bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Consumer Buying Intention of E-Commerce Airfares Based on Multivariate Demographic Segmentation: A Multigroup Structural Equation Modeling Approach
Authors: Naruetharadhol Phaninee , Wongsaichia Sasichakorn , Zhang Shenying , Phonthanukitithaworn Chanchai , Ketkaew Chavis
Published: July 2022

The internet offers enormous development opportunities for airline firms and a lot of information for consumers to pick the finest available options. This research aims to study the consumer buying intention of e-commerce airfares in an emerging economy based on the technology acceptance model. This article employed a sample of 3064 respondents at six airports in Thailand. It used cluster analysis (a multivariate analysis approach) to determine two main customer segments and then used a structural equation modeling (SEM) technique utilizing demographic segmentation as a moderator to explain the behaviors of those two segments. The findings demonstrated two customer segments: (1) the older with high and middle-income segment, and (2) the young with low-income segment. The empirical results revealed that price sensitivity and perceived ease of use substantially impacted behavioral intention to use e-commerce airfares in both segments. The users from segment (1) are more likely to look for the fun experience and entertainment value of using e-commerce airfares than those from segment (2). However, perceived usefulness is unlikely to be a vital factor in consumers’ purchasing decisions about using e-commerce airfares. It is recommended that airline companies and online travel agencies should consider perceived ease of use, price sensitivity, and hedonic motivation when implementing e-commerce airline websites for selling tickets.
Incentives to Enhance Production Reliability against Disruption: Cost-Sharing vs. Penalty
Authors: Jin Xiaodan , Zhou Hong
Published: July 2022

Two kinds of incentive strategies, cost-sharing and penalty, are examined in dealing with production disruption, with consideration of production process reliability as an endogenous factor for a two-echelon supply chain. Based on the Stackelberg game framework, we derive the optimal decisions of supply chain partners and compare their expected profits with different strategies. Considering the uncertain demand and the retailer’s preference against the risk, we further analyze how the partners’ decisions and the retailer’s expected profit are influenced by the feature of loss aversion. From theoretical analysis and numerical experiments, we find that: (1) overall, a penalty strategy dominates that of cost-sharing for the retailer, whereas the reverse applies with respect to the manufacturer; (2) a penalty strategy may outperform a cost-sharing strategy for the whole supply chain, depending on demand; and (3) a reasonable aversion against risk can help the retailer to achieve a more robust result when a penalty strategy is adopted under volatile and unpredictable demand.
Reporting University Performance through the Sustainable Development Goals of the 2030 Agenda: Lessons Learned from Italian Case Study
Authors: Fiorani Gloria , Di Gerio Chiara
Published: July 2022

The shift of sustainability paradigms within the academic context has prompted universities to develop additional accountability tools to respond to the resulting demand for information. However, in terms of social reporting, universities have complete discretion regarding the content and nature of their sustainability budgets. The resulting diversity has led to greater heterogeneity with regard to information systems, which in turn poses the risk of making reports for stakeholders difficult to understand and complicates benchmark comparisons between different institutions. This paper aims to summarize the state of the art of sustainability reporting in Italian state universities, and to assess the compliance of these reports with global sustainability trends. This is achieved by determining the extent to which the sustainable development goals, outlined by the 2030 Agenda, are integrated into the substantive indicators used in the preparation of sustainability reports. The results show that sustainability reporting in universities is still in its early stages. Although only 29 universities—which represent 42% of the total number of universities initially surveyed—have pursued a sustainability reporting process, this research highlights a gradual increase in the implementation of SDGs as a criterion for assessing sustainable performances.
The Legibility Efficacy of Historical Neighborhoods in Creating a Cognitive Map for Citizens
Authors: Askarizad Reza , He Jinliao , Khotbehsara Elham Mehrinejad
Published: July 2022

Nowadays, historical neighborhoods in Iran are in a state of decay due to the lack of spatial legibility with respect to their organic spatial layout. Hence, the level of interaction among people and their perception of the environment has gradually diminished. Historical neighborhoods no longer possess their former prosperity and function, and citizens prefer to inhabit neighborhoods with newer configurations to meet their demands. The aim of this study was to investigate the role of the spatial legibility of historical neighborhoods in creating a suitable cognitive map for citizens. Quantitative and qualitative methods were applied using a questionnaire and simulation test instruments. In addition, in order to analyze the spatial configuration of the target areas, the Space Syntax method was implemented using Depthmap 10. The findings indicated that landmarks and navigation signs were identified as the most important factors affecting legibility from the viewpoint of users. Furthermore, the correlation between integration and connectivity illustrated that the studied areas possessed poor spatial legibility due to the organic texture of their spatial configurations. Accordingly, solutions such as enhancing the interaction between highly integrated streets and identified landmarks were emphasized to ameliorate the legibility of these spaces. The present study contributes to sustainable urban design with respect to the rejuvenation of historical districts by proposing a set of pragmatic alterations of urban spatial configurations. This research may be fruitful for urban designers, planners and managers seeking to revitalize historical neighborhoods.
Multistage Economic Scheduling Model of Micro-Energy Grids Considering Flexible Capacity Allocation
Authors: Liu Hang , Wang Yongcheng , Nie Shilin , Wang Yi , Chen Yu
Published: July 2022

