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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.

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]

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]

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]

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]

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]

With a turbulent market and a desire to withstand force, many companies lower the production price of their products using different approaches. On the one hand, they often use lean principles to reduce production costs, while they are compelled by the government to adopt green practices on the other. The proposed approach (model) is a combination of green, lean sustainable improvement, leading a company through the green, lean process with measurable results and optimization functions. In addition to its return on investment (ROI), a company can calculate its output (from material to emission flow) based on its input. Based on the radar chart we have produced, they can also know what they have achieved and in which direction they need to optimize to develop in as green and sustainable a way as possible. One example of a Slovenian automotive company demonstrates how the implementation of the proposed measures can effectively reduce environmental impact, while also ensuring a quick return on investment. [ABSTRACT FROM AUTHOR]

Background: Traditional cardiovascular disease risk prediction models generate a combined risk assessment for myocardial infarction (MI) and ischemic stroke (IS), which may inadequately reflect the distinct etiologies and disparate risk factors of MI and IS. We aim to develop prediction models that separately estimate the risks of MI and IS. Methods: Our analysis included 6,242,404 individuals over 40 years old who participated in a cardiovascular health screening examination in 2009. Potential predictors were selected based on a literature review and the available data. Cox proportional hazards models were used to construct 5-year risk prediction models for MI, and IS. Model performance was assessed through discrimination and calibration. Results: During a follow-up of 39,322,434.39 person-years, 89,140 individuals were diagnosed with MI and 116,259 with IS. Both models included age, sex, body mass index, smoking, alcohol consumption, physical activity, diabetes, hypertension, dyslipidemia, chronic kidney disease, and family history. Statin use was factored into the classification of dyslipidemia. The c-indices for the prediction models were 0.709 (0.707–0.712) for MI, and 0.770 (0.768–0.772) for IS. Age and hypertension exhibited a more pronounced effect on IS risk prediction than MI, whereas smoking, body mass index, dyslipidemia, and chronic kidney disease showed the opposite effect. The models calibrated well for low-risk individuals. Conclusions: Our findings underscore the necessity of tailored risk assessments for MI and IS to facilitate the early detection and accurate identification of heterogeneous at-risk populations for atherosclerotic cardiovascular disease. [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]

In this work, we aimed to develop a computer algorithm for detecting and scoring cow teats according to udder images. In addition, by assessing three teat traits, i.e. the length, thickness, and placements, we scored them according to international standards. By applying the algorithm to 71 cows, we were able to detect all four teats in 62 of 71 (85%) cow udder images, and differentiate the front and rear teats in 56 of 62 (92%) udder images. Based on the three teat traits, we scored the teats of 56 cows. The final teat score was given by averaging the three scores for each cow. We discuss the limitations of the algorithm, and also the issues with teat conformation and image-taking itself, e.g. teats not facing perpendicular to the camera. In conclusion, we have developed the first computerised teat scoring system based on cow udder images. Further improvements require tuning parameters of the workflow and the image-taking process. [ABSTRACT FROM AUTHOR]

Background: Severe adenovirus pneumonia in children has a high mortality rate, but research on risk prediction models is lacking. Such models are essential as they allow individualized predictions and assess whether children will likely progress to severe disease. Methods: A retrospective analysis was performed on children with adenovirus pneumonia who were hospitalized at the Children's Hospital of Nanjing Medical University from January 2017 to March 2024. The patients were grouped according to clinical factors, and the groups were compared using Ridge regression and multiple logistic regression to identify risk factors associated with severe adenovirus pneumonia. A prediction model was constructed, and its value in clinical application was evaluated. Results: 699 patients were included in the study, with 284 in the severe group and 415 in the general group. Through the screening of 44 variables, the final risk factors for severe adenovirus pneumonia in children as the levels of neutrophils (OR = 1.086, 95% CI: 1.054‒1.119, P < 0.001), D-dimer (OR = 1.005, 95% CI: 1.003‒1.007, P < 0.001), fibrinogen degradation products (OR = 1.341, 95% CI: 1.034‒1.738, P = 0.027), B cells (OR = 1.076, 95%CI: 1.046‒1.107, P < 0.001), and lactate dehydrogenase (OR = 1.008, 95% CI: 1.005‒1.011, P < 0.001). The value of the area under the receiver operating characteristic curve was 0.974, the 95% CI was 0.963–0.985, and the P-value of the Hosmer-Lemeshow test was 0.547 (P > 0.05), indicating that the model had strong predictive power. Conclusion: In this study, the clinical variables of children with adenovirus pneumonia were retrospectively analyzed to identify risk factors for severe disease. A prediction model for severe disease was constructed and evaluated, showing good application value. [ABSTRACT FROM AUTHOR]

