bluuebunny/crossref_metadata_2025 huggingface dataset:
Flood Risk Assessment for Sustainable Transportation Planning and Development under Climate Change: A GIS-Based Comparative Analysis of CMIP6 Scenarios
Authors: Abuzwidah Muamer , Elawady Ahmed , Ashour Ayat Gamal , Yilmaz Abdullah Gokhan , Shanableh Abdallah , Zeiada Waleed
Published: July 2024

Climate change is causing a range of environmental impacts, including increased flood frequency and intensity, posing significant risks to human populations and transportation infrastructure. Assessing flood risk under climate change is critical, but it is challenging due to uncertainties associated with climate projections and the need to consider the interactions between different factors that influence flood risk. Geographic Information Systems (GISs) are powerful tools that can be used to assess flood risk under climate change by gathering and integrating a range of data types and sources to create detailed maps of flood-prone areas. The primary goal of this research is to create a comprehensive GIS-based flood risk map that includes various climate change scenarios derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This goal will leverage the Analytic Hierarchy Process (AHP) methodology to better understand the impacts of these climate change scenarios on the transportation network. Furthermore, this study aims to evaluate the existing flood risk map and assess the potential impacts of prospective climate scenarios on the levels of flood risk. The results showed that the northern and coastal regions of the United Arab Emirates (UAE) are at higher risk of flooding, with the majority of the population living in these areas. The projections for future flood risk levels indicate that under the SSP245 scenario, flood risk levels will generally be low, but some areas in the northern and eastern regions of the UAE may still face high to very high flood risk levels due to extensive urbanization and low-lying coastal regions. Under the SSP585 scenario, flood risk levels are projected to be significantly higher, with a widespread distribution of very high and high flood risk levels across the study area, leading to severe damage to infrastructure, property, and human lives. The recent publication of the CMIP6 models marks a significant advancement, and according to the authors’ knowledge, there have been no studies that have yet explored the application of CMIP6 scenarios. Consequently, the insights provided by this study are poised to be exceptionally beneficial to researchers globally, underscoring the urgent necessity for holistic sustainable flood risk management approaches for geography, planning, and development areas. These approaches should integrate both sustainable transportation infrastructure development and risk mitigation strategies to effectively address the anticipated impacts of flooding events within the study region.
Short-Term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer
Authors: Hu Keyong , Fu Zheyi , Lang Chunyuan , Li Wenjuan , Tao Qin , Wang Ben
Published: July 2024

The intermittent nature of solar energy poses significant challenges to the integration of photovoltaic (PV) power generation into the electrical grid. Consequently, the precise forecasting of PV power output becomes essential for efficient real-time power system dispatch. To meet this demand, this paper proposes a deep learning model, the CA-Transformer, specifically designed for PV power output prediction. To overcome the shortcomings of traditional correlation coefficient methods in dealing with nonlinear relationships, this study utilizes the Copula function. This approach allows for a more flexible and accurate determination of correlations within time series data, enabling the selection of features that exhibit a high degree of correlation with PV power output. Given the unique data characteristics of PV power output, the proposed model employs a 1D-CNN model to identify local patterns and trends within the time series data. Simultaneously, it implements a cosine similarity attention mechanism to detect long-range dependencies within the time series. It then leverages a parallel structure of a 1D-CNN and a cosine similarity attention mechanism to capture patterns across varying time scales and integrate them. In order to show the effectiveness of the model proposed in this study, its prediction results were compared with those of other models (LSTM and Transformer). The experimental results demonstrate that our model outperforms in terms of PV power output prediction, thereby offering a robust tool for the intelligent management of PV power generation.
Development of Artificial Stone through the Recycling of Construction and Demolition Waste in a Polymeric Matrix
Authors: Reis Marcelo Barcellos , Colorado Lopera Henry Alonso , Vieira Carlos Maurício Fontes , Azevedo Afonso Rangel Garcez , Carvalho Elaine Aparecida Santos , Monteiro Sérgio Neves
Published: July 2024

Civil construction is one of the oldest activities known to humanity, with reports indicating that builders from the Roman Empire were already seeking to reuse materials. Currently, considering the depletion of natural resource supplies, the recycling of solid construction and demolition waste (CDW) not only provides new products but also presents ecological and economical alternatives. In this context, this research explores new variables for the disposal of CDW, with the manufacturing of artificial finishing stones appearing as a strong possibility to be studied. This research presents the development of a new composite from CDW, using an orthophthalic polyester resin as a binder. The waste was sieved and separated by granulometry using the simplex centroid method. The best-compacted mixture was determined statistically by ANOVA and Tukey’s test. The waste was characterized by X-ray fluorescence, and the resin by Fourier transform infrared spectroscopy. Artificial stone slabs were produced with 85% waste and 15% resin by mass, using the vibro-compression and vacuum system. They were subsequently cut for mechanical, physical, and chemical tests. Microstructural analysis was performed using scanning electron microscopy on the surfaces of the fractured compositions, as well as on the grains. The artificial stone with the best results had a density of 2.256 g/cm3, a water absorption of 0.69%, and an apparent porosity of 1.55%. It also exhibited a flexural strength of 34.74 MPa and a compressive strength of 111.96 MPa, alongside good results in alterability and thermal tests. In this satisfactory scenario, the use of this waste in the composition of artificial stones is promising, as it directly aligns with the concept of sustainable development. It replaces the end-of-life concept of the linear economy with new circular flows of reuse, restoration, and renewal, in an integrated process of the circular economy. Additionally, the quality of the final product exhibits properties similar to those of commercially available artificial stones.
Textiles on the Path to Sustainability and Circularity—Results of Application Tests in the Business-to-Business Sector
Authors: Rubik Frieder , Nebel Kai , Klusch Christina , Karg Hanna , Hecht Kim , Gerbig Martina , Gärtner Sven , Boldrini Barbara
Published: July 2024

