bluuebunny/crossref_metadata_2025 huggingface dataset:
Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis
Authors: Cao Xiaoqin , Liu Ziming , Hu Chenlin , Song Xiaolong , Quaye Jonathan Atuquaye , Lu Ning
Published: July 2024

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.
Lingonberry (Vaccinium vitis-idaea L.) Skin Extract Prevents Weight Gain and Hyperglycemia in High-Fat Diet-Induced Model of Obesity in Mice
Authors: Ryyti Riitta , Hämäläinen Mari , Tolonen Tiina , Mäki Marianne , Jaakkola Mari , Peltola Rainer , Moilanen Eeva
Published: July 2024

The percentage of obese people is increasing worldwide, causing versatile health problems. Obesity is connected to diseases such as diabetes and cardiovascular diseases, which are preceded by a state called metabolic syndrome. Diets rich in fruits and vegetables have been reported to decrease the risk of metabolic syndrome and type 2 diabetes. Berries with a high polyphenol content, including lingonberry (Vaccinium vitis-idaea L.), have also been of interest to possibly prevent obesity-induced metabolic disturbances. In the present study, we prepared an extract from the by-product of a lingonberry juice production process (press cake/pomace) and investigated its metabolic effects in the high-fat diet-induced model of obesity in mice. The lingonberry skin extract partly prevented weight and epididymal fat gain as well as a rise in fasting glucose level in high-fat diet-fed mice. The extract also attenuated high-fat diet-induced glucose intolerance as measured by an intraperitoneal glucose tolerance test (IPGTT). The extract had no effect on the levels of cholesterol, triglyceride or the adipokines adiponectin, leptin, or resistin. The results extend previous data on the beneficial metabolic effects of lingonberry. Further research is needed to explore the mechanisms behind these effects and to develop further health-promoting lingonberry applications.
The Efficacy of Probiotics, Prebiotics, Synbiotics, and Fecal Microbiota Transplantation in Irritable Bowel Syndrome: A Systematic Review and Network Meta-Analysis
Authors: Wu Youhe , Li Yuetong , Zheng Qi , Li Lanjuan
Published: July 2024

Irritable bowel syndrome (IBS) is a common gastrointestinal disorder with gut microbiota imbalance playing a significant role. There are increasing numbers of research studies exploring treatment options involving probiotics, prebiotics, synbiotics, and fecal microbiota transplantation (FMT), but it is still uncertain which treatment option is superior. The research was conducted on various databases and unpublished trial data (up to February 2023). Randomized controlled trials (RCTs) were screened for adult patients with IBS comparing interventions with placebo. Probiotics, prebiotics, synbiotics, and FMT were assessed for their impact using mean difference and Bayesian network meta-analysis. Out of 6528 articles, 54 were included for probiotics, 7 for prebiotics/synbiotics, and 6 for FMT. Probiotics showed improvement in IBS symptoms, particularly with Bifidobacterium and Lactobacillus strains. Prebiotics and synbiotics did not show significant improvement. Network meta-analysis indicated the favorable effects of probiotics (OR = 0.53, 95% CI, 0.48 to 0.59) and FMT (OR = 0.46, 95% CI, 0.33 to 0.64) on IBS, with no serious adverse events reported. In short, probiotics and FMT are effective for managing IBS, with Bifidobacterium and Lactobacillus being dominant strains. However, the most effective probiotic combination or strain remains unclear, while prebiotics and synbiotics did not show significant improvement.
Research Progress of Natural Rubber Wet Mixing Technology
Authors: Zhao Qinghan , Niu Fangyan , Liu Junyu , Yin Haishan
Published: July 2024

The performance of natural rubber (NR), a naturally occurring and sustainable material, can be greatly enhanced by adding different fillers to the NR matrix. The homogeneous dispersion of fillers in the NR matrix is a key factor in their ability to reinforce. As a novel method, wet mixing technology may effectively provide good filler dispersion in the NR matrix while overcoming the drawbacks of conventional dry mixing. This study examines the literature on wet mixing fillers, such as graphene, carbon nanotubes, silica, carbon black, and others, to prepare natural rubber composites. It also focuses on the wet preparation techniques and key characteristics of these fillers. Furthermore, the mechanism of filler reinforcement is also examined. To give guidance for the future development of wet mixing technology, this study also highlights the shortcomings of the current system and the urgent need to address them.
Attention Guide Axial Sharing Mixed Attention (AGASMA) Network for Cloud Segmentation and Cloud Shadow Segmentation
Authors: Gu Guowei , Wang Zhongchen , Weng Liguo , Lin Haifeng , Zhao Zikai , Zhao Liling
Published: July 2024

