bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetic Diversity and Population Structure of Endangered Orchid Cypripedium flavum in Fragmented Habitat Using Fluorescent AFLP Markers
Authors: Hu Shijun , Wang Meizhen , Yan Xiaohui , Cheng Xiaomao
Published: October 2024

Genetic diversity is crucial for determining the evolutionary potential of a species and is essential for developing optimal conservation strategies. The impact of habitat fragmentation on the genetic diversity of food-deceptive orchids seems to be unpredictable because of their specialized seed and pollen dispersal mechanisms. The habitat of deceptive Cypripedium flavum was severely fragmented during the past half century. This study investigated the genetic diversity and structure of seven fragmented Cypripedium flavum populations in Shangrila County using AFLP markers. A total of 376 alleles were identified, with a range of 70 to 81 alleles per locus. The species exhibited considerable genetic diversity, as evidenced by an average Nei’s gene diversity (H) of 0.339 and a Shannon’s information index (I) of 0.505, with all loci being polymorphic. Based on Molecular Variance (AMOVA), 8.75% of the genetic differentiation was found among populations, while the remaining 91.25% of genetic variation occurred within populations. Population structure analysis revealed that the C. flavum germplasm can be categorized into 2 distinct groups, among which there was significant gene flow. Despite habitat fragmentation, C. flavum still retained a high level of genetic diversity, and the substantial gene flow (5.0826) is a key factor in maintaining the genetic diversity. These findings offer valuable insights for the conservation and potential use of C. flavum genetic resources.
Prediction of the Contact Behavior of Stepseals: Experimental and Numerical Investigations
Authors: Li Ran , Wei Wenshu , Cao Tianze , Chen Rongming , Li Jianfeng , Lai Yuehua , Liu Hao , Geng Xiangyu , Liu Fengcai , Li Wenbo
Published: October 2024

The contact behavior of a stepseal affects its reliability and remaining life. In this study, experimental and numerical analyses were performed to investigate the contact behavior of a typical stepseal. Its dimensions and material properties were also tested. The Mooney–Rivlin hyperelastic model was used to fit the stress–strain data of the rubber. A static contact pressure of 105.1 MPa was obtained using the Fujifilm pressure measurement film. A finite element model of a stepseal with reasonable element sizes was established. A comparison between the experimental results and finite element (FE) predictions shows that the finite element method underpredicts the maximum static contact pressure and contact length. For the maximum contact pressure, the test results were approximately 30.4% higher than those of the FE predictions.
Evolution and Characterization of Uniaxial Compression Damage of Coal Measure Rock
Authors: Sun Yanchao , Li Jianwei , Yang Yongze , Du Feng , Liu Jian , Zhang Ningbo
Published: October 2024

In order to study the evolution law of uniaxial compression damage and its characterization method, three kinds of rocks with different lithology that commonly exist in coal measure rock were taken as the research objects to explore the relationship between their mineral composition, microstructure and mechanical properties, and the feasibility of acoustic emission (AE) characteristic parameters and AE spatial location to characterize the evolution process of uniaxial compression damage of rocks was compared and analyzed. A damage constitutive model based on AE cumulative ringing count was established and verified by numerical simulation. The results show that the differences in the micromorphology and mineral composition of coal measures with different lithology directly affect their physical and mechanical properties and load damage characteristics. AE characteristic ringing count can effectively represent the damage evolution process of rock during loading. Based on rock porosity characteristics and AE cumulative ringing count during loading, a constitutive model considering initial damage and cumulative damage was established. The theoretical analysis results of the damage constitutive model are in good agreement with the results of laboratory experiments and numerical simulation, which can be used to analyze the damage process of coal measure rock under load.
Towards Strength–Ductility Synergy in Cold Spray for Manufacturing and Repair Application: A Review
Authors: Wang Yixun , Wong Bo Ching , Chan Tak-Ming , Voyle Robert
Published: October 2024

