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I examined how Pacific Islander communities are experiencing and responding to climate change, and the future of the region as a whole. For this paper, I conducted research on climate change, Pacific Islander communities, and predictions, alongside the cultural, social, economic, and political responses. Pacific Islander communities are experiencing the negative effects of climate change, and their responses—while adaptive—are often constrained due to their geography (Barnett, & Campbell, 2010). The future is full of negative effects, but the extent of the damages remains a subject of debate, with both pessimistic and optimistic perspectives. In conclusion, climate change has negative impacts on Pacific Islander communities, and this continued trend and its outcomes will persist and be determined by our actions today (Campbell, 2010). These vulnerable populations are at risk due to climate change, which makes it even more important to address the impacts in order to assist them.

A variant of the online knapsack problem is considered in the setting of predictions. In Unit Profit Knapsack, the items have unit profit, i.e., the goal is to pack as many items as possible. For Online Unit Profit Knapsack, the competitive ratio is unbounded. In contrast, it is easy to find an optimal solution offline: Pack as many of the smallest items as possible into the knapsack. The prediction available to the online algorithm is the average size of those smallest items that fit in the knapsack. For the prediction error in this hard online problem, we use the ratio r = a a ^ where a is the actual value for this average size and a ^ is the prediction. We give an algorithm which is e - 1 e -competitive, if r = 1 , and this is best possible among online algorithms knowing a and nothing else. More generally, the algorithm has a competitive ratio of e - 1 e r , if r ≤ 1 , and e - r e r , if 1 ≤ r < e . Any algorithm with a better competitive ratio for some r < 1 will have a worse competitive ratio for some r > 1 . To obtain a positive competitive ratio for all r, we adjust the algorithm, resulting in a competitive ratio of 1 2 r for r ≥ 1 and r 2 for r ≤ 1 . We show that improving the result for any r < 1 leads to a worse result for some r > 1 . [ABSTRACT FROM AUTHOR]

Objective: The aim is to evaluate the global, regional, and national trends in the burden of children and adolescents under 14 from 1990 to 2019, as well as future trend predictions. Methods: In Global Burden of Disease (GBD), we reported the incidence, prevalence rate and the years lived with disability (YLDs), the incidence per 100,000 people, and the average annual percentage change (AAPC). We further analyzed these global trends by age, gender, and social development index (SDI). We use joinpoint regression analysis to determine the year with the largest global trend change. Bayesian age-period-cohort (BAPC) was used for predictions. Results: From 1990 to 2019, the incidence rate, prevalence and YLDs of AD under 14 years old showed a downward trend. The incidence rate of AD among people under 5 years old has the largest decline [AAPC: -0.13 (95% CI: -0.15 to -0.11), P < 0.001]. The incidence rate, prevalence and YLDs of AD in women were higher than those in men regardless of age group. Regional, Asia has the highest AD incidence rate in 2019. National, Mongolia has the highest AD incidence rate in 2019. The largest drop in AD incidence rate, prevalence and YLDs between 1990 and 2019 was in the United States. Conclusion: From 1990 to 2019, the global incidence rate of children and adolescents under 14 declined. With the emergence of therapeutic drugs, the prevalence and YLDs rate declined significantly. From 2020 to 2030, there is still a downward trend. [ABSTRACT FROM AUTHOR]

The analysis of large volumes of data collected from heterogeneous sources is increasingly important for the development of megacities, the advancement of smart city technologies, and ensuring a high quality of life for citizens. This study aimed to develop algorithms for analyzing and interpreting social media data to assess citizens' opinions in real time and for verifying and examining data to analyze social tension and predict the development of situations during the implementation of urban projects. The developed algorithms were tested using an urban project in the field of transportation system development. The study's material included data from social networks, messenger channels and chats, video hosting platforms, blogs, microblogs, forums, and review sites. An interdisciplinary approach was utilized to analyze the data, employing tools such as Brand Analytics, TextAnalyst 2.32, GPT-3.5, GPT-4, GPT-4o, and Tableau. The results of the data analysis showed identical outcomes, indicating a neutral perception among users and the absence of social tension surrounding the project's implementation, allowing for the prediction of a calm development of the situation. Additionally, recommendations were developed to avert potential conflicts and eliminate sources of social tension for decision-making purposes. [ABSTRACT FROM AUTHOR]

