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Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.

Non-invasively evaluating gene expression products in human pre-implantation embryos remains a significant challenge. Here, we develop a non-invasive method for comprehensive characterization of the extracellular RNAs (exRNAs) in a single droplet of spent media that was used to culture human in vitro fertilization embryos. We generate the temporal extracellular transcriptome atlas (TETA) of human pre-implantation development. TETA consists of 245 exRNA sequencing datasets for five developmental stages. These data reveal approximately 4,000 exRNAs at each stage. The exRNAs of the developmentally arrested embryos are enriched with the genes involved in negative regulation of the cell cycle, revealing an exRNA signature of developmental arrest. Furthermore, a machine-learning model can approximate the morphology-based rating of embryo quality based on the exRNA levels. These data reveal the widespread presence of coding gene-derived exRNAs at every stage of human pre-implantation development, and these exRNAs provide rich information on the physiology of the embryo.

Background: Spontaneous preterm birth (sPTB) is a primary cause of adverse neonatal outcomes. The objective of this study is to analyze the factors influencing the occurrence of sPTB in pregnant women and to construct and validate a predictive model for sPTB risk based on big data from clinical and laboratory assessments during pregnancy. Methods: A retrospective analysis was conducted on the clinical data of 3,082 pregnant women, categorizing those who delivered before 37 weeks of gestation as the sPTB group and those who delivered at or after 37 weeks as the full-term group. The performance of five machine learning models was compared using metrics such as the AUC, accuracy, sensitivity, specificity, and precision to identify the optimal predictive model. The top 10 predictive variables were selected based on their significance in disease prediction. The data were then divided into a training set (70%) and a validation set (30%) for validation. External data were also utilized to validate the model's predictive performance. Results: A total of 24 indicators with significant differences were identified. In terms of predicting the risk of preterm birth, the XGBoost algorithm demonstrated the most outstanding performance, with an AUCROC of 0.89 (95% CI: 0.88–0.90). The top 10 critical indicators included ALP, AFP, ALB, HCT, TC, DBP, ALT, PLT, height, and SBP, which are essential for constructing an accurate predictive model. The model exhibited stable performance on both the training and validation sets, with AUC values of 0.93 and 0.87, respectively. Furthermore, the external testing set also showed superior performance, with an AUC of 0.79. Conclusions: At the time of delivery, ALP, AFP, ALB, HCT, TC, DBP, ALT, PLT, height, and SBP are influential factors for sPTB in pregnant women. The XGBoost algorithm, constructed based on these factors, demonstrated the most outstanding performance. [ABSTRACT FROM AUTHOR]

Purpose: To identify more reliable imaging and serological indicators for predicting Ki-67 expression and malignant potential in gastrointestinal stromal tumors, as well as to develop a preoperative prediction model with clinical utility. Patients and methods: This study retrospectively analyzed patients with gastrointestinal stromal tumors (GIST) diagnosed at the First Affiliated Hospital of Jinzhou Medical University between May 2018 and May 2024. Univariate logistic analyses, two-way stepwise regression, P-value stepwise regression, and LASSO regression were employed to screen for Ki-67 high expression and high malignant potential risk factors associated with GIST. Models were established using various regression methods; Nomograms, calibration curves, and clinical decision curves were generated for the two best prediction models. Results: Two-way stepwise regression analysis revealed that diameter (P=0.037; OR=1.22; 95% CI: 1.01 - 1.46), growth pattern (extraluminal type: P=0.028; OR=3.54; 95% CI: 1.14 - 10.94), enhancement model (P=0.099; OR=0.39; 95% CI: 0.12 - 1.20), EVFDM (P=0.069; OR=0.43; 95% CI: 0.17 - 1.07), PLR (P=0.099; OR=3.06; 95% CI: 0.81 - 11.59), and OPNI (P=0.058; OR=2.38; 95% CI: 0.97 - 5.84) are identified as independent risk factors for Ki-67 expression. Utilizing the two-way stepwise regression model to predict Ki-67 expression, the area under the curve (AUC) for the training group was 0.865 (95% CI: 0.807-0.922), while for the validation group it was 0.784 (95% CI: 0.631-0.937). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for the training group were 153.360 and 174.619, respectively. Two-way stepwise regression analysis revealed that volume (P <.001, OR = 1.06; 95% CI: 1.03 - 1.09), contour (P = 0.066; OR = 0.17; 95% CI: 0.05 - 0.62), ulcer (P = 0.094; OR = 0.16; 95% CI: 0.03 - 0.98), IBSC (P = 0.008; OR = 5.27; 95% CI: 1.57 - 17.69), and OPNI (P = 0.045; OR = 0.22; 95% CI: 0.05 - 0.96) are independent risk factors for malignant potential. Utilizing the two-way stepwise regression model to predict malignant potential, the AUC for the training group was 0.950 (95% CI: 0.920 - 0.980), while for the validation group it was 0.936 (95% CI: 0.867 - 1.000). The AIC and BIC values for the training group were 96.330 and 114.552, respectively. Conclusion: Diameter, growth pattern, enhancement pattern, EVFDM, PLR, and OPNI are independent risk factors for GIST with high Ki-67 expression. Additionally, volume, contour, ulceration, IBSC, and OPNI serve as independent risk factors for GIST with high malignant potential. The preoperative models developed using CT images can predict the malignant potential and Ki-67 expression status of GIST to a certain extent. When combined with serological indicators, these models' predictive performance can be further enhanced. [ABSTRACT FROM AUTHOR]

