bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design
Authors: Samsami Reihaneh
Published: October 2024

Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.
Molecular Mechanisms Responsible for Mesenchymal Stem Cell-Dependent Attenuation of Tear Hyperosmolarity and Immune Cell-Driven Inflammation in the Eyes of Patients with Dry Eye Disease
Authors: Harrell Carl Randall , Djonov Valentin , Volarevic Ana , Arsenijevic Aleksandar , Volarevic Vladislav
Published: October 2024

Background: Dry eye disease (DED) is a chronic condition characterized by a decrease in tear production or an increase in tear evaporation, leading to inflammation and damage of the ocular surface. Dysfunction of ion channels, tear hyperosmolarity and immune cell-driven inflammation create a vicious circle responsible for the pathological changes in the eyes of DED patients. Mesenchymal stem cells (MSCs) are adult, rapidly proliferating stem cells that produce a large number of immunoregulatory, angiomodulatory, and growth factors that efficiently reduce tear hyperosmolarity-induced pathological changes, inhibit harmful immune response, and provide trophic support to the injured corneal and conjuctival epithelial cells, goblet cells and acinar cells in lacrimal glands of DED patients. Methods: An extensive research in the literature was implemented in order to elucidate the role of MSCs in the attenuation of tear hyperosmolarity and eye inflammation in patients suffering from DED. Results: Findings obtained in preclinical and pilot clinical studies demonstrated that MSCs reduced tear hyperomsolaity-induced pathological changes and suppressed immune cell-driven eye inflammation. Additionally, MSC-based therapy managed to successfully address the most severe DED-related conditions and complications. Conclusions: MSCs should be considered as potentially new therapeutic agents for the treatment of severe DED.
Random Walks and Lorentz Processes
Authors: Szász Domokos
Published: October 2024

Random walks and Lorentz processes serve as fundamental models for Brownian motion. The study of random walks is a favorite object of probability theory, whereas that of Lorentz processes belongs to the theory of hyperbolic dynamical systems. Here we first present an example where the method based on the probabilistic approach led to new results for the Lorentz process: concretely, the recurrence of the planar periodic Lorentz process with a finite horizon. Afterwards, an unsolved problem—related to a 1981 question of Sinai on locally perturbed periodic Lorentz processes—is formulated as an analogous problem in the language of random walks.
Text-Enhanced Graph Attention Hashing for Cross-Modal Retrieval
Authors: Zou Qiang , Cheng Shuli , Du Anyu , Chen Jiayi
Published: October 2024

Deep hashing technology, known for its low-cost storage and rapid retrieval, has become a focal point in cross-modal retrieval research as multimodal data continue to grow. However, existing supervised methods often overlook noisy labels and multiscale features in different modal datasets, leading to higher information entropy in the generated hash codes and features, which reduces retrieval performance. The variation in text annotation information across datasets further increases the information entropy during text feature extraction, resulting in suboptimal outcomes. Consequently, reducing the information entropy in text feature extraction, supplementing text feature information, and enhancing the retrieval efficiency of large-scale media data are critical challenges in cross-modal retrieval research. To tackle these, this paper introduces the Text-Enhanced Graph Attention Hashing for Cross-Modal Retrieval (TEGAH) framework. TEGAH incorporates a deep text feature extraction network and a multiscale label region fusion network to minimize information entropy and optimize feature extraction. Additionally, a Graph-Attention-based modal feature fusion network is designed to efficiently integrate multimodal information, enhance the affinity of the network for different modes, and retain more semantic information. Extensive experiments on three multilabel datasets demonstrate that the TEGAH framework significantly outperforms state-of-the-art cross-modal hashing methods.
Regional Workforce Dynamics in West Virginia: Insights from Shift-Share and Location Quotient Analysis
Authors: Herath Bandara Saman Janaranjana
Published: October 2024