Past research on advice-taking has suggested that people are often insensitive to the level of advice independence when combining forecasts from advisors. However, this has primarily been tested for cases in which people receive numeric forecasts. Recent work by Mislavsky and Gaertig (2022) shows that people sometimes employ different strategies when combining verbal versus numeric forecasts about the likelihood of future events. Specifically, likelihood judgments based on two verbal forecasts (e.g., "rather likely") are more often extreme (relative to the forecasts) than are likelihood judgments based on two numeric forecasts (e.g., "70% probability"). The goal of the present research was to investigate whether advice-takers' use of combination strategies can be sensitive to advice independence when differences in independence are highly salient and whether sensitivity to advice independence depends on the format in which advice is given. In two studies, we found that advice-takers became more extreme with their own likelihood estimate when combining forecasts from advisors who use separate evidence, as opposed to the same evidence. We also found that two verbal forecasts generally resulted in more extreme combined likelihood estimates than two numeric forecasts. However, the results did not suggest that sensitivity to advice independence depends on the format of advice. Public Significance Statement: An important factor when combining forecasts from advisors is whether advisors are relying on the same or separate evidence to generate their forecasts. People have often been found to be insensitive to such differences in advisor independence. However, in the present work, we found that people can indeed use information about advice independence in a normative matter when differences in advice independence are sufficiently salient. Importantly, we also found that this sensitivity was not moderated by the format in which forecasts are presented. Participants in our studies attended to advice independence both when forecasts were in a verbal format (e.g., rather likely) and when both were in a numeric format (e.g., 70% probability). Replicating prior work (Mislavsky & Gaertig, 2022; Teigen et al., 2023), we also found evidence of a general advice format effect: When combining verbal as opposed to numeric forecasts, people's final likelihood estimates became more extreme (closer to certainty). [ABSTRACT FROM AUTHOR]

This study presents a new approach for predicting water levels of the Odra/Oder river using vector autoregressive models (VAR). We use water level time series from 27 gauging stations, on which we interpolate no-data gaps using the LinAR method and detect outliers with two separate methods: the extreme values (EV) approach and the isolation forest (IFO) algorithm. Before removing potential outliers, we propose a hydrological evaluation based on multivariate data analysis. Finally, we consider three separate data scenarios, i.e. LinAR (no outlier rejection), EV, and IFO. VAR models for six prediction gauges were built in a moving window manner on the most recent 720 hourly water levels prior to each prediction. The analysis covered the time range from January 2016 to May 2022 and resulted in ≈ 1,000,000 water level forecasts (3 scenarios x 6 gauges x 55,000 hourly time steps) with lead time of 72 h. The analysis of root mean squared error (RMSE) indicates that the VAR model performs well, especially for 24-hour predictions, with RMSE values ranging from 8 to 28 cm. The model was also found to have skills in predicting a rising limb of a hydrograph. Our numerical experiments showed the susceptibility of the VAR predictions to artefacts. The IFO method was found to detect outliers skilfully, which allowed to produce the most accurate VAR-based predictions. [ABSTRACT FROM AUTHOR]

Objectives: Women's attendance to cervical cancer screening (CCS) is a major concern for healthcare providers in community. This study aims to use the various algorithms that can accurately predict the most barriers of women for nonattendance to CS. Design: The real‐time data were collected from women presented at OPD of primary health centers (PHCs). About 1046 women's data regarding attendance and nonattendance to CCS were included. In this study, we have used three models, classification, ensemble, and deep learning models, to compare the specific accuracy and AU‐ROC for predicting non‐attenders for CC. Results: The current model employs 22 predictors, with soft voting in ensemble models showing slightly higher specificity (96%) and sensitivity (93%) than weighted averaging. Bagging excels with the highest accuracy (98.49%), specificity (97.3%), and ideal sensitivity (100%) with an AUC of 0.99. Classification models reveal Naive Bayes with higher specificity (97%) but lower sensitivity (91%) than Logistic Regression. Random Forest and Neural Network achieve the highest accuracy (98.49%), with an AUC of 0.98. In deep learning, LSTM has an accuracy of 95.68%, higher specificity (97.60%), and lower sensitivity (93.42%) compared to other models. MLP and NN showed the highest AUC values of 0.99. Conclusion: Employing ensemble and deep learning models proved most effective in predicting barriers to nonattendance in cervical screening. [ABSTRACT FROM AUTHOR]

The growth of Chinese multinationals has become a fascinating component within the larger literature on the growth of firms in and out of emerging economies. Leveraging the latest and most-comprehensive data, Casanova and Miroux’s (2024) research enables us to evaluate the validity of predictions made by Peng and Heath (1996), Rugman and Li (2007), and Peng (2012). Overall, the growth of Chinese multinationals reported by Casanova and Miroux has not supported earlier predictions, but has increasingly supported more recent predictions. Casanova and Miroux’s research therefore has made significant contributions by providing much-needed empirical checks for the claims made by earlier scholars, thus revealing both the strengths and weaknesses of this rapidly evolving literature.

The present contribution starts from Goldberg and Perek’s (2019) analysis of gapping within a constructionist framework. The authors promote their analysis as surpassing non-constructionist takes on gapping and ellipsis more generally. In particular, they claim predictive power. That this is not the whole truth is explained in detail in this contribution. It is shown which predictions can be made from their perspective versus from a generative perspective and it is discussed whether they are borne out. Furthermore, I highlight how the predictions relate to the fundamentals of the respective theories and, as a consequence, how they differ in kind.

