bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Lanthanum at the Heading Stage Effectively Suppresses Cadmium Accumulation in Wheat Grains by Downregulating the Expression of TaZIP7 to Increase Cadmium Retention in Nodes
Authors: Xiao Caixia , Yang Hua , Chen Xingwang , Li Jie , Cai Xiongfei , Long Jian
Published: October 2024

Reducing cadmium (Cd) accumulation in wheat is an effective way to decrease the potential threats of Cd to human health. The application of lanthanum (La) in agricultural fields is eliciting extensive attention due to its beneficial effects on improving yields and inhibiting Cd accumulation in edible parts of crops. However, the potential mechanism of La-restricted Cd accumulation in crop grains is not entirely understood. Here, we investigated the effects of La and Cd accumulation in wheat grains by implementing application at the shooting and heading stages. Some associated mechanisms were explored. Results showed that La application at the shooting and heading stages considerably promoted the thousand-grain weight. La application at the shooting and heading stages increased Cd accumulation in the first node beneath the panicle (N1) but reduced Cd levels in the other tissues. La application at the heading stage exerted greater effects on Cd storage in N1 while reducing Cd concentrations in the other tissues compared with La application at the shooting stage. La addition substantially decreased the translocation of Cd from the lower nodes to the upper internodes, but increased Cd translocation from the lower internodes to the upper nodes. The expression of TaZIP7 in N1 was downregulated by La treatment. These results suggest that the effective reduction in Cd in wheat grains by La application at the heading stage is probably a consequence of the successful promotion of Cd storage in nodes by downregulating the expression of TaZIP7 during the grain-filling stage, thereby hindering the redirection Cd from nodes to grains.
Dynamics of Hydrogen Peroxide Accumulation During Tip Growth of Infection Thread in Nodules and Cell Differentiation in Pea (Pisum sativum L.) Symbiotic Nodules
Authors: Tsyganova Anna V. , Gorshkov Artemii P. , Vorobiev Maxim G. , Tikhonovich Igor A. , Brewin Nicholas J. , Tsyganov Viktor E.
Published: October 2024

Hydrogen peroxide (H2O2) in plants is produced in relatively large amounts and plays a universal role in plant defense and physiological responses, including the regulation of growth and development. In the Rhizobium–legume symbiosis, hydrogen peroxide plays an important signaling role throughout the development of this interaction. In the functioning nodule, H2O2 has been shown to be involved in bacterial differentiation into the symbiotic form and in nodule senescence. In this study, the pattern of H2O2 accumulation in pea (Pisum sativum L.) wild-type and mutant nodules blocked at different stages of the infection process was analyzed using a cytochemical reaction with cerium chloride. The observed dynamics of H2O2 deposition in the infection thread walls indicated that the distribution of H2O2 was apparently related to the stiffness of the infection thread wall. The dynamics of H2O2 accumulation was traced, and its patterns in different nodule zones were determined in order to investigate the relationship of H2O2 localization and distribution with the stages of symbiotic nodule development in P. sativum. The patterns of H2O2 localization in different zones of the indeterminate nodule have been partially confirmed by comparative analysis on mutant genotypes.
G-Quadruplex Structures as Epigenetic Regulatory Elements in Priming of Defense Genes upon Short-Term Trichoderma atroviride Inoculation in Maize
Authors: Agostini Romina B. , Piga Ernesto J. , Bayón Candela , Binolfi Andrés , Armas Pablo , Campos-Bermudez Valeria A. , Rius Sebastián P.
Published: October 2024

Symbiosis establishment between Trichoderma atroviride and plant roots triggers the priming of defense responses, among other effects. Currently, there is no clear evidence regarding the molecular mechanisms that allow the plant to remain alert to future stimulus, either by pathogen attack or any other abiotic stress. Epigenetic modifications have emerged as a strategy to explain the increased defense response of plants in a priming state conferred by Trichoderma. Recently, various non-canonical structures of nucleic acids, especially G-quadruplex structures (G-quadruplexes or G4s), have been identified as potential targets during the establishment or maintenance of plant signals. In the present study, we developed a screening test for the identification of putative G4-forming sequences (PQSs) in previously identified Z. mays priming genes. Bioinformatic analysis revealed the presence of PQSs in the promoter region of five essential genes playing a critical role in priming in maize. Biophysical and spectroscopy studies showed the formation of G4s by these PQSs in vitro, and ChIP assays demonstrate their formation in vivo. Therefore, G4 formation could play a role as an epigenetic regulatory mechanism involved in the long-lasting primed state in maize plants.
Sustainable Composites from Waste Polypropylene Added with Thermoset Composite Waste or Recovered Carbon Fibres
Authors: Zolfaghari Ehsan , Infurna Giulia , Alessi Sabina , Dispenza Clelia , Dintcheva Nadka Tz.
Published: October 2024

