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Shifting cultivation, an age-old agricultural practice, is a major factor in forest cover change across Southeast Asia, where repeated cycles of vegetation disturbance and regrowth lead to far-reaching environmental and socio-economic impacts. The present study aims to assess the spatio-temporal patterns of vegetation disturbance and regrowth caused by shifting cultivation in Tripura state of India, over the past three decades, utilizing temporal segmentation of time-series Landsat data. The study analyzed vegetation disturbance and regrowth patterns in a shifting cultivation landscape from 1991 to 2020 using normalized burn ratio trends through LandTrendr, validated by the TimeSync tool. Six land use and land cover classes, viz., forests and trees outside forests, rubber plantation, shifting cultivation, water bodies, agriculture, and settlements, were mapped with an accuracy of 83.08% using a random forest classifier. This classification enabled the identification of the effective study area, which included forest areas with shifting cultivation patches. The analysis revealed that 2533.96 km2 of the study area remained undisturbed, 568.43 km2 experienced low-magnitude disturbance, 1501.11 km2 were moderately disturbed, and 184.82 km2 were highly disturbed. The shifting cultivation cycles in the study area show considerable variation. Low-magnitude disturbance indicates a single slash-and-burn event in the past three decades, moderate-magnitude disturbance involves fallow periods of over 10 years, and high-magnitude disturbance occurs with fallow periods of less than 8 years. The study revealed a shift from traditional shifting cultivation to more permanent agricultural practices in parts of Tripura. In response to incentives for commercial crops, cultivators are increasingly adopting long-term cultivation, including the growing of pineapple, areca nut, and rubber, as well as intercropping with papaya, banana, lemon, tapioca, pepper, and ginger. These plots are, however, abandoned if the yields become economically unviable. The expansion of monoculture cultivation significantly reduces the available area for shifting cultivation, thereby compelling jhum cultivators to revisit their jhum patches more frequently. This study is a first-of-its-kind attempt in the Indian context and sheds light on changing patterns in an age-old agricultural practice. [ABSTRACT FROM AUTHOR]

Electricity price prediction has an imperative role in the UK energy market among energy trading organisations. The price prediction directly impacts organisational policy for profitable electricity trading, better bidding plans, and the optimisation of energy storage devices for any surplus energy. Business organisations always look for the use of price-prediction models with higher accuracy to help them maximise benefits. With the enhancement of Internet of Things (IoT) technology, data availability on energy demand, and hence the associated price prediction modelling has become more effective tools than before. However, price prediction has been a challenging task because of the uncertainty in the demand and supply and other external factors such as weather, and gas prices as these factors can influence the fluctuation of electricity prices. In this regard, the selection of an appropriate prediction model is crucial for business organisations. In this paper, an analytical study has been presented to predict short-term electricity prices in the UK market as a use case for a UK-based energy trading company. ARIMA, Prophet, XGBoost as well as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM) algorithms have been analysed. In this study, UK Market Index Data (MID) from Elexon API data has been used that represent half-hourly electricity prices. In addition, gas prices, and initial demand out-turn data, as external factors, are introduced into the models for improving the accuracy and performance of these models. The comparative analysis shows that the ARIMA can handle only one external factor in its prediction model, while the Prophet and XGBoost can incorporate multiple external regressors in their models. Also, the models based on deep learning algorithms can deal with univariate and multivariate time series. The comparative analysis also revealed that the XGBoost model has better performance than the ARIMA and Prophet models, as has been found in earlier studies. With the extended analysis, it has been found that deep learning models outperform ARIMA, Prophet, and XGBoost models in terms of prediction accuracy. This extended comparative analysis gives the flexibility to choose the appropriate model selection for any organisation working in analogous business scenarios as of the business use case of this study. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to assess the prevalence of mild and moderate hearing loss spanning three decades, from 1990 to 2019, and to project the anticipated trends from 2020 to 2030 among adolescents, young adults, middle-aged adults, and age-standardised groups in Malaysia. Methods: This study involved secondary data analysis of mild and moderate hearing loss prevalence over 30 years among the Malaysian population aged 15–19, 25–29, 35–39, 45–49, and age-standardised groups. Subsequently, three time-series models were evaluated and the best models with the minimal Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) were selected for projecting the prevalence of hearing loss until 2030. Results: A relatively stable trend of mild hearing loss prevalence and gradual decline of moderate hearing loss were observed across all age groups throughout the study period. The prevalence of mild hearing loss was consistently higher than moderate hearing loss across all age groups, with its prevalence increasing with age. The projected prevalence of hearing loss exhibits a gradual declining trend in the future for all age groups, except for mild hearing loss for the 15-19-year-old group. Conclusion: Over the past 30 years, there has been a relatively stable and slightly declining trend in the prevalence of mild and moderate hearing loss among the Malaysian population, respectively with projections showing a slow reduction in the future. These findings highlighted the need for identifying the best intervention and vulnerable age groups, directing increased resources and prioritization towards them. [ABSTRACT FROM AUTHOR]

This article aims to address the issue of low recognition accuracy in existing sorting robots caused by lighting, occlusion, and environmental factors. A fruit recognition method based on a flexible tactile sensor array is described. This method enables the robot to directly perceive the attributes of the objects and identify fruits using a flexible gripper, facilitating intelligent sorting. A novel flexible tactile sensor array is utilized to construct a flexible hand tactile information acquisition platform, which collects tactile time series data for the fruits. Principal component analysis is then employed for dimensionality reduction, followed by the development of an improved particle swarm optimization for the support vector machine model. Through an experimental study, the optimized model is compared with four other models, demonstrating better classification performance. The optimized model achieves an average tactile classification accuracy of up to 98.10% for the five types of fruits. A comparison between the improved optimization algorithm, genetic algorithm, and grid search algorithm reveals the superior optimization performance of the new approach. In the future, this method is expected to be implemented in industrial sorting robots for intelligent sorting on automatic production lines. Furthermore, the algorithm will be further refined to enhance the classification accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Simple Summary: Female black soldier flies perch on artificial plants much more often than males, especially early in the day and early in their reproductive lives, times when males are competing with one another in aerial swarms. Artificial perches are implemented by many companies that mass-rear the black soldier fly (BSF), to emulate a natural breeding environment or provide additional surface area for flies to rest; however, basic information about perching behavior is lacking. This experiment tested the effect of adding 0.00, 0.04, 0.26, or 0.34 m2 of surface area to 0.93 m3 cages, each supplied with 90 male and 90 female adults. Female thoraxes marked with acrylic paint, and the number of perching flies of each sex were recorded over 6 d. A time-series analysis revealed the following: (a) females utilized perches 1.42 times more often than males across two trials; (b) especially in the morning where the difference could be as high as 2.56 times as great; (c) this decreased to 0.20–1.57 times more females than males by 1600 h; and (d) this cyclical pattern repeated each day throughout the week with a decreasing female-bias, starting from 2.41-times more females on day 1, which fell to 0.88–1.98-times more females than males on day 6. These dynamics are likely due to the presence of male flies engaging in aerial contests near ultraviolet lamps required for mating, especially during the early hours and early adulthood, aligning with and expanding prior knowledge of black soldier fly mating behavior. [ABSTRACT FROM AUTHOR]

