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Traditional approaches to improving basketball players’ shooting skills rely on coaches’ experience in adjusting players’ biomechanical motions. However, such an approach cannot provide specific instructions or facilitate immediate feedback for improvement of the shooting motion. In this article, a method is presented to quantitatively evaluate four key action indicators of shooting basketballs using a machine‐learning model based on Bayesian optimization of a light gradient boosting machine (LightGBM). Important motion data for the model are collected by micro‐inertial measurement units embedded in a wrist motion sensor and an internet of things (IoT) smart basketball. Basketball shooting motion data are collected from 16 subjects and used for model training and data testing, and four important action indicators that influence the shot quality are selected for quantitative assessment. The LightGBM model is then developed for the regression prediction of the four action indicators of shooting. In the results, it is indicated that for an individual player, the highest correlation scores of the four indexes range from 97.6% to 99.3%. The proposed approach for quantitatively assessing shooting indexes can provide objective and data‐based guidance to improve players’ shooting performance. Foreseeably, the prediction model can be embedded into a chip of a wearable device to evaluate the real‐time shot quality quantitatively.

The pink bollworm incidence and adult male moth trap catches were monitored throughout the cropping period for four years from 2017-2021 on Bt cotton (KCH-14K59) at University of Agricultural Sciences, Raichur. The pink bollworm male moth activity (95 moths/trap) was more during the month of December month (49th SMW) with the highest larval incidence (25.67 larvae/ 20 bolls) on green bolls during the month of February (6th - 9th SMW). The correlation matrix indicating relationship between the weekly mean moth catches, larval incidence and meteorological variables from 2017 to 2021 exerted negative association with mean of maximum and minimum temperature, rainfall, morning and afternoon relative humidity. However, the influence of all these whether parameters was found to be highly significant. When the data was subjected to Multi Linear Regression analysis, the results revealed that 78.70 per cent of mean pheromone trap catches (R2 = 0.787) and 92 per cent of mean larval incidence (R2 = 0.92) was negatively influenced by minimum temperature.

The study aims to explore the indicators of foreign direct investment (FDI) and their impact on economic development, with a specific focus on renewable energy in Indonesia. It is well-known that FDI plays a significant role in the development of developing countries, and Indonesia, being one of them, still faces challenges in meeting the energy needs of its citizens. The empirical analysis utilizes the Multiple Regression approach with data spanning from 1981 to 2021. The results indicate a cointegration relationship between model parameters and cross-sectional dependence. Additionally, the study finds that FDI and inflation have a negative impact on renewable energy, while population and GDP have a positive and significant effect. Furthermore, economic growth and fossil fuel consumption also positively influence renewable energy consumption. In the long term, the estimation results suggest that FDI and financial development have a simultaneous effect on energy consumption, which aligns with economic growth but not necessarily with renewable energy consumption. This implies that while FDI and financial development contribute to overall energy consumption as the economy grows, they may not directly impact the adoption of renewable energy sources. Based on the research findings, policymakers in Indonesia are encouraged to focus on sustainable development and consider policy transformations that facilitate the transition from fossil fuels to renewable energy sources. [ABSTRACT FROM AUTHOR]

Regional hydraulic geometry curves, which relate drainage area to channel morphology, are powerful tools for river restoration and watershed management. While there is a strong correlation between channel morphology and drainage area, the importance of watershed slope and relief on channel-forming flow, especially in mountainous regions, cannot be ignored. This research used single variable power functions along with multiple regression to determine which network- and or local-scale variables are influencing channel morphology in the Southern Blue Ridge Mountains. Within this region, watershed topography (i.e. relief and slope) and or local-scale characteristics (e.g. channel slope and sinuosity) are better predictors of channel morphology than drainage area. Additionally, the importance of local-scale characteristics in the multiple regression models provide strong evidence that site-specific conditions can be just as important as watershed characteristics in determining channel morphology. While multiple regression has been used to create hydraulic geometry equations in other regions, this is the first study that determined whether non-drainage area multiple regression models perform better than models that include drainage area. With the increasing ease with which network- and local-scale characteristics can be calculated, this research shows a clear need to incorporate additional network- and or site-specific characteristics into hydraulic geometry equations. [ABSTRACT FROM AUTHOR]

Multi-parametric valuation techniques, in real estate valuation, are particularly useful to understand and define all the factors that contribute to the determination of market prices. Even though a plethora of building features influence the way prices are formed, location is certainly among the most influential. As such, the goal of this research is the analysis of position and neighbourhood in the process of market value estimation for a building. Particular attention is given to the comparison of location characteristics versus construction characteristics by means of a multi-parametric econometric analysis. [ABSTRACT FROM AUTHOR]

Using a discrete-event simulation (DES) model, the current disaster plan regarding the allocation of multiple injured patients from a mass casualty incident was evaluated for an acute specialty hospital in Vienna, Austria. With the current resources available, the results showed that the number of severely injured patients currently assigned might have to wait longer than the medically justifiable limit for lifesaving surgery. Furthermore, policy scenarios of increasing staff and/or equipment did not lead to a sufficient improvement of this outcome measure. However, the mean target waiting time for critical treatment of moderately injured patients could be met under all policy scenarios. Using simulation-optimization, an optimal staff-mix could be found for an illustrative policy scenario. In addition, a multiple regression model of simulated staff-mix policy scenarios identified staff categories (number of radiologists and rotation physicians) with the highest impact on waiting time and survival. In the short term, the current hospital disaster plan should consider reducing the number of severely injured patients to be treated. In the long term, we would recommend expanding hospital capacity—in terms of both structural and human resources as well as improving regional disaster planning. Policymakers should also consider the limitations of this study when applying these insights to different areas or circumstances. [ABSTRACT FROM AUTHOR]

