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The pottery wheel machine is an essential piece of equipment in ceramics manufacturing. This paper presents the design of an intelligent pottery wheel machine aimed at addressing the issues of low clay material utilization and the challenges of pottery throwing operations. Traditional techniques require exceptional craftsmanship and proficient mechanical operation, and existing pottery wheel machines still demand attention to equipment usage and clay material knowledge. The intelligent pottery wheel machine integrates central positioning, automatic clay feeding, and clay storage. The pull mechanism stores and precisely feeds clay, regulating usage and minimizing excess. The automated settings of the clay storage and power push system replace the need for manual assessment of clay density and quality, ensuring balanced and high-quality clay extrusion. The continuous feeding setting allows for automatic addition of clay, saving replenishment time and reducing the labor intensity of adding clay. The lifting mechanism ensures that the clay body remains centered, avoiding repeated manual adjustments and allowing users to focus on creating pottery rather than spending excessive time mastering the centering technique. The machine's lightweight, modular design reduces clay waste, making the process more sustainable. By saving clay materials and improving throwing efficiency, it enhances the success rate of throwing. [ABSTRACT FROM AUTHOR]

Background and aim: Health anxiety is a mental disorder that characterized by an excessive fear about health and physical symptoms. High anxiety in pregnancy is associated with adverse outcomes. The aim of this study was to investigate the effect of prenatal education on health anxiety of primigravid women. Methods: The present study was quasi-experimental study. 122 primiparous pregnant women referred to comprehensive health services Shahrekord (A city in the southwest of Iran) clinics in 2019, after receiving consent to participate in the study, randomly divided into two intervention and control groups. The intervention group participated in 8 sessions (1.5-h), once every 2 weeks, from 20 to 37th weeks of gestation. The health anxiety questionnaire was completed on 20th (before the beginning of the courses), 28th and 37th weeks by two groups. Consequences of pregnancy included weight, Apgar score, delivery type, labor time and first breastfeeding time. SPSS version 16 software was used for data analysis. Results: No significant difference was found type of delivery, gestational age, height, weight, head length, Apgar score, duration of hospitalization and first breastfeeding time. The duration of the active and latent phase of labor was significantly lower and the weight of newborn was significantly higher in the intervention group than the control group (P < 0.05). At 37th week, the scores of illness concern, negative consequence and total health anxiety in the intervention group decreased by 3.42, 0.93 and 4.36 respectively and in control group increased by 2.82, 0.03 and 2.86. Conclusion: Pregnancy educational courses has positive effects on health anxiety, decrease duration of labor time and increased newborn weight. In order to improve the outcome of pregnancy, educational classes during pregnancy should be considered. [ABSTRACT FROM AUTHOR]

N-glycan, a critical quality attribute (CQA) of monoclonal antibodies, profoundly impacts potency, immunogenicity, safety, and clinical efficiency. Therefore, N-glycan should be monitored and controlled during development. The conventional 2-AB method is considered the gold standard for N-glycan analysis, which is labor intensive and time consuming. Moreover, its sample requirement is relatively high and cannot be met in early-stage development, including cell line development. In this study, we compared the performance of four rapid analytical methods in N-glycan analysis of mAbs, including the rapid 2-AB method, reduction method, off-line IdeS digestion method, and two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method. Our results showed that these four rapid analytical methods could provide comparable N-glycan data. Moreover, these four rapid methods shortened the testing time for the conventional 2-AB method from days to just minutes. They also reduced the sample requirement for the conventional 2-AB method from milligrams to micrograms. Among these four rapid methods, the 2D-LC-MS method demonstrated great potential for applications in time-consuming cell line development because it required less testing time and a lower sample requirement. [ABSTRACT FROM AUTHOR]

Comparable data is essential to understand biodiversity patterns. While assemblage or community inventorying requires comprehensive sampling, monitoring focuses on as few components as possible to detect changes. Quantifying species, their evolutionary history, and the way they interact requires studying changes in taxonomic (TD), phylogenetic (PD) and functional diversity (FD). Here we propose a method for the optimization of sampling protocols for inventorying and monitoring assemblages or communities across these three diversity dimensions taking sampling costs into account. We used Iberian spiders and Amazonian bats as two case-studies. The optimal combination of methods for inventorying and monitoring required optimizing the accumulation curve of α-diversity and minimizing the difference between sampled and estimated β-diversity (bias), respectively. For Iberian spiders, the optimal combination for TD, PD and FD allowed sampling at least 50% of estimated diversity with 24 person-hours of fieldwork. The optimal combination of six person-hours allowed reaching a bias below 8% for all dimensions. For Amazonian bats, surveying all the 12 sites with mist-nets and 0 or 1 acoustic recorders was the optimal combination for almost all diversity types, resulting in >89% of the diversity and <10% bias with roughly a third of the cost. Only for phylogenetic α-diversity, the best solution was less clear and involved surveying both with mist nets and acoustic recorders. The widespread use of optimized and standardized sampling protocols and regular repetition in time will radically improve global inventory and monitoring of biodiversity. We strongly advocate for the global adoption of sampling protocols for both inventory and monitoring of taxonomic, phylogenetic and functional diversity. [ABSTRACT FROM AUTHOR]

Longitudinal cutting is the most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules. However, currently, the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low. In this study, we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication. The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques, Pearson correlation coefficient for feature selection, and the Random Forest Regression (RFR) for prediction. Compared with the other three Machine-Learning (ML) algorithms, the Random Forest algorithm has the best performance. The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy so it is suitable for man-hours prediction. [ABSTRACT FROM AUTHOR]

To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these challenges, this study explores a lightweight yet efficient algorithm for recognizing cabbage transplantation states in natural settings. Initially, FasterNet is integrated as the backbone network in the YOLOv5s model, aiming to expedite convergence speed and bolster feature extraction capabilities. Secondly, the introduction of the GAM attention mechanism enhances the algorithm's focus on cabbage seedlings. EIoU loss is incorporated to improve both network convergence speed and localization precision. Lastly, the model incorporates deformable convolution DCNV3, which further optimizes model parameters and attains a superior balance between accuracy and speed. Upon testing the refined YOLOv5s target detection algorithm, improvements were evident. When compared to the original model, the mean average precision (mAP) rose by 3.5 percentage points, recall increased by 1.7 percentage points, and detection speed witnessed an impressive boost of 52 FPS. This enhanced algorithm not only reduces model complexity but also elevates network performance. The method is expected to streamline transplantation quality measurements, minimize time and labor inputs, and elevate field transplantation quality surveys' automation levels. [ABSTRACT FROM AUTHOR]