Cold spray is a solid-state additive manufacturing technology and has significant potential in component fabrication and structural repair. However, the unfavourable strength–ductility synergy in cold spray due to the high work hardening, porosity and insufficient bonding strength makes it an obstacle for real application. In recent years, several methods have been proposed to improve the quality of the cold-sprayed deposits, and to achieve a balance between strength and ductility. According to the mechanism of how these methods work to enhance metallurgical bonding, decrease porosity and reduce dislocation densities, they can be divided into four groups: (i) thermal methods, (ii) mechanical methods, (iii) thermal–mechanical methods and (iv) optimisation of microstructure morphology. A comprehensive review of the strengthening mechanism, microstructure and mechanical properties of cold-sprayed deposits by these methods is conducted. The challenges towards strength–ductility synergy of cold-sprayed deposits are summarised. The possible research directions based on authors’ research experience are also proposed. This review article aims to help researchers and engineers understand the strengths and weaknesses of existing methods and provide pointers to develop new technologies that are easily adopted to improve the strength–ductility synergy of cold-sprayed deposits for real application.
Development of Fiber-Reinforced Polymer Composites for Additive Manufacturing and Multi-Material Structures in Sustainable Applications
Authors: Faidallah Rawabe Fatima , Hanon Muammel M. , Salman Nihal D. , Ibrahim Yousef , Babu Md. Noman , Gaaz Tayser Sumer , Szakál Zoltán , Oldal István
Published: October 2024

This study investigates the mechanical properties of carbon and natural fiber-reinforced Polylactic Acid (PLA) and Polyethylene Terephthalate Glycol (PETG) composites produced via Additive Manufacturing (AM), focusing on Material Extrusion (MEX). The performance of filaments made from pre-consumer recycled PLA (rPLA) and PETG, with varying weight percentages of hemp and jute short fibers, was evaluated through tensile testing. Comparisons were made between the original filaments (PLA, carbon fiber-reinforced PLA [CF–PLA], and PETG) and their recycled versions. Multi-material compositions—neat PLA and PETG, single-graded (PLA + CF–PLA, PETG + CF–PETG), and multi-gradient (PLA + CF–PLA + PLA, PETG + CF–PETG + PETG)—were analyzed for mechanical properties. Optical microscope images of multi-material specimens were captured before and after fracture to assess failure mechanisms. The results indicate that the original CF–PETG filaments achieved a tensile strength of 50.14 MPa, which is higher than rPLA, PLA, and CF–PLA by 2%, 70%, and 6.7%, respectively. The re-manufactured PLA filaments reinforced with 7 wt% hemp fibers exhibited a tensile strength of 38.8 MPa, representing a 29% increase compared to the original PLA filaments and a 26% improvement over recycled PLA. Additionally, incorporating 7% jute fiber into PETG resulted in a tensile strength of 62.38 MPa, reflecting a 12% improvement over the original PETG filaments and a 15% increase compared to the recycled PETG filaments. Among specimens produced by AM, CF–PLA and rPLA demonstrated the highest tensile and compressive strengths. However, multi-material composites showed reduced mechanical performance compared to neat PLA and PETG, highlighting the need for improved interlayer adhesion. This study emphasizes the importance of optimizing material combinations and fiber reinforcement to enhance the mechanical properties of composites produced through AM.
Circulating Factors as Potential Biomarkers of Cardiovascular Damage Progression Associated with Type 2 Diabetes
Authors: Sartore Giovanni , Piarulli Francesco , Ragazzi Eugenio , Mallia Alice , Ghilardi Stefania , Carollo Massimo , Lapolla Annunziata , Banfi Cristina
Published: October 2024

Background: Diabetes, particularly type 2 diabetes (T2D), is linked with an increased risk of developing coronary heart disease (CHD). The present study aimed to evaluate potential circulating biomarkers of CHD by adopting a targeted proteomic approach based on proximity extension assays (PEA). Methods: The study was based on 30 patients with both T2D and CHD (group DC), 30 patients with T2D without CHD (group DN) and 29 patients without diabetes but with a diagnosis of CHD (group NC). Plasma samples were analyzed using PEA, with an Olink Target 96 cardiometabolic panel expressed as normalized protein expression (NPX) units. Results: Lysosomal Pro-X carboxypeptidase (PRCP), Liver carboxylesterase 1 (CES1), Complement C2 (C2), and Intercellular adhesion molecule 3 (ICAM3) were lower in the DC and NC groups compared with the DN groups. Lithostathine-1-alpha (REG1A) and Immunoglobulin lambda constant 2 (IGLC2) were found higher in the DC group compared to DN and NC groups. ROC analysis suggested a significant ability of the six proteins to distinguish among the three groups (whole model test p < 0.0001, AUC 0.83–0.88), with a satisfactory discriminating performance in terms of sensitivity (77–90%) and specificity (70–90%). A possible role of IGLC2, PRCP, and REG1A in indicating kidney impairment was found, with a sensitivity of 92% and specificity of 83%. Conclusions: The identified panel of six plasma proteins, using a targeted proteomic approach, provided evidence that these parameters could be considered in the chronic evolution of T2D and its complications.
Evolution and Trends in Digital Wallet Research: A Bibliometric Analysis in Scopus and Web of Science
Authors: Pizzan-Tomanguillo Nieves del Pilar , Pereyra-Gonzales Tony Venancio , León-Ramírez Segundo Victor , Bautista-Fasabi Jhon , Rosales-Bardalez Carlos Daniel , Gómez-Apaza Roel Dante , Pizzán-Tomanguillo Sandra Lucero
Published: October 2024

