bluuebunny/crossref_metadata_2025 huggingface dataset:
Trace Acetylene Gas Detection Based on a Miniaturized Y-Sphere Coupled Photoacoustic Sensor
Authors: Chen Xiaohong , Wang Sen , Li Dongming , Shi Zhao , Liang Qiang
Published: November 2024

In this work, a miniaturized Y-sphere coupled photoacoustic (YSCPA) sensor is proposed for trace C2H2 gas detection. The cavity volume of the designed YSCPA sensor is about 0.7 mL. The finite element method (FEM) has been performed to analyze the comparative performance of the YSCPA sensor and T-type PA sensor, indicating that the first-order resonance frequency (FORF) of the newly proposed YSCPA sensor has been reduced by half while the PA signal has been improved by a factor of 3 compared to the T-type PA sensor. C2H2 is employed as a target gas to test the performance of the YSCPA sensor. The experimental test results show that the response time of the gas is 26 s. The minimum detection limit (MDL) reaches 189 ppb at a lock-in integration time of 1 s. By extending the lock-in integration time to 100 s, the MDL of the designed PA sensor is reduced to 18.1 ppb. The designed YSCPA sensor has the advantages of small size, low gas consumption, simple structure, and high sensitivity, which is expected to be an effective solution for rapid and real-time monitoring of dissolved C2H2 gas in transformer oil.
Highway Safety with an Intelligent Headlight System for Improved Nighttime Driving
Authors: Nkrumah Jacob Kwaku , Cai Yingfeng , Jafaripournimchahi Ammar , Wang Hai , Atindana Vincent Akolbire
Published: November 2024

Automotive headlights are crucial for nighttime driving, but accidents frequently occur when drivers fail to dim their high beams in the presence of oncoming vehicles, causing temporary blindness and increasing the risk of collisions. To address this problem, the current study developed an intelligent headlight system using a sensor-based approach to control headlight beam intensity. This system is designed to distinguish between various light sources, including streetlights, building lights, and moving vehicle lights. The primary goal of the study was to create an affordable alternative to machine-learning-based intelligent headlight systems, which are limited to high-end vehicles due to the high cost of their components. In simulations, the proposed system achieved a 98% success rate, showing enhanced responsiveness, particularly when detecting an approaching vehicle at 90°. The system’s effectiveness was further validated through real-vehicle implementation, confirming the feasibility of the approach. By automating headlight control, the system reduces driver fatigue, enhances safety, and minimizes nighttime highway accidents, contributing to a safer driving environment.
Walk Longer! Using Wearable Inertial Sensors to Uncover Which Gait Aspects Should Be Treated to Increase Walking Endurance in People with Multiple Sclerosis
Authors: Carpinella Ilaria , Bertoni Rita , Anastasi Denise , Cardini Rebecca , Lencioni Tiziana , Ferrarin Maurizio , Cattaneo Davide , Gervasoni Elisa
Published: November 2024

Reduced walking endurance is common in people with multiple sclerosis (PwMS), leading to reduced social participation and increased fall risk. This highlights the importance of identifying which gait aspects should be mostly targeted by rehabilitation to maintain/increase walking endurance in this population. A total of 56 PwMS and 24 healthy subjects (HSs) executed the 6 min walk test (6 MWT), a clinical measure of walking endurance, wearing three inertial sensors (IMUs) on their shanks and lower back. Five IMU-based digital metrics descriptive of different gait domains, i.e., double support duration, trunk sway, gait regularity, symmetry, and local dynamic instability, were computed. All metrics demonstrated moderate–high ability to discriminate between HSs and PwMS (AUC: 0.79–0.91) and were able to detect differences between PwMS at minimal (PwMSmFR) and moderate–high fall risk (PwMSFR). Compared to PwMSmFR, PwMSFR walked with a prolonged double support phase (+100%), larger trunk sway (+23%), lower stride regularity (−32%) and gait symmetry (−18%), and higher local dynamic instability (+24%). Normative cut-off values were provided for all metrics to help clinicians in detecting abnormal scores at an individual level. The five metrics, entered into a multiple linear regression model with 6 MWT distance as the dependent variable, showed that gait regularity and the three metrics most related to dynamic balance (i.e., double support duration, trunk sway, and local dynamic instability) were significant independent contributors to 6 MWT distance, while gait symmetry was not. While double support duration and local dynamic instability were independently associated with walking endurance in both PwMSmFR and PwMSFR, gait regularity and trunk sway significantly contributed to 6 MWT distance only in PwMSmFR and PwMSFR, respectively. Taken together, the present results allowed us to provide hints for tailored rehabilitation exercises aimed at specifically improving walking endurance in PwMS.
EDMF: A New Benchmark for Multi-Focus Images with the Challenge of Exposure Difference
Authors: Li Hui , Shen Tianyu , Zhang Zeyang , Zhu Xuefeng , Song Xiaoning
Published: November 2024