The textile sector is responsible for a number of environmental impacts, e.g., climate change, and is not pursuing sustainable production and consumption patterns. Due to the increasing quantities of textiles, their share is rising, and a trend reversal from a linear to a circular and sustainable textile chain is needed. This article presents the background, methodological approach and results of a participatory textile development model. In the commercial B2B sector, three textile prototypes were developed together with users and trialled over several months in three application areas. Textile development took into account the requirements of fibre regeneration in the product design and focused on innovative more sustainable chemical recycling solutions. The three sustainably aligned textiles were subjected to spectroscopic and textile–technological tests. The sustainability tool screening life cycle assessments analysed their environmental profile and compared it with reference textiles that are used as the standard. Overall, it is clear that the three textiles can match conventional reference textiles in terms of quality and have considerable environmental benefits compared to the reference textiles. The user survey did identify concerns about a high artificial fibre content, although a general rejection of recycled fibres was not observed. The results show that a sustainable transformation is possible but must start with the fibre composition; recycling, on the other hand, is of minor importance.
Road–Rail Intermodal Travel Mode Choice Behavior Considering Attitude Factors
Authors: Wang Boqing , Li Jiajun , Jiang Fan
Published: July 2024

Road–rail intermodal transportation (RRIT) leverages the advantages of multiple transport modes and is crucial for addressing the current issue of imbalanced development in the transportation sector. However, passengers’ behavior in choosing RRIT remains unclear, and it is necessary to optimize travel service quality through analyzing RRIT choice behavior based on user perceptions. This study designed a stated preference experiment that included both direct and multi-modal travel options. A hybrid choice model considering attitude variables was constructed, and four latent attitude variables—convenience, economy, comfort, and riskiness—were extracted to analyze their impact on intercity travel mode choice behavior under conditions of ticket booking uncertainty. The results revealed that the ticket booking success rate is a critical factor in travelers’ decision-making. Passengers tend to choose travel options with higher ticket booking success rates, even if it entails a slight increase in the ticket prices for the high-speed rail to high-speed rail transfer option. The attitude variables significantly influence intercity travel mode choice behavior, with travelers generally exhibiting a preference for risk avoidance in their travel options. Moreover, there are differences among various groups of travelers in their preferences and demands for the convenience, economy, and comfort aspects of travel options. These research findings can enhance our understanding of the key factors influencing the selection of RRIT services, thereby supporting RRIT designers and planners in improving service quality and facilitating the future growth of RRIT.
Simulation Methods and Digital Strategies for Supply Chains Facing Disruptions: Insights from a Systematic Literature Review
Authors: Korder Benjamin , Maheut Julien , Konle Matthias
Published: July 2024

Supply chain disruptions pose significant economic stability and growth challenges, impacting industries globally. This study aims to systematically review the literature on the use of simulation tools in managing supply chain disruptions, focusing on the historical evolution, prevalent simulation methods, specific challenges addressed, and research gaps. A systematic literature review was conducted using the PRISMA method. An initial pool of 236 articles was identified, from which 213 publications were rigorously reviewed. This study analyzed these articles to map the academic landscape, identify key clusters, and explore the integration of digital advancements in enhancing supply chain resilience. The review identified the chronological development of research in this field, highlighting significant contributions and influential authors. It was found that various simulation methods, including discrete-event simulation, agent-based modeling, and system dynamics, are employed to address different aspects of supply chain disruptions. Two primary research frontiers emerged from the analysis: the strategic reconfiguration of supply chain networks to mitigate ripple effects and the swift implementation of countermeasures to contain disruptions. The findings suggest a need for future research focusing on dynamic analysis and control theory applications to understand and manage supply chain disruptions better. This study also notes the increasing interest and need to use digital technologies (digital twins, artificial intelligence, etc.) in future research. It underscores the necessity for continued research to develop resilient and sustainable supply chain infrastructures aligned with the United Nations’ Sustainable Development Goals. The identified research gaps offer a roadmap for future scholarly exploration and practical implementation.
A Systematic Review of Socio-Technical Systems in the Water–Energy–Food Nexus: Building a Framework for Infrastructure Justice
Authors: Shrimpton Elisabeth A. , Balta-Ozkan Nazmiye
Published: July 2024