Digital wallets have become a driving force in the global economy, with 2.4 billion users worldwide in 2020, a figure projected to reach 3.6 billion by 2026. This study conducts an in-depth bibliometric analysis to evaluate the current state and future trends of digital wallet research using scientific databases such as Scopus and Web of Science. A scoping review methodology was applied, in which we analyzed 778 documents, following an eligibility process with the following search terms: “digital wallet”, “mobile wallet”, and “e-wallet”. Results show that India, the United States, and China are leading research efforts in this field. Key factors influencing the adoption of digital wallets include data security, ease of use, and integration with traditional payment systems. While much research has focused on technological innovation and adoption, significant gaps remain in areas such as blockchain integration and AI-driven solutions. This article provides the first comprehensive bibliometric analysis of digital wallets, offering crucial insights into emerging trends such as “blockchain”, “electronic commerce”, and “digital payments” and their role in shaping the future of financial technology.
Development of Vertical Radar Reflectivity Profiles Based on Lightning Density Using the Geostationary Lightning Mapper Dataset in the Subtropical Region of Brazil
Authors: Mandú Tiago Bentes , Alves Laurizio Emanuel Ribeiro , Vendrasco Éder Paulo , Biscaro Thiago Souza
Published: October 2024

The study aims to develop vertical radar reflectivity profiles based on lightning density data from the Geostationary Lightning Mapper (GLM) on the GOES-16 satellite in the subtropical region of Brazil. The primary objective is to improve the assimilation of lightning data in numerical weather prediction models. The methodology involves the analysis of polarimetric radar data from Chapecó-SC and Jaraguari-MS, spanning from January 2019 to December 2023, and their correlation with lightning data from the GLM. Radar reflectivity profiles were created for different lightning density classes, categorized into six classes based on geometric progression. Results show a significant relationship between lightning activity and radar reflectivity, with distinct profiles for convective and stratiform events. These findings demonstrate the potential of using GLM data to enhance short-term weather forecasting, particularly for severe weather events. The study concludes that the integration of GLM data into weather models can lead to more accurate predictions of intense precipitation events, contributing to better preparedness and response strategies.
A Biometric Identification for Multi-Modal Biomedical Signals in Geriatric Care
Authors: Che Yue , Du Lingyan , Tang Guozhi , Ling Shihai
Published: October 2024

With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time–frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources.
Green Campus Transformation in Smart City Development: A Study on Low-Carbon and Energy-Saving Design for the Renovation of School Buildings
Authors: Li Yangluxi , Chen Huishu , Yu Peijun
Published: October 2024

In the context of increasingly deteriorating global ecological conditions and rising carbon emissions from buildings, campus architecture, as the primary environment for youth learning and living, plays a crucial role in low-carbon energy-efficient design, and green environments. This paper takes the case of Yezhai Middle School in Qianshan, Anhui Province, to explore wind environment optimization and facade energy-saving strategies for mountainous campus buildings under existing building stock renovation. In the context of smart city development, integrating advanced technologies and sustainable practices into public infrastructure has become a key objective. Through wind environment simulations and facade energy retrofitting, this study reveals nonlinear increases in wind speed with building height and significant effects of ground roughness on wind speed variations. Adopting EPS panels and insulation layers in facade energy retrofitting reduces energy consumption for winter heating and summer cooling. The renovated facade effectively prevents cold air intrusion and reduces external heat gain, achieving approximately 24% energy savings. This research provides a scientific basis and practical experience for low-carbon energy retrofitting of other campus and public buildings, advancing the construction industry towards green and low-carbon development goals within the framework of smart city initiatives.
The Past Is Never Dead: Soil Pollution from Mining in the Copiapó River Basin (Northern Chile)
Authors: Pérez Luna Araceli , Izquierdo Tatiana , Abad Manuel , Caraballo Manuel , Ureta Sebastián , Ruiz Francisco
Published: October 2024