Micro-energy grids integrating multiple energy sources can realize the efficient use of renewable energy and accelerate the process of energy transition. However, due to the uncertainty of renewable energy, the stability and security of system operations should be taken into account with respect to multi-energy coupling economic operations. Thus, it is essential to make flexible capacity allocations in advance of the actual scheduling of production in the micro-energy grid. With this motivation, this paper constructs a three-stage scheduling model corresponding to the running stage of the spot market. Specifically, the capacity of flexible, active devices is configured in the day-ahead stage; then, the intraday economic operation dispatching scheme is provided according to the capacity configuration. Based on the day-ahead and intraday optimization results, the system power balance is realized through the dispatching process using the reserve capacity of flexible active devices for deviations generated in the real-time stage of renewable energy. For the uncertainty of renewable energy output, the clustering method is applied to realize the clustering analysis of renewable energy output scenarios. In addition, the conditional value at risk (CVaR) theory is introduced to modify the three-stage stochastic optimization model, and the risk values caused by uncertainty are quantitatively evaluated. Finally, we simulate a practical case to verify the effectiveness of the proposed model. The results show that day-ahead flexible capacity allocation enhances the autonomy of the micro-energy grid system, ensures a certain degree of system operational security, and reduces balancing costs in the real-time stage. The higher the risk aversion factor, the more operational costs the system operator pays to avoid the risk. In addition, if the carbon penalty coefficient is higher, the overall carbon emission level of the micro-energy grid will decrease, but it will gradually converge to a minimal level. This paper guides the development of micro-energy grids and has important constructional significance for the construction of multi-energy collaborative mechanisms.
Agricultural Reservoir Operation Strategy Considering Climate and Policy Changes
Authors: Lee Jaenam , Shin Hyungjin
Published: July 2022

Agricultural water is affected by climate change and water management. Agricultural reservoirs are increasing demand on the environmental water supply because the Korean government has recently implemented an integrated water resource management policy. However, agricultural reservoirs are still in operation solely to supply agricultural water. To examine sustainable agricultural water management under climate change, we analyzed the strategy of operating regulations to efficiently distribute agricultural water as environmental water. We simulated the agricultural reservoir operation, analyzing its water supply capacity by applying operation regulations. The simulation predicted that future water supply capacity would decrease if the existing operation were maintained, and agricultural reservoir operation will be necessary in the future. The proposed reservoir operating strategy decreased the maximum water shortage and number of water shortage days compared with the existing operation with the required water supply. Our results can contribute to agricultural reservoir operation strategies and sustainable water management in response to climate change and provide decision-making guidance on water distribution for environmental use in response to water management policy changes.
Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques
Authors: Malatesta Troy , Breadsell Jessica K.
Published: July 2022

Human behaviour is a major driver and determinant of household energy consumption, with routines and practices shaping daily energy profiles. These routines and practices are made up of individual lifestyles and other contextual factors that vary from home to home. Social and psychological theories aim to explain and describe how people consume resources in the home, which has resulted in the development of the home system of practice. This evaluates how occupants live and follow multiple routines which result in varying energy consumption practices. This paper develops a methodology to identify and support the concept of the home system of practice using a data analytical approach and link it to residential energy and distribution network management. This paper utilises k-means cluster analysis to identify these different home systems of practices and routines in energy use by using real-time energy consumption data from July 2019 to March 2021 from a living laboratory in Australia. The results of the analysis show the different daily energy profiles for each of the 39 households, with some homes observing large fluctuations and changes in the way they consume energy during the day. Specific homes were discussed as case studies in this paper focusing on linking the occupants’ contextual factors to their energy profiles. This variation is discussed in terms of the routines of the occupants and associated lifestyles that explain why some energy peaks occurred at different parts of the day and differed during the COVID-19 lockdown period in Australia. The paper conducts a comparison between these case studies to show how people’s lifestyles impact household energy consumption (and variation). These case studies investigated the heating and cooling practices of the occupants to demonstrate how they impact overall consumption. This variation is discussed in relation to energy management and prediction of when homes will consume energy to assist in net-zero energy developments and grid stabilisation operations.
Development of Prediction Models for Performance of Flexible Pavements in Kansas with Emphasis on the Effects of Subgrade and Unbound Layers
Authors: Perić Dunja , Goh Gyuhyeong , Saeidaskari Javad , Rashk Olia Arash Saeidi , Ayar Pooyan
Published: July 2022