Background: Currently, technical and tactical analysis has become an indispensable task for sport in many countries. Many studies analysed players' specific technical and tactical factors, but it is rare to quantitatively analyse the importance of table tennis players' shot effectiveness. This is the first study to propose the new concept of "shot effectiveness model", and the purpose of this study is to explore the structure of the shot effectiveness model for elite table tennis, including the importance degree of shot effectiveness, and the relationship between them. Methods: 258 matches were selected between the top 50 players in the world from 2019 to 2021 as samples. Multiple regression analysis was used to obtain the standard regression coefficients and game simulation, and the total decision coefficient (TDC) was used to evaluate the importance degrees of shot effectiveness (SE) on match results. Results: (1) There was little difference in the importance degree of each shot effectiveness between men and women players. (2) The importance degree of the first and third shots (SE1), the second and fourth shots (SE2), the fifth and after shots (SE3), and the sixth and after shots (SE4) for both men and women players account for approximately 25%, 35%, 22%, and 16% respectively. (3) There was little difference in the importance degree of each shot effectiveness between Chinese women players and women players from other countries and regions with the same importance order of SE2 > SE1 > SE3 > SE4. However, the structure of the shot effectiveness model for men players was quite different from that for women players. (4) There is a compensation effect between shot effectiveness of table tennis players, and the total evaluation score of 12 and 13 is the dividing line for success or failure in both men and women matches. Conclusions: TDC could well reflect the important degrees of each shot effectiveness in various ways on winning probability in table tennis matches. And this study compared the importance of several types of players' performance on the probability of winning a match. In addition, we found that there is a compensation effect between shot effectiveness of table tennis players, and the magnitude of this effect will vary according to the type and level of shot effectiveness. [ABSTRACT FROM AUTHOR]

The study analyzed the marketing of onion in Kaduna and Katsina States, Nigeria. A survey of 100 onion farmers and 200 traders in these two states was conducted in 2022. Four villages and four markets were purposively selected. Random sampling was used to select respondents using structured questionnaire alongside oral interview. The analytical tools used were descriptive statistics, marketing margin and multiple regression. Majority of the traders (71%) had marketing experience between 5 to 25 years. Analysis on marketing margin shows that the producer’s share in the price that the final consumer pays was 56%, the wholesaler receives 14% and the retailer gets 30%, while the total marketing margin in the complete distribution chain was 43%. Analysis on the effect of marketing costs on marketing margin using multiple regression reveals that commission paid to agents was significant at 1% for regional wholesalers and 5% for inter-regional wholesalers. Transportation cost had positive coefficients significant at 10% for regional wholesalers and 5% for inter-regional wholesalers. At the level of retailers, the commission and transport charges have insignificant effect and where they exhibited significant effect, they have negative t-values. The loading/un-loading cost, revenue charges and storage cost had insignificant coefficients and negative t-values. The study recommends provision of simple drying machine for processing onion into durable products, provision of efficient transport system, onion exportation to create competition, market access to reduce glut and enlightenment of market participants on joining cooperative societies for solving many of the marketing problems. [ABSTRACT FROM AUTHOR]

Summary: Wheat tempering conditions grains before a milling process begins. Process adjustments must be made to reach a desired level of flour quality and yield, depending on multiple factors. This article aims to develop a decision support tool to help operators adjust the first‐stage tempering parameters. It is based on a regression model that predicts an increase in organic wheat moisture content according to the properties of the wheat (initial wheat moisture content, wheat protein content and wheat temperature), process parameters (targeted wheat moisture content, wheat flow rate, water flow rate, wheat quantity and resting time) and tempering conditions (water quantity, average day temperature and average day humidity). The increase in wheat moisture achieved during the first tempering stage varies between 0% and 5%. Five regression models were compared: OLS, LASSO, RIDGE, ElasticNet and XGBoost. The models have been developed and tested from a case study at an organic wheat mill. The results indicate that the LASSO model outperformed others, with an average prediction error of 0.428%. The model showed the importance of humidity and temperature factors during the tempering process. The flow of water and wheat were the most influential parameters for an increase in wheat moisture content. [ABSTRACT FROM AUTHOR]

Background and objectives: The continuation of the progressive and growing trend of global population growth and income, will lead to an increase in food demand and consequently more agricultural production. On the other hand, the escalation of climate anomalies also threatens the food security of human society in the future. It is very unlikely that this food need will be met by expanding the area under cultivation, due to the lack of water resources and suitable agricultural lands, and increasing non-agricultural uses due to urban development. Also, in an agricultural production system, there are often huge differences between the yields of a plant and a region. This prompted researchers to assess the reasons for the potential lack of performance and to fill existing gaps and improve performance. Due to the need to increase rice production in the country, the present study was conducted to determine the quantity of potential yield and yield gap in the study area and the constraints that play a role in increating a yield gap and the implementation of best management. Materials and methods: In this study, three groups of information related to crop management, soil and crop (including 160 variables), during the two agronomic years 2019 and 2020 out of a total of 164 farms including local and high-yield rice cultivars in 36 villages of Ghaemshahr located in Mazandaran province were measured and recorded in the field with continuous monitoring during the growing season and through face-to-face interviews with farmers. Statistical analysis was performed using comparison performance analysis. The relationship between yield and all variables examined with the help of multiple regression. In addition, the scope of changes and the method of performing each management operation and the proportion of farmers who had used different methods of management operations were determined. Results: From 160 variables studied, the performance model (final production equation) with nine independent variables was selected, which explained 73% of the total performance changes (P <0.0001). The model estimated the average and maximum yields to be 5163 and 11598 Kg ha-1, respectively, which are comparable to the average and maximum yields observed on the farm (at 5243 and 11052 Kg ha-1 ). The yield gap in the production equation was 6435 Kg ha-1. There is a gap of 6435 Kg ha-1 between the actual yield of farmers on the farm and the achievable yield. It was also found that low yield cultivar, planting date, number of seedlings per hill, square planting arrangement, total amount of urea fertilizer, total amount of ammonium sulfate fertilizer, total amount of potassium sulfate fertilizer, loam clay texture, and previous plant in rotation of legume type, 35, 8, 9, 3, 15, 12, 12, 2 and 4% are involved in creating this gap, respectively. Conclusion: Comparative performance analysis is one of the statistical methods that by analyzing field data is able to present the effect of various factors on the final amount of product performance. Based on the available results, it can be stated that the accuracy of the performance model (final production equation) is appropriate and by identifying the main causes of production constraints, in estimating the amount of yield gap and determining the effective contribution of each of the performance limiting variables and better giving the relevant which is done with the aim of increasing production per unit area, to provide considerable assistance. [ABSTRACT FROM AUTHOR]

