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Background: Recent analyses have shown that in health services research in Germany, healthcare organisations are often considered primarily as a study setting, without fully taking their complex organisational nature into account, neither theoretically nor methodologically. Therefore, an initiative was launched to analyse the state of Organisational Health Services Research (OHSR) in Germany and to develop a strategic framework and road map to guide future efforts in the field. This paper summarizes positions that have been jointly developed by consulting experts from the interdisciplinary and international scientific community., Methods: In July 2023, a scoping workshop over the course of three days was held with 32 (inter)national experts from different research fields centred around OHSR topics using interactive workshop methods. Participants discussed their perspectives on OHSR, analysed current challenges in OHSR in Germany and developed key positions for the field's development., Results: The seven agreed-upon key positions addressed conceptual and strategic aspects. There was consensus that the field required the development of a research agenda that can guide future efforts. On a conceptual level, the need to address challenges in terms of interdisciplinarity, terminology, organisation(s) as research subjects, international comparative research and utilisation of organisational theory was recognized. On a strategic level, requirements with regard to teaching, promotion of interdisciplinary and international collaboration, suitable funding opportunities and participatory research were identified., Conclusions: This position paper seeks to serve as a framework to support further development of OHSR in Germany and as a guide for researchers and funding organisations on how to move OHSR forward. Some of the challenges discussed for German OHSR are equally present in other countries. Thus, this position paper can be used to initiate fruitful discussions in other countries., Competing Interests: LA ist Vorstandsmitglied der DGMS und des DNVF. Sie ist Mitglied des Editorial Boards des Journal of Health Care Services and Implementation. JB erhält Fördermittel für mehrere OVF-Projekte in Australien und Europa. ME ist Vorsitzender der Society for Studies in Organizing Healthcare (SHOC). Die Arbeit des Instituts für Arbeitsmedizin, Sozialmedizin und Versorgungsforschung, Universitätsklinikum Tübingen, wird durch einen zweckungebundenen Zuschuss des Arbeitgeberverbands der Metall- und Elektroindustrie Baden-Württemberg (Südwestmetall) unterstützt. Die übrigen Autor*innen erklären, dass keine Interessenkonflikte bestehen., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

This technical note describes in detail a method for associating individual sheets of blank A4 white paper from the same ream by the physical fit of machine‐cut edges. A large‐scale laboratory trial involving ~700 sheets of paper from 24 different reams (plus one spoiled sample), and more than 20,000 potential physical fits, correctly associated and sequenced 219 pairs of sheets together with a 100% empirical success rate and no false associations. The edge profile of each short machine‐cut end of a sheet of A4 paper allows us to physically fit sheets of paper from the same ream to each other and use this to predict the sequence of sheets in a set of documents. In a real‐life scenario, it may now be possible to detect the substitution or addition of a sheet in a multipage document, link documents from different sources to each other or to a common source of paper (e.g. to paper from a seized printer or from an accused's address) or to date documents. The study provides data for the application of this method in forensic casework and supports the practitioner when forming conclusions in this type of case. [ABSTRACT FROM AUTHOR]

Introduction: The "future of work" refers to workplace modifications projected to occur following large scale events impacting workforce health and safety such as pandemics, technological advancements, or economic shifts. This paper examines the influence of the COVID-19 pandemic on the future of work., Methods: The electronic databases PubMed, Google Scholar, and Scopus were explored using search terms such as "post-pandemic," "working force," and "future of work" to retrieve papers published between 2000 and 2023., Results: The main characteristics of the future of work modification revealed are blurring of work-home boundaries, increase in telework and the gig economy, demand for artificial intelligence, smart working, quiet hiring, diversity and inclusion, and algocratic governance. Needed skills include virtual collaboration capability, cross-cultural competency, computational thinking, news media literacy, transdisciplinarity, sense making, and focusing on mental health and well-being., Conclusions: Organizations are remodeling workforces to meet employee, employer, regulatory, and market expectations to adapt to future workplace needs., Competing Interests: Conflict of interest: None declared., (Copyright © 2024 American College of Occupational and Environmental Medicine.)

Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making., (© 2024. The Author(s) under exclusive licence to International Society of Biometeorology.)

Decision prediction based on neurophysiological signals is of great application value in many real‐life situations, especially in human–AI collaboration or counteraction. Single‐trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision‐prediction system. However, previous EEG‐based decision‐prediction methods focused mainly on averaged EEG signals of all decision‐making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a single trial. In the present study, we used a rock–paper–scissors game, which is a common multichoice decision‐making task, to explore how to predict participants' single‐trial choice with EEG signals. Forty participants, comprising 20 females and 20 males, played the game with a computer player for 330 trials. Considering that the decision‐making process of this game involves multiple brain regions and neural networks, we proposed a new algorithm named common spatial pattern‐attractor metagene (CSP‐AM) to extract CSP features from different frequency bands of EEG signals that occurred during decision making. The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57% (31 of 35) of participants, verifying the classification ability of CSP features in multichoice decision‐making prediction. We believe that the CSP‐AM algorithm could be used in the development of proactive AI systems. [ABSTRACT FROM AUTHOR]

Radiological imaging is a crucial diagnostic tool for the pediatric population. However, it is associated with several unique challenges in this age group compared to adults. These challenges mainly come from the fact that children are not small-sized adults and differ in development, anatomy, physiology, and pathology compared to adults. This paper reviews relevant articles published between January 2015 and October 2023 to analyze challenges associated with imaging technologies currently used in pediatric radiology, emerging technologies, and their role in resolving the challenges and future prospects of pediatric radiology. In recent decades, imaging technologies have advanced rapidly, developing advanced ultrasound, computed tomography, magnetic resonance, nuclear imaging, teleradiology, artificial intelligence, machine learning, three-dimensional printing, radiomics, and radiogenomics, among many others. By prioritizing the unique needs of pediatric patients while developing such technologies, we can significantly alleviate the challenges faced in pediatric radiology., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The article focuses on the Institute for Supply Management's quarterly forecast, which predicts spending increases for printing and paper manufacturing. It mentions the forecast indicates that manufacturing and services sectors are expected to experience revenue growth, while capital expenditures and prices paid for raw materials are projected to increase.

This study proposes a novel method to forecast the emerging patenting area with Main Path Analysis and Word Cloud Analysis. To test the methods, we used Wind Power Energy as an example to illustrate the method's usefulness. Firstly, we used "wind power" and "wind energy" to collect 40,827 related journal papers in Scopus and 72,979 related patents in Derwent Innovation databases. Main Path Analysis was conducted to explore the development trajectory. The results of the Main Path Analysis for the papers and patents were visualized with Pajek software. Secondly, we used VOSviewer to extract the technological areas (i.e., keywords) of the collected academic papers and patents. Then, we calculated the average time lag between the first paper published and the first patent filed for each technological area (keyword). Finally, we forecasted the trend of patenting for wind power energy based on the average lag time and academic research themes in recent years. [ABSTRACT FROM AUTHOR]

Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables., Results: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R 2  = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82)., Conclusion: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry., (© 2024 Society of Chemical Industry.)

The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated as part of the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6 ∘ C to almost zero, while the daily mean absolute error decreased from 5 ∘ C to 3 ∘ C for the neural network and 3.5 ∘ C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day., (© 2024. The Author(s).)

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks 1 . Accurate and timely warnings are critical for mitigating flood risks 2 , but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings., (© 2024. The Author(s).)