West Virginia, home to approximately 1.77 million residents, has been grappling with significant economic challenges characterized by persistent poverty and sluggish growth. Despite ongoing development efforts, the state’s Gross State Product (GSP) has seen only a modest increase of 0.1% over the past five years, reaching USD 71.7 billion, while the unemployment rate remains at 4.0%. The annualized employment growth rate of 0.7% lags behind the national average, and only about 54% of West Virginia’s adult population is either employed or actively seeking employment, resulting in one of the lowest labor force participation rates in the nation. In contrast, certain industrial sectors, such as healthcare, social assistance, retail trade, and accommodation and food services, have shown intermittent growth at the county and regional levels. To explore the unique characteristics and significance of these regions in relation to employment growth, this study examines regional employment patterns in West Virginia from 2001 to 2020, focusing on the main regions of the state: Metro Valley, Mid-Ohio Valley, New River/Greenbrier Valley, Mountain Lakes, and Potomac Highlands. Utilizing shift-share and location quotient (LQ) analyses, this research identifies the sectors driving regional employment and assesses their performance. Key findings reveal strong sectoral performance in mining, manufacturing, and finance in the Mid-Ohio Valley; wholesale trade, transportation, and utilities in the Metro Valley; agriculture and administrative services in the New River/Greenbrier Valley; agriculture and manufacturing in the Potomac Highlands; and scientific services, healthcare, and utilities in the Mountain Lakes region. Based on these insights, this study recommends targeted policy interventions to address regional disparities, enhance sectors with significant short- and long-term benefits, and foster balanced economic development across the state.
A Review of the Conceptual Structure and Evolution of Digital Leadership Research in Education
Authors: Karakose Turgut , Polat Hakan , Tülübaş Tijen , Demirkol Murat
Published: October 2024

With the advent of digital technologies that have rapidly changed the functioning of societies, digital leadership has also become a novel imperative for the effective functioning of organizations. As the backbone of societal development, educational institutions have not been exempt from the influence of these new trends, which has ignited a growing research interest in the digital leadership roles of school administrators. This study aims to map the conceptual and thematic evolution of this research on digital leadership in the education field to offer a better insight into the strategic themes that are sufficiently or insufficiently addressed; that is, to identify aspects that are clearly defined or left shrouded in shadow. This study employed science mapping methodology and used SciMAT software (version 1.1.04) to map the evolution of thematic strands across three periods of the field’s development. The analysis was conducted using metadata obtained from 174 articles that are indexed on WoS and Scopus. The results indicated that with a growing focus on technology or e-leadership, considering the developments in the world of computers and the internet, research into digital leadership has gained momentum in recent years, particularly following the COVID-19 pandemic. Digital leadership is regarded as a broader concept for application in investigating the knowledge, skills, competencies, and perspectives of school leaders to facilitate the healthy integration of digital technologies into education, school administration, and student learning. The field seems to require investigations into the many aspects of digitalization in education to build a more solid and insightful knowledge base to guide the effective practice of digital leadership in schools.
Utilizing Artificial Neural Network Ensembles for Ship Design Optimization to Reduce Added Wave Resistance and CO2 Emissions
Authors: Cepowski Tomasz
Published: October 2024

Increased maritime cargo transportation has necessitated stricter management of emissions from ships. The primary source of this pollution is fuel combustion, which is influenced by factors such as a ship’s added wave resistance. Accurate estimation of this resistance during ship design is crucial for minimizing exhaust emissions. The challenge is that, at the preliminary parametric design stage, only limited geometric data about the ship is available, and the existing methods for estimating added wave resistance cannot be applied. This article presents the application of artificial neural network (ANN) ensembles for estimating added wave resistance based on dimensionless design parameters available at the preliminary design stage, such as the length-to-breadth ratio (L/B), breadth-to-draught ratio (B/T), length-to-draught ratio (L/T), block coefficient (CB), and the Froude number (Fn). Four different ANN ensembles are developed to predict this resistance using both complete sets of design characteristics (i.e., L/B, B/T, CB, and Fn) and incomplete sets, such as L/B, CB, and Fn; B/T, CB, and Fn; and L/T, CB, and Fn. This approach allows for the consideration of CO2 emissions at the parametric design stage when only limited ship dimensions are known. An example in this article demonstrates that minor modifications to typical container ship designs can significantly reduce added wave resistance, resulting in a daily reduction of up to 2.55 tons of CO2 emissions. This reduction is equivalent to the emissions produced by 778 cars per day, highlighting the environmental benefits of optimizing ship design.
Rationalization of Energy Expenditure: Household Behavior in Poland
Authors: Stańczyk Elżbieta , Szalonka Katarzyna , Niklewicz-Pijaczyńska Małgorzata , Nowak Wioletta , Stańczyk Piotr , Witczyńska Katarzyna , Ziobrowska-Sztuczka Justyna
Published: October 2024

