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Background: The vegetable‐based diet alone does not provide the lysine (Lys) needed to maximize poultry productive performance. Objectives: This experiment aimed to evaluate the effects of dietary digestible Lys (dLys) level on productive and reproductive performance, egg quality, blood metabolites and immune responses in breeding Japanese quails (Coturnix japonica). Methods: The experiment was conducted in a completely randomized design with 6 treatments, 5 replicates and 15 (12 females and 3 meals) 10‐week‐old breeding Japanese quails each. A basal diet was formulated to meet nutritional requirements of breeding quails except dLys. The basal diet was supplemented with graded (+0.82 g/kg) levels of l‐Lys‐HCl, corresponding to dietary dLys levels of 0.690%, 0.755%, 0.820%, 0.885%, 0.950% and 1.015%. The experiment lasted for 12 weeks, which was divided into 3‐4‐week periods. Results: Significant differences were observed for egg production (EP), egg mass (EM) and feed efficiency (FE) in response to increasing dietary dLys concentration with quadratic trends. The highest traits were observed in the birds fed with a diet containing 0.885% dLys. However, feed intake, egg quality, reproductive performance, blood metabolites and immune responses against sheep red blood cell inoculation were not significantly affected by increasing dietary dLys concentrations. The dLys requirements during 11–14, 15–18, 19–22 and 11–22 (overall) weeks of age for optimal EP, EM and FE, based on the quadratic broken‐line regression analysis, were estimated 272, 265, 250 and 266; 293, 285, 264 and 279; and 303, 294, 281 and 293 mg/bird/day, respectively. Conclusions: The dLys requirements vary depending on the EP phase and the trait being optimized. The estimated dLys requirement for FE was higher than those for EP and EM. During the peak stage of the first laying cycle, the dietary dLys level of 0.932% and a daily intake of 303 mg dLys/bird are sufficient for optimal performance. [ABSTRACT FROM AUTHOR]

Dengue fever remains a significant epidemiological challenge globally, particularly in Brazil, where recurring outbreaks strain healthcare systems. Traditional statistical models often struggle to accurately capture the complexities of dengue case distributions, especially when data exhibit bimodal patterns. This study introduces a novel bimodal regression model based on the log-generalized odd log-logistic exponential distribution, offering enhanced flexibility and precision for analyzing epidemiological data. By effectively addressing multimodal distributions, the proposed model overcomes the limitations of unimodal models, making it well suited for public health applications. Through regression analysis of dengue case data from the Federal District of Brazil during the epidemiological weeks of 2022, the model demonstrates its capacity to improve the fit of the disease rate. The model's parameters are estimated using maximum likelihood estimation, and Monte Carlo simulations validate their accuracy. Additionally, local influence measures and residual analysis ensure the proposed model's goodness-of-fit. While this innovative regression model offers substantial advantages, its effectiveness depends on the availability of high-quality data, and further validation is necessary to confirm its applicability across diverse diseases and regions with varying epidemiological characteristics. [ABSTRACT FROM AUTHOR]

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R2 = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R2 = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment. [ABSTRACT FROM AUTHOR]

The widespread popularization of Internet technology has facilitated the emergence of e-commerce as a novel avenue for agricultural product sales, driven by its convenience and broad reach. Nevertheless, in Wuchang City, a well-developed agricultural region in northeastern China, some farmers still exhibit low enthusiasm for participating in agricultural product e-commerce, with limited levels of engagement. To investigate the underlying causes, this study analyzes survey data from 301 farmers in Wuchang City and uses mean difference significance tests and Logistic and Tobit regression models to explore the factors influencing farmers' adoption of e-commerce for agricultural products. The results demonstrate that gender and the number of household members involved in agricultural labor significantly influence the adoption decision and the extent of adoption. There is a significant difference in the adoption of decisions among ages. Subjective willingness and policy perception positively and significantly influence the adoption decision. Risk perception significantly and negatively impacts the extent of adoption. Infrastructure exerts a significant and negative influence on the adoption decision but a significant and positive influence on the extent of adoption. Based on these findings, this study suggests localized reforms, enhanced e-commerce promotion, and differentiated training to boost farmers' adoption, promoting sustainable development of the agricultural e-commerce economy. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is crucial for monitoring the physiological processes of crops. Many studies have utilized spectral features to develop regression models for accurate LCC estimation, enabling the quantitative assessment and evaluation of crop growth status. The selection of optimal spectral features and regression algorithms significantly affects the precision of LCC estimation. In this study, we compared and analyzed the optimal spectral features for LCC estimation, as well as the consistency of machine learning methods across different crop types, phenology periods, and sensors. First, we extracted various spectral features, including the original spectral features (OS), first-order derivative spectral features (FDS), original continuum-removed spectra (CR) along with their four related derivative spectral features, principal component variables derived from different spectral features, and highly correlated spectral features with LCC. These extracted spectral features were then employed to construct the LCC models using six common regression algorithms on different datasets. Finally, we analyzed the optimal combination of spectral features and regression algorithms for accurate LCC estimation considering various dimensions, such as crop type, phenological period, and sensor used in observation conditions. The results demonstrate that the combinations of the principal component variables of continuum-removed derivative reflectance with the top 10 correlations with LCC (PCA_CRDR_R) combined with Gaussian process regression (GPR) can be considered as the optimal choice for estimating LCC under diverse observation conditions at a canopy scale, and its R2 can reach 0.62 for sugar beet LCC estimation; thus providing valuable theoretical guidance for selecting appropriate spectral features for LCC estimation. [ABSTRACT FROM AUTHOR]

This article is focused mainly on verifying the suitability of data from the experimental milling of heat-treated beech wood and on investigating the effects of the technical and technological parameters of milling on the energy consumption of this process. The independent parameters of the machining process are the cutting speed, feed speed, rake angle, and hydrothermal modification of the experimental wood material. Based on analysis of variance, it can be argued that the cutting speed and rake angle of the tool have the greatest statistically significant effect on energy consumption, while the feed speed has the least influence. The measured data on cutting power during milling were used to build a regression model and validate it, and the most suitable type of model, with a correlation of 87%, is the classification and regression tree, followed by a model created using the random forest method. [ABSTRACT FROM AUTHOR]

This research presents a machine learning (ML)‐based model that determines the DC and RF characteristics of InGaAs sub‐channel double gate high electron mobility transistors (DG‐HEMTs) to optimize the device structure. We employ technology computer‐aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub‐channel DG‐HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5‐fold cross‐validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods. [ABSTRACT FROM AUTHOR]

