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The container shipping industry has undergone significant changes in the post‐COVID‐19 era. This study examines the impact of the pandemic on container freight rates (CFR) through a demand analysis approach, using 11 major international container trade lanes from Shanghai, China as a case study. We analyze the CFR data on both weekly and monthly bases, with a focus on retaining more individual and time information using long panel data. To better reveal the pandemic's impact on CFR, we introduce interactive terms and simultaneous equations in our model. Our empirical analysis shows that the pandemic has not only directly and positively affected CFR, but, more importantly, has also indirectly promoted the growth of container shipping cost by stimulating the demand for containerized goods exported from China. Additionally, the connectivity or capacity of a given trade lane has inhibited the pandemic's effects on CFR to a certain extent. These findings provide insights into the remarkable growth of CFR from the demand side in 2020. [ABSTRACT FROM AUTHOR]

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies. [ABSTRACT FROM AUTHOR]

Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt–Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033. [ABSTRACT FROM AUTHOR]

Virtual power plants （VPPs） represent a tangible implementation in the construction of modern power systems. Addressing the current challenges of limited applicable technical standards and a lag in the development of a standardized system for VPPs， this paper， based on the collection and organization of relevant standards， conducts a comprehensive analysis of the standardization status across various facets of VPPs， including basic general standards， planning and construction， operation and maintenance， supporting technologies， and energy efficiency assessments. Adopting both top-down and bottom-up approaches of system engineering and guided by the principles of directive， coordinating， systematic， and open VPP standard system construction， a system architecture has been designed. The architecture encompasses 15 subcategories and 52 standard series. Furthermore， based on a future demand analysis， key layout recommendations have been proposed to provide reference for the establishment and revision of VPP standards.

Abstract Promoting the shift from private cars to micromobility (e.g., bike, e-bike, scooter) can represent a valuable action to improve city sustainability and liveability. Micromobility can help to replace trips by individual private cars (e.g., daily short round trips) as well as to improve coverage and accessibility of transit services, and, subsequently, to reduce the traffic impacts (e.g., pollutant emissions). It can be seen as a potential solution to move people more efficiently in urban areas, as well as to push people towards a more active mobility behaviour, contributing to the well-being goals. In this context, the paper, rather than inferring the users’ propensity to change their travel mode, proposes a methodology to identify car trips that can be considered the most compatible with micromobility. Estimation of the potential demand (e.g., the upper level of car trips that could be replaced by micromobility) is carried out by exploiting the opportunity offered by floating car data (FCD) for characterising car trips. Its goodness is therefore evaluated through an application to a real case study (i.e., the city of Trani, Apulia Region, Southern Italy), divided into seventy traffic zones, and where a FCD dataset of about 5,200 trips was available. The FCD allowed the car trips to be characterised (e.g., origin and destination, path features) instead of using the traditional surveys. The results indicate that a significant share of daily car trips can be substituted (i.e., the most compatible) by micromobility (31% of car round trips in the case study), with considerable potential environmental gains (traffic emission reduction; less than 21% of total emissions from private cars). Results can be of interest to local authorities in integrating micromobility in urban mobility planning and promoting new sustainable transport alternatives, as well as to transport companies for designing new appeal services. The developed methodology is parametric and uses easy-to-obtain data available worldwide; thus, it can be easily transferred to other city contexts.

Transfer stations are important nodes in the metro network, and it is of great significance to study the coordinated organization scheme between passenger demand and facility configuration, in order to improve the transportation efficiency. Taking the Dongfangzhimen station of the Suzhou Metro as the research object, this paper starts by analyzing the configuration of service-oriented facilities, and then dissects the spatiotemporal distribution characteristics and individual behavior characteristics of passengers via data processing, where a meticulous analysis has been carried out based on the automatic fare collection data and the field investigation data. On this basis, we have discussed the performance and bottlenecks under the current facility configuration and flow organization scheme, constructed the simulation scene of a representative peak hour in holidays using AnyLogic, and put forward a volume-based path organization scheme considering the coordination with flow demand, as well as a plan of multi-stage crowd management. As evidenced by the results, the improved scheme exhibits a higher adaptability to passenger flow and a greater balance among facility utilization, where the maximum queue length, the average time consumption and the average standing density have been reduced by 31%, 14% and 6%, respectively. The proposed methods of data analysis, flow organization and strategy decision are reliable and applicable to the management of metro transfer stations. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power 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.)

