bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region
Authors: Ullah Mohib , Tang Bingzhe , Huangfu Wenchao , Yang Dongdong , Wei Yingdong , Qiu Haijun
Published: July 2024

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers in the Himalayan region of the China–Pakistan Economic Corridor, utilizing statistical techniques and validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological (1), geological (2), and meteorological (1) factors. These variables were evaluated for multicollinearity, feature importance, and their influence on landslide incidences. Our findings indicate that Support Vector Machines and Logistic Regression were highly effective, particularly near fault zones and roads, due to their effectiveness in handling complex, non-linear terrain interactions. Conversely, Random Forest and Logistic Regression demonstrated variability in their results. Each model distinctly identified landslide susceptibility zones ranging from very low to very high risk. Significant conditioning variables such as elevation, rainfall, lithology, slope, and land use were identified, reflecting the unique geomorphological conditions of the Himalayas. Further analysis using the Variance Inflation Factor and Pearson correlation coefficient showed minimal multicollinearity among the variables. Moreover, evaluations of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values confirmed the strong predictive capabilities of the models, with the Random Forest Classifier performing exceptionally well, achieving an AUC of 0.96 and an F-Score of 0.86. This study shows the importance of model selection based on dataset characteristics to enhance decision-making and strategy effectiveness.
Potential of Former Mill Race Corridors for Urban Regeneration Strategies—A Case Study from Podolínec in Prešov Region (Slovakia)
Authors: Illes Juraj , Kristianova Katarina , Joklova Viera , Shayegani Aida
Published: July 2024

In the past, mill races were part of the urban structure of many towns in Slovakia. As regulated and artificially created waterways, they served to drive mills, rollers, or hammers. With the use of new sources of energy, they lost their functions, and most of them were dried, filled, or buried underground. In our research, we examine the former mill race corridor in Podolínec (Prešov region, Slovakia) and its potential to contribute to urban regeneration strategies. The research steps included the following, namely 1. Identification of the route of the mill race corridor according to historical sources, 2. survey of its current state and its spatial preservation in the urban structure, 3. evaluation of the possibilities of its new uses, which could increase the quality and attractiveness of urban public spaces. The research results show that the fragments of the corridor of the extinct mill race are still identifiable in the urban fabric, and in the cadaster, they are in public ownership and suitable for new uses. The fragments of the corridor of the extinct mill race in Podolínec represent a potential for strengthening the blue and green infrastructure, pedestrian and cycling greenways, and a potential for the presentation of cultural heritage values, which could contribute to the improvement of the qualities of the urban environment.
Uncovering the Triple Synergy of New-Type Urbanization, Greening and Digitalization in China
Authors: Li Qiangyi , Ge Jiexiao , Huang Mingyu , Wu Xiaoyu , Fan Houbao
Published: July 2024

The in-depth discussion and analysis of the synergistic effect of new-type urbanization, greening and digitalization (NUGD) is important for the achievement of sustainable social, ecological and economic development. Therefore, in this study, an evaluation index system composed of these three subsystems was constructed for Chinese cities from 2011 to 2021. The comprehensive and collaborative development levels of each subsystem were measured by means including the entropy weight method and the coupling coordination model, respectively. Then, methods such as ESDA and the Dagum Gini coefficient were applied to investigate the spatiotemporal evolution and spatial differences in the triple synergy effect of the NUGD system in Chinese cities. Finally, the constraining factors of the triple synergy effect were revealed using the obstacle degree model. The findings demonstrated the following: (1) Overall, the NUGD subsystems and their comprehensive levels were increasing, with moderate overall development levels. (2) The synergistic development of the NUGD system exhibited an upward trend. Spatially, the synergistic development level showed distinct differentiation, being higher in the east and lower in the west. The multidimensional dynamic variation characteristics obtained through kernel density estimation revealed that the triple synergy level exhibits high stability. (3) The differences within the east and between the eastern and western areas were the largest, with the intensity of transvariation as the main source. (4) The five criterion layers, including social and spatial urbanization variables, were the key constraints that affected the triple synergy of the NUGD in Chinese cities, and the restrictive role of factors such as the proportion of urban construction land and the per capita postal business volume should not be ignored. This study provides a valuable reference and decision-making guidance to promote China’s acceleration toward a new urbanization path supported by both digitalization and green transformation.
Exploring the Spatial Correlation Network and Its Formation Mechanisms in Urban Land Use Performance: A Case Study of the Yangtze River Economic Belt
Authors: Fang Hongjia , Chai Ji , Wang Zhanqi , Zhang Rou , Huang Chao , Luo Meiling
Published: July 2024