Experienced passage of time, the extent to which employees perceive the passage of work time as being fast or slow, is a fundamental aspect of work experience. We identify two novel temporal work design characteristics that can speed up employees' experienced passage of time: temporal predictability and task segmentation. Jobs with high temporal predictability do not make employees go through uncertain wait times before embarking on their next task. High task segmentation occurs when a large chunk of work time is segmented by categorically different temporal markers. We tested a model in which temporal predictability and task segmentation affect experienced passage of time, which in turn influences job performance, with five studies: two experiments that established the internal validity of temporal predictability and task segmentation (Studies 1a and 1b), a naturalistic field study in a factory that investigated the natural consequences of distinct temporal work design (Study 2), an organizational field study that constructively replicated the model using a sample of knowledge workers and their supervisors (Study 3), and an online survey in which we connected our model with the broader work design literature (Study 4). Altogether, the studies support a new temporal approach to work design. [ABSTRACT FROM AUTHOR]

In the field of welding inspection, radiographic non-destructive evaluation (NDE) is a widely used technique for detecting defects in welds. However, this technique requires professionally qualified workers to manually judge radiographs to determine the presence, type, and location of defects. Recently, deep learning techniques have been developed to automate this process by using image segmentation. Despite its effectiveness, small-size targets in segmentation can have blurred boundaries, making it difficult to accurately annotate them at the pixel level. In this study, we propose an automated approach using the Point-REND Res-UNet model to improve the accuracy of detecting welding defects. Our method uses the improved Point-Rend algorithm to iteratively refine coarse segmentation results, allowing for more accurate defect detection. We evaluate our approach on a set of X-ray data and demonstrate that it achieves an improvement in model dice of 6.22%. Our proposed approach can potentially save labor time and costs while enhancing the accuracy and efficiency of welding defect detection. [ABSTRACT FROM AUTHOR]

The labor force surveys (LFS) of all EU countries underwent a substantial redesign in January 2021. To ensure coherent labor market time series for the main indicators in the Norwegian LFS, we model the impact of the redesign. We use a state-space model that takes explicit account of the rotating pattern of the LFS. We also include auxiliary variables related to employment and unemployment that are highly correlated with the LFS variables we consider. The results of a parallel run are also included in the model. The purpose of the article is to quantify the structural breaks due to the redesign. This article makes two contributions to the literature on the effects of redesign in surveys with a rotating panel, such as the LFS. First, we suggest a symmetric specification of the process of the wave-specific effects. Second, we account for substantial fluctuations in the labor force estimates due to the COVID-19 pandemic in the time around the LFS redesign by applying time-varying hyperparameters for both the LFS variables and the auxiliary variables. The specification with time-varying hyperparameters shows a better fit compared to the specification with time-invariant hyperparameters. [ABSTRACT FROM AUTHOR]

Manufacturing industries struggle to devise precise planning and scheduling solutions due to unpredictable business situations. Additionally, uncertainties in production such as machine breakdowns, labour absenteeism, cycle time deviations, etc., would further deteriorate production plans and lead to uncertainty in decision-making processes. Flow shops with bottlenecks are particularly susceptible to these disturbances. Moreover, the random variations in cycle time variations can cause the bottleneck to shift between different stages. Literature indicates that conventional job release methods are ineffective in addressing these difficulties. In contrast, workload control methods would provide better solutions. Hence, a flow shop model has been developed and simulated using the variables like process time variations and bottleneck shifting on the discrete-event simulation software. The flow shop model incorporates realistic shop characteristics which are subjected to random process time variations, so as to assess the performance. The outcomes of the experimentation demonstrate that order release methods play a pivotal role in improving the performance of flow shops in more volatile situations. [ABSTRACT FROM AUTHOR]

The extraction of entities and relationships is a crucial task in the field of natural language processing (NLP). However, existing models for this task often rely heavily on a substantial amount of labeled data, which not only consumes time and labor but also hinders the development of downstream tasks. Therefore, with a focus on enhancing the model's ability to learn from small samples, this paper proposes an entity and relationship extraction method based on the Universal Information Extraction (UIE) model. The core of the approach is the design of a specialized prompt template and schema on cotton pests and diseases as one of the main inputs to the UIE, which, under its guided fine-tuning, enables the model to subdivide the entity and relationship in the corpus. As a result, the UIE-base model achieves an accuracy of 86.5% with only 40 labeled training samples, which really solves the problem of the existing models that require a large amount of manually labeled training data for knowledge extraction. To verify the generalization ability of the model in this paper, experiments are designed to compare the model with four classical models, such as the Bert-BiLSTM-CRF. The experimental results show that the F1 value on the self-built cotton data set is 1.4% higher than that of the Bert-BiLSTM-CRF model, and the F1 value on the public data set is 2.5% higher than that of the Bert-BiLSTM-CRF model. Furthermore, experiments are designed to verify that the UIE-base model has the best small-sample learning performance when the number of samples is 40. This paper provides an effective method for small-sample knowledge extraction. [ABSTRACT FROM AUTHOR]

The precise prediction of painting man-hours is significant to ensure the efficient scheduling of shipyard production and maintain a stable production pace, which directly impacts shipbuilding cycles and costs. However, traditional forecasting methods suffer from issues such as low efficiency and poor accuracy. To solve this problem, this paper proposes a selective integrated learning model (ISA-SE) based on an improved simulated annealing algorithm to predict ship painting man-hours. Firstly, the improved particle swarm optimization (MPSO) algorithm and data grouping techniques are employed to achieve the optimal selection and hyperparameter optimization of base learners, constructing a candidate set of base learners. Subsequently, the simulated annealing algorithm is improved by adding random perturbations and using a parallel perturbation search mechanism to enhance the algorithm's global search capability. Finally, an optimal set of base learners is composed of the candidate set utilizing the ISA-SE model, and a heterogeneous ensemble learning model is constructed with the optimal set of base learners to achieve the precise prediction of ship painting man-hours. The results indicate that the proposed ISA-SE model demonstrates improvements in accuracy, mean absolute error, and root mean square error compared to other models, validating the effectiveness and robustness of ISA-SE in predicting ship painting man-hours. [ABSTRACT FROM AUTHOR]

