bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Energy-Saving Potential and Visual Comfort of Electrochromic Smart Windows in Office Buildings: A Case Study in Dhahran, Saudi Arabia
Authors: Budaiwi Ismail M. , Abdul Fasi Mohammed
Published: June 2023

This study comprehensively evaluates the energy-saving potential and visual comfort aspects of electrochromic (EC) smart windows in a hot-humid climate office building. Using an advanced building simulation tool, EC windows are compared to conventional low-E glazed windows, considering two control triggers: daylighting level and glare control. The primary objective is to determine energy savings achievable with EC windows while addressing visual comfort. Detailed analysis of the building’s energy performance and indoor environment is conducted. Results show significant energy savings of 23% with EC windows using daylighting control but limited visual comfort in some zones. Conversely, EC windows with glare control achieve 17% energy savings while maintaining visual comfort throughout the building. These findings highlight the potential of EC windows with glare control in saving energy and maintaining visual comfort in hot-humid office buildings. Further research is needed to optimize performance for different building types and climates. In conclusion, this study provides insights into energy-saving capabilities and visual comfort considerations with EC smart windows, emphasizing the importance of appropriate control triggers for maximizing energy savings and occupant comfort. Future investigations should explore EC window performance across diverse building typologies and climates to enhance the benefits of this innovative technology.
Design of Cascaded Multilevel Inverter and Enhanced MPPT Method for Large-Scale Photovoltaic System Integration
Authors: Khemili Fatima Z. , Bouhali Omar , Lefouili Moussa , Chaib Lakhdar , El-Fergany Attia A. , Agwa Ahmed M.
Published: June 2023

The key goal of this effort is to develop an efficient control system for a three-phase cascaded H-bridge multilevel inverter powered by the photovoltaic (PV) system. The power for the system is generated through the use of PV modules, which serve as DC inputs for the cascaded H-bridge multilevel inverter. The authors aim to achieve a nearly sinusoidal signal at the voltage level and are specifically focused on minimizing the total harmonic distortion (THD) to the smallest possible value. Hence, an advanced N-level space vector modulation (SVM) is developed to ensure an appropriate control for the cascaded inverter. The aim is to design an effective control strategy to increase inverter efficacy and, thus, supply the best output quality. In addition, a robust approach to the maximum power point (MPP) tracking (MPPT) technique is developed based on an adaptive perturb and observe (P&O) algorithm to ensure superior tracking of the MPP. The developed algorithm eliminates 90% of the power curve area in the search space process and only maintains 10% of the area that includes the MPP. Each PV system employs its own improved MPPT control. The numerical results confirm that the enhanced P&O algorithm attains a precise response with superior efficiency and a fast response under the fast alteration of environmental conditions. Hence, the energy loss is reduced. The simulation results validate the effectiveness of this study, highlighting the high efficiency of the control strategy and the enhanced performance of the proposed scheme with lesser THD values.
A Study on the Suitable Areas for Growing Apricot Kernels in China Based on the MaxEnt Model
Authors: Sun Runze , Tong Guanjie , Zhang Qing , Xu Lingjie , Sang Zihan , Li Yanhui
Published: June 2023

Research on the climatic adaptation of the apricot kernels (Prunus armeniaca L.) has significant meaning for optimizing their cultivation and utilizing climatic resources effectively. This research utilizes geographical distribution data, climatic environmental factors, soil data, and altitude data of the apricot kernel in China. By employing the maximum entropy model (MaxEnt) and geographic information system (ArcGIS), we identify the key factors influencing the distribution of apricot kernels in China and suitable areas for their cultivation. Our findings reveal that annual precipitation, frequency of frost days in April, altitude, soil pH, and effective soil water content are the primary environmental factors impacting the distribution of apricot kernels in China. We classify the planting suitability zones into four categories. The areas characterized by annual precipitation ranging from 330.54 mm to 616.42 mm, frost day frequency of 2.68 to 19.15 days in April, altitude between 84.22 m and 831.81 m, pH values ranging from 7.5 to 8.6, and effective soil water content of 1.16 to 3.88 are deemed most suitable for growing apricot kernels. The most suitable areas correspond to the main growing areas in reality. Given the limited existing research on suitable areas for apricot kernel cultivation, this study provides a scientific foundation for promoting the cultivation of apricot kernels.
Let the Elephants Talk—Exploring Ivory, the Ivory Trade, and Exchange in Southeast Africa and the Indian Ocean World in the Early 16th Century
Authors: Roque Ana Cristina
Published: June 2023