Urban land is the primary location for manufacturing and services, facilitating the expansion and interconnectedness of economic activities and factor flows to shape various urban land-use performances (ULUP). Exploring these spatial linkages of urban land-use performance can play a crucial role in fostering cohesive urban development. Taking 109 prefecture-level cities as research samples, this paper explores the characteristics and influencing factors of the spatial network associated with ULUP in the Yangtze River Economic Belt through modified gravity model, network analysis and QAP analysis. The analysis shows that ULUP has shown an N-shaped trend over the past two decades. It has formed a network of correlations, with Chongqing, Shanghai, and Wuhan emerging as central nodes. Notably, the correlation predominantly occurs between geographically adjacent cities, with weaker links between the Yangtze River Economic Belt’s upstream, midstream, and downstream regions. The network can be divided into four distinct types: main inflow plate, main outflow plate, agent plate, and bidirectional spillover plate. Geographic location is the most significant factor influencing network formation, followed by resource mismatch, economic development, and openness. The study provides theoretical guidance and empirical support for improving the utilisation of urban land and promoting coordinated development.
A Bottom-Up Carbon Emission Assessment Model for Carbon Emission Control at the Level of Rural Detailed Planning
Authors: Song Limei , Chang Jiang , Yi Jianmei
Published: July 2024

Incorporating green and low-carbon building targets into the rural planning management system requires scientific and quantitative methods for assessing carbon emissions from rural land use. At present, the research in this field mainly focuses on urban areas, but there are fewer theoretical and practical studies on the assessment of carbon emissions from rural land use. This paper proposes a new carbon emission assessment method based on land use modes, and the model can not only assess carbon emissions but also directly reflect the carbon emission intensity of different land use spaces in rural areas and guide the carbon emission control of construction land in village planning. In this paper, we take suburban rural areas in Hunan Province as an example and establish a land use carbon emission assessment model with 13 indicators in five dimensions: total carbon emission, carbon emission efficiency, carbon emission intensity per unit of land use, carbon emission structure of land use, and carbon emission intensity of buildings, based on the bottom-up field research data. We practised our method in Jinlong Town, Hunan Province, and gave examples of model applications. It was found that the carbon emission calculation method based on the carbon emission intensity of land use can be used to calculate the current status of carbon emissions in different villages in Jinlong Town. At the same time, the carbon emission assessment results can be used as a scientific basis for carbon emission control in detailed village planning in Jinlong Town. In general, the carbon emission assessment model can complete the assessment of land carbon emissions in rural areas and provide a low-carbon land use management tool for the government.
Park Heritage of the Island of Krk between Urban Transformations and Climate Change
Authors: Vahtar-Jurković Koraljka , Sokol Jurković Renata , Jurković Jadran
Published: July 2024

The island of Krk in Primorje-Gorski Kotar County, Croatia, is also called the Golden Island because of its favorable geographical location, the diversity of natural and urban landscapes, the beauty of the coast and the sea, the wealth of tangible and intangible heritage, and especially because of the opportunities for living and working. During the last century and in this century, urban landscapes have been exposed to dramatic changes that transformed old castles or former smaller settlements of the local population into tourist centers and desirable places for permanent or temporary residence. A significant part of their complex structure is the cultural and historical heritage, within which the island’s park heritage has so far been insufficiently recognized and valued. Therefore, this paper examines forty selected public park spaces in the area of all local self-government units of the island of Krk in the context of urban transformations and climate change. It is concluded that the island’s park heritage has often been created as a result of urban transformations in which, despite being exposed to constant changes, it is mostly preserved, but that recently, new elements of this heritage are emerging. A new challenge is the threat of the consequences of climate change—increasingly frequent and long-lasting droughts, extreme precipitation and flash floods, stormy winds, rising sea level and salinization, which further emphasizes the need to preserve the park heritage of the island of Krk in the context of resistance to climate change.
The Efficacy of Early Rehabilitation Combined with Virtual Reality Training in Patients with First-Time Acute Stroke: A Randomized Controlled Trial
Authors: Chao Ta-Chung , Lin Chia-Huei , Lee Meei-Shyuan , Chang Cheng-Chiang , Lai Chia-Ying , Huang Chien-Yao , Chang Wen-Yuan , Chiang Shang-Lin
Published: July 2024

Early rehabilitation has beneficial impacts on functional outcomes for patients with acute stroke. However, whether the addition of virtual reality (VR) training could further improve these patients’ muscle strength, functional recovery, and psychological health is unknown. A randomized controlled trial was conducted on 33 patients with first-time acute ischemic stroke. The patients were randomly assigned using a 1:1 randomization ratio to either the experimental group (EG) or the comparison group (CG). Both groups received early rehabilitation, and the EG received extra VR training during their stay in the hospital. Muscle strength, functional status, and psychological health were assessed before the intervention and at discharge. Generalized estimating equations were used to examine the intervention effects via the interaction of time and group. After adjusting for potential covariates, the EG showed a more significant decrease in depression at discharge than the CG (ß = 3.77, p = 0.011). There were no differences in muscle strength and functional recovery between groups after intervention. Adding VR training into early rehabilitation facilitates substantial positive effects on psychological health, specifically depression, but not muscle strength and functional recovery, compared to receiving early rehabilitation alone in patients with first-time acute stroke during their hospitalized period.
Accuracy Analysis of Complex Transmission System with Distributed Tooth Profile Errors
Authors: Zhang Min , Zhang Zhijing , Xiong Jian , Chen Xiao
Published: July 2024