Background: The implementation of the EU climate and energy policy, along with changes in the legal environment, has led to a significant increase in energy prices in Poland. Consequently, energy expenditures are now a larger part of household budgets. These rising energy costs and the evolving legal landscape are compelling households to invest in energy-saving solutions and modify their energy consumption habits. This article aims to identify the activities of households in Poland regarding the rationalization of energy expenditures. It formulates the following research hypothesis: households invest in energy-saving appliances to rationalize energy expenditures and/or change their behaviors to reduce energy consumption. Methods: The paper is based on primary research conducted using an online questionnaire survey on a sample of 331 respondents in Poland in March and April 2023. Results: A classification tree algorithm was used to identify the level of investment activities and behavioral changes made by households to reduce energy expenditures. The authors found that low-income households and people who fear further energy price increases are the first of all to change their behaviors for more energy-efficient ones. Medium- and high-income households take investment measures. They replace household appliances with more energy-efficient ones and install heat pumps and photovoltaic panels. These investments are motivated by responsible consumption, environmental protection, cleanliness, and the ease of use of the appliances.
Comprehensive Review of Carbon Capture and Storage Integration in Hydrogen Production: Opportunities, Challenges, and Future Perspectives
Authors: Alizadeh Seyed Mehdi , Khalili Yasin , Ahmadi Mohammad
Published: October 2024

The growing emphasis on renewable energy highlights hydrogen’s potential as a clean energy carrier. However, traditional hydrogen production methods contribute significantly to carbon emissions. This review examines the integration of carbon capture and storage (CCS) technologies with hydrogen production processes, focusing on their ability to mitigate carbon emissions. It evaluates various hydrogen production techniques, including steam methane reforming, electrolysis, and biomass gasification, and discusses how CCS can enhance environmental sustainability. Key challenges, such as economic, technical, and regulatory obstacles, are analyzed. Case studies and future trends offer insights into the feasibility of CCS–hydrogen integration, providing pathways for reducing greenhouse gases and facilitating a clean energy transition.
Parametric Analysis of a Novel Array-Type Hydrogen Storage Reactor with External Water-Cooled Jacket Heat Exchange
Authors: Ye Yang , Zhang Ziyang , Zhang Yuanyuan , Liu Jingjing , Yan Kai , Cheng Honghui
Published: October 2024

Hydrogen energy is a green and environmentally friendly energy source, as well as an excellent energy carrier. Hydrogen storage technology is a key factor in its commercial development. Solid hydrogen storage methods represented by using metal hydride (MH) materials have good application prospects, but there are still problems of higher heat transfer resistance and slower hydrogen absorption and release rate as the material is applied to reactors. This study innovatively proposed an array-type MH hydrogen storage reactor based on external water-cooled jacket heat exchange, aiming to improve the heat transfer efficiency and absorption reaction performance, and optimize the absorption kinetics encountered in practical applications of LaNi5 hydrogen storage material in reactors. A mathematical model was built to compare the hydrogen absorption processes of the novel array-type and traditional reactors. The results showed that, with the same water-cooled jacket, the hydrogen absorption rate of the array-type reactor can be accelerated by 2.78 times compared to the traditional reactor. Because of the existence of heat transfer enhancement limits, the increase in the number of array elements and the flow rate of heat transfer fluid (HTF) has a limited impact on the absorption rate improvement of the array-type reactor. To break the limits, the hydrogen absorption pressure, as a direct driving force, can be increased. In addition, the increased pressure also increases the heat transfer temperature difference, thereby further improving heat transfer and absorption rate. For instance, at 3 MPa, the hydrogen absorption time can be shortened to 147 s.
Encouraging Residents to Save Energy by Using Smart Transportation: Incorporating the Propensity to Save Energy into the UTAUT Model
Authors: Gajdzik Bożena , Awdziej Marcin , Jaciow Magdalena , Lipowska Ilona , Lipowski Marcin , Szojda Grzegorz , Tkaczyk Jolanta , Wolniak Radosław , Wolny Robert , Grebski Wieslaw Wes
Published: October 2024

The rapid urbanization and technological advancements of the recent decades have increased the need for efficient and sustainable transportation solutions. This study examines the acceptance of smart transportation systems (STSs) among residents in Polish cities and explores the impact of these systems on energy-saving behaviors. Using the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model, which includes the propensity to save energy, this research seeks to understand the determinants of STS adoption. The primary research was conducted using Computer-Assisted Web Interviewing (CAWI). The sample was controlled for gender and place of residence. A sample of 471 individuals meeting the criteria of living in a city with over 200,000 residents and using smart transportation solutions in Poland were selected from the research panel. SmartPLS 4 software was used to analyze the collected data. The findings reveal that the propensity to save energy significantly influences perceived usefulness, ease of use, social influence, and hedonic motivation toward STSs. Perceived usefulness and ease of use were found to be strong predictors of the intention to use STSs, while perceived costs had a negative impact on it. This study also identified the moderating role of personal innovativeness in mitigating cost concerns. These insights underscore the importance of emphasizing energy conservation benefits and user-friendly features in promoting the use of STSs. This study concludes that aligning technological innovations with user motivations for energy conservation can enhance the adoption of sustainable transportation solutions, contributing to smarter and more sustainable urban environments.
Enhancing Thermoelectric Performance of Mg3Sb2 Through Substitutional Doping: Sustainable Energy Solutions via First-Principles Calculations
Authors: Owais Muhammad , Luo Xian , Huang Bin , Yang Yanqing , Rehman Mudassar , Mushtaq Ray Tahir
Published: October 2024

