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In international travel, customers are willing to express their experiences and feelings about the trip by posting reviews on platforms in their native language. According to Customer-based Discrepancy Theory, customers with different language backgrounds will form different anchors when browsing or reading eWOM in the corresponding language due to the differences in expression forms, nouns, and other information in different languages, and even if they have consistent offline consumption experience, customers with different language backgrounds may have different satisfaction levels due to the differences in established anchors. Prior studies have been leveraging reviews to understand the information contained in eWOM in different languages, however, the analyses of multilingual reviews still face challenges. The current joint linguistic and statistical analysis methods suffer from information overload in terms of massive online data. In this paper, we address the above challenges by utilizing cross-linguistic deep learning and multiple linear regression model of attribute-level effects on customer satisfaction. The results of the data experiment in English, Spanish, French & Dutch based on online restaurant reviews from both Yelp and TripAdvisor platforms show that there are significant differences in restaurant satisfaction across customer groups with different language backgrounds. Furthermore, there are differences in the impact of attributes level satisfaction such as restaurant service between customer groups with different language backgrounds. Our findings contribute to the development of effective marketing strategies for corporate policies for international travel services by providing a more responsive experience for customers from different language backgrounds. [ABSTRACT FROM AUTHOR]

In our contemporary cities, infrastructures face a diverse range of risks, including those caused by climatic events. The availability of monitoring technologies such as remote sensing has opened up new possibilities to address or mitigate these risks. Satellite images allow the analysis of terrain over time, fostering probabilistic models to support the adoption of data-driven urban planning. This study focuses on the exploration of various satellite data sources, including nighttime land surface temperature (LST) from Landsat-8, as well as ground motion data derived from techniques such as MT-InSAR, Sentinel-1, and the proximity of urban infrastructure to water. Using information from the Local Climate Zones (LCZs) and the current land use of each building in the study area, the economic and climatic implications of any changes in the current features of the soil are evaluated. Through the construction of a Bayesian Network model, synthetic datasets are generated to identify areas and quantify risk in Barcelona. The results of this model were also compared with a Multiple Linear Regression model, concluding that the use of the Bayesian Network model provides crucial information for urban managers. It enables adopting proactive measures to reduce negative impacts on infrastructures by reducing or eliminating possible urban disparities. [ABSTRACT FROM AUTHOR]

Chemical oxidation coupled with microbial remediation has attracted widespread attention for the removal of polycyclic aromatic hydrocarbons (PAHs). Among them, the precise evaluation of the feasible oxidant concentration of PAH-contaminated soil is the key to achieving the goal of soil functional ecological remediation. In this study, phenanthrene (PHE) was used as the target pollutant, and Fe2+-activated persulphate (PS) was used to remediate four types of soils. Linear regression analysis identified the following important factors influencing remediation: PS dosage and soil PHE content for PHE degradation, Fe2+ dosage, hydrolysable nitrogen (HN), and available phosphorus for PS decomposition. A comprehensive model of "soil characteristics-oxidation conditions-remediation effect" with a high predictive accuracy was constructed. Based on model identification, Pseudomonas aeruginosa GZ7, which had high PAHs degrading ability after domestication, was further applied to coupling repair remediation. The results showed that the optimal PS dose was 0.75% (w/w). The response relationship between soil physical, chemical, and biological indicators at the intermediate interface and oxidation conditions was analysed. Coupled remediation effects were clarified using microbial diversity sequencing. The introduction of Pseudomonas aeruginosa GZ7 stimulated the relative abundance of Cohnella, Enterobacter, Paenibacillus, and Bacillus, which can promote material metabolism and energy transformation during remediation. [ABSTRACT FROM AUTHOR]

The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of the ten cities in South America with the worst levels of air pollution. Thus, efficient and precise modeling and forecasting are critical for ozone concentrations in Lima. The focus is on developing precise forecasting models to anticipate ozone concentrations, providing timely information for adequate public health protection and environmental management. This work used hourly O$ _{3} $ data in metropolitan areas for multi-step-ahead (one-, two-, three-, and seven-day-ahead) O$ _{3} $ forecasts. A multiple linear regression model was used to represent the deterministic portion, and four-time series models, autoregressive, nonparametric autoregressive, autoregressive moving average, and nonlinear neural network autoregressive, were used to describe the stochastic component. The various horizon out-of-sample forecast results for the considered data suggest that the proposed component-based forecasting technique gives a highly consistent, accurate, and efficient gain. This may be expanded to other districts of Lima, different regions of Peru, and even the global level to assess the efficacy of the proposed component-based modeling and forecasting approach. Finally, no analysis has been undertaken using a component-based estimation to forecast ozone concentrations in Lima in a multi-step-ahead manner.