Copyright of Prävention und Gesundheitsförderung is the property of Springer Nature 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.)

Promoting the shift from private cars to micromobility (e.g., bike, e-bike, scooter) can represent a valuable action to improve city sustainability and liveability. Micromobility can help to replace trips by individual private cars (e.g., daily short round trips) as well as to improve coverage and accessibility of transit services, and, subsequently, to reduce the traffic impacts (e.g., pollutant emissions). It can be seen as a potential solution to move people more efficiently in urban areas, as well as to push people towards a more active mobility behaviour, contributing to the well-being goals. In this context, the paper, rather than inferring the users' propensity to change their travel mode, proposes a methodology to identify car trips that can be considered the most compatible with micromobility. Estimation of the potential demand (e.g., the upper level of car trips that could be replaced by micromobility) is carried out by exploiting the opportunity offered by floating car data (FCD) for characterising car trips. Its goodness is therefore evaluated through an application to a real case study (i.e., the city of Trani, Apulia Region, Southern Italy), divided into seventy traffic zones, and where a FCD dataset of about 5,200 trips was available. The FCD allowed the car trips to be characterised (e.g., origin and destination, path features) instead of using the traditional surveys. The results indicate that a significant share of daily car trips can be substituted (i.e., the most compatible) by micromobility (31% of car round trips in the case study), with considerable potential environmental gains (traffic emission reduction; less than 21% of total emissions from private cars). Results can be of interest to local authorities in integrating micromobility in urban mobility planning and promoting new sustainable transport alternatives, as well as to transport companies for designing new appeal services. The developed methodology is parametric and uses easy-to-obtain data available worldwide; thus, it can be easily transferred to other city contexts. [ABSTRACT FROM AUTHOR]

Copyright of Imagens da Educação is the property of Universidade Estadual de Maringa 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.)

India is one of the world's largest producers of tree nuts, yet it paradoxically remains a net importer of these commodities. This study aims to analyze the demand for imported tree nuts in India, motivated by the need to understand the factors contributing to this imbalance. The primary objective is to calculate income elasticities and own- and cross-price elasticities for five categories of imported tree nuts using the linear approximate almost ideal demand system model. Data is sourced from monthly import records from the United Nations Comtrade database covering 2014 to 2022. The tree nuts considered are almonds, cashews, pistachios, walnuts, and hazelnuts. Key findings reveal all imported tree nuts are normal goods. Cashews exhibit income elasticity (1.2), indicating a significant demand increase with rising incomes, while other nuts show income inelasticity. Cashews are price-elastic (−1.3), while other nuts are price-inelastic. Compensated cross-price elasticities indicate notable substitution effects, particularly between almonds and cashews. The study recommends enhancing domestic cashew production to meet growing demand and developing targeted marketing strategies to address competitive dynamics within the tree nut market. These strategies aim to reduce India's dependency on imports and promote a balanced, sustainable domestic market.

This paper introduces an innovative methodology for conducting demand analysis within various domains across multiple countries, presenting insights derived from a comprehensive analysis conducted in seven distinct nations. The proposed methodology provides a systematic process for gathering, processing, and analyzing textual data to discern global talent demand within specified domains. Three fundamental steps characterize the methodology: identification of areas of interest, calculation of relative demand, and execution of analysis and forecasting. Areas of interest within the job market are pinpointed through a combination of manual curation and automated techniques, ensuring the inclusion of only pertinent job postings. Relative demand for jobs within each specified domain is then computed for each country, furnishing a standardized metric for cross-country comparisons. Subsequently, analysis and forecasting unveil trends and patterns in job demand, enabling stakeholders to anticipate shifts in the job market landscape. Application of this methodology to scrutinize demand analysis across seven countries - the US, Canada, the UK, France, India, Singapore, and Australia - reveals substantial variations in demand across diverse regions, along with correlations and instances of skill shortages. These findings offer invaluable insights for policymakers, businesses, and researchers, facilitating informed decision-making and fostering growth and innovation within the specified domains.