This short paper analyses the concentrations of two major components (Fe, S) and eight trace elements (As, Au, Co, Cu, Hg, Ni, Mn, Zn) in soils and tailings from Tierra Amarilla (northern Chile) using ICP-Ms analysis. The levels of As, Au, Cu, Fe and S are very high and come from polymetallic sulphides from nearby mines, together with minor contributions of Co, Ni and Mn. Hg has its origin in the extreme seasonal flows of the Copiapó River, which erodes the dumps of old precious metal mines. These high concentrations require further analysis of possible metal immobilisation techniques, bioavailability or analysis in cultivated plants.
Stoichiometric and Accumulation Characteristics of Nitrogen and Phosphorus in Artificial and Natural Herbaceous Plants along Lakeshore Buffer Zone
Authors: Liu Qiang , Cheng Yan , Fan Chunnan , Bai Boyuan
Published: October 2024

Lakeshore buffer zones serve as transitional areas between terrestrial and aquatic ecosystems, playing a crucial role in intercepting non-point source pollutants, purifying river and lake water, and maintaining ecological system dynamics. This study focuses on the vegetation of the lakeshore buffer zone in Songhua Lake, the largest artificial lake in Northeast China. The nitrogen (N) and phosphorus (P) pollutant contents and accumulation characteristics of herbaceous plants were investigated and analyzed in different regions and with different species. The study results indicate that there were no significant differences in N and P content, N: P ratio, and average accumulation among vegetation in the near-water, middle, and far lakeshores of the buffer area. The herbaceous plants with the highest N and P content and N: P ratios were Artemisia sieversiana, Sium suave, and Artemisia sieversiana, respectively. Purus frumentum planted in the middle lakeshore accumulated the highest amounts of N and P, reaching 87.97 g plant−1 and 3.86 g plant−1, respectively. The aboveground parts of plants showed significantly higher accumulation of N and P compared to the underground parts. The average enrichment coefficient for aboveground parts and underground parts for N were 4.83 and 4.35, respectively, all exceeding 1. However, their capability to enrich P was relatively weak, with only the aboveground parts of F − 3 showing some enrichment ability. Among herbaceous plants, Artemisia sieversiana and Sium suave exhibited the strongest capability for transporting N and P from underground to aboveground. Overall, harvesting aboveground biomass for the ecological removal of N and P in the study area appears feasible. Biomass is a critical factor influencing the nutrient interception capacity of vegetation, with Purus frumentum identified as an optimal restoration plant for sustainable management practices, and Artemisia sieversiana and Sium suave have the potential for rapid remediation.
Consumer Motivations for Solar Energy Adoption in Economically Challenged Regions
Authors: Jabbour Al Maalouf Nada , Sayegh Elie , Inati Diala , Sarkis Nada
Published: October 2024

This study aims to investigate the factors contributing to and affecting consumer behavior toward renewable energy by following the theories of reasoned action and planned behavior. For this reason, a quantitative research method was employed and primary data were collected via a questionnaire, resulting in a random sample of 450 respondents. Structural equation modeling (SEM) revealed that concern for environmental commitment positively affects attitudes toward solar energy (β = 0.272, p < 0.001), positive subjective norms toward environmental commitment positively influence attitudes (β = 0.092, p < 0.001), perceptions of solar energy values significantly shape attitudes (β = 0.533, p < 0.001), social influence also plays a role in shaping attitudes (β = 0.047, p < 0.001), and that regulations (behavioral control) have a negative impact on attitudes (β = −0.204, p < 0.001). A positive attitude toward solar energy strongly predicts purchase intention (β = 0.944, p < 0.001). The overall model highlights the direct influence of attitude on buying intention and underscores the roles of environmental concern and functional utility in shaping consumer attitudes. This study contributes to the existing literature on renewable energy adoption by providing empirical evidence on the factors influencing consumers’ attitudes toward solar energy systems. By identifying key predictors, the study offers valuable insights into how these elements shape consumer attitudes and subsequent purchase intentions. Additionally, the study enhances the understanding of the role of positive attitudes in driving investment in solar energy, thereby contributing to both theoretical frameworks and practical applications in energy policy and marketing strategies.
Fish Viscera Hydrolysates and Their Use as Biostimulants for Plants as an Approach towards a Circular Economy in Europe: A Review
Authors: Domínguez Haizea , Iñarra Bruno , Labidi Jalel , Bald Carlos
Published: October 2024