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets. [ABSTRACT FROM AUTHOR]

This study examines the impact of large-scale suburban new town development on the commuting time of college and graduate students in the Seoul Metropolitan Area. Household travel diary data from 2016 were analyzed to categorize residential areas and quantify the impacts on commute time to school. Multiple linear regression modeling is used to explore the relationships between development type, individual, and household characteristics and their impact on commute times. The results of the study show that students living in new urban areas have significantly longer commute times than those living in central Seoul, highlighting the differences that result from urban expansion policies targeting middle-class housing. These results suggest that the development of suburban new towns, which was intended to alleviate the housing shortage, has inadvertently lengthened the daily commute time of many students. Thus, a critical reassessment of suburban development strategies is needed to better balance the advantages of residential neighborhoods against the educational and living costs associated with increased travel time. [ABSTRACT FROM AUTHOR]

The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter season, making it a public health issue. Lima, Peru, is one of the ten cities in South America with the worst levels of air pollution. Thus, efficient and precise modeling and forecasting are critical for ozone concentrations in Lima. The focus is on developing precise forecasting models to anticipate ozone concentrations, providing timely information for adequate public health protection and environmental management. This work used hourly O 3 data in metropolitan areas for multi-step-ahead (one-, two-, three-, and seven-day-ahead) O 3 forecasts. A multiple linear regression model was used to represent the deterministic portion, and four-time series models, autoregressive, nonparametric autoregressive, autoregressive moving average, and nonlinear neural network autoregressive, were used to describe the stochastic component. The various horizon out-of-sample forecast results for the considered data suggest that the proposed component-based forecasting technique gives a highly consistent, accurate, and efficient gain. This may be expanded to other districts of Lima, different regions of Peru, and even the global level to assess the efficacy of the proposed component-based modeling and forecasting approach. Finally, no analysis has been undertaken using a component-based estimation to forecast ozone concentrations in Lima in a multi-step-ahead manner. [ABSTRACT FROM AUTHOR]

In this paper, multiple linear regression (MLR) and radial basis function neural network (RBF-NN) are applied to predict nitrate (NO3-) concentration with and without reservoir volume (WV) as predictor using monthly data for ten years in three water reservoirs located in the upper Cheliff basin (NW of Algeria). The datasets were divided into training (80%) and testing (20%) sets and two different scenarios were compared. The results revealed that RBF-NN was more efficient (MAE = 0.192 and SI = 0.061) compared with the MLR model to predict NO3- in all reservoirs. RBF-NN provided the best accuracy in the testing period with a high R2 of 0.957 in reservoir II, and low MSE and PBias of 0.048 mg/l and 2.98% in the training period in reservoir III, respectively. Overall, the best results were generated by M(iii) in scenario B. [ABSTRACT FROM AUTHOR]

Fatalities caused by natural hazards are driven not only by population exposure, but also by their vulnerability to these events, determined by intersecting characteristics such as education, age and income. Empirical evidence of the drivers of social vulnerability, however, is limited due to a lack of relevant data, in particular on a global scale. Consequently, existing global‐scale risk assessments rarely account for social vulnerability. To address this gap, we estimate regression models that predict fatalities caused by past flooding events (n = 913) based on potential social vulnerability drivers. Analyzing 47 variables calculated from publicly available spatial data sets, we establish five statistically significant vulnerability variables: mean years of schooling; share of elderly; gender income gap; rural settlements; and walking time to nearest healthcare facility. We use the regression coefficients as weights to calculate the "Global‐Empirical Social Vulnerability Index (GlobE‐SoVI)" at a spatial resolution of ∼1 km. We find distinct spatial patterns of vulnerability within and across countries, with low GlobE‐SoVI scores (i.e., 1–2) in for example, Northern America, northern Europe, and Australia; and high scores (i.e., 9–10) in for example, northern Africa, the Middle East, and southern Asia. Globally, education has the highest relative contribution to vulnerability (roughly 58%), acting as a driver that reduces vulnerability; all other drivers increase vulnerability, with the gender income gap contributing ∼24% and the elderly another 11%. Due to its empirical foundation, the GlobE‐SoVI advances our understanding of social vulnerability drivers at global scale and can be used for global (flood) risk assessments. Plain Language Summary: Social vulnerability is rarely accounted for in global‐scale risk assessments. We develop an empirical social vulnerability map ("GlobE‐SoVI") based on five key drivers of social vulnerability to flooding, that is, education, elderly, income inequality, rural settlements and travel time to healthcare, which we establish based on flood fatalities caused by past flooding events. Globally, we find education to have a high and reducing effect on social vulnerability, while all other drivers increase vulnerability. Integrating social vulnerability in global‐scale (flood) risk assessments can help inform global policy frameworks that aim to reduce risks posed by natural hazards and climate change as well as to foster more equitable development globally. Key Points: We develop a global map of social vulnerability at ∼1 km spatial resolution based on five key vulnerability drivers ("GlobE‐SoVI")We establish vulnerability drivers empirically based on their contribution to predicting fatalities caused by past flooding eventsAccounting for social vulnerability in global‐scale (flood) risk assessments can inform global policy frameworks that aim to reduce risk [ABSTRACT FROM AUTHOR]