The goal of the multi-focus image fusion (MFIF) task is to merge images with different focus areas into a single clear image. In real world scenarios, in addition to varying focus attributes, there are also exposure differences between multi-source images, which is an important but often overlooked issue. To address this drawback and improve the development of the MFIF task, a new image fusion dataset is introduced called EDMF. Compared with the existing public MFIF datasets, it contains more images with exposure differences, which is more challenging and has a numerical advantage. Specifically, EDMF contains 1000 pairs of color images captured in real-world scenes, with some pairs exhibiting significant exposure difference. These images are captured using smartphones, encompassing diverse scenes and lighting conditions. Additionally, in this paper, a baseline method is also proposed, which is an improved version of memory unit-based unsupervised learning. By incorporating multiple adaptive memory units and spatial frequency information, the network is guided to focus on learning features from in-focus areas. This approach enables the network to effectively learn focus features during training, resulting in clear fused images that align with human visual perception. Experimental results demonstrate the effectiveness of the proposed method in handling exposure difference, achieving excellent fusion results in various complex scenes.
State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer
Authors: Wan Mingfei , Liu Daoguang , Wu Jun , Li Li , Peng Zhangjun , Liu Zhigui
Published: November 2024

Quadruped robots possess significant mobility in complex and uneven terrains due to their outstanding stability and flexibility, making them highly suitable in rescue missions, environmental monitoring, and smart agriculture. With the increasing use of quadruped robots in more demanding scenarios, ensuring accurate and stable state estimation in complex environments has become particularly important. Existing state estimation algorithms relying on multi-sensor fusion, such as those using IMU, LiDAR, and visual data, often face challenges on non-stationary terrains due to issues like foot-end slippage or unstable contact, leading to significant state drift. To tackle this problem, this paper introduces a state estimation algorithm that integrates an invariant extended Kalman filter (InEKF) with a disturbance observer, aiming to estimate the motion state of quadruped robots on non-stationary terrains. Firstly, foot-end slippage is modeled as a deviation in body velocity and explicitly included in the state equations, allowing for a more precise representation of how slippage affects the state. Secondly, the state update process integrates both foot-end velocity and position observations to improve the overall accuracy and comprehensiveness of the estimation. Lastly, a foot-end contact probability model, coupled with an adaptive covariance adjustment strategy, is employed to dynamically modulate the influence of the observations. These enhancements significantly improve the filter’s robustness and the accuracy of state estimation in non-stationary terrain scenarios. Experiments conducted with the Jueying Mini quadruped robot on various non-stationary terrains show that the enhanced InEKF method offers notable advantages over traditional filters in compensating for foot-end slippage and adapting to different terrains.
Resveratrol: Extraction Techniques, Bioactivity, and Therapeutic Potential in Ocular Diseases
Authors: Accomasso Giulia , Turku Flavia , Sapino Simona , Chirio Daniela , Peira Elena , Gallarate Marina
Published: November 2024

Resveratrol (RV), a natural polyphenol found in various plants, exhibits a wide range of bioactive properties and mechanisms of action. Its potential therapeutic benefits in several diseases and, more specifically, in ocular diseases have garnered significant attention, with studies exploring RV properties at cellular, molecular, and physiological levels. Like many natural derivatives, RV can be obtained through various extraction methods from plant sources, with a growing interest in sustainable techniques that align with recent trends in sustainability, circular economy, and green chemistry. This review begins by describing the most efficient and sustainable extraction techniques of RV from natural sources and then delves into its numerous bioactive properties and its synergistic effects with other active substances and drugs. Furthermore, an overview of the scientific literature on RV as a therapeutic agent for ocular diseases, both in its pure form and entrapped in nanoparticulate systems, is provided.
The Effect of Insulin-like Growth Factor-1 on the Quantitative and Qualitative Composition of Phosphoinositide Cycle Components During the Damage and Regeneration of Somatic Nerves
Authors: Parchaykina Marina , Chudaikina Elena , Revina Elvira , Molchanov Ivan , Zavarykina Anastasia , Popkov Egor , Revin Victor
Published: November 2024