This paper explores the place of technological interventions in the conceptualisation of the Water–Energy–Food Nexus (WEF Nexus). The focus is on the just infrastructure interventions required to decarbonise and adapt to the challenges of the climate crisis for sustainable livelihoods. We explore the overlap between two bodies of work, the WEF Nexus and Socio-Technical Systems, grown from different disciplinary perspectives, to scrutinise the extent to which there is a coherent synthesis of work that can examine infrastructure impacts and trade-offs in a WEF system. Following a systematic literature review and analysis, a framework is proposed for water and energy infrastructure interventions to both support sustainable development and recognise infrastructure’s role in a just and equitable society. This framework will support the creation of models that are less likely to miss vital components of a system or potential trade-offs and supports a multi-disciplinary approach to infrastructure interventions.
Application of a Multi-Model Fusion Forecasting Approach in Runoff Prediction: A Case Study of the Yangtze River Source Region
Authors: Wang Tingqi , Guo Yuting , Evgenievna Mazina Svetlana , Wu Zhenjiang
Published: July 2024

Runoff forecasting is crucial for sustainable water resource management. Despite the widespread application of deep learning methods in this field, there is still a need for improvement in the modeling and utilization of multi-scale information. For the first time, we introduce the Neural Basis Expansion Analysis with Exogenous Variable (NBEATSx) model to perform runoff prediction for a full exploration in rich temporal characteristics of runoff sequences. To harness wavelet transform (WT) multi-scale information capabilities, we developed the WT-NBEATSx forecasting model, integrating WT and NBEATSx. This model was further enhanced by incorporating a Long Short-Term Memory (LSTM) model for superior long-term dependency detection and a Random Forest (RF) model as a meta-model. The result is the advanced multi-model fusion forecasting model WT-NBEATSx-LSTM-RF (WNLR). This approach significantly enhances performance in runoff prediction. Utilizing a daily scale runoff and meteorological dataset from the Yangtze River Source region in China from 2006 to 2018, we systematically evaluated the performance of the WNLR model in runoff prediction tasks. Compared with LSTM, Gated Recurrent Units (GRUs), and NBEATSx models, the WNLR model not only significantly outperforms the original NBEATSx model but also surpasses other comparison models, particularly in accurately extracting cyclical change patterns, with NSE scores of 0.986, 0.974, and 0.973 for 5-, 10-, and 15-day forecasts, respectively. Additionally, compared to the standalone LSTM and GRU models, the introduction of wavelet transforms to form WT-LSTM and WT-GRU notably improved prediction performance and robustness, especially in long-term forecasts, where NSE increased by 32% and 1.5%, respectively. This study preliminarily proves the effectiveness of combining the cyclical characteristics of NBEATSx and wavelet transforms and creatively proposes a new deep learning model integrating LSTM and RF, providing new insights for further considering multi-scale features of complex runoff time series, thereby enhancing runoff prediction effectiveness.
Economic Order Quantity: A State-of-the-Art in the Era of Uncertain Supply Chains
Authors: Alnahhal Mohammed , Aylak Batin Latif , Al Hazza Muataz , Sakhrieh Ahmad
Published: July 2024

Inventory management is crucial for companies to minimize unnecessary costs associated with overstocking or understocking items. Utilizing the economic order quantity (EOQ) to minimize total costs is a key decision in inventory management, particularly in achieving a sustainable supply chain. The classical EOQ formula is rarely applicable in practice. For example, suppliers may enforce a minimum order quantity (MOQ) that is much larger than the EOQ. Some conditions such as imperfect quality and growing items represent variants of EOQ. Moreover, some requirements, such as the reduction of CO2 emissions, can alter the formula. Moreover, disruptions in the supply chain, such as COVID-19, can affect the formula. This study investigates which requirements must be considered during the calculation of the EOQ. Based on a literature review, 18 requirements that could alter the EOQ formula were identified. The level of coverage for these requirements has been tracked in the literature. Research gaps were presented to be investigated in future research. The analysis revealed that, despite their importance, at least 11 requirements have seldom been explored in the literature. Among these, topics such as EOQ in Industry 4.0, practical EOQ, and resilient EOQ have been identified as promising areas for future research.
Corporate Social Responsibility Disclosure Quality and Firms’ Investment Efficiency: Evidence from Chinese Listed Companies
Authors: Guo Xiaomei , Xu Rui , Li Xin , Ban Qi
Published: July 2024