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This research demonstrated whether the analytic choice of using a multiple regression or structural equation modeling methodology affected the results of faculty research productivity in Saudi Arabia. This study not only showed the differences between multiple regression and structural equation modeling results but also the disparity of results within each type of analysis. The results indicated that using either a multiple regression or structural equation modeling methodology delivered different results in terms of significant predictors and the model's overall explained variance. Further, differential outcomes produced by the various structural equation modeling models employed illustrate how the incorrect specification of formative (i.e., causal) indicators can result in worse data-fitting models. Implications for selecting analytic procedures are discussed. [ABSTRACT FROM AUTHOR]

Using the calculation of coefficients of multiple linear regression and two-dimensional correlation, we studied the mutual influence (functional connectivity) between the amplitudes of EEG rhythms within one lead in mentally retarded persons. Multiple regression equations were geometrically interpreted using polycyclic multigraphs. As a result of the studies and calculations, it was revealed that with mental retardation, the number of regression coefficients is determined more than in the norm. In the control group of sinistrals, more regression coefficients were detected between the amplitudes of EEG rhythms within one lead than in dextrals. Apparently, the results obtained reflect the features of the network semantic-topological brain. A greater number of regression coefficients within the full lead in dextrals, under normal conditions, was expressed in the sinister hemisphere, and sinistrals in the dextral one. In mentally retarded persons, on the contrary, during the reign, a greater number of regression coefficients appeared in the dextral hemisphere, and in sinistrals (although not significantly) in the sinistral. It can also be assumed that the connections-relationships found in the leads reflect projections in these leads of the EEG rhythm generators. The foregoing makes it possible to consider the regression connections-relations calculated in the analysis of the relationship between the amplitudes of EEG rhythms as units of neurophysiological activity. [ABSTRACT FROM AUTHOR]

China is currently experiencing severe water scarcity issues in its agricultural production sector. To address this challenge, the Chinese government has taken steps towards implementing a nationwide reform in agricultural water pricing to accelerate the more sustainable management of the agricultural water resources sector. The present study adopted a multiple regression model to test four alternative irrigation water charging methodologies (charges based on ladder pricing, time, land area, and electricity) accompanied by supportive agricultural pricing policies to address the inherent conflicts between water conservation and agricultural development goals. This study focused on the Wei River Basin, which is recognized as a highly water-stressed region in China. This basin was chosen as a pilot area for comprehensive reform initiatives related to agricultural water pricing and served as the geographical scope for our research. Between June and July of 2022, we conducted comprehensive field surveys within the Wei River Basin, accumulating a dataset of 415 data points pertaining to the crop year of 2022. Our results showed that the ladder water price-based method exhibited remarkable potential in achieving substantial savings, with a minimum of 60.5239 m3/mu of irrigation water conserved for food crops and an impressive 67.8090 m3/mu for cash crops. However, regarding water-saving irrigation technologies, the estimation results indicated that electricity-based charging outperformed the other methods, resulting in an impressive 55.22% increase when ladder pricing served as the benchmark. In addition, regarding agricultural green production, the results for food crops and cash crops are different, with food crops being more sensitive to the ladder water price policies. Moreover, the results suggested that different water charging methods have significant heterogeneity effects from the perspective of the farmers' scale, land fragmentation, and water price awareness capacity. This study forges an innovative path for water-stressed nations to execute agricultural water pricing reform and enhance agricultural production's sustainable growth. [ABSTRACT FROM AUTHOR]

Purpose: Many researchers and coaches hold that the ability to generate force rapidly is an important factor in athletic performance. This concept is often studied by analyzing the rate of ground reaction force development (RFD) during vertical jumps; however, many such studies disagree on whether estimates of RFD are true predictors of vertical jump height, have limited sample sizes, and have not employed multiple regression analysis. Therefore, the purpose of the study was to assess the utility of RFD as a predictor of vertical jump height. Methods: Forward sequential multiple regression models were performed using kinematic, kinetic, and demographic variables from a database of maximal countermovement vertical jumps collected via motion capture system from 2,258 NCAA Division I athletes. Results: Peak RFD was a significant bivariate predictor of vertical jump height (r = 0.408, p <.001). However, when other variables were included in the prediction model the partial variance in vertical jump height accounted for by peak RFD was nearly eliminated (r = −0.051, β = −0.051), but sex (r = 0.246, β = 0.94) and peak ground reaction force (r = 0.503, β = 1.109) emerged as predictors of partial variance in jump height. Furthermore, mediation analysis revealed the direct effect of peak RFD on vertical jump height was only 0.004. Conclusions: Multiple regression analysis enabled by a large sample size suggests Peak RFD may not be uniquely useful as a predictor of vertical jump height during maximal countermovement jumps. [ABSTRACT FROM AUTHOR]

Are there any correlations between land use and the associated prices charged for the soil? What is the significance of green infrastructure and what is the significance of public facilities and transport? For the analysis of the data, various methods of factor reduction and analysis were used to identify a multiple regression model that explained the price building. An effect was found between the pricing of the standard land reference value (SLRV), number of trees and distance to allotments. Summarizing the results, less than 231 trees in an SLRV zone causes an SLRV increase, the opposite is the case with a larger number of trees. The more accessible an allotment garden is (in terms of distance <421 m), the lower the SLV in the adjacent area. If the distance that must be covered to the allotment garden increases, the SLRV of the area increases. However, a more significant influence on the SLRV was concluded by the market economy variables. In summary, the present study indicates that (a) a uniform evaluation matrix for the SLRV should be created, and (b) the present subjective land assessments by the relevant experts should be complemented through targeting further training in the ecologically oriented planning context. [ABSTRACT FROM AUTHOR]