This study examines the balance between technical and social focus in artificial intelligence, blockchain, cybersecurity, and privacy publications in Web of Science across countries, exploring the social factors that influence these research priorities. We use regression analysis to identify predictors of research focus and cluster analysis to reveal patterns across countries, combining these methods to provide a broader view of global research priorities. Our findings reveal that liberal democracy index, life expectancy, and happiness are significant predictors of research focus, while traditional indicators like education and income show weaker relationships. This unexpected result challenges conventional assumptions about the drivers of research priorities in digital technologies. The study identifies distinct clusters of countries with similar patterns of research focus across the four technologies, revealing previously unrecognized global typologies. Notably, more democratic societies tend to emphasize social implications of technologies, while some rapidly developing countries focus more on technical aspects. These findings suggest that political and social factors may play a larger role in shaping research agendas than previously thought, necessitating a re-evaluation of how we understand and predict research focus in rapidly evolving technological fields. The study provides valuable information for policymakers and researchers, informing strategies for technological development and international collaboration in an increasingly digital world. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method for enhancing time series data preprocessing by integrating a cycling layer into a self-attention mechanism. Traditional approaches often fail to capture the cyclical patterns inherent to time series data, which affects the predictive model accuracy. The proposed method aims to improve models' ability to identify and leverage these cyclical patterns, as demonstrated using the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results show that the proposed method enhances forecast accuracy and speeds up model fitting compared to the conventional techniques. This paper contributes to the field of time series analysis by providing a more effective preprocessing approach. [ABSTRACT FROM AUTHOR]

Automotive chassis components, constructed as lap joints and produced by gas metal arc welding (GMAW), require fatigue durability. The fatigue properties of the weld in a lap joint are largely determined by weld geometry factors. When there is no gap or a consistent gap in the lap joint, improving the geometry of the weld toe can alleviate stress concentration and enhance fatigue properties. However, due to machining tolerances, it is difficult to completely eliminate or consistently manage the gap in the joint. In the case of a lap-welded joint with an inconsistent gap, it is necessary to identify the weld geometry factors related to fatigue properties. Evaluating the fatigue behavior of materials and welded joints requires significant time and cost, meaning that research that seeks to predict fatigue properties is essential. More research is needed on predicting fatigue properties related to automotive chassis components, particularly studies on predicting the fatigue properties of lap-welded joints with gaps. This study proposed a regression model for predicting fatigue properties based on crucial weld geometry factors in lap-welded joints with gaps using statistical analysis. Welding conditions were varied in order to build various weld geometries in joints configured in a lap with gaps of 0, 0.2, 0.5, and 1.0 mm, and 87 S–N curves for the lap-welded joints were derived. As input variables, 17 weld geometry factors (7 lengths, 7 angles, and 3 area factors) were selected. The slope of the S–N curve using the Basquin model from the S–N curve and the safe fatigue strength were selected as output variables for prediction in order to develop the regression model. Multiple linear regression models, multiple non-linear regression models, and second-order polynomial regression models were proposed to predict fatigue properties. Backward elimination was applied to simplify the models and reduce overfitting. Among the three proposed regression models, the multiple non-linear regression model had a coefficient of determination greater than 0.86. In lap-welded joints with gaps, the weld geometry factors representing fatigue properties were identified through standardized regression coefficients, and four weld geometry factors related to stress concentration were proposed. [ABSTRACT FROM AUTHOR]

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Land degradation is a critical issue in Ethiopia, exacerbating food insecurity by reducing agricultural productivity. Soil and water conservation (SWC) practices are essential to control erosion and increase food production. However, there is a lack of comprehensive evaluations on the impact of Ethiopia's Productive Safety Net Program (PSNP) on SWC practices. This study aimed to assess the contribution of the PSNP to SWC in the Amhara Sayint Woreda. The researchers used a mixed-method approach, combining quantitative and qualitative data. Multistage sampling was used to select households, and data were collected through questionnaires, interviews, focus groups, and observations. The study provided empirical evidence that the PSNP has a positive impact on SWC practices. Key factors influencing SWC participation include age, family size, education, plot size, livestock ownership, credit service, and access to extension services. The results suggest that the PSNP should improve payment for public work participants implementing SWC, undertake institutional reform, and increase public awareness of the benefits of SWC in reversing land degradation and improving food security. This study uniquely contributes to the understanding of how the PSNP influences the varying degrees of participation in SWC practices, filling a critical research gap. The findings can inform policymakers and program managers to enhance the PSNP's effectiveness in promoting sustainable land management and food security in Ethiopia. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Jordan faces significant food security challenges due to population growth, climate change, and urbanization, straining limited water resources. Water supply expansion is constrained by economic and environmental factors, leading to a critical impact on agriculture, which stands as the largest consumer of water. The study emphasizes the crucial importance of regional water sources in ensuring food security, as the quantity and quality of irrigation water have a direct influence on crop yield and productivity. Managing irrigation is crucial for sustainability and livelihoods, given Jordan's reliance on food imports and climate change-induced production variability. This study delves into potato cultivation, examining the interconnections among irrigation water availability, crop yield, and self-relianceMETHODS: Irrigation with freshwater and blended treated wastewater in the middle and northern Jordan Valley is compared, using stepwise regression analysis and assuming other agricultural inputs to be optimal. Food security indicators, which include agricultural and socio-economic factors, along with water scarcity indicators, encompassing both water quantity and quality, were methodically chosen and examined.FINDINGS: Regression analysis of potato production in Jordan revealed that increased blended water positively impacts yields, while higher water requirements and chloride levels negatively affect them. The negative implications of effective rainfall in combined water irrigation were evident, emphasizing the necessity for precise control over the levels of water quality and quantity. The study found that higher local potato production enhances self-sufficiency, crucial for food security. Enhanced water management techniques and advancements in agricultural practices have led to an increase in potato self-sufficiency, even in the face of dwindling water resources. Challenges from stable water requirements and decreasing rainfall can be addressed with advanced irrigation techniques and adaptive practices.CONCLUSION: Improved water and crop management contribute to enhanced potato self-sufficiency in Jordan, despite varying water quality parameters. Furthermore, the results of research offer important information regarding the adjustment of potato cultivation to evolving climate patterns, including changes in precipitation temperature. These adaptation strategies can be shared and implemented in other countries facing similar climatic challenges.