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为对菜籽油的品质监控和快速检测提供科学依据，测定49种菜籽油样品的质量指标和脂肪酸组成，分析其品质多样性，并考察其品质特征与傅里叶变换衰减全反射红外光谱的相关性。结果表明：49种菜籽油样品过氧化值和酸值的变异系数分别达63.20%和152.90%，所含主要脂肪酸中棕榈酸、油酸和亚油酸含量的变异系数均未超过21.38%，而花生一烯酸含量的变异系数高达127.09%；通过分类分析发现，油样的过氧化值、亚油酸含量和不饱和脂肪酸含量与其产地、加工工艺、品种、上架期和质量等级有关；采用化学计量学方法构建油样红外光谱的共有模式，以相关系数法分析各光谱的相似度介于0.68～0.98之间，并筛选出10个共有峰。以共有峰的峰高均值为自变量，构建脂肪酸(棕榈酸、油酸、亚油酸、花生一烯酸、不饱和脂肪酸和饱和脂肪酸)含量的多元线性回归模型均达显著水平(p＜0.05)，但过氧化值和酸值的回归模型不显著(p＞0.05)。通过对30种热处理油样的验证实验发现，回归模型能准确预测其不饱和脂肪酸含量，预测值的相对误差介于-11.15%～10.95%之间。综上，红外光谱法可初步用于市售菜籽油的脂肪酸组成的快速分析。 To provide scientific basis for quality monitoring and rapid detection of rapeseed oil, fortynine rapeseed oil samples were collected to determine their quality index and fatty acid composition, their quality diversity was analyzed, as well as the relationship between quality characteristics and attenuated total reflectance-Fourier transform infrared spectrum(ATR-FTIR). The results showed that the coefficients of variation (CV) for the peroxide value (PV) and acid value (AV) of the oil samples were 63.20% and 152.90%, respectively. The CV of the main fatty acids composition of rapeseed oils, palmitic acid, oleic acid and linoleic acid was not more than 21.38%. By comparison, the CV of eicosenoic acid content reached 127.09%. Further classification analysis indicated that the PV, linoleic acid content and unsaturated fatty acid content of the oil samples were associated with their producing areas，processing technologies, rapeseed varieties, shelf periods and quality grades. The ATR-FTIR common model of rapeseed oils was established by chemometrics. The similarity value of each spectrum was analyzed by correlation coefficient method, ranging from 0.68 to 0.98, and 10 common peaks were screened out. Taking the peak height mean value of the common peak as the independent variable, the multiple linear regression models of PV, AV, and main fatty acid content were constructed. The fatty acid models, involving palmitic acid, oleic acid, linoleic acid, eicosenoic acid, unsaturated fatty acid and saturated fatty acid, were all significant (p0.05). The results of verification test performed with 30 heat-treated samples indicated that the model could accurately predict the content of unsaturated fatty acid with a relative error range of -11.15%-10.95%. In conclusion, Fourier transform infrared spectroscopy can be preliminarily used for the rapid analysis of fatty acid composition of the commercially available rapeseed oil.

In our contemporary cities, infrastructures face a diverse range of risks, including those caused by climatic events. The availability of monitoring technologies such as remote sensing has opened up new possibilities to address or mitigate these risks. Satellite images allow the analysis of terrain over time, fostering probabilistic models to support the adoption of data-driven urban planning. This study focuses on the exploration of various satellite data sources, including nighttime land surface temperature (LST) from Landsat-8, as well as ground motion data derived from techniques such as MT-InSAR, Sentinel-1, and the proximity of urban infrastructure to water. Using information from the Local Climate Zones (LCZs) and the current land use of each building in the study area, the economic and climatic implications of any changes in the current features of the soil are evaluated. Through the construction of a Bayesian Network model, synthetic datasets are generated to identify areas and quantify risk in Barcelona. The results of this model were also compared with a Multiple Linear Regression model, concluding that the use of the Bayesian Network model provides crucial information for urban managers. It enables adopting proactive measures to reduce negative impacts on infrastructures by reducing or eliminating possible urban disparities.

Human activities and environmental changes have influenced the changes in landscape patterns, which in turn profoundly impact the variation in net primary productivity (NPP) of vegetation. Understanding the relationship between landscape patterns and NPP is of significant importance for maintaining ecosystem stability and improving the ecological environment. In this study, six land use types in the arid and semi-arid regions of Northwest China were selected, and five landscape pattern indices at the landscape level and four landscape pattern indices at the class level were used. Pearson correlation and multiple linear regression models were employed to analyze the relationship between landscape indices and NPP at a 100 km × 100 km grid scale. The results indicate that there are varying degrees of correlation between landscape pattern indices and NPP from 2001 to 2020, with different levels of variation over the 20-year period. The correlation between indices and NPP is higher at the class level than at the landscape level, and the increase in landscape abundance and fragmentation promotes an increase in NPP. At the landscape level, three landscape indices, namely NP (Number of Patches), PR (Patch Richness), and SHDI (Shannon's Diversity Index), explain 45.4% of the variation in NPP. At the class level, NP, TE (Total Edge Length), and IJI (Dispersion and Juxtaposition Index) are the main influencing factors for NPP in cropland, forestland, and grassland. Therefore, in ecological governance, it is necessary to consider landscape pattern changes appropriately to maintain ecosystem stability. [ABSTRACT FROM AUTHOR]

Abstract Fatalities caused by natural hazards are driven not only by population exposure, but also by their vulnerability to these events, determined by intersecting characteristics such as education, age and income. Empirical evidence of the drivers of social vulnerability, however, is limited due to a lack of relevant data, in particular on a global scale. Consequently, existing global‐scale risk assessments rarely account for social vulnerability. To address this gap, we estimate regression models that predict fatalities caused by past flooding events (n = 913) based on potential social vulnerability drivers. Analyzing 47 variables calculated from publicly available spatial data sets, we establish five statistically significant vulnerability variables: mean years of schooling; share of elderly; gender income gap; rural settlements; and walking time to nearest healthcare facility. We use the regression coefficients as weights to calculate the “Global‐Empirical Social Vulnerability Index (GlobE‐SoVI)” at a spatial resolution of ∼1 km. We find distinct spatial patterns of vulnerability within and across countries, with low GlobE‐SoVI scores (i.e., 1–2) in for example, Northern America, northern Europe, and Australia; and high scores (i.e., 9–10) in for example, northern Africa, the Middle East, and southern Asia. Globally, education has the highest relative contribution to vulnerability (roughly 58%), acting as a driver that reduces vulnerability; all other drivers increase vulnerability, with the gender income gap contributing ∼24% and the elderly another 11%. Due to its empirical foundation, the GlobE‐SoVI advances our understanding of social vulnerability drivers at global scale and can be used for global (flood) risk assessments.

