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This article examines the effect of leader longevity in power on world happiness. To make the assessment, a sample composed of 135 countries observed over the period 2006 to 2018 was constituted. The results obtained from OLS estimates show that longevity in power reduces individual happiness. Furthermore, the negative effect is more amplified in democratic countries. Quantile regression reveals variability in the effect over the different intervals. These results are robust to the use of alternative estimation techniques. We also identify the quality of institutions and public spending as two potential transmission channels through which longevity in power influences well‐being. These results invite political authorities to respect constitutional limits or implement constitutional reforms with the aim of limiting the duration of the mandate of the executive in order to reduce the harmful effect of an extension of the latter on individuals' well‐being. Related Articles: Flavin, Patrick, Alexander C. Pacek, and Benjamin Radcliff. 2011. "State Intervention and Subjective Well‐Being in Advanced Industrial Democracies." Politics & Policy 39(2): 251–69. https://doi.org/10.1111/j.1747‐1346.2011.00290.x. Jakubow, Alexander. 2014. "State Intervention and Life Satisfaction Reconsidered: The Role of Governance Quality and Resource Misallocation." Politics & Policy 42(1): 3–36. https://doi.org/10.1111/polp.12057. Kim, Hae S. 2017. "Patterns of Economic Development: Correlations Affecting Economic Growth and Quality of Life in 222 Countries." Politics & Policy 45(1): 83–104. https://doi.org/10.1111/polp.12190. [ABSTRACT FROM AUTHOR]

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. Cross-classified data involve observations that are nested within more than one type of higher-level unit, which are not themselves hierarchically nested. For example, if students are included within neighborhoods and schools that are not perfectly nested within each other, data on those students has a cross-classified structure. In psychology, education, and other research areas, a common approach for analyzing cross-classified data is cross-classified random effects modeling (CCREM). However, CCREM is based on strict assumptions. If its assumptions are violated, the estimates from CCREM may be systematically biased and uncertainty assessments from CCREM may be misleading. When the focus of a study is on an individual-level predictor, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) offer practical alternatives to CCREM. These alternatives are beneficial in not relying on all the assumptions required by CCREM. In this study, we compared the performance of CCREM, OLS-CRVE, and FE-CRVE in conditions where key assumptions held true and conditions where those assumptions were violated. When individual-level errors have nonconstant variance, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When there is correlation between model predictors and random effects, only FE-CRVE showed adequate performance. Further, when the random slopes were incorrectly omitted from the model, OLS-CRVE and FE-CRVE provided a more accurate statistical inference than CCREM. Thus, if there are potential concerns regarding the assumptions of the CCREM, we recommend two-way FE-CRVE as an alternative to CCREM. [ABSTRACT FROM AUTHOR]

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The paper is devoted to the research of the two-criteria estimation method for linear regressions. The first criterion corresponds to the least absolute deviations, the second – to the non-strict ordinary least squares. The implementation of the method requires solving a two-criteria linear programming problem, the solution of which involves the formation of a Pareto set. The main goal of the article was to research how the normalization of the initial variables affects the formation of the Pareto set. For this, two samples were used. The first was created artificially and contains an outlier. The second was formed on the basis of real economic data. In both cases, when normalizing the variables, the Pareto set turned out to be more representative than when working with non-normalized indicators. The example with an outlier illustrates the robustness of the least absolute deviations and the anti-robust ness of the ordinary least squares. It is shown how, based on the predicted values of the explained variable, it is possible to choose the optimal Pareto vertex.

In order to solve the problem of the traditional remote sensing drought index focuses on the monitoring of a single response factor and lacks a complete analysis of drought.In this paper， we selected TVDI， RVI， PDI， and GVMI daily products estimated from remote sensing data as independent variables， and MCI calculated from meteorological data at the adjacent moments of satellite transit as dependent variables， and uses the Random Forest Regression （RFR） model to construct a integrated remote sensing drought monitoring model.The results show that the accuracy of RFR model is better than that of the Ordinary Least Squares （OLS） model in bothtraining data and test data.The R value of the RFR training data is 0.97， the RMSE is 0.33， the R value of the RFR test data is 0.90， and the RMSE is 0.53.The R value of the OLS training data is 0.78， the RMSE value is 0.73， the R value of the OLS test data is 0.76， and the RMSE value is 0.79.The comparisons of RFR and OLS model in R and RMSE show that the RFR model is superior than the OLS model in the characterization of regional drought.In the application of drought monitoring in Southwest China in 2022， the RFR results are consistent with the spatiotemporal distribution of the MCI index， which can better characterize the spatial and temporal dynamics of the regional drought， reflecting the practicality of the RFR model in the actual drought monitoring process.However， the accuracy of RFR model is related to the number of regional stations and the spatial distribution of stations， and the accuracy of the RFR model is higher in areas with a large number of stations and uniform distribution of stations.