Background: Bone metastasis from prostate cancer severely impacts patient outcomes and quality of life. Anoikis, a form of programmed cell death triggered by the loss of cell-matrix interactions, plays a critical role in cancer progression. However, its precise relationship with prostate cancer-induced bone metastasis remains unclear. This study aims to elucidate this relationship, focusing on anoikis-related gene signatures, molecular pathways, and therapeutic implications. Methods: We used the TCGA-PRAD dataset for training, with MSKCC and GSE70769 as validation cohorts. To evaluate immunotherapy efficacy, we examined IMvigor 210 and GSE91016 datasets, and GSE137829 provided single-cell insights into prostate cancer. Specific anoikis-related genes (ARGs) were identified, and Random Survival Forest analysis and multivariate Cox regression were employed to develop anoikis-linked features. The ‘clustanoikisProfilanoikis’ and ‘GSEA’ packages were used to explore potential ARG-related pathways. Results: Analyzing 553 samples from TCGA, 231 from MSKCC, 94 from GSE70769, and single-cell data from 6 prostate cancer patients (GSE137829), we constructed a prognostic model based on 9 ARGs. GSVA revealed upregulation of carcinogenic pathways, including epithelial-mesenchymal transition, E2F targets, and angiogenesis, with downregulation of metabolic pathways. Significant differences in somatic mutations were observed between cohorts, with a positive correlation between anoikis scores and tumor mutational burden (TMB). Immune landscape analysis suggested high-risk patients might benefit more from chemotherapy than immunotherapy based on their risk score. Single-cell analysis indicated overactivation of carcinogenic pathways in the high anoikis score group. Conclusion: This study elucidates the complex interplay between anoikis and bone metastasis in prostate cancer. Our findings highlight the critical role of anoikis in metastatic progression, enhancing the understanding of key biomarkers and molecular dynamics. The identified anoikis-related gene signatures and disrupted pathways offer promising avenues for predictive and therapeutic strategies in prostate cancer management. [ABSTRACT FROM AUTHOR]

Microplastics, as an emerging pollutant, are widely distributed in freshwater environments such as rivers and lakes, posing immeasurable potential risks to aquatic ecosystems and human health. The migration behaviour of microplastics can exacerbate the degree or scope of risk. A complete understanding of the migration behaviour of microplastics in freshwater environments, such as rivers and lakes, can help assess the state of occurrence and environmental risk of microplastics and provide a theoretical basis for microplastic pollution control. Firstly, this review presents the hazards of microplastics in freshwater environments and the current research focus. Then, this review systematically describes the migration behaviours of microplastics, such as aggregation, horizontal transport, sedimentation, infiltration, stranding, resuspension, bed load, and the affecting factors. These migration behaviours are influenced by the nature of the microplastics themselves (shape, size, density, surface modifications, ageing), environmental conditions (ionic strength, cation type, pH, co-existing pollutants, rainfall, flow regime), biology (vegetation, microbes, fish), etc. They can occur cyclically or can end spontaneously. Finally, an outlook for future research is given. [ABSTRACT FROM AUTHOR]

Objective. Aim of this work is to illustrate and experimentally validate a model to evaluate the dielectric properties of biological tissues on a wide frequency band using the magnetic resonance imaging (MRI) technique. Approach. The dielectric behaviour of biological tissues depends on frequency, according to the so-called relaxation mechanisms. The adopted model derives the dielectric properties of biological tissues in the frequency range 10 MHz–20 GHz considering the presence of two relaxation mechanisms whose parameters are determined from quantities derived from MRI acquisitions. In particular, the MRI derived quantities are the water content and the dielectric properties of the tissue under study at the frequency of the MR scanner. Main results. The model was first theoretically validated on muscle and fat using literature data in the frequency range 10 MHz–20 GHz. Results showed capabilities of reconstructing dielectric properties with errors within 16%. Then the model was applied to ex vivo muscle and liver tissues, comparing the MRI-derived properties with data measured by the open probe technique in the frequency range 10 MHz–3 GHz, showing promising results. Significance. The use of medical techniques based on the application of electromagnetic fields (EMFs) is significantly increasing. To provide safe and effective treatments, it is necessary to know how human tissues react to the applied EMF. Since this information is embedded in the dielectric properties of biological tissues, an accurate and precise dielectric characterization is needed. Biological tissues are heterogenous, and their characteristics depend on several factors. Consequently, it is necessary to characterize dielectric properties in vivo for each specific patient. While this aim cannot be reached with traditional measurement techniques, through the adopted model these properties can be reconstructed in vivo on a wide frequency band from non-invasive MRI acquisitions. [ABSTRACT FROM AUTHOR]