Confronted with the unprecedented COVID-19 pandemic, millions of learners have received, are receiving, or will receive multimodal language learning education. This study aims to explore the relationships between various factors influencing learners’ continuance intention by proposing an innovative multiple linear regression model in multimodal language learning education. Participants were randomly recruited (N = 334) in China who had received multimodal language learning education by combining Massive Open Online Courses, Rain Classroom, and WeChat. The research instrument, a comprehensive questionnaire, was sent through the online system named Questionnaire Star developed by technical experts. A multiple linear regression analysis was adopted to test the proposed hypotheses and fit the research model. This study confirms the relationships between the Technology Acceptance Model-inclusive constructs such as perceived ease of use, perceived usefulness, attitudes toward multimodal language learning education, and continuance intention of participating in multimodal language learning education. The Technology Acceptance Model is also associated with other constructs, e.g. Task-technology fit, Individual-technology fit, Openness, and Reputation of multimodal language learning educational institutes, and personal investment in multimodal language learning education. However, personal investment neither directly nor indirectly predicts continuance intention. Educators and designers could make every effort to improve multimodal language learning education to enhance personal investment and foster its association with continuance intention of learners.

Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their performance is evaluated using mean square error, relative absolute mistake, and coefficient of determination. PSO utilised COD, TDS, SS, pH, and Cl- as inputs, generating a mean square error of 1029.10, an average absolute relative error of 9.41%, and a coefficient of determination of 0.89. Comparisons demonstrated that the PSO model could accurately calculate the daily BOD at Al-Rustumiya wastewater treatment plant's inlet.

The digitization and refinement of accounting are propelling accounting work towards intelligence, leading to a further restructuring of accountants’ professional competence. Based on the existing competency structure framework for accountants, this paper proposes an optimized “main-core” competency structure for accountants, taking into account the new requirements for accountants’ competency in digital transformation. Based on the optimized accounting personnel competency structure, an evaluation index system is designed and combined with the entropy weight-TOPSIS method to analyze changes in accounting personnel competency. To examine the factors that affect accounting personnel’s competence, a static panel regression model was designed based on the multiple linear regression model and combined with the competence structure. The accounting personnel ability structure is the most weighted at 0.2614 in the financial and accounting management ability index, and the information technology application ability rose from 0.346 to 0.486 between 2017 and 2023, adding up to 40.46%. Each 1% increase in accounting personnel’s professional knowledge increases their competence by 0.614%, and behavioral motivation and professional ethics have a positive effect on their competence at the 1% level. As a result, accountants’ ability structure must focus on the application of digital technology, consolidating their professional knowledge, and promoting high-quality accounting work development.

This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.

Inter-city Venture Capital (VC) exchange is a micro-embodiment of the cross-regional flow of innovative elements such as capital and technology, and also an important driving force for shaping the network of VC cities. Compared with other types of investment activities, VC has more advantages in promoting the development of innovative industries, especially high-tech industries, and then driving urban economy and regional development; hence, it is widely regarded as a "catalyst" for economic growth and technological innovation. The study of VC networks is helpful in understanding the financial activity links between cities across the country and provides key support for further rational allocation of financial resources between cities. The existing research rarely uses the actual investment amount of VC transaction events between cities to construct the city network. The analysis of the continuous dynamic evolution research and the influencing factors of the overall spatial network structure over a long period of time needs to be strengthened. To compensate for the shortcomings in the above research, this study uses the data of China's inter-city VC flow between 2000 and 2018 to construct the Chinese urban VC network. The long-term spatial evolution characteristics and overall network structure influencing factors of the national inter-city VC network are discussed based on complex network analysis, GIS spatial technology analysis, multiple linear regression model, and so on. This study derives the following results: (1) The connection between cities tends to be closer, and the agglomeration degree of the national VC network increases, displaying obvious characteristics of high aggregation and small-world network. However, the cyber spatial structure of investors/start-ups tends to be flattened, there is a trend of decentralization. (2) The VC connection between cities has expanded from the eastern coastal axis to the central and western regions as a whole, exhibiting obvious characteristics of path dependence and distance attenuation. Additionally, the VC connection continues to sink from the three major urban agglomerations to other marginal cities, and the core node cities of the network also display a tendency of quantitative expansion and power transfer. (3) The city's entrepreneurial foundation, VC industry development level, financial environment, and urban accessibility all have differentiated impacts on the spatial structure of the national VC network. Moreover, the aforementioned are affected by various mechanisms such as multi-dimensional proximity, urban economy, and path dependence, which promote the construction of national and regional VC networks and the formation and evolution of regional capital centers.

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.