This study resulted from the need for better consideration of subgrade and unbound layers on the performance of flexible pavements in Kansas. Thus, the main objective was to develop pavement performance prediction models with emphasis on the effects of subgrade and unbound layers. To this end, pavement distress data, which were collected over several years across the state of Kansas, including rutting, fatigue cracking, transverse cracking, roughness and core analysis, served as the input data into statistical models. The effects of subgrade and unbound layers were represented by the corresponding results of dynamic cone penetrometer (DCP) tests and thickness of the unbound layer. In addition, traffic volume was represented by average annual daily truck traffic (AADTT). Multiple statistical analyses identified positive correlations of dynamic cone penetration index (DPI) and rate of total rutting, and DPI and percent of good core. Negative correlation was discovered between DPI and fatigue cracking code one, and DPI and percent of poor core. AADTT was positively correlated with transverse cracking codes one and two while it had no correlation with transverse cracking code zero. Thickness of the unbound layer was negatively correlated with pavement roughness and percent of poor core, while it was positively correlated with the percent of good core. Finally, the recommendation for minimum acceptable value of California bearing ratio (CBR) was provided based on the correlation between DPI and rate of change of rutting code. The recommendation enables the selection of a CBR value based on the number of years required for unit increase in the rutting code.
A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction
Authors: Wang Qingyuan , Huang Longnv , Huang Jiehui , Liu Qiaoan , Chen Limin , Liang Yin , Liu Peter X. , Li Chunquan
Published: July 2022

To improve the accuracy of ultra-short-term wind speed prediction, a hybrid generative adversarial network model (HGANN) is proposed in this paper. Firstly, to reduce the noise of the wind sequence, the raw wind data are decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then the decomposed modalities are entered into the HGANN network for prediction. HGANN is a continuous game between the generator and the discriminator, which in turn allows the generator to learn the distribution of the wind data and make predictions about it. Notably, we developed the optimized broad learning system (OBLS) as a generator for the HGANN network, which can improve the generalization ability and error convergence of HGANN. In addition, improved particle swarm optimization (IPSO) was used to optimize the hyperparameters of OBLS. To validate the performance of the HGANN model, experiments were conducted using wind sequences from different regions and at different times. The experimental results show that our model outperforms other cutting-edge benchmark models in single-step and multi-step forecasts. This demonstrates not only the accuracy and robustness of the proposed model but also the applicability of our model to more general environments for wind speed prediction.
Farmers’ Demand for Climate Information Services: A Systematic Review
Authors: Ouedraogo Adama , Egyir Irene S. , Ouedraogo Mathieu , Jatoe John Baptist D.
Published: July 2022

The importance of climate information services (CIS) for farm decision-making is known worldwide. Its use is widely recommended by academics, governments, and development partners, especially in Africa. However, the supply of commercial CIS in Africa remains very low. Considering that the commercial CIS suppliers are business-oriented, the lack of supply is mainly due to the lack of evidence on the demand for it. The specific objectives of the review were to assess the demand for CIS, the key characteristics of the demanded CIS. and the key drivers for the demand for CIS in the Economic Community of West African States (ECOWAS). Through a systematic review, 123 articles were identified on the SCOPUS and Google Scholar databases and 52 papers were included in the study. The models of assessment done by the majority of authors were a simple description based on needs assessments and econometrics modelling to identify the key drivers. The results show that 68% of the farmers in ECOWAS demanded CIS. The average willingness to pay for CIS is estimated to be USD 2.01 for daily forecasts. The usability of CIS, daily forecasts and geolocalized CIS, and customized CIS are the key characteristics farmers are looking for in the ECOWAS region. The main drivers of CIS demand are price, income, vulnerability to climate variability, beliefs and religion, complementary services, gender, type of crops, and farm size. According to the consumer theory, information such as elasticity of price and income, ranked substitutes of CIS, which are still lacking, are key for understanding the CIS demand. However, the review showed that little research work has been conducted in this area. The review also shows the importance of determining among which type of goods CIS should be classified. Knowing whether CIS is a necessity good is vital for suppliers’ decision-making.
Effects of Heating Rate and Temperature on the Yield of Thermal Pyrolysis of a Random Waste Plastic Mixture
Authors: Riesco-Avila José Manuel , Vera-Rozo James R. , Rodríguez-Valderrama David A. , Pardo-Cely Diana M. , Ramón-Valencia Bladimir
Published: July 2022