The labor is a physiological event considered to have its own circadian (diurnal) rhythm, but some of the data remain conflicting, especially for preterm births. In this retrospective study, we analyzed the circadian trends of labor onset times in the Slovenian birth cohort from 1990 to 2018 with over 550,000 cases of singleton births. The number of term and preterm labor onsets was calculated for each hour in a day and circadian trends were evaluated for each of the study groups by modeling with a generalized Poisson distribution linked with the cosinor regression model using logarithmic link function. The induced labors were taken as the control group since the timing of labor depends mostly on the working schedule of personnel and not on the intrinsic rhythmic characteristics. For induced labors, the main peak in the number of labor cases was observed in the late morning hours (around 10 AM) for all gestational ages. The prominence of this peak becomes smaller in spontaneous premature labors with gradually disrupting rhythmicity in very preterm and extremely preterm cases. Labors starting with spontaneous contractions peak between 6 and 7 AM and lose the rhythmicity at 35 weeks of gestation while labors starting with a spontaneous rupture of membranes peak at 1 AM and lose the rhythmicity at 31 weeks of gestation, suggesting differences in underlying mechanisms. According to our knowledge, this is the first study that shows differences of circadian trends between different types of spontaneous labors, i.e., labors initiated with contraction and labors initiated with a spontaneous rupture of membranes. Moreover, the obtained results represent evidence of gradual disruption of rhythmicity from mild to extreme prematurity. [ABSTRACT FROM AUTHOR]

In a streaming environment, the characteristics and labels of instances may change over time, forming concept drifts. Previous studies on data stream learning generally assume that the true label of each instance is available or easily obtained, which is impractical in many real-world applications due to expensive time and labor costs for labeling. To address the issue, an active broad learning based on multi-objective evolutionary optimization is presented to classify non-stationary data stream. The instance newly arrived at each time step is stored to a chunk in turn. Once the chunk is full, its data distribution is compared with previous ones by fast local drift detection to seek potential concept drift. Taking diversity of instances and their relevance to new concept into account, multi-objective evolutionary algorithm is introduced to find the most valuable candidate instances. Among them, representative ones are randomly selected to query their ground-truth labels, and then update broad learning model for drift adaption. More especially, the number of representative is determined by the stability of adjacent historical chunks. Experimental results for 7 synthetic and 5 real-world datasets show that the proposed method outperforms five state-of-the-art ones on classification accuracy and labeling cost due to drift regions accurately identified and the labeling budget adaptively adjusted. [ABSTRACT FROM AUTHOR]

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage. [ABSTRACT FROM AUTHOR]

We examine the impact of unconditional cash transfers (UCTs) on fertility. We develop a theoretical model that demonstrates how UCTs affect fertility decisions, time allocations for leisure, labor and childrearing, and child health through health spending. We then empirically examine the impact of UCTs on fertility in Pakistan. Our theoretical model suggests that under certain conditions, UCTs are likely to increase fertility if UCTs increase child health regardless of how they affect parental leisure, labor and childrearing time. The empirical results suggest that UCTs have a positive effect on fertility. [ABSTRACT FROM AUTHOR]

Abstract Background To evaluate the improvement of evaluation accuracy of cervical maturity for Chinese women with labor induction by adding objective ultrasound data and machine learning models to the existing traditional Bishop method. Methods The machine learning model was trained and tested using 101 sets of data from pregnant women who were examined and had their delivery in Peking University Third Hospital in between December 2019 and January 2021. The inputs of the model included cervical length, Bishop score, angle, age, induced labor time, measurement time (MT), measurement time to induced labor time (MTILT), method of induced labor, and primiparity/multiparity. The output of the model is the predicted time from induced labor to labor. Our experiments analyzed the effectiveness of three machine learning models: XGBoost, CatBoost and RF(Random forest). we consider the root-mean-squared error (RMSE) and the mean absolute error (MAE) as the criterion to evaluate the accuracy of the model. Difference was compared using t-test on RMSE between the machine learning model and the traditional Bishop score. Results The mean absolute error of the prediction result of Bishop scoring method was 19.45 h, and the RMSE was 24.56 h. The prediction error of machine learning model was lower than the Bishop score method. Among the three machine learning models, the MAE of the model with the best prediction effect was 13.49 h and the RMSE was 16.98 h. After selection of feature the prediction accuracy of the XGBoost and RF was slightly improved. After feature selection and artificially removing the Bishop score, the prediction accuracy of the three models decreased slightly. The best model was XGBoost (p = 0.0017). The p-value of the other two models was

The timely onset of female parturition is a critical determinant for pregnancy success. The highly heterogenous maternal decidua has been increasingly recognized as a vital factor in setting the timing of labor. Despite the cell type specific roles in parturition, the role of the uterine epithelium in the decidua remains poorly understood. This study uncovers the critical role of epithelial SHP2 in parturition initiation via COX1 and COX2 derived PGF2α leveraging epithelial specific Shp2 knockout mice, whose disruption contributes to delayed parturition initiation, dystocia and fetal deaths. Additionally, we also show that there are distinct types of epithelium in the decidua approaching parturition at single cell resolution accompanied with profound epithelium reformation via proliferation. Meanwhile, the epithelium maintains the microenvironment by communicating with stromal cells and macrophages. The epithelial microenvironment is maintained by a close interaction among epithelial, stromal and macrophage cells of uterine stromal cells. In brief, this study provides a previously unappreciated role of the epithelium in parturition preparation and sheds lights on the prevention of preterm birth. The mechanism of parturition initiation remains elusive. Here, authors show that epithelium is an underestimated site for parturition initiation via epithelial PGF2a regulated by SHP2-COX1/COX2. [ABSTRACT FROM AUTHOR]

In bone fracture preoperative surgical planning, 3D fracture reduction is of great significance. However, segmenting 3D fracture bones into fragments and assembling the bone fragments to restore their original morphology are labor intensive and time consuming. Current works, however, omit their synergistic effects between reduction and segmentation, which, we envision, can significantly benefit the fracture reduction. To this end, a method is proposed to alternatively segment and assemble the fragments by incorporating the contralateral bone as template and leveraging the synergistic effect on segmentation and reduction. The 3D fracture model is initially segmented into separated outer surface of fragments. The reduction is then conducted by template matching, specifically, a whole-to-whole matching strategy through deep learning of dense features is employed. Thereafter, the segmentation is further refined with the assistance of the template. The process of reduction and segmentation is iterated until the algorithm converges. Experiments were conducted on simulated data and clinical data, the mean segmentation accuracy is 95.26%, the assembling translation error is 3.28 ± 3.01 mm, and the assembling rotation error is 1.62 ± 1.71°. Results demonstrated the superiority of our method over state-of-the-art methods, and it can be used as a feasible scheme for fracture reduction planning. Moreover, it is also proved that segmentation and reduction can promote each other. [Display omitted] • Synergistic effect is exploited between segmentation and reduction. • A novel whole-to-whole matching tactics is proposed for template-matching. • The SSM is leveraged to generate and label the training data. [ABSTRACT FROM AUTHOR]