This article explores ivory and ivory trade in early 16th century Southeast Africa, examining them through both historical and environmental lenses. It emphasizes the importance of inter and transdisciplinary research to fully understand the intricacies of the ivory trade, highlighting the need to integrate diverse data sources to understand the sociodynamics of the region and the links between hunted animals, extracted ivory, ivory work and the global networks, and markets in Africa and the Indian Ocean world. In addition, the article also discusses the limitations of the documents used, stressing the complexities of interpreting historical data, the importance of tightly weaving relevant data from other disciplines, and the need for caution when drawing conclusions based on limited evidence. This study contributes to interdisciplinary research on the history of ivory and the ivory trade in Southeast Africa, underscoring the significance of merging historical and environmental perspectives. It encourages future researchers to take a more critical approach and gather more extensive data to gain a deeper understanding of the trade’s complexities.
Coyote Optimization Algorithm-Based Energy Management Strategy for Fuel Cell Hybrid Power Systems
Authors: Venkatasatish Rudravaram , Chittathuru Dhanamjayulu
Published: June 2023

This research proposes an improved energy management strategy (EMS) for a fuel cell hybrid power system for an electric aircraft based on a recently developed coyote optimization algorithm (COA). The suggested hybrid system consists of fuel cells and an energy storage system (ESS) to supply the required load in stable conditions. The distribution and performance of the hybrid electrical power system are determined by various energy sources. Consequently, having the best energy management system is essential for completing this work. The suggested EMS’s main objectives are to reduce hydrogen energy utilization and increase power source longevity. The proposed coyote optimization algorithm with external energy maximization strategy (COA-EEMS) and coyote optimization algorithm with equivalent consumption minimisation strategy (COA-ECMS) are tested with the help of the Opal-RT 5700 real-time HIL simulator and MATLAB/Simulink. The proposed algorithms confirm their robustness and higher efficiency by minimizing hydrogen fuel consumption compared to existing algorithms. The merits of the proposed algorithms are presented in detailed and compared with existing algorithms.
Research on the Effect of Knowledge Stock on Technological Advance and Economic Growth in Republic of Korea
Authors: Jung Jaeho , Choi Sangok
Published: June 2023

The Republic of Korea’s R&D investment has been rapidly increasing over the long term. This R&D investment is accumulated as knowledge stock, which means the amount of knowledge held by individual countries. However, is the rapid increase in R&D investment contributing to the advancement of domestic science and technology and economic growth? In general, technological advance will lead to a country’s sustainable economic growth. If so, how much has it contributed? This is a very important question for a neoclassical economist. This study reviewed previous studies on “knowledge stock”, “TFP estimation”, and “R&D investment, TFP and economic growth relationship”, and sequentially analyzed the relationship among knowledge stock, TFP, and economic growth through a three-step process. The analysis procedure first performs data processing and time series trend analysis, then time series characteristics analysis, and finally Orderful Least Squares (OLS). The analysis results showed that all three models had a positive (+) relationship and most of them were derived to be statistically significant. On this basis, three policy implications are presented. First, the knowledge stock is estimated to have had a positive (+) effect on economic growth, and the continuous increase in domestic R&D investment is believed to have acted as a growth engine. In particular, financial of the government by financial resources and the mid-to-high tech industry groups by industrial characteristics are statistically positively significant. Next, domestic knowledge stocks have had a positive effect on TFP but the efficiency is judged to be decreasing. It is implied that high-tech industries need to form knowledge stocks above the threshold through continuous R&D investment. Finally, TFP was analyzed to have had a high impact on economic growth, and this confirmed that innovation-led growth was important. It is still relevant that innovation-led sustainable growth is more important than factor led in the Republic of Korea.
Optimal Model of Electric Bus Scheduling Based on Energy Consumption and Battery Loss
Authors: Xing Yan , Fu Quanbo , Li Yachao , Chu Hanshuo , Niu Enyi
Published: June 2023

The characteristics of electric buses make it difficult to estimate the energy consumption and mean that they are prone to battery loss; as such, fuel bus scheduling methods are no longer fully applicable. In current studies, the influence of these factors is ignored. This paper proposes an electric bus scheduling optimization model based on energy consumption and battery loss. Firstly, the LSTM (long short-term memory) is used to estimate trip energy consumption. Subsequently, these results are combined with the optimization objectives of minimizing the fleet size and battery loss amount. Limitations on the buses’ number, travel time, battery safety thresholds, remaining charge, and total charge are also considered. By controlling the different battery charge and discharge thresholds to minimize battery losses, the goal of sustainability is achieved. NSGA-II (non-dominated sorting genetic algorithm-II) is used to solve the model. The corresponding scheduling and charging scheme are determined. Electric bus route A is taken to validate the predictions. The results show that the annual fleet battery loss value decreases as the fleet size increases. The company has the lowest annual operating cost when the battery charge and discharge thresholds are set to [25%, 85%]. Optimizing the scheduling and charging scheme for electric bus can effectively reduce the operating cost.
Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review
Authors: Dhiman Poonam , Kaur Amandeep , Balasaraswathi V. R. , Gulzar Yonis , Alwan Ali A. , Hamid Yasir
Published: June 2023

