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The vineyards and wine industry are key pillars of the economy in the Douro Demarcated Region (DDR). However, the region’s diverse topography poses challenges, leading to high grape production costs. Additionally, ongoing difficulties in attracting labor threaten the sector's sustainability. Based on a multiple linear regression and survey data from 110 DDR farms, this study analyzed the impact of labor costs on farms with varying vineyard landscaping and grapevine training systems. Results indicate a clear relationship between certain landscaping and training practices and permanent labor costs. Traditional walled terraces, vertical planting, and cordon training systems incur the highest permanent labor expenses, while temporary labor costs remain minimal. Adopting less labor-intensive systems is a strategic priority for DDR winegrowers, with significant effects across the wine value chain. To enhance the sector’s sustainability, future policies should prioritize systems that are well-adapted to local conditions and economically viable. [ABSTRACT FROM AUTHOR]

The proportion of explained variance is an important statistic in multiple regression for determining how well the outcome variable is predicted by the predictors. Earlier research on 20 different estimators for the proportion of explained variance, including the exact Olkin–Pratt estimator and the Ezekiel estimator, showed that the exact Olkin–Pratt estimator produced unbiased estimates, and was recommended as a default estimator. In the current study, the same 20 estimators were studied in incomplete data, with missing data being treated using multiple imputation. In earlier research on the proportion of explained variance in multiply imputed data sets, an estimator called R̂SP2 was shown to be the preferred pooled estimator for regular R2. For each of the 20 estimators in the current study, two pooled estimators were proposed: one where the estimator was the average across imputed data sets, and one where R̂SP2 was used as input for the calculation of the specific estimator. Simulations showed that estimates based on R̂SP2 performed best regarding bias and accuracy, and that the Ezekiel estimator was generally the least biased. However, none of the estimators were unbiased at all times, including the exact Olkin–Pratt estimator based on R̂SP2. [ABSTRACT FROM AUTHOR]

In recent years, China's macroeconomic environment is stable, and the prosperity of e-commerce has brought new opportunities for the development of logistics enterprises. As an important part of the industrial chain and supply chain, the logistics industry plays an important role in promoting China's economic development, but also faces various opportunities and challenges. Based on the demand data of express logistics in Beijing from 2003 to 2022, this paper uses multiple linear regression model to analyze the impact of six factors on the demand of express logistics in Beijing: gross domestic product (GDP), per capita disposable income, permanent resident population, total import and export value of goods, total retail sales of social goods and total value of tertiary industry. Using EViews software to conduct regression and test, it is determined that the permanent population and the total value of the tertiary industry in Beijing are the main influencing factors. The GM (1,1) model is used to predict the dependent variable data in the next five years, and the independent variable data is predicted by combining multiple linear regression, and then the future data of the dependent variable is obtained. The comparison shows that GM (1,1) model is more stable and accurate for single prediction of dependent variable. The forecast results show that the demand for express logistics in Beijing will continue to increase with the economic development of Beijing, the popularity of e-commerce, the permanent population of Beijing and the improvement of traffic and other factors. This study provides scientific basis and reference for relevant government departments and enterprises, which can help them better optimize resource allocation and improve service quality, so as to promote the healthy development of the express logistics industry in Beijing. [ABSTRACT FROM AUTHOR]

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks. [ABSTRACT FROM AUTHOR]

Earlier studies acknowledged that the design speed does not principally assure consistency in the design of highway geometric elements. Methodologies for estimating operating speeds are limited to specific or local traffic conditions and design standards, as in various studies conducted on two-lane and multi-lane highways. The present study develops operating speed models of the vehicle types observed on horizontal curves and tangent sections on two-lane highways in southern parts of India. Geometric features of the curve section and adjacent tangent sections, such as the radius of the curve, curve length, deflection angle, degree of curvature, gradient, carriageway width, and shoulder width, are obtained from the field. The speed data collected at 24 locations on the highway, including curved and tangent gradient sections, are used to analyze and model operating speed. The results indicate that the inverse of the square root of the radius appears to be an influencing parameter for modeling the operating speed of vehicles. The operating speed is sensitive for a curve radius of up to 600 m; thereafter, the operating speed appears insignificant. On tangent sections, the gradient has a larger influence on the operating speeds of all vehicle types. The operating speed models are validated and compared with models available in the literature. The proposed models developed in the present study are helpful to traffic engineers for predicting the operating speed on two-lane highways. [ABSTRACT FROM AUTHOR]

Introduction: The presence of water significantly reduces the mechanical strength of rocks and induces various engineering geological hazards. The water weakening coefficient Kp is used to quantify this effect, defined as the ratio of wet uniaxial compressive strength to the dry value. Methods: A comprehensive physico-mechanical test was conducted on fifteen sandstones under dry and saturated conditions to predict the water weakening coefficient using easily obtainable physical parameters. Multiple linear regression was employed to establish the relationship between these parameters and the saturated water weakening coefficient. Results: The saturated water weakening coefficient decreases with increasing porosity and increases with higher Primary wave velocity (P-wave velocity). Rocks with higher porosity but lower P-wave velocity typically absorb more water. The P-wave velocity and clay mineral content were identified as the best predictors of the saturated water weakening coefficient (R2 = 0.82). Unsaturated water weakening coefficients at any water saturation level were well estimated using a previous exponential function. Discussion: The roles of different clay minerals and P-wave velocity in the water weakening process of rocks are comprehensively discussed. This study enhances the understanding of the water weakening mechanism and provides an improved evaluation model for the water weakening coefficient of sandstones using physical parameters. [ABSTRACT FROM AUTHOR]