The aim of the research is to apply a hedonic pricing model to an apartment rental market in the city of Varna, Bulgaria. This widespread model has been used extensively in the various aspects of the rental markets. The sample size of the study is 59 apartments for rent offered on the market in "Chaika" living estate in the city of Varna, published online at "ALO.bg" as of September 1, 2023. We have chosen this specific date, because at that time the market is very brisk. On one hand, the demand of the apartments increases, because of the beginning of the school year, and on the other hand, this period coincides with seasonal layoff unemployment and seasonal labor moving out of the apartments, which they have been renting for the summer jobs. The results of the study explain above 90 percent of variation in rents in Varna, emphasizing the most important factors. [ABSTRACT FROM AUTHOR]

The stress distribution function in the surface layer is created as a result of using stress measurements on the surfaces of C45 steel samples after shot peening. Stresses were measured by X-ray diffraction with the use of the PSF-3M device from the Rigaku Company. For measuring residual stresses, subsequent layers of the top surface of the material were used as a basis, and these were obtained through electrochemical etching. The test results i.e. distance into the material, sample hardness, shot type, stress) were entered into the stepwise multiple regression program. A record of residual stresses was obtained in the form of the second-degree regression function of three independent variables with interactions. The obtained analytical form of the residual stress function was used in the FUNVAL3.EXE program to calculate the tabular values of stresses permeating into the material. For the analytical description of the stress distribution, the REGPOLY.EXE regression program was used, which creates a polynomial functional form of the residual stress distribution. The plot form of the residual stress distribution was obtained using the EXCEL Microsoft Office 2000 program. [ABSTRACT FROM AUTHOR]

The stability of reservoir bank slope during impoundment is significant for the safe operation of hydropower stations. The deformation evolution of a slope adjacent to dam is analyzed based on the field monitoring data. The main influencing factors and spatial distribution of the slope deformation during impoundment are identified based on the multiple regression model and K-means cluster analysis, respectively. Subsequently, the deformation mechanism of the slope is analyzed by three-dimensional nonlinear finite element method. Then, an inversion analysis method based on improved adaptive genetic algorithm and back propagation neural network is proposed. The measured deformations of 44 monitoring points are used to inverse a total of 27 mechanical parameters, including deformation parameters, strength parameters and Biot coefficients of 9 materials. Finally, the influence of sample numbers on the prediction accuracy and the robustness of the neural network are discussed. The results indicate that the deformation rate of the slope is substantially associated with the impounding process. The water pressure component and aging component of the deformation account for a relatively high proportion while the temperature component is negligible. The deformation is mainly affected by material softening and effective stress of shallow-buried fractured rock mass, in which the latter dominates. The water load on the dam surface is the deformation inducement of the deep rock mass. The proposed inversion method can reasonably obtain the weakening rate of materials and make the numerical model accurately fit in with the actual state of the slope. [ABSTRACT FROM AUTHOR]

Analysis of relationships between the richness of three taxonomic ranks of vascular plants in the Middle Volga region and climate showed that the climatic factor explains 74% of variance in the number of species and families and 76% of variance in the number of genera. Taxonomic parameters of a floristic sample collected on 25 polygons 100 km2 in size each were compared with climatic parameters and their functions. Multiple regression models were produced for the three taxonomic ranks (numbers of families, genera, and species), and maps have been constructed on the basis of these models. The main predictors were climatic parameters at the beginning of spring and in winter months. Statistically significant correlations between the richness of species, genera, and families and some functions of climatic parameters that are usually excluded from analysis were identified. The essence of these functions is discussed in the context of their effect on snow melting processes in early spring. [ABSTRACT FROM AUTHOR]

Land value is one of the economic issues of cadastral systems which is the base of sustainable urban and regional planning. The current paper concerns the estimation of the land values according to many essential factors, which are adopted as ten variables generally. Among these ten parameters, the frontage of the parcel (width), the value of rent, the width of streets, and the level of services represent the most effective parameters that play the main role in process of land price estimation over the Erbil City. The current research introduces the nature of land values and their homogeneous distribution and evaluates the suggested algorithm of land price estimation as one of the efficient factors that affect the national economic situation. The data collection was done for 100 parcels in different locations within the Erbil city boundary, which is being selected to apply the linear multiple regression equation to find the coefficients of the effective factors and to define the correlation between them. The obtained results of the linear multiple regression equation show that the level of existing services and the value of the rent have the maximum effect among these four factors, and they have the maximum correlation with the land price, whereas the road’s width has the minimum correlation among them. The worked-out algorithm for land price estimation (which is a vital issue of the modern cadastral systems), is recommended to be applied by the institutions and organizations concerning the land prices and land taxes task. [ABSTRACT FROM AUTHOR]

Recent reform efforts have pushed toward a better understanding of the distinction between exploratory and confirmatory research, and appropriate use of each. As some utilize more exploratory tools, it may be tempting to employ multiple linear regression models. In this paper, we advocate for the use of random forest (RF) models. RF is able to obtain better predictive performance than traditional regression, while also inherently protecting against overfitting as well as detecting nonlinear effects and interactions among predictors. Given the advantages of RF compared to other statistical procedures, it is a tool commonly used within a plethora of industries, including stock trading, banking, pharmaceuticals, and patient healthcare planning. However, we find RF is used within the field of psychology comparatively less frequently. In the current paper, we advocate for RF as an important statistical tool within the context of behavioral and psychological research. In hopes of increasing the use of RF in the field of psychology, we provide information pertaining to the limitations one might confront in using RF and how to overcome such limitations. Moreover, we discuss various methods for how to optimally utilize RF with psychological data, such as nonparametric modeling, interaction and nonlinearity detection, variable selection, prediction and classification modeling, and assessing parameters of Monte Carlo simulations. Throughout, we illustrate the use of RF with visualization strategies, aimed to make RF models more comprehensible and intuitive. [ABSTRACT FROM AUTHOR]