Tooth profile errors are the internal excitations that cause gear meshing errors, which are critical error factors affecting gear transmission accuracy. In existing studies, it is usually regarded as a constant or random distribution function. However, the actual machined tooth profile error is not a constant, so this estimation is inconsistent with the actual situation, resulting in an inaccurate evaluation of transmission accuracy. This paper proposes a method for representing tooth profile errors using distribution errors (including systematic and random errors), and a mathematical model of distributed tooth profile errors is presented. The contact stresses of the complex transmission system were compared with those obtained by formulas, proving that tooth profile errors increase contact stress. A method for calculating gear meshing error is proposed to evaluate the actual output accuracy of the complex transmission system. Compared with the test, the output accuracy is reduced by 13.8% under the temperature environment and distributed tooth profile errors. The proposed methods can accurately predict the transmission accuracy of precision transmission systems at the design stage and provide theoretical support for reducing systematic and random errors at the gear machining stage.
A Digital Twin-Based Adaptive Height Control for a Shearer
Authors: You Xiusong , Guo Yinan , Miao Bing , Ge Shirong
Published: July 2024

The shearer is an important component of the smart mining workface, and its effective control is a key aspect that guarantees safe and high-efficient production in coal mines. To address the issue of autonomous height adjustment during the shearer’s cutting process, a self-adaptive speed control method driven by digital twin technology is proposed. A digital twin-based control architecture for the shearer is first established, which consists of physical and the corresponding virtual entities, as well as reality–virtual interaction between them. Based on the mathematic model formulated for height adjustment system of the shearer, an adaptive fuzzy sliding mode controller (AFSMC) with the displacement estimation is designed for the virtual entity, with the purpose of guiding the operation of the corresponding physical entity. Simulation experiments on MATLAB compares the control performance among the proposed method and four comparative ones, including PID controller, integral sliding mode controller (ISMC), feedback linearization controller (FLC), and fuzzy sliding mode controller (FSMC). The experimental results confirm the effectiveness of the proposed AFSMC. More specifically, its steady-state error is 0.024, the maximum absolute control input is 8.43, and the settling time is 1.74 s. This also proves that the digital twin-based control method enables the precise adaptive height adjustment of the shearer, providing potential reference for the intelligent development of a smart mining workface.
A Political Radicalization Framework Based on Moral Foundations Theory
Authors: Interian Ruben
Published: July 2024

Moral foundations theory proposes that individuals with conflicting political views base their behavior on different principles chosen from a small group of universal moral foundations. This study proposes using a set of widely accepted moral foundations (fairness, in-group loyalty, authority, and purity) as proxies to determine the degree of radicalization of online communities. A fifth principle, care, is generally surpassed by others that are higher in the radicalized groups’ moral hierarchy. Moreover, the presented data-driven methodological framework proposes an alternative way to measure whether a community complies with a certain moral principle or foundation: not evaluating its speech, but its behavior through the interactions of its individuals, establishing a bridge between the structural features of the interaction network and the intensity of communities’ radicalization regarding the considered moral foundations. Two foundations were assessed using the network’s structural characteristics: in-group loyalty measured by group-level modularity, and authority evaluated using group domination, for detecting potential hierarchical substructures within the network. By analyzing a set of Pareto-optimal groups regarding a multidimensional moral relevance scale, the most radicalized communities were identified among those considered extreme in some of their attitudes or views. An application of the proposed framework is illustrated using real-world datasets. The radicalized communities’ behavior exhibited increasing isolation, and their authorities and leaders showed growing domination over their audience. Differences were also detected between users’ behavior and speech, showing that individuals tended to share more “extreme” in-group content than they publish: extreme views get more likes on social media.
A Physical Insight into Computational Fluid Dynamics and Heat Transfer
Authors: Martynenko Sergey I. , Varaksin Aleksey Yu.
Published: July 2024