Pork consumption has risen significantly in many emerging nations, with producers using various systems to meet demand. However, the profitability of these systems remains largely unexplored. Therefore, the drivers of profitability of pig production systems in Northern Uganda were examined. Data were collected using a pretested structured questionnaire through a cross-sectional survey of 240 randomly selected pig farmers. Data were analyzed using descriptive statistics, gross margin analysis, and ordinary least squares model. Results revealed that the cost of initial stock (p < 0.1), cost of feed (p < 0.05), cost of vaccines (p < 0.01), output (p < 0.05), and quantity of feed (p < 0.05) were drivers of profitability in the farrow-to-finish pig production system. Further, profitability in the farrow-to-weaner pig production system was influenced by access to credit (p < 0.1), household size (p < 0.1), access to extension service (p < 0.01), and cost of initial stock (p < 0.05). In the weaner-to-slaughter pig production system, drivers of profitability included access to extension service (p < 0.1), cost of feed (p < 0.1), cost of vaccines (p < 0.05), and cost of initial stock (p < 0.05). Researchers recommend that the government arrange sufficient capacity-building initiatives and training, particularly on the farrow-to-weaner pig production system to increase the output and profitability of this production system. Further, the government and non-governmental organizations should make inputs such as vaccines, drugs, and breeding stock available to pig farmers at competitive market prices to enable farmers to make price-responsive decisions.

East Asia, particularly China, Japan, and South Korea has experienced rapid economic growth in recent decades, partly through active participation in global value chains (GVCs). Foreign direct investment (FDI) has played a crucial role in this process, enabling the transfer of technology and skills and access to international markets. This paper aims to empirically analyse the effects of FDI on GVCs in East Asian countries over the last two decades.

Leisure time and ways of spending it are one of the important parts of human daily life, which today is faced with many economic, social and cultural issues and challenges. The way of spending this time in dealing with personal and household matters has been taken away and turned into a real-virtual industry subject to various requirements and factors. The purpose of this article is to estimate the leisure time demand function of households in urban areas of Iran and to examine the factors related to the characteristics of urban households, including age, literacy level, gender, marital status, and the amount of financial resources of the head of the household on the time spent in leisure. For this purpose, ordinary least squares method was used to check the relationship between variables and to enter virtual variables in the model, and the coefficients of the model were estimated based on the data of the statistical year 2018-2019. Based on the results of the model, urban households with male heads leave less opportunity for leisure time compared to urban households with female heads; On the other hand, urban households whose heads are married have a greater tendency to devote time to leisure activities. In addition, financial availability is an effective factor in allocating more time to leisure in urban households. [ABSTRACT FROM AUTHOR]

In this work, indirect inference (II) techniques are applied to estimate the parameters of a non-linear manoeuvring model of a remotely operated vehicle (ROV). In the application of this method, a set of auxiliary statistics is established in the optimisation process in order to improve the efficiency of the estimated parameters. For the parameter estimation, data from different tests are available, which were carried out with a ROV in the facilities of the Centro de Experiencias Hidrodinámicas del Pardo INTA/CEHIPAR, Madrid. The model obtained is validated by means of graphical and statistical methods with the acquired data and the statistical properties of the estimated parameters are evaluated by means of a Monte Carlo study. [ABSTRACT FROM AUTHOR]

East Asia, particularly China, Japan, and South Korea has experienced rapid economic growth in recent decades, partly through active participation in global value chains (GVCs). Foreign direct investment (FDI) has played a crucial role in this process, enabling the transfer of technology and skills and access to international markets. This paper aims to empirically analyse the effects of FDI on GVCs in East Asian countries over the last two decades. The paper covers six East Asian countries from 2000 to 2022 and explores the relationship between FDI and GVCs using the Driscoll and Kraay (1998) estimator. The estimation techniques are ordinary least squares (OLS) and feasible generalized least squares (FGLS). The results reveal that FDI inflows into East Asia positively and significantly affect the countries participation in GVCs, both upstream and downstream. These results suggest the need to support FDI in Asian countries to improve their participation in GVCs. [ABSTRACT FROM AUTHOR]

Simultaneous causality occurs when two variables mutually influence each other, creating empirical contexts where cause and effect are not clearly unidirectional. Crime and policing often appear in urban studies presenting the following characteristic: sometimes, increased police patrols can reduce criminal activities, and other times, higher crime rates can prompt law enforcement administrations to increase patrols in affected areas. This study aims to explore the relationships between patrol dynamics and crime locations using spatial regression to support public policies. We identify spatial patterns and the potential impact of crime on policing and vice versa. Data on crimes and patrol locations were collected from the database provided by the Planning and Management Secretariat and the Social Defense Secretariat of Pernambuco, Brazil. The study employed Ordinary Least Squares (OLS) to create a spatial simultaneous regression model for integrated security zones within the Brazilian geography. This approach provides a holistic visualization, enhancing our understanding and predictive capabilities regarding the intricate relationship between police presence and crime. The results report a significant relationship, with crime locations explaining police patrols (varying in geographic domain and type of crime). No statistically significant results from most geographic locations point to the inverse relation. The quantitative analysis segregated by typology presents a potential for effective public decision support by identifying the categories that most influence the patrol security time. [ABSTRACT FROM AUTHOR]

International trade is a vital driver of economic development for nations worldwide, and Bangladesh, as a developing country in South Asia, strategically utilizes trade as a key element in its economic growth agenda. This paper examines the impact of internal factors on Bangladesh's international trade, employing Ordinary Least Squares (OLS) regression analysis to analyze data spanning from 2000 to 2022. The study focuses on factors such as GDP, inflation, real interest rates, unemployment rates, government expenditure, population growth, remittance inflows, government expenditure in education, macroeconomic management, and tariff rates. The results reveal a strong statistical significance between the predictors and Bangladesh's international trade volume. Notably, personal remittances, government expenditure in education, and macroeconomic management index exhibit significant influence on international trade. The findings provide valuable insights for policymakers and stakeholders to enhance Bangladesh's trade performance, contributing to its sustainable economic development.