Abstract Leaf chlorophyll content (LCC) is crucial for monitoring the physiological processes of crops. Many studies have utilized spectral features to develop regression models for accurate LCC estimation, enabling the quantitative assessment and evaluation of crop growth status. The selection of optimal spectral features and regression algorithms significantly affects the precision of LCC estimation. In this study, we compared and analyzed the optimal spectral features for LCC estimation, as well as the consistency of machine learning methods across different crop types, phenology periods, and sensors. First, we extracted various spectral features, including the original spectral features (OS), first-order derivative spectral features (FDS), original continuum-removed spectra (CR) along with their four related derivative spectral features, principal component variables derived from different spectral features, and highly correlated spectral features with LCC. These extracted spectral features were then employed to construct the LCC models using six common regression algorithms on different datasets. Finally, we analyzed the optimal combination of spectral features and regression algorithms for accurate LCC estimation considering various dimensions, such as crop type, phenological period, and sensor used in observation conditions. The results demonstrate that the combinations of the principal component variables of continuum-removed derivative reflectance with the top 10 correlations with LCC (PCA_CRDR_R) combined with Gaussian process regression (GPR) can be considered as the optimal choice for estimating LCC under diverse observation conditions at a canopy scale, and its R2 can reach 0.62 for sugar beet LCC estimation; thus providing valuable theoretical guidance for selecting appropriate spectral features for LCC estimation.

Despite stiff competition in the construction industry, housing quality remains a problem. From the consumer’s perspective, these quality problems are called defects. Homeowners experience inconvenience and suffering due to home defects, and developers and builders also experience severe damage in time, costs, and reputation due to defect repairs. In Korea, lawsuits are increasing due to the rise in housing defects, and the cost of repairing defects determined by lawsuits is of great concern. Litigation is a burden to consumers and producers, requiring a hefty court fee, as well as attorneys and specialist firms, and takes some years. Suppose it is possible to predict the repair costs based on the outcome of a lawsuit and present it as objective supporting data. In that case, it can be of great help in bringing a settlement between consumers and producers. According to previous studies on housing repair costs, linear regression models were mainly used. Accordingly, in this study, a linear regression model was adopted as a method to predict housing repair costs. We analyzed the defect repair costs in 100 cases in which lawsuits were filed and the verdict was finalized for housing complexes in Korea. Previous studies investigated using the following independent variables: elapsed period, litigation period, claim amount, home warranty deposit, total floor area, households, and main building’s quantity, construction cost, region, and highest floor. Among these, the floor area, elapsed period, and litigation period were determined to be valid independent variables. In addition, the construction period was discovered as a valid independent variable. The present research model, which combines these independent variables, was compared with previous research models. The results showed that the earlier research model was found to have a multicollinearity issue among some independent variables. Also, the coefficients of some independent variables were not statistically significant. This research model did not have a multicollinearity problem; all independent variables’ coefficients were statistically significant, and the coefficient of determination was higher than other linear research models. Our proposed regression model, which accounts for the interaction of each independent variable, is a significant step forward in our research. This model, using the number of households multiplied by the construction period, the construction period multiplied by the litigation period, and the litigation period multiplied by the litigation period as independent variables, has been rigorously tested and found to have no multicollinearity issue. The coefficients of all independent variables are statistically significant, further bolstering the model’s reliability. Additionally, the explanatory power of this model is comparable to the previous model, suggesting its potential to be used in conjunction with the existing model. Therefore, the linear regression model predicting the repair cost of housing defects following litigation in this study was considered the best. Utilizing the model proposed in this study is expected to play a major role in reconciling disputes between consumers and producers over housing defects.

The assessment of income-producing properties - considered as the bulk of the existing assets - has rapidly increased. An efficient assessment of the market value of this kind of properties requires an adequate involvement of the main risk factors of the local real estate market for the determination of the capitalization rate for the income approach application. The aim of the work is to identify the most significant local real estate risk factors related to the market, the tenant and the context on the residential capitalization rate. The development of a regressive methodological approach applied to the residential sector of the city of Rome (Italy) is proposed. The obtained results show the susceptibility of the analyzed capitalization rate to the variation of the local real estate risk factors, in particular the per capita income and the variation of the rental values, by also considering the influences of the exogenous shocks and the expectation of the investors. The practical implications of the work consist in the possibility for evaluators to assess the likely changes in the capitalization rate in different residential contexts if variations occur in the most influential local risk factors identified by the proposed model.

Abstract Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.

Hospital construction projects require the integration of innovative technologies, making them complex and challenging to estimate accurately. Without reliable guidelines, lists of considerations, or predictive models, decision-makers may choose an unsuitable contractor, leading to cost overruns and project delays. This research used quantitative and qualitative methods, including expert opinions and focus groups, to address this issue and identify critical factors affecting cost estimation in hospital construction. Using multi-regression analysis, historical data was analyzed to establish a cost-predictive model. The study identified 24 factors that determine the most critical criteria affecting the cost estimation of hospital construction. This model can evaluate the estimated direct cost provided to decision-makers in the early stages of the project with over 95% accuracy. The research results can assist professionals in the construction industry in better estimating and managing the costs of hospital construction projects, reducing risks, and improving project outcomes. Key findings indicate that specific components such as site and electrical works show average percentage errors of 8.02% and 5.11%, respectively, highlighting areas prone to cost estimation inaccuracies. This insight directs focus on improving accuracy in these critical sectors, thus further refining cost management strategies for hospital construction projects.

Abstract To explore the clinical characteristics of patients infected with SARS-CoV-2 nationwide, especially the effect factors of asymptomatic infection and disappearance of clinical symptoms. A total of 66,448 COVID-19 patients in China who have been diagnosed by nucleic acid test or rapid antigen test were surveyed online (December 24, 2022 to January 16, 2023). Our cross-sectional study used descriptive analyses and binary Logistics regression model to assess the correlation between the clinical characteristics and relative factors, including age, gender, pre-existing conditions, reinfection, vaccination and treatment. A total of 64,515 valid questionnaires were collected. Among included participants, 5969 of which were asymptomatic. The symptoms were mainly upper respiratory symptoms, including dry and itchy throat (64.16%), sore throat (59.95%), hoarseness (57.90%), nasal congestion (53.39%). In binary Logistics regression model, we found that male, no pre-existing conditions, reinfection and vaccination have positive correlations with the appearance of asymptomatic COVID-19 patients. In Cox proportional-hazards regression model, considering all clinical symptoms disappeared in 14 days as outcome, we found that ≤ 60 years old, male, no pre-existing conditions, vaccination and adopted treatment have positive correlations with rapid amelioration of clinical symptoms in COVID-19 patients. The clinical symptoms of the participants were mainly upper respiratory symptoms which were according with the infection of Omicron variant. Factors including age, gender, pre-existing conditions and reinfection could influence the clinical characteristics and prognosis of COVID-19 patients. Importantly, vaccination has positive significance for the prevention and treatment of COVID-19. Lastly, the use of Chinese medicine maybe beneficial to COVID-19 patients, however, reasonable guidance is necessary.