Crop production has become a priority issue in recent years because of the exponential growth of the world’s population and the need to find substitutes for chemical fertilizers. The latter is under the spotlight in order to achieve a more sustainable approach in a cost-effective way. Biostimulants have gained attention as an alternative to chemical fertilizers. Although they are not considered fertilizers as inputs of nutrients, they stimulate plants’ nutrition and tolerance to stress, among other characteristics. In the literature, amino acid-based biostimulants have been found to be effective. This review focuses on the effectiveness of biostimulants, their presence in the global market, and their production with fish by-products as a source, using enzymatic hydrolysis and autolysis, with a particular focus on fish viscera, their possibilities in the agricultural sector, and their availability in Europe for possible opportunities. Fish viscera protein hydrolysates for biostimulant production seem a feasible alternative to fishmeal production in Europe, especially in areas located far from fishmeal plants.
A Comprehensive Review and Recent Trends in Thermal Insulation Materials for Energy Conservation in Buildings
Authors: Ali Amir , Issa Anas , Elshaer Ahmed
Published: October 2024

In recent years, energy conservation became a strategic goal to preserve the environment, foster sustainability, and preserve valuable natural resources. The building sector is considered one of the largest energy consumers globally. Therefore, insulation plays a vital role in mitigating the energy consumption of the building sector. This study provides an overview of various organic and inorganic insulation materials, recent trends in insulation systems, and their applications, advantages, and disadvantages, particularly those suitable for extreme climates. Moreover, natural and composite materials that can be used as a low-cost, thermally efficient, and sustainable option for thermal insulation are discussed along with their thermal properties-associated problems, and potential solutions that could be adopted to utilize natural and sustainable options. Finally, the paper highlights factors affecting thermal performance and essential considerations for choosing a particular insulation system for a particular region. It is concluded that the most commonly used insulation materials are found to have several associated problems and there is a strong need to utilize sustainable materials along with advanced materials such as aerogels to develop novel composite insulation materials to overcome these deficiencies.
The Levels of Government Environmental Attention and Enterprises’ Green Technological Innovation
Authors: Wang Han , Han Zhuorui , He Yang
Published: October 2024

This study utilizes data from listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2023 and employs text analysis methods to investigate the impact of government environmental concerns on corporate green technology innovation and its underlying impact mechanisms. The empirical findings demonstrate that heightened government environmental concerns significantly foster corporate green technology innovation. This relationship remains robust across a series of sensitivity analyses. The impact mechanism analysis reveals that increased government environmental concern leads to a heightened intensity of environmental regulation, drives the intelligent transformation of enterprises, and enhances the implementation of corporate environmental strategies, collectively contributing to the improvement of corporate green technology innovation capabilities. Furthermore, the heterogeneity analysis indicates that the effect of government environmental concerns is more pronounced in state-owned enterprises, mature enterprises, and industries with lower pollution levels. This study underscores that the government’s intensified focus on environmental issues can effectively promote corporate green technology innovation, thereby promoting sustainable economic and social development and supporting the dual goals of environmental protection and economic growth.
The Impact of Epistemological Beliefs and School Climate on the Sustainability of Critical Thinking Dispositions in Middle School Students
Authors: Kartal Osman Yılmaz , Yazgan Akan Deniz , Temelli Dinçer , Yavuz Kartal Meral
Published: October 2024

The objective of this study is to ascertain whether school climate and students’ epistemological beliefs serve as predictors of sustainable critical thinking dispositions in middle school students. This research is designed as a correlational study. A total of 585 students from four middle schools in Çanakkale, Turkey, participated in the research. To this end, the researchers administered the Critical Thinking Disposition Scale, the Middle School Students’ Epistemological Belief Scale, and the School Climate Survey, gradually collecting the raw data. Following the collection of the data, they were subjected to analysis using multiple linear regression. The results of the statistical analysis demonstrated that the students’ critical thinking dispositions were significantly predicted by both epistemological beliefs and school climate. In other words, belief in the omniscient power of authority and quick learning were found to have a negative correlation with critical thinking, whereas belief in simple knowledge was observed to have an inverse positive relationship. The dimension of the school climate, relating to positive student–teacher relations, appeared to constitute a prospective positive predictor, while authoritarian student–teacher relations and the lack of resources were identified as the main negative classroom environment factors. The study highlights the complex mutual influence of individual beliefs and the learning environment in the development of critical thinking dispositions.
Integrating Sustainable Educational Innovations and Spatial Cognition: A Study of Factors Affecting College Students’ Sustainable Learning Behavior in Virtual Simulation Environments
Authors: Zhou Junsheng , Xiao Liyao , Li Yunfu
Published: October 2024