Different kinds of techniques are evaluated and analyzed for various classification models for the detection of diseases of citrus fruits. This paper aims to systematically review the papers that focus on the prediction, detection, and classification of citrus fruit diseases that have employed machine learning, deep learning, and statistical techniques. Additionally, this paper explores the present state of the art of the concept of image acquisition, digital image processing, feature extraction, and classification approaches, and each one is discussed separately. A total of 78 papers are selected after applying primary selection criteria, inclusion/exclusion criteria, and quality assessment criteria. We observe that the following are widely used in the selected studies: hyperspectral imaging systems for the image acquisition process, thresholding for image processing, support vector machine (SVM) models as machine learning (ML) models, convolutional neural network (CNN) architectures as deep learning models, principal component analysis (PCA) as a statistical model, and classification accuracy as evaluation parameters. Moreover, the color feature is the most popularly used feature for the RGB color space. From the review studies that performed comparative analyses, we find that the best techniques that outperformed other techniques in their respective categories are as follows: SVM among the ML methods, ANN among the neural network networks, CNN among the deep learning methods, and linear discriminant analysis (LDA) among the statistical techniques.This study concludes with meta-analysis, limitations, and future research directions.
Understanding Local Government Digital Technology Adoption Strategies: A PRISMA Review
Authors: David Anne , Yigitcanlar Tan , Li Rita Yi Man , Corchado Juan M. , Cheong Pauline Hope , Mossberger Karen , Mehmood Rashid
Published: June 2023

Digital technologies are used in various local government activities. Adopting suitable digital technology strategies could enhance service efficiency, effectiveness, and accountability. The challenges of technology adoption among local governments, however, are also evident. One of the major challenges is capacity, including the lack of knowledge or awareness of how to balance the local government’s resources and the strategies that need to be implemented. This challenge also forms a research gap. The study aims to consolidate the understanding of local government digital technology adoption strategies via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). It analyses the adoption opportunities, challenges, and strategies through the lens of people, processes, and technology frameworks. The results show that: (a) Strategies concerning the people aspects include building a platform for public participation, employees’ skills, and decision-makers' positive mindset development. (b) Strategies concerning the process aspects include recognizing the players’ roles, having a clear aim and procedure, proper regulation, and receiving user input. (c) Strategies considering the technology aspects include understanding the effect of the technology, technological preparedness, and convenience adoption. The findings inform local government policymakers in digital technology adoption and transformation endeavors.
Evaluating the Ecological Sustainability of Agrifood Land in Ethnic Minority Areas: A Comparative Study in Yunnan China
Authors: Li Chang , Tong Tong , Ge Shutong
Published: June 2023

Agrifood land plays a crucial role in indigenous communities. However, there is limited study on the overall sustainability and inter-ethnic comparison of agrifood lands across ethnic groups. To address these gaps, we developed a visual eco-efficiency framework of ecological footprint, biocapacity, and ecological benefit to evaluate the sustainability of agrifood land in these regions, as well as analyzed the sustainability of agrifood land and examined its explanatory factors across six ethnic groups in the Yunnan Province of China. The results showed that the ecological benefits of agrifood lands fluctuated in a low ecological deficit, and the eco-efficiency of different ethnic groups varied significantly, from 2010 to 2020. Moreover, redundancy analysis showed that cash crops, forestry, fishery, and livestock were major contributors to the eco-efficiency of agrifood lands in ethnic groups, rather than the commonly accepted staple foods. Another finding revealed that the contribution of urbanization rate to the eco-efficiency of agricultural food land had a rule of reversed U and was influenced by the annual average temperature and the ethnic population rate. Our study not only provided a visual framework for evaluating the sustainability of agrifood land in ethnic areas but also shed new light on its explanatory factors across different ethnic groups. The study served as a scientific foundation for the investigation, monitoring, and management of indigenous agriculture by governments and the agricultural sectors.
A Sustainable Strategy for the Conversion of Industrial Citrus Fruit Waste into Bioethanol
Authors: Vadalà Rossella , Lo Vecchio Giovanna , Rando Rossana , Leonardi Michelangelo , Cicero Nicola , Costa Rosaria
Published: June 2023