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation.
Recent Developments and Challenges in Solid-Contact Ion-Selective Electrodes
Authors: Gao Lili , Tian Ye , Gao Wenyue , Xu Guobao
Published: July 2024

Solid-contact ion-selective electrodes (SC-ISEs) have the advantages of easy miniaturization, even chip integration, easy carrying, strong stability, and more favorable detection in complex environments. They have been widely used in conjunction with portable, wearable, and intelligent detection devices, as well as in on-site analysis and timely monitoring in the fields of environment, industry, and medicine. This article provides a comprehensive review of the composition of sensors based on redox capacitive and double-layer capacitive SC-ISEs, as well as the ion–electron transduction mechanisms in the solid-contact (SC) layer, particularly focusing on strategies proposed in the past three years (since 2021) for optimizing the performance of SC-ISEs. These strategies include the construction of ion-selective membranes, SC layer, and conductive substrates. Finally, the future research direction and possibilities in this field are discussed and prospected.
Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques
Authors: Musthafa Muhammad Bisri , Huda Samsul , Kodera Yuta , Ali Md. Arshad , Araki Shunsuke , Mwaura Jedidah , Nogami Yasuyuki
Published: July 2024

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs’ ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models’ performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of 96.92% and 99.77% and overfitting values of 0.33% and 0.04% on the two datasets, respectively. The model’s ROC is also shaped with a sharp bend, with AUC values of 0.9665 and 0.9971 for the UNSW-NB15 dataset and the NSL-KD dataset, respectively.
Triboelectric Nanogenerators: State of the Art
Authors: Shi Zhan , Zhang Yanhu , Gu Jiawei , Liu Bao , Fu Hao , Liang Hongyu , Ji Jinghu
Published: July 2024

The triboelectric nanogenerator (TENG), as a novel energy harvesting technology, has garnered widespread attention. As a relatively young field in nanogenerator research, investigations into various aspects of the TENG are still ongoing. This review summarizes the development and dissemination of the fundamental principles of triboelectricity generation. It outlines the evolution of triboelectricity principles, ranging from the fabrication of the first TENG to the selection of triboelectric materials and the confirmation of the electron cloud overlapping model. Furthermore, recent advancements in TENG application scenarios are discussed from four perspectives, along with the research progress in performance optimization through three primary approaches, highlighting their respective strengths and limitations. Finally, the paper addresses the major challenges hindering the practical application and widespread adoption of TENGs, while also providing insights into future developments. With continued research on the TENG, it is expected that these challenges can be overcome, paving the way for its extensive utilization in various real-world scenarios.
Converter Capacitor Temperature Estimation Based on Continued Training LSTM under Variable Load Conditions
Authors: Dai Xiaoteng , Chen Yiqiang , Chen Jie , Qiu Ruichang
Published: July 2024

Capacitors are crucial components in power electronic converters, responsible for harmonic elimination, energy buffering, and voltage stabilization. However, they are also the most susceptible to damage due to their operational environment. Accurate temperature estimation of capacitors is essential for monitoring their condition and ensuring the reliability of the converter system. This paper presents a novel method for estimating the core temperature of capacitors using a long short-term memory (LSTM) algorithm. The approach incorporates a continued training mechanism to adapt to variable load conditions in converters. Experimental results demonstrate the proposed method’s high accuracy and robustness, making it suitable for real-time capacitor temperature monitoring in practical applications.
Influence of Impaired Upper Extremity Motor Function on Static Balance in People with Chronic Stroke
Authors: Mallo-López Ana , Cuesta-Gómez Alicia , Fernández-Pardo Teresa E. , Aguilera-Rubio Ángela , Molina-Rueda Francisco
Published: July 2024