Climate warming, agricultural intensity, and urban growth are main forces triggering land degradation in advanced economies. Being active over different spatial and temporal scales, they usually reflect—at least indirectly—the impact of additional factors, such as wellbeing, demographic dynamics, and social development, on land quality. Using descriptive statistics and a multiple regression analysis, we analyzed the impact of these three processes comparatively over a decadal scale from 1960 to 2020 at the provincial level (Nuts-3 sensu Eurostat) in Italy. We enriched the investigation with a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic approach. Land degradation was estimated adopting the Environmental Sensitive Area Index (ESAI) measured at the spatio-temporal scale mentioned above. Computing on multiple observations at nearly 300,000 locations all over Italy, provinces were regarded as representative spatial units of the territorial pattern of land degradation. Between 1960 and 1990, the three predictors (climate, agriculture, and urbanization) explained a relatively high proportion of variance, suggesting a modest role for any other (unobserved) factor. All of these factors were found to be highly significant predictors of land degradation intensity across provinces, the most impactful being farming intensity. The highest adjusted-R2 coefficient was observed in both 1990 and 2000, and suggests that the three predictors still reflect the most powerful drivers of land degradation in Italy at those times, with a marginal role for additional (unobserved) factors. The impact of farming intensity remained high, with the role of urbanization increasing moderately, and the role of climate aridity declining weakly between 2000 and 2010. In more recent times (2010 and 2020), and in future (2030) scenarios, the adjusted R2 diminished moderately, suggesting a non-negligible importance of external (unobserved) factors and the rising role of spatial heterogeneity. The climate factor became progressively insignificant over time, while increasing the role of urbanization systematically. The impact of farming intensity remained high and significant. These results underlie a latent shift in the spatial distribution of the level of land vulnerability in Italy toward a spatially polarized model, influenced primarily by human pressure and socioeconomic drivers and less intensively shaped by biophysical factors. Climate aridity was revealed to be more effective in the explanation of land degradation patterns in the 1960s rather than in recent observation times. [ABSTRACT FROM AUTHOR]

Introduction: Sepsis is one of the most prevalent causes of death among hospitalised patients. Individuals with sepsis-associated acute kidney injury (SAKI) have worse outcomes and are usually more seriously ill than sepsis-non-associated acute kidney injury (SnAKI). To determine acute kidney injury, it is crucial to estimate and rapidly identify the function of the static kidney to avoid a poorer prognosis. However, clinical investigations take longer time compared to analysing vital signs that are accessible at the bedside. Therefore, this study aimed to investigate differences in serum creatinine and vital signs between patients with sepsis-associated acute kidney injury (SAKI) and those with sepsis-non-associated acute kidney injury (SnAKI). Materials and methods: This study was conducted on retrospective data on adults aged more than 18 years for 30 sepsis SAKI and 30 SnAKI patients from Hospital Universiti Sains Malaysia (HUSM) and analysed using multiple regression. Results: The findings show that the serum creatinine was significantly different between SAKI and SnAKI (p<0.05). The regression model on vital signs and creatinine for SAKI had better accuracy with a lower root mean square error (RMSE) (3.440) and higher R-squared (0.409) compared to SnAKI. Conclusion: Vital signs and creatinine both have the potential to increase the accuracy of SAKI. However, the model with vital signs only able to provide prediction within a shorter period. [ABSTRACT FROM AUTHOR]

This study aims to explore the spatial distribution of false kelpfish (Sebastiscus marmoratus) in the mussel farming area, artificial reef areas of Gouqi Island (Shengsi, China), and natural areas using eDNA detection methods. Surface and bottom water samples were collected at 12 stations in November 2022 and April 2023, totaling 52 samples. We used species-specific primers and probes for quantitative PCR (qPCR) detection of Sebastiscus marmoratus eDNA. The eDNA concentrations differed seasonally (p < 0.05) and did not differ (p > 0.05) among the three sampling areas and two water layers. The greatest eDNA concentrations occurred in the surface layer during the spring. Higher concentrations of Sebastiscus marmoratus eDNA were also found in the mussel aquaculture area. Temperature exhibited a significant positive correlation with Sebastiscus marmoratus eDNA concentration (p < 0.05). Additionally, we developed linear equations predicting the relationship between environmental factors and environmental factors, providing a reference for future fishery resource surveys. [ABSTRACT FROM AUTHOR]

It is very important to clarify the optimization method of the rock-like material ratio for accurately characterizing mechanical properties similar to the original rock. In order to explore the optimal ratio of rock-like materials in gneissic granite, the water–paste ratio, iron powder content and coarse sand content were selected as the influencing factors of the ratio. An orthogonal test design and sensitivity analysis of variance were used to obtain the significant influencing factors of the ratio factors on seven macroscopic mechanical parameters, including compressive strength σc, tensile strength σt, shear strength τf, elastic modulus E, Poisson's ratio ν, internal friction angle φ and cohesion c. A multivariate linear regression equation was constructed to obtain the quantitative relationship between the significant ratio factors and the macroscopic mechanical parameters. Finally, a rock-like material ratio optimization program based on genetic algorithm inversion was written. The results show that the water–paste ratio had extremely significant effects on σc, σt, τf, E, ν and c. The iron powder content had a highly significant effect on σc, σt, τf and c, and it had a significant effect on ν and φ. Coarse sand content had a significant effect on σc, E and c. The multiple linear regression model has good reliability after testing, which can provide theoretical support for predicting the macroscopic mechanical parameters of rock-like materials to a certain extent. After testing, the ratio optimization program works well. When the water–paste ratio is 0.5325, the iron powder content is 3.975% and the coarse sand content is 15.967%, it is the optimal ratio of rock-like materials. [ABSTRACT FROM AUTHOR]

This study investigated the mechanical properties of a low-plasticity clay soil reinforced with polypropylene (PP) fiber in various contents (0.05%, 0.10%, 0.15%, and 0.20%) and lengths (6, 12, and 19 mm). The reinforced specimens were subjected to unconsolidated-undrained (UU) triaxial compression tests under three different confining pressures (50, 100, and 200 kPa). The optimum fiber contents in specimens reinforced with 6-, 12-, and 19-mm PP fiber were determined as 0.15%, 0.15%, and 0.20%, respectively. As a result, the highest values regarding deviator stress at failure (σdev), energy absorption capacity (EAC), and shear strength parameters occurred in specimens containing 0.20% PP (19 mm). As a result of the reinforcement process, the most remarkable improvements in the σdev, cohesion, internal friction angle, and EAC values of the natural soil are 59.95%, 21.80%, 63%, and 34.70%, respectively. Linear and nonlinear relationships between σdev and fiber length, fiber content, and confining pressure were investigated by multiple linear regression and artificial neural network methods. Equations were generated to predict σdev of a low-plasticity clay soil reinforced with PP fiber and were made available to geotechnical researchers. [ABSTRACT FROM AUTHOR]