Objective: This study investigated the factors influencing college students’ continuous utilization intention of virtual simulation software (Animation Character Virtual Simulation Software V1.0) in the context of current efforts to promote sustainable educational practices and explored design strategies to enhance this willingness in a sustainable manner. Method: Based on the Expectation Confirmation Model of Information System Continuance (ECM-ISC), this study developed a model to examine the impact of college students’ spatial ability (SA) on their virtual simulation learning behavior. Upon administering a questionnaire to the selected participants (N = 164), the survey data were analyzed for reliability and validity. Subsequently, the relationships among the model’s variables were explored. Result: For college students majoring in Digital Media Art, spatial ability significantly positively affects their sustainable utilization intention of virtual simulation software within 3D virtual scenarios. Meanwhile, it positively impacts perceived ease of use (PEU) and flow experience (FE), which are key factors in promoting the sustainable adoption of such technologies. The introduced spatial ability, perceived ease of use, and flow experience influence the endogeneity of the ECM-ISC. Moreover, gender differences in spatial ability are profound among these students. Conclusion: The primary influencing pathway is spatial ability → flow experience → expectation confirmation → perceived usefulness → continuous utilization intention. Enhancing spatial ability is one effective way to advance virtual simulation software, offering new insights for its design and long-term improvement in alignment with sustainable educational practices.
An Enhanced Particle Swarm Optimization Long Short-Term Memory Network Hybrid Model for Predicting Residential Daily CO2 Emissions
Authors: Hu Yuyi , Wang Bojun , Yang Yanping , Yang Liwei
Published: October 2024

This study aims to establish an accurate hybrid model for predicting residential daily carbon dioxide (CO2) emissions, offering essential theoretical insights and data support for decision-makers in the construction industry. A hybrid model named CRLPSO-LSTM was proposed, which integrates an enhanced particle swarm optimization (CRLPSO) algorithm with a long short-term memory (LSTM) network. The CRLPSO algorithm enhances population quality, diversity, and global search efficiency by introducing improved circle chaotic mapping, optimizing worst mutations, and incorporating the Lévy flight strategy. The performance of the CRLPSO algorithm was rigorously evaluated using 23 internationally recognized standard test functions. Subsequently, the CRLPSO algorithm was employed to optimize the parameters of the LSTM model. Experimental validation was performed on three datasets from China, the United States, and Russia, each exhibiting distinct emissions characteristics: China with high emissions and high volatility, the United States with medium emissions and medium volatility, and Russia with low emissions and low volatility. The results indicate that the CRLPSO-LSTM hybrid model outperformed other hybrid models in predicting residential daily CO2 emissions, as demonstrated by superior R2, MAE, and MSE metrics. This study underscores the effectiveness and broad applicability of the CRLPSO-LSTM hybrid model, offering a robust theoretical foundation and data support for advancing the sustainable development goals.
Research on the Impact Mechanism of ETS on Green Innovation in China’s High-Carbon Industries: A Perspective of Enterprise Heterogeneity
Authors: Liu Xiao , Zhu Yue
Published: October 2024

Green technology innovation is an important driving force for low-carbon development of enterprises. As a market-based environmental policy to promote greenhouse gas emission reduction, whether carbon emission trading scheme (ETS) can encourage enterprises to carry out green technology innovation under the background of “dual carbon” goal deserves further research. Taking Chinese A-share listed enterprises in the five major sectors as samples, this study constructed a modified Difference-in-Differences (DID) model to test the causal effect of ETS on green innovation in high-carbon industries. Three significant results can be summarized from the empirical study. Firstly, the ETS has a significant promoting effect on green innovation of high-carbon enterprises. And it can effectively promote high-carbon enterprises to achieve an average of a 13.24–19.56% increase in low-carbon innovation capabilities. Secondly, enterprises with different characteristics have heterogeneity in the impact of ETS implementation on green innovation. Secondly, the implementation of ETS exerts heterogeneous effects on green technology innovation across enterprises that possess diverse characteristics. Enterprises with a large capital scale (low equity concentration) have a more significant promoting effect than those with a small capital scale (high equity concentration). Thirdly, the green innovation effect of ETS exhibits significant heterogeneity across different types of industries. In the mining, manufacturing, and construction sectors, the ETS has effectively stimulated green innovation to a certain extent. There has been no significant change in green innovation in the sector of electricity, heat, gas, and water production and supply. In particular, after the implementation of the ETS, green innovation has actually been weakened in the transportation sector. As such, for policy makers, differentiated ETS policies should be implemented based on the actual situation of different industries and types of carbon-emitting enterprises.
Economic Assessment of Hydrologic Ecosystem Services in Morocco’s Protected Areas: A Case Study of Ifrane National Park
Authors: Sadgui Oumayma , Khattabi Abdellatif
Published: October 2024