Mg3Sb2-based materials, part of the Zintl compound family, are known for their low thermal conductivity but face challenges in thermoelectric applications due to their low energy conversion efficiency. This study addressed these limitations through first-principles calculations using the CASTEP module in Materials Studio 8.0, aiming to enhance the thermoelectric performance of Mg3Sb2 via strategic doping. Density functional theory (DFT) calculations were performed to analyze electronic properties, including band structure and density of states (D.O.S.), providing insights into the influence of various dopants. The semiclassical Boltzmann transport theory, implemented in BoltzTrap (version 1.2.5), was used to evaluate key thermoelectric properties such as the Seebeck coefficient, electrical conductivity, electronic thermal conductivity, and electronic figure of merit (eZT). The results indicate that doping significantly improved the thermoelectric properties of Mg3Sb2, facilitating a transition from p-type to n-type behavior. Bi doping reduced the band gap from 0.401 eV to 0.144 eV, increasing carrier concentration and mobility, resulting in an electrical conductivity of 1.66 × 106 S/m and an eZT of 0.757. Ge doping increased the Seebeck coefficient to −392.1 μV/K at 300 K and reduced the band gap to 0.09 eV, achieving an electronic ZT of 0.859 with low thermal conductivity (11 W/mK). Si doping enhanced stability and achieved an electrical conductivity of 1.627 × 106 S/m with an electronic thermal conductivity of 11.3 W/mK, improving thermoelectric performance. These findings established the potential of doped Mg3Sb2 as a highly efficient thermoelectric material, paving the way for future research and applications in sustainable energy solutions.
Pathways to Net-Zero—Innovations and Challenges in Achieving Carbon Neutrality
Authors: Pan Shu-Yuan
Published: October 2024

The global pursuit of net-zero carbon emissions has become one of the most critical challenges of the 21st century, as extreme climate events, rising temperatures, and environmental degradation force a reckoning with the carbon-intensive practices that define many of our industrial, agricultural, and energy systems [...]
Three-Stage Solid-State Fermentation Technology for Distillers’ Grain Feed Protein Based on Different Microorganisms Considering Oxygen Requirements
Authors: Kong Songlin , Wang Shilei , He Yun , Wang Nan , Wang Zhi , Weng Longfei , Liu Dong , Zhao Xiaoling , Chen Jinmeng , Xu Jingliang , Cai Yafan , Ying Hanjie
Published: October 2024

The shortage of feed protein has plagued the development of the animal husbandry industry in China. In this study, a new three-stage fermentation technology for producing distillers’ grain feed protein was developed by introducing Aspergillus niger, yeast, and lactic acid bacteria. During the aerobic stage, there was a negative correlation between the reducing sugar content in the distillers’ grains and the amount of Aspergillus niger. The maximum reducing sugar concentration (36.89 mg g−1) was obtained when the oxygen supply was 30 mL min−1 and the fermentation time was two days. During the microaerophilic stage, the natural exchange of oxygen achieved optimal true protein enhancement (from 10.8% to 16.4%) among the three oxygen supply modes (natural exchange, forced ventilation, and filling supplement). During the anaerobic stage, lactic acid bacteria were inoculated for feed protein preservation and flavor enhancement. Our results provided insight and practical guidance for the high-value use of distillers’ grains.
High-Density Fermentation of Lactobacillus plantarum P6: Enhancing Cell Viability via Sodium Alginate Enrichment
Authors: Sun Feiyang , Liu Siyi , Che Xinying , Wang Gang , Wang Xiufeng , Li Yanli , Zhang Sitong , Chen Huan
Published: October 2024

Lactobacillus plantarum exhibits a wide range of beneficial physiological functions, including maintaining intestinal microbiota balance, reducing serum cholesterol, and promoting digestive health. According to the specific nutrient requirements of Lactobacillus plantarum P6, we investigated the effects of various carbon sources, nitrogen sources, trace elements, growth-promoting substances, as well as the initial pH and inoculum size on the growth of Lactobacillus plantarum P6 under fermentation conditions. The optimal growth conditions for Lactobacillus plantarum P6 were identified to facilitate high-density fermentation in small-scale fermenter production, achieving a cell concentration of 1.03 × 1011 CFU/mL. This resulted in a 2.5-fold increase in bacterial wet weight, and fermentation time was reduced to 12 h when utilizing a specific medium enriched with 0.2% sodium alginate. It is hypothesized that sodium alginate forms a protective film around the bacterial cells, promoting cell aggregation and enhancing self-coalescence, potentially triggering a bacterial community effect. These results provide a basis for the industrial-scale high-density cultivation of Lactobacillus plantarum, offering potential for enhanced biotechnological applications.
Impacts of Harvest Year and Cultivation Location on Off-Flavor Compounds and Functionality of Pea Protein Isolate
Authors: Manouel Tamar , Subasi Busra Gultekin , Abdollahi Mehdi
Published: October 2024