Mathematical equations that describe all physical processes are valid only under certain assumptions. One of them is the minimum scales used for the given description. In fact, this prohibits the use of derivatives in the mathematical models of the physical processes. This article represents a derivative-free approach for the mathematical modelling. The proposed approach for CFD and numerical heat transfer is based on the conservation and phenomenological laws, and physical constraints on the minimum problem-dependent spatial and temporal scales (for example, on the average free path of molecules and the average time of their collisions for gases). This leads to the derivative-free governing equations (the discontinuum approximation) that are very convenient for numerical simulation. The theoretical analysis of governing equations describing the fundamental conservation laws in the continuum and discontinuum approximations is given. The article demonstrates the derivative-free approach based on the correctly defined macroparameters (pressure, temperature, density, etc.) for the mathematical description of physical and chemical processes. This eliminates the finite-difference, finite-volume, finite-element or other approximations of the governing equations from the computational algorithms.
OLF-ML: An Offensive Language Framework for Detection, Categorization, and Offense Target Identification Using Text Processing and Machine Learning Algorithms
Authors: Hasan MD. Nahid , Sakib Kazi Shadman , Preeti Taghrid Tahani , Allohibi Jeza , Alharbi Abdulmajeed Atiah , Uddin Jia
Published: July 2024

The pervasiveness of offensive language on social media emphasizes the necessity of automated systems for identifying and categorizing content. To ensure a more secure online environment and improve communication, effective identification and categorization of this content is essential. However, existing research encounters challenges such as limited datasets and biased model performance, hindering progress in this domain. To address these challenges, this research presents a comprehensive framework that simplifies the utilization of support vector machines (SVM), random forest (RF) and artificial neural networks (ANN). The proposed methodology yields notable gains in offensive language detection, automatic categorization of offensiveness, and offense target identification tasks by utilizing the Offensive Language Identification Dataset (OLID). The simulation results indicate that SVM performs exceptionally well, exhibiting excellent accuracy scores (77%, 88%, and 68%), precision scores (76%, 87%, and 67%), F1 scores (57%, 88%, and 68%), and recall rates (45%, 88%, and 68%), proving to be practically successful in identifying and moderating offensive content on social media. By applying sophisticated preprocessing and meticulous hyperparameter tuning, our model outperforms some earlier research in detecting and categorizing offensive language tasks.
Characterizing Finite Groups through Equitable Graphs: A Graph-Theoretic Approach
Authors: Altassan Alaa , Saleh Anwar , Hamed Marwa , Muthana Najat
Published: July 2024

This paper introduces equitable graphs of Type I associated with finite groups. We investigate the connectedness and some graph-theoretic properties of these graphs for various groups. Furthermore, we establish the novel concepts of the equitable square-free number and the equitable group. Our study includes an analysis of the equitable graphs for specific equitable groups. Additionally, we determine the first, second and forgotten Zagreb topological indices for the equitable graphs of Type I constructed from certain groups. Finally, we derive the adjacency matrix for this graph type built from cyclic p-groups.
Unsteady Magnetohydrodynamics PDE of Monge–Ampère Type: Symmetries, Closed-Form Solutions, and Reductions
Authors: Polyanin Andrei D. , Aksenov Alexander V.
Published: July 2024

The paper studies an unsteady equation with quadratic nonlinearity in second derivatives, that occurs in electron magnetohydrodynamics. In mathematics, such PDEs are referred to as parabolic Monge–Ampère equations. An overview of the Monge–Ampère type equations is given, in which their unusual qualitative features are noted. For the first time, the Lie group analysis of the considered highly nonlinear PDE with three independent variables is carried out. An eleven-parameter transformation is found that preserves the form of the equation. Some one-dimensional reductions allowing to obtain self-similar and other invariant solutions that satisfy ordinary differential equations are described. A large number of new additive, multiplicative, generalized, and functional separable solutions are obtained. Special attention is paid to the construction of exact closed-form solutions, including solutions in elementary functions (in total, more than 30 solutions in elementary functions were obtained). Two-dimensional symmetry and non-symmetry reductions leading to simpler partial differential equations with two independent variables are considered (including stationary Monge–Ampère type equations, linear and nonlinear heat type equations, and nonlinear filtration equations). The obtained results and exact solutions can be used to evaluate the accuracy and analyze the adequacy of numerical methods for solving initial boundary value problems described by highly nonlinear partial differential equations.
Global Sensitivity Analysis of Structural Reliability Using Cliff Delta
Authors: Kala Zdeněk
Published: July 2024

This paper introduces innovative sensitivity indices based on Cliff’s Delta for the global sensitivity analysis of structural reliability. These indices build on the Sobol’ method, using binary outcomes (success or failure), but avoid the need to calculate failure probability Pf and the associated distributional assumptions of resistance R and load F. Cliff’s Delta, originally used for ordinal data, evaluates the dominance of resistance over load without specific assumptions. The mathematical formulations for computing Cliff’s Delta between R and F quantify structural reliability by assessing the random realizations of R > F using a double-nested-loop approach. The derived sensitivity indices, based on the squared value of Cliff’s Delta δC2, exhibit properties analogous to those in the Sobol’ sensitivity analysis, including first-order, second-order, and higher-order indices. This provides a framework for evaluating the contributions of input variables on structural reliability. The results demonstrate that the Cliff’s Delta method provides a more accurate estimate of Pf. In one case study, the Cliff’s Delta approach reduces the standard deviation of Pf estimates across various Monte Carlo run counts. This method is particularly significant for FEM applications, where repeated simulations of R or F are computationally intensive. The double-nested-loop algorithm of Cliff’s Delta maximizes the extraction of information about structural reliability from these simulations. However, the high computational demand of Cliff’s Delta is a disadvantage. Future research should optimize computational demands, especially for small values of Pf.
Optimal Investment for Defined-Contribution Pension Plans with the Return of Premium Clause under Partial Information
Authors: Liu Zilan , Zhang Huanying , Wang Yijun , Huang Ya
Published: July 2024