Climate change has made energy management a global priority. In France, the Grenelle Environment has set very ambitious progress targets for positive-energy buildings, particularly in terms of reducing and managing energy consumption. However, effective energy management in multi-zone buildings presents significant challenges, particularly when considering the inter-zone dynamics and heat transfer. This study examines multi-zone heating control, using a data-driven model for predictive indoor temperature modeling in intelligent buildings taking into account the influence of interconnected adjacent zones. The research methodology uses dynamic thermal simulation, parallel predictive models based on multiple linear regressions, and a multi-objective non-dominated sorting genetic algorithm II (NSGA-II) for the optimization process, which evaluates various generated heating strategies. This research introduces an approach to improve building energy efficiency by considering inter-zone dynamics and reducing heating-related energy consumption compared to a conventional heating strategy. By applying this model predictive control on a simulated case, a reduction in energy consumption due to heating is observed while respecting thermal comfort. This work contributes by implementing a method that independently controls temperatures in different building zones simultaneously while applying distinct constraints to each zone. This approach empowers occupants to manage heating consumption based on their preferences, ensuring personalized comfort. In addition, a comparison was made using a model that did not account for inter-zone interactions. This comparison demonstrates that incorporating these interactions into the predictive model enhances the effectiveness of the model predictive control approach. The multi-zone approach was also validated experimentally by using real experimental data, demonstrating significant reductions in energy consumption. [ABSTRACT FROM AUTHOR]

Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute. [ABSTRACT FROM AUTHOR]

Deformation plays an important role in dam monitoring, especially in judging potential damage. Excessive deformation serves as an indicator of the abnormal evolution of dam structures, but the pivotal issue lies in definition for "excessive" precisely. Deformation prediction serves as reference for detection, but it is challenged by multiple factors. Sequence decomposition is an approach to select related factors and predict precisely, but the mechanism in application is a critical problem. This study proposes a decomposition Long short-term memory (DLSTM) model for the deformation prediction of arch dams. Segmented correlation feature extraction method balances feature correlation and consistency. Multiple linear regression is introduced for the integration, and the periodic extension is used to simplify unnecessary predicting operations. The proposed DLSTM model is evaluated using monitoring data from the LYX arch dam. The results show an 18% improvement in the accuracy, as well as better stability of the prediction model. The DLSTM-based multiple linear regression outperforms nonlinear neural network methods in terms of both physical interpretation and performance. The superiority of the proposed model over BP neural network, wavelet neural network, and LSTM model demonstrates that the DLSTM model is a promising approach for practical applications in dam deformation monitoring. [ABSTRACT FROM AUTHOR]

Urban air pollution, a combination of industry, traffic, forest burning, and agriculture pollutants, significantly impacts human health, plants, and economic growth. Ozone exposure can lead to mortality, heart attacks, and lung damage, necessitating the creation of complex environmental safety regulations by forecasting ozone concentrations and associated pollutants. This study proposes a hybrid method, RFNN-GOA, combining recurrent fuzzy neural network (RFNN) and grasshopper optimization algorithm (GOA) to estimate and forecast the daily ozone (O 3 ) in specific urban areas, specifically Kopački Rit and Osijek city in Croatia, aiming to improve air quality, human health, and ecosystems. Due to the intricate structure of atmospheric particles, modeling of O 3 likely poses the biggest challenge in air pollution today. The dataset used by the proposed RFNN-GOA model for the prediction of O 3 concentrations in each explored area consists of the following air pollutants, NO, NO 2 , CO, SO 2 , O 3 , PM 10 , and PM 2.5 ; and five meteorological elements, including temperature, relative humidity, wind direction, speed, and pressure. The RFNN-GOA method optimizes membership functions' parameters and the rule premise, demonstrating robustness and reliability compared to other identifiers and indicating its superiority over competing methods. The RFNN-GOA method demonstrated superior accuracy in Osijek city and Kopački Rit area, with variance-accounted for (VAF) values of 91.135%, 83.676%, 87.807%, 79.673% compared to the RFNN method's corresponding values of 85.682%, 80.687%, 80.808%, 74.202% in both training and testing phases, respectively. This reveals that RFNN-GOA increased the average VAF in Osijek city and Kopački Rit area by over 5% and 8%, respectively. [ABSTRACT FROM AUTHOR]

International Energy Agency (IEA) predicts India's AC stock will reach 1144 million units by 2050, making it the second largest ACs holder globally. Studies on the effect of building geometry on cooling load reduction are primarily focused on material and envelope specifications. However, studies on building morphological parameters in the Indian context are scarce. Therefore, this research quantifies the effect of four morphology predictors, namely, FL (floor number), ESA (exposed surface area), CZB (conditioned zones per building), and CZF (conditioned zones per floor) on cooling load in 75 dominant residential built forms of Navi Mumbai. The selected buildings are simulated using the Rhinoceros 6 tool with the energy plus plugin. Despite having the same simulation inputs, envelope parameters, and conditioned volume, the results indicated a 90% variation between the compact and loosely designed forms. Multiple Linear Regression shows that the four predictors explain 78% (R² = 0.78) of variation in the cooling load. It is observed that tall buildings show greater efficiency in cooling load reduction due to lesser CZF values. Also, an increase in CZB and a decrease in ESA significantly reduce the mean cooling load due to compactness and wall sharing, respectively. [ABSTRACT FROM AUTHOR]