In China, where the corporate social responsibility (CSR) process is relatively underdeveloped, the government has promulgated a series of laws and regulations on CSR disclosure in recent years to promote the sustainable transformation of the economy. Using data from A-share listed Chinese firms from 2009 to 2021, this study empirically examines the relationship between CSR disclosure quality and firm investment efficiency in China. The results indicate that (1) improvements in CSR disclosure quality significantly mitigate firms’ underinvestment and overinvestment, thereby enhancing investment efficiency. (2) Further analysis shows that high-quality CSR information also promotes investment efficiency by reducing agency costs and financing constraints and improving media evaluations of firms. (3) A heterogeneity analysis suggests that the positive effect of CSR disclosure on investment efficiency is stronger for firms with lower equity incentives, more severe financing constraints, and higher media attention. Our study extends the understanding of the mechanisms through which CSR disclosure affects firms’ investment efficiency, potentially providing insights for research in related fields and guiding future CSR disclosure practices in other developing countries.
The Impact of City Anti-Contagion Policies (CAPs) on Air Quality Evidence from a Natural Experiment in China
Authors: Yang Zili , Yoon Yong
Published: July 2024

In order to control the spread of the Coronavirus Disease 2019 (COVID-19), many countries around the world adopted aggressive anti-contagion policies (APs), the most common of which was to restrict people’s transportation and economic activities, which not only curbed the spread of the epidemic but also improved urban air quality during the APs’ implementation. However, the impact that these policies had in the post-AP period is unclear. Using daily air quality data for prefecture-level cities in China in early 2020 and the Difference-in-Differences (DiD) models, we measured the short-term (AP implementation period) and medium-term (post-AP period) impacts of the city APs (CAPs) on different kinds of air pollutants and considered the meteorological conditions. We found that the policies significantly reduced air pollution (i.e., particulate matter [PM2.5, PM10] and nitrogen dioxide [NO2]) in the short term; although the medium-term impacts are in line with the short-term impacts, they are not significant. The effects were reduced in cities with higher incomes, larger populations, more industrial activities, and greater traffic volumes, and without a central heating system. Although the CAPs did not improve air quality in the long run, they improved air quality and health benefits in the short term. In addition, the policies’ experiments verified the complexity of environmental governance.
The Use of AI-Driven Automation to Enhance Student Learning Experiences in the KSA: An Alternative Pathway to Sustainable Education
Authors: Mutambik Ibrahim
Published: July 2024

The relevance of virtual learning platforms has been increasingly recognised, and their merit in contributing to sustainable education is ever growing. Depending on the context, the benefits of these virtual platforms were revealed during the COVID-19 pandemic. Moreover, their impact has lingered on post-COVID-19, and virtual learning is now considered a viable option for continuing and sustainable education. Therefore, many countries have taken advantage of these virtual platforms to maximise student engagement, as evidenced by the reports in the existing literature. However, while these studies have explored how this can best be achieved, there are very few studies which have examined how the use of virtual platforms can help to deliver an educational approach that prepares young people to address the many and complex sustainability challenges of the future, i.e., the delivery of sustainable education. This study addresses this gap in the literature by exploring the question of how AI-powered automation can enhance student learning experiences in the Kingdom of Saudi Arabia (hereafter, KSA) as an alternative pathway for sustainable education. Data were collected from 1991 undergraduate and postgraduate students across 10 different Saudi universities using an online survey. The data were analysed using advanced structural equation modelling (SEM) to examine the relationship between student readiness and the (AI-powered) automation of administrative processes. The findings highlight the transformative potential of AI as an alternative pathway to sustainable education and for streamlining learning management system (LMS) operations. The implications of this study extend beyond the immediate instructional context, offering strategic direction for educators, LMS designers, policymakers, and institutional leaders in harnessing AI to equip individuals with the knowledge, skills, values, and attitudes necessary to contribute to a sustainable future.
Global Trends in the Research and Development of Petrochemical Waste Gas from 1981 to 2022
Authors: Wu Mengting , Liu Wei , Ma Zhifei , Qin Tian , Chen Zhiqin , Zhang Yalan , Cao Ning , Xie Xianchuan , Chi Sunlin , Xu Jinying , Qi Yi
Published: July 2024

As a highly energy-intensive and carbon-emitting industry with significant emissions of volatile organic compounds (VOCs), the petroleum and chemical industry is a major contributor to the global greenhouse effect and ozone layer destruction. Improper treatment of petrochemical waste gas (PWG) seriously harms human health and the natural environment. This study uses CiteSpace and VOSviewer to conduct a scientometric analysis of 1384 scholarly works on PWG and carbon sequestration published between 1981 and 2022, revealing the basic characteristics, knowledge base, research topic evolution, and research hotspots of the field. The results show the following: (1) In the early stages of the petrochemical industry, it was processed tail gas, plant leakage waste gas, and combustion flue gas that were investigated in PWG research. (2) Later, green environmental protection technology was widely studied in the field of PWG treatment, such as biotechnology, catalytic oxidation technology, membrane separation technology, etc., in order to achieve efficient, low energy consumption and low emissions of waste gas treatment, and the number of publications related to this topic has increased rapidly. In addition, researchers studied the internet of things and technology integration, such as the introduction of artificial intelligence, big data analysis, and other technologies, to improve the accuracy and efficiency of exhaust gas monitoring, control, and management. (3) The department has focused on how to reduce emissions by optimizing petrochemical process lines or improving energy efficiency. Emission reduction and low-carbon transition in the petrochemical industry will become the main trend in the future. Switching from renewable carbon to feedstock carbon derived from captured carbon dioxide, biomass, or recycled chemicals has become an attractive strategy to help curb emissions from the chemical industry. The results of our analysis can provide funding agencies and research groups with information to better understand the global trends and directions that have emerged in this field from 1981 to 2022 and serve as a reference for future research.
The Past, Present and Future of Land Use and Land Cover Changes: A Case Study of Lower Liaohe River Plain, China
Authors: Wu Rina , Wang Ruinan , Lv Leting , Jiang Junchao
Published: July 2024