The optimal investment problem for defined contribution (DC) pension plans with partial information is the subject of this paper. The purpose of the return of premium clauses is to safeguard the rights of DC pension plan participants who pass away during accumulation. We assume that the market price of risk consists of observable and unobservable factors that follow the Ornstein-Uhlenbeck processes, and the pension fund managers estimate the unobservable component from known information through Bayesian learning. Considering maximizing the expected utility of fund wealth at the terminal time, optimal investment strategies and the corresponding value function are determined using the dynamical programming principle approach and the filtering technique. Additionally, fund managers forsake learning, which results in the presentation of suboptimal strategies and utility losses for comparative analysis. Lastly, numerical analyses are included to demonstrate the impact of model parameters on the optimal strategy.
Solving Dynamic Full-Truckload Vehicle Routing Problem Using an Agent-Based Approach
Authors: Çabuk Selin , Erol Rızvan
Published: July 2024

In today’s complex and dynamic transportation networks, increasing energy costs and adverse environmental impacts necessitate the efficient transport of goods or raw materials across a network to minimize all related costs through vehicle assignment and routing decisions. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full-truckload vehicle assignment and routing problem was investigated. First, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks was conducted. Then, a new original problem with distinctive features compared with similar studies in the literature was formulated, including pickup/delivery time windows, nodes with different functions (pickup/delivery, washing facilities, and parking), a heterogeneous truck fleet, multiple trips per truck, multiple trailer types, multiple freight types, and setup times between changing freight types. This dynamic optimization problem was solved using an intelligent multi-agent model with agent designs that run on vehicle assignment and routing algorithms. To assess the performance of the proposed approach under varying environmental conditions (e.g., congestion factors and the ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., the latest response time to orders and activating the interchange of trip assignments between vehicles), a detailed scenario analysis was conducted based on a set of designed simulation experiments. The simulation results indicate that the proposed dynamic approach is capable of providing good and efficient solutions in response to dynamic conditions. Furthermore, using longer latest response times and activating the interchange mechanism have significant positive impacts on the relevant costs, profitability, ratios of loaded trips over the total distance traveled, and the acceptance ratios of customer orders.
Modeling and Optimal Control of Infectious Diseases
Authors: Lefebvre Mario
Published: July 2024

We propose a stochastic model of infectious disease transmission that is more realistic than those found in the literature. The model is based on jump-diffusion processes. However, it is defined in such a way that the number of people susceptible to be infected decreases over time, which is the case for a population of fixed size. Next, we consider the problem of finding the optimal control of the proposed model. The dynamic programming equation satisfied by the value function is derived. Estimators of the various model parameters are obtained.
Federated Transfer Fault Diagnosis Method Based on Variational Auto-Encoding with Few-Shot Learning
Authors: Ge Yang , Ren Yong
Published: July 2024

Achieving accurate equipment fault diagnosis relies heavily on the availability of extensive, high-quality training data, which can be difficult to obtain, particularly for models with new equipment. The challenge is further compounded by the need to protect sensitive data during the training process. This paper introduces a pioneering federated transfer fault diagnosis method that integrates Variational Auto-Encoding (VAE) for robust feature extraction with few-shot learning capabilities. The proposed method adeptly navigates the complexities of data privacy, diverse working conditions, and the cross-equipment transfer of diagnostic models. By harnessing the generative power of VAE, our approach extracts pivotal features from signals, effectively curbing overfitting during training, a common issue when dealing with limited fault samples. We construct a federated learning model comprising an encoder, variational feature generator, decoder, classifier, and discriminator, fortified with an advanced training strategy that refines federated averaging and incorporates regularization when handling non-independent data distributions. This strategy ensures the privacy of data while enhancing the model’s ability to discern subtleties in fault signatures across different equipment and operational settings. Our experiments, conducted across various working conditions and devices, demonstrate that our method significantly outperforms traditional federated learning techniques in terms of fault recognition accuracy. The innovative integration of VAE within a federated learning framework not only bolsters the model’s adaptability and accuracy but also upholds stringent data privacy standards.
Novel Dynamic Defense Strategies in Networked Control Systems under Stochastic Jamming Attacks
Authors: Mejdi Hana , Ezzedine Tahar
Published: July 2024