Urban morphology significantly affects the ecological effects of urban heat islands, ventilation, and atmospheric pollution. Here, we reveal the mechanisms linking the ecological effects of urban morphology to develop a planning approach for the collaborative optimization of multiple ecological effects. Considering Shenyang, a cold city in northern China, as the study area, a multiple regression model of morphological parameters and ecological effects was established, and the impact of morphological parameters on ecological effects was explored. The results show that the aspect ratio of the streets, building density, and vegetation coverage are sensitive to multiple ecological effects. The inflection point of the ecological effect function curve occurs when the aspect ratio of the building and building density are 0.2 and 0.3, respectively. In addition, for optimal design applications in typical areas of the city, to obtain a Pareto-optimal urban morphology, Grasshopper is used to establish a parametric platform, wherein a genetic algorithm solves the multiple regression equation set. Ultimately, five ecological effect indicators are optimized and show 8.4%, 5.0%, 31.6%, 33.1%, and 12.5% improvement. The study effectively constructs a collaborative optimization planning and design method for multiple ecological effects. [ABSTRACT FROM AUTHOR]

The purpose of this study is the quantitative investigation of the basketball tournament of the FIBA World Cup 2019. Firstly, it identified the performance of a team by using Principal Components Analysis (PCA). Then, the contributions of shooting, rebounding, turnover, and free-throw factors are identified and compared with Offense vs. Defense in terms of their contribution to the team's performance. Moreover, other factors are identified that affected the performance, the teams which performed better than expected are detected, and finally, machine learning models which enhance the 'Power Rankings' for the prediction of the final position of the teams in the tournament are suggested. [ABSTRACT FROM AUTHOR]

Abstract Depression is measured in most studies by surveys that sum individual symptom scores into one common variable. Given the high heterogeneity of depressive disorders and the diversity of symptom profiles at the same levels of depression, a significant amount of information is, therefore, not evaluated. In this study, we aimed to investigate how distinct depression symptoms from the tripartite model of anxiety and depression relate to the dimensions of core affect. The study included N = 1102 individuals who completed depression, anxiety and stress, and core affect scales. Participants were recruited from the convenience sample and were aged between 18 and 59 years (M = 39.70; SD = 12.03) with 38.2% men and 61.8% women, whose average number of years spent in education was M = 14.17; SD = 3.63. Correlation and regression analysis with JASP and R software showed that all depressive symptoms were significantly related to the core affect dimensions (valence and activation), and network analysis indicated which symptoms formed undirected interrelationships and what their possible roles were in the model. We concluded that not all depression symptoms in the network model formed similar relationships with the dimensions of core affect, which may be explained through both validity and nonclinical sampling aspects.

The role of demographic factors (age, gender, income, education, and occupation) and various apparel product categories on the online impulse buying behaviour (OIBB) of apparel consumers is investigated in this research. Data was collected using a convenience sampling (non-probability) method. The 404 apparel customers in Delhi (NCR) participated in the survey using a structured questionnaire. Multiple regression, percentage analysis, and chi-square tests were performed for data analysis. The multiple regression results revealed that gender, age, education, and occupation were significantly and inversely associated with the impulse buying behaviour of apparel consumers. Further, the results indicated that income was significant and directly associated with the OIBB for apparel consumers. ANOVA findings indicated that apparel consumers’ demographic factors (age, gender, income, education, and occupation) have a significant simultaneous impact on the OIBB of apparel consumers. It was found that the T-shirt was the most popular online apparel product category for the impulsively purchased item (32.43% out of 11 apparel product categories). The research findings provide recommendations for e-retailers to improve marketing strategies to enhance online buying among apparel consumers.

Walnut (Juglans regia L.) is a particularly significant plant for humans in terms of its useful properties and in the Russian Federation it can be attributed to the most valuable introducers for forestry and horticulture. It is grown in many countries of the world and the area of its cultivation is constantly expanding. Breeding species for food purposes requires a selection assessment of the quality of its fruits. For this, at different times, appropriate methods were developed and the existing gene pool of the culture was assessed. By now, new varieties and forms have been bred and brought, which also need to be evaluated for rational use for scientific and practical purposes. The descriptions of varieties and forms of walnut often contain insufficient information about all selected traits, which requires the development of new models to assess the variety fund using incomplete data. The purpose of the research is to develop the missing models of selection evaluation using incomplete data and evaluate the world walnut variety fund in terms of fruit quality, to identify varieties and forms of the highest quality category. The well-known methodology and new models have been used for the evaluation. When developing evaluation models for incomplete data, a database of 50 varieties evaluated for all indicators has been used. Using the method of excluding individual indicators, the corresponding multiple regression models have been calculated on its basis, taking into account a different combination of features. The licensed programs Stadia 8.0, Microsoft Excel have been used in the research. The proposed models provide an estimate with an average error of ± 0.48 – 3.72 points at R2 = 0.63 – 0.99. A promising walnut gene pool from 23 countries has been assessed, 69 representatives with fruits of the breeding category of the highest quality identified, and a list of them compiled. Of the 512 ancestors, 19.73% had the selection category of fruits of the highest quality, 62.88% of high-quality fruits, 16.80% of ordinary fruits, and 0.59% of varieties and forms of low-quality fruits.