Several regression models for predicting returns of pink salmon in the Kamchatka region are presented. The data for 1990–2023 were analyzed. Among available climatic and oceanological indices, the most suitable for using as predictors for forecasting of pink salmon returns were the Pacific Decadal Oscillation (PDO) index, Western Pacific Cyclonic Index (WP), Arctic Oscillation (AO) index, and the sea surface temperature anomaly in the North Pacific. Multi-dimensional models of the «stock–recruitment» type were built on identified statistical patterns, which allowed to estimate potential abundance of the pink salmon returns to northeastern and western Kamchatka. Besides, methods for predicting the abundance of pink salmon returns on the data of fish counting in the sea are considered, using the materials of TINRO trawl surveys conducted in the Bering and Okhotsk Seas in the fall seasons of 2012–2023. To determine the abundance of pink salmon originated from West Kamchatka, genetic identification of regional composition of juveniles in mixed trawl catches was used. All tested methods have a high level of determination, but simpler regressive models are more prospective for practical forecasting of general trend in dynamics of pink salmon stocks in the Kamchatka region due to very weak generalization ability of more complicated models.

Aims: Transfusion after primary total hip arthroplasty (THA) has become rare, and identification of causative factors allows preventive measures. The aim of this study was to determine patient-specific factors that increase the risk of needing a blood transfusion. Methods: All patients who underwent elective THA were analyzed retrospectively in this single-centre study from 2020 to 2021. A total of 2,892 patients were included. Transfusion-related parameters were evaluated. A multiple logistic regression was performed to determine whether age, BMI, American Society of Anesthesiologists (ASA) grade, sex, or preoperative haemoglobin (Hb) could predict the need for transfusion within the examined patient population. Results: The overall transfusion rate was 1.2%. Compared to the group of patients without blood transfusion, the transfused group was on average older (aged 73.8 years (SD 9.7) vs 68.6 years (SD 10.1); p = 0.020) and was mostly female (p = 0.003), but showed no significant differences in terms of BMI (28.3 kg/m2 (SD 5.9) vs 28.7 kg/m2 (SD 5.2); p = 0.720) or ASA grade (2.2 (SD 0.5) vs 2.1 (SD 0.4); p = 0.378). The regression model identified a cutoff Hb level of < 7.6 mmol/l (< 12.2 g/dl), aged > 73 years, and a BMI of 35.4 kg/m² or higher as the three most reliable predictors associated with postoperative transfusion in THA. Conclusion: The possibility of transfusion is predictable based on preoperatively available parameters. The proposed thresholds for preoperative Hb level, age, and BMI can help identify patients and take preventive measures if necessary. Cite this article: Bone Jt Open 2024;5(7):560–564.

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios.

Abstract In this research, the star gold structure with beta graphene is thoroughly examined. We mainly focus on computing degree-based topological indices, which provide information about the network’s connectivity and complexity as well as structural features. In addition, we compute an entropy measure to represent the uncertainty, information richness, and degree of unpredictability in the network. Furthermore, this study explores the relationships between topological descriptors and entropy using regression models that are logarithmic, linear, and quadratic. By merging these regression models, we uncover hidden patterns and understand the underlying ideas governing the network’s behaviour. Our findings shed light on the connection between topological indices and entropy. This work improves our understanding of star gold structure dynamics and provides a visual framework for interpreting their behaviour.

Palm fat has uniquely optimal melting characteristics that are difficult to replace in products such as baked goods and chocolate-based items. This study investigates the efficacy of using Pickering emulsions derived from Swiss plant oils and their micromilled press cakes. Emulsification was carried out at both the lab and pilot scales using sunflower- and rapeseed-based recipes, with and without additional surfactants, for both oil-in-water and water-in-oil emulsions. The resulting emulsions were measured for viscosity and short- and long-term stability and linked to the properties of the raw materials. The results indicated that the contact angle, size, and macronutrient composition of the particles significantly impact emulsion quality, though differences in oil pressing methods might predominate these effects. The combination of particles and surfactants demonstrated a clear advantage with respect to interface stabilisation, with a suggested link between the wax content of the oil and particles and the resulting emulsion quality and stability. [ABSTRACT FROM AUTHOR]

Recently, there has been a shift in the global energy landscape to move to reliable, clean, and eco-friendly renewable energy sources to address global issues such as climate change and greenhouse gas emissions. One such energy source is wave energy; researchers attempt to develop models that can accurately forecast the availability of wave energy as an alternative energy source. In this paper, an Artificial Neural Network (ANN) model along with statistical models such as time series models, and regression models are proposed for forecasting wave energy at a site in Fiji using the wave height and wave period as the independent variables. The performance of the proposed models developed is compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the goodness-of-fit ($R^2$R2) value. The proposed model is then further benchmarked with the naïve model. The empirical results reveal that the proposed ANN model outclassed all the other models and was more efficient and accurate in forecasting wave energy than the regression and time series models. By accurate wave modelling and by incorporating impedance matching, maximum power generation can be achieved. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) from road traffic is a non-negligible part of global greenhouse gas (GHG) emissions, and it is a challenge for the world today to accurately estimate road traffic CO2 emissions and formulate effective emission reduction policies. Current emission inventories for vehicles have either low-resolution, or limited coverage, and they have not adequately focused on the CO2 emission produced by new energy vehicles (NEV) considering fuel life cycle. To fill the research gap, this paper proposed a framework of a high-resolution well-to-wheel (WTW) CO2 emission estimation for a full sample of vehicles and revealed the unique CO2 emission characteristics of different categories of vehicles combined with vehicle behavior. Based on this, the spatiotemporal characteristics and influencing factors of CO2 emissions were analyzed with the geographical and temporal weighted regression (GTWR) model. Finally, the CO2 emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies. The results show that the distribution of vehicle CO2 emissions shows obvious heterogeneity in time, space, and vehicle category. By simply adjusting the existing NEV promotion policy, the emission reduction effect can be improved by 6.5%-13.5% under the same NEV penetration. If combined with changes in power generation structure, it can further release the emission reduction potential of NEVs, which can reduce the current CO2 emissions by 78.1% in the optimal scenario. [ABSTRACT FROM AUTHOR]

In this paper, we present the unit-power half-normal distribution, derived from the power half-normal distribution, for data analysis in the open unit interval. The statistical properties of the unit-power half-normal model are described in detail. Simulation studies are carried out to evaluate the performance of the parameter estimators. Additionally, we implement the quantile regression for this model, which is applied to two real healthcare data sets. Our findings suggest that the unit power half-normal distribution provides a robust and flexible alternative for existing models for proportion data. [ABSTRACT FROM AUTHOR]