Background: Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis. Methods: A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features. Results: Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets. Conclusion: This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs. [ABSTRACT FROM AUTHOR]

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus causing a debilitating febrile illness with rheumatic disease symptoms of arthralgia and arthritis. Since its spread outside of Africa in 2005, it continues to cause outbreaks and disseminates into new territories. Intervention strategies are urgently required, including vaccination and antiviral approaches. To test efficacy, the use of small animal models is required. Two mouse strains, A129, with a deficiency in their type-I interferon (IFN) receptor, and C57BL/6 are widely used. A direct comparison of these strains alongside the wild-type parental strain of the A129 mice, 129Sv/Ev, was undertaken to assess clinical disease progression, viral loads in key tissues, histological changes and levels of sera biomarkers. Our results confirm the severe disease course in A129 mice which was not observed in the parental 129Sv/Ev strain. Of the two wild-type strains, viral loads were higher in 129Sv/Ev mice compared to C57BL/6 counterparts. Our results have established these models and parameters for the future testing of vaccines and antiviral approaches. [ABSTRACT FROM AUTHOR]

In this work, we actively investigated a betavoltaic cell model that demonstrated high accuracy and reproducibility, utilizing both Monte Carlo (MC) simulations and Technology Computer-Aided Design (TCAD) for its development. This model integrates diamond-based metal/intrinsic/p-doped (MIP) Schottky diode structures and employs the following commonly utilized radioactive isotopes: 63Ni, 147Pm, 14C, and 3H. The optimal response of the MIP structure devices to these four radiation sources has been refined through computational modeling. To achieve optimal efficiency from betavoltaic cells when exposed to various radiation sources, design parameters were obtained, a 9 µm thick i-layer with a doping concentration of 1E14 cm− 3 under 2 µm of 63Ni, yielding an efficiency of 10.7%; a 40 µm thick i-layer with a doping concentration of 1E10 cm− 3 under 30 µm of 147Pm, resulting in an efficiency of 7.58%; a 30 µm thick i-layer with a doping concentration of 1E12 cm− 3 under 70 µm of 14C, achieving an efficiency of 9.6%; a 3 µm thick i-layer with a doping concentration of 1E15 cm− 3 under 1 µm of 3H with the lowest energy, which can obtain an efficiency of up to 30.7%. Due to the high energy density of 147Pm, it can theoretically achieve a power density of 63 µW·cm− 2. Ultimately, we propose the design of a one-third optimization strategy for high-performance diamond MIP Schottky betavoltaic cells. [ABSTRACT FROM AUTHOR]

The Yellow River Basin is geographically critical to protect the ecological environment in China. But there is a large demand for agriculture, rapid economy as well as ecological and environmental pressure in the Yellow River Basin, especially in the Middle Reaches of the Yellow River (MRYR) at present. An ecological security pattern (ESP) is also very urgent to clarify the ecological bottom line and areas for restoration, in order to maintain a green ecological protection barrier. Taking MRYR as the study area, this study aims to construct a scientific ESP for the restoration and protection of the ecological environment. Firstly, the core area was calculated using morphological spatial pattern analysis. The national nature reserve was also superimposed to screen the ecological sources. Secondly, a comprehensive system was constructed for the resistance surface indicator. Then the circuit theory model was used to identify the ecological corridors, ecological pinch points, and barrier areas. The ESP of MRYR was constructed to obtain the dynamic trends and features of ESP from 2000 to 2020. Finally, some strategies were proposed to optimize the ESP for ecological and environmental protection, according to specific ecological conditions. The results showed that the number and area of ecological sources remained basically stable. The eastern basin and mountains were distributed densely, while the western plateau was distributed more sparsely. The number of ecological corridors evolved from 96 in 2000 to 103 in 2020. There was a dense distribution in the eastern part of the country, where the mountains were widespread. While in the western part of the country, the ecological corridors were sparse and spanned longer distances. The area of ecological pinch points remained stable. A slight decreasing trend was observed in the area of barrier areas. The pinch points and barrier areas were most densely distributed in the connecting area of the source and the corridor and in the middle of the corridor. The ESP presented a dense spatial pattern in the southeast, while the sparse spatial pattern in the northwest. Pinch-point protection areas were changed frequently in the sparse western corridors. The barrier restoration areas were concentrated in the basins or plains with high human activity intensities. The sparse source-protected areas required stable "staging areas" to maintain corridor connectivity over long distances. The number and length of corridors showed an increasing trend. The ESP shared the promising potential during this time. The spatial difference in the ESN between the eastern and western parts of the MRYR was attributed to the combination of high-intensity human activities and ecologically sound mountainous areas concentrated in the eastern part of the region, and fragile ecological environments and irrational development activities in the western part of the region. According to the specific ESP and the actual ecological situation in the region, some measures were proposed to restore and protect the ecological sources, and corridor elements. The finding can provide a strong reference to optimize the ESP in MRYR, particularly for the high-quality development of the ecological environment. [ABSTRACT FROM AUTHOR]