The photothermal sensitivity of tobacco refers to the degree to which tobacco responds to changes in light and temperature conditions in its growth environment, which is crucial for determining the planting area of cultivars and improving tobacco yield and quality. In order to accurately and effectively evaluate the photothermal sensitivity of tobacco cultivars, this study selected five cultivars and their hybrid combinations with significant differences planted under different ecological conditions from 2021 to 2022 as materials. The experiment was conducted in two locations with significant differences in temperature and light. We measured the agronomic traits and biomass of the experimental materials, and constructed an effective tobacco photothermal sensitivity evaluation model using principal component analysis, membership function, and regression analysis. The reliability of the model was evaluated by utilizing the photosynthetic characteristics, chlorophyll content, and antioxidant enzyme system activity of the experimental materials. The results showed that tobacco biomass is the most important principal component in agricultural traits, and the comprehensive evaluation model for tobacco photothermal sensitivity is: y = 0.4571y1 + 0.2406y2 + 0.1725y3, where the fitting coefficients R2 of y1, y2, and y3 are 0.945, 0.851, and 0.977, respectively; The photothermal sensitivity of the experimental materials was calculated using this model, and the comprehensive ranking of the 11 experimental materials is: G3 < G5 < G10 < G9 < G11 < G6 < G7 < G2 < G4 < G8 < G1. Conventional identification methods have found that G2, G4, G6, G7, G8, and G11 are sensitive materials, G3, G5, and G10 are insensitive materials, and G1 and G9 are intermediate materials. The consistency rate of the evaluation results of the two methods reached 90.91%. And there is a significant correlation between the agronomic traits selected in the model and the physiological indicators selected by conventional evaluation methods, providing a scientific basis for evaluating the light temperature sensitivity of tobacco cultivars using agronomic traits in this study. The results indicate that the photothermal sensitivity evaluation model established in this study provides an efficient, convenient, and reliable method for evaluating the photothermal sensitivity of tobacco. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Method Results Discussion Avoidant/restrictive food intake disorder (ARFID) is an eating disorder characterized by a severely restrictive diet leading to significant physical and/or psychosocial sequelae. Largely owing to the phenotypic heterogeneity, the underlying pathophysiological mechanisms are relatively unknown. Recently, the communication between microorganisms within the gastrointestinal tract and the brain—the so‐called microbiota‐gut‐brain axis—has been implicated in the pathophysiology of eating disorders. This Spotlight review sought to investigate and conceptualize the possible ways that the microbiota‐gut‐brain axis is involved in ARFID to drive future research in this area.By relating core symptoms of ARFID to gut microbiota and its signaling pathways to the brain, we evaluated how the gut microbiota is potentially involved in the pathophysiology of ARFID.We hypothesized that the restricted type and amount of food intake characteristic of ARFID diminishes gut microbial diversity, including beneficial bacteria and their metabolites capable of signaling to the brain, to modulate biopsychological pathways relevant to ARFID: homeostatic signaling, food reward, interoception, sensory sensitivity, disgust, perseveration, fear‐based learning, and mood. Candidate signaling mechanisms include microbial‐induced effects on inflammation, cortisol, and neurotransmitters such as dopamine and serotonin.Through reviewing the extant evidence, we conceptualized a new theoretical framework of ARFID with an emphasis on microbiota‐gut‐brain axis signaling to inform future research. Although more research is necessary to evaluate this theoretical model, the tentative evidence suggests that therapeutics specifically targeting the gut microbiota for the treatment of ARFID symptomatology warrants more investigation. [ABSTRACT FROM AUTHOR]

In the design and assessment of thick concrete shell structures subjected to out‐of‐plane loads, there is often a need to evaluate the response of shear critical components or to evaluate the influence of combined loading on shear response. This paper first presents the results from three shear‐critical large‐scale shell strip experiments subjected to combinations of shear, moment, and axial load. The experiments were monitored with digital image correlation equipment to capture the full deformation field response throughout loading. The paper then presents a simplified three‐dimensional model to predict the shear response of shear critical shell elements. The model called the 3D SEM, is compared to the shell strip experiments presented in this paper and is used to predict the interaction of in‐plane versus out‐of‐plane shear for several series of shear critical shell tests in the literature. [ABSTRACT FROM AUTHOR]

Background: Malaria remains a significant public health burden, necessitating evidence-based strategies to reduce prevalence and associated morbidity. This study explores the potential of integrated healthcare, encompassing both modern and traditional herbal medicine (THM), for malaria control in Ghana. Methods: Employing a qualitative approach, semi-structured interviews were conducted with medical doctors, pharmacists, and THM providers. Thematic analysis approach was utilized to inductively analyse interview data and integrate participants' lived experiences and suggestions. Results: Six themes emerged: Standardization of THM practice; Training on THM broadened; Research on THM expanded; Increasing awareness of THM integration hospitals and inclusion of THM in national health insurance scheme; Constant supply of certified herbal medications; and Tax relief provision. These recommendations form the 'STRICT' framework for developing functional health system for promoting an effective malaria control through integrated healthcare in Ghana. Conclusion: It was evident that the 'STRICT' framework can potentially transform healthcare delivery and improve service quality for malaria patients. Policymakers, healthcare providers, and managers can utilize these insights to advocate for and implement integrated healthcare strategies, ultimately enhancing service delivery for all Ghanaians, particularly those suffering from malaria. [ABSTRACT FROM AUTHOR]

Two lignin model compounds, eugenol (Eu) or 2-methoxy-4-propylphenol (MPP), were esterified with a protonating acetic acid (AcA), H2SO4 catalyst and water. Initial product analysis showed a high related number of the unspecific process species of Eu reactions. Therefore, research was continued only with MPP. Temperature, equivalent MPP/AA molar ratio and quantified H2SO4 mass amount were varied, reactants were monitored, and it was determined that the functionalisation's best conditions were 130 °C, indicated total contained MPP/AA 1:3 and 1 mol% rate-increasing H2SO4. Yields exceed 15 mol%. Other H+ donors, HCl or p-toluene-sulfonic acid, were used, but resulted in lower yields (2 mol% or less). Afterwards, MPP esterification was attempted with five bio-based molecules, but only levulinic acid produced a measurable ester value (4 mol%). Two enzymes, lipase from Rhyzopus oryzae or immobilized lipase B from Candida antarctica, were also applied, not forming products. A micro-kinetic system representation was developed, based on a proposed elementary mechanism, which revealed the comparable activation energy of MPP substitution, catalysed by H2SO4. Barrier (24 kJ mol–1) was less than that with H-beta (zeolite) (39 kJ mol–1), while higher than that with AmberLite XAD 16 (resin) (10 kJ mol–1). [ABSTRACT FROM AUTHOR]

The purpose of this article is to propose a fuzzy logic system as a tool for automated risk identification of potential technical challenges and social barriers during the implementation of artificial intelligence-based co-bots on work-stations in manufacturing enterprises. On the basis of an extensive literature review, as well as industry reports and expert consultations, the basic challenges and enterprise barriers occurring during the implementation of changes in enterprises, especially during the implementation of the latest technologies, were selected. A fuzzy logic model was then developed that, based on the values of the input factors, generates an answer as to whether there is a risk of technical or social challenges in an enterprise when implementing the latest technologies. The results generated by the developed model, when confronted with expert knowledge, experience and subjective assessments, showed that the model works as expected. The results of the study suggest that the use of fuzzy logic can effectively support companies in detecting challenges and obstacles, thereby facilitating decision-making in reducing the risk of their occurrence. Adaptation to the conditions currently prevailing in the company allows for dynamic adjustment of co-bot deployment strategies, which in turn can lead to more effective management of technological changes and minimization of potential operational disruptions. [ABSTRACT FROM AUTHOR]