Problematic mobile phone use is characterized by its "impulsive" nature; users engage in it despite their negative attitude toward it. From a behavioral‐economic perspective, this attitude–behavior discrepancy is generated by competing contingencies that involve smaller‐sooner social reinforcers associated with mobile phone use and larger‐later prosocial reinforcers potentially compromised by phone use. Based on this conceptualization, the reinforcer‐pathology model of problematic mobile phone use is proposed, which posits that such phone use stems from excessive delay discounting of the social and prosocial reinforcers and/or excessive demand for the social reinforcers. A secondary data analysis of previously published studies was conducted, with the novel addition of principal component analysis and hierarchical cluster analysis of these data. The results generated evidence that supports the reinforcer‐pathology model proposed in this article. Based on the theoretical analyses and accumulated empirical evidence, theory‐driven prevention and intervention strategies for problematic mobile phone use are proposed. Overall, the reinforcer‐pathology model of problematic mobile phone use provides a comprehensive framework for understanding and addressing this growing issue. [ABSTRACT FROM AUTHOR]

This research investigates the relationship between consumer perception and the demand for organic products. As sustainability and health consciousness continue to shape consumer choices, understanding the factors influencing their perception becomes crucial for the organic industry. The study employs a mixed-methods approach, combining surveys and interviews to analyze consumer attitudes towards organic products. Examining factors such as perceived health benefits, environmental impact, and trust in organic certification, the research aims to unveil the drivers and barriers impacting consumer perception. Additionally, it explores the influence of marketing strategies on shaping consumer views. [ABSTRACT FROM AUTHOR]

Copyright of Steel Pipe is the property of Steel Pipe Magazine 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.)

Agricultural big data is of great importance for developing smart agriculture and promoting agricultural modernization, which could be promoted by the combination of the industrial chain data and the area data of county level or agricultural park level. The national modern agricultural industrial park is an important carrier for the development of agricultural big data platform. Based on the construction of the Tea and Flower Industry Big Data Platform in Zhangping Modern Agricultural Industrial Park, Fujian Province, China, this paper recognized the main problems of local tea and flower industry, analyzed the demands of different users of the platform, and proposed the general framework, main functions, and applications of the platform. Centering on the leading industries (tea and flower industry) in Zhangping Modern Agricultural Industrial Park, the platform takes organization, standardization, branding, informatization and industrialization as the development direction, undertakes the functions of data integration, industrial analysis, application service and display demonstration, and builds an application management platform and corresponding operation service support, database, display and service terminals for government departments and business entities. The main applications of the platform include digital certificate of tea gardens, standardization management of planting and processing, soil nutrition management, general management of tea and flower industries, quality-and-safety traceability management, credit management of business entities, and the decision cockpit. This paper summarizes and suggests that the government should attach importance to the construction of data resource system, pay attention to data application and value mining, retain data and application service interfaces for future function expansion, and establish a big data talent team. The construction of big data platform can promote the integration of industrial basic data and the information management of industrial chain in Zhangping Modern Agricultural Industrial Park, which is conducive to breaking through the blocking points of industrial development, making up the shortcomings of industrial chain and smoothing the supply chain channels. [ABSTRACT FROM AUTHOR]

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies.