The urban heat island (UHI) phenomenon caused by rapid urbanization has become an important global ecological and environmental problem that cannot be ignored. In this study, the effect of influencing factors on ground surface temperature in Karaj city was measured and by using ordinary least square (OLS) regression, geographic weighted regression (GWR) and multiscale geographic weighted regression (MGWR) models, spatial heterogeneities of the factors were measured. We checked the influencer and LST.The results indicated that, compared with traditional OLS models, GWR improved the model fit by considering spatial heterogeneity, whereas MGWR outperformed OLS and GWR in terms of goodness of fit by considering the effects of different bandwidths on LST. The results showed that compared to the traditional OLS models, GWR improved the fit of the model by considering spatial heterogeneity, while MGWR outperformed OLS and GWR by considering the effects of different bandwidths on LST. Building density Normalized Difference Impervious Surface Index (NDISI) and traffic density had the greatest effect on high LST by 0.323, 0.246 and 0.260 respectively, while NDVI index, MNDWI and population density were negatively correlated with temperature. These findings show the need to consider spatial heterogeneity in the analysis of impact factors. This study can be used to provide guidance on strategies to reduce temperature in different regions. [ABSTRACT FROM AUTHOR]

Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000–2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria’s six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively. [ABSTRACT FROM AUTHOR]

AbstractIn this article, we begin by providing a theoretical comparison between Farebrother’s estimator and the ridge estimator, in terms of the MSE matrix criterion, under three distinct restrictions when r is random. Additionally, we propose an estimate of parameter k for Farebrother’s estimator. Subsequently, we present a Monte Carlo simulation experiment to compare Farebrother’s estimator with OLS, RLS, and ridge estimators. [ABSTRACT FROM AUTHOR]

Indoor positioning has been extensively studied for at least the past twenty years. In the list of the most common solutions, those based on the Received Strength Signal Indicator (RSSI) have gained importance due to the simplicity of RSSI as well as the fact that it is available in several wireless sensor networks. In this work, we propose SeALS (Selection Strategy of Access Points with Least Squares Estimation), a new RSSI-based indoor positioning system using Bluetooth Low-Energy (BLE) access points, whose accuracy is improved by a new selection strategy of collected RSSI combined with the Ordinary Least Squares (OLS) estimation method. The main advantage of the proposed solution is the fact that it requires less time in the training phase allied with better system accuracy if compared to traditional methods. The proposed system is validated in a large-scale, real-world scenario, and the obtained results for the positioning error are reduced by up to 13% concerning the pure OLS method, and by up to 30% concerning the widely deployed K-Nearest Neighbors technique. [ABSTRACT FROM AUTHOR]

International trade is a vital driver of economic development for nations worldwide, and Bangladesh, as a developing country in South Asia, strategically utilizes trade as a key element in its economic growth agenda. This paper examines the impact of internal factors on Bangladesh's international trade, employing Ordinary Least Squares (OLS) regression analysis to analyze data spanning from 2000 to 2022. The study focuses on factors such as GDP, inflation, real interest rates, unemployment rates, government expenditure, population growth, remittance inflows, government expenditure in education, macroeconomic management, and tariff rates. The results reveal a strong statistical significance between the predictors and Bangladesh's international trade volume. Notably, personal remittances, government expenditure in education, and macroeconomic management index exhibit significant influence on international trade. The findings provide valuable insights for policymakers and stakeholders to enhance Bangladesh's trade performance, contributing to its sustainable economic development. [ABSTRACT FROM AUTHOR]