One of the pressing issues in regenerative medicine is the restoration of somatic nerve function after injury. In this study, extraction methods were used to obtain lipids from nervous tissue, followed by chromatographic separation, quantitative analysis via densitometry, and qualitative and quantitative analyses of the fatty acid composition through gas chromatography. The results showed that nerve cutting results in the accumulation of all forms of phosphoinositides and a decrease in diacylglycerol (DAG) levels in both the proximal and distal segments of the nerve conductor. This phenomenon is likely attributable to the inactivation of phosphoinositide-specific phospholipase C and the activation of lipolytic enzymes, particularly phospholipases A1 and A2, resulting in an increase in the amount of free fatty acids (FFAs). The intramuscular administration of insulin-like growth factor-1 (IGF-1) was associated with enhanced phosphoinositide metabolism, increased DAG levels, reduced FFA levels, and a redistribution of fatty acids within the studied lipid fractions. The registration method of action potentials demonstrated the restoration of nerve conduction in the proximal segment of somatic nerves following the introduction of IGF-1. This correlates with our findings regarding alterations in the lipid fraction composition of damaged nerve conductors in response to the drug’s effects. Most likely, IGF-1 exerts its effects through activation of the phosphoinositide-specific phospholipase C and phosphatidylinositol-3 kinase signaling pathways, which are necessary for axonal regeneration and the restoration of functioning damaged nerve conductors.
Stress-Related Hormonal and Psychological Changes to Simulated and Official Judo Black Belt Examination in Older Tori and Adult Uke: An Exploratory Observational Study
Authors: Ciaccioni Simone , Martusciello Francesca , Di Credico Andrea , Guidotti Flavia , Conte Daniele , Palumbo Federico , Capranica Laura , Di Baldassarre Angela
Published: November 2024

This study investigated the psycho-physiological impact of a black belt examination. Older brown-belt judoka (Tori, F = 2, M = 4; age = 75.6 ± 4.5 yrs) and their 2nd–5th Dan black-belt coaches (Uke; M = 6; age = 36.5 ± 10.8 yr) were evaluated during a simulated and official examination and a resting day. Participants’ trait anxiety (STAI-Y2) was recorded prior to the study. State anxiety (STAI-Y1), ratings of perceived exertion (RPE), enjoyment (ENJ), and fear of falling (FoF) were collected 15 min before and after the experimental conditions. Saliva samplings at awakening (T0), PRE (T1), and POST (T2) exercise and during the recovery (15 min-T3, 30 min-T4, 60 min-T5) were collected for cortisol (sC), testosterone (sT), and alpha-amylase (sAA). Participants showed normal age-reference population trait anxiety. A difference (p ≤ 0.05) for role emerged for ENJ and sT only. For STAI-Y1, higher PRE values with respect to POST ones emerged (p = 0.005), and the highest values (p = 0.007) for PRE of the examination were with respect to the simulation. For sAA, differences for sampling were found in the examination conditions only, with peak values at T2 (370.3 ± 78.6 U/mL, p = 0.001). For sC, a significant peak value (0.51 ± 0.09 μg/dL, p = 0.012) emerged at T2 in the examination condition. With respect to Tori, Uke showed higher mean sT values in all conditions (p ≤ 0.05) and the highest T2 during examination (712.5 ± 57.2 pg/mL). Findings suggest the relevance of monitoring psycho-physiological stress-related responses in judo for optimizing both coaching effectiveness and sport performance, especially in older judo practitioners.
Analyzing the Impacts of Inter-Provincial Trade on the Quantitative and Spatial Characteristics of Six Embodied Air Pollutants in China Through Multi-Scenario Simulation
Authors: Zhou Tianfeng , Chen Cong , Dong Cong , Li Qinghua
Published: November 2024

Inter-provincial trade is accompanied by the transfer of embodied pollution emissions, leading to emissions leakage, thereby hindering the sustainable development of society. Therefore, it is imperative to analyze the characteristics of embodied pollutant emission and spatial transfer driven by inter-provincial trade. In this study, the quantitative and spatial characteristics of the six main embodied pollutants (i.e., SO2, NOX, CO, VOC, PM2.5, and PM10) were analyzed by a hypothetical extraction method (HEM) and complex network analysis (CNA) under an input–output analysis (IOA) framework. Then, the row arrange series (RAS) method was employed to simulate the impacts of varying levels of trade intensity, economic growth rate, and technological progress on embodied pollutants and spatial-transfer characteristics. The major findings are as follows: (i) the increase in inter-provincial trade led to a corresponding rise in embodied pollutant emissions due to the relocation of production activities towards provinces with higher emission intensity. Excessive responsibility was assumed by provinces such as Shanxi and Hebei, engaging in production outsourcing for reducing pollutants. (ii) The macro direction of pollutant transfer paths was from the resource-rich northern and central provinces towards the trade-developed southern provinces. Sectors in the transfer path, such as the industry sectors of Shanxi, Guangdong, Henan, and the transport sector of Henan, exhibited high centrality and dominated pollutant transfer activities in the network. (iii) The industry sector, characterized by substantial energy consumption, was the predominant emitter of all pollutant production-based emissions, accounting for more than 40% of total emissions. This study is conducive to analyzing the impacts of inter-provincial trade on embodied pollutant emissions and developing emissions reduction policies considering equitable allocation of emissions responsibilities from both production and consumption perspectives.
Exploring the Heat Mitigation Effects of Urban Climate Adaptation Facilities
Authors: Kwon Tae Kyung , Kim Tae Hyoung , Lim Young-Shin
Published: November 2024