Background: Stroke is a leading cause of disability, especially due to an increased fall risk and postural instability. The objective of this study was to analyze the impact of motor impairment in the hemiparetic UE on static balance in standing, in subject with chronic stroke. Methods: Seventy adults with chronic stroke, capable of independent standing and walking, participated in this cross-sectional study. The exclusion criteria included vestibular, cerebellar, or posterior cord lesions. The participants were classified based on their UE impairment using the Fugl-Meyer Assessment of Motor Recovery after Stroke (FMA-UE). A posturographic evaluation (mCTSIB) was performed in the standing position to analyze the center of pressure (COP) displacement in the mediolateral (ML) and anteroposterior (AP) axes and its mean speed with eyes open (OE) and closed (EC) on stable and unstable surfaces. Results: A strong and significant correlation (r = −0.53; p < 0.001) was observed between the mediolateral (ML) center of pressure (COP) oscillation and the FMA-UE, which was particularly strong with eyes closed [r(EO) = 0.5; r(EC) = 0.54]. The results of the multiple linear regression analysis indicated that the ML oscillation is influenced significantly by the FMA-Motor, and specifically by the sections on UE, wrist, coordination/speed, and sensation. Conclusions: The hemiparetic UE motor capacity is strongly related to the ML COP oscillation during standing in individuals with chronic stroke, with a lower motor capacity associated with a greater instability. Understanding these relationships underpins the interventions to improve balance and reduce falls in people who have had a stroke.
Recommendation System for Sustainable Day and Night-Time Cultural Tourism Using the Mean Signed Error-Centric Recurrent Neural Network for Riyadh Historical Sites
Authors: Jeribi Fathe , Perumal Uma , Alhameed Mohammed Hameed
Published: July 2024

To accommodate user-specific requirements and preferences, a travel Recommendation System (RS) gives a customized place of interest. The prevalent research did not provide solutions to some essential situations for cultural tourism, including relevant time, environmental conditions, and stay places. Thus, the existing RS models led to unreliable cultural tourism recommendations by neglecting essential factors like personalized itineraries, environmental conditions of the cultural sites, sentiment analysis of the hotel reviews, and sustainable cultural heritage planning. To overcome the above factors, a day- and night-time cultural tourism RS utilizing the Mean Signed Error-centric Recurrent Neural Network (MSE-RNN) is proposed in this paper. The proposed work develops an efficient RS by considering historical data, Geographic Information System (GIS) map location, hotel (stay place) reviews, and environmental data to access day and night cultural tourism. First, from the Geographic Information System (GIS) map and hotel data, the historical and hotel geolocations are extracted. Currently, these locations are fed to Similarity-centric Hamilton Distance-K-Means (SHD-KM) for grouping the nearest locations. Next, hotels are ranked utilizing the Tent Mapping-centric Black Widow Optimization (TM-BWO) approach centered on the locations. In addition, using Bidirectional Encoder Representations from Transformers (BERT), the essential keywords from the historical geo-locations are embedded. In the meantime, the sites’ reviews and timings are extracted from Google. The extracted reviews go through pre-processing, and the keywords from the pre-processed data are extracted. For the extracted keywords, polarity is calculated centered on the Valence-Aware Dictionary for Sentiment Reasoning (VADER). Concurrently, utilizing the Reference-centric Pearson Correlation Coefficient (R-PCC), the timings of the sites are segregated. Lastly, for providing a recommendation of tourist sites, the embedded words, ranked hotels, and segregated timings, along with the pre-processed environment and season data, are fed to the MSE-RNN classifier. At last, the experimental evaluation verified that other recommendation systems were surpassed by the proposed approach.
The Impact of Information Distortions on Decision-Making: A Case Study in Land–Sea Transport Chain Planning
Authors: Torok Adam , Semenov Iouri , Filina-Dawidowicz Ludmiła , Durczak Wojciech
Published: July 2024

Management based on reliable, complete, and up-to-date information is key to increasing a transport chain’s effectiveness and sustainability. The instability of the business environment and the increase in competition have contributed to the growth of challenges faced by managers of transport and logistics companies who make decisions using low-value information exposed to significant distortions. This article aims to investigate the impact of information distortions on decision-making quality and to determine the probability of making trustworthy decisions in freight land–sea transport chain planning. The research was carried out in several stages, which included the formulation of rules for detecting information distortions, as well as distortion clustering and evaluation of their impact on decision quality. A methodology to assess the probability of making trustworthy decisions was developed. It was shown that information value directly impacts the quality of decisions related to transport chain planning for both traditional and unique/occasional freight. In the case of significant information distortion, the manager’s ability to assess available information considerably increases, especially in crises, when the lack of time to verify information threatens the accuracy of decisions. This study’s results may be useful for transport and logistics companies’ managers, who make decisions using information obtained from various sources.
Challenges and Problems of Nature Conservation: A Case Study from Poland
Authors: Kozera-Kowalska Magdalena , Jęczmyk Anna , Piekutowska Magdalena , Uglis Jarosław , Niedbała Gniewko
Published: July 2024