Effects of heating rate and temperature on thermal-pyrolytic yield of a plastic-waste mixture were studied in a semi-batch reactor. The temperature in the range of 380–460 °C and heating rates of 10, 19, and 28 °C/min were evaluated through an experimental multi-level design. The results show that higher temperatures or lower residence time reduce the yield of pyrolytic oil at the expense of increasing the yield of gaseous products. The maximum liquid yield was 69%, obtained at 410 °C and a heating rate of 10 °C/min. The composition of pyrolytic oil covers a wide range of hydrocarbons; thus, a fractionation is necessary before using it as fuel in internal combustion engines. The fractionation process yielded 21.12 wt% of light fraction (gasoline-like), 56.52 wt% of medium fraction (diesel-like), and 22.36 wt% of heavy fraction (heavy diesel-like). The light fraction has an octane index and caloric value within the range of the typical gasoline values. On the other hand, the cetane index and caloric value of the medium fraction meet the requirements of the standards for diesel.
Exploring the Influence of Problematic Mobile Phone Use on Mathematics Anxiety and Mathematics Self-Efficacy: An Empirical Study during the COVID-19 Pandemic
Authors: Chen Suting , Jiang Haozhe , Guan Jia
Published: July 2022

Problematic mobile phone use is a pervasive issue globally and has aroused wide public concerns. Prior studies have indicated that problematic mobile phone use has a series of negative effects on individuals’ physical and mental health. However, the effects on student learning have seldom been investigated. During the COVID-19 pandemic, home quarantine and social distancing have led to individuals’ greater problematic mobile phone use, and it is essential to have a better understanding of individuals’ problematic mobile phone use and its negative effects during this unprecedented period. Given this, the present study investigates the effects of university students’ problematic mobile phone use on mathematics self-efficacy and mathematics anxiety, which play critical roles in mathematics learning. This study collected data from 420 students in March 2022, when a large-scale COVID-19 lockdown took place in Shanghai, China. Structural equation modeling was used to analyze the data. Our findings show that university students’ problematic mobile phone use can significantly impact mathematics anxiety and indirectly—yet considerably—influence mathematics self-efficacy. This study calls for increased public concern regarding students’ problematic mobile phone use during the COVID-19 pandemic.
Knowledge, Attitude and Practice towards the Use of Over-the-Counter Medicines: An Online Survey among Bruneian Adults amid the COVID-19 Pandemic
Authors: Bahrin Dzulkharnain Faiqah Batrisyia Syaza , Shafqat Naeem , Hermansyah Andi , Tan Ching Siang , Koh David , Goh Khang Wen , Ming Long Chiau
Published: July 2022

Globally, self-medication has increased, where 25% of adults use OTC medicines. This research is intended to assess the knowledge, attitude and practice regarding OTC medicines among adults in Brunei Darussalam. An online cross-sectional survey was performed using a questionnaire adapted from similar research conducted among students in Brunei Darussalam. A total of 364 responses were collected, where the median age of the study participants was 23 years. The mean knowledge score was 7.3 out of 9, with most respondents (77.7%) having good knowledge of OTC medicines. Almost all (92.9%) showed a positive attitude towards OTC use. A statistically significant difference (p ≤ 0.05) was observed in attitude scores between age groups and education levels. Most of the study participants (88.2%) have practiced self-medication with OTC medicines, mainly due to their easy accessibility (79.4%). A small number practiced improper habits, such as consuming more than the recommended dose (6.0%) and not checking the expiry date (0.5%). The practice of self-medicating with OTC medicines can be advantageous when patients fully know the medications and nature of their disease. Knowledge of proper OTC medicine use among adults in Brunei Darussalam is essential to avoid improper user practices and potential health hazards associated with the misuse of medications.
Location of Logistics Distribution Center Based on Improved Bald Eagle Algorithm
Authors: Tong Yanfen , Cheng Xianbao
Published: July 2022

The location of a logistics distribution center is a complex combinatorial optimization problem, and it is difficult to achieve the best results by traditional mathematical methods. This paper proposes an improved bald eagle search applied to logistics distribution center location selection for the first time, one which uses the chaotic operator to initialize the population, increases the diversity of populations, and introduces a sine and cosine algorithm in the search stage. It increases the global search ability of the algorithm and the ability to jump further out of the local space. Through test function and location simulation experiments, it is proved that the improved algorithm has obvious advantages over other common algorithms in solution accuracy and convergence speed. It can effectively improve the efficiency of logistics distribution when applied to the location of logistics distribution centers. Finally, the improved bald eagle search algorithm is used to optimize the location model of logistics distribution center. The experimental results show that the improved bald eagle search algorithm has good solving ability in this problem, can be obtained to minimize the distribution cost, save the distribution cost effectively and improve the distribution efficiency. It can further optimize the logistics management system and increase the efficiency of logistics enterprises. Compared with similar algorithms, such as WOA, WCA, PSO, the results are improved.
Wildfire Smoke, Air Quality, and Renewable Energy—Examining the Impacts of the 2020 Wildfire Season in Washington State
Authors: Bertoletti Augusto Zanin , Phan Theresa , Campos do Prado Josue
Published: July 2022