Despite stiff competition in the construction industry, housing quality remains a problem. From the consumer's perspective, these quality problems are called defects. Homeowners experience inconvenience and suffering due to home defects, and developers and builders also experience severe damage in time, costs, and reputation due to defect repairs. In Korea, lawsuits are increasing due to the rise in housing defects, and the cost of repairing defects determined by lawsuits is of great concern. Litigation is a burden to consumers and producers, requiring a hefty court fee, as well as attorneys and specialist firms, and takes some years. Suppose it is possible to predict the repair costs based on the outcome of a lawsuit and present it as objective supporting data. In that case, it can be of great help in bringing a settlement between consumers and producers. According to previous studies on housing repair costs, linear regression models were mainly used. Accordingly, in this study, a linear regression model was adopted as a method to predict housing repair costs. We analyzed the defect repair costs in 100 cases in which lawsuits were filed and the verdict was finalized for housing complexes in Korea. Previous studies investigated using the following independent variables: elapsed period, litigation period, claim amount, home warranty deposit, total floor area, households, and main building's quantity, construction cost, region, and highest floor. Among these, the floor area, elapsed period, and litigation period were determined to be valid independent variables. In addition, the construction period was discovered as a valid independent variable. The present research model, which combines these independent variables, was compared with previous research models. The results showed that the earlier research model was found to have a multicollinearity issue among some independent variables. Also, the coefficients of some independent variables were not statistically significant. This research model did not have a multicollinearity problem; all independent variables' coefficients were statistically significant, and the coefficient of determination was higher than other linear research models. Our proposed regression model, which accounts for the interaction of each independent variable, is a significant step forward in our research. This model, using the number of households multiplied by the construction period, the construction period multiplied by the litigation period, and the litigation period multiplied by the litigation period as independent variables, has been rigorously tested and found to have no multicollinearity issue. The coefficients of all independent variables are statistically significant, further bolstering the model's reliability. Additionally, the explanatory power of this model is comparable to the previous model, suggesting its potential to be used in conjunction with the existing model. Therefore, the linear regression model predicting the repair cost of housing defects following litigation in this study was considered the best. Utilizing the model proposed in this study is expected to play a major role in reconciling disputes between consumers and producers over housing defects. [ABSTRACT FROM AUTHOR]

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters. [ABSTRACT FROM AUTHOR]

Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City's SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally. [ABSTRACT FROM AUTHOR]

Milk has a high nutritional value and is also one of the most versatile products in the food industry. Unfortunately, fraud involving the product is constant and needs to be combated. This work presents a measurement methodology to perform the electrical impedance analysis in food. The influence of temperature on the measurements has been evaluated through mathematical models specially developed for identifying the fraudulent activity of adding water to milk. The bioimpedance measurements have been performed in a sample container with a four-electrode measurement system and a digital oscilloscope. Samples of 150 mL were analysed, prepared using whole UHT milk adulterated with distilled water in different % by volume (V/V), at temperatures of 16, 28, and 37 °C. The proposed measurement methodology has been validated and showed sensitivity for analysis in milk, revealing the influence of temperature on the impedance values; from the results mathematical models were proposed for investigating adulteration in milk. This work deals with the design of a device capable of measuring bioimpedance in milk with a view to its application in physicochemical analyses. The influence of temperature on the impedance values was also evaluated to propose mathematical models to facilitate analysis with complex factors and matrices. [ABSTRACT FROM AUTHOR]

The exploration of non-cancer medications with potential anti-cancer activity offers a promising avenue for drug repurposing, accelerating the development of new oncological therapies. This study employs Quantitative Structure-Property Relationship (QSPR) modeling to identify and predict the anti-cancer efficacy of various non-cancer drugs, utilizing topological indices as key descriptors. Topological indices, which capture the molecular structure's geometric and topological characteristics, provide critical insights into the pharmacological interactions relevant to anti-cancer activity. By analyzing a comprehensive dataset of non-cancer medications, this research establishes robust QSPR models that correlate topological indices with anti-cancer activity. The models demonstrate significant predictive power, highlighting several non-cancer drugs with potential anti-cancer properties. Further, we will use linear, quadratic and logarithmic regression to understand the structures of anti-cancer drugs and strengthen our ability to manipulate the molecular structures. The findings underscore the utility of topological indices in drug repurposing strategies and pave the way for further experimental validation and clinical trials. This integrative approach enhances our understanding of drug action mechanisms and offers a cost-effective strategy for expanding the repertoire of anti-cancer agents. [ABSTRACT FROM AUTHOR]

The transition from wakefulness to sleep occurs when the core body temperature decreases. The latter is facilitated by an increase in the cutaneous blood flow, which dissipates internal heat into the micro-environment surrounding the sleeper's body. The rise in cutaneous blood flow near sleep onset causes the distal (hands and feet) and proximal (abdomen) temperatures to increase by about 1 °C and 0.5 °C, respectively. Characterizing the dynamics of skin temperature changes throughout sleep phases and understanding its relationship with sleep quality requires a means to unobtrusively and longitudinally estimate the skin temperature. Leveraging the data from a temperature sensor strip (TSS) with five individual temperature sensors embedded near the surface of a smart bed's mattress, we have developed an algorithm to estimate the distal skin temperature with a minute-long temporal resolution. The data from 18 participants who recorded TSS and ground-truth temperature data from sleep during 14 nights at home and 2 nights in a lab were used to develop an algorithm that uses a two-stage regression model (gradient boosted tree followed by a random forest) to estimate the distal skin temperature. A five-fold cross-validation procedure was applied to train and validate the model such that the data from a participant could only be either in the training or validation set but not in both. The algorithm verification was performed with the in-lab data. The algorithm presented in this research can estimate the distal skin temperature at a minute-level resolution, with accuracy characterized by the mean limits of agreement [−0.79 to +0.79 °C] and mean coefficient of determination R 2 = 0.87 . This method may enable the unobtrusive, longitudinal and ecologically valid collection of distal skin temperature values during sleep. Therelatively small sample size motivates the need for further validation efforts. [ABSTRACT FROM AUTHOR]