Exploited forage fishes serve a dual role in marine ecosystems by supporting directed fisheries and predator productivity, and thus both harvest and predatory removals should be accounted for when developing stock assessments and evaluating management trade-offs. Predator catch and stomach content data collected on the Northeast US Shelf from 1978 to 2019 by two fisheries-independent surveys were combined within multivariate spatiotemporal models to estimate time-series of consumptive removals during spring and fall for four commercially exploited prey; Atlantic herring (Clupea harengus), silver hake (Merluccius bilinearis), butterfish (Peprilus triacanthus), and longfin squid (Doryteuthis pealeii). Seasonal consumption trends were mostly synchronous for Atlantic herring and silver hake, asynchronous for butterfish and longfin squid, and predatory removals were generally greater during fall. Consumption has increased since the 1990s for all prey except Atlantic herring and butterfish during fall, which coincides with the widespread implementation of harvest constraints meant to rebuild predator and prey populations. These time-series were linked to hypothesized drivers using state-space regression models; prey availability (bottom-up; positive relationships) and commercial catch (top-down; primarily negative relationships) were the strongest predictors of consumption. Although the mechanisms underlying these relationships remain unresolved, these linkages highlight connections among the systemic drivers of productivity on the Northeast Shelf. [ABSTRACT FROM AUTHOR]

Background and aims: Public health concerns regarding pregnant women's health after the enactment of the Cannabis Act in Canada (CAC) (a law that allowed non‐medical cannabis use), and the potential impact of the COVID‐19 pandemic, call for a contemporary assessment of these two events. Our study measured associations between the CAC, the COVID‐19 pandemic and the monthly prevalence rates of cannabis‐, all drug‐ and alcohol‐related diagnosed disorders among pregnant women in the province of Quebec. Design, setting and participants: This was a quasi‐experimental design applying an interrupted time‐series methodology in the province of Quebec, Canada. The participants were pregnant women aged 15–49 years, between January 2010 and July 2022. Measurements: Administrative health data from the Québec Integrated Chronic Disease Surveillance System were used to classify pregnant women according to cannabis‐, all drug (excluding cannabis)‐ and alcohol‐related disorders. The CAC (October 2018) and the COVID‐19 pandemic (April 2020) were evaluated as (1) slope changes and (2) level changes. Cannabis‐, all drug (excluding cannabis)‐ and alcohol‐related disorders were measured by total monthly age‐standardized monthly prevalence rate of each disorder for pregnant women aged 15–49 years. Findings Before the CAC, the prevalence rate of cannabis‐related diagnosed disorders significantly increased each month by 0.5% [95% confidence interval (CI) = 0.3–0.6] in the pregnant population. After the CAC, there were significant increases of 24% (95% CI = 1–53) of cannabis‐related diagnosed disorders. No significant changes were observed for all drug (excluding cannabis)‐ and alcohol‐related diagnosed disorders associated with the CAC. A non‐significant decrease of 20% (95% CI = −38 to 3) was observed during the COVID‐19 pandemic in alcohol‐related disorders. Conclusions: The monthly incidence rates of diagnosed cannabis‐related disorders in pregnant women in Quebec increased significantly following the enactment of the Cannabis Act in Canada. Diagnoses of all drug (excluding cannabis)‐ and alcohol‐related disorders remained relatively stable. [ABSTRACT FROM AUTHOR]

Vegetation height, and its spatial and temporal changes, is an important environmental parameter required for understanding natural habitats, estimating carbon stores and monitoring forestry activities. Recent satellite LiDAR altimetry sensors have discontinuous spatial coverage but can be combined with spatially complete remote sensing data to extrapolate to large regions. Earlier studies have focused on producing a single spatially continuous vegetation height product. This research builds on past studies, using Landsat (annual surface reflectance and fractional cover products) and the Global Ecosystem Dynamics Investigation (GEDI) data to generate annual vegetation height layers from 1988 to 2022. GEDI data for 2019 were used to train and validate the model, resulting in a root mean square error (RMSE) of 5.45 m, mean absolute error (MAE) of 3.82 m, and coefficient of determination (R2) of 0.63. This accuracy reduces when the modelled height for 2020, 2021, and 2022 is compared to GEDI data for the same years (RMSE = 6.08–6.29 m, MAE = 4.36–4.73 m, and R2 = 0.48–0.54). Validation with independent field measurements across Australia from 2011 to 2021 shows an RMSE, MAE, and R2 of 8.2 m, 5.2 m, and 0.48, respectively. One source of error is the saturation of the Landsat signal in tall, closed canopy vegetation. While model accuracy is correlated with plot-based vegetation height measurements, results indicate that accuracy reduces for the years outside of the model calibration year (i.e. 2019). When compared to other vegetation height products (also produced using GEDI and spatial remote sensing data) from three independent published studies (one for 2009, one for 2019, and one for 2020), the model developed here tends to estimate 2–4 m taller than the first two studies and around 5 m shorter when compared to the third study. This investigation demonstrates the potential to produce multiyear vegetation height at a continental scale but also highlights the large uncertainty in modelled estimates especially when extrapolating to years other than the model calibration year. [ABSTRACT FROM AUTHOR]

Multi-channel Electroencephalogram (EEG) based emotion recognition is focused on several analysis of frequency bands of the acquired signals. In this paper, spectral properties appeared on five EEG bands (δ , θ , α , β , γ ) and gated transformer network (GTN) based emotion recognition using EEG signal are proposed. Spectral energies and differential entropies of 62-channel signals are converted to 3D (sequence-channel-trial) form to feed the GTN. The GTN with enhanced gated two tower based transformer architecture is fed by 3D sequences extracted from SEED and SEED-IV emotional datasets. 15 participants' states in session 1–3 are evaluated using the proposed GTN based sequence classification, and the results are repeated by 3 × shuffling. Totally, 135 times training and testing are performed on each dataset, and the results are presented. The proposed GTN model achieves mean accuracy rates of 98.82% on the SEED dataset and 96.77% on the SEED-IV dataset for three and four emotional state recognition tasks, respectively. The proposed emotion recognition model can be employed as a promising approach for EEG emotion recognition. [ABSTRACT FROM AUTHOR]

