSE), and 0.63 (RMSE) than other machine learning models namely, support vector, random forest and XGBoost regressors. Hence, it is preferable for growth monitoring and yield prediction of the lettuce crop in the real-time aeroponics system. [ABSTRACT FROM AUTHOR]

Objectives: Prenatal growth affects short- and long-term morbidity, mortality and growth, yet communication between prenatal and postnatal healthcare teams is often minimal. This paper aims to develop an integrated, interdisciplinary framework for foetal/infant growth assessment, contributing to the continuity of care across the first 1000 d of life. Design: A multidisciplinary think-tank met regularly over many months to share and debate their practice and research experience related to foetal/infant growth assessment. Participants' personal practice and knowledge were verified against and supplemented by published research. Setting: Online and in-person brainstorming sessions of growth assessment practices that are feasible and valuable in resource-limited, low- and middle-income country (LMIC) settings. Participants: A group of obstetricians, paediatricians, dietitians/nutritionists and a statistician. Results: Numerous measurements, indices and indicators were identified for growth assessment in the first 1000 d. Relationships between foetal, neonatal and infant measurements were elucidated and integrated into an interdisciplinary framework. Practices relevant to LMIC were then highlighted: antenatal Doppler screening, comprehensive and accurate birth anthropometry (including proportionality of weight, length and head circumference), placenta weighing and incorporation of length-for-age, weight-for-length and mid-upper arm circumference in routine growth monitoring. The need for appropriate, standardised clinical records and corresponding policies to guide clinical practice and facilitate interdisciplinary communication over time became apparent. Conclusions: Clearer communication between prenatal, perinatal and postnatal health care providers, within the framework of a common understanding of growth assessment and a supportive policy environment, is a prerequisite to continuity of care and optimal health and development outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Anthropometric parameters play vital role in monitoring growth in pediatrics. Many etiological factors lead to short stature. So, before assessing the etiological factors short stature needs to be addressed. This study aimed to screen short stature for age in school-going children aged 5 to 16 years in Uttarakhand. Material and Methods: In this cross-sectional observational study, the height (through stadiometer) and weight (through weight machine) of 4189 students of government and private school in Rishikesh (Uttarakhand) aged 5–16 years were measured after the verbal assent of the students and individual's height is in the 3rd percentile for the mean height of a given age, sex, and population group and was considered short stature. The data collection was performed from October 2019 to July 2021. The data were categorized according to different age groups to 5–8 years, 9–12 years, and 13–16 years. The data were recorded in Microsoft (MS) Excel spreadsheet program. Statistical Package for the Social Sciences (SPSS) v23 (IBM Corp.) was used for data analysis. Descriptive statistics were elaborated in the form of means or standard deviations and medians or Interquartile range IQRs for continuous variables and frequencies and percentages for categorical variables. The Chi-square test was used for group comparisons for categorical data. Results: 7.1% of children were short stature (height 143.16 ± 15.09 cm) in the Himalayan belt, and males were more prone to short stature at age of 9–12 years. Conclusion: In the growing phase of children, the etiology of short stature has to be rectified, so the children can achieve such proper growth. Parents and physicians have to assess and monitor the growth of children timely. This study can be a stepping stone for further epidemiological studies.

Dynamic monitoring and quantitative estimation of forage crop growth are of great importance to the large-scale production of forage crop. UAV remote sensing has the advantages of high resolution, strong flexibility and low cost. In recent years, it has developed rapidly in the field of forage crop growth monitoring. In order to clarify the development status of forage crop growth monitoring and find the development direction, first, methods of UAV crop remote sensing monitoring were briefly described from two aspects of data acquisition and processing. Second, three key technologies of forage crop including canopy information extraction, spectral feature optimization and forage biomass estimation were described. Then the development trend of related research in recent years was analyzed, and it was pointed out that the number of papers published on UAV remote sensing forage crop monitoring showed an overall trend of rapidly increasing. With the rapid development of computer information technology and remote sensing technology, the application potential of UAV in the field of forage crop monitoring has been fully explored. Then, the research progress of UAV remote sensing in forage crop growth monitoring was described in five parts according to sensor types, i.e., visible, multispectral, hyperspectral, thermal infrared and LiDAR, and the research of each type of sensor were summarized and reviewed, pointing out that the current researches of hyperspectral, thermal infrared and LiDAR sensors in forage crop monitoring were less than that of visible and multispectral sensors. Finally, the future development directions were clarified according to the key technical problems that have not been solved in the research and application of UAV remote sensing forage crop growth monitoring: (1) Build a multi-temporal growth monitoring model based on the characteristics of different growth stages and different growth years of forage crops, carry out UAV remote sensing monitoring of forage crops around representative research areas to further improve the scope of application of the model. (2) Establish a multi-source database of UAV remote sensing, and carry out integrated collaborative monitoring combined with satellite remote sensing data, historical yield, soil conductivity and other data. (3) Develop an intelligent and user-friendly UAV remote sensing data analysis system, and shorten the data processing time through 5G communication network and edge computing devices. This paper could provide relevant technical references and directional guidelines for researchers in the field of forage crops and further promote the application and development of precision agriculture technology.