There is an increasing demand for materials capable of withstanding ever-higher temperatures. However, because the difficulties associated with carrying out experimental investigations at very high temperatures are significant, the published information relating to the phase stability of potential refractory materials is, in many instances, still lacking. The work described in this paper attempts to rectify this deficit through the on-going development of a thermodynamic database for high-temperature materials based on carbides, nitrides, borides, and silicides. Because of technological requirements of high-temperature stability and strength, combined with lightness, these are the materials that come into question most frequently for high-temperature applications. In developing the present database, and to ensure reliability of its use, emphasis has been placed on the need to maintain the compatibility of data and modeling when assessing experimental data and estimating missing values. The methods used to achieve compatibility of published information are described and calculations of phase equilibria relevant to the industrial application of various refractory materials are presented. The similarity to, and the importance of the scientific background and published work of Ted Massalski for the work of the present author is stressed. [ABSTRACT FROM AUTHOR]

Predicting breakthrough papers holds great significance; however, prior studies encountered challenges in this task, indicating a need for substantial improvement. We propose that the failure to capture the dynamic structural-evolutionary features of citation networks is one of the major reasons. To overcome this limitation, this paper introduces a new method for constructing citation cascades of focus papers, allowing the creation of a time-series-like set of citation cascades. Then, through a thorough review, three types of structural indicators in these citation networks that could reflect breakthroughs are identified, including certain basic topological metrics, PageRank values, and the von Neumann graph entropy. Based on the time-series-like set of citation cascades, the dynamic trajectories of these indicators are calculated and employed as predictors. Using the Nobel Prize-winning papers as a landmark dataset, our prediction method yields approximately a 7% improvement in the ROC-AUC score compared to static-based prior methods. Additionally, our method advances in achieving earlier predictions than other previous methods. The main contribution of this paper is proposing a novel method for creating citation cascades in chronological order and confirming the significance of predicting breakthroughs from a dynamic structural perspective. [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) is the prominent recurrent climatic pattern in the tropical Pacific Ocean with global impacts on regional climates. This study utilizes deep learning to predict the Niño 3.4 index by encoding non-linear sea surface temperature patterns in the tropical Pacific using an autoencoder neural network. The resulting encoded patterns identify crucial centers of action in the Pacific that serve as predictors of the ENSO mode. These patterns are utilized as predictors for forecasting the Niño 3.4 index with a lead time of at least 6 months using the Long Short-Term Memory (LSTM) deep learning model. The analysis uncovers multiple non-linear dipole patterns in the tropical Pacific, with anomalies that are both regionalized and latitudinally oriented that should support a single inter-tropical convergence zone for modeling efforts. Leveraging these encoded patterns as predictors, the LSTM - trained on monthly data from 1950 to 2007 and tested from 2008 to 2022 - shows fidelity in predicting the Niño 3.4 index. The encoded patterns captured the annual cycle of ENSO with a 0.94 correlation between the actual and predicted Niño 3.4 index for lag 12 and 0.91 for lags 6 and 18. Additionally, the 6-month lag predictions excel in detecting extreme ENSO events, achieving an 85% hit rate, outperforming the 70% hit rate at lag 12 and 55% hit rate at lag 18. The prediction accuracy peaks from November to March, with correlations ranging from 0.94 to 0.96. The average correlations in the boreal spring were as large as 0.84, indicating the method has the capability to decrease the spring predictability barrier. [ABSTRACT FROM AUTHOR]

In supervised learning, feature selection methods identify the most relevant predictors to include in a model. For linear models, the inclusion or exclusion of each variable may be represented as a vector of bits playing the role of the genetic material that defines the model. Genetic algorithms reproduce the strategies of natural selection on a population of models to identify the best. We derive the distribution of the importance scores for parallel genetic algorithms under the null hypothesis that none of the features has predictive power. They, hence, provide an objective threshold for feature selection that does not require the visual inspection of a bubble plot. We also introduce the eradication strategy, akin to forward stepwise selection, where the genes of useful variables are sequentially forced into the models. The method is illustrated on real data, and simulation studies are run to describe its performance. [ABSTRACT FROM AUTHOR]

Biological samples of lipids and metabolites degrade after extensive years in −80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of −80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS–DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of −80 °C storage. With receiver operator characteristic curves, sparse PLS–DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at −80 °C when producing predictive metrics from polar metabolites, more so than lipids. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios. Methods: We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning. Results: Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively. Conclusions: By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions. [ABSTRACT FROM AUTHOR]