Weft-knitted spacer fabrics are thick 3D knitted structures prized for their cushioning properties which have gathered increasing attention in the last decade. The thickness of a spacer fabric is one of its most influential parameters and strongly impacts its cushioning properties, wearability, thermal insulation or permeability. However, the fabric's natural undulation and high deformability make its thickness measurement uneasy. The current standard measurement methods require to measure the fabric thickness after compressing it until a fixed threshold stress value is reached to flatten it. The diversity of these threshold values is confusing, and each of them is unsuitable to variety of fabric rigidity. In this article, a standard for thickness evaluation was proposed and used to measure the thickness of 20 samples knitted with 5 independent parameters. The measured thickness was compared to the thickness measured at a threshold value of 1 kPa and to a theoretical thickness. The proposed measurement standard was proved reproducible and efficient for all fabrics when the threshold measures showed large errors on the softer and stiffer samples. The flattening stress of the fabrics ranged from 86 to 5262 Pa and could not be approximated by a single standard value. The theoretical thickness was more accurate, predicting the thickness with an average error of 3.8%. [ABSTRACT FROM AUTHOR]

ABSTRACT Different cultures have distinct conceptions of knowledge and ways of defining, generating and communicating knowledge. Therefore, multilingualism in academia has been shown to be instrumental in preserving the various systems of knowledge, equality of opportunity in science and enhancing the international competitiveness of scholars whose native language is not the lingua franca. The actors of translation and the strategies they work with (domestication and foreignisation) play a unique role in this, as their decisions may foster or hinder linguistic and epistemic diversity. Despite its pronounced significance, however, relatively little is known about the stereotypical competences required for academic translation. As translation in science is not merely a philological issue, this conceptual study follows a consilient approach and integrates the outcomes of research from several disciplines and research perspectives to propose a theory‐based, multi‐componential model of academic translation competence as a macrocompetence. Based on the model, a conceptual definition of the construct of academic translation competence is provided to identify what exactly is to be observed and measured for its study and to generate preliminary hypotheses related to the construct. The study has implications not only for the theory, practice and training of academic translation but also for the disciplinary training of researchers. [ABSTRACT FROM AUTHOR]

Introduction: The Global Adult Tobacco Survey (GATS) shows a drop in tobacco use worldwide. Despite the drop, there still continues to be a significant number of tobacco users in India. Research on tobacco use among young persons is commonly prioritised in India, while studies on tobacco use among middle-aged (45–59 years) and elderly (≥ 60 years) adults are noticeably lacking. We have conducted this study with objective to estimate the distribution, determinants and socioeconomic inequalities of smoking (SM) and smokeless tobacco (SLT) consumption across Indian states and union territories. Methods: This study was based on 66,606 participants aged ≥ 45 years using Longitudinal Aging Study in India (LASI)-1 (2017–2018) data. Distribution of tobacco consumption (any form, smoking (SM), smokeless (SLT) and both) was documented as per Indian states and union territories with spatial distribution by Indian map. Demographic, socioeconomic, health related and behavioural determinants were established using nested multilevel regression modelling. Socioeconomic disparities were documented using concentration curve. P-value < 0.05 was considered as statistically significant. Results: Overall, 36.78% participants documented using any form of tobacco; with higher consumption of SLT (19.88%) than smoking/SM (13.92%). Only 2.98% consumed both. Mizoram had highest consumption of tobacco in any form (78.21%) and smoking (35.18%). Elderly participants had higher odds of consuming tobacco (any 1.23 (1.18–1.28), SM 1.99 (1.14–1.27), SLT 1.08 (1.03–1.14) and both 1.27 (1.14–1.40 times) than middle aged participants. Females, OBC (other backward castes), urban residence had lower odds in all the categories, while being widow/ separated/ divorced, belonging to Muslim community, having clerical and skilled occupation, poor self-rated health, comorbidity and multimorbidity had higher odds. With decrease in the wealth index, educational status and frequency of physical activity the odds of tobacco consumption increased. The odds of higher tobacco consumption were documented from northeast region (2.56 (2.37–2.76) higher than north). Alcohol consumption had the highest odds (4.94 (4.69–5.21)). Participants exposed to media had lower odds (11% lower) of consuming tobacco. The socioeconomic inequalities in tobacco consumption were significantly distributed more among the poorest (any -0.064 (-0.072 to -0.056) and SLT -0.069 (-0.072 to -0.056)). Conclusion: Prioritising tobacco prevention and increasing availability and accessibility of cessation programmes that are suited with unique requirements and circumstances, even for elderly population, are essential focusing on the higher determinants across poorest section in the country. [ABSTRACT FROM AUTHOR]

Intercropping can improve light interception and crop yield on limited farmlands. The light interception rate in intercropping is determined by row configuration. Quantifying the spatio-temporal light interception of intercrops is very important for improving crop yields by optimizing the row configuration. A two-year field experiment was conducted at two sites to quantify the responses of the light interception rate of intercrops to five treatments: two rows of maize alternated with three rows of soybean (2M3S), two rows of maize alternated four rows of soybean (2M4S), two rows of maize alternated five rows of soybean (2M5S), sole soybean (SS), and sole maize (SM). We developed a multiple regression model based on the sine of the solar elevation angle (sin(h)) and crop leaf area density (LAD) to quantify the spatio-temporal light interception of intercrops. The predicted light interception rate was positively correlated with the measured values of photosynthetically active radiation (R2 > 0.814) and dry matter (R2 > 0.830). Increasing soybean rows led to an increase in light interception of both soybean and the lower layer of maize. However, this also resulted in a decrease in light interception in the upper layer of maize. At the two sites, compared to 2M3S, the annual average cumulative light interception of soybean in 2M5S increased by 44.73% and 47.18%, that of the lower layer of maize in 2M5S increased by 9.25% and 8.04%, and that of whole canopy of maize decreased by 13.77% and 17.74% respectively. The changes in dry matter and yield of intercrops were consistent with the change in light interception, which further verified the high accuracy of the light interception model. The annual average maize yield of 2M5S was 6.03% and 6.16% lower but the soybean yield was 23.69% and 28.52% higher than that of 2M3S. On the basis of system yield, the best performance was recorded in 2M4S at the two sites. In summary, the newly created light interception model performs well in the quantification of the temporal and spatial changes in crop light interception in strip intercropping and has potential applications in other configurations. Optimizing row configurations across climatic regions to enhance light interception and yield at the system level will become a future target. [ABSTRACT FROM AUTHOR]