The present study focused on the development of a methodology for the production of ethanol from the fermentation of waste resulting from citrus fruit processing. The experimental design included a number of steps, each optimized to make the whole process cost-effective, energy-saving, and ecofriendly. Particular emphasis was given to the pretreatment of citrus waste (CW), which was carried out through a combination of physical means, namely milling, heating, sonication, and microwave irradiation. Following this, an enzymatic hydrolysis was performed by loading a mix of enzymes, i.e., cellulase, pectinase, and β-glucosidase. Different combinations and concentrations were assayed with respect to the effective degree of saccharification. Afterwards, the hydrolysate was transferred to a bioreactor, added with nutrients and inoculated with two yeast strains, i.e., Saccharomyces cerevisiae and Saccharomyces bayanus. Fermentation lasted 48 h, leading to an amount of 40.1 g·L−1 ethanol. The process involved an extra step of fed batch that allowed the entire potential productivity of CW to be exploited by yielding 52.3 g·L−1 ethanol at a rate of 1.09 g·L−1·h−1. In accord with previously published data, this investigation has proven to be successful in reaching its prefixed objectives of sustainability.
Spatial Characteristics and Influencing Factors of Commuting in Central Urban Areas Using Mobile Phone Data: A Case Study of Nanning
Authors: Wang Jinfeng , Luo Guowei , Huang Yanjia , Liu Min , Wei Yi
Published: June 2023

Urban commuting characteristics have important implications for both the spatial planning and governance of cities. However, the traditional methods of surveying the characteristics of commuting are very time- and labour-intensive, with the results susceptible to subjective influences. In this work, taking the central city of Nanning as the research object, the commuting space of the population was constructed on the grid-block-subdistrict scale, and the distribution characteristics of the commuting space were systematically analysed. In addition, the influencing factors of the commuting volume were explored by combining multi-source and spatiotemporal data with a geodetector. From our analysis, it was demonstrated that the population density in the central city of Nanning showed a spatial distribution pattern of “decaying distance from the city centre”, with a weak agglomeration effect of large-scale commuters at the grid scale and a larger east-west than north-south commuter scale. At the block scale, large-scale commuters were more concentrated, and the commuting distances were shorter in areas with large commuter populations. At the subdistrict scale, the internal commuting population was also more than the cross-subdistrict commuting population, with more cross-subdistrict commuting flows and an uneven distribution of the flow sizes, with most commuters concentrating on two or three subdistricts for commuting. Various important factors that affect the size of the commuting population should be controlled, including the permanent population, residential distribution, medical facilities, recreational facilities, food services and workplace distribution; the interactions between the permanent population, the residential distribution and the house price factors have the strongest impact values. Our work provides valuable insights for the understanding of commuting patterns in cities and can be used as a scientific basis for urban spatial decision-making.
How Tripartite Stakeholders Promote Green Technology Innovation of China’s Heavily Polluting Enterprises?
Authors: Zhao Ying , Huang Yongchun , Hu Shiliang , Sun Jun
Published: June 2023

Green technology innovation of heavily polluting enterprises is a critical way to alleviate environmental pressure and promote sustainable development. However, the ways in which the interaction of influencing factors affects heavily polluting enterprises in green technology innovation in China have not been fully addressed and need to be investigated in this field. This paper explored the impact of government environmental regulation, corporate social responsibility (CSR), and public attention on promoting green technology innovation of heavily polluting enterprises based on S-O-R (stimulus–organism–response) and stakeholder theories. The panel data of A-share listed companies in China’s heavily polluting industries from 2008 to 2020 was used to investigate their interactions by adopting POLS (Pooled Ordinary Least Square) method. The main results show that (1) environmental regulation has a significant positive effect on green technology innovation; it especially has a great impact on state-owned heavily polluting enterprises; (2) CSR plays a mediating role between environmental regulation and green technology innovation; (3) public attention has a moderating effect between CSR and green technology innovation and also moderates the indirect effect of environmental regulation on green technology innovation through CSR. The results illustrate that green technology innovation should be not only guided by governmental regulation, but also supplemented by enterprises’ internal driven force and public supervision, which can give implications for promoting the development of green technology innovation and optimizing environmental policy tools.
An Analysis of Runoff Variation in a Small Basin in the Loess Plateau: Identifying the Variation Causes and Implications for Sustainable Water Management
Authors: Li Wenqing , He Guohua , Zhao Yong , Liu Zhao , He Fan , Wang Haibin
Published: June 2023

Analyzing the characteristics and causes of runoff variation in a typical small basin is beneficial for ecological restoration in the Loess Plateau. This study employed a series of statistical methodologies to examine the characteristics of meteorological changes and underlying surface evolution in the Qishui River Basin (QRB). To differentiate the impacts of climate change and human activities on runoff variation, we applied the Choudhury–Yang formula and the Double Mass Curve (DMC) method. Subsequently, by incorporating future watershed protection strategies and various SSP scenarios, we utilized the Soil and Water Assessment Tool to simulate future runoff while employing the DMC to identify underlying causes of runoff variation. The results suggested that human activity has a slightly greater impact than climate change on reducing runoff during the historical period, with only a 1% difference. However, this will change in the future as human impact becomes increasingly significant. Human activities such as afforestation have dual effects, encompassing positive effects such as improving water quality and mitigating soil erosion, as well as negative consequences such as diminishing local water availability and exacerbating drought. Effective policies should be implemented, involving the use of appropriate tree species and planting methods, finding an appropriate value of forest area, monitoring and evaluation, etc., in order to ensure that the policies are aligned with the broader social, economic, and environmental goals of the QRB. These findings provide valuable guidance for policy-makers in developing management strategies for future environmental changes.
Correction: Yu et al. Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math. Sustainability 2023, 15, 2226
Authors: Yu Xianyu , Xia Yang , Zhou Jianguo , Jiang Weiwei
Published: June 2023