Kai-Fang Hua,1,2 Bo-Yang Jing,3 Yan-Hui Wu1,4 1Department of Endocrinology, Xiang’an Hospital of Xiamen University, Xiamen, Fujian, People’s Republic of China; 2School of Medicine, Xiamen University, Xiamen, Fujian, People’s Republic of China; 3Hai Yuan College, Kunming Medical University, Kunming, Yunnan, People’s Republic of China; 4National Metabolic Management Center, Xiang’an Hospital of Xiamen University, Xiamen, Fujian, People’s Republic of ChinaCorrespondence: Yan-Hui Wu, Department of Endocrinology, Xiang’an Hospital of Xiamen University, 2000 Xiang’ an East Road, Xiamen, Fujian, 361102, People’s Republic of China, Tel +8613089998806, Email mabelwyh@163.comPurpose: This study aimed to further quantify the relationship between insulin antibodies (IAs) and the 2-hour insulin to C-peptide molar ratio (2h-ICPR) using a multiple linear regression model in T2DM patients.Methods: A total of 274 T2DM patients from April 2019 to December 2022 in Xiang’an Hospital of Xiamen University were included in this study. Multiple Linear Model Fitting was conducted on the candidate independent variables (age, BMI, HbA1c, and 2h-ICPR) for the multiple linear regression. The linear relationship between insulin antibodies (IAs) and the significant independent variables was presented by making multiple linear regression equations.Results: The total demographic characteristics of the included patients were as follows: age (51.92± 13.10 years), BMI (24.94± 3.99 kg/m2), HbA1c (9.70± 2.39%), 2h-ICPR (0.12± 0.11), and IAs (0.37± 1.12COI). Linear relationships of independent variables: age (r=0.163, p=0.007), 2h-ICPR (r=0.259, p=0.001), BMI (r=0.007, p=0.907) and 2h-ICPR (r=0.092, p=0.129). Multiple linear regression: age (unstandardized β=0.014, 95% CI: 0.004– 0.024, p=0.004), 2h-ICPR (unstandardized β=2.758, 95% CI: 1.555– 3.962, p≤ 0.001). The regression equation: .Conclusion: The quantitative relationship between 2h-ICPR and insulin antibodies was . 2h-ICPR can be a preliminary screening indicator for insulin antibody testing in patients with type 2 diabetes.Keywords: insulin, insulin antibodies, C-peptide, multiple linear regression model

This study examines the long-term climate predictability in the Seomjin River basin using statistical methods, and explores the effects of incorporating the duration of climate indices as predictors. A multiple linear regression model is employed, utilizing 44 climate indices as predictors, including global climate patterns and local meteorological factors specific to the area. The analysis focuses on teleconnections between the target variables and climate indices, considering the value of each index, not only for the corresponding month, but also for an average value over a duration of 2 and 3 months. The correlation analysis reveals that considering the duration of climate indices allows for the inclusion of predictors with higher correlation, leading to improved forecasting accuracy. The goodness of fit analysis, which compares predicted mean values with observed values on a monthly basis, indicates that neither precipitation nor temperature is significantly affected by the duration. However, the tercile hit rate analysis, comparing the results with historical data, shows a 34.7% hit rate for precipitation, both before and after, reflecting the duration of indices. Notably, for long lead times (10–12 months), the hit rate improves after incorporating the duration. In contrast, for temperature, the tercile hit rate is higher before considering the duration. Nonetheless, both precipitation and temperature exhibit hit rates higher than the baseline probability of 33.3%, affirming the reliability of long-term forecasts in the Seomjin River basin. Incorporating the duration of climate indices enhances the selection of predictors with higher correlation, resulting in a notable impact on long-lead precipitation forecasting. However, since temperature demonstrates little irregularity and displays a consistent pattern according to the month and season, the effect of considering the duration is relatively insignificant compared to precipitation. Future research will explore the decrease in hit rate due to reflecting the duration in temperature by extending the analysis to other regions. [ABSTRACT FROM AUTHOR]

Inter-city Venture Capital (VC) exchange is a micro-embodiment of the cross-regional flow of innovative elements such as capital and technology, and also an important driving force for shaping the network of VC cities. Compared with other types of investment activities, VC has more advantages in promoting the development of innovative industries, especially high-tech industries, and then driving urban economy and regional development; hence, it is widely regarded as a "catalyst" for economic growth and technological innovation. The study of VC networks is helpful in understanding the financial activity links between cities across the country and provides key support for further rational allocation of financial resources between cities. The existing research rarely uses the actual investment amount of VC transaction events between cities to construct the city network. The analysis of the continuous dynamic evolution research and the influencing factors of the overall spatial network structure over a long period of time needs to be strengthened. To compensate for the shortcomings in the above research, this study uses the data of China's inter-city VC flow between 2000 and 2018 to construct the Chinese urban VC network. The long-term spatial evolution characteristics and overall network structure influencing factors of the national inter-city VC network are discussed based on complex network analysis, GIS spatial technology analysis, multiple linear regression model, and so on. This study derives the following results: (1) The connection between cities tends to be closer, and the agglomeration degree of the national VC network increases, displaying obvious characteristics of high aggregation and small-world network. However, the cyber spatial structure of investors/start-ups tends to be flattened, there is a trend of decentralization. (2) The VC connection between cities has expanded from the eastern coastal axis to the central and western regions as a whole, exhibiting obvious characteristics of path dependence and distance attenuation. Additionally, the VC connection continues to sink from the three major urban agglomerations to other marginal cities, and the core node cities of the network also display a tendency of quantitative expansion and power transfer. (3) The city's entrepreneurial foundation, VC industry development level, financial environment, and urban accessibility all have differentiated impacts on the spatial structure of the national VC network. Moreover, the aforementioned are affected by various mechanisms such as multi-dimensional proximity, urban economy, and path dependence, which promote the construction of national and regional VC networks and the formation and evolution of regional capital centers. [ABSTRACT FROM AUTHOR]