IntroductionConsidering the current process of destruction of natural resources in the country and the problems faced by the present and future generations, the measures taken in the field of conservation and restoration and development of natural resources do not seem to be enough. Although, to solve these problems, the role of the government as a planner and supporter of natural resources projects is clear and important (Arayesh & Farajilah Hosseini, 2010). However, the projects designed to conserve natural resources are implemented based on people's participation. In general, people's participation in projects in the field of natural resources protection can take place in various fields. The most important of these issues have been considered in the behavioral examples of the Helpers of Nature project which can be including planting seedlings, people's participation in times of crisis such as fire, protection of forests, contributing to environmental protection associations, joining environmental associations, waste management in terms of separation and volume, joining the natural resources projects through the organization's systems, introducing people to the natural resources organization in the form of nature's helper, paying the green tax and etc (Natural resources & watershed management organization-I.R of IRAN, 2023). However it is the question as how to attract people's participation in the mentioned issues requires tools that can influence human behavior. Behavioral economics, as a new scientific field of economic sciences, can measure and analyze the impact of intentions, beliefs, and motivations on human behavior and decisions, and based on this, it can also provide policy tools (Asgari et al., 2021). For this reason, it can influence people's behavior to participate in the conservation of natural resources using behavioral economics approach. Therefore, to realize the goal of attracting people's participation, the current study considers to examine nudges and the effectiveness of nudges on people's behavior using behavioral economics approach Materials and MethodsThe sampling method of the research is convenience sampling. The number of samples is 213 people using the online questionnaire in two separate groups. This study is a quasi-experimental design and its type is a comparison between two groups. The number of the control group includes 108 people and the number of the treatment group includes 105 people. The control and treatment groups were independent. Each of the groups had completely common questions and response criteria. In this study, the control and treatment groups did not receive any training, but only the treatment group was given additional information about cognitive errors along with related questions. First, the Mann-Whitney-Wilcoxon test is used to check and compare the ratings regarding supplementary information as well as the answers of people in the two groups. The ordered probit regression is used to analyze the relationship between ordinal or ranked dependent variables related to natural resource protection behavior and independent treatment (nudge) variables and other variables. For the relative dependent variables (the time of registration of cooperation request and the number of people introduced as a nature helper), the ordinary least square regression is used to analyze the effect of the treatment binary variable on the people's behavior in the field of natural resources conservation Results and DiscussionThis study results showed that out of 19 nudges, 14 nudges includes; Normative default, time limit, anchor and exemplify, carrot and stick, personalization, decoy effect, authority confirmation bias, bandwagon effect, present bias, automatic recommendation, halo effect and ownership effect (1) and (2) and (3) had a significant impact on these people's behavior, respectively, the number of seedlings, the time of registering a request for cooperation, participation in firefighting, waste production, the number of members introduced as a nature’s helper, choosing tasks, membership in associations, membership in a special association, recycling, people's action preferences for forest protection, the percentage of perceived success for projects, willingness to spend taxes to beautify one's neighborhood, willingness to spend taxes to protect forests in one's area, and applying zoning to protect forests. The direction of influence in all nudges (except for the normative default and time limit) on people's behavior has been positive and significant. The marginal effects also showed that all nudges had the positive effect (with ordinal or ranked dependent variable) on the selection of the target option(s) in the treatment group compared to the control group. The carrot and stick policy had no significant effect on the ordinal variable of waste production, but the effect of this nudge on dummy variable of waste production was significant, This means that this nudge has had a positive and significant effect on maintaining the existing situation (garbage collection every day of the week) and reducing the amount of garbage (choosing 20 kg of garbage and less per week). The normative default and the time limit had a negative and significant effect (respectively) on the number of seedlings and the registration time of cooperation requests for planting seedlings. These negative effects have also confirmed the positive effect of nudging on people's behavior. Although the normative default resulted in fewer seedlings being planted by individuals, this occurred because the default was set at a minimal level and individuals were significantly more inclined to follow the default. The nudge of the time limit also led to a reduction in the time to register cooperation requests by individuals, so that people tended to register their request faster. ConclusionAccording to the results of this research, to attract the people's participation in the conservation of natural resources, these following should be considered: defaults, low-cost anchors, clear examples, incentives and punishments, highlight individual performance through personalization, using existing privileges for more cooperation, confirming people's sovereignty, presenting reports during performance, immediate rewards, making SMS and telephone systems available to compensate for people's lack of action, considering time limits for registering people in programs and projects, providing success reports to join people in an action, applying people's ownership of the green tax to further encourage them to pay taxes, allocating each zone to an environmental association for forests protection.

Abstract The OLS model is built on the assumption of normality in the distribution of error terms. However, this assumption can be easily violated, especially when there are outliers in the data. A single outlier can disrupt the normality assumption of error terms, making the OLS model less effective. In such situations, M-estimators (MEs) come into play to obtain reliable estimates. We introduce a redescending M-estimators (RME) for robust regression to handle datasets with outliers. The proposed RME produces more robust estimates by effectively managing the influence of outliers, even at lower values of the tuning constant. We compared the performance of this estimator with existing RMEs using real-life data examples and an extensive simulation study. The results show that our suggested RME is more efficient than the compared ME in various situations.