This study aims to rapidly and accurately identify the origin of spearmint. A feature wavelength extraction was proposed to combine the sequential fusion of Kernel Principal Component Analysis (KPCA) with Kernel Locality Preserving Projections (KLPP) and Wilks' Λ statistics. Effective discrimination was realized among the five origins of spearmint. Firstly, the three-dimensional fluorescence data was collected from the 300 spearmint samples in five locations. The interferences of Rayleigh and Raman scattering were removed from the original spectra by triangular interpolation. Savitzky-Golay (SG) was used to smooth and then eliminate the noise. Secondly, the preprocessed three-dimensional fluorescence spectrum matrix was merged into the data matrix, according to the emission wavelength. KPCA, KPCA + KLPP, KPCA + Wilks Λ statistics, and KPCA + KLPP + Wilks Λ statistics were used to extract the feature wavelengths of the three-dimensional fluorescence spectrum matrix after preprocessing. Among them, KPCA and KPCA + Wilks Λ statistics were used to extract 40 wavelengths of feature emission and eight wavelengths of feature excitation, respectively. KPCA+KLPP and KPCA + KLPP + Wilks Λ statistics were to extract 50 wavelengths of feature emission and 10 wavelengths of feature excitation, respectively. Thirdly, the spectral values of feature emission wavelength were converted into row vectors, according to the wavelength order of feature excitation. Four 300-row spectral matrices of feature wavelength were then obtained from the 300 samples. Furthermore, Fisher discriminant analysis (FDA) was applied to the spectral matrix of feature wavelengths for data separable fusion for the separable fisher discriminant (FD) variables. The first four FD variables were selected with a cumulative discriminant ability of 99% as the input vectors of the discriminant model for the spearmint origins. Finally, the support vector machine (SVM) was used to analyze the four FD variables. The FDA + SVM identification model showed that there was a four-feature wavelength extraction. The correct rates for the training set were 92.00%, 97.78%, 95.11%, and 100%, respectively, and the correct rates for the test set were 92.00%, 96.00%, 94.67%, and 100%, respectively. The results show that both KLPP and Wilks Λ statistics improved the performance of KPCA. The sequential fusion significantly improved the overall feature extraction, in order to better remove the redundant information of spectral data for the high correct of identification. Therefore, the sequential fusion of KPCA + KLPP + Wilks Λ statistics + FDA + SVM can be expected to effectively identify the origin of spearmint. The extraction of feature wavelength with sequential fusion of KPCA + KLPP + Wilks Λ statistics can effectively reduce the redundancy of three-dimensional fluorescence spectral data for the informative features of the original fluorescence data. Better identification was achieved in the five kinds of spearmint origins. In addition, these research findings can lay a foundation for the subsequent quantitative analysis of the important components in spearmint by three-dimensional fluorescence spectroscopy. [ABSTRACT FROM AUTHOR]