Agriculture is one of the most important sectors and driving factors of the economy of Bangladesh, which plays a significant role in the prosperity of large rural communities by increasing productivity, income, and creating employment. Presently, this sector has faced a severe challenge in its production, due to the construction of unplanned infrastructure in rural areas. This study investigates the effect of rural infrastructure on agricultural production in Bangladesh. Using the purposive sampling technique, 50 respondents were interviewed through a structured questionnaire to collect primary data from six unions of Sadar Upazila in the Kushtia district. Statistical methods of multiple regression and paired-sample t-test have been utilized to analyze the collected data. The results of the multiple regression model show that the co-efficient of cultivable and infrastructural land size is statistically significant at 1 percent of level, which depicts cultivable land positively affects agricultural production, whereas infrastructural land negatively affects agricultural production in the study area. This means that infrastructure built on cultivable land has reduced agricultural production. Paired-sample t-test result also shows that the mean difference between agricultural production before and after constructing infrastructure is TK.134847.94 per year. The primary reasons for the construction of infrastructure in the study area are unanticipated population expansion, urbanization, unplanned human settlement, and a rise in nuclear families. Lastly, suitable policies have been offered to develop the infrastructure as well as agricultural production in rural areas.

Abstract Background Evaluation of the extent of genetic variation within and between the populations of crop genetic resources are of paramount importance in any breeding program. An experiment aimed at assessing the extent of variation among barley lines and the degree of association between hordein polypeptide and agronomic traits was hence executed. Methods Field experiment was conducted in six environments between 2017–2019 involving 19 barley lines. Hordein bands were separated using vertical Sodium Dodecyl Sulphate Poly- acrylamide Gel Electrophoresis (SDS-PAGE). Results The analysis of variance revealed significant variation among lines and wider range units were observed for the agronomic traits. The line (Acc# 16,811–6) was superior, producing the highest grain yield (2.97 ton ha−1) across environments, 3.6 ton ha−1 at Holleta, and 1.93 ton ha−1 at Chefedonsa. At Arsi Negelle a different line Acc# 17146–9 was the highest yielding (3.15ton ha−1). SDS-PAGE-based analysis of barley lines separated 12 hordein bands between C (four bands) and B (eight bands) subunits. Interestingly bands 52, 46a, and 46b were uniquely conserved in the four naked barley lines (Acc#16809–14,16956–11, 17240–3, 17244–19). A considerably high proportion of genetic diversity within the populations than among the populations could be a repercussion of high gene flow which substantiates the longstanding and dominant informal seed exchange system among the farmers. The significant positive association between grain yield and band 50 evocates the expression of this allele may code for higher grain yield. The negative association between days to maturity and band 52 perhaps stipulates earliness in barely lines upon the manifestation of the band. Band 52 and 60 appeared to be associated with more than one agronomic trait (days to maturity and thousand kernel weight; grain filling period and grain yield respectively) and could be the result of pleiotropic characteristics of the genes residing in these banding regions. Conclusion The barley lines exhibited substantial variation for hordein protein and agronomic traits. However, imparted the need for the implementation of decentralized breeding as a consequence of genotype-by-environment interaction. Significant hordein polypeptide and agronomic traits association advocated the utilization of hordein as a protein marker and perhaps consider them in the parental line selection.

In this study, challenges experienced by dairy cattle enterprises in Southwestern Uganda and the factors influencing their profitability were respectively analyzed using exploratory factor analysis and multiple regression in STATA 15.0 statistical software. Eighteen questions relating to the challenges experienced by dairy producers in the study area were factor analyzed using principal components analysis with varimax rotation. Kaiser-Meyer-Olkin’s measure of sampling adequacy was 0.643, above the commonly recommended value of 0.6, and Bartlett’s test of sphericity was significant (ꭓ² (153) = 1670.13, P

Deviations in driving time (DT), or significant variations, occur frequently on urban public transport (PT) lines, except in subsystems with separate routes. DT variability is the main reason for disturbances in operation, leading to unstable and unreliable transport service. Moreover, it also causes variability in total user travel time, which is one of the main parameters of transport service quality. Identifying and quantifying factors that influence PT vehicle DT characteristics is significant for designing advanced prediction and passenger information systems and prioritising investments to reduce bus travel time and improve the scheduling process, and thus the level of transport service quality. An analysis of the elements of the route and other static elements of the line that influence DT was carried out in this paper. A model for determining and quantifying influential factors and methodologies for collecting all necessary data was created. The multiple regression model, developed as a result of the conducted multivariate statistical analysis using the specialised SPSS software, was applied to the selected representative set of lines in a real urban PT system. The created regression model explains between 18.2% and 97.4% of the variance of average, minimum and maximum DT and its deviation in the peak and off-peak periods.

Selecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents the MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when extracting drainage from DEMs. This work employs 36 sample sub-basins in Hubei (China) province. Firstly, topography, the normalized difference vegetation index (NDVI), and water storage change are used in building multiple regression models to calculate the drainage length. Power functions are fit to calculate the FAT of each sub-basin. Nine randomly chosen regions served as test sub-basins. The results show that: (1) water storage change and NDVI have high correlation with the drainage length, and the coefficient of determination (R2) ranges between 0.85 and 0.87; (2) the drainage length obtained from the Multiple Regression model using water storage change, NDVI, and topography as influence factors is similar to the actual drainage length, featuring a coefficient of determination (R2) equal to 0.714; (3) the MR-AP method calculates suitable FATs for each sub-basin in Hubei province, with a drainage length error equal to 5.13%. Moreover, drainage network extraction by the MR-AP method mainly depends on the water storage change and the NDVI, thus being consistent with the regional water-resources change.