Introduction Considering the current process of destruction of natural resources in the country and the problems faced by the present and future generations, the measures taken in the field of conservation and restoration and development of natural resources do not seem to be enough. Although, to solve these problems, the role of the government as a planner and supporter of natural resources projects is clear and important (Arayesh & Farajilah Hosseini, 2010). However, the projects designed to conserve natural resources are implemented based on people's participation. In general, people's participation in projects in the field of natural resources protection can take place in various fields. The most important of these issues have been considered in the behavioral examples of the Helpers of Nature project which can be including planting seedlings, people's participation in times of crisis such as fire, protection of forests, contributing to environmental protection associations, joining environmental associations, waste management in terms of separation and volume, joining the natural resources projects through the organization's systems, introducing people to the natural resources organization in the form of nature's helper, paying the green tax and etc (Natural resources & watershed management organization-I.R of IRAN, 2023). However it is the question as how to attract people's participation in the mentioned issues requires tools that can influence human behavior. Behavioral economics, as a new scientific field of economic sciences, can measure and analyze the impact of intentions, beliefs, and motivations on human behavior and decisions, and based on this, it can also provide policy tools (Asgari et al., 2021). For this reason, it can influence people's behavior to participate in the conservation of natural resources using behavioral economics approach. Therefore, to realize the goal of attracting people's participation, the current study considers to examine nudges and the effectiveness of nudges on people's behavior using behavioral economics approach Materials and Methods The sampling method of the research is convenience sampling. The number of samples is 213 people using the online questionnaire in two separate groups. This study is a quasi-experimental design and its type is a comparison between two groups. The number of the control group includes 108 people and the number of the treatment group includes 105 people. The control and treatment groups were independent. Each of the groups had completely common questions and response criteria. In this study, the control and treatment groups did not receive any training, but only the treatment group was given additional information about cognitive errors along with related questions. First, the Mann-Whitney-Wilcoxon test is used to check and compare the ratings regarding supplementary information as well as the answers of people in the two groups. The ordered probit regression is used to analyze the relationship between ordinal or ranked dependent variables related to natural resource protection behavior and independent treatment (nudge) variables and other variables. For the relative dependent variables (the time of registration of cooperation request and the number of people introduced as a nature helper), the ordinary least square regression is used to analyze the effect of the treatment binary variable on the people's behavior in the field of natural resources conservation Results and Discussion This study results showed that out of 19 nudges, 14 nudges includes; Normative default, time limit, anchor and exemplify, carrot and stick, personalization, decoy effect, authority confirmation bias, bandwagon effect, present bias, automatic recommendation, halo effect and ownership effect (1) and (2) and (3) had a significant impact on these people's behavior, respectively, the number of seedlings, the time of registering a request for cooperation, participation in firefighting, waste production, the number of members introduced as a nature's helper, choosing tasks, membership in associations, membership in a special association, recycling, people's action preferences for forest protection, the percentage of perceived success for projects, willingness to spend taxes to beautify one's neighborhood, willingness to spend taxes to protect forests in one's area, and applying zoning to protect forests. The direction of influence in all nudges (except for the normative default and time limit) on people's behavior has been positive and significant. The marginal effects also showed that all nudges had the positive effect (with ordinal or ranked dependent variable) on the selection of the target option(s) in the treatment group compared to the control group. The carrot and stick policy had no significant effect on the ordinal variable of waste production, but the effect of this nudge on dummy variable of waste production was significant, This means that this nudge has had a positive and significant effect on maintaining the existing situation (garbage collection every day of the week) and reducing the amount of garbage (choosing 20 kg of garbage and less per week). The normative default and the time limit had a negative and significant effect (respectively) on the number of seedlings and the registration time of cooperation requests for planting seedlings. These negative effects have also confirmed the positive effect of nudging on people's behavior. Although the normative default resulted in fewer seedlings being planted by individuals, this occurred because the default was set at a minimal level and individuals were significantly more inclined to follow the default. The nudge of the time limit also led to a reduction in the time to register cooperation requests by individuals, so that people tended to register their request faster. Conclusion According to the results of this research, to attract the people's participation in the conservation of natural resources, these following should be considered: defaults, low-cost anchors, clear examples, incentives and punishments, highlight individual performance through personalization, using existing privileges for more cooperation, confirming people's sovereignty, presenting reports during performance, immediate rewards, making SMS and telephone systems available to compensate for people's lack of action, considering time limits for registering people in programs and projects, providing success reports to join people in an action, applying people's ownership of the green tax to further encourage them to pay taxes, allocating each zone to an environmental association for forests protection. [ABSTRACT FROM AUTHOR]

The OLS model is built on the assumption of normality in the distribution of error terms. However, this assumption can be easily violated, especially when there are outliers in the data. A single outlier can disrupt the normality assumption of error terms, making the OLS model less effective. In such situations, M-estimators (MEs) come into play to obtain reliable estimates. We introduce a redescending M-estimators (RME) for robust regression to handle datasets with outliers. The proposed RME produces more robust estimates by effectively managing the influence of outliers, even at lower values of the tuning constant. We compared the performance of this estimator with existing RMEs using real-life data examples and an extensive simulation study. The results show that our suggested RME is more efficient than the compared ME in various situations. [ABSTRACT FROM AUTHOR]

For chemical measurements, calibration is typically conducted by regression analysis. In many cases, generalized approaches are required to account for a complex-structured variance–covariance matrix of (in)dependent variables. However, in the particular case of highly correlated independent variables, the ordinary least squares (OLS) method can play a rational role with an approximated propagation of uncertainties of the correlated independent variables into that of a calibrated value for a particular case in which standard deviation of fit residuals are close to the uncertainties along the ordinate of calibration data. This proposed method aids in bypassing an iterative solver for the minimization of the implicit form of the squared residuals. This further allows us to derive the explicit expression of budgeted uncertainties corresponding to a regression uncertainty, the measurement uncertainty of the calibration target, and correlated independent variables. Explicit analytical expressions for the calibrated value and associated uncertainties are given for straight-line and second-order polynomial fit models for the highly correlated independent variables. [ABSTRACT FROM AUTHOR]

The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann–Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (ξ) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing ξ. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when ξ is positive. [ABSTRACT FROM AUTHOR]