The paper introduces a deep‐learning model fine‐tuned for detecting authoritarian discourse in political speeches. Set up as a regression problem with weak supervision logic, the model is trained for the task of classification of segments of text for being/not being associated with authoritarian discourse. Rather than trying to define what an authoritarian discourse is, the model builds on the assumption that authoritarian leaders inherently define it. In other words, authoritarian leaders talk like authoritarians. When combined with the discourse defined by democratic leaders, the model learns the instances that are more often associated with authoritarians on the one hand and democrats on the other. The paper discusses several evaluation tests using the model and advocates for its usefulness in a broad range of research problems. It presents a new methodology for studying latent political concepts and positions as an alternative to more traditional research strategies. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the relationship between the operating parameters of electric vehicles (EVs) and parameters related to road conditions. The data for analysis came from urban driving trips conducted at different times of day. The average energy consumption was the dependent variable in the model. The following parameters were used as independent variables: the stopping time;; the journey time; the average speed; the quartiles of speed, acceleration, and deceleration, and their maximum values; the number of braking; stops; and the acceleration time and regenerative braking. A multiple linear regression model was developed to predict the average energy demand of an EV based on its kinematic parameters. The proposed model enables the analysis of the influence of various factors related to the route and driving style of the driver on the average energy consumption in the vehicle. [ABSTRACT FROM AUTHOR]

Entering an era where humans and synthetic agents are supposed to collaborate and cooperate, adequate models of human intent are crucial for coordinated teamwork. Unfortunately, although there is a need for such models, the concept of intent is ambiguous and approaches to model intent from a human-centric perspective are scarce. Building upon theoretical and methodological foundations, this study aims to address these gaps by presenting a conceptualisation of intent alongside an approach. Specifically, leveraging the six levels of cognitive control outlined in the Joint Control Framework, a provisional model of human intent alongside a defined and operationalised concept is presented. Building on these foundations, a novel approach is proposed. Utilising seven scenario-based interviews, the value of these contributions is demonstrated through an example case in the context of Manned-Unmanned Teaming. It is concluded that intent should be understood as a multi-faceted concept shaped by situated constraints, where intent is formed through a commitment to choices by context-situation and means-end reasoning. It is also concluded that the approach is useful, particularly since it can glean insights from choices considered and committed, both being essential in the design of synthetic teammates' capability to adapt to their human partner's agency. Relevance To human factors/ergonomics theory: With theoretical and methodological foundations, a provisional model of human intent is accompanied by a definition and operationalisation of intent to enhance understanding of this often ambiguous concept. Building upon these foundations, this paper introduces and demonstrates an approach for modelling situated intent from a human-centric perspective. These contributions deepen the understanding of human intent, particularly in the context of designing and developing systems that can effectively account for and adapt to human agency. [ABSTRACT FROM AUTHOR]

Nestmate recognition is a widespread phenomenon and evolutionary important trait in the social insects. Yet evidence accumulates that the responses to non‐nestmates varies more than previously thought. We present a simple frequency‐dependent cost‐benefit model of nestmate recognition to understand conditions that might or might not favor the evolution of nestmate recognition that is based on cuticular hydrocarbon (CHC) profiles. Costs accounted for are costs for 1) maintaining a functional recognition system and 2) keeping a CHC profile that may be sub‐optimal regarding other functionalities like desiccation control, whereas the benefit of recognition is the prevention of nest raiding by other colonies. Unsurprisingly, the model indicates that recognition systems only evolve if costs are sufficiently low and benefits sufficiently high. In addition, the model suggests that nestmate recognition is more likely to evolve if colony turnover is fast (colony life‐expectancy is low). Our model creates evolutionary cycles that are typically longest under parameter combinations that just allow the evolution of recognition systems at all; the system expresses attributes of a rock‐paper‐scissors game. The model shows that a breakdown of nestmate recognition may occur under changing ecological situations, e.g. as a result of reduced intraspecific competition or increased abiotic stress. We speculate that such effects may be involved in the formation of supercolonies during invasions. Nestmate recognition may have evolved more to prevent interspecific predation or parasitism by antagonists that managed to mimic their host's CHC profile than as a mechanism to prevent exploitation by conspecific colonies. [ABSTRACT FROM AUTHOR]

Rainfall intensity and land use changes and from natural vegetation to cultivated fields both impact on river streamflows and sediment dynamics. Assessing the magnitude of these flow and sediment changes is important for water management. However, many rivers are still under-studied/monitored and data sparse. Fully distributed rainfall–runoff models, such as the Jena Adaptable Modelling System (JAMS/J2000), allow for simulation of streamflow, its flow components and potential sediment yield given land use and hydrological changes. This study demonstrates the use of this method in the Bot River, a data-scarce catchment in South Africa. A hydrological model was implemented, and sediment yield was estimated under three land use and two hydrological flow component scenarios. The model reported Nash-Sutcliffe efficiencies (E2) of 0.64 and 0.32 (for streamflow) for model calibration and validation periods, respectively. Modelled mean annual sediment yield increased from 423 t/km2/year in 1990 to 490 t/km2/year in 2018, attributable to land use changes. [ABSTRACT FROM AUTHOR]

Computational models are powerful tools to quantify physical phenomena to gain valuable insights into a manufacturing process. Their accuracy is hindered, however, by uncertainty in the input data. Furthermore, when calibrating models with plant measurements, it helps to understand which variables have greatest effect on the critical model outputs. This work applies uncertainty quantification and sensitivity analysis to determine the most influential input parameters in the CON1D model of heat transfer and solidification in steel continuous casting with slag. Results show that the slag rim greatly affects heat flux near the meniscus, so control of its size is important. Heat flux and temperature down the mold depend greatly on velocity of the solid slag layer, and slag solidification temperature, which control the slag layer thickness, which in turn affects the interfacial resistance that controls heat transfer in the process. Scale formation on the mold coldface greatly increases mold temperatures. Based on the results presented here, models of heat transfer in continuous casting such as CON1D would benefit from plant measurements such as slag rim size and solid slag velocity, and lab measurements such as slag viscosity at lower temperatures, to better characterize this important slag property. [ABSTRACT FROM AUTHOR]