Understanding and managing land use/cover changes (LUCC) is crucial for ensuring the sustainability of the region. With the support of remote sensing technology, intensity analysis, the geodetic detector model, and the Mixed-Cell Cellular Automata (MCCA) model, this paper constructs an integrated framework linking historical evolutionary pattern-driving mechanisms for future simulation for LUCC in the Lower Liaohe Plain. From 1980 to 2018, the increasing trends were in built-up land and water bodies, and the decreasing trends were in grassland, cropland, forest land, unused land, and swamps. Overall, the changes in cropland, forest land, and built-up land are more active, while the changes in water bodies are more stable; the sources and directions of land use conversion are more fixed. Land use changes in the Lower Liaohe Plain are mainly influenced by socio-economic factors, of which population density, primary industry output value, and Gross Domestic Product (GDP) have a higher explanatory power. The interactive influence of each factor is greater than any single factor. The results of the MCCA model showed high accuracy, with an overall accuracy of 0.8242, relative entropy (RE) of 0.1846, and mixed-cell figure of merit (mcFoM) of 0.1204. By 2035, the built-up land and water bodies will increase, while the rest of the land use categories will decrease. The decrease is more pronounced in the central part of the plains. The findings of the study provide a scientific basis for strategically allocating regional land resources, which has significant implications for land use research in similar regions.
Enhancement of a Simple, Economic and Eco-Friendly Analytical Approach for the Extraction and Determination of Endocrine Disruptors from Plastics in Shrimp
Authors: Soliz Dulce Lucy , García-González Jorge , Paniagua González Gema , Garcinuño Rosa Mª , Bravo Yagüe Juan Carlos , Fernández Hernando Pilar
Published: July 2024

The economic significance of the shrimp industry relies heavily on the comprehensive utilization of all of the shrimp’s parts. However, this importance is often threatened by common challenges such as disease and pollution, caused by prominent contaminants that are capable of exerting adverse effects either directly as physical pollutants or indirectly through the incorporation of additives or adsorbed chemicals. Among these substances are endocrine disruptors, which pose risks to both wildlife and human populations. In this study, 11 endocrine-disrupting compounds were determined (3 bisphenols, 3 phthalates, 3 pesticides, and 2 nonylphenols) through the development of a cost-effective, greener and cost-friendly method based on solid-phase matrix dispersion (MSPD) with high-performance liquid chromatography coupled with diode array detection (HPLC-DAD). Determinations were performed on different parts of the shrimp: the cephalothorax, abdomen, intestine and shell. Several variables were optimized in the extraction, separation and detection phases, resulting in average recoveries of about 90%. The limit of detection (LOD) varies depending on the analyte and matrix. At concentrations of 1 mg/kg in the cephalothorax+shells and 1.25 mg/kg in the abdomen+intestine, all compounds were detected, except for nonylphenols. The developed method has allowed the simultaneous determination of 11 endocrine disruptors in different parts of the shrimp samples. Furthermore, the MSPD has been demonstrated to be an efficacious, selective, and streamlined sample extraction method, eliminating the necessity for pretreatment steps such as centrifugation and filtration, as well as the use of large volumes of solvents.
Unveiling Istanbul’s City Dynamics: Spatiotemporal Hotspot Analysis of Vegetation, Settlement, and Surface Urban Heat Islands
Authors: Cigerci Hazal , Balcik Filiz Bektas , Sekertekin Aliihsan , Kahya Ceyhan
Published: July 2024