This paper presents Dynamic Gait Signature Analysis (DGSA), an innovative approach to gait analysis that leverages deep graph learning techniques. Unlike conventional methods, DGSA leverages multifaceted parameters and advanced deep graph learning techniques, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). These techniques enable a comprehensive analysis of gait dynamics, including the use of dynamic graph representation methods like Gait Cycle Joint Angles Graph and Gait Cycle Joint Power Graph. DGSA's unique framework allows for simultaneous prediction of neurological diseases, gait classification, and early detection of cognitive impairments. By modeling gait as dynamic graph structures, DGSA captures intricate relationships between body movements and foot positions, ultimately enhancing accuracy in classification and prediction tasks. Comprehensive experiments on real-world datasets demonstrate DGSA's robustness, generalization, and superiority in accuracy. Our approach achieves notable accuracy metrics: gait velocity (1.6 m/s), dynamic stability margin (5.6 cm), gait variability (2.4%), joint range of motion (56 degrees), dynamic balance index (0.4), minimum toe clearance (2.3 cm), foot progression angle (8.6 degrees), and dynamic joint stiffness (172). This study includes a comparative analysis of gait analysis approaches based on these key performance metrics, demonstrating DGSA's significant advancement in gait analysis methodology. [ABSTRACT FROM AUTHOR]

Dementia poses a substantial global health challenge, warranting an exploration of its intricate pathophysiological mechanisms and potential intervention targets. Leveraging multi-omic technology, this study utilizes data from 2251 participants to construct classification models using lipidomic, gut metabolomic, and cerebrospinal fluid (CSF) proteomic markers to distinguish between the states of cognitive decline, namely, the cognitively unimpaired state, mild cognitive impairment, and dementia. The analysis identifies three CSF proteins (apolipoprotein E, neuronal pentraxin-2, and fatty-acid-binding protein), four lipids (DEDE.18.2, DEDE.20.4, LPC.O.20.1, and LPC.P.18.1), and five serum gut metabolites (Hyodeoxycholic acid, Glycohyodeoxycholic acid, Hippuric acid, Glyceric acid, and Glycodeoxycholic acid) capable of predicting dementia prevalence from cognitively unimpaired participants, achieving Area Under the Curve (AUC) values of 0.879 (95% CI: 0.802–0.956), 0.766 (95% CI: 0.700–0.835), and 0.717 (95% CI: 0.657–0.777), respectively. Furthermore, exclusively three CSF proteins exhibit the potential to predict mild cognitive impairment prevalence from cognitively unimpaired subjects, with an AUC of 0.760 (95% CI: 0.691–0.828). In conclusion, we present novel combinations of lipids, gut metabolites, and CSF proteins that showed discriminative abilities between the states of cognitive decline and underscore the potential of these molecules in elucidating the mechanisms of cognitive decline. [ABSTRACT FROM AUTHOR]

BACKGROUND: The potential reappearance and/or expansion of vector‐borne diseases is one of the terrifying issues awaiting humanity in the context of climate change. The presence of competent Anopheles vectors, as well as suitable environmental circumstances, may result in the re‐emergence of autochthonous Malaria, after years of absence. In Morocco, international travel and migration movements from Malaria‐endemic areas have recently increased the number of imported cases, raising awareness of Malaria's possible reintroduction. Using machine learning we developed model predictions, under current and future (2050) climate, for the prospective distribution of Anopheles claviger, Anopheles labranchiae, Anopheles multicolor, and Anopheles sergentii implicated or incriminated in Malaria transmission. RESULTS: All modelled species are expected to find suitable habitats and have the potential to become established in the northern and central parts of the country, under present‐day conditions. Distinct changes in the distributions of the four mosquitoes are to be expected under climate change. Even under the most optimistic scenario, all investigated species are likely to acquire new habitats that are now unsuitable, placing further populations in danger. We also observed a northward and altitudinal shift in their distribution towards higher altitudes. CONCLUSION: Climate change is expected to expand the potential range of malaria vectors in Morocco. Our maps and predictions offer a way to intelligently focus efforts on surveillance and control programmes. To reduce the threat of human infection, it is crucial for public health authorities, entomological surveillance teams, and control initiatives to collaborate and intensify their actions, continuously monitoring areas at risk. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In this paper, some properties of the order dependent time-homogeneous load-sharing model are obtained, including an algorithmic procedure to simulate samples from this model. Then, the problem of how to get predictions of the future failure times is analysed in a sample from censored data (early failures). Punctual predictions based on the median, the mean and the convolutions of exponential distributions are proposed and prediction bands are obtained. Some illustrative examples show how to apply the theoretical results. An application in the study of lifetimes of coherent systems is proposed as well. [ABSTRACT FROM AUTHOR]