Measurement of river bathymetry has been revolutionized by high‐resolution remote sensing that combines UAV platforms with SfM‐MVS photogrammetry. Mapping inundated and exposed areas simultaneously are possible using either two‐media refraction correction or some form of the Beer–Lambert Law to estimate water depths. If, as in turbid glacially‐fed braided streams, the bed is not visible then traditional survey techniques (e.g. differential GPS systems) are required. As an alternative, here we test whether the spatial distribution of water depths in a shallow braided stream can be modelled from basic planimetric data and used to estimate inundated zone bathymetry. We develop heuristic rules using; (1) distance from the nearest river bank; (2) total inundated width along a line tangential to the local flow direction; (3) local curvature magnitude and direction; and distance from the nearest flow (4) divergence and (5) convergence regions. We parameterize them using a sample of measured water depths in stepwise multiple linear regressions and validate them using independent data. Resulting water depth distribution maps explain between 50% and 60% of the measured water depth spatial variability when compared to independent data. After incorporating modelled water depths into digital elevation models (DEMs) of exposed areas, we show that the developed method is suitable for volumetric change calculations in both dry and inundated areas. [ABSTRACT FROM AUTHOR]

Introduction: The learning experience has undergone significant changes recently, particularly with the adoption of advanced technology and online lectures to address challenges such as pandemics. In fields like engineering, where hands-on classes are essential, the online learning environment plays a crucial role in shaping students' experiences and satisfaction. Methods: This study aimed to explore the key factors affecting engineering students' satisfaction with online learning. A structured survey was administered to 263 students across various engineering disciplines and academic levels, all of whom had experienced both in-person learning before the pandemic and online learning during the pandemic. Factor analysis and multiple linear regression were employed to analyze the data. Results: The analysis identified interactions, services, and technology as the main factors positively influencing online learning satisfaction. The regression analysis further revealed that students' satisfaction is significantly dependent on the availability and quality of online learning services, assessment and interaction tools, and technology. Discussion: This study highlights the critical factors that enhance engineering students' satisfaction with online learning. It offers strategies for educators to improve online learning environments, emphasizing the importance of quality services, assessment, and interaction tools. These findings can guide the development of more effective online learning experiences in engineering education. [ABSTRACT FROM AUTHOR]

In remote watersheds or large water bodies, monitoring water quality parameters is often impractical due to high costs and time-consuming processes. To address this issue, a cost-effective methodology based on remote sensing was developed to predict water quality parameters over a large and operationally challenging area, especially focusing on East Lake. Sentinel-2 satellite image data were used as a proxy, and a multiple linear regression model was developed to quantify water quality parameters, namely chlorophyll-a, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand. This model was then applied to East Lake to obtain the temporal and spatial distribution of these water quality parameters. By identifying the locations with the highest concentrations along the boundaries of East Lake, potential pollution sources could be inferred. The results demonstrate that the developed multiple linear regression model provided a satisfactory relationship between the measured and simulated water quality parameters. The coefficients of determination R2 of the multiple linear regression models for chlorophyll-a, total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand were 0.943, 0.781, 0.470, 0.624 and 0.777, respectively. The potential pollution source locations closely matched the officially published information on East Lake pollutant discharges. Therefore, using remote sensing imagery to establish a multiple linear regression model is a feasible approach for understanding the exceedance and distribution of various water quality parameters in East Lake. [ABSTRACT FROM AUTHOR]

As urban expansion continues, the intensifying land surface temperature (LST) underscores the critical need for accurate predictions of future thermal environments. However, no study has investigated which method can most effectively and consistently predict the future LST. To address these gaps, our study employed four methods—the multiple linear regression (MLR), geographically weighted regression (GWR), random forest (RF), and artificial neural network (ANN) approach—to establish relationships between land use/cover and LST. Subsequently, we utilized these relationships established in 2006 to predict the LST for the years 2012 and 2018, validating these predictions against the observed data. Our results indicate that, in terms of fitting performance (R2 and RMSE), the methods rank as follows: RF > GWR > ANN > MLR. However, in terms of temporal stability, we observed a significant variation in predictive accuracy, with MLR > GWR > RF > ANN for the years 2012 and 2018. The predictions using MLR indicate that the future LST in 2050, under the SSP2 and SSP5 scenarios, is expected to increase by 1.8 ± 1.4 K and 2.1 ± 1.6 K, respectively, compared to 2018. This study emphasizes the importance of the MLR method in predicting the future LST and provides potential instructions for future heat mitigation. [ABSTRACT FROM AUTHOR]

Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

Introduction Life on Earth is influenced by precipitation. Precipitation is one of the most significant factors that affect the hydrological cycle. Considering that precipitation is non-linear, complex, and can be changed according to spatial and temporal, estimating the amount of this important atmospheric factor in each month or year for each region and watershed is particularly important in managing and optimizing water resources. Various optimization models and algorithms have been proposed for modeling hydrological systems in recent decades. These algorithms have significantly reduced errors and increased accuracy. Still, since hydrological systems rely on random events, none of the methods can be completely and accurately selected as a superior model for modeling and estimating. The honey badger algorithm is an innovative algorithm that requires a few iterations to achieve an optimal solution, and this increases the speed of reaching the desired results. In current study investigates the performance of three models, including multiple linear regression (MLR), artificial neural network (ANN), and hybrid artificial neural network with honey badger optimization algorithm (HBA-ANN) for modeling the temporal and spatial precipitation in East Azarbaijan province. The best-developed model was selected by evaluation criteria such as R, RMSE, NRMSE, MBE, and NSE, the best model is selected. Materials and Methods The MLR model is one of the methods to analyze and investigate several variables. In this method, the model has one dependent variable and several independent variables, so that a linear equation is generated between the independent variables called X1, X2, ..., Xn and the dependent variable Y. ANN is a black box model of neural networks in the human brain. One of the most used methods is the BP method, which includes two stages. In the first stage, which is entitled feed-forward, the error value is calculated, after comparing output and objective values. In the second stage, which is labeled the back-propagation, the error value calculated in the previous step is corrected. The mentioned two stages continue until the output of the model approaches the desired output. The HBA is a new algorithm that simulates the honey-seeking behavior of a creature called the honey badger. The HBA includes two stages. In the first phase, the locations of this creature are calculated, and in the second phase, the exact distance between the HBA and the prey (dj) is calculated based on the honey intensity (S) and the honey smell intensity (Ij), as well as its new and optimal location for the prey Xnew. In the HBA-ANN model, the HBA algorithm is used to determine the most optimal output value in the ANN and increase performance in modeling. Therefore, the developed hybrid model can have the characteristics of both ANN and HBA methods. Results and Discussion In this study, in the first stage, the temporal modeling, and in the second stage, the spatial modeling of the monthly precipitation of 18 stations in East Azarbaijan province during the period of 1996-2022 using MLP, ANN, and HBA-ANN models has been paid. For temporal modeling of precipitation, one and two-month precipitation delay steps of the stations were used as input parameters. The first 70% of the dataset was selected for the training phase and the last 30% of the dataset was selected for the testing phase. Based on the results obtained from evaluation criteria and graphic diagrams, it can be concluded that the HBA-ANN model indicated significant accuracy compared to other models in the temporal modeling of precipitation. Also, by comparing the results of the stations in the HBA-ANN model, the Heris station with R =0.94, RMSE=2.25, NSE=0.79, NRMSE=0.04, and MBE=1.06 in the testing stage performed better compared with other stations. For spatial modeling of precipitation, the geographic coordinates of the stations, which include longitude, latitude, and altitude, are used as input parameters, and average monthly precipitation is used as the output parameter. From eighteen stations, 70% of the stations were selected for the training phase and 30% of the stations were selected for the testing phase. Based on the results obtained from R=0.95, RMSE=1.03, NSE =0.92, NRMSE = 0.03, and MBE = -0.81 and graphical diagrams, it can be concluded that the HBA-ANN model revealed significant accuracy compared to other models in spatial modeling of precipitation. Conclusion Precipitation is one of the most important factors that significantly change the hydrological cycle. Therefore, modeling and estimating this parameter is vital. In this study, the performance of multiple linear regression (MLR), artificial neural network (ANN), and hybrid ANN using honey badger algorithm (HBA-ANN) models were used for the spatial and temporal modeling of precipitation in East Azarbaijan province. For spatial modeling, the time delay steps of one and two months of station precipitation were selected as input parameters. Also, for temporal modeling, the longitude, latitude, and altitude parameters were used. The mentioned models were evaluated by R, RMSE, NSE, NRMSE, and MBE assessment criteria. According to the results of temporal modeling, the HBA-ANN model for all stations, especially Heris station with R equal to 0.94, RMSE equal to 2.25, NSE equal to 0.79, NRMSE equal to 0.04, and MBEequal to 1.06 is selected as the superior model. Also, based on the results obtained from spatial modeling, the HBA-ANN model with R equal to 0.95, RMSE equal to 1.03, NSE equal to 0.92, NRMSE equal to 0.03, and MBEequal to -0.81 was selected as the best model. The MLR and ANN models, respectively, presented a relatively poor performance compared to the developed hybrid model. [ABSTRACT FROM AUTHOR]

A QSPR (Quantitative Structure-Property Relationship) model links mathematically various physicochemical properties with the structure of a molecule. Establishing such quantitative relationships is of great technological importance as in this way one can predict the properties of new untested molecules by means of a linear or nonlinear equation that expresses a certain property as an explicit function of one or more independent variables. These functional relationships (usually called QSPR models) are obtained by performing specific QSPR studies on a class of similar compounds whose properties have been already determined and were appropriately correlated to their molecular structures. In this study we are interested to determine the best QSPR models for the normal boiling point (bp) and molar refraction (mr) of 64 alkyl halides by using the topological index ZEP, the structural parameter (Hd) and the number of carbon atoms (N) as predictor variables. In order to validate the developed models with respect to the goodness-of-fit, robustness and predictive ability for the two properties considered in the class of alkyl halides, external validation, cross-validation (leave-one-out) and randomization (y-randomization) were performed. The obtained results have shown that the three descriptors above could be efficiently used for modelling and predicting the normal boiling points and molar refractions of the considered class of compounds. [ABSTRACT FROM AUTHOR]

This study examines trust in public sector institutions during the coronavirus (COVID-19) pandemic and the impact factors. A survey was administered to a convenience sample of 1,372 local and expatriate residents in the United Arab Emirates during 2022–2023. Bivariate and multiple regression analyses were utilized. There was a high level of general public trust among respondents (mean = 4.11), with the highest trust in the police (mean = 4.21) followed by trust in the information disseminated by the government (mean = 4.06) and in the performance of public sector institutions (mean = 3.89). On the bivariate and multivariate levels, demographic variables had an insignificant impact on public trust. General trust was significantly correlated with institutional performance (β = 0.417), institutional integrity (β = 0.397), perceived gains (β = 0.378), and government-disseminated information (β = 0.374). Public trust is a complex reality linked to numerous organizational and nonorganizational factors. Improving public trust requires enhancing ethical conduct—crucial for maintaining trust—as well as good governance practices and leadership behavior. The sample size and quantitative focus might have limited the generalizability of the findings. In the future, researchers should use in-depth interviews or mixed methodologies. [ABSTRACT FROM AUTHOR]

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The safety of road traffic is facing increasing challenges from a range of factors, and this study aims to address this issue. The paper describes the development of a model that assesses both the quantitative and qualitative aspects of the current traffic situation and can also predict future trends based on monthly data on traffic accidents over a period of years. The dataset is composed of the number of accidents that occurred in the Pristina region over a 10-year period, and these are categorized based on the type of accident and safety factors, including human, vehicle, and road factors. By using machine learning algorithms, a model has been developed that determines the factor with the greatest impact on traffic safety. To create the model, the algorithms Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Random Trees (RT) were used. The model evaluates the contribution of human, road, and vehicle factors to traffic accidents, using machine learning algorithms and 36 types of traffic accidents to analyze the relevant statistics. The results indicate a very good fit of the model according to the MLR algorithm, and this model also identifies the road factor as the main influencer of the traffic safety level. [ABSTRACT FROM AUTHOR]