Understanding patterns of species diversity is crucial for ecological research and conservation, and this understanding may be improved by studying patterns in the two components of species diversity, species richness and evenness of abundance of species. Variation in species richness and evenness has previously been linked to variation in total abundance of communities as well as productivity gradients. Exploring both components of species diversity is essential because these components could be unrelated or driven by different mechanisms.The aim of this study was to investigate the relationship between species richness and evenness in European bird communities along an extensive latitudinal gradient. We examined their relationships with latitude and Net Primary Productivity, which determines energy and matter availability for heterotrophs, as well as their responses to territory densities (i.e. the number of territories per area) and community biomass (i.e. the bird biomass per area).We applied a multivariate Poisson log‐normal distribution to unique long‐term, high‐quality time‐series data, allowing us to estimate species richness of the community as well as the variance of this distribution, which acts as an inverse measure of evenness.Evenness in the distribution of abundance of species in the community was independent of species richness. Species richness increased with increasing community biomass, as well as with increasing density. Since both measures of abundance were explained by NPP, species richness was partially explained by energy‐diversity theory (i.e. the more energy, the more species sustained by the ecosystem). However, species richness did not increase linearly with NPP but rather showed a unimodal relationship. Evenness was not explained either by productivity nor by any of the aspects of community abundance.This study highlights the importance of considering both richness and evenness to gain a better understanding of variation in species diversity. We encourage the study of both components of species diversity in future studies, as well as use of simulation studies to verify observed patterns between richness and evenness. [ABSTRACT FROM AUTHOR]

Febrile seizures are convulsions predominately occurring in young children. The effects of various exposomes, including influenza infection and external environmental factors, on febrile seizures have not been well-studied. In this study, we elucidated the relationships between ambient temperature, air pollutants, influenza infection, and febrile seizures using 22-year territory-wide hospitalization data in Hong Kong. The aggregated data were matched with the meteorological records and air pollutant concentrations. All-type and type-specific influenza-like illness positive (ILI+) rates were used as proxies for influenza activity. Distributed lag non-linear model in conjunction with the quasi-poisson generalized additive model was used to examine the associations of interest. According to the results, all-type influenza infections were significantly associated with an increased risk of hospital admissions for febrile seizures (cumulative adjusted relative risk [ARR] = 1.59 at 95th percentile vs. 0; 95% CI, 1.51–1.68). The effect of ILI + A/H3N2 on febrile seizure was more pronounced than other type-specific ILI + rates. A low mean ambient temperature was identified as a significant risk factor for febrile seizures (cumulative ARR = 1.50 at 5th percentile vs. median; 95% CI, 1.35–1.66), while the redox-weighted oxidant capacity and sulfur dioxide were not associated with febrile seizures. In conclusion, our study underscores that influenza infections and exposure to cold conditions were related to an increased risk of febrile seizures in children. Thus, we advocate for influenza vaccination before the onset of the cold season for children to mitigate the burden of febrile seizures. [ABSTRACT FROM AUTHOR]

We investigated long-term changes in copepod community body size in a temperate estuary in the Southeastern United States. Mesozooplankton samples were collected twice each lunar cycle from 1981 to 2020 during the months of March to July. We found strong evidence for a long-term increase in body size, likely driven by shifts in species composition, with the most rapid increase occurring during the most recent decade (2011–2020). Between the 1980s and 2010s, we documented an increase in the proportion of Oithona spp. (small species) and Acartia tonsa and Pseudodiaptomus pelagicus (large species) and decreased proportion of Parvocalanus crassirostris (small species). Temperature was inversely related with monthly mean body size, potentially driving the seasonal shifts in size, but was not correlated with the observed long-term trend. We detected strong seasonality in the normalized biomass size spectra slope, but the slope for each month did not vary interannually with changes in temperature. Overall, our study showed a long-term increase in copepod community body size that was not directly linked to changes in temperature but instead to changes in species composition. [ABSTRACT FROM AUTHOR]

In this paper, we propose a correlation‐based test for the evaluation of two competing forecasts. Under the null hypothesis of equal correlations with the target variable, we derive the asymptotic distribution of our test using the Delta method. This null hypothesis is not necessarily equivalent to the null of equal Mean Squared Prediction Errors (MSPE). Specifically, it might be the case that the forecast displaying the lowest MSPE also exhibits the lowest correlation with the target variable: this is known as "The MSPE paradox." In this sense, our approach should be seen as complementary to traditional tests of equality in MSPE. Monte Carlo simulations indicate that our test has good size and power. Finally, we illustrate the use of our test in an empirical exercise in which we compare two different inflation forecasts for a sample of OECD economies. We find more rejections of the null of equal correlations than rejections of the null of equality in MSPE. [ABSTRACT FROM AUTHOR]

Abstract Shifting cultivation, an age-old agricultural practice, is a major factor in forest cover change across Southeast Asia, where repeated cycles of vegetation disturbance and regrowth lead to far-reaching environmental and socio-economic impacts. The present study aims to assess the spatio-temporal patterns of vegetation disturbance and regrowth caused by shifting cultivation in Tripura state of India, over the past three decades, utilizing temporal segmentation of time-series Landsat data. The study analyzed vegetation disturbance and regrowth patterns in a shifting cultivation landscape from 1991 to 2020 using normalized burn ratio trends through LandTrendr, validated by the TimeSync tool. Six land use and land cover classes, viz., forests and trees outside forests, rubber plantation, shifting cultivation, water bodies, agriculture, and settlements, were mapped with an accuracy of 83.08% using a random forest classifier. This classification enabled the identification of the effective study area, which included forest areas with shifting cultivation patches. The analysis revealed that 2533.96 km2 of the study area remained undisturbed, 568.43 km2 experienced low-magnitude disturbance, 1501.11 km2 were moderately disturbed, and 184.82 km2 were highly disturbed. The shifting cultivation cycles in the study area show considerable variation. Low-magnitude disturbance indicates a single slash-and-burn event in the past three decades, moderate-magnitude disturbance involves fallow periods of over 10 years, and high-magnitude disturbance occurs with fallow periods of less than 8 years. The study revealed a shift from traditional shifting cultivation to more permanent agricultural practices in parts of Tripura. In response to incentives for commercial crops, cultivators are increasingly adopting long-term cultivation, including the growing of pineapple, areca nut, and rubber, as well as intercropping with papaya, banana, lemon, tapioca, pepper, and ginger. These plots are, however, abandoned if the yields become economically unviable. The expansion of monoculture cultivation significantly reduces the available area for shifting cultivation, thereby compelling jhum cultivators to revisit their jhum patches more frequently. This study is a first-of-its-kind attempt in the Indian context and sheds light on changing patterns in an age-old agricultural practice.