The applicability of remote sensing enables the prediction of nutritional value, phytosanitary conditions, and productivity of crops in a non-destructive manner, with greater efficiency than conventional techniques. By identifying problems early and providing specific management recommendations in bean cultivation, farmers can reduce crop losses, provide more accurate and adequate diagnoses, and increase the efficiency of agricultural resources. The aim was to analyze the efficiency of vegetation indices using remote sensing techniques from UAV multispectral images and Sentinel-2A/MSI to evaluate the spectral response of common bean (Phaseolus vulgaris L.) cultivation in different phenological stages (V4 = 32 DAS; R5 = 47 DAS; R6 = 60 DAS; R8 = 74 DAS; and R9 = 89 DAS, in 99 days after sowing—DAS) with the application of doses of magnesium (0, 250, 500, and 1000 g ha−1). The field characteristics analyzed were mainly chlorophyll content, productivity, and plant height in an experimental area by central pivot in the midwest region of Brazil. Data from UAV vegetation indices served as variables for the treatments implemented in the field and were statistically correlated with the crop’s biophysical parameters. The spectral response of the bean crop was also detected through spectral indices (NDVI, NDMI_GAO, and NDWI_GAO) from Sentinel-2A/MSI, with spectral resolutions of 10 and 20 m. The quantitative values of NDVI from UAV and Sentinel-2A/MSI were evaluated by multivariate statistical analysis, such as principal components (PC), and cophenetic correlation coefficient (CCC), in the different phenological stages. The NDVI and MCARI vegetation indices stood out for productivity prediction, with r = 0.82 and RMSE of 330 and 329 kg ha−1, respectively. The TGI had the best performance in terms of plant height (r = 0.73 and RMSE = 7.4 cm). The best index for detecting the relative chlorophyll SPAD content was MCARI (r = 0.81; R2 = 0.66 and RMSE = 10.14 SPAD), followed by NDVI (r = 0.81; R2 = 0.65 and RMSE = 10.19 SPAD). The phenological stage with the highest accuracy in estimating productive variables was R9 (Physiological maturation). GNDVI in stages R6 and R9 and VARI in stage R9 were significant at 5% for magnesium doses, with quadratic regression adjustments and a maximum point at 500 g ha−1. Vegetation indices based on multispectral bands of Sentinel-2A/MSI exhibited a spectral dynamic capable of aiding in the management of bean crops throughout their cycle. PCA (PC1 = 48.83% and PC2 = 39.25%) of the satellite multiple regression model from UAV vs. Sentinel-2A/MSI presented a good coefficient of determination (R2 = 0.667) and low RMSE = 0.12. UAV data for the NDVI showed that the Sentinel-2A/MSI samples were more homogeneous, while the UAV samples detected a more heterogeneous quantitative pattern, depending on the development of the crop and the application of doses of magnesium. Results shown denote the potential of using geotechnologies, especially the spectral response of vegetation indices in monitoring common bean crops. Although UAV and Sentinel-2A/MSI technologies are effective in evaluating standards of the common bean crop cycle, more studies are needed to better understand the relationship between field variables and spectral responses.

Multi-parametric valuation techniques, in real estate valuation, are particularly useful to understand and define all the factors that contribute to the determination of market prices. Even though a plethora of building features influence the way prices are formed, location is certainly among the most influential. As such, the goal of this research is the analysis of position and neighbourhood in the process of market value estimation for a building. Particular attention is given to the comparison of location characteristics versus construction characteristics by means of a multi-parametric econometric analysis.

We have developed the wearable ground reaction force measurement system, however, the number of sensors, twelve, was considered to be large because the estimation error of test data rather increased by adding the explanatory variables. This study objected to confirming the proper number and positions of the sensors. We tried all the combinations of the twelve sensors to use as the explanatory variables. Test subjects were eight; half was teacher data and the other was test data. As a result, the estimating error was the minimum with eight sensors, removing two sensors on the lateral mid-foot and one on the lateral heel. The root mean square error was reduced from 5.90kgf to 4.71kgf. The proper number of sensors might be nine. Still, it might not be enough to use eight test subjects. Our next task is to extend the test subjects to confirm the result. [ABSTRACT FROM AUTHOR]

Abstract This paper aims to assess efficiency in the public irrigation projects of Brazil. A Data Envelopment Analysis (DEA) model using a limited set of significant variables and adapted to the specific characteristics of existing public irrigation projects in the country was used. Then a Multiple Regression Analysis was performed to efficient irrigation projects to estimate other inputs that did not have been considered in the DEA model. The results indicate that 15 public projects out of the 34 studied, reached the technical efficiency score, as well as pure efficiency and scale efficiency. The work brings several new contributions to the literature on irrigation management and practical implications for decision makers. It is noteworthy that the results of the study can be useful for a better understanding of the general efficiency of public irrigation and what are its most determining factors.

Remote sensing is widely utilized in agriculture for estimating corn (Zea mays L.) grain yield (CGY). Few studies have determined if the Normalized Difference Vegetation Index (NDVI) and/or Soil Plant Analysis Development (SPAD) can estimate CGY in Florida. From April to August 2022, in Live Oak, Florida, a field-scale experiment was conducted in two sites with irrigated corn using a complete randomized block design with six nitrogen (N) rates and four replicates per site. This study aimed to estimate CGY using NDVI alone or in combination with SPAD, plant height (PH), and N rate. CGY response curve served as a comparison standard. Fifteen data subsets were selected, and stepwise selection multiple linear regression analysis was utilized to generate each reduced equation (Model). In addition, the relative significance of the predictor variables was evaluated. The strongest correlations with CGY were demonstrated by N rate (r = 0.93), PH103 (r = 0.91), NDVI39 (r = 0.81), and SPAD60 (r = 0.93). Models with multiple variables showed a better fit than single-variable models. Model 15 (variables until tasseling - 60 DAP) demonstrated comparable performance with 92.8% of variance explained and RMSE = 1,315.685 kg ha−1. Regardless of the model, the N rate has always contributed the most to CGY. Although Model 1 had the best overall performance, it may not be feasible for growers to utilize a model with multiple terms. Consequently, Model 15 could estimate CGY in Florida based on PH and NDVI at 60 and 32 DAP, respectively.