An efficient Bayesian approach is proposed to infer fault slip from geodetic data in a Slow Slip Event (SSE). The physical model of the slip process reduces to a multiple linear regression with constraints. Assuming a Gaussian model for the geodetic data and considering a multivariate truncated normal prior distribution for the unknown fault slip, the resulting posterior distribution is also a multivariate truncated normal. A prior slip distribution having a detailed correlation structure to impose natural coherence in the fault slip is proposed. Regarding the posterior, an ad hoc algorithm based on a Hybrid Optimal Directional Gibbs sampler is proposed that allows to sample efficiently from the resulting high-dimensional posterior slip distribution without supercomputing resources. A synthetic fault slip example illustrates the flexibility and accuracy of the proposed approach. This methodology is also applied to a real data set for the 2006 Guerrero, Mexico, SSE, where the objective is to recover the fault slip on a known interface that produces displacements observed at ground geodetic stations. As a by-product, our approach further allows us to estimate the Moment Magnitude for the 2006 Guerrero SSE with uncertainty quantification. [ABSTRACT FROM AUTHOR]

Background: Sperm quality, including semen volume, sperm count, concentration, and total and progressive motility (collectively, "semen parameters"), has declined in the recent decades. Computer-assisted sperm analysis (CASA) provides sperm kinematic parameters, and the temporal trends of which remain unclear. Our objective is to examine the temporal trend of both semen parameters and kinematic parameters in Shanghai, China, in the recent years. Methods: This retrospective study analyzed semen parameters and kinematic parameters of 49,819 men attending our reproductive center by using CASA during 2015-2021. The total sample was divided into two groups: samples that surpassed the WHO guideline (2010) low reference limits ("above reference limit" group, ARL; n = 24,575) and samples that did not ("below reference limit" group, BRL; n = 24,614). One-way analysis of variance, Kruskal-Wallis test, independent samples t-test, and covariance analysis were used to assess the differences among groups. Year, age, and abstinence time were included in the multiple linear regression model of the ARL group to adjust the confounders and depict the trends in sperm quality. Results: Among all the total sample and the ARL and BRL groups, the age of subjects increased in recent years. Semen volume and sperm count showed declined tendency with years in the total sample, the ARL and BRL groups, and the subgroup of age or abstinence time, whereas sperm velocities showed increased tendency with years on the contrary. The multiple linear regression model of the ARL group, adjusting for age and abstinence time, confirmed these trends. Semen volume (β1= -0.162; CI: -0.172, -0.152), sperm count (β1= -9.97; CI: -10.813, -9.128), sperm concentration (β1 = -0.535; CI: -0.772, -0.299), motility (β1 = -1.751; CI: -1.830, -1.672), and progressive motility (β1 = -1.12; CI: -0.201, -0.145) decreased with year, whereas curvilinear line velocity (VCL) (β1 = 3.058; CI: 2.912, 3.203), straight line velocity (VSL) (β1 = 2.075; CI: 1.990, 2.161), and average path velocity (VAP) (β1 = 2.305; CI: 2.224, 2.386) increased over time (all p < 0.001). In addition, VCL, VSL, and VAP significantly declined with age and abstinence time. Conclusion: The semen parameters declined, whereas the kinematic parameters increased over the recent years. We propose that, although sperm count and motility declined over time, sperm motion velocity increased, suggesting a possible compensatory mechanism of male fertility. [ABSTRACT FROM AUTHOR]

In this work, the Marcinkiewicz–Zygmund type strong law of large numbers for weighted sums of widely orthant dependent random variables is established under mild conditions including infinite variance. As applications, some new results such as the Cesàro strong law of large numbers, the strong consistency of the least squares estimator in multiple linear regression models, and the strong consistency of the wavelet estimator in nonparametric regression models are presented. Simulation studies are also provided to support the theoretical results. [ABSTRACT FROM AUTHOR]

Glacier response patterns at the catchment scale are highly heterogeneous and defined by a complex interplay of various dynamics and surface factors. Previous studies have explained heterogeneous responses in qualitative ways but quantitative assessment is lacking yet where an intrazone homogeneous climate assumption can be valid. Hence, in the current study, the reason for heterogeneous mass balance has been explained in quantitative methods using a multiple linear regression model in the Sikkim Himalayan region. At first, the topographical parameters are selected from previously published studies, then the most significant topographical and geomorphological parameters are selected with backward stepwise subset selection methods. Finally, the contributions of selected parameters are calculated by least square methods. The results show that, the magnitude of mass balance lies between −0.003±0.24 to −1.029±0.24 m.w.e.a−1 between 2000 and 2020 in the Sikkim Himalaya region. Also, the study shows that, out of the terminus type of the glacier, glacier area, debris cover, ice-mixed debris, slope, aspect, mean elevation, and snout elevation of the glaciers, only the terminus type and mean elevation of the glacier are significantly altering the glacier mass balance in the Sikkim Himalayan region. Mathematically, the mass loss is approximately 0.40 m.w.e.a−1 higher in the lake-terminating glaciers compared to the land-terminating glaciers in the same elevation zone. On the other hand, a thousand meters mean elevation drop is associated with 0.179 m.w.e.a−1 of mass loss despite the terminus type of the glaciers. In the current study, the model using the terminus type of the glaciers and the mean elevation of the glaciers explains 76% of fluctuation of mass balance in the Sikkim Himalayan region. [ABSTRACT FROM AUTHOR]