Regional budget assessments of methane (CH4) are critical for future climate and environmental management. CH4 emissions from rice cultivation (CH4-rice) constitute one of the most significant sources. However, previous studies mainly focus on historical emission estimates and lack consideration of future changes in CH4-rice under climate change or anthropogenic policy intervention, which hampers our understanding of long-term trends and the implementation of targeted emission reduction efforts. This study investigates the spatiotemporal variations of CH4-rice over the past two decades, using an integrated method to identify the major drivers and predict future emissions under climate change scenarios and policy perspectives. Results indicate that the CH4-rice emissions in China ranged between 6.21 and 6.57 Tg yr−1 over the past two decades, with a spatial distribution characterized by decreases in the south and increases in the north, associated with economic development, dietary shifts, technological advancements, and climate change. Factors such as the rate of straw added (RSA), fertilization, soil texture, temperature, and precipitation significantly influence CH4 emissions per unit rice production (CH4-urp), with RSA identified as the most significant tillage management factor, explaining 32 % of the variance. Lowering RSA to 8 % is beneficial for reducing CH4-urp. Scenario analysis indicates that under policies focusing on production or demand, CH4-rice is expected to increase by 0.3 % to 5.6 %, while adjusting RSA can reduce CH4-rice by 9.4 % to 10.0 %. Structural adjustments and regional cooperation serve as beneficial starting points for controlling and reducing CH4-rice in China, while optimizing industrial layouts contributes to regional development and CH4-rice control. Implementing policies related to maintaining field and crop yields can achieve a balance between rice supply and demand ahead of schedule. Dynamic adjustment of rice cultivation based on supply–demand balance can effectively reduce CH4-rice from excess rice production. By 2060, the reduction effect could reach 8.95 %–12.01 %. Introducing policy-driven tillage management measures as reference indicators facilitates the reduction of CH4-rice.

With the continuous evolution of the coronavirus, the Omicron variant has gradually replaced the Delta variant as the prevalent strain. Their inducing epidemics last longer, have a higher number of asymptomatic cases, and are more serious. In this article, we proposed a nonautonomous time-delayed susceptible-infected-removed (NATD-SIR) model to predict them in different regions of China. We obtained the maximum and its time of current infected persons, the final size, and the end time of COVID-19 epidemics from January 2022 in China. The method of the fifth-order moving average was used to preprocess the time series of the numbers of current infected and removed cases to obtain more accurate parameter estimations. We found that usually the transmission rate $ \beta(t) $ was a piecewise exponential decay function, but due to multiple bounces in Shanghai City, $ \beta(t) $ was approximately a piecewise quadratic function. In most regions, the removed rate $ \gamma(t) $ was approximately equal to a piecewise linear increasing function of (a*t+b)*H(t-k), but in a few areas, $ \gamma(t) $ displayed an exponential increasing trend. For cases where the removed rate cannot be obtained, we proposed a method for setting the removed rate, which has a good approximation. Using the numerical solution, we obtained the prediction results of the epidemics. By analyzing those important indicators of COVID-19, we provided valuable suggestions for epidemic prevention and control and the resumption of work and production.

Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoskeletons and prostheses, functional electrical stimulation (FES)-based Neuroprostheses, and more. Non-stationary temporal development of these indices in dynamic contractions makes forecasting difficult. This study aims at incorporating transfer learning into a deep learning model, Myoelectric Fatigue Forecasting Network (MEFFNet), to forecast myoelectric indices of fatigue (both time and frequency domain) obtained during voluntary and FES-induced dynamic contractions in healthy and post-stroke subjects respectively. Different state-of-the-art deep learning models along with the novel MEFFNet architecture were tested on myoelectric indices of fatigue obtained during ${a}\text {)}$ voluntary elbow flexion and extension with four different weights (1 kg, 2 kg, 3 kg, and 4 kg) in sixteen healthy subjects, and ${b}\text {)}$ FES-induced elbow flexion in sixteen healthy and seventeen post-stroke subjects under three different stimulation patterns (customized rectangular, trapezoidal, and muscle synergy-based). A version of MEFFNet, named as pretrained MEFFNet, was trained on a dataset of sixty thousand synthetic time series to transfer its learning on real time series of myoelectric indices of fatigue. The pretrained MEFFNet could forecast up to 22.62 seconds, 60 timesteps, in future with a mean absolute percentage error of 15.99 ± 6.48% in voluntary and 11.93 ± 4.77% in FES-induced contractions, outperforming the MEFFNet and other models under consideration. The results suggest combining the proposed model with wearable technology, prosthetics, robotics, stimulation devices, etc. to improve performance. Transfer learning in time series forecasting has potential to improve wearable sensor predictions.

AbstractThe development of a data-driven surrogate model (SM) is extensively studied in Eulerian–Lagrangian simulations for its advantage of high computational speed. However, in the application of granular systems with violent fluid-solid flows, how to select sufficient training data to ensure consistency between the high-fidelity model and SM remains unknown and highly challenging. The accuracy of SM can be easily deteriorated due to insufficient training data. This necessitates a trial-and-error process and hinders its industrial applications. To address this issue, this study newly reveals a finding that data density is a key to sufficient training, and we propose a novel technique for deciding the sufficient training data of SM. Specifically, a feasibility index is proposed based on posterior error analysis. It is demonstrated that when the training data is determined under the proposed feasibility index [Formula: see text] 2, the consistency of granular dynamics between SM and the high-fidelity model can be guaranteed. Employed in a representative SM, a reduced order model (ROM), this technique enables the successful decision of sufficient training data, resulting in the remarkable predictability in violent fluid-solid flows without trial-and-error. This technique holds great potential in solving the predicament of deciding training data for data-driven models.