This study employs numerical and statistical approaches to investigate the entropy optimization of steady Casson nanofluid flow over three different geometries subject to boundary conditions induced by convective flow. Multiple linear regression is employed to statistically examine. The present model incorporates several novel elements, such as Arrhenius activation energy, Brownian motion, the Cattaneo-Christov dual flux, thermophoresis, thermal radiation, and so on. Moreover, a comparison is presented between Newtonian and non-Newtonian fluids. By applying the proper similarity transformations, ordinary differential equations (ODEs) are ob- tained by converting foundational partial differential equations (PDEs). The Runge-Kutta fourth- order method is utilised to solve the obtained ODEs along with the shooting technique. The out- comes are visually depicted via tables and graphs. The velocity drops with increasing Grashof number, and the magnetic field becomes progressively more forceful as the suction parameter in- creases. The temperature gets reduced with the increase of the suction parameter, solute Grashof number increases with the magnetic field, thermophoresis, and radiation parameters. The entropy is observed to rise with the increase of the effective parameters (magnetic field, Brinkmann num- ber and radiation). The MAD (mean absolute deviation), MSE (mean squared error), and RMSE (root mean square error) values are approaching zero, indicating that the derived outcomes are highly accurate. A lower MAPE (mean absolute percentage error) suggests that the model has a higher level of precision. Therefore, the outcomes of the present model are more precise and eliable. This study has various potential applications such as power plant heat exchangers, ma- terial processing industries, and solar thermal energy systems. [ABSTRACT FROM AUTHOR]

Rice cultivation in Madurai district, Tamil Nadu, spans distinct cropping seasons: Kar (May -- Jun), Semi-dry (Jul - Aug), Samba/ Late Samba (Aug - Sep), and Navarai (Dec - Jan), each with unique weather conditions and rice varieties. This study explores the correlation between weather parameters temperature, rainfall, humidity, sunshine hours, and wind speed and rice blast disease severity from 2021 to 2023. Using multiple linear regression with ordinary least squares (OLS), the analysis achieves high predictive accuracy (R² = 0.98). Results show that the maximum temperatures correlated negatively with disease severity (r = -0.869 to -0.892), while rainfall (r = 0.768 to 0.804) and wind speed (r = 0.766 to 0.938) correlated positively during the semi-dry season. Relative humidity exhibits varying impacts across seasons. These findings underscore the importance of tailored disease management strategies, such as targeted fungicidal applications during warmer seasons and optimized water management in others. By elucidating these dynamics, the study enhances understanding of weather-disease interactions, providing actionable insights to optimize disease management and enhance crop resilience in Madurai district. [ABSTRACT FROM AUTHOR]

In the recent past, several weathering indicators have been developed to describe its state of weathering. The state of rock weathering is a useful indicator to estimate the integrity of tropically weathered rock material and mass which weatherability plays an important role in a tropical region. Through a ground assessment tool, the strength and durability of the rock mass could be estimated and complex or adopted to simplify the early prediction of the complex engineering parameter. This paper presents several models of the Weathering Index (WI) using selected significant parameters using statistical analysis. For this purpose, several sites have been chosen to represent granitic rock mass. Forty (40) numbers of samples were collected and tested comprising from four (4) sites in Malaysia. Several laboratory tests have been conducted such as Point Load Index (Is(50)), dry density, Slake Durability 1 (SD1), Slake Durability 2 (SD2) and moisture content. The field and laboratory data sets are used to determine the WI by using simple regression and MLR analysis Significant parameters found to be useful in determining the WI are selected namely SD1, dry density, Is(50), and block volume. These parameters were selected based on stepwise analysis using Statistical Package for the Social Sciences (SPSS). Following the models' implementation, the models were evaluated and the best prediction model was selected after considering statistical coefficients, such as coefficient of determination (R2), variance account for (VAF), and root mean squared error (RMSE), as well as utilizing a straightforward ranking approach. The findings of this study could contribute to the more accurate prediction of WI using a more simplistic field and laboratory parameters. Therefore, the WI is useful during the initial stages and planning of rock excavation work and provides a good description of weathering grade and rock mass properties, which will affect excavatability in granitic areas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Finance & Financial Law / Finanse i Prawo Finansowe is the property of Wydawnictwo Uniwersytetu Lodzkiego and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wildfires present a major global environmental issue, exacerbated by climate change. The Iranian Northern Zagros Forests, characterized by a Mediterranean climate, are particularly vulnerable to fires during hot, dry summers. This study investigates the impact of climate change on forest fires in these forests from 2006 to 2023. The analysis revealed significant year-to-year fluctuations, with notable fire occurrence in years 2007, 2010, 2021, and 2023. The largest burned area occurred in 2021, covering 2655.66 ha, while 2006 had the smallest burned area of 175.27 ha. Climate variables such as temperature, humidity, precipitation, wind speed, heat waves, and solar radiation were assessed for their effects on fire behavior. Strong correlations were found between higher average temperatures and larger burned areas, as well as between heat waves and increased fire frequency. Additionally, higher wind speeds were linked to larger burned areas, suggesting that increased wind speeds may enhance fire spread. Multiple linear regression models demonstrated high predictive accuracy, explaining 84% of the variance in burned areas and 69.6% in the variance in fire frequency. These findings document the growing wildfire risk in the Northern Zagros region due to climate change, highlighting the urgent need to integrate scientific research with policies to develop effective wildfire management strategies for sustainable forest management. [ABSTRACT FROM AUTHOR]