The impact of four different harvest years and two cultivation locations (CLs) of pea seeds on their protein wet fractionation yield, volatile and non-volatile beany flavors, and functionality were investigated. Both harvest years and CLs significantly affected protein recovery, but protein purity was primarily influenced by CLs. Seed age emerged as a dominant factor causing the reduction in linolenic/linoleic acid content and lipoxygenase (LOX) activity which surpassed the effect of harvest years in the seeds but not in their proteins. CL significantly affected fatty acid composition in both seeds and proteins, whereas its effect on LOX activity was discernible only in the proteins. Volatile beany compounds in the proteins were affected by both harvest years and CLs, correlating with their polyunsaturated fatty acid (PUFA) content and LOX activity. Both factors minimally impacted the emulsification capacity of the proteins but imposed a significant effect on their rheological properties. Altogether, the results revealed that seed crop years and especially locations affect pea protein quality, calling for proper adaptation strategies.
Development of Nutrient Rich Morning Meals for Students by Concept of Tapas
Authors: Obradović Valentina , Ergović Ravančić Maja , Marčetić Helena , Vuksanović Nikola , Škrabal Svjetlana
Published: October 2024

A correlation between nutritional status and academic achievement has been established by many studies, but students′ eating habits often do not meet nutritional recommendations. Breakfast consumption has a positive effect on cognitively demanding tasks and results in better attention and memory. The goal of this work was to develop well-balanced, nutritionally rich morning meals for students based on the concept of tapas, a combination of several different small meals composed of different ingredients. An introductory survey about students’ morning eating habits was conducted among students of the Faculty of Tourism and Rural Development in Požega, Croatia. Forty-six full-time students (9.50% of total number of students at the faculty) participated in the survey. Sixteen types of tapas were prepared combining ingredients which students rated as desirable (cheese, prosciutto, peppers, milk spread) and undesirable (blue fish, dry fruit, cauliflower, chickpeas) in the initial survey. Tapas (one tapa = one sample) were scored by a sensory panel of 16 students, and nutritional value of the samples was assessed by web application Program Prehrane® (The Nutrition Program). All samples except two were scored as desirable by more than 60% of students, meaning that even ingredients which have been initially scored as undesirable, can be incorporated into desirable meal when properly combined. Based on the analysis of energy and nutritional value of samples, students should combine 3 to 5 different tapas to fulfill their energy needs for breakfast or 1 to 2 different tapas for a morning snack. Developed tapas can provide a perfectly balanced meal rich in different micronutrients because they include ingredients which students normally do not include in their breakfast. Especially important ingredients were blue fish rich in unsaturated acids and selenium, nuts rich in selenium and vitamin E, and red peppers rich in vitamin C and carotenoids. Tapas containing cheese and prosciutto, students’ favorite ingredients, had the highest level of sodium and fats, but not above recommended values when combined with other tapas.
Hybrid Meat Sausages with Cereal Ingredients: A Systematic Review and Development Trial with the Assessment of Physicochemical and Sensory Attributes
Authors: Olewnik-Mikołajewska Anna , Guzek Dominika , Głąbska Dominika , Gutkowska Krystyna
Published: October 2024