In contemporary networked control systems (NCSs), ensuring robust and adaptive security measures against dynamic threats like jamming attacks is crucial. These attacks can disrupt the control signals, leading to degraded performance or even catastrophic failures. This paper introduces a novel approach to enhance NCS security by applying stochastic game theory to model and resolve interactions between a defender and a jammer. We develop a two-player zero-sum game where the defender employs mixed strategies to minimize the expected cost of maintaining system stability and control effectiveness in the face of potential jamming. Our model discretizes the state space and employs backward induction to dynamically update the value functions associated with various system states, reflecting the ongoing adjustment of strategies in response to the adversary’s actions. Utilizing linear programming in MATLAB, we optimize the defender’s mixed strategies to systematically mitigate the impact of jamming. The results from extensive simulations demonstrate the efficacy of our proposed strategies in attack scenarios, indicating a substantial enhancement in the resilience and performance of NCSs against jamming attacks. Specifically, the proposed method improved network state stability by 75%, reducing the fluctuation range by over 50% compared with systems without defense mechanisms. This study not only advances the theoretical framework for security in NCSs but also provides practical insights for the design of resilient control systems under uncertainty.
Attentional Bias for Opioids in Taiwanese Heavy Smokers with Chronic Noncancer Pain
Authors: Liu Ling-Jun , Lin Edward Meng-Hua , Tsao Shao-Lun , Wang Hsin-Yu , Ho Ming-Chou
Published: July 2024

Background and Objectives: Attentional bias (AB) for addictive substances is a feature of attention found in individuals with substance misuse or diagnosed with substance use disorders. When AB exists, the attention of the addicted individual may be quickly oriented to cues related to the addictive substance or be maintained on these cues for a longer time. AB toward opioids was found in Western samples of smokers with chronic noncancer pain. The level of AB was dose-responsive. However, similar studies in the Taiwanese population are lacking. This study compared the patterns of AB for opioid analgesics in Taiwanese participants with chronic noncancer pain to that of individuals without pain. This study aimed to investigate if AB toward opioids is presented in Taiwanese heavy smokers who are on long-term opioid therapy for pain control. Materials and Methods: Participants were grouped into chronic noncancer pain smokers, chronic pain nonsmokers, and smokers without pain, according to smoking habits and whether or not on long-term opioid therapy for pain control. Each participant completed demographic questionnaires, mood scales, and the opioid-related visual probe task. Differences in AB among the groups were compared using a three-way analysis of covariance controlling for daily cigarette consumption. Results: Chronic noncancer pain smokers (n = 17) and chronic pain nonsmokers (n = 16) displayed more severe levels of depression, anxiety, and pain, compared to smokers without pain (n = 28). Only did chronic pain nonsmokers show significant AB for opioid cues that were displayed for a short time. Analysis on reaction time found that smokers without pain consistently responded faster to the tasks. No difference in reaction time was found between the pain groups. Conclusions: The current study did not fully replicate findings from studies that were based in Western countries. Formulary availability and regulatory limitations might have affected patient’s perception of prescription opioids in Taiwan. However, chronic pain nonsmokers exhibited initial orientation toward opioid-related cues when daily cigarette consumption was accounted for. According to previous research, this AB for shortly displayed opioid cues can be associated with the expectation of pain relief. The current finding also indicated general psychomotor retardation in individuals who were on long-term use of opioids.
Antimicrobial Polymer Films with Grape Seed and Skin Extracts for Food Packaging
Authors: Ivanov Yavor , Godjevargova Tzonka
Published: July 2024

The development of antimicrobial food packaging is a very important and current goal, but it still difficult to implement in practice. Reducing microbial contamination and preserving food quality are very important tasks for food manufacturers as the use of antimicrobial packaging can preserve the health of consumers. On the other hand, the difficulty of degrading packaging materials, leading to environmental pollution, is also an important problem. These problems can be solved by using biodegradable biopolymers and antimicrobial agents in the production of food packaging. Very suitable antimicrobial agents are grape seed and skin extracts as they have high antioxidant and antimicrobial capacity and are obtained from grape pomace, a waste product of winemaking. The present review presents the valuable bioactive compounds contained in grape seeds and skins, the methods used to obtain the extracts, and their antimicrobial and antioxidant properties. Then, the application of grape seed and skin extracts for the production of antimicrobial packaging is reviewed. Emphasis is placed on antimicrobial packaging based on various biopolymers. Special attention is also paid to the application of the extract of grape skins to obtain intelligent indicator packages for the continuous monitoring of the freshness and quality of foods. The focus is mainly placed on the antimicrobial properties of the packaging against different types of microorganisms and their applications for food packaging. The presented data prove the good potential of grape seed and skin extracts to be used as active agents in the preparation of antimicrobial food packaging.
Necrotizing Laryngitis in Patients with Hematologic Disease: The First Case-Report Due to PDR Acinetobacter baumannii and Literature Review
Authors: Tatouli Ioanna , Dedes Nikolaos , Bozikas Andreas , Melliou Stamatoula , Pavlou Maria-Markella , Kontogiannis Sofoklis , Kyrodimos Efthymios , Kanioura Eftychia , Ntanasis-Stathopoulos Ioannis , Dimopoulos Meletios-Athanasios , Dimopoulos George , Kastritis Efstathios , Gavriatopoulou Maria
Published: July 2024