Comprehending the interactions between humans and their environment necessitates modeling human–environment interactions. This study employs time-series satellite imagery from Landsat Thematic Mapper (1995 and 2009) and Landsat 8 Operational Land Imager (2022) to examine land use/land cover (LULC) change in Dar es Salaam, Tanzania, after image pre-processing with the Google Earth engine code editor, while random forest machine learning in R classified LULC. Geographically weighted regression (GWR) correlates LULC changes to socioeconomic factors spatially. Analysis reveals a dynamic LULC transformation between 1995 and 2022, with a 14.9% increase in built-up areas and a 14.6% decline in bushland. Out of the total LULC, 65.8% experienced gains and losses, while 34.2% remained stable. The GWR model, surpassing the ordinary least squares (OLS) model, achieves an R2 value of 0.73, indicating a strong relationship between LULC changes and socioeconomic factors, explaining 73% of the variation. The influences of these factors exhibit variations across different LULC change types. Population density and proximity to the city center significantly contribute to LULC changes, while the impacts of gross domestic product and distance to roads are comparatively less significant. Poverty does not drive LULC changes significantly. The findings indicate that urbanization and urban sprawl, influenced by population density and distance from the city center, significantly impact land use and cover changes. Effective urban planning strategies should be prioritized to address this, considering factors such as population density and distance from the city center to mitigate the considerable effects on land use and cover changes in the study area. [ABSTRACT FROM AUTHOR]

Brazil has experienced a significant escalation in crime rates, resulting in many municipalities ascending to top positions in international rankings of the most violent cities. Various socioeconomic factors contribute to this surge in crime rates, prompting public policies to address not only policing but also elements such as education and social inequality. This study aims to conduct a regression analysis on education, inequality, and crime indicators in the municipalities of the state of Pernambuco based on the Ordinary Least Squares (OLS) method. The findings provide insights into the interplay between these factors and guide the formulation of more effective, multifaceted public policies, supporting policymakers on the critical importance of integrating educational and social inequality measures into crime reduction strategies. Additionally, by highlighting specific socioeconomic drivers of crime, the research may lead to more targeted and sustainable interventions in the most affected municipalities.

This paper aims to examine the role of economic diplomacy in attracting foreign capital to emerging countries, by developing a composite index measuring diplomatic activity. We focus on what extent does economic diplomacy influence the inflow of foreign capital to emerging countries. Then, we used data from fifty-five (55) developing economies in 2018. The composite index for diplomatic activity is constructed using principal component analysis. Further, we investigated the effect of this index on foreign capital inflows using linear regression based on the ordinary least squares method. The results indicate that the increase in the number of embassies alone does not significantly influence the evolution of diplomatic action. However, diplomacy plays a non-negligible role in attracting foreign capital. Our results demonstrate a positive and significant link between diplomacy and foreign funding, highlighting the importance of this tool for attracting investment and supporting growth in these countries. The findings of this work are going to serve both scientific and practitioners’ communities as it sheds light on the larger debate around the growing role of economic diplomacy in emerging countries in the context of globalization. Moreover, it provides a useful tool for measuring the effectiveness of foreign policies and their impact on economic expansion.