This comparative study is an attempt to explore the determinants of capital structure for Malaysian firms listed in various sectors level. Within the framework of traditional and moderate dynamic capital structure theories, the key determinants such as fixed assets, current assets, return on equity, size, earning per share and total assets are tested in relation to the debt-equity ratio. The large-scale study entails data collected from 551 listed firms of Bursa Malaysia main market over 12 years period i.e. 2005-2016. Notably, this study combines Time Series econometrics with Panel Data analysis to enhance methodological robustness. Moreover, the comparative analysis approach is designated to recognize the most persistent capital structure determinants. In the first place, the Multiple Regression analysis (MRA) is selected as a baseline estimation method. Subsequently, the Auto Regression Distributed Lag model (ARDL), the Panel Data Static models, and Dynamic model via the Generalized Method of Moments (GMM) are employed to identify the capital structure determinants for the firms listed at Bursa Malaysia. The outcomes are surprising and indicate that the entire market is primarily controlled by the studied determinant total assets, which is significant in both construction and property sectors through MRA, ARDL, and GMM analysis. Technically, the significant role of tangibility and the existence of speed of adjustment across sectors imply that the Dynamic Capital Structure is the most prominent among all, followed by the Dynamic Trade-off theory.

Organic waste generation has increased massively around the world during the last decades, especially the waste produced by the olive-growing industry. In order to manage the waste accumulation, composting process is an appropriate biotechnological solution which allows the waste organic matter biotransformation into a useful product the “compost”, used as an amendment for agricultural soils. The classical composting process presents several disadvantages; the major difficulty is to find the best feedstocks proportion to be used, leading to a final C/N ratio ranged between 12 and 15, a neutral pH, a humidity between 40% and 60% and organic matter (OM) content of 20–60%, at ambient temperature. Consequently, an accurate optimization of the composting process is needed for predicting the process parameters progress. To optimize these parameters and the waste rates initially mixed, the multiple regression method was used to determine the compost final parameters values, referring to the initial mixture of the different waste types. The best model filling the required standardized values included 49% of olive mill wastewater, 19.5% of exhausted olive mill cake, 15.5% of poultry manure, and 16% of green waste. This combination provides a pH of 7.5, a C/N ratio of 12.5 and an OM content of 44%. Such modelization would enshorten the composting required time.

Using the calculation of coefficients of multiple linear regression and two-dimensional correlation, we studied the mutual influence (functional connectivity) between the amplitudes of EEG rhythms within one lead in mentally retarded persons. Multiple regression equations were geometrically interpreted using polycyclic multigraphs. As a result of the studies and calculations, it was revealed that with mental retardation, the number of regression coefficients is determined more than in the norm. In the control group of sinistrals, more regression coefficients were detected between the amplitudes of EEG rhythms within one lead than in dextrals. Apparently, the results obtained reflect the features of the network semantic-topological brain. A greater number of regression coefficients within the full lead in dextrals, under normal conditions, was expressed in the sinister hemisphere, and sinistrals in the dextral one. In mentally retarded persons, on the contrary, during the reign, a greater number of regression coefficients appeared in the dextral hemisphere, and in sinistrals (although not significantly) in the sinistral. It can also be assumed that the connections-relationships found in the leads reflect projections in these leads of the EEG rhythm generators. The foregoing makes it possible to consider the regression connections-relations calculated in the analysis of the relationship between the amplitudes of EEG rhythms as units of neurophysiological activity.

This study aimed to evaluate the influence of meteorological variables and geographic factors on the selection of soybean lines concerning grain yield in Brazil and Paraguay soybean-producing regions. The study was conducted in seven different environments: Bela Vista do Norte - PY, Palotina - PR, Mangueirinha - PR, Major Vieira - SC, Três Passos - RS, Toledo – PR, and Passo Fundo - RS. The randomized block design in an incomplete factorial scheme with six soybean genotypes (G1, G2, G3, G4, G5, and G6) was used for the experiments. The harvest occurred in the first half of March, and grain yield was measured through the total harvest of the plot and expressed in kg ha-1, with grain moisture at 13%. The climatic variables used in the study were maximum air temperature (Tmax, ºC), average air temperature (Tavg, ºC), minimum air temperature (Tmin, ºC), relative air humidity (RH, %), precipitation (Prec, mm), wind speed (WS, m/s), dew point (DP, °C), incident radiation (Rad_Inc, MJ/m²), and total radiation (RAD_OL, MJ/m²); and geographic factors were altitude (ALT), longitude (LON), and latitude (LAT). The G5 genotype with a genetic value for grain yield above the general average is the most adapted to favorable environments. Altitude had the greatest influence on the biological variability of the genotypes, with a negative correlation of moderate magnitude with grain yield. Grain yield was enhanced in environments with altitudes lower than 338 m at latitudes below 24.17S.

To examine and analyze the applicability of UAV multispectral images to urban river monitoring, this paper, taking the Fuyang River in the urban area of Handan Municipality as the object, the orthogonal image data of the river in different seasons were acquired by unmanned aerial vehicles (UAVs) equipped with multispectral sensors, and at the same time, the water samples were collected for physical and chemical indexes detection. Based on the image data, a total of 51 modeling spectral indexes were obtained by constructing three forms of band combinations ranging from the difference index (DI), ratio index (RI), and normalization index (NDI) and combining six single-band spectral values. Through the partial least squares (PLS), random forest (RF), and lasso prediction models, six fitting models of water quality parameters were constructed: turbidity (Turb), suspended, substance (SS), chemical oxygen demand (COD), ammonia nitrogen (NH4-N), total nitrogen (TN), and total phosphorus (TP). After verifying the results and evaluating the accuracy, the following conclusions were drawn: (1) The inversion accuracy of the three types of models is generally the same—summer is better than spring, and winter is the worst. (2) Water quality parameter inversion model based on two kinds of machine learning algorithms has more prominent advantages than PLS. RF model has good performance in the inversion accuracy and generalization ability of water quality parameters in different seasons. (3) The prediction accuracy and stability of the model are positively correlated to a certain extent with the size of the standard deviation of sample values. To sum up, by using the multispectral image data acquired by UAV and adopting the prediction models built upon machine learning algorithms, water quality parameters in different seasons can be predicted in different degrees. [ABSTRACT FROM AUTHOR]