In order to limit the ever-increasing consumption of new resources for material formulations, regulations and legislation require us to move from a linear to a circular economy and to find efficient ways to recycle, reuse and recover materials. Taking into account the principles of material circularity and waste reuse, this research study aims to produce thermoplastic composites using two types of industrial waste from neighbouring companies, namely waste polypropylene (wPP) from household production and carbon-fibre-reinforced epoxy composite scrap from a pultrusion company. The industrial scrap of the carbon-fibre-reinforced epoxy composites was either machined/ground to powder (pCFRC) and used directly as a reinforcement agent or subjected to a chemical digestion process to recover the carbon fibres (rCFs). Both pCFRC and rCF, at different weight ratios, were melt-blended with wPP. Prior to melt blending, both pCFRC and rCF were analysed for morphology by scanning electron microscopy (SEM). The pCFRC powder contains epoxy resin fragments with spherical to ellipsoidal shape and carbon fibre fragments. The rCFs are clean from the matrix, but they are slightly thicker and corrugated after the matrix digestion. Further, the morphologies of wPP/pCFRC and wPP/rCF were also investigated by SEM, while the thermal behaviour, i.e., transitions and changes in crystallinity, and thermal resistance were evaluated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. The strength of the interaction between the filler (i.e., pCFRC or rCF) and the wPP matrix and the processability of these composites were assessed by rheological studies. Finally, the mechanical properties of the systems were characterised by tensile tests, and as found, both pCFRC and rCF exert reinforcement effects, although better results were obtained using rCF. The wPP/pCFRC results are more heterogeneous than those of the wPP/rCF due to the presence of epoxy and carbon fibre fragments, and this heterogeneity could be considered responsible for the mechanical behaviour. Further, the presence of both pCFRC and rCF leads to a restriction of polymer chain mobility, which leads to an overall reduction in ductility. All the results obtained suggest that both pCFRC and rCF are good candidates as reinforcing fillers for wPP and that these complex systems could potentially be processed by injection or compression moulding.
An Exploration of Alkaline Degumming in the Printing and Dyeing Process of Silk Georgette
Authors: Wu Huihui , Zhou Jiali , Zhu Panpan , Li Jing , Li Yufeng
Published: October 2024

Alkali printing was one of the traditional techniques employed for printing on silk georgette in ancient China. This study investigates two degumming methods in alkaline printing processes, namely alkaline boiling and alkaline steaming, based on the principles of Tang Dynasty alkaline printing techniques. The effects of slaked lime concentration, steam temperature, and steam duration on the degumming rate of silk georgette are studied. Alkaline boiling is found to be rapid and effective, achieving a degumming rate of 27% at 80 °C in 30 min, whereas alkaline steaming requires a prolonged process with a maximum degumming rate of less than 20% before the water reaches its boiling point. Additionally, the differences in dyeing effects at various degumming rates, and the variations in pattern clarity and detail under alkaline steaming, were compared. Although both degumming methods can achieve the desired amount of degumming rate through process control, alkaline steaming allows for integration with methods like screen printing and rotary printing, offering better control over pattern freedom and detail. The combination of these two processes can further expand the artistic expression and application of traditional alkaline printing techniques in contemporary silk degumming printing.
Thermodynamic Justification for the Effectiveness of the Oxidation—Soda Conversion of Ilmenite Concentrates
Authors: Akhmetova Kuralay , Tusupbayev Nesipbay , Kenzhaliyev Bagdaulet , Gladyshev Sergey , Akhmadiyeva Nazym , Imangaliyeva Leila
Published: October 2024

This article presents the results of a thermodynamic analysis of the oxidation soda conversion reactions of minerals in ilmenite concentrates in the temperature range of 373–2273 K. The thermodynamic parameters of pseudorutile, pseudobrukite, and the new minerals, zhikinite and spessartine, were calculated for the first time. It has been established that the most important criterion relating to the stability of titanium minerals and related elements, as well as the reaction properties of the structural oxides of metals and silicon, is their degree of oxidation. Oxides of silicon (IV) and manganese have the best reactivity in solid-phase oxidizing alkaline environments (VI). Modeling this process scientifically substantiates the mechanism involved in the destruction of minerals in ilmenite concentrates in the low-temperature region in the presence of atmospheric oxygen and sodium oxide of soda ash, which are decomposed through the absorption of heat and the evaporation of moisture during the dehydration of hydrated minerals of iron and manganese and the dehydration of the soda–ilmenite batch. Tests conducted during pilot metallurgical production at the Institute of Metallurgy and Enrichment (PMP of JSC) confirmed the feasibility of processing high-chromium and siliceous rutile leucoxene ilmenite concentrates, which are unsuitable for traditional pyro- and hydro-metallurgical enrichment methods, through single-stage oxidation soda roasting, followed by the leaching of easily soluble sodium salts of iron and associated impurities with water and a dilute hydrochloric acid solution. The proposed energy-saving method ensures the production of high-purity (>98%) synthetic rutile while eliminating the formation of strong deposits on the lining of roasting units.
Numerical Investigation on the Effect of Air Humidification and Oxygen Enrichment on Combustion and Emission Characteristics of Gas Boiler
Authors: Wang Haoyu , Yang Xiong , Li Ziyi , Zhang Chuanzhao , Zhu Xianqiang , Zhang Ruijuan , Du Jing , Zhang Shuyuan
Published: October 2024