The 2020 wildfire season was devastating, setting negative records in many states and regions around the world, especially in North America. Five of the six largest fires in California’s recorded history burned in 2020. In the Pacific Northwest region of the United States, Oregon and eastern Washington almost doubled their 10-year average of burnt acres recently. Depending on wind speed and direction conditions, the smoke from wildfires may significantly impact the air quality and reduce solar photovoltaic (PV) generation even in regions located hundreds of kilometers away from high-risk zones. Thus, during those periods, power system operators must ensure reliability and resilience across power generation, transmission, and distribution, while minimizing carbon emissions that can harm the air quality of the affected communities during wildfire events even more. This paper analyzes the impact of the 2020 wildfire season in the state of Washington, verifying the wind speed and solar irradiance data, and correlating these with the particulate matter 2.5 (PM 2.5) concentration and aerosol optical thickness (AOT) through a multi-variable regression model. The results show that PV production may be significantly reduced during the periods of high concentration of wildfire smoke and reduced wind speeds, thus highlighting the need for efficient and sustainable power system operations during wildfire events.
In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data
Authors: Crusiol Luís Guilherme Teixeira , Sun Liang , Sun Zheng , Chen Ruiqing , Wu Yongfeng , Ma Juncheng , Song Chenxi
Published: July 2022

China is one the largest maize (Zea mays L.) producer worldwide. Considering water deficit as one of the most important limiting factors for crop yield stability, remote sensing technology has been successfully used to monitor water relations in the soil–plant–atmosphere system through canopy and leaf reflectance, contributing to the better management of water under precision agriculture practices and the quantification of dynamic traits. This research was aimed to evaluate the relation between maize leaf water content (LWC) and ground-based and unoccupied aerial vehicle (UAV)-based hyperspectral data using the following approaches: (I) single wavelengths, (II) broadband reflectance and vegetation indices, (III) optimum hyperspectral vegetation indices (HVIs), and (IV) partial least squares regression (PLSR). A field experiment was undertaken at the Chinese Academy of Agricultural Sciences, Beijing, China, during the 2020 cropping season following a split plot model in a randomized complete block design with three blocks. Three maize varieties were subjected to three differential irrigation schedules. Leaf-based reflectance (400–2500 nm) was measured with a FieldSpec 4 spectroradiometer, and canopy-based reflectance (400–1000 nm) was collected with a Pika-L hyperspectral camera mounted on a UAV at three assessment days. Both sensors demonstrated similar shapes in the spectral response from the leaves and canopy, with differences in reflectance intensity across near-infrared wavelengths. Ground-based hyperspectral data outperformed UAV-based data for LWC monitoring, especially when using the full spectra (Vis–NIR–SWIR). The HVI and the PLSR models were demonstrated to be more suitable for LWC monitoring, with a higher HVI accuracy. The optimal band combinations for HVI were centered between 628 and 824 nm (R2 from 0.28 to 0.49) using the UAV-based sensor and were consistently located around 1431–1464 nm and 2115–2331 nm (R2 from 0.59 to 0.80) using the ground-based sensor on the three assessment days. The obtained results indicate the potential for the complementary use of ground-based and UAV-based hyperspectral data for maize LWC monitoring.
Allocation and Utilization of Coal Mine Water for Ecological Protection of Lakes in Semi-Arid Area of China
Authors: Liu Changshun , Liang Lili , Wang Lin , Zheng Shuai
Published: July 2022

In the background of water ecological protection, how to utilize the superfluous coal mine water efficiently has become an urgent problem, especially in northwest China, where the fragile ecological environment needs to be protected but lacks water. To solve this problem, this study proposes a new procedure for the allocation and utilization of mine water aimed at the ecological protection of lakes in an arid and semi-arid area. Based on the water balance method, the ecological water supplement of regional lakes is first estimated according to their different protection goals. Next, a trend analysis of water demand and supply is carried out, and the mine water inflow and available quantity are calculated. Meanwhile, the water resource allocation plan is evaluated systematically. In this study, the procedure is applied to the mine water and lakes in Wushen Banner, Inner Mongolia Autonomous Region of China. The results show that: (1) the lakes can be divided into three classifications according to their ecological protection goals, (2) the available amount of mine water will reach 57.17–81.97 million m3 in 2030, and there are about 46.7 million m3 of water that can be adjusted to reach the optimal utilization in 2020, and (3) the mine water after advanced treatment could meet the requirement of lakes. Finally, it outputs the water supplement path and the water quantity, as well as the water transmission pipelines to each lake, which makes up a new water resources allocation plan and a utilization mode of regional mine water. This utilization mode can provide solutions and ideas for improving the ecological environment of regional lakes and promote the construction of regional ecological safety barriers. Moreover, it can be very helpful for optimizing the allocation of regional water resources, and for improving the reasonable utilization of coal mine water.
An Assessment of the Impact of Temperature Rise Due to Climate Change on Asphalt Pavement in China
Authors: Miao Yinghao , Sheng Jiajia , Ye Jin
Published: July 2022