Immunocompromised patients with hematologic diseases may experience life-threatening infections with rather uncommon manifestations. Laryngitis has been described as a potential infection in such vulnerable patients and may result in major complications, ranging from impending airway obstruction to total laryngeal necrosis. Immediate laryngoscopy is of paramount importance, as it provides quantification of laryngeal edema and evidence of necrosis. Documentation of the causative pathogen is usually feasible through tissue culture. In the literature, 14 cases of necrotizing laryngitis have already been published. Here, we present the case of a 38-year-old male with a recent diagnosis of multiple myeloma, who received the first cycle of therapy a few days before admission. The patient presented with neutropenic fever, diarrhea, and multiple organ dysfunction. His course was complicated with hemophagocytic lymphohistiocytosis and stridor. A diagnosis of necrotizing laryngitis attributed to Acinetobacter baumannii invasion of the larynx was established. This manuscript highlights that the management of patients with hematologic disease and necrotizing laryngitis should be coordinated in highly specialized centers and clinicians should have a high level of clinical suspicion and act promptly.
The Relationship between Exposome and Microbiome
Authors: Merra Giuseppe , Gualtieri Paola , La Placa Giada , Frank Giulia , Della Morte David , De Lorenzo Antonino , Di Renzo Laura
Published: July 2024

Currently, exposome studies include a raft of different monitoring tools, including remote sensors, smartphones, omics analyses, distributed lag models, etc. The similarity in structure between the exposome and the microbiota plus their functions led us to pose three pertinent questions from this viewpoint, looking at the actual relationship between the exposome and the microbiota. In terms of the exposome, a bistable equilibrium between health and disease depends on constantly dealing with an ever-changing totality of exposures that together shape an individual from conception to death. Regarding scientific knowledge, the exposome is still lagging in certain areas, like the importance of microorganisms in the equation. The human microbiome is defined as an aggregate assemblage of gut commensals that are hosted by our surfaces related to the external environment. Commensals’ resistance to a variety of environmental exposures, such as antibiotic administration, confirms that a layer of these organisms is protected within the host. The exposome is a conceptual framework defined as the environmental component of the science-inspired systems ideology that shifts from a specificity-based medical approach to reasoning in terms of complexity. A parallel concept in population health research and precision public health is the human flourishing index, which aims to account for the numerous environmental factors that affect individual and population well-being beyond ambient pollution.
Study on Chalcopyrite Dissolution Mechanism and Bioleaching Community Behavior Based on Pulp Concentration Gradient at 6 °C
Authors: Jiang Xiao , Man Meilian , Zeng Weimin
Published: July 2024

Low-temperature bioleaching is relevant to the recovery of metals in alpine mines, but its development has been constrained by low bioleaching rates at high pulp concentrations. To this end, the bioleaching effect of the microbial community after the domestication of pulp concentration at 6 °C was studied. Domestication improved the bioleaching rate of copper. Environmental scanning electron microscopy (ESEM), X-ray diffraction (XRD), and electrochemical measurements revealed that the domestication process aggravated the corrosion of the chalcopyrite surface by accelerating its dissolution reaction. High-throughput sequencing technology indicated that Acidithiobacillus spp., Leptospirillum spp., and Acidiphilium spp. were the major lineages of the domesticated microbial community. The analysis of the microbial community revealed that domestication changed the microbial structure, enhancing the adaptability of the microbial community to pulp concentrations and acidic conditions. This study uncovered the mechanism by which domestication enhanced the bioleaching efficiency of the microbial community at low temperatures.
Base-Free Oxidation of HMF to FDCA over Ru/Cu-Co-O·MgO under Aqueous Conditions
Authors: Zhang Shuang , Chu Guoning , Wang Sai , Ma Ji , Wang Chengqian
Published: July 2024