A number of consumers in developed countries are now reducing the amount of meat in their diets, so the development of novel alternatives for conventional meat products is becoming a challenge for the meat industry. The aim of this study was to analyse the possibility of developing hybrid meat sausages with cereal ingredients, based on a systematic review of the literature, as well as a development trial of a hybrid dry snack stick sausage with groats with an assessment of its physicochemical and sensory attributes. A systematic review of peer-reviewed studies about hybrid meat sausages with cereal ingredients, including bibliometric network analysis, was conducted. The development trial was conducted including physicochemical analyses and sensory assessment of the hybrid semi-dry sausages, produced as a ready-to-eat snack (cabanossi) with groats, obtained on an industrial scale. Among the studied hybrid meat products with cereal ingredients, there were patties, frankfurters, salami, and other sausages, while the cereal products added included various components obtained from rice, wheat, chia, and oats. The usefulness of the applied cereal components was emphasised in order to obtain a product of a potential better nutritional value and higher health-promoting properties, as well as being acceptable, or sometimes even better, and described as being products of a good quality. The development trial allowed us to obtain the hybrid semi-dry ready-to-eat cabanossi sausages with groats, which were compared with the hybrid semi-dry ready-to-eat cabanossi sausages with sunflower seeds. Both studied products were characterised by a composition in agreement with requirements, but of a reduced fat content (with hybrid semi-dry ready-to-eat cabanossi sausages with groats being even lower than for those with sunflower seeds; p < 0.0001). Their sensory properties were acceptable, even if the plant-based components were recognisable. The characteristics of the hybrid meat products with cereal ingredients both in the literature and development trial were acceptable, and what is even more important is that they are characterised by a potential to be presented as a product of a better nutritional value and higher health-promoting properties.
Double Encapsulation of Resveratrol and Doxorubicin in Composite Nanogel—An Opportunity to Reduce Cardio- and Neurotoxicity of Doxorubicin
Authors: Radeva Lyubomira , Yordanov Yordan , Spassova Ivanka , Kovacheva Daniela , Tzankova Virginia , Yoncheva Krassimira
Published: October 2024

The simultaneous encapsulation of drugs into nanosized delivery systems could be beneficial for cancer therapies since it could alleviate adverse reactions as well as provide synergistic effects. However, the encapsulation of hydrophobic drugs into hydrophilic nanoparticles, such as nanogels, could be challenging. Therefore, innovative technological approaches are needed. In this research, a composite nanogel system was prepared from chitosan, albumin, and hydroxypropyl-β-cyclodextrin for co-delivery of the hydrophilic anticancer drug doxorubicin and hydrophobic antioxidant resveratrol. The nanoparticles were characterized using dynamic light scattering and found to have a hydrodynamic diameter of approx. 31 nm, narrow size distribution (PDI = 0.188), positive ƺ-potential (+51.23 mV), and pH-dependent release of the loaded drugs. FTIR and X-ray analyses proved the successful development of the composite nanogel. Moreover, the double-loaded system showed that the loading of resveratrol exerted protection against doxorubicin-induced toxicity in cardioblast H9c2 and neuroblast SH-SY5Y cells. The simultaneous loading did not influence the cytostatic effect of the antitumor agent in lymphoma L5178Y and L5178MDR cell lines.
Assessing Nitrogen Fertilization in Processing Pepper: Critical Nitrogen Curve, Yield Response, and Crop Development
Authors: Vadillo Jose Maria , Campillo Carlos , González Valme , Prieto Henar
Published: October 2024

Groundwater pollution in intensive horticultural areas is becoming an increasingly important problem. Over-fertilization of these crops, combined with poor irrigation management, leads to groundwater contamination through leaching. Previous research on the effect of N on sweet peppers grown in greenhouses is abundant, but data on outdoor cultivation, especially considering variety and site influences, are lacking. Therefore, this study evaluates nitrogen (N) fertilization in open-field processing-pepper crop in Extremadura, Spain to mitigate this environmental impact. Field trials were conducted in 2020, 2021, and 2022 to determine the optimum N fertilizer rate for processing peppers, with the aim of reducing environmental impacts such as nitrate leaching while maintaining crop yields. The trial consisted of applying different N doses, 0, 60, 120, and 180 kg N/ha in 2020 and 2021 and 0, 100, and 300 kg N/ha in 2022. There were four replications of each treatment, arranged in randomized blocks. Measurements included crop yield, biomass, intercepted photosynthetically active radiation (PAR), and canopy cover. The study also developed a critical nitrogen curve (CNC) to determine the minimum N concentration required for optimal growth. The commercial yield results showed that there were no significant differences between the two treatments with higher N inputs in the three years; therefore, the application of more than 120 kg N/ha did not significantly increase yield. Nitrogen-free treatments resulted in earlier fruit maturity, concentrating the harvest and reducing waste. In addition, excessive N application led to environmental problems such as groundwater contamination due to nitrate leaching. The study concludes that outdoor pepper crops in this region can achieve optimal yields with lower N rates (around 120 kg N/ha) compared to current practices, taking into account that initial soil N values were higher than 100 kg N/ha, thereby reducing environmental risks and fertilizer costs. It also established relationships between biomass, canopy cover, and N uptake to improve fertilization strategies. These data support future crop modeling and sustainable fertilization practices.
Mapping the Knowledge Structure of Image Recognition in Cultural Heritage: A Scientometric Analysis Using CiteSpace, VOSviewer, and Bibliometrix
Authors: Ju Fei
Published: October 2024