Gas boilers exhibit thermal inefficiency and unsatisfying pollutant emissions. In this study, numerical simulations were conducted to examine the effect of humidified oxygen-enriched air on methane combustion in a furnace and the effects of different premixed ratios of air on the temperature field inside the furnace, intermediate product OH groups, component concentration distribution, and pollutants. Although humidification of ambient air effectively reduced the flame center temperature and mass concentration of the NOx generated during combustion in the furnace, the highest growth rate of CO concentration at the furnace outlet was 18.6%. Humidification of oxygen-enriched air increased the center temperature and outlet NO concentration of the furnace compared with those during no oxygen enrichment, but the outlet CO concentration showed a decreasing trend, with the highest decrease rate of 34.6%. This study determined an optimal CO–air premix ratio with a moisture concentration of 50 g/kg dry air and an oxygen concentration of 23%. The air humidification and oxygen enrichment technology proposed in this article provides a technical reference for low nitrogen transformation of existing gas boilers.
Artificial Neural Networks for Mineral Production Forecasting in the In Situ Leaching Process: Uranium Case Study
Authors: Aizhulov Daniar , Tungatarova Madina , Kurmanseiit Maksat , Shayakhmetov Nurlan
Published: October 2024

This study was conducted to assess the applicability of artificial neural networks (ANN) for forecasting the dynamics of uranium extraction over exploitation time during the process of In Situ Leaching (ISL). Currently, ISL process simulation involves multiple steps, starting with geostatistical interpolation, followed by computational fluid dynamics (CFD) and reactive transport simulation. While extensive research exists detailing each of these steps, machine learning techniques may offer the potential to directly obtain extraction curves (i.e., the concentration of the mineral produced over the exploitation time of the deposit), thereby bypassing these computationally expensive steps. As a basis, both an empirical experimental configuration and reactive transport simulations were used to generate training data for the neural network model. An ANN was constructed, trained, and tested on several test cases with different initial parameters, then the expected outcomes were compared to those derived from conventional modeling techniques. The results indicate that for the employed experimental configuration and a limited number of features, artificial intelligence technologies, specifically regression-based neural networks can model the recovery rate (or extraction degree) of the ISL process for mineral production, achieving a high degree of accuracy compared to traditional CFD and mass transport models.
Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence
Authors: Nallakaruppan M. K. , Chaturvedi Himakshi , Grover Veena , Balusamy Balamurugan , Jaraut Praveen , Bahadur Jitendra , Meena V. P. , Hameed Ibrahim A.
Published: October 2024

The greatest technological transformation the world has ever seen was brought about by artificial intelligence (AI). It presents significant opportunities for the financial sector to enhance risk management, democratize financial services, ensure consumer protection, and improve customer experience. Modern machine learning models are more accessible than ever, but it has been challenging to create and implement systems that support real-world financial applications, primarily due to their lack of transparency and explainability—both of which are essential for building trustworthy technology. The novelty of this study lies in the development of an explainable AI (XAI) model that not only addresses these transparency concerns but also serves as a tool for policy development in credit risk management. By offering a clear understanding of the underlying factors influencing AI predictions, the proposed model can assist regulators and financial institutions in shaping data-driven policies, ensuring fairness, and enhancing trust. This study proposes an explainable AI model for credit risk management, specifically aimed at quantifying the risks associated with credit borrowing through peer-to-peer lending platforms. The model leverages Shapley values to generate AI predictions based on key explanatory variables. The decision tree and random forest models achieved the highest accuracy levels of 0.89 and 0.93, respectively. The model’s performance was further tested using a larger dataset, where it maintained stable accuracy levels, with the decision tree and random forest models reaching accuracies of 0.90 and 0.93, respectively. To ensure reliable explainable AI (XAI) modeling, these models were chosen due to the binary classification nature of the problem. LIME and SHAP were employed to present the XAI models as both local and global surrogates.
Attribution of Vegetation Dynamics in the Yellow River Water Conservation Area Based on the Deep ConvLSTM Model
Authors: Liang Zhi , Sun Ruochen , Duan Qingyun
Published: October 2024

Climate change and human activities have significantly impacted the long-term growth of vegetation, thereby altering the ecosystem’s response mechanisms. The Yellow River Water Conservation Area (YRWCA) is a critical ecological functional zone in China. Since 1982, the vegetation in the YRWCA has changed significantly, and the primary drivers of vegetation which changed before and after 2000 were identified as climate change and human activities, respectively. However, the extent to which different drivers contribute to the vegetation dynamics of the YRWCA remains uncertain. In this study, we introduced a modified deep Convolutional Long Short-Term Memory (ConvLSTM) model to quantify the contributions of climate change and human activities to vegetation change while considering the spatiotemporal heterogeneity. We identified areas with minimal human activity before 2000 using the residual trend method, and used the regional data from these areas to train the model. Subsequently, we applied the trained deep ConvLSTM model to perform an attribution analysis after 2000. The results show that the deep ConvLSTM effectively captures the impacts of climate change on vegetation growth and outperforms the widely used Random Forest model (RF). Despite the fact that the input data of RF were optimized, ConvLSTM still distinctly outperformed RF, achieving R2, MAE, and RMSE values of 0.99, 0.013, and 0.018, respectively, compared to RF’s corresponding values of 0.94, 0.038, and 0.045. Since 2000, the regional normalized difference vegetation index (NDVI) has shown a broad increasing trend, particularly in dryland, primarily induced by human activities from 2006 to 2015. Furthermore, an analysis of changes in regional land use, particularly in drylands, revealed that the highest magnitude of conversion of farmland back to forest or grass was recorded from 2000 to 2005. However, the most significant contributions from human activities occurred from 2006 to 2015, indicating a time lag in vegetation recovery from these ecological programs. The attribution results provide valuable insights for the implementation of ecological programs, and the introduced deep ConvLSTM proves the suitability of deep learning models that capture spatiotemporal features in vegetation growth simulations, allowing for broader applications.
A Novel Detection Transformer Framework for Ship Detection in Synthetic Aperture Radar Imagery Using Advanced Feature Fusion and Polarimetric Techniques
Authors: Ahmed Mahmoud , El-Sheimy Naser , Leung Henry
Published: October 2024