The article aims to show the attitudes and views of Polish residents on the problem of preserving the natural environment from the perspective of their place of residence. The need for research in this area stems from the insufficient number of available studies on this very important issue given the global environmental challenges we are facing. The research gap noted relates particularly to the aspects of engagement in environmental measures, knowledge levels, and motivations for conservation efforts by local citizens. Environmentally and socially responsible behavior is part of the concept of sustainable development. Empirical research covered a sample of 500 adult residents of Poland using the CAWI technique. The results showed that the vast majority of respondents noticed numerous problems in preserving the natural environment in their place of residence. According to respondents, the way to reduce these problems is to increase care for green areas, promote renewable energy sources, and strive to reduce waste. Moreover, the research results show that respondents take initiatives to segregate waste, save energy, and apply the zero-waste concept. The main reason for taking action to solve environmental problems is to preserve the environment for our children and future generations. The results of these studies showed that for men, pro-environmental activities are more important than for women. These findings are valuable for policymakers, local authorities, and fellow citizens.
Traditional Regionalism or Modern Minimalism? Unveiling the Psychological Impact of Architectural Styles in Sustainable Urban Planning
Authors: Cao Zihao , Mustafa Muhizam , Mohd Isa Mohd Hafizal
Published: July 2024

Life satisfaction is one of the key indicators of people’s well-being. However, the choice between traditional regionalism and modern minimalism in urban planning remains controversial from a human-centered perspective. Using a sample of 438 respondents from Huangshan City in 2024, we explored the relationships between architectural style, general belongingness, social identity, and life satisfaction through structural equation modeling (SEM). The results indicate significant correlations among the four variables in the model, with general belongingness and social identity acting as mediators in the relationship between architectural style and life satisfaction. Furthermore, residents living in traditional regional architecture scored higher on the scales of general belongingness, social identity, and life satisfaction compared to those living in modern minimalist architecture. These findings provide new insights for urban planning, highlighting the importance of considering the potential psychological and social welfare impacts of architectural styles on residents during the design process. Additionally, they emphasize the value of preserving and promoting traditional regional architecture. This study provides empirical evidence for urban planners and architects, supporting the integration of more human-centered design principles into modern urban environments to enhance residents’ well-being.
Analysis of Water Quality Prediction in the Yangtze River Delta under the River Chief System
Authors: Wu Guanghui , Zhang Cheng
Published: July 2024

Water quality prediction is essential for effective water resource management and pollution prevention. In China, research on predictive analytics for various water bodies has not kept pace with environmental needs. This study addresses this gap by conducting a comprehensive analysis and modeling of water quality monitoring data from multiple distributed water bodies specifically within the Yangtze River Delta. Using a novel approach, this paper introduces a distributed water quality prediction system enhanced by a CNN-LSTM joint model. This model synergistically combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks to robustly extract and utilize spatiotemporal data, thereby significantly improving the accuracy of predicting dynamic water quality trends. Notably, the excellent predictive performance of the joint model enables its prediction results to achieve RMSE and MAPE as low as 1.08% and 6.8%, respectively. Empirical results from this study highlight the system’s superior predictive performance. Based on these findings, this paper offers targeted recommendations for water quality monitoring, treatment, and management strategies tailored to the specific needs of the Yangtze River Delta. These contributions are poised to aid policymakers and environmental managers in making more informed decisions.
Coupling and Coordination Development, Spatiotemporal Evolution, and Driving Factors of China’s Digital Countryside and Inclusive Green Growth in Rural Areas
Authors: Chen Liupeng , Wang Yiting , Yan Yingzheng , Zhou Ziwei , Xie Bangsheng , You Xiaodong
Published: July 2024