The electricity industry plays a pivotal role in a country's economic growth and development. Therefore, it is imperative to assess its performance and identify the strengths and weaknesses of its different sectors, such as production, transmission, and distribution, to enhance economic growth in diverse areas. Given the significance of the transmission sector, this research focuses on analyzing and evaluating the performance of 16 regional electricity companies in Iran from 1390 to 1398, with the aim of comprehending the impact of contextual variables on efficiency. To achieve this, the study will utilize two techniques - Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS) - to determine the efficiency score and estimate the effect of contextual variables on efficiency, respectively. In the first stage, the DEA technique is employed to calculate the technical efficiency of each company, considering their specific inputs and outputs. In the second stage, the logarithm of the efficiency scores obtained is regressed on contextual variables to establish their effect on efficiency. The residual derived from the regression is referred to as managerial ability. Finally, the companies are ranked based on their modified efficiency after removing the impact of contextual variables. Introduction The electricity industry comprises three key sectors: production, transmission, and distribution. It stands as one of the most crucial economic infrastructures in the country, exerting significant influence on industrial, agricultural, service, and other sectors. Undoubtedly, the growth of the electricity industry drives the nation's economic development and progress, contributing to the prosperity and comfort of its citizens (Tavassoli et al., 2020). Consequently, analyzing and examining the growth trajectory of each sector across different years becomes pivotal in mitigating adverse effects and fostering progress within this domain. In recent years, numerous researchers have conducted studies in this field. Some have independently evaluated each production, transmission, and distribution sector, while others have adopted a comprehensive approach by considering the integrated three-stage network structure. The research background highlights that the transmission sector has received less attention from researchers than other sectors. This is noteworthy because, following electricity production, the transmission process and energy accessibility to consumers are paramount. The absence of proper energy transfer can result in consumer dissatisfaction, financial losses, and stagnation within the competitive economic market. Therefore, identifying the strengths and weaknesses of the transmission sector's performance and comparing regional electricity transmission companies can effectively help enhance the performance level of each. One technique that has captured researchers' attention for evaluating the electricity industry's performance is the data envelopment analysis (DEA) technique. DEA is a non-parametric method used to assess the performance of homogeneous units, considering multiple inputs and outputs. It was initially introduced in 1978 by Charnes et al. The initial model was built upon the assumption of constant returns to scale. Subsequently, Banker et al. (1984) extended it by presenting a model under the assumption of returns to a variable scale. Importantly, traditional DEA models evaluate a system's performance based on specific inputs and outputs consumed and produced by the unit. However, various factors, such as contextual variables, managerial ability, and skill, can significantly influence performance and productivity. A crucial point to consider is that managerial abilities are not always overtly visible. This lack of direct visibility can impede accurate measurement. Hence, recognizing these variables among the existing indicators and assessing their influence on the performance and efficiency of each unit holds particular significance. This procedure enhances the precision of evaluation and opens avenues for delivering enhanced solutions aimed at improving the system's overall performance. Methodology The objective of this study is to analyze and evaluate the performance of Iran's regional electricity transmission sector while considering contextual variables and establishing a ranking methodology based on managerial ability. This perspective enables the identification of strengths and weaknesses in the system's structure from various angles and offers appropriate solutions for enhancement. To accomplish this, the first step involves identifying all variables within the transmission section, encompassing inputs, outputs, and contextual factors. Subsequently, we determine the technical efficiency of each regional power transmission company, taking into account specific inputs and outputs, using meta-frontier technology. The concept of meta-frontier in DEA measures the gap or distance between decision-making units (DMUs) across different boundaries. This approach assumes a unified boundary for all subgroups, enabling efficiency estimation based on a single boundary (Battese, 2004; O'Donnell, 2008). Its primary advantage lies in resolving the challenge of evaluating efficiency at varying levels. As a result, meta-frontier technology enhances the precision of evaluating regional power companies over multiple periods. After assessing the efficiency of each regional electricity transmission company, we employ the linear regression method to estimate the impact of contextual variables on efficiency, subsequently yielding a measure of managerial ability. Ultimately, we introduce a method for ranking each company based on managerial ability. The advantage of the proposed method is that, in addition to reviewing and analyzing the technical efficiency of each of the companies in the regional electricity transmission sector during different periods, it will be possible to evaluate the managerial ability of each of the companies. Such a perspective allows for companies to be compared from different dimensions. Moreover, providing a new ranking criterion based on managerial ability also facilitates a better and more accurate comparison. Results In this study, the performance of Iran's regional power companies was analyzed and evaluated from two systems and management perspectives during the years 1390-1398. Additionally, a new rating criterion based on managerial ability was presented to compare the performance of companies during 9 time periods. In this regard, firstly, the technical efficiency of 16 regional electricity companies during 9 time periods was calculated based on the inputs of the number of employees and receiving energy from neighboring companies and the outputs of sending energy to neighboring companies and delivering energy to distribution companies, using meta-frontier technology and the DEA approach. Then, the effect of contextual variables, such as line length, transformer capacity, and loss magnitude, on the efficiency score of each company was estimated using the ordinary least squares method (OLS). Furthermore, the managerial ability of each company was determined during different periods. Ultimately, a ranking criterion was established based on the results of technical efficiency after removing the effect of contextual variables. Conclusion The results of efficiency measurements over 9 time periods indicate that the highest and lowest average efficiencies were observed in the years 1390 and 1398, respectively. Furthermore, it's evident that, in general, the performance of Iran's 16 regional electricity companies exhibited a consistent upward trend from 1390 to 1398. Among the 16 evaluated companies, the Guilan regional electricity company consistently achieved the highest level of efficiency across all 9 time periods, reflecting its strong performance. Conversely, the Fars regional electricity company consistently had the lowest efficiency, indicating its weaker performance compared to other companies. When analyzing the companies' performance by year, it's noteworthy that the Tehran regional electricity company secured the highest rank in 1390, 1391, and 1394, while the Fars regional electricity company held the top spot in the remaining years. In contrast, the Sistan regional electricity company consistently displayed the lowest performance throughout all periods. The assessment of management performance over the 9 time periods indicates that the Kerman regional electricity company demonstrated superior performance from 1390 to 1393, whereas the Guilan regional electricity company excelled from 1394 to 1398, outperforming other companies. Conversely, the Gharb regional electricity company exhibited weaker performance compared to its counterparts. Additionally, the results of the regression analysis highlight a positive relationship between the efficiency score and two variables: line length and transformer capacity. Conversely, the relationship with loss magnitude is observed to be inversely correlated.

Oil is Ecuador's main export product and its financial income an important part of the General State Budget. Being Ecuador a country dependent on crude oil is the reason why this investigation is carried out. For this purpose, an investigation with a quantitative approach was used, with a descriptive and explanatory scope strengthened in the Solow model, for which an econometric model with time series data was estimated using the Ordinary Least Squares (OLS) method. The results show that oil barrel exports have a positive effect on economic growth. Likewise, it is concluded that oil imports, the price of oil, will have a greater dynamism in economic growth, since there is a strong causality with the aforementioned variables.