Energy demand forecasting is crucial for effective resource management within the energy sector and is aligned with the objectives of Sustainable Development Goal 7 (SDG7). This study undertakes a comparative analysis of different forecasting models to predict future energy demand trends in Brazil, improve forecasting methodologies, and achieve sustainable development goals. The evaluation encompasses the following models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Exogenous SARIMA (SARIMAX), Facebook Prophet (FB Prophet), Holt–Winters, Trigonometric Seasonality Box–Cox transformation, ARMA errors, Trend, and Seasonal components (TBATS), and draws attention to their respective strengths and limitations. Its findings reveal unique capabilities among the models, with SARIMA excelling in tracing seasonal patterns, FB Prophet demonstrating its potential applicability across various sectors, Holt–Winters adept at managing seasonal fluctuations, and TBATS offering flexibility albeit requiring significant data inputs. Additionally, the investigation explores the effect of external factors on energy consumption, by establishing connections through the Granger causality test and conducting correlation analyses. The accuracy of these models is assessed with and without exogenous variables, categorized as economical, industrial, and climatic. Ultimately, this investigation seeks to add to the body of knowledge on energy demand prediction, as well as to allow informed decision-making in sustainable energy planning and policymaking and, thus, make rapid progress toward SDG7 and its associated targets. This paper concludes that, although FB Prophet achieves the best accuracy, SARIMA is the most fit model, considering the residual autocorrelation, and it predicts that Brazil will demand approximately 70,000 GWh in 2033.

Developing socialized agricultural services is an effective means to solve the problem of “who will farm and how to farm”. In order to gain a deeper understanding of the socialized services of grain crop agriculture in Beijing, a combination of survey questionnaires, on-site visits, and interviews was conducted to conduct research in six districts: Fangshan, Miyun, Yanqing, Shunyi, Pinggu, and Tongzhou. The results indicate that the main problems in the development of socialized services for grain crop agriculture in Beijing are as follows: There is a gap in the demand for socialized agricultural services for small farmers; Insufficient capacity of service subjects in the suburbs of Beijing, and inconsistent service standards; The integration and promotion capabilities of agricultural technology need to be further strengthened; Intermediary entities lack financial and insurance services. The following measures can be taken to promote the development of socialized services for grain crop agriculture in Beijing: Promote the moderate scale of grain crop cultivation; Supplement the shortcomings of socialized agricultural services for grain crops; Improve the yield of grain crops and the level of agricultural technology promotion; Increase support for socialized services and financial insurance for grain crops. [ABSTRACT FROM AUTHOR]

In the transition to a low carbon economy, minerals are crucial. The demand for the minerals required to create and install green energy technology, such as solar panels, wind turbines, electric vehicles, and energy storage, is rising along with it. In particular, the countries that hold these mineral reserves should be thought of as thriving economically from the rising demand for essential mineral resources (such as cobalt, lithium, and others). This study uses import demand function analysis to look at how the major mineral importing countries' mineral import demand changed in response to the clean energy transitions between 2000 and 2021 for selected 14 countries. In the study, the cross-sectional autoregressive distributed lag (CS-ARDL) method was used. Findings show that long-term renewable energy production has a largely favorable impact on mineral import demand. Additionally, CO2 emissions have a long-term negative impact on mineral import demands, but energy intensity and exchange rate are favorable for mineral imports. The findings have significant ramifications for using the mineral trade to speed up the transition to sustainable energy around the world. Therefore, the study's key proposed policy is to emphasize the value of mineral resources in clean energy while maximizing their use in the transition to carbon-free energy. [ABSTRACT FROM AUTHOR]

Media multitasking has brought concerns (e.g., lower productivity and performance) in multiple settings including college classrooms. The present study examined the behavior of texting in the classroom (TIC) by applying the behavioral economic demand theory while taking college students' different attitudes and behaviors of TIC into consideration. Undergraduate students (109 females and 73 males for valid data, whose average age was 19.4 [SD = 2.5]) completed questionnaires on demographic characteristics, TIC-related attitude and behavior, and a demand task with a hypothetical scenario, which aimed to quantify the value of social rewards from text messaging with demand intensity (i.e., excessiveness) and elasticity (i.e., persistence). A cluster analysis identified four distinct subgroups who had varying levels of attitude (i.e., perceiving TIC as appropriate or inappropriate) and frequency of TIC: Inappropriate-Low-Frequency, Inappropriate-High-Frequency, Appropriate-Moderate-Frequency, and Appropriate-High-Frequency subgroups. The subsequent analysis revealed that demand intensity and elasticity of the Inappropriate-Low-Frequency subgroup were significantly lower and higher, respectively, than those of the Inappropriate-High-Frequency and Appropriate-High-Frequency subgroups. In addition, in supplemental analyses of multiple regression, demand intensity, but not elasticity, significantly predicted frequencies of TIC. Considering these findings, excessive valuation of social rewards from text messaging, particularly characterized by its demand intensity, appears to play an important role in the frequency of TIC. The present study contributes to the literature on media multitasking by suggesting excessive valuation as a potential factor related to TIC and providing practical implications that can help explore effective interventions tailored for distinct types of college students who have varying TIC-related attitudes and behaviors. [ABSTRACT FROM AUTHOR]