Urban heat islands (UHI), which are exacerbated by climate change, significantly increase heat stress, particularly affecting vulnerable populations, such as the elderly and children. This study evaluates the effectiveness of various urban heat mitigation technologies, including cooling fog systems, cool roofs (reflective paint), shading structures, and small water paths, in reducing temperatures and enhancing thermal comfort. Field tests were conducted from 2021 to 2023 across Gimhae-si, Yechun-gun, Geyang-gu, and Sangju-si, with support from the Korean Ministry of Environment. The results demonstrated that cooling fog systems provided immediate temperature reductions, lowering ambient temperatures by up to 3.1 °C, while cool roofs reduced surface temperatures by 2–3 °C. Shading structures reduced surface temperatures by up to 10 °C, and small water paths cooled air temperatures by up to 1.5 °C, also increasing humidity and improving thermal comfort. The findings suggest that a combination of these technologies can effectively mitigate urban heat stress, especially in areas with vulnerable populations.
Is the Ratoon Rice System More Sustainable? An Environmental Efficiency Evaluation Considering Carbon Emissions and Non-Point Source Pollution
Authors: Qiao Hui , Pu Mingzhe , Wang Ruonan , Zheng Fengtian
Published: November 2024

The sustainability of rice-cropping systems hinges on balancing resources, output, and environmental impacts. China is revitalizing the ancient ratoon rice (RR) system for input savings and environmental benefits. Prior research has explored the RR system’s performance using various individual indicators, but few studies have focused on its overall balance of these factors. Environmental efficiency (EE) analysis addresses this gap. Using field survey data from Hunan Province in China and the slacks-based data envelopment analysis method, we quantified the EE of the RR, double-season rice (DR), and single-season rice (SR) systems. Key findings include: (1) the RR system outperforms in carbon emissions and non-point source pollution; (2) the RR system’s EE is 0.67, significantly higher than the DR (0.58) and SR (0.57) systems, indicating superior performance; and (3) despite its relatively high EE, the RR system can still improve, mainly due to input redundancy and production value shortfall. These findings provide strategies for optimizing RR systems to enhance agricultural sustainability.
Scientometric Analysis of Publications on Household Electricity Theft and Energy Consumption Load Profiling in a Smart Grid Context
Authors: de Rezende José Antonio Moreira , Leão Junior Reginaldo Gonçalves , Gomes Otávio de Souza Martins
Published: November 2024

This study provides a scientometric analysis of research focused on energy theft detection and load profiling in smart grid networks. Data were retrieved from the Web of Science and Scopus databases, covering publications from 2003 to April 2024. Using the Bibliometrix package and VOSviewer software, we analyzed trends in publications, author productivity, collaborative networks, and key journals. The study highlights significant growth in the research field, with China and the USA emerging as the most productive countries, with strong international collaboration. Nadeem Javaid is identified as a leading author, contributing to publications with a strong focus on the application of deep learning techniques for energy consumption analysis in smart grids. Key journals such as IEEE Access, Applied Energy, and Energies were found to be central to this research area. Our findings highlighted the importance of this area, as smart grid technologies continue to evolve, requiring advanced methodologies to detect non-technical losses and analyze consumption patterns. This research supports the United Nations’ (UN) Sustainable Development Goals (SDGs), particularly goals related to sustainable energy and infrastructure development, by emphasizing the importance of technological innovation and collaboration in tackling energy theft.
Moving Toward the Expansion of Energy Storage Systems in Renewable Energy Systems—A Techno-Institutional Investigation with Artificial Intelligence Consideration
Authors: Razmjoo Armin , Ghazanfari Arezoo , Østergaard Poul Alberg , Jahangiri Mehdi , Sumper Andreas , Ahmadzadeh Sahar , Eslamipoor Reza
Published: November 2024