The copper–cobalt metal oxide composite magnesium oxide catalyst loaded with Ru has achieved the aerobic oxidation of 5-hydroxymethylfurfural (HMF) to the bio-based polyester monomer 2,5-furandicarboxylic acid (FDCA) under base-free conditions. Several Ru/Cu-Co-O·MgO catalysts were prepared, with Cu-Co-O being a combination of CuO and Co3O4. The catalyst’s activity was boosted by the synergistic interaction between copper and cobalt, as well as an optimal copper-to-cobalt molar ratio. Optimal catalytic activity was observed in the Ru4/Cu1-Co1-O·MgO catalyst, loaded with 4 wt% Ru when copper-to-cobalt molar ratio of 1:1 and magnesium oxide compounding amount of 6 mmol were employed. The inclusion of MgO and the load of Ru not only expanded the specific surface area of the catalyst but also heightened its basicity. Additionally, the presence of loaded Ru improved the catalyst’s reducibility at low temperatures. In aqueous solution under oxygen pressure, the conversion rate of HMF achieved 100%, and the yield of FDCA was 86.1%. After five reaction cycles, examining the catalyst and solution revealed that Ru nanoparticles resisted leaching or oxidation, and MgO exhibited only slight dissolution. The green separation of the product was achieved using semi-preparative liquid chromatography, selectively collecting the FDCA-containing solution by exploiting variations in interactions between solutes and the stationary/mobile phases. The subsequent steps involved rotary evaporation and drying, resulting in FDCA powder with a purity exceeding 99%. Notably, this approach eliminated the need to introduce concentrated hydrochloric acid into the system for FDCA separation, providing a novel method for synthesising powdered FDCA.
How the Chemical Properties of Polysaccharides Make It Possible to Design Various Types of Organic–Inorganic Composites for Catalytic Applications
Authors: Akhmetova Sandugash , Zharmagambetova Alima , Talgatov Eldar , Auyezkhanova Assemgul , Malgazhdarova Makpal , Zhurinov Murat , Abilmagzhanov Arlan , Jumekeyeva Aigul , Kenzheyeva Alima
Published: July 2024

Recently, the use of plant-origin materials has become especially important due to the aggravation of environmental problems and the shortage and high cost of synthetic materials. One of the potential candidates among natural organic compounds is polysaccharides, characterized by a number of advantages over synthetic polymers. In recent years, natural polysaccharides have been used to design composite catalysts for various organic syntheses. This review is devoted to the current state of application of polysaccharides (chitosan, starch, pectin, cellulose, and hydroxyethylcellulose) and composites based on their catalysis. The article is divided into four main sections based on the type of polysaccharide: (1) chitosan-based nanocomposites; (2) pectin-based nanocomposites; (3) cellulose (hydroxyethylcellulose)-based nanocomposites; and (4) starch-based nanocomposites. Each section describes and summarizes recent studies on the preparation and application of polysaccharide-containing composites in various chemical transformations. It is shown that by modifying polysaccharides, polymers with special properties can be obtained, thus expanding the range of biocomposites for catalytic applications.
Recent Advances on the Functionalities of Polyoxometalate-Based Ionic Liquids
Authors: Wang Hongxue , Li Bao
Published: July 2024

Polyoxometalate (POM)-based ionic liquids (POM-ILs) are gaining increasing attention due to their diverse structures and functionalities. POMs in POM-ILs not only act as essential structural building blocks but also play a crucial role in their functional performance. With the incorporation of POMs, POM-ILs find applications in various fields such as chemical catalysis, energy science, materials science, sensors, and more. The abundant availability of POMs and other building blocks in POM-ILs, along with their versatile combination possibilities, present promising opportunities for the future. Rather than focusing solely on discovering new structures of POM-ILs, current developments in this field emphasize exploring their functions, leading to the emergence of numerous new applications. Summarizing these advancements aids in understanding the latest trends and facilitates rapid evolution. This review examines the recent five years’ worth of results to analyze the new functions of POM-ILs, categorizing them based on their unique characteristics.
A Strategy for Anode Recovery and Upgrading by In Situ Growth of Iron-Based Oxides on Microwave-Puffed Graphite
Authors: Chen Wenxin , Sun Jing , Jia Pingshan , Wang Wenlong , Song Zhanlong , Wang Ziliang , Zhao Xiqiang , Mao Yanpeng
Published: July 2024

Faced with the increasing volume of retired lithium-ion batteries (LIBs), recycling and reusing the spent graphite (SG) is of great significance for resource sustainability. Here, a facile method for transforming the SG into a carbon framework as well as loading Fe2O3 to form a composite anode with a sandwich structure is proposed. Taking advantage of the fact that the layer spacing of the spent graphite naturally expands, impurities and intercalants are eliminated through microwave thermal shock to produce microwave-puffed graphite (MPG) with a distinct three-dimensional structure. Based on the mechanism of microwave-induced gasification intercalation, a Fe2O3-MPG intercalation compound (Fe2O3-MPGIC) anode material was constructed by introducing iron precursors between the framework layers and subsequently converting them into Fe2O3 through annealing. The Fe2O3-MPGIC anode exhibits a high reversible capacity of 1000.6 mAh g−1 at 200 mA g−1 after 100 cycles and a good cycling stability of 504.4 mAh g−1 at 2000 mA g−1 after 500 cycles. This work can provide a reference for the feasible recycling of SG and development of high-performance anode materials for LIBs.