Tomatoes are among the world's most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: To effectively address the disposal issues of gold tailings and alleviate the shortage of sandstone resources,this study investigated the potential of utilizing gold tailings as a partial replacement for natural sand in the production of gold tailings concrete. Method: Through a series of cube compressive strength tests and orthogonal experimental methods,this research conducted a comprehensive analysis of the impacts of four factors: water-binder ratio, go Id tailings substitution rate, sand ratio, and fly ash content, on the 28-day compressive strength of gold tailings concrete. The systematic analysis was performed using range and variance analysis methods, which determined the relative importance of each factor in influencing the 28-day compressive strength of the concrete. Furthermore,a multiple regression equation was established for predicting the compressive strength of gold tailings concrete based on the orthogonal experimental data. Conclusion: The results indicated that the water-binder ratio had the most significant effect on the compressive strength, followed by the gold tailings substitution rate, fly ash content,and sand ratio. The regression equation,with an R2 value of 0. 872,can predict the compressive strength to a considerable degree. Significance: The research results can provide some reference value for the comprehensive utilization of gold tailings resources. [ABSTRACT FROM AUTHOR]

The revitalization of historic districts is crucial for the sustainable development of cities, with street corner spaces being a vital component of the public space in these districts. However, street corner spaces have been largely overlooked in previous research on crowd dynamics within historic districts. This study investigates the key factors influencing crowd dynamics in street corner spaces within historic districts. First, a hierarchical model of vitality-influencing factors was developed based on prior research. Potential factors influencing the vitality of street corners were quantified using multi-source data collection methods, including deep learning algorithms, and crowd vitality within these spaces was assessed through multidimensional measurements. The impact of each element on crowd vitality was then analyzed through a multivariate linear regression model. The findings revealed that eight factors—corner building historicity, first-floor functional communality, transparency, openness, density of functional facilities, greenness, functional variety of buildings, and walkability—significantly influence the vitality of corner spaces, collectively explaining 77.5% of the vitality of these spaces. These conclusions offer new perspectives and scientific evidence for the revitalization and conservation of historic districts. [ABSTRACT FROM AUTHOR]

The Artificial Neural Network (ANN) is a high-precision modelling technique applied across various applications, but their use in modelling the proton exchange membrane fuel cell (PEMFC) at the stack scale remains limited. This study aimed to enhance the modelling of a 1.2 kWp PEMFC stack using ANN integrated with the LabVIEW program, and to evaluate its accuracy compared to Multiple Linear Regression (MLR) and Analytical Mathematical Model (AMM) methods based on experimental data. The ANN model was tested on the polarization curve under pressures ranging from 1 to 9 bar and currents from 1 to 40 A. Accuracy was assessed using analysis of variance (ANOVA) with a significance level of p = 0.05, focusing on key statistical performance metrics. The results indicate that the ANN model significantly enhances PEMFC stack modelling accuracy, especially for activation and concentration loss processes. The ANN demonstrates a substantial improvement, reducing error metrics by at least 28% compared to MLR and 75% compared to AMM. [ABSTRACT FROM AUTHOR]

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks.

Background Responsiveness is a key goal in evaluating the performance of the health system, and is also an essential basis for improving the capacity of primary health services. Currently, many studies confuse the concepts of responsiveness and satisfaction, and the evaluation of responsiveness is not comprehensive enough, focusing on the level of responsiveness but ignoring its distribution. Objective To understand the responsiveness evaluations of community health services among residents of different ages in Zhejiang province and to analyze its influencing factors. Methods Utilizing the method of purposive sampling and convenience sampling, six community health service centers in Hangzhou and Taizhou, Zhejiang province, were selected as the investigation sites from July to August 2022 to assess the responsiveness scale of outpatient residents. The weights of the eight components of responsiveness recommended by the WHO were used to calculate the overall level and distribution of responsiveness. Univariate analysis and multiple linear regression were used to explore the influencing factors on the responsiveness level among residents of different ages. Results The score of responsiveness for community health services in Zhejiang was (8.31±1.04) points, and the responsiveness distribution index was 0.13; residents of different age groups expressed their higher satisfaction with the dimensions of dignity and social support, with scores of (8.60±1.24) points and (8.63±1.20) points, respectively. In contrast, the dimensions of selectivity and autonomy received lower ratings, with scores of (7.96±1.56) points and (8.04±1.34) points, respectively. Household registration was an influencing factor for responsiveness evaluation of community health services among young people, while education level and monthly income were influencing factors for responsiveness evaluation among middle-aged individuals (P

International Energy Agency (IEA) predicts India's AC stock will reach 1144 million units by 2050, making it the second largest ACs holder globally. Studies on the effect of building geometry on cooling load reduction are primarily focused on material and envelope specifications. However, studies on building morphological parameters in the Indian context are scarce. Therefore, this research quantifies the effect of four morphology predictors, namely, FL (floor number), ESA (exposed surface area), CZB (conditioned zones per building), and CZF (conditioned zones per floor) on cooling load in 75 dominant residential built forms of Navi Mumbai. The selected buildings are simulated using the Rhinoceros 6 tool with the energy plus plugin. Despite having the same simulation inputs, envelope parameters, and conditioned volume, the results indicated a 90 % variation between the compact and loosely designed forms. Multiple Linear Regression shows that the four predictors explain 78% (R2 = 0.78) of variation in the cooling load. It is observed that tall buildings show greater efficiency in cooling load reduction due to lesser CZF values. Also, an increase in CZB and a decrease in ESA significantly reduce the mean cooling load due to compactness and wall sharing, respectively.

This study employs numerical and statistical approaches to investigate the entropy optimization of steady Casson nanofluid flow over three different geometries subject to boundary conditions induced by convective flow. Multiple linear regression is employed to statistically examine. The present model incorporates several novel elements, such as Arrhenius activation energy, Brownian motion, the Cattaneo-Christov dual flux, thermophoresis, thermal radiation, and so on. Moreover, a comparison is presented between Newtonian and non-Newtonian fluids. By applying the proper similarity transformations, ordinary differential equations (ODEs) are obtained by converting foundational partial differential equations (PDEs). The Runge-Kutta fourth-order method is utilised to solve the obtained ODEs along with the shooting technique. The outcomes are visually depicted via tables and graphs. The velocity drops with increasing Grashof number, and the magnetic field becomes progressively more forceful as the suction parameter increases. The temperature gets reduced with the increase of the suction parameter, solute Grashof number increases with the magnetic field, thermophoresis, and radiation parameters. The entropy is observed to rise with the increase of the effective parameters (magnetic field, Brinkmann number and radiation). The MAD (mean absolute deviation), MSE (mean squared error), and RMSE (root mean square error) values are approaching zero, indicating that the derived outcomes are highly accurate. A lower MAPE (mean absolute percentage error) suggests that the model has a higher level of precision. Therefore, the outcomes of the present model are more precise and reliable. This study has various potential applications such as power plant heat exchangers, material processing industries, and solar thermal energy systems.