Visual attention is often characterized as being guided by precise memories for target objects. However, real-world search targets have dynamic features that vary over time, meaning that observers must predict how the target could look based on how features are expected to change. Despite its importance, little is known about how target feature predictions influence feature-based attention, or how these predictions are represented in the target template. In Experiment 1 (N = 60 university students), we show observers readily track the statistics of target features over time and adapt attentional priority to predictions about the distribution of target features. In Experiments 2a and 2b (N = 480 university students), we show that these predictions are encoded into the target template as a distribution of likelihoods over possible target features, which are independent of memory precision for the cued item. These results provide a novel demonstration of how observers represent predicted feature distributions when target features are uncertain and show that these predictions are used to set attentional priority during visual search. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

With the continuous evolution of the coronavirus, the Omicron variant has gradually replaced the Delta variant as the prevalent strain. Their inducing epidemics last longer, have a higher number of asymptomatic cases, and are more serious. In this article, we proposed a nonautonomous time-delayed susceptible-infected-removed (NATD-SIR) model to predict them in different regions of China. We obtained the maximum and its time of current infected persons, the final size, and the end time of COVID-19 epidemics from January 2022 in China. The method of the fifth-order moving average was used to preprocess the time series of the numbers of current infected and removed cases to obtain more accurate parameter estimations. We found that usually the transmission rate β (t) was a piecewise exponential decay function, but due to multiple bounces in Shanghai City, β (t) was approximately a piecewise quadratic function. In most regions, the removed rate γ (t) was approximately equal to a piecewise linear increasing function of (a*t+b)*H(t-k), but in a few areas, γ (t) displayed an exponential increasing trend. For cases where the removed rate cannot be obtained, we proposed a method for setting the removed rate, which has a good approximation. Using the numerical solution, we obtained the prediction results of the epidemics. By analyzing those important indicators of COVID-19, we provided valuable suggestions for epidemic prevention and control and the resumption of work and production. [ABSTRACT FROM AUTHOR]

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Introduction: The present research highlights the promising factors that suggest the possible occurrence of the fourth COVID wave in India. India is among the major victim of this pandemic and has already witnessed the three dangerous COVID waves, and the lives of Indian people are put to a major setback. When it appears as the destruction caused by the third wave seems to getting over and people are trying to return to their normal lives, meanwhile with the mutation of the virus, the new COVID variants (COVID XBB 1.16 or Arcturus, XBB.1.5, and Delta Omicron) are again shaking our universe. Methods: Research studies and developed predictive models are suggesting the possible occurrence of the fourth COVID wave in the coming months. Considering the predictions and model developed, a critical insight is provided in the present study, which predicts the prospect of the fourth COVID wave in India. Results: Research proposed that the new COVID variants (COVID XBB 1.16, XBB.1.5, and Delta Omicron) are again leading to an increase in the number of active infected cases. Several factors, as mentioned in the research, are responsible for this spread of infection. Conclusions: Important factors are highlighted, which portray the probability of the fourth coronavirus wave in India. Important factors are presented in individuals, and an attempt is made to relate them with the occurrence or nonoccurrence of the fourth wave. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) presents a severe and urgent threat to global health. In response to the COVID-19 pandemic, many countries have implemented nonpharmaceutical interventions (NPIs), including national workplace and school closures, personal protection, social distancing, contact tracing, testing, home quarantine, and isolation. To evaluate the effectiveness of these NPIs in mitigating the spread of early COVID-19 and predict the epidemic trend in the United Kingdom, we developed a compartmental model to mimic the transmission with time-varying transmission rate, contact rate, disease-induced mortality rate, proportion of quarantined close contacts, and hospitalization rate. The model was fitted to the number of confirmed new cases and daily number of deaths in five stages with a Markov Chain Monte Carlo method. We quantified the effectiveness of NPIs and found that if the transmission rate, contact rate, and hospitalization rate were approximately equal to those in the second stage of the most strict NPIs, and the proportion of quarantined close contacts increased by 3%, then the epidemic would die out as early as January 12, 2021, with around 1,533,000 final cumulative number of confirmed cases, and around 55,610 final cumulative number of deaths. [ABSTRACT FROM AUTHOR]

The metallurgical sector is one of the important sectors of the Slovak economy. Its integral part is the storage of metallurgical waste, which is accompanied by noise that bothers the inhabitants of the surrounding urban areas. This paper focuses on the analysis of the problem of noise propagation into protected areas located in the vicinity of the metallurgical plant. The paper describes a number of measurements that have been carried out at the slag landfill. Based on these measurements, simulations were performed using a mathematical model, and predictions of noise propagation in the exterior were made. Subsequently, noise reduction measures were proposed. The results obtained by the authors form a methodological basis for addressing such situations, since, during the solution, it was often necessary to deal with non-standard situations that were specific to the area of the technology addressed. This solution was then applied in real practice. [ABSTRACT FROM AUTHOR]