Investigation of cities’ spatiotemporal dynamics, including vegetation and urban areas, is of utmost importance for understanding ecological balance, urban planning, and sustainable development. This study investigated the dynamic interactions between vegetation, settlement patterns, and surface urban heat islands (SUHIs) in Istanbul using spatiotemporal hotspot analysis. Utilizing Landsat satellite imagery, we applied the Getis-Ord Gi* statistic to analyze Land Surface Temperature (LST), Urban Index (UI), and Normalized Difference Vegetation Index (NDVI) across the city. Using satellite images and the Getis-Ord Gi* statistic, this research investigated how vegetation and urbanization impact SUHIs. Based on the main results, mean NDVI, UI, and LST values for 2009 and 2017 were analyzed, revealing significant vegetation loss in 37 of Istanbul’s 39 districts, with substantial urbanization, especially in the north, due to new infrastructure development. On the other hand, hotspot analysis was conducted on normalized NDVI, UI, and LST images by analyzing 977 neighborhoods. Results showed a significant transformation of green areas to non-significant classes in NDVI, high urbanization in UI, and the formation of new hot areas in LST. SUHIs were found to cluster in areas with increasing residential and industrial activities, highlighting the role of urban development on SUHI formation. This research can be applied to any region since it offers crucial perspectives for decision-makers and urban planners aiming to mitigate SUHI effects through targeted greening strategies and sustainable urban development. By integrating environmental metrics into urban planning, this study underscores the need for comprehensive and sustainable approaches to enhance urban resilience, reduce environmental impact, and improve livability in Istanbul.
Bringing Back Reef Fish: Sustainable Impacts of Community-Based Restoration of Elkhorn Coral (Acropora palmata) in Vega Baja, Puerto Rico (2008–2023)
Authors: Hernández-Delgado Edwin A. , Laureano Ricardo
Published: July 2024

In response to the severe fragmentation of Elkhorn coral, Acropora palmata (Lamarck, 1816), stands caused by a major winter swell (“Holy Swell”) in March 2008, an emergency community-based low-tech restoration was initiated in Vega Baja, Puerto Rico. Over a 15-year period, coral demographic performance and fish assemblages were monitored across four restored and four control (non-restored) 100 m2 plots. The restoration effort proved to be highly successful, leading to successful coral survival and growth, and to sustained recovery of fish assemblages, particularly herbivore guilds. Significantly increased abundance, biomass, and diversity were observed across all trophic functional groups, fishery target species, and geo-ecological functional groups in both restored and control plots. These positive outcomes were attributed to enhanced spatial complexity by long-term coral growth, “nutrient hotspots” within restored plots, the refugia effect from enhanced benthic spatial complexity, and the recovery of fish dispersal paths promoting spillover effects from restored to adjacent non-restored areas. Restoring herbivore guilds and geo-ecological functional groups played a crucial role in restoring vital ecological processes promoting reef ecosystem resilience. Recommendations include integrating fish assemblage recovery into coral restoration strategies, establishing natural coral nursery plots for future coral sourcing, and incorporating the concept of nursery seascapes for a holistic and ecosystem-based approach to restoration.
Utilizing Agricultural By-Products for Sustainable Agouti (Dasyprocta leporina) Diets: Evaluating Low-Cost Feed Alternatives in Trinidad and Tobago, West Indies
Authors: Small Shecania Elysha , John Eden Natalia , Lall Kavita Ranjeeta , Jones Kegan Romelle
Published: July 2024

The agouti (Dasyprocta leporina) is a promising protein source in the Caribbean, Mexico, and South America. This study assessed the impact of three by-product diet formulations on intensively reared agoutis, including a cost analysis and nutritional evaluation as well as the nutritional composition, digestibility, and economic viability of three experimental diets. Sixteen adult male agoutis were individually housed at the University of the West Indies’ Field Station. After a two-week acclimatization, a four-week data collection phase followed. The four treatments included a control diet of commercial rabbit pellets and three experimental diets with varying by-product ratios. Key findings revealed that Treatment 2 had elevated Dry Matter, Crude Fat, and fiber but potentially compromised digestibility due to higher fiber and lignin. Treatment 1 resulted in the highest morbidity and was discontinued after two weeks due to significant weight loss but demonstrated the most cost-effective results, with the lowest variable cost per 45 kg, the lowest feeding cost to reach 3 kg, and the highest gross margin. Treatment 2 effectively maintained animal weight. Treatment 3 showed promising growth due to low levels of Neutral Detergent Fiber (NDF) and Acid Detergent Lignin (ADL). Alternative dietary feedstuffs from agricultural by-products show potential for sustaining body weight in adult male agoutis, but further research is needed to refine nutritional requirements for growing agoutis.
Assessing the Flexibility of Power Systems through Neural Networks: A Study of the Hellenic Transmission System
Authors: Kaskouras Christos D. , Krommydas Konstantinos F. , Baltas Ioannis , Papaioannou George P. , Papayiannis Georgios I. , Yannacopoulos Athanasios N.
Published: July 2024