Ship detection in synthetic aperture radar (SAR) imagery faces significant challenges due to the limitations of traditional methods, such as convolutional neural network (CNN) and anchor-based matching approaches, which struggle with accurately detecting smaller targets as well as adapting to varying environmental conditions. These methods, relying on either intensity values or single-target characteristics, often fail to enhance the signal-to-clutter ratio (SCR) and are prone to false detections due to environmental factors. To address these issues, a novel framework is introduced that leverages the detection transformer (DETR) model along with advanced feature fusion techniques to enhance ship detection. This feature enhancement DETR (FEDETR) module manages clutter and improves feature extraction through preprocessing techniques such as filtering, denoising, and applying maximum and median pooling with various kernel sizes. Furthermore, it combines metrics like the line spread function (LSF), peak signal-to-noise ratio (PSNR), and F1 score to predict optimal pooling configurations and thus enhance edge sharpness, image fidelity, and detection accuracy. Complementing this, the weighted feature fusion (WFF) module integrates polarimetric SAR (PolSAR) methods such as Pauli decomposition, coherence matrix analysis, and feature volume and helix scattering (Fvh) components decomposition, along with FEDETR attention maps, to provide detailed radar scattering insights that enhance ship response characterization. Finally, by integrating wave polarization properties, the ability to distinguish and characterize targets is augmented, thereby improving SCR and facilitating the detection of weakly scattered targets in SAR imagery. Overall, this new framework significantly boosts DETR’s performance, offering a robust solution for maritime surveillance and security.
Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure
Authors: Bae HongGuk , Lee JaeGon , Park SangWook
Published: October 2024

As metasurfaces are in the spotlight, research is being conducted to incorporate them into transmitarray (TA) antennas. Among these, as an attempt to create a low-profile design, a patch antenna classified as low-gain can be utilized as an appropriate source antenna. However, for high efficiency of the TA, the gain of the source antenna must be fundamentally improved. For this, a simple side metal structure was applied to a metallic cross-type slot transmitarray. This acts as a resonant element and reflector by utilizing the electromagnetic wave radiated from the source antenna. The changes in the center frequency and gain due to the application of the side metal structure to the source antenna were analyzed. The gain of the source antenna was improved by a total of 4.63 dB. This is expected to be applied to create various source waves and to conduct future research on improving the gain in transmitarray antennas.
Exploring the Role of Tourist Pro-Environmental Behavior in Autonomous Vehicle Adoption: A TPB and PLS-SEM Approach
Authors: Zhang Qingyu , Zada Muhammad , Khan Salman , Haddad Hossam , Alramahi Nidal , Ahmed Nureldeen Waleed
Published: October 2024

As businesses and governments increasingly explore innovative strategies to promote pro-environmental behavior, autonomous vehicles (AVs) have emerged as a key initiative for fostering sustainable communities. However, the specific norms—personal, descriptive, and subjective—that influence behavioral intentions regarding AV adoption remain underexplored. This research addresses this gap by integrating the extended theory of planned behavior (TPB) with pro-environmental variables to examine both direct and indirect influences on behavioral intentions. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the proposed model was validated with data from 516 domestic Chinese tourists. The findings highlight that subjective norms and trust significantly shape positive intentions toward adopting autonomous vehicles. Furthermore, environmental knowledge and concerns exert a significant indirect impact on these intentions, underscoring the complex interplay of factors that drive pro-environmental behavior. These results provide valuable insights for policymakers and practitioners aiming to enhance the acceptance of AVs in China and offer a foundation for future research on the role of norms in technology adoption.
A Study on Revenue Distribution of Chinese Agricultural E-Commerce Supply Chain Based on the Modified Shapley Value Method
Authors: Zhou Xiangyang , Chi Liang , Li Jiaxuan , Xing Liwei , Yang Lu , Wu Jianzhai , Meng Han
Published: October 2024