Water and nitrogen are the primary constraints on improving agricultural productivity. The aims of this study are to investigate the synergistic effects of water and nitrogen, optimize their combination schemes under mulched drip irrigation systems in the northwest region of China, and offer scientific insight into enhancing water and nitrogen use efficiency in potato cultivation. The traditional cultivar "Qing Shu 10" was chosen for the test material. A two-year field study on potato water–nitrogen interaction was conducted in the central Hexi Corridor, within Ganzhou District of Zhangye City, with three irrigation levels (W1 (336 mm), W2 (408 mm), and W3 (480 mm)) and three nitrogen application rates (N1 (44 kg ha−1), N2 (192 kg ha−1), and N3 (240 kg ha−1)) using a fully randomized combination design, resulting in nine treatments. This study examined the varying responses in potato yield and water–nitrogen use efficiency to different water–nitrogen combinations in the Hexi Corridor region, developed a mathematical regression model to predict the economic benefit of potatoes based on water–nitrogen interactions, and refined the application strategy. The results indicated that both the volume of irrigation water and the rate of nitrogen application significantly influenced potato yield and water–nitrogen utilization efficiency. A distinct interactive effect was observed between irrigation volume and nitrogen application rate. The reduced irrigation volume restricted nitrogen uptake, with an average increase of 31.87% in nitrogen fertilizer partial productivity and 31.54% in potato yield when moving from W1 to W2 over two years and only a 6.02% and 5.48% increase from W2 to W3, respectively. Similarly, reduced nitrogen application rates also hindered water uptake by potatoes, with increases of 9.05% in water use efficiency, 12.14% in irrigation water use efficiency, 12.12% in yield from N1 to N2, and only 1.98% and 1.69% increases in irrigation water use efficiency and yield from N2 to N3, while water use efficiency decreased by 1.17%. The highest yield values over the two-year period were observed in the N2W3 treatment, with 43,493.54 and 43,082.19 kg ha−1. The irrigation volume, nitrogen application rate, and potato economic benefit were well modeled by a quadratic regression, with an R2 of 0.996 for both predicted and actual economic benefit over two years, indicating a trend of initial increase followed by a decrease as water and nitrogen levels increased. Through simulation optimization and a thorough analysis of multiple indicators, the N2W3 treatment yielded an economic benefit exceeding 25,391.13 CNY ha−1 and demonstrated a high water–nitrogen utilization efficiency. This treatment not only enhances potato economic benefit but also minimizes agricultural resource inputs, establishing it as the optimal water and fertilizer management strategy for this study. [ABSTRACT FROM AUTHOR]

The study of estimating rapeseed above-ground biomass (AGB) is of significant importance, as it can reflect the growth status of crops, enhance the commercial value of crops, promote the development of modern agriculture, and predict yield. Previous studies have mostly estimated crop AGB by extracting spectral indices from spectral images. This study aims to construct a model for estimating rapeseed AGB by combining spectral and LiDAR data. This study incorporates LiDAR data into the spectral data to construct a regression model. Models are separately constructed for the overall rapeseed varieties, nitrogen application, and planting density to find the optimal method for estimating rapeseed AGB. The results show that the R² for all samples in the study reached above 0.56, with the highest overall R² being 0.69. The highest R² for QY01 and ZY03 varieties was 0.56 and 0.78, respectively. Under high- and low-nitrogen conditions, the highest R² was 0.64 and 0.67, respectively. At a planting density of 36,000 plants per mu, the highest R² was 0.81. This study has improved the accuracy of estimating rapeseed AGB. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to investigate the soil infiltration patterns under different land uses in karst trough valley and to propose a suitable soil infiltration model for karst trough valley. [Methods] Four typical land uses (forest, grassland, cropland and orchard) in the karst trough valley were taken as the research objects to determine the differences in soil infiltration characteristics and influencing factors as affected by different land uses, and to evaluate the adaptability of three common infiltration models: Horton, Kostiakov and Philip, the in this area. [Results] (1) Soil bulk density, water content and electrical conductivity were markedly different among different land uses (p＜0.05). Soil bulk density (1.1 g/cm³), water content (26.9%) and electrical conductivity (75.43 mS/cm) in cropland were lower than those in other land uses. Soil bulk density (1.32 g/cm³) in orchard, water content in forest (35.02%) and electrical conductivity in grassland (164.6 mS/cm) were higher than those in other land uses, respectively. (2) The infiltration process of different land use types showed rapid decline, slow decline and stabilization. (3) There were differences in the fitting results of Horton model, Kostiakov model and Philip model for different land use modes in karst trough valley. Horton model had the best results, with the fit R² reaching 0.922～0.951, and Philip model had the worst results, with the fit R² ranging from 0.754 to 0.908. (4) The pH value was highly significantly positively correlated with initial infiltration rate and average infiltration rate (p＜0.01) and significantly positively correlated with stable infiltration rate (p＜0.05); 0.05～0.25 mm and ＜0.05 mm fractions of micro-aggregates were significantly positively and negatively correlated with stable infiltration rate and average infiltration rate, respectively (p＜0.05). The multiple linear regression model showed that soil bulk density, water content and ＜0.05 mm fraction of micro-aggregates were the main factors influencing infiltration rate, which had an inhibitory effect on infiltration. [Conclusion] Cropland has better infiltration performance among different land uses, and the Horton model is more suitable for modeling soil infiltration in karstic troughs and valleys. The results of the study can provide a scientific basis for the assessment of soil infiltration performance and vegetation allocation in karst trough valley areas. [ABSTRACT FROM AUTHOR]

Existing sales forecasting models are not comprehensive and flexible enough to consider dynamic changes and nonlinearities in sales time-series at the store and product levels. To capture different big data characteristics in sales forecasting data, such as seasonal and trend variations, this study develops a hybrid model combining adaptive trend estimated series (ATES) with a deep neural network model. ATES is first used to model seasonal effects and incorporate holiday, weekend, and marketing effects on sales. The deep neural network model is then proposed to model residuals by capturing complex high-level spatiotemporal features from the data. The proposed hybrid model is equipped with a feature-extraction component that automatically detects the patterns and trends in time-series, which makes the forecasting model robust against noise and time-series length. To validate the proposed hybrid model, a large volume of sales data is processed with a three-dimensional data model to effectively support business decisions at the product-specific store level. To demonstrate the effectiveness of the proposed model, a comparative analysis is performed with several state-of-the-art sales forecasting methods. Here, we show that the proposed hybrid model outperforms existing models for forecasting horizons ranging from one to 12 months. [ABSTRACT FROM AUTHOR]