Paper, a porous-fibrous network, is made up of hydrophilic fibres, which are notably susceptible to deformations due to variations in moisture content. The response of paper sheets to full or partial wetting, based on the water-induced fibre swelling, has mostly been predicted in the past using highly idealised two-dimensional network geometries. The purpose of the present paper is to establish how precisely the two-stage process of determining effective hygro-expansivity under uniform wetting by a two-dimensional model followed by bending analysis using a continuum model predicts the curl of a fully three-dimensional network. In the present multi-scale modelling work, the moisture-induced homogenised sheet scale curl, derived from hygro-elastic deformations using non-uniform moisture profiles, is analysed based on an idealised non-woven three-dimensional fibrous network finite element model. First, the effective paper sheet swelling properties, derived computationally for the uniform wetting condition, are compared with analytically homogenised hygro-elastic properties. Whereas the analytically predicted elastic modulus has an inaccuracy of only ∼ 5%, the hygro-expansivity coefficient is over-predicted by roughly a third for a network with straight fibres. The difference is smaller if waviness of the fibres and wrap-around in the fibre bonds are taken into account. Fibre alignment turns out to have little influence. Subsequently, paper sheet curl predictions made by the analytical model are compared with that computed from the three-dimensional network model. The inaccuracy of the fully analytical model, based on the two-dimensional network description, relative to the fully detailed three-dimensional computational model with straight fibres is on the order of 40%–50%. The fidelity of the simple model is primarily constrained by the inaccuracy of the effective hygro-expansion coefficient. If the expansivity extracted from the three-dimensional simulations under uniform wetting is inserted, the inaccuracy drops to less than 10%. The continuum bending description hence performs adequately, provided it is based on a reliable estimation of the uniform expansion behaviour of the material. • Hygroscopic curl of paper sheets may be predicted using homogenised models. • We assess the accuracy of such predictions. • As a reference simulations of an idealised three-dimensional fibre network are used. • The predictions are accurate provided an accurate hygro-expansivity is used. • Two-dimensional network models may not be sufficient for this. [ABSTRACT FROM AUTHOR]

The thin-walled tubular deployable composite boom (DCB) can realize folding and deploying functions, and it has a good application prospect in space field. This paper investigates the folding behaviour of the tubular DCB by analytical modeling. Based on the Archimedes' helix, the geometrical model of the DCB was established. By combining equilibrium equation and energy principle, an analytical model to predicted the folding moment versus rotational displacement of the DCB was presented. The failure indices in the equal-sense and opposite-sense folding processes were calculated utilizing Tsai‐Hill and maximum stress criteria. Analytical results agreed well with experimental and numerical results. At last, the influence of geometric parameters (i.e., radius, central angle and thickness of cross-section) on the folding behaviour of the DCB was further studied using the analytical model. [Display omitted] • An analytical model is presented to predict the folding behaviour of the deployable composite boom (DCB). • Failure indices are calculated using two criteria in equal-sense and opposite-sense folding processes. • The analytical model is validated against experimental and numerical results, showing good prediction accuracy. • Geometric parameters (radius, central angle, and thickness) significantly affect the folding behavior of the DCB. [ABSTRACT FROM AUTHOR]

This study examined the effect of different parameters on the collapsibility of reconstituted sandy soils. The effect of related parameters including silt content, type of fines, initial water content, drying and wetting process, and wetting pressure have been studied. Collapse potential and matric suction were measured via the single oedometer collapse test and the ASTM filter paper method, respectively. The current study revealed that matric suction has a major role in determining the collapsibility of sandy soils. The effect of parameters related to soil index properties were reflected on the initial matric suction. In addition, an equation was proposed to predict collapse potential as a function of the two stress-state variables that control the behavior of an unsaturated soil, i.e. net normal stress and matric suction before wetting. The matric suction measurements were also used to generate soil water characteristic curves (SWCCs) based on the most widely used models. Finally, a simplified relationship between initial matric suction and the degree of collapse based was established. [ABSTRACT FROM AUTHOR]

The problem of emission control in chemical fiber industry and construction of multifactor model of pollution index prediction is studied. A neural network method that makes it possible to derive regularities of a unidimensional series for PM 2.5 and simultaneously determine the influence of a set of factors of weather conditions is described. To reduce the complexity of the model, instead of the range of factors of influence, its main components are used. The interconnection of the number of factors of influence, depth of retrospective sampling, type and scale of neural network, and accuracy of prediction is studied. [ABSTRACT FROM AUTHOR]