In recent years, research reached a very high level of development and validation of augmented prototyping systems in support of collaborative design activities. However, there is still great scepticism in companies when it comes to integrating these new technologies within a consolidated working model. Among others, the main barrier to overcome concerns the lack of understanding of the impact of AR systems on the key objectives of a business, such as improving its efficiency and revenue. For this reason, this paper aims to quantify these indicators by observing the technological impact not on a single design session but on an entire product development process, during which the aspects related to its integration are also considered. Thanks to the collaboration with a design agency, it was possible to compare parameters such as the lead time, number of iterations, person-hours and costs between two similar and realistic projects in which only one was supported by projection-based AR technology. [ABSTRACT FROM AUTHOR]

Background: To evaluate the improvement of evaluation accuracy of cervical maturity for Chinese women with labor induction by adding objective ultrasound data and machine learning models to the existing traditional Bishop method. Methods: The machine learning model was trained and tested using 101 sets of data from pregnant women who were examined and had their delivery in Peking University Third Hospital in between December 2019 and January 2021. The inputs of the model included cervical length, Bishop score, angle, age, induced labor time, measurement time (MT), measurement time to induced labor time (MTILT), method of induced labor, and primiparity/multiparity. The output of the model is the predicted time from induced labor to labor. Our experiments analyzed the effectiveness of three machine learning models: XGBoost, CatBoost and RF(Random forest). we consider the root-mean-squared error (RMSE) and the mean absolute error (MAE) as the criterion to evaluate the accuracy of the model. Difference was compared using t-test on RMSE between the machine learning model and the traditional Bishop score. Results: The mean absolute error of the prediction result of Bishop scoring method was 19.45 h, and the RMSE was 24.56 h. The prediction error of machine learning model was lower than the Bishop score method. Among the three machine learning models, the MAE of the model with the best prediction effect was 13.49 h and the RMSE was 16.98 h. After selection of feature the prediction accuracy of the XGBoost and RF was slightly improved. After feature selection and artificially removing the Bishop score, the prediction accuracy of the three models decreased slightly. The best model was XGBoost (p = 0.0017). The p-value of the other two models was < 0.01. Conclusion: In the evaluation of cervical maturity, the results of machine learning method are more objective and significantly accurate compared with the traditional Bishop scoring method. The machine learning method is a better predictor of cervical maturity than the traditional Bishop method. [ABSTRACT FROM AUTHOR]

Within public health control strategies for SARS-CoV-2, whole genome sequencing (WGS) is essential for tracking viral spread and monitoring the emergence of variants which may impair the effectiveness of vaccines, diagnostic methods, and therapeutics. In this manuscript different strategies for SARS-CoV-2 WGS including metagenomic shotgun (SG), library enrichment by myBaits® Expert Virus-SARS-CoV-2 (Arbor Biosciences), nCoV-2019 sequencing protocol, ampliseq approach by Swift Amplicon® SARS-CoV-2 Panel kit (Swift Biosciences), and Illumina COVIDSeq Test (Illumina Inc.), were evaluated in order to identify the best approach in terms of results, labour, and costs. The analysis revealed that Illumina COVIDSeq Test (Illumina Inc.) is the best choice for a cost-effective, time-consuming production of consensus sequences.

Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions. [ABSTRACT FROM AUTHOR]

Background and Objectives: Local anesthetics administered via epidural catheters have evolved from intermittent top-ups to simultaneous administration of continuous epidural infusion (CEI) and patient-controlled epidural analgesia (PCEA) using the same device. The latest programmed intermittent epidural bolus (PIEB) model is believed to create a wider and more even distribution of analgesia inside the epidural space. The switch from CEI + PCEA to PIEB + PCEA in our department began in 2018; however, we received conflicting feedback regarding workload from the quality assurance team. This study aimed to investigate the benefits and drawbacks of this conversion, including the differences in acute pain service (APS) staff workload, maternal satisfaction, side effects, and complications before and after the changeover. Materials and Methods: Items from the APS records included total delivery time, average local anesthetic dosage, and the formerly mentioned items. The incidence of side effects, the association between the duration of delivery and total dosage, and hourly medication usage in the time subgroups of the CEI and PIEB groups were compared. The staff workload incurred from rescue bolus injection, catheter adjustment, and dosage adjustment was also analyzed. Results: The final analysis included 214 and 272 cases of CEI + PCEA and PIEB + PCEA for labor analgesia, respectively. The total amount of medication and average hourly dosage were significantly lower in the PIEB + PCEA group. The incidences of dosage change, manual bolus, extra visits per patient, and lidocaine use for rescue bolus were greater in the PIEB + PCEA group, indicating an increased staff workload. However, the two groups did not differ in CS rates, labor time, maternal satisfaction, and side effects. Conclusions: This study revealed that while PIEB + PCEA maintained the advantage of decreasing total drug doses, it inadvertently increased the staff burden. Increased workload might be a consideration in clinical settings when choosing between different methods of PCEA. [ABSTRACT FROM AUTHOR]

Multi-phase computed tomography (CT) images have gained significant popularity in the diagnosis of hepatic disease. There are several challenges in the liver segmentation of multi-phase CT images. (1) Annotation: due to the distinct contrast enhancements observed in different phases (i.e., each phase is considered a different domain), annotating all phase images in multi-phase CT images for liver or tumor segmentation is a task that consumes substantial time and labor resources. (2) Poor contrast: some phase images may have poor contrast, making it difficult to distinguish the liver boundary. In this paper, we propose a boundary-enhanced liver segmentation network for multi-phase CT images with unsupervised domain adaptation. The first contribution is that we propose DD-UDA, a dual discriminator-based unsupervised domain adaptation, for liver segmentation on multi-phase images without multi-phase annotations, effectively tackling the annotation problem. To improve accuracy by reducing distribution differences between the source and target domains, we perform domain adaptation at two levels by employing two discriminators, one at the feature level and the other at the output level. The second contribution is that we introduce an additional boundary-enhanced decoder to the encoder–decoder backbone segmentation network to effectively recognize the boundary region, thereby addressing the problem of poor contrast. In our study, we employ the public LiTS dataset as the source domain and our private MPCT-FLLs dataset as the target domain. The experimental findings validate the efficacy of our proposed methods, producing substantially improved results when tested on each phase of the multi-phase CT image even without the multi-phase annotations. As evaluated on the MPCT-FLLs dataset, the existing baseline (UDA) method achieved IoU scores of 0.785, 0.796, and 0.772 for the PV, ART, and NC phases, respectively, while our proposed approach exhibited superior performance, surpassing both the baseline and other state-of-the-art methods. Notably, our method achieved remarkable IoU scores of 0.823, 0.811, and 0.800 for the PV, ART, and NC phases, respectively, emphasizing its effectiveness in achieving accurate image segmentation. [ABSTRACT FROM AUTHOR]