Bioeconomic modeling and seafood market analysis both have rich intellectual traditions that have contributed insights to understanding the economics of aquatic foods. This paper argues that these traditions, which developed mostly in parallel, should be combined more purposefully to understand management problems in fisheries and aquaculture. First, modeling the feedback between economic incentives and biological mechanisms is essential for avoiding management failure, and prices provide important incentives. Second, the form of management affects opportunities to generate value, influencing patterns of exploitation and the types of products that come from fishery resources. Third, price incentives in fisheries and responses to management depend on market context, including competition with aquaculture. By combining these insights with a modern empirical focus on counterfactuals, including both reduced-form and structural modeling approaches to causal inference, economists can inform policy and help to deliver a wide range of values from the production and consumption of aquatic foods. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

The development of voluntary health insurance is characteristic of post-Communist and CEE countries. The aim of the paper is to identify the factors determining the demand for private voluntary health insurance. The main finding of the research is that the most important factor is the demographic factor. Other factors include the factor related to the ineffectiveness of treatment under universal health insurance, the factor related to health condition, the demographic factor related to longevity, and the factor related to insurance awareness and income. The research confirms conclusions from previously published studies. The most important is to confirm that the income factor positively influences the studied phenomenon. However, the results shed new light on the aspect of the demand factor, as this indicates the ageing of society and the consequent increase in private spending on health, which are the key mechanisms in the discussion, with the other factors constituting a complementary element. [ABSTRACT FROM AUTHOR]

Ethiopia is a country in East Africa experiencing significant economic growth in recent years, with an increasing electricity demand. Ensuring sustainable and efficient energy for newly developed industries and economic zones is crucial. In this study, a 15-year electric power demand forecast for the new economic zone under construction is conducted. The electrical power demand forecast is done for the year 2025–2040 by using bottom-up forecasting approach for three different scenarios. Long-range Energy Alternatives Planning (LEAP) system software is used to analyze residential, industrial, and general business sector electric power demand. The analysis of the assessed scenario shows that the economic zone's electric power demand increases by 52.2 % from the base year 2025–2040 for the baseline scenario, due to anticipated rapid urbanization, growth in population, economic expansion, and anticipated political stability. Compared to the baseline scenario, the total power demand shows a growth of 68 % from the forecast year (2025) to 2040 for the aggressive scenario, which ensures sustainable and efficient energy options that can draw businesses from both domestic and international baselines. In contrast, the total power demand in the conservative scenario shows a growth of 30.3 % from the base year (2025) to 2040. This reduction in demand compared to the two scenarios indicates a reflection of how much electricity power demand could be if certain development conditions failed to be realized in the economy. In general, both results show a rapid increase in power demand compared to the base year. To address this increasing demand, a supply-side demand analysis can be done for reference and aggressive scenarios. The analysis result indicated that by 2040, supply-side demand from the national grid will increase by 93.5 % and 175.9 % for reference and aggressive scenarios, respectively, compared to the base year 2025 demand. Due to the huge gap between the supply and demand in the country, onsite off-grid generation can be considered to cover 25 % of the demand in the economic zone. Hence, with the support of off-grid generation, the demand from the national grid was reduced to 45 % and 107 % for reference and aggressive scenarios with the support of onsite generation. Hence, this research clearly shows that there is a serious need for large scale electricity generation and distribution planning and preparation to meet the continually increasing electric power demand in a sustainable manner to accommodate the growth and change required to develop the modern economic zones in the country.