With the rapid development of e-commerce in China, “live broadcast + e-commerce” has become an emerging mode of the e-commerce industry. The speedy growth of the digital economy has also provided a favorable external environment for fresh agricultural products to ride on the e-commerce express train. As a result, more and more suppliers of fresh agricultural products choose to sell their products on e-commerce platforms and conduct live streaming. This paper utilizes the e-commerce supply chain field survey data, takes tomato as the specific analyzed variety, and studies the revenue distribution pattern of each subject in the supply chain. The purpose of this paper is to explore the optimization strategy of the revenue distribution of an agricultural e-commerce supply chain, and the results show that the agricultural e-commerce supply chain effectively promotes the matching of supply and demand of medium- and high-end agricultural products, realizes the quality premium of medium- and high-end agricultural products, and significantly improves the overall revenue level of the supply chain. The field survey found that under the supply chain model of “farmers + cooperatives + e-commerce platform”, farmers, cooperatives, and e-commerce platforms gained $2865, $3098, and $1111, respectively, for operating one mu of tomatoes in a year. According to the traditional Shapley value method, from the point of view of the contribution to the cooperative income, part of the income of the farmers and cooperatives should be compensated to the e-commerce platform, and the specific results are as follows: the income of the farmers should be reduced from $2865 to $2729, the income of the cooperatives should be reduced from $3099 to $2955, and the income of the e-commerce platform should be increased from $1111 to $1390. According to the Shapley value method based on the risk coefficient correction, from the perspective of risk compensation, part of the earnings of the e-commerce platform should be compensated to farmers and cooperatives, so as to establish a more equitable and reasonable pattern of earnings distribution, with the specific results: the earnings of farmers should be increased to $3205, the earnings of cooperatives should be increased to $3148, and the earnings of the e-commerce platform should be reduced to $722.
Relationship Between Land Use Transformation and Ecosystem Service Value in the Process of Urban–Rural Integration: An Empirical Study of 17 Prefecture-Level Cities in Henan Province, China
Authors: Liang Xin , Zhang Pei
Published: October 2024

Urban–rural integration, which aims to balance economic growth with sustainable land use, is becoming an increasingly critical strategy for regional development. This study provides crucial insights into the relationship between land use changes and ecosystem service values (ESVs) in rapidly urbanizing areas by analyzing the urban–rural integration process in Henan Province, a typical agricultural province in China. This research investigated the relationship between land use transformation and ESVs in Henan Province, China, from 1990 to 2020. Utilizing land use data and employing the equivalent factor method and elasticity model, we analyzed shifts in land use and their impacts on ecosystem services across 17 prefecture-level cities. Results indicated a gradual improvement in the urban–rural integration development index of Henan Province, particularly after 2000, but with notable disparities among cities. Zhengzhou, the provincial capital, consistently demonstrated high urban–rural integration development index (URII) values, influencing the integration efforts of neighboring cities. Conversely, peripheral cities exhibited lower integration indices. Notable shifts in land use patterns characterized by diverse transfer dynamics distinctively influenced ESVs across regions. Urban sprawl initially exerted substantial impacts on ecosystem services and stabilized over time. Suburbanization impacts peaked in the early and middle stages, while agricultural intensification initially affected ecosystem services, but their effects diminished with increased efficiency. Ecological restoration efforts consistently enhanced ESVs. The findings contribute to a more comprehensive understanding of the dynamic interactions between land use transitions and ecosystem services in the context of urban–rural integration.
Sewage Treatment Equipment Supply Chain Collaboration and Resilience Improvement Path Analysis: Collaborative Decision-Making, Information Sharing, Risk Management
Authors: Xu Xu , Wang Jie , He Chan , Jiang Xuting , An Qianru
Published: October 2024

Since the dawn of the 21st century, supply chains (SCs) have faced an array of unprecedented challenges, encompassing economic volatility, escalating geopolitical conflicts, intensified trade disputes, and persistent environmental degradation. These challenges have imposed immense pressures on SCs, amplifying both the risks and occurrences of disruptions. In response to these critical demands, there has been a substantial increase in academic, industrial, and governmental focus on the resilience and recovery capabilities of SCs. In this context, the supply chain (SC) for sewage treatment equipment has been significantly affected. This study aims to systematically investigate collaborative strategies and mechanisms for bolstering the resilience of manufacturing SCs within the sewage treatment sector, with particular attention to collaborative decision-making, information sharing, and risk mitigation throughout the SC lifecycle. Following a comparative analysis of various methodologies, this paper employed the structural equation model (SEM) and subsequently conducted a comprehensive survey of manufacturers specializing in glass fiber sewage treatment systems, yielding 385 valid responses. Through this data, a structural equation model was developed to analyze pathways for resilience enhancement. A thorough analysis of the results indicates that Supply Chain Collaboration (SCC) and collaborative decision-making are instrumental in strengthening Glass Fiber Sewage Treatment Supply Chain Resilience (SCR) through the implementation of effective information sharing and risk management practices. These findings contribute to the theoretical framework of SCC and SCR by clarifying the influence of collaborative practices while also providing practical guidance for the governance and strategic management of sewage treatment equipment enterprises and their associated SCs. The conclusions drawn from this study can guide us in establishing a more resilient and sustainable SC ecosystem, making it more directive and capable of mitigating complex disruptions.
Improved Generalized-Pinball-Loss-Based Laplacian Twin Support Vector Machine for Data Classification
Authors: Damminsed Vipavee , Wangkeeree Rabian
Published: October 2024