The role of energy storage as an effective technique for supporting energy supply is impressive because energy storage systems can be directly connected to the grid as stand-alone solutions to help balance fluctuating power supply and demand. This comprehensive paper, based on political, economic, sociocultural, and technological analysis, investigates the transition toward electricity systems with a large capacity for renewable energy sources combined with energy storage systems (ESS), along with a comprehensive overview of energy storage technologies; the role of AI in the development of ESS is also presented. This study aims to demonstrate how energy storage systems can be implemented with successful integration to increase electric grid flexibility. The results of the study indicate that this goal can be achieved with suitable planning and cooperation by the national, provincial, and local governments, while taking into account stakeholders’ needs and environmental concerns. In this regard, comprehensive analysis has revealed that procedures such as planning, increasing rewards for renewable energy storage, technological innovation, expanding subsidies, and encouraging investment in infrastructure for renewable energy and large-scale battery storage are crucial for the development of energy storage systems. Furthermore, stakeholders should be able to comprehend the benefits of energy storage systems and their provided valuable services, and engage in the adoption process. Moreover, leveraging AI can significantly enhance the implementation and operation of energy storage systems in energy systems, enabling governments and policymakers to optimize the storage and distribution of energy from renewable sources.
Strengthening Sustainable Higher Education with Digital Technologies: Development and Validation of a Digital Competence Scale for University Teachers (DCS-UT)
Authors: Ličen Sabina , Prosen Mirko
Published: November 2024

The integration of digital competences into higher education is essential for the promotion of effective and sustainable teaching and learning environments. The aim of this study was to develop and validate the Digital Competence Scale for University Teachers (DCS-UT), an instrument to assess key digital competences of teachers. The development of the scale involved the creation of items based on an extensive literature review, followed by rigorous testing for content and face validity. The psychometric properties of the scale were assessed using data from 411 university teachers, with confirmatory factor analysis (CFA) and structural equation modelling (SEM) to examine the underlying structure of the scale. Reliability was assessed using Cronbach’s alpha, which confirmed the internal consistency of the instrument (0.974). The analysis revealed a robust four-factor structure: digital literacy, digital skills, digital interaction, and technology integration, which together explained 70.284% of the variance. These findings underscore the value of the DCS-UT as a tool to promote sustainable teaching practises by assessing digital competences. By equipping teachers with essential digital competences, the scale supports the long-term adaptability and effectiveness of higher education institutions in an increasingly digital field.
Dynamic Response Study of Coral Reef Revetment Project Under Extreme Wave Action
Authors: Hu Jielong , Chen Songgui , Chen Hanbao , Nie Zhichao , Qi Zuoda , Duan Zihao
Published: November 2024

It is crucial for reef revetments to respond dynamically to rigorous wave actions for structural stability and safety. A comprehensive analysis of the interaction between the wave force and wave overtopping in a reef revetment project was conducted based on wave flume experiments. This study explored how wave conditions, the water depth along the reef flat, and the proximity of the reef edge to the revetment project influenced wave overtopping and wave force patterns. The results indicate that as the incident wave height, period, and water depth along the reef flat increased, the average wave overtopping within the revetment project also increased. Additionally, higher levels of average wave overtopping occurred with the decrease in the distance between the revetment project and the reef edge. The peak wave force on the seawall of the revetment project was studied in response to various factors, including wave period, wave height, water depth along the reef flat, and distance to the reef edge. The changes in the maximum wave force reflected those of the average wave overtopping, with a strong linear correlation. The quantitative relationship between these variables was determined, and the wave forces on the seawall could be indirectly estimated using the average wave overtopping volume. This study provides an efficient methodology for assessing the dynamic attributes of revetment projects and the disaster risk of these structures.
Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands
Authors: Mahajan Akash , Das Srijita , Su Wencong , Bui Van-Hai
Published: November 2024

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building-performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. This study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification.
The Impact of the Digital Divide on Labor Mobility and Sustainable Development in the Digital Economy
Authors: Chen Jiawei , Xu Zhijin
Published: November 2024

This paper explores the ways in which the digital divide affects labor in the context of sustainable development within the digital economy. It discusses the effects of major indicators such as digital infrastructure construction, digital industry development, and digital-inclusive finance on labor mobility. Although existing research has analyzed the ways in which the digital economy enhances economic vitality, there is insufficient research that investigates how the divide between digital access and usage can be effectively reduced to promote sustainable development. Therefore, through empirical analysis and mechanism research, this study used quantitative measurement and regression analysis methods to conduct an in-depth analysis of the dual effects of digital access and usage divides on the long-term marginal impact for labor. The results show that improving digital infrastructure such as broadband and fiber optic networks not only significantly boosts the economic vitality of underdeveloped areas, but also enhances their ability to participate in sustainable development. This enables more laborers to access new job opportunities and resources provided by the digital economy. While narrowing the digital use divide initially increases labor mobility, uneven dissemination may create barriers to information access, thus limiting mobility. Our research indicates that the development of the digital economy promotes cross-regional labor mobility, which is particularly prominent in the digital platform economy, facilitating more sustainable economic growth. After controlling for variables such as the level of economic development, this positive impact remains robust. This paper suggests that digital infrastructure construction and training in digital skills should be strengthened to narrow the digital divide and promote sustainable, balanced regional development and increased economic vitality.
Sustainable Mobility: Machine Learning-Driven Deployment of EV Charging Points in Dublin
Authors: Mutua Alexander Mutiso , de Fréin Ruairí
Published: November 2024