The conventional packed bed humidifiers encounter significant drawbacks such as scale formation, spray nozzle clogging, corrosion, and relatively poor effectiveness, which adversely affect their reliability and lifespan, thereby escalating their operational and capital costs. To tackle these issues, bubble column humidifiers have been proposed. The bubble column design ensures maximum air–water interaction, enhancing component effectiveness, while obviating the need for atomizers, thereby decreasing maintenance expenses, and enhancing heat and mass transfer efficiency. Moreover, studies related to a moving streams multi-stage bubble column HDH design are lacking. Therefore, this study aims to provide a theoretical analysis of various configurations of multi-stage moving streams bubble column humidifiers, aiming to bridge the performance knowledge gap between moving stream and nonmoving stream bubble column humidifiers. A mathematical model based on heat and mass transfer was developed and validated against published data. The effect of multistaging, water inlet temperature, air inlet temperature, and inlet relative humidity on the total effectiveness, outlet humidity ratio and outlet air temperature are studied. Results reveal that multistaging is effective in boosting the maximum effectiveness of single stage bubble column humidifier from 27% and 33% to about 94% and 94%, for the parallel and series configurations, respectively. Furthermore, both series and parallel configurations show similar performance with a slightly higher performance for the series arrangement. Additionally, this study discusses the operational and maintenance differences between the parallel and series multi-stage bubble column humidifiers, with the series configuration having a more straightforward operation compared to the parallel arrangement. However, the parallel configuration requires less complicated maintenance in comparison to the series multi-stage configuration. The outcomes of this study are pivotal to understanding the optimum operating conditions of the humidifier for its possible integration with the dehumidifier. Consequently, an improved humidification dehumidification (HDH) desalination system can be attained. [ABSTRACT FROM AUTHOR]

Unlike the hydrodynamic model used in the current popular numerical simulation of buried gas pipeline leakage, this paper adopts the Darcy equation of underground seepage, mass balance equation, and equation of state of gas to establish a buried gas pipeline leakage model. Geometrically, the established model is a soil cylinder with the ground as the top surface and the vertical line through the pipeline leakage point as the axis. Due to the axial symmetry, the three-dimensional buried pipeline leakage model is expressed as a two-dimensional mathematical problem. A series of key concepts about the hydrodynamic model of buried pipeline leakage and its solution are presented. A comprehensive description of the physical process (pressure and flow rate) of buried gas pipeline leakage is provided through numerical examples. Quantitative analysis was conducted on the relevant structural parameters of the model and relations of leakage flow rate with related factors of leakage. By providing a comprehensive and quantitative understanding of buried gas pipeline leakage, this paper proposes a flow rate formula and an estimation formula of leak orifice equivalent diameter of leaking buried pipelines, and provides a theoretical and data processing framework for experimental study of buried gas pipeline leakage. [ABSTRACT FROM AUTHOR]

This study introduces an ensemble model designed for real-time monitoring of bedridden patients. The model was developed using a unique dataset, specifically acquired for this study, that captures six typical movements. The dataset was balanced using the Synthetic Minority Over-sampling Technique, resulting in a diverse distribution of movement types. Three models were evaluated: a Decision Tree Regressor, a Gradient Boosting Regressor, and a Bagging Regressor. The Decision Tree Regressor achieved an accuracy of 0.892 and an R2 score of 1.0 on the training dataset, and 0.939 on the test dataset. The Boosting Regressor achieved an accuracy of 0.908 and an R2 score of 0.99 on the training dataset, and 0.943 on the test dataset. The Bagging Regressor was selected due to its superior performance and trade-offs such as computational cost and scalability. It achieved an accuracy of 0.950, an R2 score of 0.996 for the training data, and an R2 score of 0.959 for the test data. This study also employs K-Fold cross-validation and learning curves to validate the robustness of the Bagging Regressor model. The proposed system addresses practical implementation challenges in real-time monitoring, such as data latency and false positives/negatives, and is designed for seamless integration with hospital IT infrastructure. This research demonstrates the potential of machine learning to enhance patient safety in healthcare settings. [ABSTRACT FROM AUTHOR]

Nigeria faces significant challenges related to violence, conflict, and social unrest, often stemming from ethnic, religious, and political differences. The escalation of violence and its consequences pose severe threats to the stability, development, and overall well-being of the nation. Traditional approaches to conflict resolution and peace-building have often been inadequate in addressing these deep-rooted issues. In light of these challenges, there is a need to explore alternative models of peace-building that are rooted in nonviolence and draw inspiration from the principles outlined in the New Testament. This article adopted a theological method in this research. The study examines the theoretical foundations of nonviolence as espoused in the New Testament scriptures and analyzes its relevance to contemporary peacebuilding efforts. By delving into the principles of love, forgiveness, and reconciliation advocated in the New Testament, the paper presents a compelling argument for the adoption of nonviolence as a practical and ethical framework for peace-building in Nigeria. In conclusion, this paper asserts that embracing the New Testament model of nonviolence can significantly contribute to transforming the culture of violence in Nigeria and beyond Africa. The findings of this research provide valuable insights for policymakers, practitioners, and scholars working in the field of peace-building, offering a roadmap for cultivating enduring peace in regions afflicted by conflict and violence. [ABSTRACT FROM AUTHOR]