The application of image recognition techniques in the realm of cultural heritage represents a significant advancement in preservation and analysis. However, existing scholarship on this topic has largely concentrated on specific methodologies and narrow categories, leaving a notable gap in broader understanding. This study aims to address this deficiency through a thorough bibliometric analysis of the Web of Science (WoS) literature from 1995 to 2024, integrating both qualitative and quantitative approaches to elucidate the macro-level evolution of the field. Our analysis reveals that the integration of artificial intelligence, particularly deep learning, has significantly enhanced digital documentation, artifact identification, and overall cultural heritage management. Looking forward, it is imperative that research endeavors expand the application of these techniques into multidisciplinary domains, including ecological monitoring and social policy. Additionally, this paper examines non-invasive identification methods for material classification and damage detection, highlighting the role of advanced modeling in optimizing the management of heritage sites. The emergence of keywords such as ‘ecosystem services’, ‘models’, and ‘energy’ in the recent literature underscores a shift toward sustainable practices in cultural heritage conservation. This trend reflects a growing recognition of the interconnectedness between heritage preservation and environmental sciences. The heightened awareness of environmental crises has, in turn, spurred the development of image recognition technologies tailored for cultural heritage applications. Prospective research in this field is anticipated to witness rapid advancements, particularly in real-time monitoring and community engagement, leading to the creation of more holistic tools for heritage conservation.
Characteristics of Microbial Diversity and Metabolic Versatility in Dynamic Mid-Okinawa Trough Subsurface Sediments
Authors: Xin Youzhi , Zhang Tao , Chen Ye , Wu Linqiang , Jiang Chengzhu , Wu Nengyou
Published: October 2024

Large-scale and multi-sample datasets have revealed that microbial diversity and geographic distribution patterns are distinct across various habitats, particularly between hydrothermal vent and cold seep ecosystems. To date, our understanding of the effects of spatial and geochemical gradients on marine microbial communities remains limited. Here, we report the microbial diversity and metabolic versatility of a remote seafloor sediment ecosystem at different sites (GC-2, -4, -5, -6, -8) in the Mid-Okinawa Trough (Mid-OT) using high-throughput metagenomic sequencing technology. Desulfobacteraceae (3.1%) were detected in a high abundance at GC-2 with intense methane concentrations (353 μL/L), which showed a clear correlation with cold seeping. Whereas Candidatus Brocadiaceae (1.7%), Rhodobacteraceae (0.9%), and Rhodospirillaceae (0.7%), which are commonly involved in denitrification and sulfur oxidation, were enriched at GC-8. Concurrently investigating the potential of deep-sea microbial metabolism, we gained insights into the adaptive capabilities and metabolic mechanisms of microorganisms within seafloor environments. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, the analysis of functional modules revealed a significant enrichment (71–74%) of genes associated with metabolic pathways. These results expand our knowledge of the relationship between microbial biodiversity and metabolic versatility in deep-sea extreme environments.
Flow Field Analysis and Development of a Prediction Model Based on Deep Learning
Authors: Yu Yingjie , Zhang Xiufeng , Wang Lucai , Tian Rui , Qian Xiaobin , Guo Dongdong , Liu Yanwei
Published: October 2024

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future.
High-Efficient Exploration and Development of Oil & Gas from Ocean
Authors: Meng Mianmo , Ji Wenming , Cui Guodong
Published: October 2024

The ocean takes up 71% of the Earth’s surface, and hosts abundant resources, such as oil/gas [...]
Insider Trading and CEO Pay-Gap Induced Turnover
Authors: Le Viet , Nguyen Ann-Ngoc , Gregoriou Andros , Forbes William
Published: October 2024

We explore how insider trading returns, disparities in executive pay, and CEO turnover are interrelated. Our findings reveal both independent and interactive effects for insider trading returns, the CEO pay gap, and the likelihood of CEO turnover. First, an increase in abnormal returns from insider purchases lowers the probability of a CEO’s turnover, while an increase in abnormal returns from insider sales increases the likelihood of a CEO’s dismissal. Second, the CEO pay gap negatively affects the probability of CEO turnover for insider purchases, but it does not have a similar effect on insider sales. Third, the interaction between insider abnormal returns and any CEO pay disparity influences the impact of these returns on CEO turnover. Specifically, this interaction diminishes the positive effect of insider selling on the probability of a CEO’s dismissal, offsets the negative effect of insider purchasing on CEO dismissal, and, finally, amplifies the negative impact of CEO pay disparity on the probability of a CEO’s dismissal during periods witnessing insider purchases.
Microcrediting and Investment Analysis in the Context of Environmental, Social, and Corporate Governance
Authors: Adambekova Ainagul , Adambekov Nurbek , Randhir Timothy O. , Adambekova Zhuldyz , Yezhebekov Manat
Published: October 2024