Agrobacterium -mediated transformation (AMT) potentially has great advantages over other DNA introduction methods: e.g., long DNA and numerous recipient strains can be dealt with at a time merely by co-cultivation with donor Agrobacterium cells. However, AMT was applied only to several laboratory yeast strains, and has never been considered as a standard gene-introduction method for yeast species. To disseminate the AMT method in yeast species, it is necessary to develop versatile AMT plasmid vectors including shuttle type ones, which have been unavailable yet for yeasts. In this study, we constructed a series of AMT plasmid vectors that consist of replicative (shuttle)- and integrative-types and harbor a gene conferring resistance to either G418 or aureobasidin A for application to prototrophic yeast strains. The vectors were successfully applied to five industrial yeast strains belonging to Saccharomyces cerevisiae after a modification of a previous AMT protocol, i.e., simply inputting a smaller number of yeast cells to the co-cultivation than that in the previous protocol. The revised protocol enabled all five yeast strains to generate recombinant colonies not only at high efficiency using replicative-type vectors, but also readily at an efficiency around 10−5 using integrative one. Further modification of the protocol demonstrated AMT for multiple yeast strains at a time with less labor. Therefore, AMT would facilitate molecular genetic approaches to many yeast strains in basic and applied sciences. [ABSTRACT FROM AUTHOR]

Many odontocetes produce whistles that feature characteristic contour shapes in spectrogram representations of their calls. Automatically extracting the time × frequency tracks of whistle contours has numerous subsequent applications, including species classification, identification, and density estimation. Deep-learning-based methods, which train models using analyst-annotated whistles, offer a promising way to reliably extract whistle contours. However, the application of such methods can be limited by the significant amount of time and labor required for analyst annotation. To overcome this challenge, a technique that learns from automatically generated pseudo-labels has been developed. These annotations are less accurate than those generated by human analysts but more cost-effective to generate. It is shown that standard training methods do not learn effective models from these pseudo-labels. An improved loss function designed to compensate for pseudo-label error that significantly increases whistle extraction performance is introduced. The experiments show that the developed technique performs well when trained with pseudo-labels generated by two different algorithms. Models trained with the generated pseudo-labels can extract whistles with an F1-score (the harmonic mean of precision and recall) of 86.31% and 87.2% for the two sets of pseudo-labels that are considered. This performance is competitive with a model trained with 12 539 expert-annotated whistles (F1-score of 87.47%). [ABSTRACT FROM AUTHOR]

Introduction: Women with gestational diabetes mellitus (GDM) have higher rates of adverse perinatal outcomes compared with women without GDM, including an increased risk for having labor induced and for cesarean section. The findings from previous studies analyzing duration of labor in women with GDM are contradictory. The aim of the study was to evaluate the impact of GDM on time in spontaneous and induced active labor. Material and methods: This was a population‐based cohort study including 247 524 primiparous women who gave birth to a singleton fetus with cephalic presentation, ≥34+0 (completed gestational weeks + additional days) between January 2014 and May 2020 in Sweden. Data was obtained from the Swedish Pregnancy Register. Time in active labor was compared between women with GDM and without GDM with a spontaneous labor onset or induction of labor using Kaplan Meier survival analysis and Cox regression analysis. Results: Women with GDM had significantly longer time in active labor, both with a spontaneous onset and induction of labor compared to women without GDM. Women with GDM had a decreased chance of vaginal delivery at a certain time‐point compared to women without GDM, with adjusted hazard ratio of 0.92 (0.88–0.96) and 0.83 (0.76–0.90) for those with spontaneous onset and induction of labor, respectively. Women with GDM had increased risk for time in active labor ≥12 h both in spontaneous labor onset (adjusted odds ratio 1.14 [1.04–1.25]) and in induction of labor (adjusted odds ratio 1.55 [1.28–1.87]). Conclusions: Women with GDM seem to spend a longer time in active labor, both in spontaneous and induced active labor compared to women without GDM. To be able to individualize care intrapartum, there is a need for more studies demonstrating the impact of hyperglycemia during pregnancy on outcomes during childbirth. [ABSTRACT FROM AUTHOR]

The role of cost, time and labor intensity in the construction process is very important, but there is a problem of poor optimization effect. The standard genetic algorithm cannot solve the problem of index optimization in the construction process, and the optimization rationality is low. Therefore, this paper proposes an improved genetic algorithm to study the objectives of construction projects. Firstly, the k-value clustering theory is used to analyze the characteristic values of the index and divide them according to the changing trend to reduce the disturbance factors in optimization. Then, the k-value clustering theory is used to search for the child-parent node of the indicator to form the final optimization result and complete the multi-objective optimization. MATLAB simulation shows that under certain optimization criteria, improving the genetic algorithm can improve the rationality of multi-objective optimization, and the optimization time is shorter than that standard improves genetic algorithms. [ABSTRACT FROM AUTHOR]

In the digital era, Robotic Process Automation (RPA) has become an important technology in the field of information technology. The purpose of this article is to apply RPA technology to the digital labor time management system of enterprises to improve management efficiency and accuracy. This article mainly used data comparison and mathematical model analysis methods to draw conclusions and provide specific solutions. The experimental results showed that the minimum time reduction rate for robot process automation was 19.5%. The RPA solution for digital labor time management system has the characteristics of sustainable development, convenient management, and strong systematicity, which has great promotion and application value. [ABSTRACT FROM AUTHOR]

This paper presents a flexible method for designing a bandpass filter (BPF) using pixel structure and genetic algorithm (GA) optimization. The pixel structure is made up of a grid of metallic microstrip stubs, and the GA is utilized to determine the connections between these stubs. The pixel structure enables the construction of step impedance and shunt branches, which are used to design a traditional BPF. To enhance the design freedom, one side of the discrete grids is connected to the ground via metallic holes. For verification, a BPF was designed, simulated, and measured. The experimental results showed that the 10 dB return loss bandwidth ranges from 1.1 to 1.9 GHz and the insertion loss is approximately 2.5 dB. There is good agreement between the calculation, EM simulation, and measurement results. The proposed GA-based design method offers significant advantages in terms of one-time EM simulation, feasibility, and labor time savings, making it more convenient than the traditional design method. [ABSTRACT FROM AUTHOR]