The authors would like to make the following corrections about the published paper [...]
Economic Policy Uncertainty and Firm Value: Impact of Investment Sentiments in Energy and Petroleum
Authors: Hussain Sarfraz , Ali Rosalan , Emam Walid , Tashkandy Yusra , Mishra Pradeep , Fahlevi Mochammad , Matuka Adelajda
Published: June 2023

This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. This study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020 and macroeconomic variables such as EPU. This study investigates the moderating effects of EPU on investments in fixed and intangible assets and its effect on Tobin’s Q and the market price per share. The outcomes demonstrate that EPU reduces the costs of both tangible and intangible assets for businesses. In addition, companies with a higher Tobin’s Q and market price per share are more impacted by uncertain corporate investment policies. However, financial leverage is negatively correlated with share price and positively correlated with earnings per share and earnings per unit. Tobin’s Q positively correlates with financial leverage, indicating that firms that raise capital through debt are more likely to create value for investors. The research indicates that market-dependent enterprises are more susceptible to the unpredictability of monetary policy. According to this study, consistent application and open communication of economic policies are likely to increase the efficacy of company investments, resulting in more effective resource allocation and business decision-making.
Car Ownership Behavior Model Considering Nonlinear Impacts of Multi-Scale Built Environment Characteristics
Authors: Wu Lan , Yuan Xiaorui , Yin Chaoyin , Yang Ming , Ouyang Hongjian
Published: June 2023

To explore the nonlinear influence of a multi-scale built environment on residents’ car ownership behavior, combined with the data set of residents’ individual information and travel-related data from the China Labor Force Dynamic Survey report, eight variables are selected to describe the built environment from multiple scales. The gradient-boosting iterative decision tree model including individual family attributes and neighborhood-scale and city-scale built-environment attributes is constructed. The results show that the individual family attributes have the greatest cumulative impact on car ownership behavior (46.3%). The built environment based on neighborhood scale and city scale also has a significant impact on residents’ car ownership behavior, these being 33.94% and 19.76%, respectively. The distance to the city center at the neighborhood scale is positive correlated with car ownership. The number of buses per 10,000 people and road area per capita in the city scale are also positive correlated with car ownership. Therefore, in order to slow down the increase in car ownership, the built environment can be optimized and adjusted at neighborhood scale and city scale.
Towards Sustainable Safe Driving: A Multimodal Fusion Method for Risk Level Recognition in Distracted Driving Status
Authors: Chen Huiqin , Liu Hao , Chen Hailong , Huang Jing
Published: June 2023

Precise driving status recognition is a prerequisite for human–vehicle collaborative driving systems towards sustainable road safety. In this study, a simulated driving platform was built to capture multimodal information simultaneously, including vision-modal data representing driver behaviour and sensor-modal data representing vehicle motion. Multisource data are used to quantify the risk of distracted driving status from four levels, safe driving, slight risk, moderate risk, and severe risk, rather than detecting action categories. A multimodal fusion method called vision-sensor fusion transformer (V-SFT) was proposed to incorporate the vision-modal of driver behaviour and sensor-modal data of vehicle motion. Feature concatenation was employed to aggregate representations of different modalities. Then, successive internal interactions were performed to consider the spatiotemporal dependency. Finally, the representations were clipped and mapped into four risk level label spaces. The proposed approach was evaluated under different modality inputs on the collected datasets and compared with some baseline methods. The results showed that V-SFT achieved the best performance with an recognition accuracy of 92.0%. It also indicates that fusing multimodal information effectively improves driving status understanding, and V-SFT extensibility is conducive to integrating more modal data.
Analysis of the Coupling Coordination and Obstacle Factors between Sustainable Development and Ecosystem Service Value in Yunnan Province, China: A Perspective Based on the Production-Living-Ecological Functions
Authors: Li Jiaxi , Li Changyuan , Liu Chenyang , Ge Hailong , Hu Zheneng , Zhang Zhuoya , Tang Xueqiong
Published: June 2023