Increasing the generation of electric power from renewable energy sources (RESs) creates important challenges to transmission system operators (TSOs) for balancing the power system. To address these challenges, adequate system flexibility is required. In this context, TSOs carry out flexibility assessment studies to evaluate the flexibility level of the power system and ensure that a stable operation of the transmission system under high RESs integration can be achieved. These studies take into consideration numerous scenarios incorporating different assumptions for temperature, RESs penetration, load growth, and hydraulic conditions. Until now, flexibility studies usually solve the standard unit commitment problem and evaluate if the flexibility level is adequate. Although this approach provides quite accurate results, the computational requirements are significant, resulting in limiting the scenarios chosen for examination. In this paper, deep learning approaches are examined, and more precisely, an integrated system of two recurrent neural networks with long short-term memory cells is designed to carry out the flexibility assessment task, aiming at the reduction in the computational time required by the optimization process. The output of this neural network system is then used to calculate the probability of flexibility shortages. The proposed method is evaluated based on data from the Hellenic transmission system, providing quite promising results in (a) accurately calculating the probability of insufficient flexibility and (b) achieving a significant decrease in computational time. This novel approach could notably facilitate TSOs since more scenarios can be included, exploiting the computational efficiency of the method. In this way, a more complete evaluation of the flexibility level of the power system can be achieved and thus help to ensure the stable and reliable operation of the transmission system.
A New Approach to Circular Inversion in l1-Normed Spaces
Authors: Ermiş Temel , Şen Ali Osman , Gielis Johan
Published: July 2024

While there are well-known synthetic methods in the literature for finding the image of a point under circular inversion in l2-normed geometry (Euclidean geometry), there is no similar synthetic method in Minkowski geometry, also known as the geometry of finite-dimensional Banach spaces. In this study, we have succeeded in creating a synthetic construction of the circular inversion in l1-normed spaces, which is one of the most fundamental examples of Minkowski geometry. Moreover, this synthetic construction has been given using the Euclidean circle, independently of the l1-norm.
Partially Nonclassical Method and Conformal Invariance in the Context of the Lie Group Method
Authors: Burde Georgy I.
Published: July 2024

The basic idea of the ‘partially nonclassical method’, developed in the present paper, is to apply the invariance requirement of the Lie group method using not all differential consequences of the invariant surface condition but only part of them. It differs from the ‘classical’ method, in which the invariant surface condition is not used, and from the ‘nonclassical’ method, in which all the differential consequences are used. It provides additional possibilities for the symmetry analysis of partial differential equations (PDEs), as compared with the ‘classical’ and ‘nonclassical’ methods, in the so-named no-go case when the group generator, associated with one of the independent variables, is identically zero. The method is applied to the flat steady-state boundary layer problem, reduced to an equation for the stream function, and it is found that applying the partially nonclassical method in the no-go case yields new symmetry reductions and new exact solutions of the boundary layer equations. A computationally convenient unified framework for the classical, nonclassical and partially nonclassical methods (λ-formulation) is developed. The issue of conformal invariance in the context of the Lie group method is considered, stemming from the observation that the classical Lie method procedure yields transformations not leaving the differential polynomial of the PDE invariant but modifying it by a conformal factor. The physical contexts, in which that observation could be important, are discussed using the derivation of the Lorentz transformations of special relativity as an example.
The Combined Additive Effect of Inter-Limb Muscle Mass Asymmetries and Body Composition Indices on Lower Limb Injuries in Physically Active Young Adults
Authors: Domaradzki Jarosław
Published: July 2024

Biological measurements that predict injury risk are crucial diagnostic tools. Yet, research on improving diagnostic accuracy in detecting accidents is insufficient. Combining multiple predictors and assessing them via ROC curves can enhance this accuracy. This study aimed to (1) evaluate the importance of lower limb muscle mass asymmetry and body composition (BMI and FMI) as predictors of injuries, (2) explore the role of the most effective body composition index in the relationship between muscle asymmetry and injury, and (3) assess the prognostic potential of combined predictors. Cross-sectional sampling was used to select students from a university. The sample included 237 physically active young adults (44% males). The independent variables were inter-limb muscle mass asymmetry (absolute asymmetry, AA), BMI, and FMI; the dependent variable was the number of injuries in the past year. Using zero-inflated Poisson regression, we examined the relationships, including a moderation analysis (moderated multiple ZIP regression). The mediation by body composition was tested using ZIP and logistic regression. The predictive power was assessed via ROC curves. The significance level was set at an α-value of 0.05. No significant difference in injury incidence between males and females was found (χ2 = 2.12, p = 0.145), though the injury types varied. Males had more muscle strains, while females had more bone fractures (χ2 = 6.02, p = 0.014). In males, the inter-limb asymmetry and FMI predicted injuries; in females, the BMI and FMI did, but not asymmetry. No moderating or mediating effects of body composition were found. In males, combined asymmetry and the FMI better predicted injuries (AUC = 0.686) than separate predictors (AA: AUC = 0.650, FMI: AUC = 0.458). For females, the FMI was the best predictor (AUC = 0.662). The most predictive factors for injuries in males were both muscle asymmetry and the FMI (as combined predictors), while in females, it was the single FMI. The hypothesis regarding the mediating role of body composition indicators was rejected, as no moderation or mediation by the FMI was detected in the relationship between absolute asymmetry (AA) and injuries. For clinical practice, the findings suggest that practitioners should incorporate assessments of both muscle asymmetry and body composition into routine screenings for physically active individuals. Identifying those with both high asymmetry and an elevated FMI can help target preventative interventions more effectively. Tailored strength training and conditioning programs aimed at reducing asymmetry and managing body composition may reduce the risk of injury, particularly in populations identified as high-risk.
A Novel Blind Double-Color Image Watermarking Algorithm Utilizing Walsh–Hadamard Transform with Symmetric Embedding Locations
Authors: Trinadh Reddy KVSV , Reddy S. Narayana
Published: July 2024