Exploring the spatial non-stationary relationships between land surface temperature (LST) and their driving environmental factors is important for selecting appropriate strategies to mitigate and regulate the thermal environment of watersheds. To examine the influence of various biophysical factors on LST in the Upper Awash Basin (UAB) of Ethiopia, the study used two models: Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. As a global model, the OLS model was initially used to capture the overall relationship between LST and some biophysical factors. And then the GWR, a local spatial modeling approach, was used to examine the spatial non-stationary relationships between LST and its influencing biophysical factors. Landsat 8 OLI/TIRS image and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) were used to generate the LST and its influencing biophysical factors. Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST. The result showed that the GWR model, with a higher coefficient of determination (R2) (GWR: 0.98; OLS: 0.52) and a smaller Akaike Information Criterion (AIC) (GWR: 12354; OLS: 65412), provides a better prediction than the traditional OLS model, reflecting the spatial non-stationarity relationships. The results also showed that increased LST was significantly affected by NDBI, NDBaI, and albedo, with NDBI having the greatest effect. Conversely, EVI, MNDVI, and DEM showed a negative correlation with LST, with EVI having the greatest impact. These findings highlighted the importance of considering the spatial non-stationarity relationships between LST and pertinent driving factors, and they also offer recommendations for mitigating measures to control the thermal environment of a river basin.

Abstract The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann–Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (ξ) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing ξ. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when ξ is positive.

1,4-dihydropyridines (1,4-DHPs) are widely recognized as highly effective L-type calcium channel blockers with significant therapeutic benefits in the treatment of cardiovascular disorders. 1,4-DHPs can also target T-type calcium channels, making them promising drug candidates for neurological conditions. When exposed to light, all 1,4-DHPs tend to easily degrade, leading to an oxidation product derived from the aromatization of the dihydropyridine ring. Herein, the elaboration of a quantitative structure–property relationships (QSPR) model was carried out by correlating the light sensitivity of structurally different 1,4-DHPs with theoretical molecular descriptors. Photodegradation experiments were performed by exposing the drugs to a Xenon lamp following the ICH rules. The degradation was monitored by spectrophotometry, and experimental data were elaborated by Multivariate Curve Resolution (MCR) methodologies to assess the kinetic rates. The results were confirmed by the HPLC-DAD method. PaDEL-Descriptor software was used to calculate molecular descriptors and fingerprints related to the chemical structures. Seventeen of the 1875 molecular descriptors were selected and correlated to the photodegradation rate by means of the Ordinary Least Squares (OLS) algorithm. The chemometric model is useful to predict the photosensitivity of other 1,4-DHP derivatives with a very low relative error percentage of 5.03% and represents an effective tool to design new analogs characterized by higher photostability. [ABSTRACT FROM AUTHOR]

By 2050, one in ten people in OECD countries will be 80 aged or older. The aging population will have several impacts on countries, such as increased demand for healthcare and social services, a smaller workforce, and a growing dependency ratio. Türkiye relies heavily on family members to provide long-term care (LTC) for their elderly. In this paper, we study the relationship between a comprehensive range of demographic and socioeconomic variables and informal long-term caregiving using the Turkish Statistical Institute's Time Use Survey, a nationally representative micro dataset. Employing a multivariate regression analysis, we find that women are more likely to provide unpaid informal caregiving, albeit lacking strong statistical significance. The findings also reveal that being married and older are significant predictors of providing informal LTC. In contrast, we find no significant evidence that income level and house characteristics are crucial determinants of informal LTC. The findings of this study have a number of important policy implications for future practice, such as investing in healthcare and social services and developing policies to encourage LTC workforce participation. [ABSTRACT FROM AUTHOR]

Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature—collected via remote sensing or weather stations—are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature ('organismal climatologies') during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or 'corrected' environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered. [ABSTRACT FROM AUTHOR]

Interpolation of scalar data, in the 2D space, is an important topic in many fields of environmental and geoscience studies, and uncertainty assessment is as important as interpolation itself. An example of this is the Kriging method, which is well-established in geostatistics and enables the automatic evaluation of uncertainties by solving a linear equation, taking into account the bivariate spatial continuity of the data. The Sibson interpolation method (natural neighbour) has the important property of providing unambiguous and reproducible results. However, since it is fundamentally a deterministic method, it does not have qualitative and/or quantitative control of the uncertainty based on the sampling spatial distribution geometry. In this paper, we show the different steps leading to an analytical approach to evaluate the uncertainties of the Sibson method. After a series of tests with a synthetic data set and a surface with a known differentiable function, we show an example using the data set of accelerometric data from the M 6.5 Norcia earthquake of 30 October, 2016. [ABSTRACT FROM AUTHOR]

Music cities leverage live music as a tool for urban revitalization. Identifying influential industries in U.S. urban areas that have shaped the country music landscape can provide valuable insights into the role of the music industry in urban development. The 'big data' of country music concerts obtained from Spotify were examined to discern the relative importance of food and transportation services in explaining the spatial distribution of country music concerts from 2009 to 2019. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) analyses show that both food and transportation services have a positive relationship with country music concerts. The analysis also reveals that the majority of country music concerts occurred in urbanized areas. Although country music has successfully spread throughout the entire country, there are distinctive regional clusters in large cities such as Nashville, Dallas, New York City, and Austin. The result also indicates the strength of GWR in improving and sustaining the explanatory power of models. The GWR was implemented to execute four models separately considering different explanatory variables and a comparative analysis of the model performance then suggested that food service appears to perform best, whilst bus service performs better than train service and air service. These findings highlight the roles of food and transportation service facilities that have made country music — a form of Southern culture visible in the urban landscape. This study encourages music cities to harness the potential of big data's power to foster vibrant industrial ecosystems in urban environments. [ABSTRACT FROM AUTHOR]