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a 6 km by 10 km grid size scenario in Dublin in Ireland so that the best CP distribution is obtained; (2) to use a feedforward neural network (FNNs) model to predict the best parameter weight combinations and the corresponding CPs. CP deployment solutions are classified as successful when an EV is located within 100 m of a CP at the end of a trip. We find that (1) integrating the GEECharge and EV Portacharge algorithms with FNNs optimises the distribution of CPs; (2) the normalised optimal weights for the population density, POIs, and most used road parameters determined by this approach result in approximately 109 CPs being allocated in Dublin; (3) resizing the grid from 6 km by 10 km to 10 km by 6 km and rotating it at an angle of −350∘ results in a 5.7% rise in the overall number of CPs in Dublin; (4) reducing the grid cell size from 1 km2 to 500 m2 reduces the mean distance between CPs and the EVs. This research is vital to city planners as we show that city planners can use readily available data to generate these parameters for urban planning decisions that result in EV CP networks, which have increased efficiency. This will promote EV usage in urban transportation, leading to greater sustainability.
The Synergy Between Industry 5.0 and Circular Economy for Sustainable Performance in the Chinese Manufacturing Industry
Authors: Shafique Muhammad Noman , Adeel Umar , Rashid Ammar
Published: November 2024

The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.
Research Progress on the Design of Surface Texture in Tribological Applications: A Mini-Review
Authors: Chen Keyang , Tang Yunqing
Published: November 2024

Surface texturing technology, as an advanced method to improve surface tribological properties of friction pairs, has been widely used in many fields. In this work, the influence of surface texture parameters on tribological properties of friction pair surfaces are reviewed. For the currently most developed surface textures with symmetry and simple geometries and distributions, it is found that they could help reduce friction mainly by enhancing their dynamic pressure lubrication capability, storing abrasive debris and lubricants for dynamic lubrication or promoting the formation of friction films on surfaces of friction pairs. The dominant design parameters of surface textures influencing their tribological performance are found to be shape, geometry and density, while working condition, including contact mode and lubrication situation, also has a significant influence on the performance of surface textures with specific features. Asymmetric textures and multi-scale composite textures also show great tribological performance, while the coupling mechanism across different factors is still unclear, which makes it a challenge to maximize the advantage of asymmetric or multi-scale composite textures. The development of machine learning provides promising approaches for the multi-parameter optimization of surface textures, which is expected to promote and accelerate the design of advanced surface textures.
Co-Dominant Piezoelectric and Flexoelectric Effects in Twisted Double Bilayer Graphene
Authors: Wei Yuanhao , Li Yuhao , Zhang Hanhao , Lin Shengsheng , Taniguchi Takashi , Watanabe Kenji , Gao Cun-Fa , Shi Yan
Published: November 2024

Controlling the balance between piezoelectric and flexoelectric effects is crucial for tailoring the electromechanical responses of a material. In twisted graphene, it is found that the electromechanical response near the domain walls (DWs) is dominated by either the flexoelectric effect as in twisted bilayer graphene (tBLG) or the piezoelectric effect as in twisted monolayer–bilayer graphene (tMBG). The codominance of both effects in a single system is rare. Here, utilizing lateral piezoresponse force microscopy (LPFM), we show that piezoelectric and flexoelectric effects can coexist and are equally important in twisted double bilayer graphene (tDBG), termed as the piezo-flexoelectric effect. Unlike tBLG and tMBG, distinctive two-step LPFM spatial profiles are captured across the moiré DWs of tDBG. By decomposing the LPFM signal into axisymmetric and antisymmetric components, we find that the angular dependence of both components satisfies sinusoidal relations. Quantitatively, the in-plane piezoelectric coefficient of DWs in tDBG is determined to be 0.15 pm/V by dual AC resonance tracking (DART) LPFM measurement. The conclusion is further supported by continuum mechanics simulations. Our results demonstrate that the stacking configuration serves as a powerful tuning knob for modulating the electromechanical responses of twisted van der Waals materials.
Viewpoints Concerning Crystal Structure from Recent Reports on Schiff Base Compounds and Their Metal Complexes
Authors: Akitsu Takashiro , Nakane Daisuke , Miroslaw Barbara
Published: November 2024