Rainfall is one of the remarkable hydrologic variables that is directly connected to the sustainable environment for any region over the globe. The present study aims to review different research papers on rainfall forecasting using artificial intelligence (AI) models including a bibliographic assessment of the most popular AI models and a comparison of the results based on the accuracy parameters. 39 journal papers, published in renowned international journals from 2000 to 2023, were studied extensively to categorize modeling techniques, best models, characteristics of input data, the period for the input variables, data division, and so forth. Although certain drawbacks still exist, the results of reviewed studies suggest that AI models may help simulate rainfall in various geographic locations. In some cases, the data splitting mechanism was delivered to the model itself so that the model accuracy gets improved. The recommendations from the reviewed papers will help future researchers fill the research gaps, especially tuning the hyperparameters while building the training models. Hybrid models were advised in some cases to minimize the gap between the simulated and the observed data. All recommendations from reviewed papers aimed to achieve a resilient rainfall forecasting model in the era of climate change. [ABSTRACT FROM AUTHOR]

We contribute to the empirical literature on the predictability of oil-market volatility by comparing the predictive role of aggregate versus several disaggregated metrics of policy-related and equity-market uncertainties of the USA and geopolitical risks for forecasting the future realized volatility of oil-price (WTI) returns over the monthly period from 1985:01 to 2021:08. Using machine-learning techniques, we find that adding the disaggregated metrics to the array of predictors improves the accuracy of forecasts at intermediate and long forecast horizons, and mainly when we use random forests to estimate our forecasting model., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

With a sample of 228 psychology papers that failed to replicate, we tested whether the trajectory of citation patterns changes following the publication of a failure to replicate. Across models, we found consistent evidence that failing to replicate predicted lower future citations and that the size of this reduction increased over time. In a 14-y postpublication period, we estimated that the publication of a failed replication was associated with an average citation decline of 14% for original papers. These findings suggest that the publication of failed replications may contribute to a self-correcting science by decreasing scholars’ reliance on unreplicable original findings. [ABSTRACT FROM AUTHOR]

With the ever-increasing number of submissions in top-tier conferences and journals, finding good reviewers and meta-reviewers is becoming increasingly difficult. Writing a meta-review is not straightforward as it involves a series of sub-tasks, including making a decision on the paper based on the reviewer's recommendation and their confidence in the recommendation, mitigating disagreements among the reviewers, and other such similar tasks. In this work, we develop a novel approach to automatically generate meta-reviews that are decision-aware and which also take into account a set of relevant sub-tasks in the peer-review process. More specifically, we first predict the recommendation scores and confidence scores for the reviews, using which we then predict the decision on a particular manuscript. Finally, we utilize the decision signals for generating the meta-reviews using a transformer-based seq2seq architecture. Our proposed pipelined approach for automatic decision-aware meta-review generation achieves significant performance improvement over the standard summarization baselines as well as relevant prior works on this problem. We make our codes available at https://github.com/saprativa/seq-to-seq-decision-aware-mrg. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

Purpose: The present study aims to show the similarities and differences of the 18 sub-fields related to each of the six concepts of futures studies, foresight, futurology, future thinking, futurism and future mining by studying and comparing the topics investigated in the scientific productions of futures studies (in its general sense). Method: The method of collecting information and the method of analysis are scientific and specialized library research. Data analysis was done using Excel software. The research is descriptive-exploratory in terms of practical purpose and in terms of nature and method, was carried out using scientometric techniques and qualitative approach in order to collect data to conceptualize and provide prescriptive analysis. The basis of analyzes is scientific research and scientific promotion articles indexed in the database of Iranian scientific publications (Magiran). Findings: The results show that the highest frequency of articles in the field of futures studies is urban and regional 205 articles, religious and religious 128 articles, and defense and military 102 articles. In addition, the highest rate of growth of articles in the studied fields is in 2022. Conclusion: The results of this research are in line with the comparison of the frequency of scientific articles published in the six studied concepts related to the future in the fifteen -year period of 2008 to 2022, and the results of the analysis show that the highest frequency of articles is related to the concept of futures studies with 822 and foresight with 279 respectively. The research title is dedicated. In addition, the highest growth of articles in these fifteen years is for the concept of futures studies. [ABSTRACT FROM AUTHOR]

Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables. [ABSTRACT FROM AUTHOR]