Inclusive green growth is an effective strategy for achieving sustainable development in rural areas. In the digital economy era, it is crucial to examine whether rural digital development and inclusive green growth can be harmoniously integrated. This study investigates the spatial and temporal evolution of the coupled coordination between digital village construction and rural inclusive green growth in China. Utilizing panel data from 30 provinces from 2011 to 2022, we assess development levels using the entropy weighting method and analyze interdependencies with a coupling coordination model. The results indicate an upward trend in coupling coordination, with significant regional disparities, and it is slowly taking on the characteristics of spatial clustering. Economically advanced regions exhibit higher coordination levels, attributed to stronger economic foundations and better fiscal resources, enabling effective investments in digital infrastructure and green growth initiatives. Additionally, factors such as urbanization rate, innovation levels, reduction in natural disasters, increased financial support for agriculture, and improved large-scale operations positively contribute to this coordination. These findings offer insights for targeted regional development strategies, enhancing the synergy between digital transformation and sustainable rural development.
Scenarios in an Urban Planning Studio: The Perception of Multidisciplinary Students
Authors: Sedrez Maycon , Cheshmehzangi Ali , Xie Linjun , Wang Yimeng
Published: July 2024

With the ongoing development of increasingly complex and uncertain urban contexts, imagining and envisioning future scenarios have become essential competences in the education of urban planners. We applied scenario planning in a postgraduate studio titled Sustainable Urbanism Project that aimed at facilitating students’ learning and improving their ability to craft context-based and future-oriented urban solutions. We believe that this method effectively guided students with multidisciplinary backgrounds to engage with the subject, enhancing their creativity and capability in co-designing sustainable futures. This paper investigates the expectations and challenges faced by students throughout the project process, discussing the applicability of the method and its outcomes, pitfalls, and advantages. The study highlights the reflective role of scenario planning in urban planning studios to guide students in recognising sustainable and unsustainable scenarios for the future. It is important that scenario planning allows students to fully understand the planning process rather than just focusing on generic planning solutions or interventions.
Mapping the Nexus: A County-Level Analysis and Visualization of Iowa’s Food–Energy–Water Systems
Authors: Brittes Tuthill Júlia , Kaleita Amy
Published: July 2024

This study presents a county-level analysis and visual assessment of Iowa’s Food–Energy–Water systems (IFEWs), focusing on the interdependencies of agricultural practices, nitrogen management, and energy production. We use data from the USDA National Agricultural Statistics Service and other sources to assess cropland-nitrogen sources, animal-based-nitrogen contributions, and ethanol production capacity across Iowa’s counties. Our methodology leverages geoprocessing and interpolation tools to address data availability challenges and refine nitrogen surplus (Ns) estimates. The results reveal spatial–temporal dynamics of cropland-nitrogen, quantify non-point nitrogen sources at the county level, and evaluate the impact of energy systems on the IFEWs balance. We discuss the implications of our findings for sustainable agriculture, environmental management, and energy production in Iowa. The study highlights the need for integrated approaches to address the complex interactions within the IFEWs and informs policy development for sustainable resource management.
Sustainability of the Housing Market: Post-COVID-19 Effects on Housing Preferences in Cyprus
Authors: Evangelou Maria , Katafygiotou Martha
Published: July 2024

After the announcement of the outbreak of the COVID-19 pandemic, people around the world had to adopt online education, working from home and social-distancing policies in an effort to stop the spread of COVID-19. The lockdowns and home isolation brought to the surface the current limitations of the existing housing stock as people needed to re-organize their home space to host various daytime functionalities. Therefore, one could question whether the priorities of potential homebuyers have been changed due to technology, health, and socialization alteration? This study explores the preferences for housing attributes and trends in Cyprus post-COVID-19 pandemic and compares these against the views of real estate professionals who represent the Cypriot residential demand prior the pandemic. Data were gathered via qualitative surveys from 117 potential homebuyers and 36 real estate professionals from across Cyprus. A survey methodology with questionnaires was used to analyze the changes in the real estate demand between the pre- and post-COVID-19 periods. Descriptive statistics were used to determine the mean importance of the housing attributes and to compare the results from the pre- and post-COVID-19 pandemic periods. The analysis found a shift toward houses rather than apartments, and the results suggest that new housing should contain more capacity for private use spaces, and at the same time, it should be designed with flexible and adaptive layouts to accommodate more activities. This study discusses whether these shifts are likely to be temporary or permanent and the implications for real estate professionals.
Predicting High Technology Exports of Countries for Sustainable Economic Growth by Using Machine Learning Techniques: The Case of Turkey
Authors: Gulzar Yonis , Oral Ceren , Kayakus Mehmet , Erdogan Dilsad , Unal Zeynep , Eksili Nisa , Caylak Pınar Celik
Published: July 2024