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The branching plasticity of soybean, i.e. its ability to adjust branch development to planting density, differs among cultivars. Field experiments are required to measure the degree of branching plasticity, but such experiments require a great deal of time and labor, and it is difficult to analyze and compare a large number of cultivars. A simple evaluation method needs to be established to investigate branching plasticity for a wide range of materials. Therefore, we conducted two methods to estimate this value. In the first method, we investigated the relationship between the number of branching nodes and intra-row planting distance with a gradient of distance between plants (5, 7.5, 10, 15, 20, 25, and 30 cm). The slope of the regression line between these two factors revealed significant differences among soybean cultivars, and was correlated with the measured values of branching plasticity determined in field experiments in 2015 and 2017. In the second method, the top of the stem was pinched out between the first and second leaf nodes at the V4 stage, and then the number of branch nodes was counted at maturity. There were differences in the number of branching nodes among cultivars, and a significant positive correlation between these values and the branching plasticity values measured in the 2015 and 2017 field experiments. Considering the time and effort required for field management and morphological surveys, the pinching method is considered to be an effective and simple method to evaluate branching plasticity. [ABSTRACT FROM AUTHOR]

Internships have skyrocketed in popularity as they become the new entry-level position for professional careers across the country. Despite their popularity, the legality of internships falls in a gray area created by a vague statute and a flexible, factor-based judicial test. The Fair Labor Standards Act (FLSA), which regulates employment relationships and importantly mandates a minimum wage and hour requirements, was written long before internships became commonplace and provides little direction for how to regulate these positions. In this void, both the Department of Labor and federal courts have developed guidance, the ultimate culmination of which is the modern primary beneficiary test. The equestrian industry's attempt at internships is a position known as a "working student." These positions did not arise in the same Petrie dish or era as many other internships. Instead, this type of internship is an ages-old derivative of the barter economy and apprenticeships. In short, a young, developing equestrian exchanges her labor for benefits such as training and housing--but, notably, not FLSA-complaint wages--with a professional equestrian. This position is seen both as a rite of passage into a career as an equestrian but also as an opportunity to test the waters of the industry. Working students work long hours, take on physically intensive tasks, and are often fully immersed in the industry and their positions. Do these positions pass the modern primary beneficiary test? This Note applies the factors of the test to the facts of the working student position and asserts that, for many working students, the answer is 'no.' This analysis is largely based on an extensive survey and interviews with working students, which were compiled by the Author and further explained within. Finally, this Note suggests solutions that will preserve the significant benefits of the working student position while bringing the equestrian industry into compliance. [ABSTRACT FROM AUTHOR]

Abstract In this field study of the labor process of food delivery workers, we examine the new rules of time and new forms of labor time control in the food delivery industry. Food delivery platforms attract laborers with the flexibility of working time and place but simultaneously strictly surveil the labor process of delivery workers, thus establishing a multidimensional body of control consisting of the platform and customers. At the same time, platform mechanisms of “grab the order” and “wait for the order” help platforms subtly control delivery workers’ experience, thoughts, and emotions. These mechanisms create a sense of time characterized by “punctuality” and “speed,” making delivery workers “all-day workers.” Delivery workers come to delivery platforms in search of work freedom, but in the end, they become constrained by platforms. Helpless, they voluntarily subject themselves to the time control of the platform, while the latter obtains profit under the guise of freedom.

This explains why Flamant devotes the third part of the book to showing how prices and distribution come about - rather than taking prices as derived directly from labour value and surplus. Flamant's treatment of fixed capital highlights its two-fold character: on one hand, it is a reproducible product of labour required for production, like intermediate inputs. Profit as "surplus" is determined in the distribution of the product rather than deriving from a physical surplus product. [Extracted from the article]

Traditional identification methods for Papaver somniferum and Papaver rhoeas (PSPR) consume much time and labor, require strict experimental conditions, and usually cause damage to the plant. This work presents a novel method for fast, accurate, and nondestructive identification of PSPR. First, to fill the gap in the PSPR dataset, we construct a PSPR visible capsule image dataset. Second, we propose a modified MobileNetV3-Small network with transfer learning, and we solve the problem of low classification accuracy and slow model convergence due to the small number of PSPR capsule image samples. Experimental results demonstrate that the modified MobileNetV3-Small is effective for fast, accurate, and nondestructive PSPR classification. [ABSTRACT FROM AUTHOR]

Inbreeding depression can reduce the viability of wild populations. Detecting inbreeding depression in the wild is difficult; developing accurate estimates of inbreeding can be time and labor intensive. In this study, we used a two-step modeling procedure to incorporate uncertainty inherent in estimating individual inbreeding coefficients from multilocus genotypes into estimates of inbreeding depression in a population of Weddell seals (Leptonychotes weddellii). The two-step modeling procedure presented in this paper provides a method for estimating the magnitude of a known source of error, which is assumed absent in classic regression models, and incorporating this error into inferences about inbreeding depression. The method is essentially an errors-in-variables regression with non-normal errors in both the dependent and independent variables. These models, therefore, allow for a better evaluation of the uncertainty surrounding the biological importance of inbreeding depression in non-pedigreed wild populations. For this study we genotyped 154 adult female seals from the population in Erebus Bay, Antarctica, at 29 microsatellite loci, 12 of which are novel. We used a statistical evidence approach to inference rather than hypothesis testing because the discovery of both low and high levels of inbreeding are of scientific interest. We found evidence for an absence of inbreeding depression in lifetime reproductive success, adult survival, age at maturity, and the reproductive interval of female seals in this population. [ABSTRACT FROM AUTHOR]

The Bukchon area in Seoul boasts a high density of Hanok, the traditional Korean architecture representing the region. Because the Hanok facade plays a vital role in the streetscape formation, we must record it in terms of social, cultural, historical, artistic, and scenic values. However, recording the facade of an existing Hanok building through drawing or image information is time consuming and labor intensive, and therefore costly. Further, its digital conversion is inherently difficult. This study proposes the use of deep learning to identify the form elements that comprise the Hanok facade. Three-dimensional modeling was performed on 405 well-preserved Hanok facades in the region, and 2808 items of image data were created under similar conditions and at differing angles. Labeling was performed on the shape elements of the Hanok facade, and a methodology was established to identify the facade elements using MASK R-CNN. The type of roof, windows, the lower part of the outer wall, and the design were identified with high accuracy. [ABSTRACT FROM AUTHOR]

Power take-off (PTO) shafts are widely used in agricultural machinery to directly power implements with the tractor's engine. While the operating conditions such as dirt, weather, peak loads and utilization greatly influence a PTO shaft's lifetime, proper maintenance, especially lubrication, is necessary for an appropriate operation and maximum lifetime. Hence, this paper evaluates the potential for saving resources during maintenance by using a digital maintenance assistant. To simulate and study the cost of PTO shafts for agricultural implements, an investigation of the lubrication process of PTO shafts was carried out on several test farms. In practice, two extremes are common: Either far too much grease is used for best possible lubrication, or the PTO shaft is not lubricated at all. To simulate the annual cost of maintenance, three scenarios were calculated and compared: "Maintenance before usage" and "no maintenance" as extreme scenarios and "maintenance according to the manufacturer's specifications" by a digital maintenance assistant as an optimum scenario. The simulation of different maintenance scenarios shows a reduction in maintenance costs of up to 93.8%, an average reduction of lubricants by 86.3% and an average labor time reduction by 81.8% for the case of "maintenance according to the manufacturer's specifications". The results of this work show that there is potential for major savings using a digital maintenance assistant. This development could be the first step towards the digitalization of maintenance activities on agricultural farms. [ABSTRACT FROM AUTHOR]