Abstract Electricity price prediction has an imperative role in the UK energy market among energy trading organisations. The price prediction directly impacts organisational policy for profitable electricity trading, better bidding plans, and the optimisation of energy storage devices for any surplus energy. Business organisations always look for the use of price-prediction models with higher accuracy to help them maximise benefits. With the enhancement of Internet of Things (IoT) technology, data availability on energy demand, and hence the associated price prediction modelling has become more effective tools than before. However, price prediction has been a challenging task because of the uncertainty in the demand and supply and other external factors such as weather, and gas prices as these factors can influence the fluctuation of electricity prices. In this regard, the selection of an appropriate prediction model is crucial for business organisations. In this paper, an analytical study has been presented to predict short-term electricity prices in the UK market as a use case for a UK-based energy trading company. ARIMA, Prophet, XGBoost as well as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM) algorithms have been analysed. In this study, UK Market Index Data (MID) from Elexon API data has been used that represent half-hourly electricity prices. In addition, gas prices, and initial demand out-turn data, as external factors, are introduced into the models for improving the accuracy and performance of these models. The comparative analysis shows that the ARIMA can handle only one external factor in its prediction model, while the Prophet and XGBoost can incorporate multiple external regressors in their models. Also, the models based on deep learning algorithms can deal with univariate and multivariate time series. The comparative analysis also revealed that the XGBoost model has better performance than the ARIMA and Prophet models, as has been found in earlier studies. With the extended analysis, it has been found that deep learning models outperform ARIMA, Prophet, and XGBoost models in terms of prediction accuracy. This extended comparative analysis gives the flexibility to choose the appropriate model selection for any organisation working in analogous business scenarios as of the business use case of this study.

Background: A substantial number of patients treated systemically with synthetic glucocorticoids undergo emotional disturbances during treatment. Patients with cutaneous T-cell lymphoma frequently experience skin inflammation and itching and often require glucocorticoid treatment. Objective: This case-series study aimed to examine how emotional and skin-related symptoms interact throughout glucocorticoid treatment. Methods: Five cutaneous T-cell lymphoma patients undergoing systemic glucocorticoid treatment completed daily ecological momentary assessments for on average 30 assessments. Fluctuations in their emotions and symptoms were analyzed using undirected and directed dynamic time warp analyses, and were visualized in symptom networks. Results: Toward the end of the glucocorticoid treatment, a decline was found in positive psychological symptoms. Idiographic dynamic time warp analyses revealed highly variable symptom networks. Directed time-lag group-level analyses revealed irritability, enthusiastic, and excited as variables with highest outstrength, in which mainly decreasing levels of positive emotions were associated with a higher likelihood of experiencing increases in itchy skin and skin problems the next day. Conclusion: The end of glucocorticoid treatment, potentially via the induction of hypocortisolism, seems to coincide with decreased energy, motivation, and enthusiasm. Itch and skin problems could be a consequence of low-positive emotions the day before.

In the IIoT, billions of devices continually provide information that is extremely diverse, variable, and large-scale and presents significant hurdles for interpretation and analysis. Additionally, issues about data transmission, scaling, computation, and storage can result in data anomalies that significantly affect IIoT applications. This work presents a novel anomaly detection framework for the IIoT in the context of the challenges posed by vast, heterogeneous, and complex data streams. This paper proposes a two-staged multi-variate approach employing a composition of long short-term memory (LSTM) and a random forest (RF) Classifier. Our approach leverages the LSTM's superior temporal pattern recognition capabilities in multi-variate time-series data and the exceptional classification accuracy of the RF model. By integrating the strengths of LSTM and RF models, our method provides not only precise predictions but also effectively discriminates between anomalies and normal occurrences, even in imbalanced datasets. We evaluated our model on two real-world datasets comprising periodic and non-periodic, short-term, and long-term temporal dependencies. Comparative studies indicate that our proposed method outperforms well-established alternatives in anomaly detection, highlighting its potential application in the IIoT environment. [ABSTRACT FROM AUTHOR]

The recent advances in technological capabilities have led to a massive production of time‐series data and remarkable progress in longitudinal designs and analyses within psychological research. However, implementing time‐series analysis can be challenging due to the various characteristics and complexities involved, as well as the need for statistical expertise. This paper introduces a statistical pipeline on time‐series analysis for studying the changes in a single process over time at either a population or individual level, both retrospectively and prospectively. This is achieved through systemization and extension of existing modelling and inference techniques. This analytical approach enables practitioners not only to track but also to model and evaluate emerging trends and apparent seasonality. It also allows for the detection of unexpected events, quantifying their deviations from baseline and forecasting future values. Given that other discernible population‐ and individual‐level changes in psychological and behavioural processes have not yet emerged, continued surveillance is warranted. A near real‐time monitoring tool of time‐series data could guide community psychological responses across multiple ecological levels, making it a valuable resource for field practitioners and psychologists. An empirical study is conducted to illustrate the implementation of the introduced analytical pipeline in practice and to demonstrate its capabilities. [ABSTRACT FROM AUTHOR]

Abstract Background The relationship between human gut microbiota and high-altitude hypoxia acclimatization remains highly controversial. This stems primarily from uncertainties regarding both the potential temporal changes in the microbiota under such conditions and the existence of any dominant or core bacteria that may assist in host acclimatization. Results To address these issues, and to control for variables commonly present in previous studies which significantly impact the results obtained, namely genetic background, ethnicity, lifestyle, and diet, we conducted a 108-day longitudinal study on the same cohort comprising 45 healthy Han adults who traveled from lowland Chongqing, 243 masl, to high-altitude plateau Lhasa, Xizang, 3658 masl, and back. Using shotgun metagenomic profiling, we study temporal changes in gut microbiota composition at different timepoints. The results show a significant reduction in the species and functional diversity of the gut microbiota, along with a marked increase in functional redundancy. These changes are primarily driven by the overgrowth of Blautia A, a genus that is also abundant in six independent Han cohorts with long-term duration in lower hypoxia environment in Shigatse, Xizang, at 4700 masl. Further animal experiments indicate that Blautia A-fed mice exhibit enhanced intestinal health and a better acclimatization phenotype to sustained hypoxic stress. Conclusions Our study underscores the importance of Blautia A species in the gut microbiota’s rapid response to high-altitude hypoxia and its potential role in maintaining intestinal health and aiding host adaptation to extreme environments, likely via anti-inflammation and intestinal barrier protection.