Schiff bases are organic compounds that are often used as ligands in metal complexes. In addition to the C=N double bond, which is characteristic of Schiff bases, intermolecular hydrogen bonds are frequently observed in both the twisting of planar substituents in organic compounds and the geometric structure of the coordination environment in metal complexes. The results of the crystal structure analyses are stored in databases, which can be used to assess three-dimensional structures. To examine the important structural aspects for novel molecular and material designs, this review examines the important discussion of crystal structure “features” from various viewpoints based on papers on Schiff bases and Schiff base metal complexes from recent years.
Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer
Authors: Zhou Yixiong , Ye Xiaofei , Yan Xingchen , Wang Tao , Chen Jun
Published: November 2024

The increase in motorized vehicles in cities and the inefficient use of parking spaces have exacerbated parking difficulties in cities. To effectively improve the utilization rate of parking spaces, it is necessary to accurately predict future parking demand. This paper proposes a deep learning model based on multi-graph convolutional Transformer, which captures geographic spatial features through a Multi-Graph Convolutional Network (MGCN) module and mines temporal feature patterns using a Transformer module to accurately predict future multi-step parking demand. The model was validated using historical parking transaction volume data from all on-street parking lots in Nanshan District, Shenzhen, from September 2018 to March 2019, and its superiority was verified through comparative experiments with benchmark models. The results show that the MGCN–Transformer model has a MAE, RMSE, and R2 error index of 0.26, 0.42, and 95.93%, respectively, in the multi-step prediction task of parking demand, demonstrating its superior predictive accuracy compared to other benchmark models.
An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences
Authors: Cai Wanxin , Yang Mingqing , Lin Li
Published: November 2024

Group preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed. Firstly, the K-means algorithm is employed to cluster users based on a variety of features. The fixed association rule is then applied to filter and identify relevant subsets, forming the foundational basis for constructing a user portrait. The Nonlinear Bayesian Personalized Ranking (NBPR) is constructed to explore common preferences using explicit feedback. Finally, the item preference matrix is enriched with implicit feedback to compile a comprehensive recommendation list that caters to group preferences. Using a multi-user joint evaluation approach, we compare the performance of IR with baseline models across multiple metrics. This comparison demonstrates the robust reliability of the IR system and its ability to prioritize ISAD with preference-aligned groups. Our research overcomes data sparsity in the automotive recommendation system, providing a new method for embedding human elements in decision support systems.
Efficient Degradation of Tetracycline by Peroxymonosulfate Activated with Ni-Co Bimetallic Oxide Derived from Bimetallic Oxalate
Authors: Zhang Qi , Yu Mingling , Liu Hang , Tang Jin , Yu Xiaolong , Wu Haochuan , Jin Ling , Sun Jianteng
Published: November 2024

In this work, NiCo2O4 was synthesized from bimetallic oxalate and utilized as a heterogeneous catalyst to active peroxymonosulfate (PMS) for the degradation of tetracycline (TC). The degradation efficiency of TC (30 mg/L) in the NiCo2O4 + PMS system reached 92.4%, with NiCo2O4 exhibiting satisfactory reusability, stability, and applicability. Radical trapping test and electron paramagnetic resonance (EPR) results indicated that SO4•−, •OH, O2•−, and 1O2 were the dominating reactive oxygen species (ROS) for TC degradation in the NiCo2O4 + PMS system. Seven intermediates were identified, and their degradation pathways were proposed. Toxicity assessment using T.E.S.T software (its version is 5.1.1.0) revealed that the identified intermediates had lower toxicity compared to intact TC. A rice seed germination test further confirmed that the NiCo2O4 + PMS system effectively degraded TC into low-toxicity or non-toxic products. In conclusion, NiCo2O4 shows promise as a safe and efficient catalyst in advanced oxidation processes (AOPs) for the degradation of organic pollutants.
Associations Between Urinary Phthalate Metabolites and Decreased Serum α-Klotho Level: A Cross-Sectional Study Among US Adults in Middle and Old Age
Authors: Liu Yuyan , Zhao Xiaoyu , Ma Shuxian , Li Yongfang
Published: November 2024