The maximum slope of the sunspot number during the rising phase of a sunspot cycle has an excellent correlation with the maximum value of the sunspot number during that cycle. This is demonstrated using a Savitzky–Golay filter to both smooth and calculate the derivative of the sunspot-number data. Version 2 of the International Sunspot Number (S ) is used to represent solar activity. The maximum of the slope during the rising phase of each cycle was correlated against the peaks of solar activity. Using three different correlation fits, the average predicted amplitude for Solar Cycle 25 is 130.7 ± 0.5, among the best correlations in solar predictions. A possible explanation for this correlation is given by the similar behavior of a shape function representing the time variation of the sunspot number. This universal function also provides the timing of the solar maximum by the time from the slope maximum to the peak in the function as late 2023 or early 2024. A Hilbert transform gives similar results, which are caused by the dominance of the 11-yr sunspot-cycle period in a Fourier fit of the sunspot number. [ABSTRACT FROM AUTHOR]

This 17-year prospective study applied a social-development lens to the challenge of identifying long-term predictors of adult depressive symptoms. A diverse community sample of 171 individuals was repeatedly assessed from age 13 to age 30 using self-, parent-, and peer-report methods. As hypothesized, competence in establishing close friendships beginning in adolescence had a substantial long-term predictive relation to adult depressive symptoms at ages 27–30, even after accounting for prior depressive, anxiety, and externalizing symptoms. Intervening relationship difficulties at ages 23–26 were identified as part of pathways to depressive symptoms in the late twenties. Somewhat distinct paths by gender were also identified, but in all cases were consistent with an overall role of relationship difficulties in predicting long-term depressive symptoms. Implications both for early identification of risk as well as for potential preventive interventions are discussed. [ABSTRACT FROM AUTHOR]

In recent years, several places all over the world have experienced devastating wildfire events that have resulted in exorbitant losses. With the climate warming every year, the trend of destructive fires is expected to continue in the future as well. To be better prepared for such events, an understanding of interaction between wildfires and the built environment is required. In this study, a graph-based approach is utilized to predict the damage to individual buildings in a test area selected from the 2022 Marshall Fire–affected region. The validity of the graph model is first shown by comparing the predicted damage with the observed damaged patterns, showing 72% accuracy. The model is further utilized to investigate the sensitivity of the damage patterns to wind conditions to better understand the correlation between wind characteristics and the resulting damage. For two wind speeds, 12 and 25 m/s , polar fragilities are developed by evaluating the damage for different wind directions. The results showed the variation in damage distribution among all wind directions for 25 m/s to be more than twice that for 12 m/s , highlighting the fact that the impact of wind conditions is nonlinear and needs to be carefully considered for wildfire risk assessment. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of dynamic multi- objective optimization problems (DMOPs), by demonstrating new approaches to change detection and change prediction in an evolutionary algorithm framework. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic. First, we propose a new change detection method which achieves greater sensitivity by considering changes in both the PS and the PF, unlike most previous approaches. Second, when changes occur, a second-order (acceleration-based) prediction strategy is proposed to predictively reinitialize the population close to the new set of optima. We compare the performance of the proposed algorithm against two other state-of-the-art algorithms from the literature, using ten different dynamic benchmark problems. Experimental results show that the proposed change detection strategy in this paper can not only consider the effect of the optimal individuals but also can consider the effect of their corresponding objective values. Compared with the other two methods, the DMOPs achieved both the ability of precisely predicting the direction of changes and the ability of predicting the future trend of change direction. So, the DMOPs can also converge to the true PF in much less iterations compared with other methods. After multiple experiments, the proposed method outperforms the other algorithms on most of the test problems. [ABSTRACT FROM AUTHOR]

Industry 4.0 uses artificial intelligence and machine learning algorithms to optimize processes. The main goal of the manuscript is to analyze the possibilities of applying automated machine learning to predict company bankruptcy. The data sample consists of financial data of 9,771 manufacturing companies for the years 2020 and 2021 collected from the Finstat database. Two methods of automated machine learning, AutoML and H2O, were tested. The results were compared with five other methods - linear discriminant analysis, logistic regression, naive Bayes classifier, CatBoost and XGBoost. The resulting model was cross-validated through the 10-fold approach. The best results were achieved by H2O automated machine learning algorithm with an AUC of 90.13%, followed by the gradient boosting methods CatBoost with AUC of 90.05% and XGBoost (AUC of 88.61%) and another automated learning algorithm AutoML with AUC of 81.17%. The findings of this paper indicate possibilities to apply automated machine learning methods in predicting bankruptcy. However, it is necessary to distinguish between individual automated machine learning algorithms since they provide a different range of results. [ABSTRACT FROM AUTHOR]