This study delves into the complex interplay between land use dynamics, hydrological services, and intangible benefits within the context of Ifrane National Park (INP) in Morocco. Due to its extensive mountain forests and numerous wetlands, INP is a crucial contributor to the nation’s water supply and a vital source of hydrological ecosystem services (HES). However, climate change and evolving land use patterns have led to diminishing water resources and the desiccation of certain wetlands. This research used the Integrated Valuation of Ecosystem Services and Tradeoffs software (InVEST 3.10.2) for HES quantification and environmental economics approach for monetization to comprehend how HES values respond to challenges posed by urbanization, intensive agriculture, and other land use alterations. This work underscores INP’s role as a significant “water tower”, emphasizing the evolution of its services amidst challenges. Our findings reveal an annual decrease in HES economic value by USD 4000. This economic assessment serves as a compelling tool to enlighten decision-makers and park users about the imperative need to preserve natural ecosystems and use water resources judiciously. It advocates for investments in conservation and restoration within protected areas to sustain these vital services.
A Decision Support Model for Lean Supply Chain Management in City Multifloor Manufacturing Clusters
Authors: Wiśnicki Bogusz , Dzhuguryan Tygran , Mielniczuk Sylwia , Petrov Ihor , Davydenko Liudmyla
Published: October 2024

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions.
Medium- and Long-Term Distributed Photovoltaic Power Prediction Based on Multiple Time Series Feature and Multiple-Model Fusion
Authors: Yang Xinting , Ye Shengyong , Jiang Keteng , Sun Chongbo , Lu Zongxiang , Liu Liyang , Han Yuqi , Zhang Bin
Published: October 2024

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R2, and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust.
Delayed Interval-Valued Symmetric Stochastic Integral Equations
Authors: Malinowski Marek T.
Published: October 2024

In this paper, delayed stochastic integral equations with an initial condition and a drift coefficient given as interval-valued mappings are considered. These equations have a certain symmetric form that distinguishes them from classical single-valued stochastic integral equations and has implications for the properties of the diameter of the values of the solutions of the equations. The main result of the paper is the theorem that there is a unique solution to the equation considered. It was obtained under the assumptions of continuity of the kernels and Lipschitz continuity of the drift and diffusion coefficients. The proof of the existence of the solution is carried out by the method of iterating successive approximations. The paper ends with theorems about the continuous dependence of the solution on the initial function, kernels and nonlinearities.
Current Developments in Soil Ecotoxicology
Authors: Fernández-Marcos Maria Luisa
Published: October 2024

Ecotoxicology focuses on how chemicals affect organisms in the environment, with the ultimate goal of safeguarding the structure and function of ecosystems [...]
Frequency of Major Transmitted Integrase Resistance in Poland Remains Low Despite Change in Subtype Variability
Authors: Mielczak Kaja , Serwin Karol , Urbańska Anna , Aksak-Wąs Bogusz , Karasińska-Cieślak Malwina , Mularska Elżbieta , Witor Adam , Jakubowski Paweł , Hlebowicz Maria , Bociąga-Jasik Monika , Jabłonowska Elżbieta , Szymczak Aleksandra , Szetela Bartosz , Łojewski Władysław , Parczewski Miłosz
Published: October 2024

With the widespread use of integrase inhibitors and the expanding use of long-acting cabotegravir in both pre-exposure prophylaxis and antiretroviral treatment, molecular surveillance on the transmission of integrase resistance has regained clinical significance. This study aimed to determine the frequency of INSTI-transmitted drug resistance mutations (DRMs) among treatment-naïve individuals in Poland from 2016 to 2023. INSTI resistance was analyzed in 882 antiretroviral treatment-naïve individuals using Sanger sequencing. Integrase DRMs were defined based on the Stanford HIV drug resistance database scores. Phylogeny was used to investigate subtyping and clustering. For the analysis of time-trends, logistic regression was used. Major (E138K and R263K) integrase mutations were detected in 0.45% of cases with minor resistance observed in 14.85%, most commonly (13.95%) E157Q. Overall, no major clusters of transmitted drug resistance were identified, and the transmission of E157Q showed a decreasing trend (p < 0.001). While the frequency of sub-subtype A6 increased, it was predominantly found among migrants and associated with L74 mutations. The frequency of major integrase-transmitted DRMs remains low, despite the changes in subtype variability. Surveillance of changing HIV molecular variation patterns is vital from the perspective of the optimal use of integrase inhibitors, especially due to expanding long-acting cabotegravir implementation.
Microbiological Profile of the Upper and Lower Respiratory Tract of Suckling and Weaned Dairy Calves with Acute Respiratory Disease
Authors: Alfieri Alice Fernandes , Fritzen Juliana Torres Tomazi , Yasumitsu Carolina Yuka , Alfieri Amauri Alcindo
Published: October 2024