Nowadays, unlabeled data are abundant, while supervised learning struggles with this challenge as it relies solely on labeled data, which are costly and time-consuming to acquire. Additionally, real-world data often suffer from label noise, which degrades the performance of supervised models. Semi-supervised learning addresses these issues by using both labeled and unlabeled data. This study extends the twin support vector machine with the generalized pinball loss function (GPin-TSVM) into a semi-supervised framework by incorporating graph-based methods. The assumption is that connected data points should share similar labels, with mechanisms to handle noisy labels. Laplacian regularization ensures uniform information spread across the graph, promoting a balanced label assignment. By leveraging the Laplacian term, two quadratic programming problems are formulated, resulting in LapGPin-TSVM. Our proposed model reduces the impact of noise and improves classification accuracy. Experimental results on UCI benchmarks and image classification demonstrate its effectiveness. Furthermore, in addition to accuracy, performance is also measured using the Matthews Correlation Coefficient (MCC) score, and the experiments are analyzed through statistical methods.
Sequence of Bounds for Spectral Radius and Energy of Digraph
Authors: Zhao Jietong , Hameed Saira , Ahmad Uzma , Tabassum Ayesha , Asgharsharghi Leila
Published: October 2024

The graph spectra analyze the structure of the graph using eigenspectra. The spectral graph theory deals with the investigation of graphs in terms of the eigenspectrum. In this paper, the sequence of lower bounds for the spectral radius of digraph D having at least one doubly adjacent vertex in terms of indegree is proposed. Particularly, it is exhibited that ρ(D)≥αj=∑p=1m(χj+1(p))2∑p=1m(χj(p))2, such that equality is attained iff D=G↔+ {DE∉ Cycle}, where each component of associated graph is a k-regular or (k1,k2) semiregular bipartite. By utilizing the sequence of lower bounds of the spectral radius of D, the sequence of upper bounds of energy of D, where the sequence decreases when eU≤αj and increases when eU>αj, are also proposed. All of the obtained inequalities are elaborated using examples. We also discuss the monotonicity of these sequences.
Basic Properties and Development Status of Aluminum Adjuvants Used for Vaccines
Authors: Lan Jingyang , Feng Disong , He Xueshan , Zhang Qianru , Zhang Rong
Published: October 2024

Background: Aluminum adjuvants, renowned for their safety and efficacy, act as excellent adsorbents and vaccine immunogen enhancers, significantly contributing to innate, endogenous, and humoral immunity. An ideal adjuvant not only boosts the immune response but also ensures optimal protective immunity. Aluminum adjuvants are the most widely used vaccine adjuvants and have played a crucial role in both the prevention of existing diseases and the development of new vaccines. With the increasing emergence of new vaccines, traditional immune adjuvants are continually being researched and upgraded. The future of vaccine development lies in the exploration and integration of novel adjuvant technologies that surpass the capabilities of traditional aluminum adjuvants. One promising direction is the incorporation of nanoparticles, which offer precise delivery and controlled release of antigens, thereby enhancing the overall immune response. Conclusions: This review summarizes the types, mechanisms, manufacturers, patents, advantages, disadvantages, and future prospects of aluminum adjuvants. Although aluminum adjuvants have certain limitations, their contribution to enhancing vaccine immunity is significant and cannot be ignored. Future research should continue to explore their mechanisms of action and address potential adverse reactions to achieve improved vaccine efficacy.
Effect of Bed Material on Roughness and Hydraulic Potential in Filyos River
Authors: Aksoy Berna , Öztürk Melisa , Özölçer İsmail Hakkı
Published: October 2024

Seasonal changes, sea level rise, and global warming make flood events more frequent, which necessitates watershed management and efficient use of water resources. In this context, understanding the hydrodynamic behavior of basins is critical for the development of flood prevention strategies. The contributions of hydrological and hydraulic modeling techniques in this process are among the key determinants of sustainable water resources management. The Filyos Sub-Basin, located in the Western Black Sea Basin, stands out as one of the regions where flood risk assessment is a priority, as it has two important floodplains. This study aims to analyze the flood risk in the Filyos River Sub-Basin with hydraulic modeling methods, and to determine the Manning roughness coefficient. In the study, the parameters affecting the roughness of the river bed were analyzed using the Cowan method, and the effects of vegetation on river bed resistance were evaluated in the laboratory environment. Flood simulations were carried out for four different flow rates (Q1000, Q500, Q100 and Q50) using the HEC-RAS model, and the performance of flood protection structures were analyzed. The findings show that a significant portion of the existing protection structures are unable to meet the potential flood flows, which can cause serious damage to residential and agricultural areas. In basins with limited historical discharge data, such as the Filyos River, these findings provide important contributions to sustainable water resources management and regional planning processes. The results of the study serve as a reference for flood risk assessment, not only for the Filyos River Basin, but also for other basins with similar hydrodynamic characteristics. It is envisaged that future research, supported by larger data sets, can improve the accuracy of flood simulations. Furthermore, the Cowan method and HEC-RAS model used in this study are expected to contribute to strategic planning and engineering solutions to minimize flood risk in other watershed management projects. In future studies, we plan to further develop methodological approaches for determining the roughness coefficient, and to address applications to increase the effectiveness of flood protection structures.
A Hybrid Model Combined Deep Neural Network and Beluga Whale Optimizer for China Urban Dissolved Oxygen Concentration Forecasting
Authors: Wang Tianruo , Ding Linzhi , Zhang Danyi , Chen Jiapeng
Published: October 2024