The relationship and obstacles between sustainable development (SD) and ecosystem services (ESs) are crucial factors for SD decision-making and ecological conservation strategies. The production-living-ecological (PLE) functional perspective provides a new research entry point to analyze the interrelationship between the SD of human society and ESs. In view of this, based on the Sustainable Development Goals (SDGs), this study established an SD localization evaluation framework from the perspective of the PLE functions and subsequently evaluated the SD levels of 16 cities in Yunnan Province from 2005 to 2020. Furthermore, changes in the ecosystem service value (ESV) were explored by combining the PLE land classification and dynamic equivalence methods. The relationship between SD and ESV was then analyzed using the modified coupling coordination degree (CCD) model. Finally, the obstacle degree model was introduced to identify the main factors affecting the CCD level of the two systems. The results revealed the SD level in Yunnan Province to be high in the central region and low in other areas. From 2005 to 2020, the province’s SD level increased from 0.146 to 0.341. Furthermore, the main contribution of ESV originated from ecological land (over 90%). The ESV was higher in the southwest of Yunnan Province compared to that in the northeast. From 2005 to 2020, the ESV decreased by 2.231 billion CNY and was dominated by the regulation services. The CCD level increased significantly from 2005 to 2020. The CCD subcategory changed from SD lag to ESV lag and ESV-SD balanced type. Moreover, the dominant obstacle factors did not change significantly during the study period. The ESV and SD systems were dominated by regulation services and sustainable production subsystems, respectively. The research results have important guiding significance for further optimization of regional development strategies and ecological protection measures.
General Circulation Model Downscaling Using Interpolation—Machine Learning Model Combination—Case Study: Thailand
Authors: Prathom Chotirose , Champrasert Paskorn
Published: June 2023

Climate change, a global problem, is now impacting human life and nature in many sectors. To reduce the severity of the impacts, General Circulation Models (GCMs) are used for predicting future climate. The prediction output of a GCM requires a downscaling process to increase its spatial resolution before projecting on local area. In order to downscale the output to a higher spatial resolution (less than 20 km), a statistical method is typically considered. By using this method, a large amount of historical observed data, up to 30 years, is essential. In some areas, the historical data is insufficient. Hence, the statistical method may not be suitable to downscale the output on the area which lacks the required data. Hence, this research aims to explore a high spatial resolution downscaling process that is able to provide a valid and high accuracy result in the Thailand area with a limitation in quantity of historical data. In this research, a combination of an interpolation and machine learning model called `IDW-ANN’ is proposed for downscaling the data under the condition. The prediction of temperature and precipitation from a GCM, IPSL-CM6A-LR in CMIP6 is downscaled by the proposed combination into a 1 km spatial resolution. After the performance evaluation, the IDW-ANN downscaling process showed good accuracy (RMSE, MAE, and R2) and valid downscaled results. The future climate situation in Thailand, in particular temperature, and precipitation level, in 2040 and 2100 under two scenarios of SSPs (SSP1-2.6 and SSP3-7.0) is also projected at 1 km resolution by using IDW-ANN. From the projection, the level of precipitation sums, and temperature seem to be increased in most of Thailand in all future scenarios.
Identifying Smart City Leaders and Followers with Machine Learning
Authors: Liu Fangyao , Damen Nicole , Chen Zhengxin , Shi Yong , Guan Sihai , Ergu Daji
Published: June 2023

Smart cities have been a popular topic for the city stakeholders. A smart city is the next urban lifestyle that citizens expect. Due to the hypercompetitive and globalized economy, many cities have already started or are about to start their smart city projects. There is no uniform benchmark to evaluate the smart cities’ performance. Several organizations use their own indicators to evaluate smart cities worldwide or nationwide. This research paper leverages fuzzy logic to label smart city leaders and followers based on various organization’s evaluation meta results and then uses machine learning techniques to identify the key characteristics of leaders and followers. Based on the training data performance, the Support Vector Machine (SVM) is used to predict who will be the next smart city leader or follower. According to the proposed prediction framework, we have successfully predicted 30 smart city leaders and 20 followers.
Sustainable Food in Teacher Training: Evaluation of a Proposal for Educational Intervention
Authors: Ortega-Quevedo Vanessa , Santamaría-Cárdaba Noelia , Gil-Puente Cristina
Published: June 2023

The sustainability of the food system is a particularly relevant issue today as it is a challenge to ensure environmental sustainability and the need to guarantee access to food in all parts of the world to promote social equity. Given this, the need to promote a sustainable food transition in consumption habits becomes imperative. It is crucial that teachers be sensitised to this issue so that they can try to develop a critical and globally aware student body that is committed to a social transformation towards sustainability. The main objective of this study is discovering the opinions of preservice teachers (PSTs) on sustainable food. It also has two specific objectives: (1) analyze the initial opinions of prospective teachers on sustainable food before and after carrying out an educational intervention on this topic, and (2) study the relationship between the opinions of PSTs and variables related to their personality or their relationship with nature, among others. A longitudinal pre-test-intervention-post-test design using quantitative methods was carried out to explore the opinions of 49 pre-service teachers studying for a degree in Primary Education before and after the educational intervention. The results show changes in the opinions of future teachers after the application of the educational intervention and differences towards food sustainability according to some socio-demographic variables such as gender.
An Integrated Decision-Making Framework for Mitigating the Impact of Urban Heat Islands on Energy Consumption and Thermal Comfort of Residential Buildings
Authors: Turhan Cihan , Atalay Ali Serdar , Gokcen Akkurt Gulden
Published: June 2023