In this article, we address the potential effects of construction activities around a permanent Global Navigation Satellite System (GNSS) station. The study is based on a consistent time series of GNSS observations collected over a span of almost 13 years and comprises an analysis in both observation and coordinate domains. The analysis in the observation domain revealed a significant increase of the code multipath for the C1C signal of GPS and GLONASS. In terms of standard deviations the values increased by 37 and 31%, respectively. No significant increase was observed for other signals. Interestingly, the analysis showed that the Galileo E5 AltBOC signal outperformed the other signals in distinguishing direct from indirect signals. In the coordinate domain, the relative GNSS solution results demonstrated an increase in root mean square error for the eastern component during the same period. Noteworthy, the observed increase in both domains coincided with the period of the highest level of construction activity around the GNSS station. These findings suggest that the construction activity near the station impacted the quality of GNSS observations and that the Galileo E5 AltBOC signal was better at compensating for local changes than other signals.

Accurate, consistent, and high-resolution Land Use & Cover (LUC) information is fundamental for effectively monitoring landscape dynamics and better apprehending drivers, pressures, state, and impacts on land systems. Nevertheless, the availability of such national products with high thematic accuracy is still limited and consequently researchers and policymakers are constrained to work with data that do not necessarily reflect on-the-ground realities impending to correctly capture details of landscape features as well as limiting the identification and quantification of drivers and rate of change. Hereafter, we took advantage of the Switzerland’s official LUC statistical sampling survey and dense time-series of Sentinel-2 data, combining them with Machine and Deep Learning methods to produce an accurate and high spatial resolution land cover map over the Lake Geneva region. Findings suggest that time-first approach is a valuable alternative to space-first approaches, accounting for the intra-annual variability of classes, hence improving classification performances. Results demonstrate that Deep Learning methods outperform more traditional Machine Learning ones such as Random Forest, providing more accurate predictions with lower uncertainty. The produced land cover map has a high accuracy, an improved spatial resolution, while at the same time preserving the statistical significance (i.e. class proportion) of the official national dataset. This work paves the way towards the objective to produce a yearly high resolution land cover map of Switzerland and potentially implement a continuous land change monitoring capability. However further work is required to properly address challenges such as the need for increased temporal resolution for LUC information or the quality of training samples.

Abstract Background Extracellular vesicles (EVs) hold the potential for elucidating the pathogenesis of amyotrophic lateral sclerosis (ALS) and serve as biomarkers. Notably, the comparative and longitudinal alterations in the protein profiles of EVs in serum (sEVs) and cerebrospinal fluid (CSF; cEVs) of sporadic ALS (SALS) patients remain uncharted. Ropinirole hydrochloride (ROPI; dopamine D2 receptor [D2R] agonist), a new anti-ALS drug candidate identified through induced pluripotent stem cell (iPSC)-based drug discovery, has been suggested to inhibit ALS disease progression in the Ropinirole Hydrochloride Remedy for Amyotrophic Lateral Sclerosis (ROPALS) trial, but its mechanism of action is not well understood. Therefore, we tried to reveal longitudinal changes with disease progression and the effects of ROPI on protein profiles of EVs. Methods We collected serum and CSF at fixed intervals from ten controls and from 20 SALS patients participating in the ROPALS trial. Comprehensive proteomic analysis of EVs, extracted from these samples, was conducted using liquid chromatography/mass spectrometer (LC/MS). Furthermore, we generated iPSC-derived astrocytes (iPasts) and performed RNA sequencing on astrocytes with or without ROPI treatment. Results The findings revealed notable disparities yet high congruity in sEVs and cEVs protein profiles concerning disease status, time and ROPI administration. In SALS, both sEVs and cEVs presented elevated levels of inflammation-related proteins but reduced levels associated with unfolded protein response (UPR). These results mirrored the longitudinal changes after disease onset and correlated with the revised ALS Functional Rating Scale (ALSFRS-R) at sampling time, suggesting a link to the onset and progression of SALS. ROPI appeared to counteract these changes, attenuating inflammation-related protein levels and boosting those tied to UPR in SALS, proposing an anti-ALS impact on EV protein profiles. Reverse translational research using iPasts indicated that these changes may partly reflect the DRD2-dependent neuroinflammatory inhibitory effects of ROPI. We have also identified biomarkers that predict diagnosis and disease progression by machine learning-driven biomarker search. Conclusions Despite the limited sample size, this study pioneers in reporting time-series proteomic alterations in serum and CSF EVs from SALS patients, offering comprehensive insights into SALS pathogenesis, ROPI-induced changes, and potential prognostic and diagnostic biomarkers.

Monitoring active volcanoes is necessary to analyze their current status to pose a mitigation hazard. Mount Melbourne is an active volcano that has erupted in the past, and future eruptions are possible. This condition could threaten future eruptions, particularly near scientific bases. Jang Bogo, a South Korean research station, is located only 30 km from the summit and could be affected by significant ash fallout in case of an explosive eruption. This condition leads to the necessity of observing Mount Melbourne's activity frequently. This study used Sentinel-1 SAR data acquired from 2017 to 2024 to monitor the volcanic activity of Mount Melbourne by utilizing InSAR multitemporal time-series analysis implementing the improved combined scatterers interferometry with optimized point scatterers (ICOPS) method. The ICOPS method combined persistent scatterer (PS) and distributed scatterer (DS) with measurement point (MP) optimization based on convolutional neural network (CNN) and optimized hot spot analysis (OHSA). The ICOPS measurement results maintain reliable MP along the Mount Melbourne summit and around Jang Bogo station. The absence of GPS stations around these two areas makes it difficult to validate the result with the ground truth measurement, so the comparison with another method, small baseline (SBAS) measurement, is made to evaluate the reliability of the ICOPS measurement points. The comparison between the MP from ICOPS and the SBAS methods shows a good correlation with R2 of about 0.8134 in the Melbourne area and 0.8678 in the Jang Bogo area. The selected time-series plot around the summit of Mount Melbourne and the Jang Bogo area shows a stable trend of surface deformation. Thus, a total accumulated deformation of around 0.82 cm and an average deformation of about 0.10 cm/year was found around Mount Melbourne. Meanwhile, the Jang Bogo area exhibits a total deformation of about 0.15 cm with an average deformation of about 0.02. Overall, this research is a preliminary study of the ability of the ICOPS algorithm to monitor volcanic activity in snow-covered areas. [ABSTRACT FROM AUTHOR]