Automatic medical document classification using machine learning techniques can enhance the productivity of healthcare services by reducing processing time and cost. This work proposes an ensemble learning approach to develop a model that classifies electronic medical documents in Afaan Oromo. The main tasks in this work are preparing the corpus, pre-processing, training the models, and the classification process. We used the term frequency-inverse document frequency (TF-IDF) and bag of words (BOW) feature extraction methods. An ensemble technique in this work is that it creates multiple individual classifier predictions from naïve Bayes, random forest, SVM, and logistic regression and then combines them to advance a reliable and more accurate classifier. Evaluation measures were employed using accuracy, F1-score, recall, and precision for performance comparison. The efficiency of the proposed method is compared with the two existing boosting approaches, namely gradient boosting and adaboost. The experimental result shows the efficiency of BOW feature extraction over TF-IDF in this work on our dataset. These results also illustrated the effectiveness of the proposed model by scoring 94.81% accuracy and 94.84% F1-score. This work significantly contributes to the technological enhancement of service delivery, managing documents through classification methods, and advancing the data processing systems in healthcare sectors.Article Highlights: Introduce ensemble learning techniques for efficient classification of medical document in Afaan Oromo These ensemble techniques combine the predictions of utilized base learner and applied the voting methods to obtain the improved final prediction model. The results demonstrate the effectiveness of the proposed ensemble method by achieving 94.81% accuracy. [ABSTRACT FROM AUTHOR]

Monosodium methanearsonate (MSMA), a sodium salt of monomethylarsonic acid (MMA), is a selective contact herbicide used for the control of a broad spectrum of weeds. In water, MSMA dissociates to ions of sodium (Na+) and monomethylarsonate (MMA−) that is stable and does not transform abiotically. In soils characteristic of MSMA use, several simultaneous processes can occur: (1) microbial methylation of MMA to dimethylarsinic acid (DMA), (2) microbial demethylation of MMA to inorganic arsenic (iAs), (3) methylation of iAs to MMA, and (4) sorption and sequestration of MMA and its metabolites to soil minerals. Sequestered residues are residues that cannot be desorbed from soil in environmental conditions. Sequestration is rapid in the initial several days after MSMA application and continues at a progressively slower rate over time. Once sequestered, MMA and its metabolites are inaccessible to soil microorganisms and cannot be transformed. The rate and extent of the sorption and sequestration as well as the mobility of MMA and its metabolites depend on the local edaphic conditions. In typical MSMA use areas, the variability of the edaphic conditions is constrained. The goal of this research was to estimate the amount of iAs potentially added to drinking water as a result of the use of MSMA, with models and scenarios developed by the US Environmental Protection Agency for pesticide risk assessment. In this project, the estimated drinking water concentrations (EDWCs) for iAs were assessed as the average concentration in the reservoir over a 30‐year simulation with annual applications of MSMA at maximum label rates. When the total area of suitable land was assumed to be treated, EDWCs ranged from <0.001 to 0.12 µg/L. When high estimates of actually treated acreage are considered, the EDWCs are below 0.06 µg/L across all scenarios. Integr Environ Assess Manag 2024;20:2076–2087. © 2024 The Author(s). Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: Inorganic arsenic (iAs) potentially added to drinking water from the use of monosodium methanearsonate (MSMA) was estimated with models (Pesticide Root Zone Model, version 5 [PRZM5], Variable Volume Water Model [VVWM], and aerial spray prediction model [AgDRIFT]) and based on scenarios developed by the USEPA for pesticide risk assessment.Sequestered residues (residues that could not be extracted by water or weak acids) were considered as losses to the modeled systems in PRZM5 and VVWM.Rate constants used for modeling sequestration and transformation were derived from empirical data representative of MSMA use environments.The modeled estimated drinking water concentration for iAs ranged from <0.001 to 0.12 µg/L when 100% of the suitable area of a watershed is treated with MSMA and below 0.06 µg/L across all scenarios when the actual area treated with MSMA is considered. [ABSTRACT FROM AUTHOR]

The results of experimental and modeling studies of the distributed generation sources (DG) influence on the occurrence in the Interconnected Power System (IPS) of Ukraine asynchronous modes (AM) caused by accidents in its main electrical network are presented. According to simulation scenarios it was assumed that the DG power was introduced into electrical distribution networks instead of the power units of thermal power plants of the IPS of Ukraine which were destroyed as a result of missile attacks by the Russians. In the absence of information about DGs, certain generalizations and assumptions are made regarding the possibility of DG groups modeling of in the form of turbine generators with different inertia constants. Various scenarios of the accidents occurrence and the functioning of relay protection and anti-emergency automation devices were considered. Conducted studies results show that the DG introduction into distribution electrical networks does not contribute to the AM occurrence in the main electrical network of the IPS of Ukraine. References 11, figures 10, tables 3. [ABSTRACT FROM AUTHOR]

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Aim: This multicenter retrospective study aimed to develop a novel prognostic system for extranodal natural killer/T-cell lymphoma (ENKTL) patients in the era of pegaspargase/L-asparaginase. Materials & methods: A total of 844 newly diagnosed ENKTL patients were included. Results: Multivariable analysis confirmed that Eastern Cooperative Oncology Group performance status, lactate dehydrogenase, Chinese Southwest Oncology Group and Asia Lymphoma Study Group ENKTL (CA) system, and albumin were independent prognostic factors. By rounding up the hazard ratios from four significant variables, a maximum of 7 points were assigned. The model of Huaihai Lymphoma Working Group-Natural killer/T-cell Lymphoma prognostic index (NPI) was identified with four risk groups and the 5-year overall survival was 88.2, 66.7, 54.3 and 30.5%, respectively. Conclusion: Huaihai Lymphoma Working Group-NPI provides a feasible stratification system for patients with ENKTL in the era of pegaspargase/L-asparaginase. Article highlights Patients with dual positivity for EBER and Epstein–Barr virus DNA status had significantly poorer survival outcomes. The survival of patients who used the DDGP regimen was superior to those who used the SMILE regimen in this study. According to the maximal Chi-square method, 292 and 34.2 were the optimal cutoff points for lactate dehydrogenase and albumin, which distinguished the two prognostic groups most effectively. Eastern Cooperative Oncology Group performance status, lactate dehydrogenase, CA stage, and albumin were independent prognostic factors for extranodal natural killer/T-cell lymphoma patients in the era of pegaspargase/L-asparaginase. The prognostic model of extranodal natural killer/T-cell lymphoma based on the LASSO regression was more accurate than the random forest in this study. We developed a prognostic stratification index (HHLWG-NPI) and divided patients into four risk groups. The area under the curve of HHLWG-NPI was higher than the Korean Prognostic Index, International Prognostic Index, and prognostic index of natural killer lymphoma models in this study. HHLWG-NPI appears to more effectively identify high-risk patients. [ABSTRACT FROM AUTHOR]