In this study, the estimation of high-tech exports for Turkey’s foreign trade target in line with sustainable development was carried out. The research was carried out for Turkey since it has been focusing on sustainable and environmentally friendly production and an export-oriented growth model, with a transformation in its economic growth strategy as of 2021, and high-tech products are a determining factor in the export target. In this research, three different machine learning techniques, namely artificial neural networks, logistic regression, and support vector regression, were used to determine a successful prediction method close to the ideal scenario. In the models, high technology exports for the period of 2007–2023 with data obtained from the World Bank were taken as the dependent variable, while the gross national product, number of patents, and research and development expenditures were taken as independent variables. By calculating the R2, MAPE, and MSE metrics, the success of the model with the least error was evaluated, and it was seen that artificial neural networks (ANNs) were the most successful model, with values of 94.2%, 0.011, and 0.073, respectively. The ANN model was followed by support regression and logistic regression.
Environmental Regulation, Foreign Direct Investment, and Green Total Factor Productivity: An Empirical Test Based on Chinese City-Level Panel Data
Authors: Chen Lei , Hu Lijun , He Fang , Zhang Heqi
Published: July 2024

Environmental regulation is a crucial tool for government intervention in the field of green technology innovation. It can boost an enterprise’s competitiveness and encourage green technology innovation, both of which have a major effect on luring foreign investment. This paper first systematically elaborates on the relationship between environmental regulation, foreign direct investment (FDI), and green total factor productivity (GTFP) and then combines panel data from Chinese cities to empirically test these relationships using various methods, such as the mediation effect model, two-stage least squares, and difference-in-differences method. The study found that environmental regulation significantly boosts FDI and GTFP. FDI helps to improve GTFP, and environmental regulation can impact GTFP indirectly through FDI. The way that FDI and environmental regulations affect GTFP demonstrates regional variation. Large cities with high economic growth gain more from environmental regulation. FDI has a stronger promotion effect on GTFP in medium- and small-sized cities than in large-sized cities, and it does not significantly impact GTFP in cities with high levels of economic development or in the eastern region.
Evaluating the Influence of Sustainable Urban Disaster Prevention Planning Satisfaction on Residents’ Living Willingness: A Structural Equation Modeling Approach in Kaohsiung City
Authors: Chen Yih-Tzoo , Yang Annette Yee-Yen
Published: July 2024

Rapid urban development and increasing climate-related challenges underscore the need for effective urban disaster mitigation strategies. This study, conducted in Kaohsiung City, Taiwan, uses a Structural Equation Model to assess how satisfaction with urban disaster prevention planning influences residents’ intentions to stay there. We found that satisfaction with sustainable urban disaster prevention increases residents’ willingness to remain and acts as a key mediator between satisfaction with public facilities and residential intentions. The methodology provides practical insights for urban planners and policymakers, highlighting that improving public facilities and effective disaster planning significantly strengthens residents’ attachment to urban environments. These findings suggest a pragmatic way to enhance urban sustainability and resilience by incorporating disaster preparedness into urban development plans.
Core Indicators for Monitoring the Sustainable Urban Mobility Plan: A Case Study
Authors: Rutka Anna , Michnej Maciej , Zwoliński Tomasz , Gabory Maciej
Published: July 2024

The primary objective of this paper is to validate core indicators for monitoring Sustainable Urban Mobility Plans (SUMPs), focusing on the Functional Urban Area in Southern Poland. Given the high concentration of the population in urban areas within the European Union (EU)—currently at 70.9% and expected to rise to 83.7% by 2050—there is an urgent need to address associated challenges such as congestion and the significant contribution of cities to transport-related greenhouse gas emissions. Effective urban mobility planning is, therefore, more critical than ever. This study aims to provide a methodological framework for determining baseline values for four essential monitoring indicators: road safety, accessibility to public mass transit, greenhouse gas emissions, and air quality. These indicators are crucial for the European Commission’s evaluation of SUMPs. By focusing on these aspects, the research aims to enhance the effectiveness of urban mobility plans, ensuring they contribute meaningfully to the EU’s transport, climate, health, and social goals as outlined in the European Green Deal and the Sustainable and Smart Mobility Strategy.
Removal and Recovery of Europium with a New Functionalized Mesoporous Silica-Based Adsorbent
Authors: Fonseka Charith , Ryu Seongchul , Kandasamy Jaya , Ratnaweera Harsha , Vigneswaran Saravanamuthu
Published: July 2024