Clustering algorithms such as k-means and agglomerative hierarchical clustering (HCA) may provide a unique opportunity to analyze time-series kinematic data. Here we present an approach for determining number of clusters and which clustering algorithm to use on time-series lumbar and pelvis kinematic data. Cluster evaluation measures such as silhouette coefficient, elbow method, Dunn Index, and gap statistic were used to evaluate the quality of decision making. The result show that multiple clustering evaluation methods should be used to determine the ideal number of clusters and algorithm suitable for clustering time-series data for each dataset being analyzed. [ABSTRACT FROM AUTHOR]

The Tigris and Euphrates Basin (TEB) encompasses a wide area, and due to its different geographical and political conditions, each environmental factor in different conditions has different effects on the process of surface water changes. Accordingly, in this research, we aim to evaluate the trend of surface water changes in this basin in 2001-2021 by using the time series of 16 different parameters and the products available on the Google Earth Engine (GEE) platform. Based on the findings, the general trend of surface water changes is increasing, and the water area has reached 8605.9 km2 in 2001 to 10021.8 km2 in 2021. Nevertheless, the spatial-temporal changes of water have been different because the extent of lakes and wetlands in the southern areas of the basin has decreased drastically. On the contrary, it has increased upstream of the basin due to the expansion of various dams and channels. In addition, our findings indicated a high correlation between climatic variables, especially evapotranspiration, and temperature, with temporal changes in water in the region. Thus, the impact of global climate changes on the hydrology and environment of the basin highlights the importance and high sensitivity of the major lakes in the region, such as Razazeh, Tharthar, Hamrin, and Habbaniyah, to climate changes. The present research results may be used to assess surface water in other regions and provide valuable information for the planning and management of global surface water resources. [ABSTRACT FROM AUTHOR]

We propose a novel methodology for forecasting spatio-temporal data using supervised semi-nonnegative matrix factorization (SSNMF) with frequency regularization. Matrix factorization is employed to decompose spatio-temporal data into spatial and temporal components. To improve clarity in the temporal patterns, we introduce a nonnegativity constraint on the time domain along with regularization in the frequency domain. Specifically, regularization in the frequency domain involves selecting features in the frequency space, making an interpretation in the frequency domain more convenient. We propose two methods in the frequency domain: soft and hard regularizations, and provide convergence guarantees to first-order stationary points of the corresponding constrained optimization problem. While our primary motivation stems from geophysical data analysis based on GRACE (Gravity Recovery and Climate Experiment) data, our methodology has the potential for wider application. Consequently, when applying our methodology to GRACE data, we find that the results with the proposed methodology are comparable to previous research in the field of geophysical sciences but offer clearer interpretability. [ABSTRACT FROM AUTHOR]

As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at the same time, the problem arises that the AI-based models cannot explain the rationale of their predictions to users. To address this issue, we propose a time-series explanatory fault prediction framework to provide an explainability even when using AI-based fault prediction models. It consists of a data feature reduction process, a fault prediction model training process using long short-term memory, and an interpretation process of the fault prediction model via an explainable AI method. In particular, the proposed framework can explain a fault prediction based on time-series data. Therefore, it indicates which part of the data was significant for the fault prediction not only in terms of sensor type but also in terms of time. Through extensive experiments, we evaluate the proposed framework using various fault data by comparing the prediction performance of fault prediction and by assessing how well the main pre-symptoms of the fault are extracted when predicting a fault. [ABSTRACT FROM AUTHOR]

In this work, we explored the feasibility of using a transformer-based time-series forecasting architecture, known as the Temporal Fusion Transformer (TFT), for anomaly detection using threaded track data from the MITRE Corporation's Transportation Data Platform (TDP) and digital flight data. The TFT architecture has the flexibility to include both time-varying multivariate data and categorical data from multimodal data sources and conduct single-output or multi-output predictions. For anomaly detection, rather than training a TFT model to predict the outcomes of specific aviation safety events, we train a TFT model to learn nominal behavior. Any significant deviation of the TFT model's future horizon forecast for the output flight parameters of interest from the observed time-series data is considered an anomaly when conducting evaluations. For proof-of-concept demonstrations, we used an unstable approach (UA) as the anomaly event. This type of anomaly detection approach with nominal behavior learning can be used to develop flight analytics to identify emerging safety hazards in historical flight data and has the potential to be used as an on-board early warning system to assist pilots during flight. [ABSTRACT FROM AUTHOR]

Cancer Antigen 15-3 (CA 15-3) is a glycoprotein often linked to breast cancer. Elevated levels of CA 15-3, above the normal reference range of 30 U/mL (units per milliliter), are frequently found in the blood of patients with metastatic breast cancer, where the cancer has spread to other parts of the body, such as the bones. This study examines the importance of cluster analysis in evaluating time-series measurements of plasma CA 15-3 in a male patient with metastatic breast cancer that has spread to the trochanteric region of the left leg. Clustering is a statistical method used to organize data based on similarity, though it may not directly reflect underlying physical properties. The trend of CA 15-3 time-series measurement presented here is familiar to oncologists. The novelty of this study lies in evaluating the significance of applying cluster analysis to the specific time-series data. The results indicate that this approach is indeed meaningful. Notably, two distinct clusters were identified within the data, as anticipated. The first cluster corresponds to the period before the recurrence of illness (metastatic breast cancer), while the second cluster reflects the advanced (metastatic) stage of the disease. The boundary between these clusters provides valuable insights into the onset of the metastatic stage. To our knowledge, this is the first study to apply cluster analysis to CA 15-3 time-series data. The results are promising. Its potential use in identifying the onset of the metastatic stage merits further examination. [ABSTRACT FROM AUTHOR]

Accurate resource utilization estimation is crucial for efficient resource allocation, capacity planning, and cost optimization in cloud systems. In the past, several artificial intelligence, machine learning, and deep learning-based techniques have been evolved to forecast cloud cluster workload. Despite the abundance of available techniques, most existing techniques fail to achieve the desired prediction efficiency and generalization capability. They are computationally inefficient in accurately determining resource utilization at a machine-level granularity. The current research proposes a computationally less-expensive hybrid approach combining cluster analysis and deep neural learning with transfer learning to estimate the machine-level workload. The method implements clustering to identify the similarity patterns among the non-linear usage profiles of machines present in the input dataset. Subsequently, the generalized deep neural learning models are developed considering only a sample dataset belonging to each identified cluster. Lastly, the concept of transfer learning is deployed using pre-trained generalized models to estimate the workload for all remaining machines relating to the clusters. The performance validation of the proposed approach is carried out on the real-world traces dataset of the google cluster. The comparative evaluation of the proposed approach with benchmark approaches verifies the achieved performance benefits and accuracy. [ABSTRACT FROM AUTHOR]