Addictions are physical and/or psychological dependency disorders characterized by repetitive and compulsive behaviours in which the individual has difficulty controlling impulses. In this study, behaviours are defined as chronic alcohol or substance use. Thus, compulsions will generate negative consequences on a person's quality of life. Among the causes of addiction are genetic and biological factors, environmental factors, trauma, abuse, lack of emotional support, stress, social pressures, poor coping mechanisms, social and cultural factors. Material and method In this study, part of a larger study, 81 patients diagnosed with toxicnutritional liver cirrhosis, chronic alcohol users, aged between 32 and 68 years were included. The study period was of two years, and anamnestic data (number of hospitalization days, hospitalization frequency) were collected for the period 2015-2022. In order to carry out the linear research, SPSS statistical software was used. Patients, after obtaining consent, were administered a psychological questionnaire designed to assess unconditional selfacceptance, based on the hypothesis that low self-tolerance generates selfdestructive behaviours, i.e. addictions. Results The necessary statistical steps were followed in order to check the database and it was possible to obtain correlations between the number of hospitalization days, the hospitalization frequency and the scores obtained in the questionnaire. Finally, it was possible to generate a simple linear regression prediction with the number of hospitalisation days/inpatient frequency as the dependent variable and the test score as the independent variable. The results showed that as unconditional self-acceptance decreases (decreasing score), the hospitalization duration or the number of patient presentations to the doctor increases. Conclusions Unconditional self-acceptance, environmental tolerance, coping mechanisms have a major impact on the patient's well-being and compliance with treatment. Psychosomatic disorders accompany the addiction patient to a much greater extent than the diagnosis made by clinicians. This demonstrates the need for a diagnostic tool, the lack of collaboration with the psychiatrist, ultimately generating costs on the health system and reducing the quality of the patient's life. In order to optimise the diagnosis, a tool within the clinician's reach (internal medicine doctor, gastroenterologist, etc.) and a real collaboration with the psychiatrist or clinical psychologist is necessary. [ABSTRACT FROM AUTHOR]

Urban green spaces improve quality of life by mitigating urban temperatures. However, there are challenges in obtaining urban data to analyze and understand their influence. With the aim of developing innovative methodologies for this type of research, Artificial Neural Networks (ANNs) were developed to predict daily and hourly temperatures in urban green spaces from sensors placed in situ for 41 days. The study areas were four urban allotment gardens (with dynamic and productive vegetation) and a forested urban park in the city of Valladolid, Spain. ANNs were built and evaluated from various combinations of inputs (X), hidden neurons (Y), and outputs (Z) under the practical rule of "making networks simple, to obtain better results". Seven ANNs architectures were tested: 7-Y-5 (Y = 6, 7, ..., 14), 6-Y-5 (Y = 6, 7, ..., 14), 7-Y-1 (Y = 2, 3, ..., 8), 6-Y-1 (Y = 2, 3, ..., 8), 4-Y-1 (Y = 1, 2, ..., 7), 3-Y-1 (Y = 1, 2, ..., 7), and 2-Y-1 (Y = 2, 3, ..., 8). The best-performing model was the 6-Y-1 ANN architecture with a Root Mean Square Error (RMSE) of 0.42 °C for the urban garden called Valle de Arán. The results demonstrated that from shorter data points obtained in situ, ANNs predictions achieve acceptable results and reflect the usefulness of the methodology. These predictions were more accurate in urban gardens than in urban parks, where the type of existing vegetation can be a decisive factor. This study can contribute to the development of a sustainable and smart city, and has the potential to be replicated in cities where the influence of urban green spaces on urban temperatures is studied with traditional methodologies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a profound impact on people's lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively assess their preventive effects. The results show that the GTNNWR model exhibits superior predictive capacity to the conventional infectious disease dynamics model (SEIR model), which is approximately 9% higher, and reflects the spatial and temporal heterogeneity well. In scenario simulations, this study established five scenarios for epidemic prevention measures, and the results indicate that masks are the most influential single preventive measure, reducing deaths by 5.38%, followed by vaccination at 3.59%, and social distancing mandates at 2.69%. However, implementing single stringent preventive measures does not guarantee effectiveness across all states and months, such as California in January 2025, Florida in August 2024, and March–April 2024 in the continental U.S. On the other hand, the combined implementation of preventive measures proves 5 to-10-fold more effective than any single stringent measure, reducing deaths by 27.2%. The deaths under combined implementation measures never exceed that of standard preventive measures in any month. The research found that the combined implementation of measures in mask wearing, vaccination, and social distancing during winter can reduce the deaths by approximately 45%, which is approximately 1.5–3-fold higher than in the other seasons. This study provides valuable insights for COVID-19 epidemic prevention and control in America. [ABSTRACT FROM AUTHOR]