Summary In the face of anthropogenic warming, drought poses an escalating threat to food production. C4 plants offer promise in addressing this threat. C4 leaves operate a biochemical CO2 concentrating mechanism that exchanges metabolites between two partially isolated compartments (mesophyll and bundle sheath), which confers high‐productivity potential in hot climates boosting water use efficiency. However, when C4 leaves experience dehydration, photosynthesis plummets. This paper explores the physiological mechanisms behind this decline. In a fast dehydration experiment, we measured the fluxes and isotopic composition of water and CO2 in the gas exchanged by leaves, and we interpreted results using a novel biochemical model and analysis of elasticity. Our findings show that, while CO2 supply to the mesophyll and to the bundle sheath persisted during dehydration, there was a decrease in CO2 conductance at the bundle sheath‐mesophyll interface. We interpret this as causing a slowdown of intercellular metabolite exchange – an essential feature of C4 photosynthesis. This would impede the supply of reducing power to the bundle sheath, leading to phosphoglycerate accumulation and feedback inhibition of Rubisco carboxylation. The interplay between this rapid sensitivity and the effectiveness of coping strategies that C4 plants deploy may be an overlooked driver of their competitive performance. [ABSTRACT FROM AUTHOR]

In this article, we have chosen to explore the performance of the piezoresistive accelerometer, favoured for its comparative advantages over other accelerometers. Our approach includes modelling this accelerometer according to the law of motion and validating the model through simulation tests and experimental trials. With this approach, we can determine the optimal damping rate and frequency range for the piezoresistive accelerometer, the optimisation of these parameters is crucial to maximise the performance of the accelerometer in various operating conditions, ensuring high sensitivity and fast response while minimising distortions or errors in the collected data. These optimal values of damping rate and frequency range guarantee precise and reliable accelerometer measurements. This paper provides a comprehensive overview of sensor design principles and signal processing methods from a theoretical standpoint. Beginning with sensor design, we delve into the foundational aspects of transduction principles, emphasising the conversion of physical quantities into electrical signals. The selection and optimisation of sensing elements are explored, highlighting material properties, geometrical considerations, and packaging strategies to ensure sensitivity, linearity, and robustness. [ABSTRACT FROM AUTHOR]

K‐changes are observed as step‐like increases in the thundercloud electric fields. The K‐changes occur in the late part of intra‐cloud lightning or during negative cloud‐to‐ground lightning between return strokes. It has been shown that the processes leading to K‐changes initiate in the decayed part of a positive leader channel and propagate toward the flash origin. They are often accompanied by microsecond‐scale electric field pulses. We introduce a new model to simulate processes leading to the K‐changes in cloud‐to‐ground lightning. Our method is based on the full solution of Maxwell's equations coupled to Poisson's equation for the thundercloud charge structure. To model the K‐changes, we gradually increase the decayed channel conductivity. The modeled current wavefront propagates due to the K‐processes downward along a vertical channel and completely attenuates before reaching the ground. We derive the evolution of the linear charge densities and the scalar electric potential along the channel leading to K‐changes. We model electrostatic step‐like changes in the measured electric field together with the approximate rates and amplitudes of the microsecond scale pulses. Step‐like changes increase their amplitudes with the length of the simulated channel and with a higher conductivity of the channel. The microsecond‐scale pulse waveshapes depend mainly on the propagation velocity of the current wave, and the time scale of the conductivity increase. We show that our modeled waveforms are in a good agreement with observations conducted in Florida. Plain Language Summary: K‐changes are the step‐like increases in the measured electric fields in the late part of an intra‐cloud lightning and between return strokes in cloud‐to‐ground lightning. The K‐changes are often accompanied by microsecond‐scale electric field pulses. We have developed a new model to simulate the K‐changes in cloud‐to‐ground lightning. We solve electrodynamic equations to model these processes and we are able to reproduce the observed electric field waveforms recorded in Florida by a flat‐plane antenna. We model how the current wave propagates along the vertical channel and attenuates before reaching the ground. We show how the charges are distributed along the simulated channel and we model why in some cases the breakdown occurs at the positive end of the channel after the recoil leader propagates several hundreds of meters along a decayed channel. Key Points: We model for the first time the simultaneous occurrence of step‐like changes and microsecond‐scale pulses related to K‐changesThe waveshape of the microsecond‐scale pulses depends mainly on the current front propagation velocity and the conductivity rise timeThe modeled amplitudes and repetition rates are in a good agreement with observations [ABSTRACT FROM AUTHOR]

Background: Current evidence linking long-term exposure to fine particulate matter (PM2.5) exposure and mortality is primarily based on persons that live in the same residence, city and/or country throughout the study, with few residential moves or relocations. We propose a novel method to quantify the health impacts of PM2.5 for United States (US) diplomats who regularly relocate to international cities with different PM2.5 levels. Methods: Life table methods were applied at an individual-level to US mortality statistics using the World Health Organization's database of city-specific PM2.5 annual mean concentrations. Global Burden of Disease concentration-response (C-R) functions were used to estimate cause-specific mortality and days of life lost (DLL) for a range of illustrative 20-year diplomatic assignments for three age groups. Time lags between exposure and exposure-related mortality risks were applied. Sensitivity analysis of baseline mortality, exposure level, C-R functions and lags was conducted. The effect of mitigation measures, including the addition of air purifiers, was examined. Results: DLL due to PM2.5 exposure for a standard 20-year assignment ranged from 0.3 days for diplomats' children to 84.1 days for older diplomats. DLL decreased when assignments in high PM2.5 cities were followed by assignments in low PM2.5 cities: 162.5 DLL when spending 20 years in high PM2.5 cities compared to 62.6 DLL when spending one of every four years (5 years total) in a high PM2.5 city for older male diplomats. Use of air purifiers and improved home tightness in polluted cities may halve DLL due to PM2.5 exposure. The results were highly sensitive to lag assumptions: DLL increased by 68% without inception lags and decreased by 59% without cessation lags for older male diplomats. Conclusion: We developed a model to quantify health impacts of changing PM2.5 exposure for a population with frequent relocations. Our model suggests that alternating assignments in high and low PM2.5 cities may help reduce PM2.5-related mortality burdens. Adding exposure mitigation at home may help reduce PM2.5 related mortality. Further research on outcome-specific lag structures is needed to improve the model. [ABSTRACT FROM AUTHOR]