Objective To establish a comprehensive, scientific, and reasonable smart home automation system for patients with ischemic stroke and functional impairment by conducting a questionnaire survey on the requirements of a smart home automation system. Methods The researchers developed a questionnaire titled "Requirements of a smart home automation system for patients with ischemic stroke and functional impairment" to assess the specific needs of patients who suffered from ischemic stroke and were unable to live independently. A total of 100 patients participated in the survey. Results The results of the questionnaire survey involving 100 individuals revealed that only 43. 00%(43/100)of the respondents were familiar with intelligent home systems, and a mere 21. 00%(21/100)had previous experience with similar smart systems. When asked about their willingness to accept a smart home automation system that could be installed and customized to provide convenience in daily life, including monitoring vital signs and medication usage, as well as aiding in rehabilitation training, 46. 00%(46/100) of the participants expressed their willingness of acceptance. Further investigation revealed that complex operation, impractical function and expensive cost were main reasons behind the reluctance of the rest 54 individuals to accept the system. The respondents expressed the highest demand for the electronic medication box monitoring system, followed by the sleep monitoring system and the vital sign monitoring system. Conclusion The findings indicated that patients with ischemic stroke and functional impairment had limited awareness and acceptance of smart home automation system. In order to ensure that the implementation of this automation system provides tangible benefits to patients, it is crucial to enhance its functionality, actively promote awareness among the target population, and establish an effective foundation for subsequent clinical trials of the intelligent home management system software. (目的 调查脑梗死伴功能障碍患者对智慧居家管理模块的需求, 为进一步建立完善、科学、合理的智慧居家管理模块提供依据。方法 采用自拟“脑梗死伴功能障碍患者对智慧居家模块的需求”调查问卷, 选取2020年4月—2023年4月医院收治的100例脑梗死伴功能障碍中生活不能完全自理的患者进行调查。结果 43(43. 00%)例患者了解智慧居家系统, 但只有21(21. 00%)例患者使用过类似的智能系统。当被问及“有一个智慧居家管理系统, 可在现有居家电器、门、窗等基础上进行安装改造, 旨在为您解决居家不便、进行生命体征及服药监测、帮助您进行康复训练等, 是否愿意接受”时, 46(46. 00%)例患者表示愿意接受, 而不愿意接受的原因包括操作复杂、实用性低、价格昂贵。患者对智慧居家管理系统功能的需求程度排前三位的分别是电子药盒监测系统、睡眠监测系统和生命体征监测系统。结论 脑梗死伴功能障碍患者对于智慧居家模块的了解程度低、接受意愿较低; 应通过加强功能完善、积极宣传普及等措施, 为智慧居家管理系统软件后期投入临床试验提供有效基础, 确保智慧居家模块的实施能够给患者带来确切益处。)

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Major arterial roads in city centres often face ever-increasing traffic demand periodically addressed through developments to ease congestion on road links and improve integrated road connectivity. Towards this end, three flyovers are proposed in the Slave Island area in Colombo. This high-investment project of the Sri Lankan Government expects the flyovers to prevent peak-hour traffic delays from the frequent road closures at two railway gates while providing for the higher traffic generated from the land use changes in the area. The project also expects to relieve congestion in the adjacent road network. This research investigates the outcomes expected of integrated traffic management from the project. A traffic analysis was carried out for the entire road network of the project impact area using the PTV Vissim software. Moreover, current and future land use was considered in assessing network connectivity over the design life. Alternative traffic management options were tested to identify the most feasible arrangement for connecting the proposed flyovers. Origin-Destination data from 2018 were used to determine intersection capacities and pedestrian movements. Results reveal that delays due to railway gate closure reduced, though a significant traffic diversion from the proposed flyover network was not evident.