In the global warming context, understanding the impact of temperature rise on asphalt pavement is the basis for making adaptation strategies. An approach based on historical climate data and pavement performance models was employed to assess the potential impact of temperature rise on asphalt pavement in China. It is shown that permanent deformation is one sensitive aspect of asphalt pavement performance, which increased on average by 20.70% from 1992 to 2019. Another one is low-temperature cracking, which decreased by 20.99% from 1970 to 1997, but has remained almost unchanged since 1997. Global mean surface temperature anomalies of 1.5 °C and 2.0 °C will increase the permanent deformation of asphalt pavement by 18.63% and 36.71%, respectively, compared to 2019. Global warming is bringing serious challenges to the structure and material design of asphalt pavement due to the increasing service temperature range.
Estimation of Parameters of Triple Diode Photovoltaic Models Using Hybrid Particle Swarm and Grey Wolf Optimization
Authors: Ellithy Hazem Hassan , Taha Adel M. , Hasanien Hany M. , Attia Mahmoud A. , El-Shahat Adel , Aleem Shady H. E. Abdel
Published: July 2022

The quality of the photovoltaic (PV) cell model impacts many simulation studies for PV systems, such as maximum power point tracking and other assessments. Moreover, due to limited information found in the datasheets of the PV cells, several parameters of the model are unavailable. Thus, this paper introduces a novel approach using a hybrid Particle Swarm and Grey Wolf Optimization algorithm to figure out these parameters under different environmental conditions. The proposed algorithm is used with two types of PV cells–Kyocera KC200GT and Canadian solar cell CS6K-280M–and can be used with any commercial type of PV module needing only parameters in the datasheet. The absolute error of the model’s simulation results is compared to the actual results collected from sites in Egypt, in an attempt to investigate the effectiveness of the suggested approach.
Analysis of Urbanization and Climate Change Effects on Community Resilience in the Rio Grande Valley, South Texas
Authors: Atisa George , Racelis Alexis E.
Published: July 2022

Disruptive development events have tested and will continue to test community resilience as people work to balance healthy living, economic growth, and environmental quality. Aspects of urbanization, if not designed and guided by healthy living strategies, convert natural areas into built environments, thus reducing the diversity of plant and animal species that are the foundation of resilience in communities. In this study, we attempted to answer the following question: What are the most effective ways to ensure that ongoing urbanization and climate change do not negatively affect ecological services and community resilience in the Rio Grande Valley (RGV)? The region is experiencing a high urban growth rate and is also one of the poorest regions in Texas. Thus, it has an inadequate capacity to prevent or mitigate climate change-related threats and take advantage of opportunities associated with urbanization. Using qualitative analysis, we consulted existing literature to identify relevant leverage points that can help foster regional resilience capacity. The findings show that there are very strong leverage points that can produce cumulative desired resilience outcomes, but these have not been incorporated into policy and natural systems in the RGV.
Identifying the Key Drivers and Barriers of Smart Home Adoption: A Thematic Analysis from the Business Perspective
Authors: Basarir-Ozel Birgul , Turker Hande Bahar , Nasir Vesile Aslihan
Published: July 2022

Smart homes embrace advanced technologies and the connectedness of devices that aim to increase consumers’ life quality. They are based on data integration over shared platforms collected via sensors and wireless networks. However, although consumers’ current and potential adoption of smart homes have received some research interest, there is a low number of studies considering the foreseeable future of smart homes from the business perspective. To fulfill this gap in the literature, this study presents the results of an exploratory research attempting to reveal the foresight of the business side regarding the penetration of smart home technologies (SHTs) into consumers’ lives. Based on the opinions of industry experts collected through 13 semistructured in-depth interviews, numerous drivers of and barriers to SHT adoption are uncovered and displayed in their intertwined relationship in a thematic map. In creating this map, the qualitative data gathered through the interviews are integrated with widely used theories/models of technology adoption in the literature to develop a full-fledged set of determinants. As a result, drivers of SHT adoption (five sub-themes) and barriers that hinder smart home penetration (eight subthemes) were determined. Drivers consist of relative advantage, enjoyment, image enhancement, modern and multifunctional design, and consumers’ technology innovativeness. In contrast, the main barriers are high cost, complexity, lack of compatibility, lack of trialability, lack of observability, lack of a trusted brand in the market, lack of facilitating conditions and support services, and consumers’ technology anxiety. This rich set of SHT adoption determinants can be used in future studies to examine their relative impact on consumers’ adoption of SHT.
A Comparative Study of Machine Learning and Spatial Interpolation Methods for Predicting House Prices
Authors: Kim Jeonghyeon , Lee Youngho , Lee Myeong-Hun , Hong Seong-Yun
Published: July 2022