Transitioning to an individualized risk-based approach can significantly enhance cervical cancer screening programs. We aimed to derive and internally validate a prediction model for assessing the risk of cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and cancer in women eligible for screening. This retrospective study utilized data from the Estonian electronic health records, including 517,884 women from the health insurance database and linked health registries. We employed Cox proportional hazard regression, incorporating reproductive and medical history variables (14 covariates), and utilized the least absolute shrinkage and selection operator (LASSO) for variable selection. A 10-fold cross-validation for internal validation of the model was used. The main outcomes were the performance of discrimination and calibration. Over the 8-year follow-up, we identified 1326 women with cervical cancer and 5929 with CIN3+, with absolute risks of 0.3% and 1.1%, respectively. The prediction model for CIN3 + and cervical cancer had good discriminative power and was well calibrated Harrell's C of 0.74 (0.73–0.74) (calibration slope 1.00 (0.97–1.02) and 0.67 (0.66–0.69) (calibration slope 0.92 (0.84–1.00) respectively. A developed model based on nationwide electronic health data showed potential utility for risk stratification to supplement screening efforts. This work was supported through grants number PRG2218 from the Estonian Research Council, and EMP416 from the EEA (European Economic Area) and Norway Grants. [ABSTRACT FROM AUTHOR]

Hydrate-based gas separation (HBGS) has good potential in the separation of helium from helium-rich natural gas. HBGS should be carried out under a pressure higher than the thermodynamic equilibrium hydrate formation pressure (Peq) to ensure the formation of hydrate so that the accurate prediction of Peq is the basis of the determination of HBGS pressure. In this work, the Peq of the helium-rich natural gases with different helium contents (1 mol%, 10 mol%, and 50 mol%) in gas and different tetrahydrofuran (THF) contents (5 wt%, 10 wt%, and 19 wt%) in liquid at different temperatures were experimentally investigated through the isothermal pressure search method. A new thermodynamic model was proposed to predict the Peq of helium-rich natural gas. This model can quantitatively describe the effects of THF and helium on Peq, and it predicts the Peq of the helium-rich natural gases in this work accurately. The average relative deviation of the model is less than 3%. This model can guide the determination of the operating condition of the HBGS of helium-rich natural gas. [ABSTRACT FROM AUTHOR]

Monopterus albus is one of the popular aquatic fish in Asia. Manual processing of eel cannot fully meet the large- scale production at present, due to the time-consuming, laborious, and low safety. In view of its slender and slimy body, an eel- cutting machine is required for the consumption of Monopterus albus. In this study, a miniature machine of automatic cutting was designed for the living Monopterus albus with a sticky surface. The machine consisted of conveying, clamping, and cutting parts. The double-clamped wheel to roller was used to capture the eel for transportation during cutting. The switch between laparotomy and back dissection was realized to change the feeding channel. The adaptive mechanism was equipped to meet the requirements of different sizes of eel cutting. Firstly, the geometry and weight parameters of Monopterus albus were measured using a straight ruler and precision electronic balance. The tribological parameters on the surface of Monopterus albus were measured by the inclined plane. The mechanical parameters of Monopterus albus meat were verified by compression test. Secondly, according to the rigid-flexible coupling principle, the rigid-damping model was equivalent to the contact between the Monopterus albus body and the clamping wheel and slide. The rigid-flexible coupling model was established by Abaqus and Adams simulation software. The analysis model was optimized to take the conveying time of the fish body, output speed, clamping force, and whole machine vibration as the test indexes in single-factor tests, such as the spring stiffness, slope Angle of the slide, and the speed of the clamping wheel. The results show that the clamping force first increased and then decreased with the increase of spring stiffness and the vibration amplitude of the whole machine decreased. But there was little influence on conveying time and output speed. The output time of the fish body decreased with the increase of slope angle, while the output speed, clamping force, and vibration of the whole machine increased. There was an increase in the conveying time of the fish body, the output speed, the clamping force, and the vibration of the whole machine, with the increase of the speed of the clamping wheel. The single- and multi-factor analyses were carried out to obtain the optimal working conditions. Specifically, the clamping wheel speed was 600 r/min, the spring stiffness was 1 200 N/m, and the slope angle was 16°. Finally, the processing experiment was performed on the automatic cutting machine for eel. There was consistency in the vibration amplitude range and the relationship between the three directions of the machine measuring points with the theoretical analysis. At the same time, the output speed of the fish body output machine was also measured to better agree with the theoretical analysis. The cutting of the machine under optimal working conditions has fully met the requirements. The correctness of the theoretical analysis was also verified by the test. The rigid-flexible coupling model can provide a strong reference to research and develop the subsequent equipment. [ABSTRACT FROM AUTHOR]

Intercropping can improve light interception and crop yield on limited farmlands. The light interception rate in intercropping is determined by row configuration. Quantifying the spatio-temporal light interception of intercrops is very important for improving crop yields by optimizing the row configuration. A two-year field experiment was conducted at two sites to quantify the responses of the light interception rate of intercrops to five treatments: two rows of maize alternated with three rows of soybean (2M3S), two rows of maize alternated four rows of soybean (2M4S), two rows of maize alternated five rows of soybean (2M5S), sole soybean (SS), and sole maize (SM). We developed a multiple regression model based on the sine of the solar elevation angle (sin(h)) and crop leaf area density (LAD) to quantify the spatio-temporal light interception of intercrops. The predicted light interception rate was positively correlated with the measured values of photosynthetically active radiation (R² > 0.814) and dry matter (R² > 0.830). Increasing soybean rows led to an increase in light interception of both soybean and the lower layer of maize. However, this also resulted in a decrease in light interception in the upper layer of maize. At the two sites, compared to 2M3S, the annual average cumulative light interception of soybean in 2M5S increased by 44.73% and 47.18%, that of the lower layer of maize in 2M5S increased by 9.25% and 8.04%, and that of whole canopy of maize decreased by 13.77% and 17.74% respectively. The changes in dry matter and yield of intercrops were consistent with the change in light interception, which further verified the high accuracy of the light interception model. The annual average maize yield of 2M5S was 6.03% and 6.16% lower but the soybean yield was 23.69% and 28.52% higher than that of 2M3S. On the basis of system yield, the best performance was recorded in 2M4S at the two sites. In summary, the newly created light interception model performs well in the quantification of the temporal and spatial changes in crop light interception in strip intercropping and has potential applications in other configurations. Optimizing row configurations across climatic regions to enhance light interception and yield at the system level will become a future target. [ABSTRACT FROM AUTHOR]