In the rapidly changing climate of arid desert regions, evaluating the comprehensive characteristics of humidity levels is crucial for agricultural, urban, and infrastructural planning, as well as for minimizing potential public health impacts. We investigated variability and trends of humidity levels in major Saudi Arabian cities during 1982–2022, focusing on the influence of meteorological factors such as average, maximum and minimum temperature, rainfall, and windspeed. Employing the Probability Density Function and descriptive statistics, variability of climatic factors was analyzed. The Mann–Kendall Test (MKT) and Innovative Trend Analysis (ITA) were employed to identify monthly and annual trends. The magnitude and changing patterns were determined by calculating Sen's Slope and ITA slope. Findings of the MKT and ITA showed similar trends in humidity levels across all the cities. ITA result revealed that humidity in Riyadh and Taif decreased at a rate of 0.012% and 0.016% per year, respectively, while increased in Jeddah, Makkah, and Madinah at a 0.05 confidence level. The influence of climatic factors on humidity was assessed using Pearson's correlation coefficients, multiple regression model, and wavelet transform coherence (WTC) for each city, pinpointing temperature as the key driver of humidity variability. The dominance of temperature features was corroborated by strong power spectrums in the WTC across various time periods and scales. The in-depth analysis of humidity dynamics in this study provides critical insights for the development of climate-resilient infrastructure and formulation of public health strategies in Saudi Arabian cities. [ABSTRACT FROM AUTHOR]

The construction of tunnels in conditions of dense urban development affects buildings, structures, and engineering communications located at the surface. In this work, through dispersion analysis, factors influencing the settlement of the earth's surface and buildings during tunneling were selected. Subsequently, a model based on statistically significant parameters that can predict deformations at the pre-design stage was created. This research was conducted using data from geotechnical monitoring obtained during the construction of underground structures, with information about the technological parameters of shield tunneling in the construction of the single-track lines of the Moscow Metro using TBM with face-support pressure and engineering–geological conditions. In the obtained model, there is a clear dependency between the additional displacement of the monitoring object located above the projected tunnel and the average face-support pressure causing the mentioned deformations. The response is also affected by the soil deformation model at the tunnel face, the depth of the tunnel, and the soil excavation for the installation of one ring. [ABSTRACT FROM AUTHOR]

This study aimed to develop a model using experimentally obtained convective heat and mass transfer coefficients to predict the effect of temperature, humidity, and drying rate on wood drying. Tangential wood samples of Eucalyptus nitens (H. Deane & Maiden) were used in the investigation. The experimental design consisted of two temperature levels (40 °C and 55 °C), two relative humidity levels (55% and 75%), and two air velocity settings (2 m·s−1 and 3 m·s−1). The experiments were conducted under a constant evaporation rate, spanning the maximum and critical moisture content in the wood. A statistical model using multivariate regression was created to predict the convective heat and mass transfer coefficients. The results indicated that the experimental data and empirical correlations exhibited an error margin of 37.77% and 37.86%, respectively. A significant positive correlation was found between the convective heat transfer coefficient and air velocity, temperature, and relative humidity, while the convective mass transfer coefficient showed a significant positive correlation only with air velocity and temperature. The model predicted the convective heat and mass transfer coefficients with high accuracy and statistical significance. Using the proposed method, we successfully obtained both convective coefficients, which enable accurate description of heat and mass flow during the convective drying of Eucalyptus nitens wood. [ABSTRACT FROM AUTHOR]

Malaria is a deadly disease that can take a person's life if not predicted or cured correctly. Numerous factors like temperature, humidity, precipitation, etc., impact India's increasing cases of malaria diseases. This research presents an advanced machine learning regression technique recently developed to anticipate the prevalence of malaria in Tripura using a real-world data. The proposed structure uses nine different regression methods, such as multilayer perceptron (MLP), random forest, support vector regressor, gradient boosting regressor, Bayesian ridge, kernel ridge, extreme gradient boost regressor (XGB), light gradient boosting machine (LGBM regressor and linear regression, to predict malaria using the most affecting factors of malaria diseases, namely temperature, humidity, precipitation, month, and years as input. Furthermore, to opt out the best suited technology for malaria cases prediction, the range of value method (ROVM)–multi-criteria decision methods (MCDM) technique has been applied, considering various statistical measurements as criteria. Ultimately, a comparison of various MCDM techniques reveals that MLP, XGB, and RF emerge as the top three choices. MLP regression, with root-mean-square error (RMSE) value of 0.03357, yields the lowest RMSE value, and the coefficient of determination (R2) is 0.97616, yielding the maximum among other regressions. To effectively battle the illness in Tripura, it could be useful for continuing intervention tactics by governmental, profit and nonprofit organizations. [ABSTRACT FROM AUTHOR]

In this research, the star gold structure with beta graphene is thoroughly examined. We mainly focus on computing degree-based topological indices, which provide information about the network's connectivity and complexity as well as structural features. In addition, we compute an entropy measure to represent the uncertainty, information richness, and degree of unpredictability in the network. Furthermore, this study explores the relationships between topological descriptors and entropy using regression models that are logarithmic, linear, and quadratic. By merging these regression models, we uncover hidden patterns and understand the underlying ideas governing the network's behaviour. Our findings shed light on the connection between topological indices and entropy. This work improves our understanding of star gold structure dynamics and provides a visual framework for interpreting their behaviour. [ABSTRACT FROM AUTHOR]

The Brazilian electrical system (BES) relies heavily on hydrothermal energy, specifically hydroelectric power plants (HPPs), which are highly dependent on rainfall patterns. The São Francisco River Basin (SFRB) is a critical component of the BES, playing a key role in electricity generation. However, climate extremes have increasingly impacted energy production in recent decades, posing challenges for HPP management. This study, explores the relationship between extreme precipitation events in the SFRB and two crucial energy variables: Stored Energy (STE) and Affluent Natural Energy (ANE). We analyze the spatial distribution and trends of 11 extreme precipitation indices and investigate the seasonality, trends, and correlations between these energy variables and the extreme indices. Our findings reveal downward trends in both ANE and STE. Additionally, we identify a seasonal pattern influenced by extreme precipitation rates at various time scales. The results indicate that it is possible to estimate ANE and STE efficiently by employing three machine learning (ML) algorithms (Random Forest, Artificial Neural Networks and k-Nearest Neighbors) using extreme precipitation data. These results offer valuable insights for the strategic planning and management of the BES, aiding in decision-making and the development of energy security. [ABSTRACT FROM AUTHOR]

The article proposes a new regression based on the generalized odd log-logistic family for interval-censored data. The survival times are not observed for this type of data, and the event of interest occurs at some random interval. This family can be used in interval modeling since it generalizes some popular lifetime distributions in addition to its ability to present various forms of the risk function. The estimation of the parameters is addressed by the classical and Bayesian methods. We examine the behavior of the estimates for some sample sizes and censorship percentages. Selection criteria, likelihood ratio tests, residual analysis, and graphical techniques assess the goodness of fit of the fitted models. The usefulness of the proposed models is red shown by means of two real data sets. [ABSTRACT FROM AUTHOR]