As the volume of spatial data has rapidly increased over the last several decades, there is a growing concern about missing and incomplete observations that may result in biased conclusions. Several recent studies have reported that machine learning techniques can more efficiently address this limitation in emerging data sets than conventional interpolation approaches, such as inverse distance weighting and kriging. However, most existing studies focus on data from environmental sciences; so, further evaluations are required to assess their strengths and limitations for socioeconomic data, such as house price data. In this study, we conducted a comparative analysis of four commonly used methods: neural networks, random forests, inverse distance weighting, and kriging. We applied these methods to the real estate transaction data of Seoul, South Korea, and demonstrated how the values of the houses at which no transactions are recorded could be predicted. Our empirical analysis suggested that the neural networks and random forests can provide a more accurate estimation than the interpolation counterparts. Of the two machine learning techniques, the results from a random forest model were slightly better than those from a neural network model. However, the neural network appeared to be more sensitive to the amount of training data, implying that it has the potential to outperform the other methods when there are sufficient data available for training.
A Random-Parameter Negative Binomial Model for Assessing Freeway Crash Frequency by Injury Severity: Daytime versus Nighttime
Authors: Zhang Ping , Wang Chenzhu , Chen Fei , Cui Suping , Cheng Jianchuan , Bo Wu
Published: July 2022

This study explored the effects of contributing factors on crash frequency, by injury severity of all, daytime, and nighttime crashes that occurred on freeways. With three injury severity outcomes classified as light injury, minor injury, and severe injury, the effects of the explanatory variables affecting the crash frequency were examined in terms of the crash, traffic, speed, geometric, and sight characteristics. Regarding the model estimations, the lowest AIC and BIC values (2263.87 and 2379.22, respectively) showed the superiority of the random-parameter multivariate negative binomial (RPMNB) model in terms of the goodness-of-fit measure. Additionally, the RPMNB model indicated the highest R2 (0.25) and predictive accuracy, along with a significantly positive α parameter. Moreover, transferability tests were conducted to confirm the rationality of separating the daytime and nighttime crashes. Based on the RPMNB models, several explanatory variables were observed to exhibit relatively stable effects whereas other variables presented obvious variations. This study can be of certain value in guiding highway design and policies and developing effective safety countermeasures.
Research on Comprehensive Evaluation Model of a Truck Dispatching System in Open-Pit Mine
Authors: Kou Xiangyu , Xie Xuebin , Zou Yi , Kang Qian , Liu Qi
Published: July 2022

In this paper, a comprehensive evaluation factor model of a truck dispatching system in open-pit mines is constructed from the three dimensions of optimal route, traffic flow planning, and real-time dispatching, and the final combined weight of the factor is determined according to game theory. On this basis, a comprehensive evaluation model of a truck dispatching system in open-pit mines based on gray relational analysis—technology for order preference by similarity to an ideal solution (GRA-TOPSIS) is established. Taking the truck dispatching system in five open-pit mines as the research background, the advantages and disadvantages of the dispatching system were comprehensively evaluated, and the differences between the dispatching systems were analyzed using the radar chart method. The research shows that the evaluation results of the comprehensive evaluation model of the truck dispatching system in open-pit mines based on GRA-TOPSIS are in line with the reality, which is more conducive to analyzing and comparing the advantages and disadvantages of the systems, effectively identifying the differences of various systems, and making the evaluation of truck dispatching systems more scientific. The research results of this paper broaden the evaluation of truck dispatching systems and provide a theoretical basis for the optimization of truck dispatching systems.
Cognitive IoT Vision System Using Weighted Guided Harris Corner Feature Detector for Visually Impaired People
Authors: Rajendran Manoranjitham , Stephan Punitha , Stephan Thompson , Agarwal Saurabh , Kim Hyunsung
Published: July 2022

India has an estimated 12 million visually impaired people and is home to the world’s largest number in any country. Smart walking stick devices use various technologies including machine vision and different sensors for improving the safe movement of visually impaired persons. In machine vision, accurately recognizing an object that is near to them is still a challenging task. This paper provides a system to enable safe navigation and guidance for visually impaired people by implementing an object recognition module in the smart walking stick that uses a local feature extraction method to recognize an object under different image transformations. To provide stability and robustness, the Weighted Guided Harris Corner Feature Detector (WGHCFD) method is proposed to extract feature points from the image. WGHCFD discriminates image features competently and is suitable for different real-world conditions. The WGHCFD method evaluates the most popular Oxford benchmark datasets, and it achieves greater repeatability and matching score than existing feature detectors. In addition, the proposed WGHCFD method is tested with a smart stick and achieves 99.8% recognition rate under different transformation conditions for the safe navigation of visually impaired people.
An Empirical Study for Senior Citizens Using a Customized Medical Informatics System for Dementia Diagnosis and Analysis
Authors: Ho Hsu-Hua , Lin Jien-Jou , Gong Jia-Qiao , Yu Tzu-Yi
Published: July 2022