Copyright of Revista Espacios is the property of Talleres de Impresos Oma 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.)

Air temperature ( T a ) is a common meteorological element involved in many fields, such as surface energy exchange and water circulation. Consequently, accurate T a estimation is essential for the establishment of hydrological, climate, and environmental models. Unlike most studies concerned with the estimation of daily T a from land surface temperature, this study focused on the estimation of instantaneous T a from Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric profile products aboard the Terra and Aqua satellites. The applicability of various estimation methods was examined in two regions with different geomorphological and climate conditions, North and Southwest China. Specifically, the spatiotemporal trend of T a under clear sky conditions can be reflected by the atmospheric profile extrapolation and average methods. However, the accuracy of T a estimation was poor, with root mean square error (RMSE) ranging from 3.5 to 5.2 °C for North China and from 4.0 to 7.7 °C for Southwest China. The multiple linear regression model significantly improved the accuracy of T a estimation by introducing auxiliary data, resulting in RMSE of 1.6 and 1.5 °C in North China and RMSE of 2.2 and 2.3 °C in Southwest China for the Terra and Aqua datasets, respectively. Since atmospheric profile products only provide information under clear sky conditions, a new multiple linear regression model was established to estimate the instantaneous T a under cloudy sky conditions independently from atmospheric profile products, resulting in RMSE of 1.9 and 1.9 °C in North China and RMSE of 2.5 and 2.8 °C in Southwest China, for the Terra and Aqua datasets, respectively. Finally, instantaneous T a products with high accuracy were generated for all-weather conditions in the study regions to analyze their T a spatial patterns. The accuracy of T a estimation varies depending on MODIS datasets, regions, elevation, and land cover types. [ABSTRACT FROM AUTHOR]

[Objective] The optimal feature set was determined in order to explore the reasonable number of climatic factors to establish an evaporation model, based on filtering by feature selection algorithm. Methods] 1 6 years (2005-01 to 2021-03) of Yulin, Jinghe and Hanzhong weather stations of hour-by-hour observation data were taken. The model parameters and the number of characteristic variables were optimized by using feature selection and traversal loop methods. Based on the best parameters combined with two machine learning algorithms, the random forest model and the multiple linear regression model, evaporation model suitable for 3 regions was established, and the three indicators of average absolute error, root mean square error and square correlation coefficient were used to evaluate the model accuracy. [Results] The number of feature variables and decision trees are 8 and 61, the prediction effect of the model is the best. The root mean square error was used to evaluate the prediction effects of the optimized random forest model and the multiple linear regression model, and the error size of the two models was 0. Except for the Jinghe area, the root mean square error of Yulin and Hanzhong was smaller than that of the optimized multiple linear regression model. Predict effects of evaporation in Yulin, Jinghe and Hanzhong fitted by the optimized random forest model are 0.85, 0.9() and 0.86, and the optimized multiple linear regression model fitting effects are 0.77, ().83 and ().79. The optimized random forest model of the average absolute error and the root mean square error are lower than optimized multiple linear regression model. Conclusion] On the whole, the optimized two models have good prediction effects and the prediction effect of the random forest model is better than the multiple linear regression model. [ABSTRACT FROM AUTHOR]

The main objective of this study is to investigate the adaptation of wind turbines at the Gibara II Wind Farm in Cuba, which operates in a tropical climate that differs from the typical conditions in which these turbines are designed and manufactured in the northern hemisphere. The study utilizes condition monitoring techniques supported by Big Data acquired through a supervisory control and data acquisition (SCADA) system. By statistically processing normalized databases using multiple linear regression equations, the study establishes mathematical models that characterize the behavior of critical variables such as bearing, oil and winding temperatures, electrical generation, and specific climatic conditions unique to the wind farm under analysis. These models are essential for advancing condition-based maintenance (CBM) practices and developing preventive measures to mitigate functional failures. The significance of this research lies in the historical technical performance of the equipment under investigation, highlighting the importance of addressing the challenges posed by different environmental conditions. The study was conducted using the relevant regulatory technical documentation pertaining to the design of the wind turbines at the Gibara II Wind Farm. [ABSTRACT FROM AUTHOR]

Appendicitis is a most common abdominal condition worldwide, and appendectomy especially laparoscopic appendectomy is among the most commonly performed general surgeries. In this study, data were collected from patients who underwent laparoscopic appendectomy surgery at the Evangelical Hospital "Betania" in Naples, Italy. Linear multiple regression was used to obtain a simple predictor that can also assess which of the independent variables considered to be a risk factor. The model with R2 of 0.699 shows that comorbidities and complications during surgery are the main risk factors for prolonged LOS. This result is validated by other studies conducted in the same area. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to introduce how to combine the propensity score analysis to reasonably carry out multiple linear regression analysis. Firstly， it introduced 3 basic concepts related to the propensity score analysis. Secondly， it presented the core contents of the propensity score analysis， that was， three matching methods. Thirdly， through an epidemiological survey example， it gave the whole process of how to use SAS software for the analysis. The contents were as follows： ① for the original data set， test whether the difference of covariates between the treatment group and the control group was statistically significant； ② directly implement the multiple linear regression analysis for the original data set； ③ the propensity score analysis was used to generate the matched data set； ④ for the matched data set， test whether the difference of covariates between the treatment group and the control group was statistically significant； ⑤ a reasonable multiple linear regression analysis was used for the matched data set.