Accurate cloud and shadow detection is a crucial prerequisite for optical remote sensing image analysis and application. Multi-temporal-based cloud and shadow detection methods are a preferable choice to detect clouds in complex scenes (e.g., thin clouds, broken clouds and clouds with interference from artificial surfaces with high reflectivity). However, such methods commonly require cloud-free reference images, and this may be difficult to achieve in time-series data since clouds are often prevalent and of varying spatial distribution in optical remote sensing images. Furthermore, current multi-temporal-based methods have limited feature extraction capability and rely heavily on prior assumptions. To address these issues, this paper proposes a Siamese network (Siamnet) for cloud and shadow detection based on Time-Series cloudy Images, namely TSI-Siamnet, which consists of two steps: 1) low-rank and sparse component decomposition of time-series cloudy images is conducted to construct a composite reference image to cope with dynamic changes in the cloud distribution in time-series images; 2) an extended Siamnet with optimal difference calculation module (DM) and multi-scale difference features fusion module (MDFM) is constructed to extract reliable disparity features and alleviate semantic information feature dilution during the decoder part. TSI-Siamnet was tested extensively on seven land cover types in the well-known Landsat 8 Biome dataset. Compared to six state-of-the-art methods (including four deep learning-based methods and two classical non-deep learning-based methods), TSI-Siamnet produced the best performance with an overall accuracy of 95.05% and MIoU of 84.37%. In three more challenging experiments, TSI-Siamnet showed enhanced detection of thin and broken clouds and greater anti-interference to highly reflective surfaces. TSI-Siamnet provides a novel strategy to explore comprehensively the valid information in time-series cloudy images and integrate the extracted spectral-spatial–temporal features for reliable cloud and shadow detection. [ABSTRACT FROM AUTHOR]

The hadopelagic environment remains highly understudied due to the inherent difficulties in sampling at these depths. The use of sediment environmental DNA (eDNA) can overcome some of these restrictions as settled and preserved DNA represent an archive of the biological communities. We use sediment eDNA to assess changes in the community within one of the world's most productive open‐ocean ecosystems: the Atacama Trench. The ecosystems around the Atacama Trench have been intensively fished and are affected by climate oscillations, but the understanding of potential impacts on the marine community is limited. We sampled five sites using sediment cores at water depths from 2400 to ~8000 m. The chronologies of the sedimentary record were determined using 210Pbex. Environmental DNA was extracted from core slices and metabarcoding was used to identify the eukaryote community using two separate primer pairs for different sections of the 18S rRNA gene (V9 and V7) effectively targeting pelagic taxa. The reconstructed communities were similar among markers and mainly composed of chordates and members of the Chromista kingdom. Alpha diversity was estimated for all sites in intervals of 15 years (from 1842 to 2018), showing a severe drop in biodiversity from 1970 to 1985 that aligns with one of the strongest known El Niño events and extensive fishing efforts during the time. We find a direct impact of sea surface temperature on the community composition over time. Fish and cnidarian read abundance was examined separately to determine whether fishing had a direct impact, but no direct relation was found. These results demonstrate that sediment eDNA can be a valuable emerging tool providing insight in historical perspectives on ecosystem developments. This study constitutes an important step toward an improved understanding of the importance of environmental and anthropogenic drivers in affecting open and deep ocean communities. [ABSTRACT FROM AUTHOR]

This study aimed to improve the predictive accuracy of the Braden assessment for pressure injury risk in skilled nursing facilities (SNFs) by incorporating real‐world data and training a survival model. A comprehensive analysis of 126 384 SNF stays and 62 253 in‐house pressure injuries was conducted using a large calibrated wound database. This study employed a time‐varying Cox Proportional Hazards model, focusing on variations in Braden scores, demographic data and the history of pressure injuries. Feature selection was executed through a forward‐backward process to identify significant predictive factors. The study found that sensory and moisture Braden subscores were minimally contributive and were consequently discarded. The most significant predictors of increased pressure injury risk were identified as a recent (within 21 days) decrease in Braden score, low subscores in nutrition, friction and activity, and a history of pressure injuries. The model demonstrated a 10.4% increase in predictive accuracy compared with traditional Braden scores, indicating a significant improvement. The study suggests that disaggregating Braden scores and incorporating detailed wound histories and demographic data can substantially enhance the accuracy of pressure injury risk assessments in SNFs. This approach aligns with the evolving trend towards more personalized and detailed patient care. These findings propose a new direction in pressure injury risk assessment, potentially leading to more effective and individualized care strategies in SNFs. The study highlights the value of large‐scale data in wound care, suggesting its potential to enhance quantitative approaches for pressure injury risk assessment and supporting more accurate, data‐driven clinical decision‐making. [ABSTRACT FROM AUTHOR]

Multiple evidence has supported that air pollution exposure has detrimental effects on the cardiovascular and respiratory systems. However, most investigations focus on the general population, with limited research conducted on medically insured populations. To address this gap, the current research was designed to examine the acute effects of inhalable particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ground-level ozone (O3), and sulfur dioxide (SO2) on the incidence of upper respiratory tract infections (URTI), utilizing medical insurance data in Wuhan, China. Data on URTI were collected from the China Medical Insurance Basic Database for Wuhan covering the period from 2014 to 2018, while air pollutant data was gathered from ten national monitoring stations situated in Wuhan city. Statistical analysis was performed using generalized additive models for quasi-Poisson distribution with a log link function. The analysis indicated that except for ozone, higher exposure to four other pollutants (NO2, SO2, PM2.5, and PM10) were significantly linked to an elevated risk of URTI, particularly during the previous 0–3 days and previous 0–4 days. Additionally, NO2 and SO2 were found to be positively linked with laryngitis. Furthermore, the effects of air pollutants on the risk of URTI were more pronounced during cold seasons than hot seasons. Notably, females and the employed population were more susceptible to infection than males and non-employed individuals. Our findings gave solid proof of the link between ambient air pollution exposure and the risk of URTI in medically insured populations. [ABSTRACT FROM AUTHOR]