The dissolved oxygen concentration (DOC) is an important indicator of water quality. Accurate DOC predictions can provide a scientific basis for water environment management and pollution prevention. This study proposes a hybrid DOC forecasting framework combined with Variational Mode Decomposition (VMD), a convolutional neural network (CNN), a Gated Recurrent Unit (GRU), and the Beluga Whale Optimization (BWO) algorithm. Specifically, the original DOC sequences were decomposed using VMD. Then, CNN-GRU combined with an attention mechanism was utilized to extract the key features and local dependency of the decomposed sequences. Introducing the BWO algorithm solved the correction coefficients of the proposed system, with the aim of improving prediction accuracy. This study used 4-h monitoring China urban water quality data from November 2020 to November 2023. Taking Lianyungang as an example, the empirical findings exhibited noteworthy enhancements in performance metrics such as MSE, RMSE, MAE, and MAPE within the VMD-BWO-CNN-GRU-AM, with reductions of 0.2859, 0.3301, 0.2539, and 0.0406 compared to a GRU. These results affirmed the superior precision and diminished prediction errors of the proposed hybrid model, facilitating more precise DOC predictions. This proposed DOC forecasting system is pivotal for sustainably monitoring and regulating water quality, particularly in terms of addressing pollution concerns.
COVID-19: fatores associados ao uso adequado de equipamentos de proteção individual por trabalhadores da atenção primária
Authors: Costa Kelly Aline Rodrigues , Assis Camila Cristina Gregório de , Carvalho Ana Paula Mendes , Lanza Fernanda Moura , Dutra Herica Silva , Leite Isabel Cristina Gonçalves , Correa-Oliveira Rodrigo , Coelho Angélica da Conceição Oliveira
Published: October 2024

O estudo tem como objetivo analisar os fatores associados ao uso adequado dos Equipamentos de Proteção Individual (EPI) pelos trabalhadores da Atenção Primária à Saúde (APS). Estudo transversal, realizado no Brasil, em ambiente virtual entre os meses de agosto/2020 e março/2021. Para a coleta de dados utilizou-se o instrumento validado “E.P.I. - APS COVID-19”. Foram utilizados testes qui-quadrado ou exato de Fisher, razão de prevalência, Intervalo de Confiança de 95% e regressão de Poisson com significância p≤0,005. Pesquisa aprovada pelo Comitê de Ética em Pesquisa com Seres Humanos. Participaram da pesquisa 455 trabalhadores. Os trabalhadores com 37 anos ou mais apresentaram prevalência de 1,59 vezes maior para uso adequado de óculos/protetor facial, 1,39 vezes maior para máscara N95 e 1,23 vezes maior para a higienização correta das mãos. O uso de luvas apresentou uma prevalência 35% maior para os trabalhadores com carga horária ≤ 40 horas. A faixa etária 37 anos ou mais foi associada ao uso de máscara N95 (RP=1,107) e a higiene das mãos (RP=1,075). A carga horária ≤ 40 horas foi associada ao uso de luvas (RP=0,846). Conclui-se que são fatores associados ao uso adequado de EPI a faixa etária 37 anos ou mais e a carga horária ≤ 40 horas.
Lesão por pressão: incidência e associação ao perfil de risco clínico e nutricional de pacientes hospitalizados em uso de terapia nutricional enteral e/ou parenteral
Authors: Lima Renata Conceição Pimentel de , Almeida Carolina de , Gonzalez Renata de Brito , Oliveira Ana Lívia de , Moreira Ana Paula Boroni
Published: October 2024

Introdução: A lesão por pressão (LPP) é um dano à pele ou ao tecido subcutâneo, capaz de gerar diversos prejuízos aos pacientes. Nesse sentido, a nutrição possui importância significativa para a manutenção da integridade da pele, logo é extremamente necessário entender qual o seu impacto diante de tantos fatores de risco para o desenvolvimento de LPP. Objetivo: Identificar a incidência de LPP e avaliar a oferta nutricional, o perfil de risco clínico e nutricional para o surgimento de LPP dos pacientes em terapia nutricional enteral e/ou parenteral exclusivas em um hospital universitário. Material e Métodos: Trata-se de um estudo prospectivo, longitudinal, observacional, realizado com pacientes internados no Hospital Universitário da Universidade Federal de Juiz de Fora (HU-UFJF). Foram avaliadas variáveis sociodemográficas, clínicas e nutricionais, além da classificação de risco para o desenvolvimento de LPP pela escala de Braden. Resultados: A amostra final foi composta por 40 pacientes. A média de idade encontrada foi de 60 anos (DP= 15,93), sendo 52,5% (n= 21) dos participantes adultos e 60% (n= 24) do sexo masculino. A incidência de LPP foi de 47,5%. O tempo de internação prolongado, risco aumentado na escala de Braden e doença pulmonar prévia foram considerados fatores associados ao desenvolvimento de LPP. O perfil nutricional desta amostra indicou que 55,55% (n= 20) se encontravam em risco nutricional. Conclusão: Este estudo revelou o perfil clínico dos pacientes que desenvolveram LPP e a importância da atuação da Equipe Multiprofissional de Terapia Nutricional na prevenção ou no adiamento da LPP.
ВЫБОР СУБЪЕКТА ИННОВАЦИОННОЙ ИНФРАСТРУКТУРЫ ДЛЯ ОБЕСПЕЧЕНИЯ ТРАНСФЕРА ТЕХНОЛОГИЙ В ДОНЕЦКОЙ НАРОДНОЙ РЕСПУБЛИКЕ
Authors: Шабалина Л.В. , Шавкун Г.А. , Тимко М.В.
Published: October 2024