Environmental variables are widely recognized as a cause of differences in efficiency measurement. However, the existing literature on data envelopment analysis (DEA) in environmental factors ignores the impact of demand on output. To address this gap, we propose the Point-wise Minimization DEA model (PWMDEA), which considers contextual variables that affect demand and lead to differences in efficiency. The model obtains efficiency value by considering the minimum of virtual inputs and virtual demand. Then, efficiency is evaluated by minimizing the ratio of above minimum to virtual output. This one-step model avoids issues of multi-stage assumptions and requires less data, making it more applicable. Moreover, we demonstrate the accuracy of our new model by conducting simulations with given true efficiency values. The simulation results demonstrate that our model has the lowest ranking error when the output is affected by multiple inputs or when demand has a significant impact. In addition, we evaluate the efficiency of healthcare in 31 Chinese provinces by considering two environmental factors. The results suggest that provinces with lower financial investments or population loss received higher rankings from our proposed model. These findings provide plausible explanations and demonstrate the practical usefulness of our model. [ABSTRACT FROM AUTHOR]

Rats were studied in social reinforcement procedures in which lever presses opened a door separating two adjacent spaces, permitting access to social interaction with a partner rat. The number of lever presses required for social interaction was systematically increased across blocks of sessions according to fixed-ratio schedules, generating demand functions at three different social reinforcement durations: 10 s, 30 s, and 60 s. The social partner rats were cagemates in one phase, and non-cagemates in a second phase. The rate at which social interactions were produced declined with the fixed-ratio price, and was well described by an exponential model that has been successfully employed with a range of social and non-social reinforcers. None of the main parameters of the model varied systematically with social interaction duration or with the social familiarity of the partner rat. On the whole, the results provide further evidence of the reinforcing value of social interaction, and its functional parallels with nonsocial reinforcers. [ABSTRACT FROM AUTHOR]

This study tests the assumption of weak separability between demand for dairy and nondairy milk products by using food scanner data from 2012 to 2017 and estimating linear-approximate EASI demand systems. Our results show that the weak separability structures can be rejected. First, this finding shows that nondairy milk products compete with dairy milk for consumers' budget allocated to milk. Second, although milk demand studies often do not include nondairy milk, or assume weak separability, the exclusion of these products--or the separability assumptions--may lead to biased estimates. [ABSTRACT FROM AUTHOR]

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In Latin America and developing countries, urban planners often lack sufficient data on cyclists’ modal share and profile to establish effective cycling lane networks. Mendoza, Argentina, is such a city. This research aimed to identify adequate locations for new cycle lanes by speculating on potential cycle trips from observed patterns of existing cyclists for a coherent demand-based network. The study proposed a novel, simple, cost-effective Geographic Information Systems model that uses spatial and human data and presents results cartographically for policymakers. Statistical analysis of observed bicycle trips enabled constructing a local profile of cyclists, from which ‘potential trips’ were speculated: trips made by other modes, but which could have been biked given their distance, duration, purpose, and the traveler’s age. Geo-referencing observed and potential trips to their ‘sub-zones’ of origin and destination produced a migratory synthesis map highlighting general flows and densities. Superimposing this on the current cycling infrastructure exposed the critical gaps. As a broader aim, this geomatic method is readily transferable to other developing world settings. Actions for Mendoza and broader applications are proposed, significant limitations discussed, and further research suggested, noting issues of gender equity and safety.

Informal public transport has been growing rapidly in many developing countries. Because urban infrastructure development tends to lag behind rapid population growth, informal public transport often meets the growing gap between demand and supply in urban mobility. Despite the rich literature primarily focused on formal transport modes, the informal transport sector is relatively unknown. The paper analyzes the demand behavior in the "informal" minibus sector in Antananarivo, Madagascar, taking advantage of a recent user survey of thousands of people. It is found that the demand for informal public transport is generally inelastic. Essentially, people have no other choice but to use this kind of public transport. While the time elasticity is estimated at − 0.02 to − 0.05, the price elasticity is − 0.05 to − 0.06 for short-distance travelers, who may have alternative choices, such as motorcycle taxi or walking. Unlike formal public transportation, the demand also increases with income. Regardless of the income level, everyone uses minibuses. The estimated demand functions indicate that people prefer safety and more flexibility in transit. The paper shows that combining these improvements and fare adjustments, the informal transport sector can contribute to increasing people's mobility and reducing traffic congestion in the city. [ABSTRACT FROM AUTHOR]

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