Objective: The measurement of the quality of life (QOL) in patients with breast cancer can evaluate the therapeutic effects of medical treatments and help to provide reference for clinical decisions. The minimum clinically important difference (MCID) can be better used in clinical interpretation than the traditional statistical significance. Based on the anchors, a variety of ways including traditional and updated anchor-based methods were used to explore most suitable MCID, so that to find better interpretation on scores of the scale QLICP-BR(V2.0) (Quality of Life Instruments for Cancer Patients-Breast cancer). Methods: According to the investigation data of breast cancer patients before and after treatment, the most relevant indicators in various domains of QLICP-BR (V2.0) was found as an anchor to statistically analyze the value of MCID, and three analysis methods of anchors were used: Traditional anchor-based method, ROC curve method, multiple linear regression model analysis. Anchors are divided into four standards according to the degree of change in the treatment effect: one grade difference (Standard A), at least one grade difference (Standard B), one grade better (Standard C), better (Standard D). The final MCID value is selected from different statistical methods and classification standards that are most suitable for clinicians to use. Results: Using Q29 of the EORTC QLQ-C30 as an anchor has the highest correlation with each domain of QLICP. The order of magnitude of MCID values among the four standard groups is: standard A< Standard C< Standard B< Standard D. The MCID value obtained by the ROC curve method is the most stable and is least affected by the sample size, and the MCID value obtained by the multiple linear regression model is the least. After comparisons and discussions, Standard C in the multiple linear regression model is used to determine the final MCID, which is the closest to other methods. After integer the MCID values of Physical domain (PHD), Psychological domain (PSD), Social domain (SOD), Common symptoms and side effect domain (SSD), Core/general module (CGD), Specific domain (SPD), Total score(TOT) can be taken as 15,10, 10, 11, 10, 9 and 9, respectively. Conclusion: In the evaluation of the QOL of breast cancer patients, although the results of MCID values produced by different methods are different, the results are relatively close. The anchor-based methods make the results of MCID more clinically interpretable by introducing clinical variables, and clinicians and researchers can choose the appropriate method according to the research purpose. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences 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.)

This paper focused on the performance monitoring and modeling of a 6.0 kW, 2000 L hybrid direct expansion solar assisted heat pump (DX-SAHP) water heater used for the production of hot water in a university students' accommodation with 123 females. The data of total electrical energy consumed, volume of hot water consumed, ambient temperature, relative humidity, and solar irradiance were obtained from the data acquisition systems and analyzed in conjunction with the energy factor (EF) of the system. A multiple linear regression model was developed to predict the EF. The EF of the hybrid DX-SAHP water heater was determined from the summation of the coefficient of performance (COP) of the heat pump unit and the solar fraction (SF) of the solar collectors. The operations of the hybrid energy system were analyzed based on three phases (first phase from 00:00–08:00, second phase from 08:30–18:30, and third phase from 19:00–23:30) over 24 h for the entire monitoring period. The average EF of the hybrid energy system per day during the second phase of operation was 4.38, while the SF and COP were 0.697 and 3.683, respectively. The developed multiple linear regression model for the hybrid DX-SAHP water heater accurately predicted the determined EF. [ABSTRACT FROM AUTHOR]

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This study was aimed at evaluating the application of the canonical correlation analysis (CCA) to predict monthly precipitation amounts (predictands) by benefitting from 17 large-scale climate indices (predictors) in Iran. Monthly precipitation data, covering the period of 1987–2017, were collected from 100 weather stations across the country. Monthly precipitations were predicted using the multiple linear regression (MLR) models, based on the 1- to 6-month lead times of the original and canonical predictors. The cross-validation was conducted to compare the prediction skills of the two sets of MLR models constructed on the basis of the original predictors (MLOrigPr) and the canonical predictors (MLCCAPr). The analyses revealed the dominant teleconnections and that there are the interannual variations in responses of precipitation to them suggesting that a signal only is not sufficient to achieve a robust understanding of the associations. At the 1-month lead time, the MLR models based on the canonical predictors outperformed those based on the original predictors. However, the skill of both models was reduced by increasing the lead times up to 6 months. Averaging on all stations, around 61.4% and 26.3% of the observed values, falls into the cross-validated 95% prediction intervals of the MLCCAPr and MLOrigPr models, respectively. Furthermore, the MLCCAPr models were found to be more spatially universal than the MLOrigPr ones and decrease multicollinearity symptoms strengthening the predictions. These findings corroborated the advantage of using the CCA in improving the teleconnective predictability of precipitation in Iran. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower 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.)