This article is devoted to the analysis and development of ranking criteria for microcredit organizations to increase their investment attractiveness. The need to solve problematic issues is associated with the need to minimize risks before the start of the lending process through the correct selection of participants in the credit transaction. This study used the methods of content analysis and interpretation, correlation analysis and regression modeling, ranking, and clustering to assess the factors affecting the effectiveness of microcredit organizations. The most attention is paid to identifying key indicators that help improve the quality of financial services provided and their availability for various categories of borrowers. The results show that factors related to lending volumes and borrower characteristics have a significant impact on the quality of microcredit organizations. Of interest is the interpretation of classical financial indicators of microcredit organizations in the context of the principles of environmental, social, and corporate governance (ESG). The proposed approaches and conclusions can be used to improve the practice of microfinance and develop management and regulation strategies in this area.
A Study of the Mechanisms of Government Embedment and Organizational Environment Related to Supervisory Effectiveness in the Collective Governance of Rural Residential Land
Authors: Yang Zhongjian , Tang Hong , Zhao Wenxiang , Ran Ruiping
Published: October 2024

Rural Residential Land is a kind of public pond resource, and the implementation of the collective governance of Rural Residential Land is a response to the dilemma of utilizing the land. This paper takes the attribute of using Rural Residential Land as a public pond resource as an entry point, draws on Autonomous Governance Theory, and utilizes the OLS model and the mediated effect model to explore the influence of Government Embedment on supervisory effectiveness in the collective governance of Rural Residential Land, based on the fieldwork data of 450 farming households in three districts (cities and counties) in Sichuan Province. This study shows that Government Embedment, the Technological Environment and the Cultural Environment can significantly enhance supervisory effectiveness in the collective governance of Rural Residential Land, while the Resource Environment has a negative effect on it. The Resource Environment has a masking effect on the influence of Government Embedment on supervisory effectiveness in the collective governance of Rural Residential Land, while the Technological Environment and the Cultural Environment play a part in the mediating effect. The anonymous whistleblowing mechanism positively moderates the influence of the Technological and Cultural Environments on supervisory effectiveness. Additionally, there is obvious locational heterogeneity in the influence of Government Embedment and the Organizational Environment on supervisory effectiveness. Therefore, a coordinated supervision and sanctioning mechanism should be constructed in towns and villages to promote the integration and complementarity of formal and informal systems; a sound mechanism for anonymous reporting of illegal and irregular use of Rural Residential Land should be established; and the effective implementation of the collective governance of Rural Residential Land should be promoted in accordance with the differences in the location of the villages.
A Comparison of the Efficacy of Fuzzy Overlay and Random Forest Classification for Mapping and Shaping Perceptions of the Post-Mining Landscape of Gauteng, South Africa
Authors: Khanyile Samkelisiwe
Published: October 2024

Post-mining landscapes are multifaceted, comprising multiple characteristics, more so in big metropolitan regions such as Gauteng, South Africa. This paper evaluates the efficacy of Fuzzy overlay and Random Forest classification for integrating and representing post-mining landscapes and how this influences the perception of these landscapes. To this end, this paper uses GISs, MCDA, Fuzzy overlay, and Random Forest classification models to integrate post-mining landscape characteristics derived from the literature. It assesses the results using an accuracy assessment, area statistics, and correlation analysis. The findings from this study indicate that both Fuzzy overlay and Random Forest classification are applicable for integrating multiple landscape characteristics at varying degrees. The resultant maps show some similarity in highlighting mine waste cutting across the province. However, the Fuzzy overlay map has higher accuracy and extends over a larger footprint owing to the model’s use of a range of 0 to 1. This shows both areas of low and high memberships, as well as partial membership as intermediate values. This model also demonstrates strong relationships with regions characterised by landscape transformation and waste and weak relationships with areas of economic decline and inaccessibility. In contrast, the Random Forrest classification model, though also useful for classification purposes, presents a lower accuracy score and smaller footprint. Moreover, it uses discrete values and does not highlight some areas of interaction between landscape characteristics. The Fuzzy overlay model was found to be more favourable for integrating post-mining landscape characteristics in this study as it captures the nuances in the composition of this landscape. These findings highlight the importance of mapping methods such as Fuzzy overlay for an integrated representation and shaping the perception and understanding of the locality and extent of complex landscapes such as post-mining landscapes. Methods such as Fuzzy overlay can support research, planning, and decision-making by providing a nuanced representation of how multiple landscape characteristics are integrated and interact in space and how this influences public perception and policy outcomes.