• Landsat archive presents unique opportunities for multitemporal analysis. • Specific month selection in time series effects classification accuracy. • Benefit of increased month observations varied among climatic regions and classes. • Grass/shrub and cultivated classes saw highest improvements. • Implementation scenario showed F1 improvements as high as 10% The Landsat archive having consistent revisit times, near global extent and extensive multi-decadal temporal coverage offers a unique opportunity for land cover land use product generation. Along with this vast volume of freely available data, new classification methods based on deep learning have improved modeling capabilities. This manuscript investigates the effect of intra-annual Landsat scene availability in the accuracy of land cover land use classification in the conterminous United States. More specifically, we seek to quantify the effect of: i) increased monthly scene availability, and ii) specific months that may result in higher classification accuracy across different classes. Identifying specific months with comparable classification accuracy to the entire time series could offer significant computational gains for large-scale mapping. Our experiment incorporated deep learning classifiers and a wide range of reference data across the continental United States. Results were contrasted between five large U.S. climatic regions to further differentiate this intra-annual effect. Our findings indicate that the total number of months can have a highly variable effect in the classification accuracy ranging from minor (a few percentage points in terms of class F1 accuracy) to extremely beneficial (approaching 50% F1 improvement moving from four to twelve month observations). The benefit of increased month observations varied among climatic regions and classes: when all climate regions were combined, the grass/shrub and cultivated classes improved their F1 accuracy up to 30%, while the water class saw the least improvement of about 5%, partially due to its limited room for improvement. The effect of specific month combinations was also examined, where the total number of months was kept constant and the included months varied. The difference between the best month combination and the median combination value was estimated to be as high as about 30% for the four monthly observations scenario and the grass/shrub class. Further validation of the month selection importance comes from an example implementation scenario where F1 improvements can be as high as 10%. Our work demonstrated that month selection may offer such benefits that in some classes and climatic regions this time selection optimization is an inevitable choice due to large accuracy improvements. Also, the potential data reduction with targeted month selection would be particularly appealing to large-scale classification tasks. Due to the large extent of the climatic regions further studies are needed to quantify a more localized effect along with explanation of potential drivers. [ABSTRACT FROM AUTHOR]

In this study, employing the IEEE Xplore database as the data source, articles on different topics (keywords) and their usage data generated from January 2011 to December 2020 were collected and analyzed. The study examined the temporal relationships between these usage data and publication counts at the topic level via Granger causality analysis. The study found that almost 80% of the topics exhibit significant usage-publication interactions from a time-series perspective, with varying time lag lengths depending on the direction of the Granger causality results. Topics that present bidirectional Granger causality show longer time lag lengths than those exhibiting unidirectional causality. Additionally, the study found that the direction of the unidirectional Granger causality was influenced by the significance of a topic. Topics with a greater preference for article usage as the Granger cause of publication counts were deemed more important. The findings' reliability was confirmed by varying the maximum lag period. This study provides strong support for using usage data to identify hot topics of research. [ABSTRACT FROM AUTHOR]

As big data technologies for IoT services develop, cross-service distributed learning techniques of multivariate deep learning models on IoT time-series data collected from various sources are becoming important. Vertical federated deep learning (VFDL) is used for cross-service distributed learning for multivariate IoT time-series deep learning models. Existing VFDL methods with reasonable performance require a large communication amount. On the other hand, existing communication-efficient VFDL methods have relatively low performance. We propose TT-VFDL-SIM, which can achieve improved performance over centralized training or existing VFDL methods in a communication-efficient manner. TT-VFDL-SIM derives partial tasks from the target task and applies transfer learning to them. In our task-driven transfer approach for the design of TT-VFDL-SIM, the SIM Partial Training mechanism contributes to performance improvement by introducing similar feature spaces in various ways. TT-VFDL-SIM was more communication-efficient than existing VFDL methods and achieved an average of 0.00153 improved MSE and 7.98% improved accuracy than centralized training or existing VFDL methods. [ABSTRACT FROM AUTHOR]

The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model's viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert's pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers' pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included. [ABSTRACT FROM AUTHOR]

In future smart grid environment, local energy markets will become a reality to provide flexibility. Consequently, it will be essential not only to implement accurate energy consumption forecasters at the building level to determine which buildings can provide the required flexibility, but also at an aggregated level to anticipate power system boundary conditions. Thus, both forecasters play a key role in supporting the reliable and secure operation of smart grids and developing future demand response strategies. Although there is a piece of literature that addressed energy demand forecasting for day-ahead horizons, proposed algorithms only focused on improving accuracy neglecting energy markets technical boundary conditions. This study presents a novel methodology based on random forest machine learning algorithm to predict day-ahead energy demand at individual buildings with a 15-minute resolution. Furthermore, an analysis has been conducted to assess whether the application of time-series decomposition techniques or shape factors can enhance the accuracy of the proposed methodology. The results indicate that the proposed methodology is effective and accurate, exhibiting a MAPE of 10.77% – 31.52% and an R2 of 0.51–0.70 for individual buildings. These findings demonstrate the potential of the methodology for future energy markets.

Cancer Antigen 15-3 (CA 15-3) is a glycoprotein often linked to breast cancer. Elevated levels of CA 15-3, above the normal reference range of 30 U/mL (units per milliliter), are frequently found in the blood of patients with metastatic breast cancer, where the cancer has spread to other parts of the body, such as the bones. This study examines the importance of cluster analysis in evaluating time-series measurements of plasma CA 15-3 in a male patient with metastatic breast cancer that has spread to the trochanteric region. Clustering is a statistical method used to organize data based on similarity, though it may not directly reflect underlying physical properties. The trend of CA 15-3 time-series measurement presented here is familiar to oncologists. The novelty of this study lies in evaluating the significance of applying cluster analysis to the specific time-series data. The results indicate that this approach is indeed meaningful. Notably, two distinct clusters were identified within the data, as anticipated. The first cluster corresponds to the period before the recurrence of illness (metastatic breast cancer), while the second cluster reflects the advanced (metastatic) stage of the disease. The boundary between these clusters provides valuable insights into the onset of the metastatic stage. To our knowledge, this is the first study to apply cluster analysis to CA 15-3 time-series data. The results are promising. Its potential use in identifying the onset of the metastatic stage merits further examination.

Invasive Emerald Ash Borer (EAB) damage pose significant challenges for sustainable forest management, necessitating accurate mapping of damaged ash trees. Traditional change detection methods, using time-series imagery, are essential for monitoring forest disturbances but complicated by abnormal fluctuations in original time-series features. Tree phenology also complicates this process by masking the reflectance characteristics indicative of EAB infestation. To address these challenges, we propose an improved change detection method integrating patterns from host tree phenology and EAB life history. This improved method includes: (1) select the indices with time stability to enhance detection reliability by partial least squares method (PLS); (2) correction on negative change values before and positive change values after the phenological peak based on known patterns of tree phenology and EAB life history. Result confirms that this method effectively reflects the seasonal growth and decline dynamics of ash trees, revealing the impacts of phenology and EAB infestation. EAB-damaged trees exhibited slower growth in May and premature decline in July compared with healthy tree, with the damage severity influencing the rate of leaf decline. This proposed method achieved an overall accuracy of 53.4%-76.7% across different months for ash trees with health, light and severe damage. This study highlights the capabilities of integrating pest life history and phenology in change detection method and provide a new method to monitor individual tree health across large areas by high-resolution satellite imagery.