Studying the expression of model body language exploring the visual guidance and influencing factors of model body language on the viewer in fashion shoots and obtaining the visual experience rules of the viewer' s body language on the model can provide new ideas for fashion shoots and brand shooting. Based on the fashion shoots of VOGUE in the past five years four types of model body language expressions were summarized and refined through a large number of picture comparisons and literature theories and eye tracking technology was introduced to collect and analyze the eye tracking data of 41 professional models and 41 ordinary people. By comparing and analyzing the hotspot maps trajectory maps and specific eye movement values of the participants the directional effect of the model' s body language and the different effects of different types of body language on vision were explored. The article started from an innovative perspective that is people's body language in order to obtain more influencing factors of fashion shoots and provide a certain theoretical research basis for better conveying fashion concepts and clothing effects. Body language is particularly important in fashion shoots and reasonable design of model body language can improve the visual communication effect of fashion shoots which has high practical research and practical guiding significance. The rapid development of social networks has accelerated the speed of visual image dissemination and the scope of dissemination is constantly expanding at an incredible speed. With the deepening development of the social economy and cultural industry people's attention to the fashion industry is also constantly increasing. Therefore it is urgent to promote the flourishing development of the fashion industry through social networks. Fashion shoots are a comprehensive form of artistic expression often widely used for commercial promotion conveying fashion information and concepts due to their advantages of good dissemination performance and sensory experience. In fashion shoots models serve as the main subject of the image and their expressions and postures often guide people' s attention points. Therefore research on the use of model body language has high value. Eye tracking technology as an emerging technology has been widely applied in various fields such as humancomputer interaction transportation industry military human factor analysis medicine and scene research. The use of eye tracking systems can track the movement trajectory of the eyes thereby obtaining eye movement indicators such as the position of the observation fixation point fixation trajectory fixation time fixation frequency and iris size of human eye movement. By observing and studying the visual trajectory map and fixation focus map the most subjective views of humans on things can be analyzed. In the field of clothing eye tracking technology is also widely used in research on brand recognition visual evaluation feature extraction display design applications and other aspects. Based on literature research and expert discussions this study intends to reclassify the forms of body language expression to maximize the coverage of all forms of body language expression. The main classification method is according to the relative amplitude of the model's body movements the body expression forms are divided into four types strong upper and weak lower limbs weak upper and strong lower limbs both strong upper and lower limbs and both weak upper and lower limbs. Based on expert discussions the article defines the body movement amplitude exceeding 30 degrees of the original fixed position as high intensity otherwise it is classified as weak intensity. On this basis this study combines eye tracking technology to analyze the visual focus of model body language in fashion shoots and explores the intrinsic relationship between different types of body language and visual language in professional models and ordinary people so as to provide suggestions and assistance for the composition and layout of fashion shoots as well as new ideas for fashion brand advertising and promotion. [ABSTRACT FROM AUTHOR]

Although a memristor model, subjected to electrochemical metallization mechanism, has been proposed based on the spontaneous decay of clusters in the previous work, it does not agree with the human forgetting accurately. Therefore, an improved model is meaningfully presented for the memristor with the cluster spontaneous decay by adding the residual effect. The former is due to the inward contraction of atoms driven by surface energy, while the latter is because of the balance of attractive and repulsive forces between atoms. The model fits well with the actual device. The forgetting is caused by the spontaneous decay. Memory retention is generated due to the added effect, which is also the internal cause of good agreement with the actual forgetting. Additionally, short‐term plasticity is converted to long‐term plasticity through the repeated learning. The efficiency of experiential learning using this model is much higher than that using the previous. It is shown that the physical mechanism of spontaneous decay in the cluster‐based channel is different from that in vacancy‐based or atom‐based channel. The model working under a non‐ideal condition with the temperature influence is discussed. Potential applications based on the model are stated. [ABSTRACT FROM AUTHOR]

Introduction: Autogenous arteriovenous fistula (AVF) is the preferred vascular access in patients undergoing maintenance hemodialysis (MHD). However, complications such as thrombosis may occur. This study aimed to construct and validate a machine learning-based risk-prediction model for AVF thrombosis, hypothesizing that such a model can effectively predict occurrences, providing a foundation for early clinical intervention. Methods: The retrospective longitudinal study included a total of 270 patients who underwent MHD at the Hemodialysis Center of the Second Affiliated Hospital of Harbin Medical University between March 2021 and December 2022. During this study, baseline data and scale information of patients between March 2020 and December 2021 were collected. We recorded outcome indicators between March 2021 and December 2022 for subsequent analyses. Five machine learning models were developed (artificial neural network, logistic regression, ridge classification, random forest, and adaptive boosting). The sensitivity (recall), specificity, accuracy, and precision of each model were evaluated. The effect size of each variable was analyzed and ranked. Models were assessed using the area under the receiver-operating characteristic (AUROC) curve. Results: Among the 270 included patients, 105 had AVF thrombosis (55 male and 50 female patients; age range, 29–79 years; mean age, 56.72 years; standard deviation [SDs], ±13.10 years). Conversely, 165 patients did not have AVF thrombosis (99 male and 66 female patients; age range, 23–79 years; mean age, 53.58 years; SD, ± 13.33 years). During the observation period, approximately 52.6% of patients with AVF experienced long-term complications. The most common complications associated with AVF were thrombosis (105; 38.9%), aneurysm formation (27; 10%), and excessively high output flow (10; 3.7%). Fifty-four (20%) patients with AVF required intervention because of complications associated with vascular access. The AUROC curve of the testing set was between 0.858 and 0.903. Conclusion: In this study, we developed five machine learning models to predict the risk of AVF thrombosis, providing a reference for early clinical intervention. [ABSTRACT FROM AUTHOR]