The discharge of acid mine drainage (AMD), characterized by a high concentration of rare earth elements (REEs), poses a significant threat to the health of ecosystems surrounding water sources. The global market demand for REEs has experienced a notable surge in the past decade. Consequently, recovering REEs from waste streams like AMD not only benefits the environment but also offers financial advantages. Europium (Eu), the rarest among REEs, constitutes only 0.1% w/w in monazite and bastnaesite ores. Eu is extensively used in the production of phosphors, alloys, and additives, and is a critical raw material for developing smart devices, ranging from high-resolution color screens to circuitry. Traditional adsorbents typically exhibit limited selectivity towards REE recovery. Mesoporous silica materials, such as SBA15 (Santa Barbara Amorphous-15), provide excellent tunability and modification capabilities, making them an attractive and cost-effective alternative. This research focused on two key aspects: (i) evaluating the dynamic adsorption column performance of granulated SBA15–NH–PMIDA to preferentially recover Eu, and (ii) employing mathematical modeling to optimize the dynamic adsorption column’s operating conditions for real-world applications with a minimal number of experimental runs. Granulated SBA15–NH–PMIDA was chosen as the adsorbent due to its high adsorptive capacity and selectivity in capturing Eu. The study revealed that granulated SBA15–NH–PMIDA exhibited 57.47 mg/g adsorption capacity and an 81% selectivity towards Eu. Furthermore, SBA15–NH–PMIDA demonstrated preferential adsorption toward Eu in complex multi-component solutions, such as AMD. The linear driven force approximation model (LDFAM) provided an acceptable simulation (R2 > 0.91) under varying operational conditions. This validates the use of the model as a tool to effectively simulate and optimize column experiments that used granulated SBA15–NH–PMIDA to recover Eu.
Between Wires and Wings: What Are the Impacts of Power Transmission Lines on the Diversity of Insectivorous Bats?
Authors: Falcão Fábio , Mira-Mendes Caio Vinícius de , Herrera-Lopera Jorge Mario
Published: July 2024

Energy consumption in the world is growing every year, and there is an increasing demand on the energy system to meet the increase in consumption, resulting in the installation of new power transmission lines. The understanding of how power transmission lines affect biodiversity is predominantly focused on birds, with limited information available on other organisms. In this study, we assessed the potential effect of power transmission lines on bat communities in a locality in the Cerrado biome in Brazil. More specifically, we used a paired sample design and acoustically sampled bats in locations near and far from the transmission lines. Our findings suggest that power transmission lines do not have a generally positive or negative effect on insectivorous bat communities in the study area. However, their presence seems to be associated with increased diversity in specific functional groups and changes in the activity patterns of some bat species and families. We believe that this information is of particular importance for establishing appropriate programs during the environmental licensing process, assisting in the development of projects in the different stages of construction as well as in monitoring programs during operation.
Open-Source Design of Infiltration Trenches for Sustainable Soil and Water Conservation in Rural Areas of Central Chile
Authors: Pizarro Roberto , Vidal-Silva Cristian , Jaque Dayana , Ibáñez Córdova Alfredo , Sangüesa Claudia , Toledo Cristóbal , Garcia-Chevesich Pablo A.
Published: July 2024

Specific algorithms are developed to solve the equations that define the physical dimensions under various conditions. In this sense, the storm index method was incorporated for the variable precipitation intensity, expanding the number of rainfall stations with the intensity duration frequency (IDF) curves from 9 to 31 within the considered territory (the Biobio Region of Central Chile). Likewise, the infiltration values and runoff coefficients necessary for calculating the dimensions of the trenches were obtained using the Python programming language. The results show that an open-source Python solution allows high reliability and efficiency based on the tests developed. For this reason, this prototype is expected to add new mathematical expressions that may arise to better account for an efficient design of soil and water conservation works or infiltration trenches. In this way, it is concluded that it is possible to develop simulation models for the efficient design of trenches based on well-defined and limited theoretical modeling, adding to computer language tools. This allows for a virtuous synergy that can help address efficient public policies to conserve soil and water in Chile and elsewhere.
Examining the Impact of Urban Connectivity on Urban Innovation Efficiency: An Empirical Study of Yangtze River Delta in China
Authors: Yang Chuankai , Xue Shuaijun , Gao Peng , Guo Xu
Published: July 2024