Urban heat island (UHI) is a zone that is significantly warmer than its surrounding rural zones as a result of human activities and rapid and dense urbanization. Excessive air temperature due to the UHI phenomenon affects the energy performance of buildings and human health and contributes to global warming. Knowing that most of the building energy is consumed by residential buildings, therefore, developing a framework to mitigate the impact of the UHI on residential building energy performance is vital. This study develops an integrated framework that combines hybrid micro-climate and building energy performance simulations and multi-criteria decision-making techniques. As a case study, an urban area is analyzed under the Urban GreenUP project funded by the European Union’s Horizon 2020 Programme. Four different strategies to mitigate the UHI effect, including the current situation, changing the low-albedo materials with high-albedo ones, nature-based solutions, and changing building façade materials, are investigated with a micro-climatic simulation tool. Then, the output of the strategies, which is potential air temperature, is used in a dynamic building energy simulation software to obtain energy consumption and thermal comfort data of the residential buildings in the case area. Finally, a multi-criteria decision-making model, using real-life criteria, such as total energy consumption, thermal comfort, capital cost, lifetime and installation flexibility, is used to make a decision for decreasing the UHI effect on residential energy performance of buildings. The results showed that applying NBSs, such as green roofs and changing existing trees with high leaf area density ones, have the highest ranking among all mitigation strategies. The output of this study may help urban planners, architects, and engineers in the decision-making processes during the design phase of urban planning.
Do Eco-Based Adaptation Measures Enhance Ecosystem Adaptation Services? Economic Evidence from a Study of Hillside Forests in a Fragile Watershed in Northeastern Taiwan
Authors: Chen Wan-Jiun , Jan Jihn-Fa , Chung Chih-Hsin , Liaw Shyue-Cherng
Published: June 2023

As the risks of climate change keep increasing, countries have emphasized the ecosystem adaptation policy, and the United Nation Environmental Program (UNEP) aids countries to adapt to a warming world with eco-based adaptation (EbA) measures for good ecosystem governance for boosting ecosystem adaptation services (EAS). With the purpose of helping to indicate the magnitude of the benefits of EAS from local EbA measures, this study assesses the economic value of the EAS of hillside forests regarding the residents in a climate vulnerable watershed, the Lanyang River watershed, by applying a single-bounded contingent evaluation method. The demographic variables and motivation variables indexed by perceived impacts are influencing factors in the residents’ willingness-to-pay. These variables are of significance in EbA policy application. The average economic value for each responding resident was estimated to be NT$ 793.65 on the basis of a survey of the residents’ willingness to pay for EAS and the single-boundary contingent valuation method. The results verified that the residents depend on the protection of natural hillside ecosystems. Considering the complex interactions between ecosystems and humans, the EbA is demonstrated to be a crucial method for mitigating the consequences of climate change. Protecting hillside ecosystems in the Lanyang River watershed through soil and water management presents critical policy implications. Now that climate change has become an emergency, this case study shows the success of Taiwan’s long manipulated EbA for EAS, with evidence of residents benefiting. This Taiwan case study has policy implications for the world and UNEP’s global EbA program to maintain EAS.
Research on the Trade-Offs and Synergies of Ecosystem Services and Their Impact Factors in the Taohe River Basin
Authors: Zhou Jing , Zhang Bo , Zhang Yaowen , Su Yuhan , Chen Jie , Zhang Xiaofang
Published: June 2023

The Taohe River Basin is an essential ecological function area in the upper reaches of the Yellow River. Understanding the intricate trade-offs and synergies between ecosystem services (ESs) and exploring the impact of different factors are essential for achieving win–win outcomes in ecosystem management and socioeconomic development. The role of impact factors on the relationship between ESs, nevertheless, is more challenging to spatialize. This study used different models to estimate the net primary productivity (NPP), water yield (WY), and soil conservation (SC), and analyzed synergies and trade-offs between Ess. The spatial heterogeneity of the effects of natural and social factors on the relationships between Ess was explored using a geographic detector and a multi-scale geographically weighted regression (MGWR) model. The results show that: (1) NPP, WY, and SC all exhibit a rising trend, with multi-year averages of 488.99 gC/m2, 157.29 mm, and 1441.51 t/hm2, respectively; (2) NPP–WY and NPP–SC exhibit trade-offs in the majority of regions, while WY–SC are primarily synergistic in the upper and middle reaches, and they have the highest percentage of cropland, forest, and grassland; and (3) precipitation (PRE) has the greatest impact on the trade-off between NPP–WY and NPP–SC in the upper and middle reaches, and the gross domestic product (GDP), population density (POP), and distance from cropland (CROP) are the primary factors determining the synergy between NPP and WY in the lower reaches of the Loess Plateau cropping sector. PRE, digital elevation model (DEM), and CROP are the primary impact factors affecting the synergy of WY–SC. This study may serve as a reference for examining the evolutionary mechanism underlying the trade-offs and synergies between ESs and provide a scientific basis for future ecological environmental protection and regional land management in the Taohe River Basin.
Embedding Sustainable Mathematics Higher Education in the Fourth Industrial Revolution Era Post-COVID-19: Exploring Technology-Based Teaching Methods
Authors: Naidoo Jayaluxmi , Reddy Sarasvathie
Published: June 2023