Data lakes are storage repositories that contain large amounts of data in its native format; either structured ssemi-structured or unstructured, to be used when needed. Data lakes are open to a wide range of use cases such as carrying out advanced analytics, extracting knowledge patterns, etc. However, simply dumping all the data into a data lake would only lead to a so-called data swamp. To prevent such a situation, enterprises can adopt best practices among which to build and maintain metadata. In recent years there has been a growing body of research about managing metadata in data lake environments. Existing research efforts deal separately with different activities such as metadata modeling, metadata capture and extraction, metadata usage, etc. Nevertheless, despite its importance, a global view about the research landscape about metadata management for data lakes is still missing. This survey congregates different facets of metadata management in data lakes and presents a global view along with the technological implications and the required features for building successful metadata management systems. Besides, this survey summarizes and discusses research gaps, open problems and main challenges facing both industrialists and academics. This survey pertains to the broader field of Big Data and especially to the data platforms that manage enterprise big data assets. Furthermore, considering the parallels between data lakes and digital libraries regarding their dependence on metadata for content management, this study could offer valuable insights to the digital library community, offering them a technological outlook on metadata management. [ABSTRACT FROM AUTHOR]

Introduction: Respiratory syncytial virus (RSV) leads to significant morbidity in newborn infants in the United Kingdom (UK). Nirsevimab, a long-acting monoclonal antibody, received approval from the European Medicines Agency and has been licensed by the Medicines and Healthcare products Regulatory Agency for preventing RSV lower respiratory tract disease (LRTD) in neonates and infants during their first RSV season. The objective of this study was to assess the potential impact of nirsevimab on RSV-associated LRTDs, related costs, and loss of quality-adjusted life years (QALYs) in infants experiencing their first RSV season. Methods: The impact of administering nirsevimab across all infant populations compared to palivizumab in the high-risk palivizumab-eligible population was assessed via a static decision-analytic model specified for a UK birth cohort experiencing their first RSV season. The RSV-related health events of interest included primary care (PC), accident and emergency (A&E) visits, hospitalizations [including hospitalizations alone and those resulting in intensive care unit (ICU) admissions], recurrent wheezing in infants who were previously hospitalized, and all-cause LRTD hospitalizations. Results: Under the current standard of practice (SoP), RSV was estimated to result in 329,425 RSV LRTDs annually, including 24,381 hospitalizations and ICU admissions, representing £117.8 million (2024 GBP) in costs. Comparatively, universal immunization of all infants with nirsevimab could avoid 198,886 RSV LRTDs, including 16,657 hospitalizations and ICU admissions, resulting in savings of £77.2 million in RSV treatment costs. Considering the impact on all-cause LRTD of a universal immunization strategy, nirsevimab could be valued between £243 and £274, assuming willingness-to-pay (WTP) thresholds of £20,000 and £30,000 per QALY saved, respectively. Conclusions: This analysis demonstrated that the health and economic burden of RSV would be substantially reduced in all infants experiencing their first RSV season in the UK (including term, preterm, and palivizumab-eligible infants) as a result of a universal immunization strategy with nirsevimab. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the evaporation (E) and transpiration (T) components of evapotranspiration (ETc), and the growth parameters of curly lettuce (Lactuca sativa L. cv. Caipira) grown under different irrigation treatments. The study was conducted in a Mediterranean-type plastic greenhouse located in Antalya, Türkiye, in the fall and spring growing seasons of 2020 and 2021, respectively. To assess the impact of water stress on ETc and its components, three different irrigation water levels [ I100 for full irrigation treatment (100%), I66 for 66% (I100×0.66), and I33 for 33% (I100×0.33)] were selected. Planted and unplanted pots were used to measure ETc and E independently. The values obtained from these measurements were used as inputs to calculate the evaporation that occurred in the soil under the crop canopy and plant transpiration. In the present study, T was determined indirectly from the difference of measured evapotranspiration and evaporation and estimated with the modified Hernandez-Suarez model (Te). The modified model for the different irrigation treatments