Target-based point cloud registration methods are still widely used by many laser scanning professionals due to their direct and manipulable nature. However, placing and moving multiple targets such as spheres for registration is a time-consuming and tactical process. When the number of scans gets large, the time and labor costs will accumulate to a high level. In this paper, we propose a flexible registration method that requires the installation of only a low-cost cubical target: a die-like object. The method includes virtual coordinate system construction and two error compensation techniques, in which the non-orthogonality of the scanned facets, along with the unknown sizes of the dice are estimated based on projection geometry and cubical constraints so that three pairs of conjugate points can be accurately identified along the axes of the constructed coordinate systems for the registration. No scan overlap of the facet is needed. Two different low-cost dice (with a volume of 0.125 m3 and 0.027 m3) were used for verifying the proposed method, which shows that the proposed method delivers registration accuracy (with an RMSE discrepancy of less than 0.5 mm for check planes) comparable to the traditional sphere- based method using four to six spherical targets spanning the scene. Therefore, the proposed method is particularly useful for registering point clouds in harsh scanning environments with limited target-setting space and high chances of target interruption. [ABSTRACT FROM AUTHOR]

Epidural analgesia (EA) increases the risks of maternal fever during labor, which is associated with adverse maternal and neonatal outcomes, while the risk factors for epidural-associated fever and strategies for minimizing these effects remain limited. A total of 325 pregnant women were retrospectively analyzed who had attended our hospital for a vaginal in-hospital delivery, including 208 who voluntarily accepted EA and 117 who did not receive EA. During labor, 208 EA women were allocated to a fever group (n = 42, a tympanic temperature ≥37.5 °C during labor), and a no fever group (n = 166). The outcome measures included main maternal and neonatal outcomes, labor times, duration of EA and the total EA dosage administered. 42 out of 208 women given EA exhibited fever temperatures during labor, which were higher than in women who did not receive EA (20.19% vs. 0.85%). Maternal fever had an increased risks for conversion to surgery (adjusted odds ratio (AOR), 4.05; 95% CI, 1.44–11.39) and neonatal infections (5.13; 1.98–13.29) compared to the no fever group. While maternal fever did not increase the risks for assisted vaginal delivery, fetal distress or admission to the neonatal intensive care unit (NICU), it was predominantly associated with primiparity and lesser times of gravity. Frequent cervical examinations, the duration of first stage and total labor, and the duration of EA and its total dosage were positively correlated with the incidence of fever. Furthermore, after stratifying risk factors into subgroups, we found that more frequent cervical examinations (≥7 times) and longer duration of first stage (≥442.5 min), total labor time (≥490 min), EA (≥610.0 min) increased the risk for epidural-associated fever after adjustment for potential confounding factors. EA increased the risk of intrapartum epidural-associated fever, which was correlated with adverse perinatal outcomes. Nulliparity, less times of gravidity, ≥7 cervical examinations, increased volume of the EA dosage, prolonged duration of EA and total labor time were risk factors for epidural-associated fever. The findings provide clinicians with insights and strategies to prevent epidural-associated fever more safely and effectively. [ABSTRACT FROM AUTHOR]

Based on the appearance of tomatoes, it is possible to determine whether they are diseased. Detecting diseases early can help the yield losses of tomatoes through timely treatment. However, human visual inspection is expensive in terms of the time and labor required. This paper presents an automatic tomato disease monitoring system using modular and extendable mobile robot we developed in a greenhouse. Our system automatically monitors whether tomatoes are diseased and conveys the specific locations of diseased tomatoes to users based on the location information of the image data collected by the robot, such that users can adopt timely treatment. This system consists of two main parts: a modular, extendable mobile robot that we developed and a server that runs a tomato disease detection program. Our robot is designed to be configured and extended according to the actual height of the tomato vines, thus ensuring that the monitoring range covers most tomatoes. It runs autonomously between two rows of tomato plants and collects the image data. In addition to storing the image data of tomatoes, the data server runs a program for detecting diseases. This program contains a two-level disease detection model: a detection network for detecting diseased tomatoes and a validation network for verifying the detection results. The validation network verifies the results of the detection network by classifying the outputs of the detection network, thus reducing the false positive rate of the proposed system. Experimentally, this work focuses on the blossom-end rot of tomatoes. In this paper, YOLOv5, YOLOv7, Faster R-CNN, and RetinaNet are trained and compared on datasets divided by different conditions. YOLOv5l showed the best results on the randomly divided dataset: the mAP@0.5 reached 90.4%, and the recall reached 85.2%. Through the trained YOLOv5l, a dataset was created for training the classification networks: ResNet, MobileNet, and DenseNet. MobileNetv2 achieved the best overall performance with a 96.7% accuracy and a size of 8.8 MB. The final deployment to the system included YOLOv5l and MobileNetv2. When the confidence threshold of YOLOv5l was set to 0.1, the two-level model's false positive and false negative rates were 13.3% and 15.2%, respectively. Compared to using YOLOv5l alone, the false positive rate decreased by 5.7% and the false negative rate increased by only 2.3%. The results of the actual operation of the proposed system reveal that the system can inform the user of the locations of diseased tomatoes with a low rate of false positives and false negatives, and that it is an effective and promotable approach. [ABSTRACT FROM AUTHOR]

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability. [ABSTRACT FROM AUTHOR]