Отсутствие в Донецкой Народной Республике эффективной системы трансфера технологий, а также субъектов, обеспечивающих данный процесс, приводит к снижению эффективности технологического обмена между представителями науки, образования, бизнеса и государства, что ведет к технологическому отставанию и повышению ресурсоемкости производства. Целью данного исследования является выбор и создание субъекта инновационной инфраструктуры, обеспечивающего трансфер технологий в Донецкой Народной Республике. В статье на основе анализа нормативных правовых актов Российской Федерации и Республики Беларусь, а также научной литературы определены субъекты инновационной инфраструктуры, участвующие в трансфере технологий (технопарк, научный парк, бизнес-инкубатор, центр трансфера технологий, технологическая платформа и др.), а также их цель и задачи функционирования. Для обеспечения трансфера технологий в Донецкой Народной Республике предложено использовать технологическую платформу, как наиболее оптимальный вариант с позиции издержек и сформированной инфраструктуры поддержки субъектов малого и среднего предпринимательства в Республике. Посредством системного анализа практики создания и функционирования технологических платформ в Российской Федерации предложены этапы формирования данного субъекта в Донецкой Народной Республике, а также его организационная структура. Представленные результаты могут быть использованы при организации трансфера технологий в Республике.

The lack of an effective system of technology transfer in the Donetsk People's Republic, as well as entities that ensure this process, leads to a decrease in the efficiency of technological exchange between representatives of science, education, business and the state, which leads to a technological lag and an increase in the resource intensity of production. The purpose of this study is to select and create an innovative infrastructure entity that ensures technology transfer in the Donetsk People's Republic. Based on the analysis of regulatory legal acts of the Russian Federation and the Republic of Belarus, as well as scientific literature, the article identifies the subjects of innovation infrastructure involved in technology transfer (technology park, science park, business incubator, technology transfer center, technology platform, etc.), as well as their purpose and objectives of functioning. To ensure technology transfer in the Donetsk People's Republic, it was proposed to use a technological platform as the most optimal option in terms of costs and the formed infrastructure to support small and medium-sized businesses in the Republic. Based on the system analysis of the practice of creating and functioning technological platforms in the Russian Federation, the stages of the formation of this subject in the Donetsk People's Republic, as well as its organizational structure, are proposed. The presented results can be used in the organization of technology transfer in the Republic.
ПРОБЛЕМЫ РЕАЛИЗАЦИИ ГОСУДАРСТВЕННОЙ ПОЛИТИКИ В ОБЛАСТИ ЗАНЯТОСТИ В РЕГИОНЕ
Authors: Шигильчева С.А. , Задорова Т.В.
Published: October 2024

В статье приводятся результаты оценки ключевых показателей рынка труда в Чувашской Республике и раскрываются проблемы, с которыми сталкиваются региональные органы власти при реализации государственной политики в области занятости. В ходе исследования использовались различные методы: системный и сравнительный анализ, графический метод. На основе оценки состояния занятости и безработицы в Чувашской Республике были выявлены определённые проблемы на рынке труда. Во-первых, наличие естественной убыли населения, что оказывает неблагоприятное влияние на численность рабочей силы. Во-вторых, низкий уровень заработной платы в регионе приводит к росту механической убыли, особенно для сельских территорий. В условиях пандемии 2019 г. основные меры были направлены на избежание значительных колебаний численности безработных за счет предоставления организациям льготных кредитов в целях сохранения занятости. Оценивая реализацию политики в области занятости со стороны региональных органов, можно отметить, что в регионе произошло сокращение уровня безработицы и достижение показателей занятости докризисных значений. Однако в настоящее время возникают новые угрозы дисбаланса на рынке труда. Прежде всего они связаны с внедрением искусственного интеллекта, который внесет существенные трансформации во все аспекты трудовых отношений.

The article presents the results of the assessment of key indicators of the labor market in the Chuvash Republic and reveals the problems faced by regional authorities in the implementation of state policy in the field of employment. Various methods were used in the course of the study: system and comparative analysis, graphical method. Based on the assessment of the state of employment and unemployment in the Chuvash Republic, certain problems in the labor market were identified. Firstly, the presence of a natural population decline, which has an adverse effect on the size of the workforce. Secondly, the low level of wages in the region leads to an increase in mechanical loss, especially for rural areas. In the context of the 2019 pandemic, the main measures were aimed at avoiding significant fluctuations in the number of unemployed by providing preferential loans to organizations in order to maintain employment. Assessing the implementation of the employment policy by the regional authorities, it can be noted that the unemployment rate has decreased in the region and employment indicators have reached pre-crisis values. However, new threats of an imbalance in the labor market are currently emerging. First of all, they are connected with the introduction of artificial intelligence, which will make significant transformations in all aspects of labor relations.