showed high Te estimation performance. Evaporation from the soil in the planted pots (Es) was calculated by considering the canopy cover and soil water content. The study revealed that water stress significantly affected lettuce plant height, root length, cover percentage, leaf area index (LAI), number of leaves, fresh and dry head weights, and root weights (p<0.01). The study also investigated the relationship between Es/ETc , and LAI using an exponential method and established a strong nonlinear relationship in all irrigation treatments (R2>0.90). The modified model developed for different irrigation treatments in Mediterranean-type greenhouses can be used to predict lettuce ETc values with greater precision and to better understand the partitioning of ETc into its constituent components. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Whether plasma biomarkers play roles in predicting incident dementia among the general population is worth exploring. METHODS: A total of 1857 baseline dementia‐free older adults with follow‐ups up to 13.5 years were included from a community‐based cohort. The Recursive Feature Elimination (RFE) algorithm aided in feature selection from 90 candidate predictors to construct logistic regression, naive Bayes, bagged trees, and random forest models. Area under the curve (AUC) was used to assess the model performance for predicting incident dementia. RESULTS: During the follow‐up of 12,716 person‐years, 207 participants developed dementia. Four predictive models, incorporated plasma p‐tau217, age, and scores of MMSE, STICK, and AVLT, exhibited AUCs ranging from 0.79 to 0.96 in testing datasets. These models maintained robustness across various subgroups and sensitivity analyses. DISCUSSION: Plasma p‐tau217 outperforms most traditional variables and may be used to preliminarily screen older individuals at high risk of dementia. Highlights: Plasma p‐tau217 showed comparable importance with age and cognitive tests in predicting incident dementia among community older adults.Machine learning models combining plasma p‐tau217, age, and cognitive tests exhibited excellent performance in predicting incident dementia.The training models demonstrated robustness in subgroup and sensitivity analysis. [ABSTRACT FROM AUTHOR]

In this work, a harmonic oscillator with elastic elements is adopted as a model of a mechanical system sensitive to various force disturbances. The developed physical and mathematical models for the interaction of the oscillator with the tribosystem model formed the basis for the creation of a method and a set of experimental tools for assessing the tribological state of the contact and the behavior of the sliding tribosystem under dynamic operating conditions. For this purpose, an original installation was designed and built with a specially prepared test chamber using a harmonic oscillator as a sensitive unit. The installation was equipped with a specially developed measuring system for monitoring the state of the tribomodel and the oscillator, implemented using experimental data collection tools (products of National Instruments). According to the methodology, specialized software has been developed using LabVIEW, which allows the collection, processing, and storage of experimental data in large volumes and with high productivity. The experimental tools together with the developed software expand the range of test conditions for sliding tribosystems and make it possible to evaluate their behavior in various dynamic operating modes, including frictional self-oscillations. [ABSTRACT FROM AUTHOR]

Background/Objectives: In paediatric liver transplantation, donor–recipient compatibility depends on graft size. We explored whether the graft weight can be predicted using the donor's biometric parameters. Methods: We used seven easily available biometric variables in 142 anonymised paediatric and adult donors, with data collected between 2016 and 2022. The whole or partial liver was transplanted in our hospital from these donors. We identified the variables that had the strongest correlation to our response variable: whole liver graft weight. Results: In child donors, we determined two linear models: using donor weight and height on the one hand and using donor weight and right liver span on the other hand. Both models had a coefficient of determination R2 = 0.86 and p-value < 10−5. We also determined two models in adult donors using donor weight and height (R2 = 0.33, p < 10−4) and donor weight and sternal height (R2 = 0.38, p < 10−4). The models proved valid based on our external dataset of 245 patients from two institutions. Conclusions: In clinical practise, our models could provide rapidly accessible estimates to determine whole graft dimension compatibility in liver transplantation in children and adults. Determining similar models predicting the left lobe and lateral segment weight could prove invaluable in paediatric transplantation. [ABSTRACT FROM AUTHOR]