Higher Education Institutions have adopted technology-based teaching methods to prevent the spreading of the contagious coronavirus (COVID-19). In the Fourth Industrial Revolution (4IR) era, technology-based teaching methods are indispensable for scaffolding teaching, learning and assessment. Mathematics is an important discipline in education, and provides a basis for problem solving, critical thinking and analytical skills, which is important to consider when focusing on sustainability. Thus, to add knowledge to the field about integrating technology-based teaching methods in mathematics, Higher Education environments during and post-COVID-19 need to be interrogated. This added knowledge is be valuable in the 4IR era post-COVID-19 for sustaining mathematics in Higher Education. This study explored participants’ experiences, views, implications and suggestions for technology-based teaching methods for mathematics. These participants (N = 45) were postgraduate students and mathematics school teachers at the research site in KwaZulu-Natal, South Africa. The research location for this qualitative study was a South African Higher Education Institution, and the study was conducted during the COVID-19 pandemic. The Community of Inquiry theoretical framework and the Substitution, Augmentation, Modification and Redefinition (SAMR) theoretical model guided this study. Participants were invited to two interactive online workshops. At these workshops, participants were introduced to different technology-based teaching methods. Then, they were invited to individual online interviews. The findings of this study suggest important experiences, views and suggestions for using technology-based teaching methods in Higher Education mathematics contexts in the 4IR era. These findings provide important implications and further research possibilities for embedding sustainable mathematics in Higher Education in the 4IR era, post-COVID-19.
Urethral Sheath to Evacuate Blood Clots through Mitrofanoff Appendicovesicostomy
Authors: Della Corte Marcello , Clemente Erica , Sibona Mattia , Cerchia Elisa , Tulelli Berenice , Gontero Paolo , Gerocarni Nappo Simona
Published: June 2023

Background: the Mitrofanoff appendicovesicostomy provides a catheterizable submucosal tunnel between umbilicus and bladder (or neobladder). In patients with a closed bladder neck, the Mitrofanoff channel is the only way to access the bladder. We describe our case of a 17 year-old girl with a Mitrofanoff appendicovesicostomy and a previous surgical closure of the bladder neck and who developed a large bladder clot due to hematuria after a surgical cystolithotomy in an augmented bladder; Methods: after an unsuccessful trans-appendicovesicostomy bladder washing, the endoscopic evaluation was performed using a 14 Ch rigid cystoscope and surrounded by its own urethral sheath. The clot was progressively fragmented through the cystoscope under direct vision. Clot fragments were aspirated to obtain a complete evacuation; Results: the urethral sheath prevented damages to the appendicovesicostomy, allowing at the same time repeated accesses of the cystoscope into the neobladder and ensuring the procedural success. The postoperative period was uneventful, and the neobladder catheter was removed after two days. Neither channel stenosis nor anastomosis dehiscence nor incontinence were reported after five months; Conclusions: the use of urethral sheath 14 Ch through an appendicovesicostomy preserves both the stoma and the channel, making possible endoscopic procedures such as blood clot evacuation into the neobladder.
Finite Reservoirs Corrections to Hamiltonian Systems Statistics and Time Symmetry Breaking
Authors: Colangeli Matteo , Di Francesco Antonio , Rondoni Lamberto
Published: June 2023

We consider several Hamiltonian systems perturbed by external agents that preserve their Hamiltonian structure. We investigate the corrections to the canonical statistics resulting from coupling such systems with possibly large but finite reservoirs and from the onset of processes breaking the time-reversal symmetry. We analyze exactly solvable oscillator systems and perform simulations of relatively more complex ones. This indicates that the standard statistical mechanical formalism needs to be adjusted in the ever more investigated nano-scale science and technology. In particular, the hypothesis that heat reservoirs be considered infinite and be described by the classical ensembles is found to be critical when exponential quantities are considered since the large size limit may not coincide with the infinite size canonical result. Furthermore, process-dependent emergent irreversibility affects ensemble averages, effectively frustrating, on a statistical level, the time reversal invariance of Hamiltonian dynamics that are used to obtain numerous results.