The treatment of dementia-related diseases is a global issue. Taiwan is facing a more serious dementia problem due to the combination of an aging society and a declining birthrate. A great portion of healthcare resources has been utilized for dementia among the aged population. In order to understand how dementia develops in rural areas in Taiwan, a cooperated effort between the university and a regional hospital was formed to develop a customized medical information system to collect and track dementia patients. This efficient customized system compiled information on 768 patients with dementia-released diseases. Big data technology and data mining approaches were then applied to analyze the relevant information. Using statistical analysis, we then extracted useful medical findings from the large amounts of collected medical data. Some of the findings indicate that the patients’ education level and care practices have a major effect on the dementia severity in these local senior populations.
The Influence of Taxation on Income Inequality: Analysis of the Practice in the EU Countries
Authors: Dianov Sergey , Koroleva Lyudmila , Pokrovskaia Natalia , Victorova Natalia , Zaytsev Andrey
Published: July 2022

The growing economic inequality around the world is recognized as a global problem of mankind. At the same time, the key tool for reducing inequality and ensuring the achievement of sustainable development goals is the taxation system given its distributive function. That is why this paper puts forward and proves a scientific hypothesis according to which direct taxation has a significant impact on economic inequality, with its scale and sphere depending on the level of economic development and the specific architecture of the tax system adopted in a particular country. The study relies on data from 28 European Union countries, including the United Kingdom, whose tax systems are not identical but harmonized in accordance with European Union directives, the same as the legislation in other economic sectors. Accordingly, it can be concluded that similar institutional characteristics are present. We have used the method of two-stage cluster analysis, which is meant for identifying the natural splitting of the mass of data into groups, then carried out regression analysis and built some models. The contribution of the study is revealing a number of important regularities that are significant for characterizing the dependence of income inequality on direct taxation as well as formulation recommendations for improving the tax policies of European Union countries, with the potential of policy implications. The results obtained can play a significant role in the development and further harmonization of tax systems and resolving the global problem of increased inequality within and between countries.
Improving the Effectiveness of Organisational Collaborative Innovation in Megaprojects: An Agent-Based Modelling Approach
Authors: Zhao Na , Lei Congcong , Liu Hui , Wu Chunlin
Published: July 2022

As the complexity, breadth of expertise and number of agents involved in megaprojects grow, collaborative innovation models become invaluable for helping to achieve sustainable project development. On this basis, the purpose of this study is to explore the innovation output mechanisms used for collaborative innovation in megaproject (CIMP) systems by the agent-based modelling (ABM) approach, and to promote the efficiency and effectiveness of organisational collaborative innovation through variable controls. A multi-agent simulation CIMP model was developed using the NetLogo tool. The model encompasses the behavioural factors and interaction rules that affect organisational CIMP. Four simulations were conducted, and the results showed that (1) the innovation environment, including policy environment, cultural climate, and engineering demand, has a positive effect on the output rate of CIMP; (2) a larger scale of innovative network organisation accelerates innovation output; (3) innovative organisations must avoid enforcing high standards for cooperation, communication, and recognition abilities when selecting partner organisations; (4) innovative organisations’ ability to absorb technology, information, and knowledge is positively related to output, while an increase in behavioural costs reduces the scale of innovative network organisations, thereby affecting their output. This study developed its CIMP theory from the perspective of organisational behaviour. The findings are expected to provide methodological and practical guidance for the selection of innovation agents, behavioural patterns, and for guaranteeing efficient innovation in collaborative megaproject organisations.
Evaluation of Hesperidin on Sausages Stuffed in a New Modified Casing during Long-Term Storage—A Preliminary Study
Authors: Feng Chao-Hui , Arai Hirofumi
Published: July 2022

The effects of hesperidin and different casings on pH, moisture content, water-holding capacity, and total viable count (TVC) of sausages stored for 171 days were evaluated by principal component analysis (PCA) and discriminant analysis (DA). Sausages stuffed in a modified casing with treatment B (soy lecithin concentration: 1:30, soy oil concentration: 2.5%, lactic acid concentration: 21 mL/kg NaCl [solid], treated time: 90 min) had a significantly lower pH value (6.89 ± 0.01) at d 31 but higher (6.55 ± 0.03) than that of control casings at d 171 (p < 0.05). Hesperidin plays an important role in antimicrobial property that renders the sausages with modified casing by treatment A (soy lecithin concentration: 1:27.5, soy oil concentration: 1.25%, lactic acid concentration: 19.5 mL/kg NaCl [solid], treated time: 75 min) stable (p > 0.05), with the final TVC of 5.03 ± 0.10 log cfu/g. According to the PCA results, water-holding capacity has a positive correlation to pH. Moisture content was the best discriminator for differentiating sausages with control and modified casings, whilst pH was able to discriminate sausages stored after 138 days from other days.