Bamboo scrimber is a new type of biomass fiber-based composite material with broad application. In this study, self-developed bio-oil phenolic resin (BPF) was used to prepare bamboo scrimber. The effects of hot-pressing temperature, hot-pressing time, and BPF resin solid content on the modulus of rupture (MOR) and modulus of elasticity (MOE) were systematically investigated through single-factor experiments and response surface methodology (RSM). According to the Box-Behnken design (BBD) experiment of the RSM, the effects of all three factors on MOR and MOE are significant. The effects of the main factors affecting the MOR and MOE decreased in the order of resin solid content, hot-pressing temperature, and hot-pressing time. Based on BBD, the optimal conditions for the preparation of bamboo scrimber were determined as follows: a hot-pressing temperature of 150 °C, a hot-pressing time of 27.5 min, and a resin solid content of 29%. Under these conditions, the MOR is 150.05 MPa and the MOE is 12,802 MPa, which are close to the theoretical values, indicating that the optimization results are credible. This study helps to promote the full utilization of bamboo components and provides a reference for the development of high-quality bamboo scrimber. [ABSTRACT FROM AUTHOR]

The environmental effects on the response of Date Palm Fiber (DPF) composites are explored through an in-depth analysis of water absorption behavior. This study comprehensively investigates the influence of fiber content, immersion time, and water type on mass gain. Rigorous experimentation and advanced statistical analyses quantify the percent contribution of each factor, emphasizing the dominant role of fiber content at 96.61%. While immersion time and water type contribute relatively smaller percentages (2.11% and 1.28%, respectively), their insights are invaluable for tailored composite design. The study extends to interaction effects, showcasing the combined influence of factors. Regression models, progressing from linear and reciprocal formulations to comprehensive global models, are developed. Meticulous examination of prediction accuracy, using diverse statistical metrics, highlights the superior performance of the global reciprocal model across different water types. This work provides essential insights for optimizing DPF composite design, fabrication, and application, empowering engineers and researchers to make informed decisions in industries demanding tailored water absorption behavior.

The paper investigates the effect of machine setting on breaking strength, breaking elongation, and pick-up percentage of sized yarn properties. The factors considered are concentration of size solution, squeezing pressure and counts of yarn, the aim to develop a regression model that shows the effect of factors on size pick-up percentage, breaking strength regain, and loss in breaking elongation. The experiment is conducted by using 16 Ne and 21 Ne of cotton yarn, 3 Bar and 5 Bar of squeezing pressure, and 1.9% and 6% of size concentration with maize starch as sizing agent. A factorial design with two factors and 5 replicants has been used to design the experiment and analyze the results. The result showed that a slight change in squeezing pressure and concentration significantly affects the pick-up percentage, breaking strength regain and breaking elongation loss while a change of yarn count would not have a significant effect on the pick-up percentage of sized yarn. Furthermore, for achieving optimum properties of the sized yarn the pressure of squeezing, the count of the yarn and the concentration of the solution should be adjusted, and a regression model is developed to investigate the properties of the sized yarn by considering considered factors.

ABSTRACTThis study evaluates the effect of mixed washing treatment on the bagging behavior of denim fabrics. Six types of denim fabrics were washed under different conditions. Then, the bagging test was applied. Furthermore, the most influential factors of this treatment on the bagging ability of denim fabric were discussed using the main effect plots. This method shows that the quantity of pumice stone, the quantity of enzyme and the fabric are the most influencing factors. The increase in the amount of enzyme or pumice stone increases the occurrence of the bagging problem. Since the fabric is the dominant factor for all bagging properties, the characterization with Kawabata instruments of treated and untreated fabrics was performed. Then, we used the principal component analysis method to select the most important parameters. A reduction of 52.78% in parameters was achieved. Finally, the linear regression models were obtained to predict the bagging properties. The regression coefficients (R2) range between 96.85% and 99.43% and the adjusted R2 is between 92.34% and 98.13%, which confirms the efficiency of our models. These models are crucial for the manufacturers of washing denim since they could help the industrials to predict denim fabric’s bagging behavior after mixed washing.

AbstractAll across the world, the construction industry is a very crucial sector for development. Cost prediction is the process of forecasting the cost of construction projects at an early stage. An effective estimation technique is necessary to reduce the effort, time, and cost. Therefore, the study aims to develop a mathematical model to predict the cost of Ethiopian Roads Authority road projects in the case of Ethiopia. The data were collected from primary and secondary sources. The method of data analysis for the study was multiple regression analysis, and it was done using SP SS and Microsoft Excel. The data were obtained from a road document review and a questionnaire survey of 62 respondents and 15 interviewers who are working in ERA. The major cost-influential factors were found to be: inflation rate, bridges, culverts, project length, width, type and scope, contract duration, and location. As a result, a multiple regression model was developed using nine significant parameters taken from 18 completed road projects. The multiple regression analysis result revealed that the model can perform at a success rate of 83.8%. The MAPE of the model was ±16.44%. The conclusion and recommendations are likely to support and reduce ineffective estimation trends.

Abstract Background The vegetable‐based diet alone does not provide the lysine (Lys) needed to maximize poultry productive performance. Objectives This experiment aimed to evaluate the effects of dietary digestible Lys (dLys) level on productive and reproductive performance, egg quality, blood metabolites and immune responses in breeding Japanese quails (Coturnix japonica). Methods The experiment was conducted in a completely randomized design with 6 treatments, 5 replicates and 15 (12 females and 3 meals) 10‐week‐old breeding Japanese quails each. A basal diet was formulated to meet nutritional requirements of breeding quails except dLys. The basal diet was supplemented with graded (+0.82 g/kg) levels of l‐Lys‐HCl, corresponding to dietary dLys levels of 0.690%, 0.755%, 0.820%, 0.885%, 0.950% and 1.015%. The experiment lasted for 12 weeks, which was divided into 3‐4‐week periods. Results Significant differences were observed for egg production (EP), egg mass (EM) and feed efficiency (FE) in response to increasing dietary dLys concentration with quadratic trends. The highest traits were observed in the birds fed with a diet containing 0.885% dLys. However, feed intake, egg quality, reproductive performance, blood metabolites and immune responses against sheep red blood cell inoculation were not significantly affected by increasing dietary dLys concentrations. The dLys requirements during 11–14, 15–18, 19–22 and 11–22 (overall) weeks of age for optimal EP, EM and FE, based on the quadratic broken‐line regression analysis, were estimated 272, 265, 250 and 266; 293, 285, 264 and 279; and 303, 294, 281 and 293 mg/bird/day, respectively. Conclusions The dLys requirements vary depending on the EP phase and the trait being optimized. The estimated dLys requirement for FE was higher than those for EP and EM. During the peak stage of the first laying cycle, the dietary dLys level of 0.932% and a daily intake of 303 mg dLys/bird are sufficient for optimal performance.