The purpose of the article. The relationship between economic growth and financial development has long been widely discussed in scientific circles. Most empirical research has led to the clear conclusion that financial markets stimulate economic development. However, in the case of financial markets in developing countries, this conclusion is not so obvious. The aim of this study is to empirically verify the relationship between economic growth and the development of the stock market (Kazakhstan Stock Exchange). Methodology. Empirical verification of the strength and shape of the cause-and-effect relationship between real GDP growth and independent predictors will be carried out using classic linear regression models with many independent variables. Results of the research. The results of empirical verification of the strength and shape of the cause-and-effect relationship between the growth of real GDP and independent predictors indicate that the development of the stock market has a positive impact on the growth of the country's real GDP but is not the basic factor of Kazakhstan's economic growth.

This study investigates how various service quality dimensions affect customer satisfaction in commercial banks across Nepal. The objective of this study is to examine the impact of key service quality dimensions (responsiveness, empathy, assurance, communication, and overall service quality) on customer satisfaction in commercial banks in Nepal. A survey-based quantitative approach was employed, collecting data from 399 customers in Nepal. The study utilizes multiple linear regression analysis and hypothesis testing to determine the relationship between these factors and customer satisfaction. The results indicate that communication, assurance, and service quality positively influence customer satisfaction, with a moderate mean score of 2.317949 out of 5 and moderate variability (SD = 0.479612). These three factors are key contributors, accounting for 41.5% of the variation in satisfaction, whereas empathy and responsiveness have weaker or insignificant impacts. The study concludes that enhancing communication and service quality is crucial for improving customer satisfaction, which is essential for sustainable growth in Nepal’s financial sector.

Abstract Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved.

Abstract Objective To identify factors that influence the severity of tinnitus via a hierarchical multiple linear regression model. Methods The study was a retrospective cross‐sectional analysis. The study included 331 patients experiencing tinnitus as their primary concern, who visited Shanghai Changzheng Hospital of the Navy Medical University between 2019 and 2021. Data on general health status and disease characteristics were collected from all patients. With their consent, participants underwent audiological evaluatons and completed questionnaires to analyze the characteristics of their tinnitus and the factors influencing its severity. Results The correlation analysis showed a positive relationship between tinnitus frequency, tinnitus loudness, SAS scores, and PSQI scores with THI scores (P

ABSTRACTA substantial amount of wastewater is generated during the extraction of oil and gas. Wastewater that is generated as a result of oil and gas extraction activities is known as produced water (PW), and managing PW is one of the greatest challenges faced by the oil and gas industry. Effective management of PW requires an understanding of the complex impact it may have on both oil production and water treatment operations. Here, a numerical model has been applied to a real-world case study, that of Kuwait Oil Company (KOC), to examine the relationships among various input and output factors in PW supply chain management. Specifically, the numerical model is a multi-objective optimisation model for the management of PW supply chains that takes into account economic as well as environmental objectives. A global sensitivity analysis has also been performed to assess the economic and operational factors that can influence the management of PW in KOC. The findings of this study show that the operational costs, treatment costs and environmental impacts can be assessed and used to guide the management of the PW supply chain in KOC.

This paper presents a study on the interlaminar shear strength (ILSS) retention of three types of glass fiber–reinforced polymer (GFRP) bars with different surface textures subjected to four types of conditioning environments (alkaline, alkaline, salt, acidic, and water) at two temperature levels (ambient laboratory and high temperature) for 3, 6, and 12 months. The conditioning temperature plays a critical role in reducing the strength of the bars. Scanning electron microscopy revealed the extent of damage to the fibers, resin, interface, and fracture morphologies in the cross sections. The causes of fiber cracking and lower strength upon exposure were validated by point energy-dispersive X-ray spectroscopy analyses, which detected the leaching of silicon from the fiber structure. Prediction models using multiple linear regression (MLR) and artificial neural networks (ANNs) were developed using Matrix Laboratory (MATLAB R2023b) software and compared. The coefficients of determination of the MLR and ANN prediction models were found to be 0.29 and 0.90, respectively, indicating the superiority of machine learning–based models in identifying and accounting for nonlinearities and highlighting their potential application in GFRP bars. Finally, the correlation between the transverse shear strength (TSS) and ILSS of the tested GFRP bars was identified. The ILSS of the bars was found to be approximately 0.26 times the TSS for any given conditioning scenario. [ABSTRACT FROM AUTHOR]

Exploring the relationships between cropland expansion and their driving factors is crucial to implement cropland protection policies. Previous research primarily explored their linear relationships but seldom discussed the non-linear effects that better reflect the changes in the real world. Thus, a machine learning method, namely, the gradient boosting decision tree (GBDT) model, is introduced to identify the threshold effect and the non-linear correlations between cropland expansion and driving factors in the middle reaches of the Yangtze River urban agglomerations (MRYR), China. Results show the following: (1) During 2000–2020, a 13,141.76 km2 of cropland is expended by taking in other land use types, among which forest and construction land have the top proportions. (2) The relative importance (RI) identified by the GBDT model indicated that area of the county (AC) and cropland area (CA) were the most important variables that affected cropland expansion values of RI at 56.94% and 12.14%, respectively. (3) A non-linear correlation exists between cropland expansion and each explanatory variable, which can be represented by a curve with thresholds. In addition, three correlation types were identified: positive, negative, and piecewise non-linear correlations. This research emphasizes that the non-linear relationships and threshold effect of the socioeconomic development factors on cropland protection should be considered when formulating cropland protection policies. [ABSTRACT FROM AUTHOR]