Background: The implementation of person-centred care (PCC) is advocated worldwide. Stakeholders in charge of implementing PCC as a broad-scale change across the health care sector face two intertwined and complex challenges. First, making sense of PCC as an intervention with complex innovation characteristics and second, staging implementation of PCC by choosing appropriate implementation strategies. We aimed to explore one of these challenges by tracking, naming, specifying, and comparing which strategies and how strategies were enacted to support the implementation of more PCC in a real-world setting represented by one health care region in Sweden.Methods: A case study with seven embedded units at two organisational levels within a health care region was conducted from 2016 to 2019. Data were collected from three sources: activity logs, interviews, and written documents. Strategies were identified from all sources and triangulated deductively by name, definition, and cluster in line with the taxonomy Expert Recommendations for Implementing Change (ERIC) and specified according to recommendations by Proctor and colleagues as actor, action, action target, temporality, dose, outcome, and justification.Results: Four hundred thirteen activities were reported in logs, representing 43 discrete strategies identified in ERIC (n = 38), elsewhere (n = 1), or as emerging strategies (n = 4). The highest reported frequencies of discrete strategies were identified as belonging to two clusters: Train and educate stakeholders (40%) and Develop stakeholder interrelationships (38%). We identified a limited number of strategies belonging to the cluster Use evaluative and iterative strategies (4.6%) and an even smaller number of strategies targeting information to patients about the change initiative (0.8%). Most of the total dose of 11,076 person-hours in the 7 units was spent on strategies targeting health care professionals who provide PCC (81.5%) while the dose of strategies targeting support functions was 18.5%.Conclusions: Our findings show both challenges and merits when strategies for implementation of PCC are conducted in a real-world setting. The results can be used to support and guide both scientists and practitioners in future implementation initiatives. [ABSTRACT FROM AUTHOR]

An objective stereo video quality assessment (SVQA) strives to be consistent with human visual perception while ensuring a low time and labor cost of evaluation. The temporal–spatial characteristics of video make the data processing volume of quality evaluation surge, making an SVQA more challenging. Aiming at the effect of distortion on the stereoscopic temporal domain, a stereo video quality assessment method based on the temporal–spatial relation is proposed in this paper. Specifically, a temporal adaptive model (TAM) for a video is established to describe the space–time domain of the video from both local and global levels. This model can be easily embedded into any 2D CNN backbone network. Compared with the improved model based on 3D CNN, this model has obvious advantages in operating efficiency. Experimental results on NAMA3DS1-COSPAD1 database, WaterlooIVC 3D Video Phase I database, QI-SVQA database and SIAT depth quality database show that the model has excellent performance. [ABSTRACT FROM AUTHOR]

To determine the optimal week for labor induction in women with diet-controlled gestational diabetes mellitus by comparing differences in perinatal and neonatal outcomes of labor induction to expectant management at different gestational weeks. Methods: This was a retrospective analysis of a prospectively recruited cohort of 797 singleton pregnancies complicated by diet-controlled gestational diabetes mellitus that were diagnosed, treated, and delivered after 37 weeks in a tertiary, university-affiliated perinatal center between January 2016 and December 2021. Results: The incidence of neonatal complications was highest when delivery occurred at 37 weeks, whereas fetal macrosomia occurred mostly at 41 weeks (20.7%); the frequency of large for gestational age infants did not differ between the groups. Conversely, the best neonatal outcomes were observed at 40 weeks due to the lowest number of neonates requiring phototherapy for neonatal jaundice (1.7%) and the smallest proportion of neonates experiencing composite adverse neonatal outcomes defined as neonatal hypoglycemia, phototherapy, clavicle fracture, or umbilical artery pH < 7.15 (10.4%). Compared with expectant management, the risk for neonatal hypoglycemia was increased for induction at 39 weeks (adjusted odds ratio 12.29, 95% confidence interval 1.35–111.75, p = 0.026) and that for fetal macrosomia was decreased for induction at 40 weeks (adjusted odds ratio 0.11, 95% confidence interval 0.01–0.92, p = 0.041), after adjusting for maternal pre-pregnancy body mass index, nulliparity, and mean pregnancy A1c. Conclusions: The lowest rate of neonatal complications was observed at 40 weeks. Labor induction at 40 weeks prevented fetal macrosomia. [ABSTRACT FROM AUTHOR]

Bio-analysis is related to the analysis of analytes (drugs, metabolites, biomarkers) in biological matrix and it involves several steps from sample collection to analysis and data reporting. Sample collection from clinical or preclinical studies; then sending the samples to laboratory for analysis, later the sample preparation will be carried. Stable sample preparation method could be achieved. The role of sample preparation is to remove interferences from the biological matrix and to extract the analyte of interest and to improve analytical system performance. Sample preparation is often labor intensive and time consuming. Final step of the analysis is the quantification of analyte by using analytical instruments. Among them, Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is method of choice in bioanalytical laboratories. This is due to high selectivity and high sensitivity of the LC-MS/MS technique. Development and validation of analytical method play an important role in the discovery, development and manufacturing of pharmaceuticals. Every year, number of drugs entered into the market; hence it is mandatory to develop newer analytical methods for such drugs. After the development, it becomes necessary to validate the new analytical method. Method development is the process which proves that the analytical method is acceptable for use. Validation of analytical method gives information about various stages and parameters like accuracy, precision, linearity, Limit of Detection, Limit of Quantification, specificity, range and robustness. Validation should be done as per ICH regulatory guidelines. [ABSTRACT FROM AUTHOR]

Owing to the continuous increase in the damage to farms due to wild animals' destruction of crops in South Korea, various methods have been proposed to resolve these issues, such as installing electric fences and using warning lamps or ultrasonic waves. Recently, new methods have been attempted by applying deep learning-based object-detection techniques to a robot. However, for effective training of a deep learning-based object-detection model, overfitting or biased training should be avoided; furthermore, a huge number of datasets are required. In particular, establishing a training dataset for specific wild animals requires considerable time and labor. Therefore, this study proposes an Extract–Append data augmentation method where specific objects are extracted from a limited number of images via semantic segmentation and corresponding objects are appended to numerous arbitrary background images. Thus, the study aimed to improve the model's detection performance by generating a rich dataset on wild animals with various background images, particularly images of water deer and wild boar, which are currently causing the most problematic social issues. The comparison between the object detector trained using the proposed Extract–Append technique and that trained using the existing data augmentation techniques showed that the mean Average Precision (mAP) improved by ≥2.2%. Moreover, further improvement in detection performance of the deep learning-based object-detection model can be expected as the proposed technique can solve the issue of the lack of specific data that are difficult to obtain. [ABSTRACT FROM AUTHOR]

CRISPR/Cas9 systems were established in some edible fungi based on in vivo expressed Cas9 and guide RNA. Compared with those systems, the in vitro assembled Cas9 and sgRNA ribonucleoprotein complexes (RNPs) have more advantages, but only a few examples were reported, and the editing efficiency is relatively low. In this study, we developed and optimized a CRISPR/Cas9 genome-editing method based on in vitro assembled ribonucleoprotein complexes in the mushroom Flammulina filiformis. The surfactant Triton X-100 played a critical role in the optimal method, and the targeting efficiency of the genomic editing reached 100% on a selective medium containing 5-FOA. This study is the first to use an RNP complex delivery to establish a CRISPR/Cas9 genome-editing system in F. filiformis. Moreover, compared with other methods, this method avoids the use of any foreign DNA, thus saving time and labor when it comes to plasmid construction. [ABSTRACT FROM AUTHOR]