Innovation serves as a vital catalyst for sustainable urban development, with the enhancement of urban innovation efficiency representing a critical strategy to bolster cities’ innovative capacity. Rigorous scientific measurement of urban innovation efficiency and thorough investigation into the key factors influencing it are imperative for advancing urban innovation capacity. Despite this significance, prior research has largely overlooked the impact of urban connections on urban innovation efficiency. Therefore, this paper undertakes the task of measuring the innovation efficiency of 27 cities within China’s Yangtze River Delta (YRD) region using an improved DEA model, while also examining the associated influencing factors. The primary findings are as follows: (1) The comprehensive efficiency of the 27 cities in the YRD remains relatively low, with pure technical efficiency representing a notable constraint, while scale efficiency demonstrates higher overall performance. (2) The cities can be classified into four distinct categories: innovation-leading, innovation-optimizing, innovation-breaking, and innovation-improving cities. (3) The urban innovation efficiency within the YRD exhibits negative spatial spillover effects. (4) And notably, local economic and social characteristics such as human capital and degree of openness play a positive role in enhancing innovation efficiency in YRD cities. Conversely, factors such as economic foundation and government involvement exhibit negative contributions to innovation efficiency enhancement in YRD cities. Additionally, population mobility between cities is identified as a significant contributor to urban innovation efficiency. This study sheds light on the complex dynamics shaping urban innovation efficiency and underscores the importance of leveraging urban connections to bolster innovation capacity in the YRD and beyond.
Investigating the Impact of Recent and Future Urbanization on Flooding in an Indian River Catchment
Authors: Thaivalappil Sukumaran Sonu , Birkinshaw Stephen J.
Published: July 2024

Socioeconomic growth in India has caused massive infrastructure development which has resulted in extensive damage to the natural environment. A consequence of this urbanization has been extensive monsoon flooding in many locations within the country. The impact of recent land use and land cover (LULC) change because of urbanization and a series of future LULC scenarios is assessed for the Meenachil river basin in central Kerala, India. This catchment flows into the Kuttanad administrative area, which has the country’s lowest elevation, an increasing population, and currently suffers from regular flooding. Hydrological modeling using SHETRAN and hydraulic modeling using HEC-RAS predicts that an extreme event will produce a 105% rise in flood depth in 2100 compared to 2005. A scenario that incorporates Nature-based Solutions suggests the rise in flood depth could be reduced by 44%. A catchment response for future development is needed but is hindered by different administrative boundaries within the river basins that flow into the Kuttanad administrative area, and so this study concludes by providing regional-scale planning recommendations that integrate hydrologic components.
Assessing Salinity Reduction by Environmental Restoration in the Seomjin River Estuary (South Korea): A Numerical Model Approach for Corbicula Habitat Conservation
Authors: Lee Gayeong , Park Jongyoon , Jung Chunggil , An Yunkeun
Published: July 2024

This study modelled possible ways of reducing salinity in the Seomjin River estuary to improve habitats for corbicula, which are important components of the ecosystem (ecologically and commercially) in the Seomjin River. Additionally, we analyzed the salinity reduction effects of structural measures to sustain optimal salinity. To do this, salinity measurement facilities were installed at crucial habitat locations in the Seomjin River estuary. After ensuring the reliability of the model, numerical simulations were conducted. Salinity changes were analyzed for four major locations (Dugok, Shinbi, Mokdo, Hwamok) and simulated under various conditions, resulting in concentrations of 4.7 psu in Dugok, 16.0 psu in Shinbi, 19.2 psu in Mokdo, and 28.2 psu in Hwamok. Additionally, this study analyzed reduction effects by applying three reduction scenarios (riverbed restoration, submerged weir, and groyne) by simulating the numerical model. The reductions in salinity for the submerged weir (SWS-1 to 3) and groyne (GS-1 to 3) scenarios were minimal, below 1 psu, indicating a very limited reduction effect. In conclusion, the salinity concentration in the Seomjin River estuary is dominated by the neap tide, and the salinity intrusion distance is influenced more by the spring tide. The Songjeong discharge strongly impacts the spring tide, while the neap tide is less dominant due to salinity stratification. Among the salinity reduction scenarios, riverbed restoration has the most critical effect, but artificial reduction measures may pose challenges in terms of cost and practicality.