This paper introduces an effective blind watermarking algorithm for double-color images utilizing the Walsh–Hadamard Transform (WHT) with symmetric embedding locations to enhance imperceptibility. The proposed algorithm leverages the energy accumulation capability and significant correlations among coefficients of the WHT. First, the color host image undergoes partitioning into its respective red (R), green (G), and blue (B) channels, followed by further subdivision into 4 × 4 blocks. Through research, the algorithm determines which WHT coefficients are least visually sensitive to embedding a color image, and as a result, optimizes the embedding locations to achieve better imperceptibility. The extensive simulation results verify the superior performance of the proposed algorithm compared to other related approaches, showcasing its excellence not only in imperceptibility but also in embedding capacity and robustness.
An Analytical Study of the Mikhailov–Novikov–Wang Equation with Stability and Modulation Instability Analysis in Industrial Engineering via Multiple Methods
Authors: Hossain Md Nur , Miah M. Mamun , Abbas M. S. , El-Rashidy K. , Borhan J. R. M. , Kanan Mohammad
Published: July 2024

Solitary waves, inherent in nonlinear wave equations, manifest across various physical systems like water waves, optical fibers, and plasma waves. In this study, we present this type of wave solution within the integrable Mikhailov–Novikov–Wang (MNW) equation, an integrable system known for representing localized disturbances that persist without dispersing, retaining their form and coherence over extended distances, thereby playing a pivotal role in understanding nonlinear dynamics and wave phenomena. Beyond this innovative work, we examine the stability and modulation instability of its gained solutions. These new solitary wave solutions have potential applications in telecommunications, spectroscopy, imaging, signal processing, and pulse modeling, as well as in economic systems and markets. To derive these solitary wave solutions, we employ two effective methods: the improved Sardar subequation method and the (℧′/℧, 1/℧) method. Through these methods, we develop a diverse array of waveforms, including hyperbolic, trigonometric, and rational functions. We thoroughly validated our results using Mathematica software to ensure their accuracy. Vigorous graphical representations showcase a variety of soliton patterns, including dark, singular, kink, anti-kink, and hyperbolic-shaped patterns. These findings highlight the effectiveness of these methods in showing novel solutions. The utilization of these methods significantly contributes to the derivation of novel soliton solutions for the MNW equation, holding promise for diverse applications throughout different scientific domains.
Convolution Theorem for (p,q)-Gamma Integral Transforms and Their Application to Some Special Functions
Authors: Al-Omari Shrideh , Salameh Wael , Zureigat Hamzeh
Published: July 2024

This article introduces (p,q)-analogs of the gamma integral operator and discusses their expansion to power functions, (p,q)-exponential functions, and (p,q)-trigonometric functions. Additionally, it validates other findings concerning (p,q)-analogs of the gamma integrals to unit step functions as well as first- and second-order (p,q)-differential operators. In addition, it presents a pair of (p,q)-convolution products for the specified (p,q)-analogs and establishes two (p,q)-convolution theorems.
Novel Uncertainty Principles Related to Quaternion Linear Canonical S-Transform
Authors: Damang Dahnial , Bahri Mawardi , Toaha Syamsuddin
Published: July 2024

In this work, we introduce the quaternion linear canonical S-transform, which is a generalization of the linear canonical S-transform using quaternion. We investigate its properties and seek the different types of uncertainty principles related to this transformation. The obtained results can be looked as an extension of the uncertainty principles for the quaternion linear canonical transform and the quaternion windowed linear canonical transform.
Estimating the Confidence Interval for the Common Coefficient of Variation for Multiple Inverse Gaussian Distributions
Authors: Chankham Wasana , Niwitpong Sa-Aat , Niwitpong Suparat
Published: July 2024

The inverse Gaussian distribution is a two-parameter continuous probability distribution with positive support, which is used to account for the asymmetry of the positively skewed data that are often seen when modeling environmental phenomena, such as PM2.5 levels. The coefficient of variation is often used to assess variability within datasets, and the common coefficient of variation of several independent samples can be used to draw inferences between them. Herein, we provide estimation methods for the confidence interval for the common coefficient of variation of multiple inverse Gaussian distributions by using the generalized confidence interval (GCI), the fiducial confidence interval (FCI), the adjusted method of variance estimates recovery (MOVER), and the Bayesian credible interval (BCI) and highest posterior density (HPD) methods using the Jeffreys prior rule. The estimation methods were evaluated based on their coverage probabilities and average lengths, using a Monte Carlo simulation study. The findings indicate the superiority of the GCI over the other methods for nearly all of the scenarios considered. This was confirmed for a real-world scenario involving PM2.5 data from three provinces in northeastern Thailand that followed inverse Gaussian distributions.