Bovine respiratory disease (BRD) is a significant global health issue in cattle farming, leading to substantial economic losses. This study analyzed the microbiological profiles of BRD outbreaks in nine dairy cattle herds in southern Brazil. We examined 36 biological samples, including 24 deep nasopharyngeal swabs (NS) and 12 lung tissue, from 29 suckling and 7 weaned heifer calves with acute BRD. PCR and RT-PCR techniques were used to partially amplify the genes of five viruses and four respiratory bacteria. A total of 8 different microorganisms, 4 viruses (bovine viral diarrhea virus, n = 5; bovine coronavirus, n = 3; bovine alphaherpesvirus 1, n = 3; and bovine parainfluenza virus 3, n = 2), and 4 bacteria (Pasteurella multocida, n = 16; Mycoplasma bovis, n = 8; Histophilus somni, n = 7; and Mannheimia haemolytica, n = 4) were identified in 29 (80.5%) samples. Seven samples (four lung tissue and three NS) were negative for all the microorganisms. Mixed infections were more common (62.1%) than single infections (37.9%). Bacterial nucleic acids were more commonly co-detected in NS than in lung tissue. Nucleic acids from a single pathogen were more frequently detected in lung tissues than in NS. M. bovis was the only bacterium detected in the lower respiratory tract. Understanding the microbiological profiles of the respiratory tracts of dairy calves with clinical signs of BRD is crucial for implementing effective biosecurity measures to prevent BRD in suckling and weaned dairy heifer calves.
Optimal Scheduling of Integrated Energy System Considering Virtual Heat Storage and Electric Vehicles
Authors: Liu Yinjun , Zhu Yongqing , Yu Shunjiang , Wang Zhibang , Li Zhen , Chen Changming , Yang Li , Lin Zhenzhi
Published: October 2024

Integrated energy systems (IESs) are complex multisource supply systems with integrated source, grid, load, and storage systems, which can provide various flexible resources. Nowadays, there exists the phenomenon of a current power system lacking flexibility. Thus, more research focuses on enhancing the flexibility of power systems by considering the participation of IESs in distribution network optimization scheduling. Therefore, the optimal scheduling of IESs considering virtual heat storage and electric vehicles (EVs) is proposed in this paper. Firstly, the basic structure of IESs and mathematical models for the operation of the relevant equipment are presented. Then, an optimal scheduling strategy of an IES considering virtual heat storage and electric vehicles is proposed. Finally, an IES with an IEEE 33-node distribution network, 20-node Belgian natural gas network, and 44-node heating network topologies is selected to validate the proposed strategy. The proposed models of integrated demand response (IDR), EV orderly charging participation, virtual heat storage, and actual multitype energy storage devices play the role of peak shaving and valley filling, which also helps to reduce the scheduling cost from CNY 11,253.0 to CNY 11,184.4. The simulation results also demonstrate that the proposed model can effectively improve the operational economy of IESs, and the scheduling strategy can promote the consumption of renewable energy, with the wind curtailment rate decreasing from 63.62% to 12.50% and the solar curtailment rate decreasing from 56.92% to 21.34%.
Simulation Modeling of Time for Moving a Fallen Tree by Harvester to the Zone of Its Bucking
Authors: Sergeeva Tatiana , Gilyazova Tatiana , Rukomojnikov Konstantin
Published: October 2024

The aim of the research was to create a mathematical dependence to justify the labor costs of moving a fallen tree by harvester to the zone of its bucking, depending on the variety of values of natural factors characterizing the operation of the harvester during the partial cut of the forest. For this research, the computer simulation model of the technological process of the forest harvester was created. Production experiments were carried out to test the model. The computer experiment was implemented on the model, and regression dependence was obtained. The results of this study showed that the relationship between the residual density of plantings and the execution time of the moving operations using regression dependence. The multiple coefficients of determination of the nonlinear model was 0.845. This value indicates that the change in the average time of moving a fallen tree to the zone of its bucking depends on changes in the factors included in the regression model, and this dependence is not accidental. The results can be used by research organizations when planning the production process of logging operations.