Phthalates are widely used chemicals with ubiquitous human exposure. Evidence indicated that phthalate exposure was associated with an increased risk of aging-related diseases. Klotho is a transmembrane protein with anti-aging functions, and its association with phthalates remains unknown. To find the association between phthalate exposure and serum α-Klotho, a cross-sectional study was performed in 4482 adults (40–79 years old) who completed the National Health and Nutrition Examination Survey (NHANES) (2007–2016). As shown in the results of multivariable linear regression analyses, mono(carboxynonyl) phthalate (MCNP) and mono-n-butyl phthalate (MBP) were inversely associated with α-Klotho, and the regression coefficients of MCNP and MBP were −1.14 (95% confidence interval (CI): −2.00, −0.27) and −0.08 (95% CI: −0.14, −0.02). Subgroup analyses based on the quartiles of each phthalate metabolite showed that both MCNP and MBP were only inversely associated with α-Klotho in the subgroups of the highest levels. For mono-isobutyl phthalate (MIBP), the inverse association with α-Klotho was only statistically significant in the subgroup of the lowest level, and the regression coefficient was −26.87 (95% CI: −52.53, −1.21). Our findings suggest that α-Klotho might be involved in the association of phthalate exposure with aging-related diseases. Future research investigating the causality between phthalates and α-Klotho and its underlying mechanisms is encouraged.
The Immune Escape Strategy of Rabies Virus and Its Pathogenicity Mechanisms
Authors: Kiflu Abraha Bahlbi
Published: November 2024

In contrast to most other rhabdoviruses, which spread by insect vectors, the rabies virus (RABV) is a very unusual member of the Rhabdoviridae family, since it has evolved to be fully adapted to warm-blooded hosts and spread directly between them. There are differences in the immune responses to laboratory-attenuated RABV and wild-type rabies virus infections. Various investigations showed that whilst laboratory-attenuated RABV elicits an innate immune response, wild-type RABV evades detection. Pathogenic RABV infection bypasses immune response by antagonizing interferon induction, which prevents downstream signal activation and impairs antiviral proteins and inflammatory cytokines production that could eliminate the virus. On the contrary, non-pathogenic RABV infection leads to immune activation and suppresses the disease. Apart from that, through recruiting leukocytes into the central nervous system (CNS) and enhancing the blood–brain barrier (BBB) permeability, which are vital factors for viral clearance and protection, cytokines/chemokines released during RABV infection play a critical role in suppressing the disease. Furthermore, early apoptosis of neural cells limit replication and spread of avirulent RABV infection, but street RABV strains infection cause delayed apoptosis that help them spread further to healthy cells and circumvent early immune exposure. Similarly, a cellular regulation mechanism called autophagy eliminates unused or damaged cytoplasmic materials and destroy microbes by delivering them to the lysosomes as part of a nonspecific immune defense mechanism. Infection with laboratory fixed RABV strains lead to complete autophagy and the viruses are eliminated. But incomplete autophagy during pathogenic RABV infection failed to destroy the viruses and might aid the virus in dodging detection by antigen-presenting cells, which could otherwise elicit adaptive immune activation. Pathogenic RABV P and M proteins, as well as high concentration of nitric oxide, which is produced during rabies virus infection, inhibits activities of mitochondrial proteins, which triggers the generation of reactive oxygen species, resulting in oxidative stress, contributing to mitochondrial malfunction and, finally, neuron process degeneration.
Oncolytic Virotherapies and Adjuvant Gut Microbiome Therapeutics to Enhance Efficacy Against Malignant Gliomas
Authors: Meléndez-Vázquez Natalie M. , Gomez-Manzano Candelaria , Godoy-Vitorino Filipa
Published: November 2024

Glioblastoma (GBM) is the most prevalent malignant brain tumor. Current standard-of-care treatments offer limited benefits for patient survival. Virotherapy is emerging as a novel strategy to use oncolytic viruses (OVs) for the treatment of GBM. These engineered and non-engineered viruses infect and lyse cancer cells, causing tumor destruction without harming healthy cells. Recent advances in genetic modifications to OVs have helped improve their targeting capabilities and introduce therapeutic genes, broadening the therapeutic window and minimizing potential side effects. The efficacy of oncolytic virotherapy can be enhanced by combining it with other treatments such as immunotherapy, chemotherapy, or radiation. Recent studies suggest that manipulating the gut microbiome to enhance immune responses helps improve the therapeutic efficacy of the OVs. This narrative review intends to explore OVs and their role against solid tumors, especially GBM while emphasizing the latest technologies used to enhance and improve its therapeutic and clinical responses.
Computational Evidence for Bisartan Arginine Blockers as Next-Generation Pan-Antiviral Therapeutics Targeting SARS-CoV-2, Influenza, and Respiratory Syncytial Viruses
Authors: Ridgway Harry , Apostolopoulos Vasso , Moore Graham J. , Gadanec Laura Kate , Zulli Anthony , Swiderski Jordan , Tsiodras Sotirios , Kelaidonis Konstantinos , Chasapis Christos T. , Matsoukas John M.
Published: November 2024

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza, and respiratory syncytial virus (RSV) are significant global health threats. The need for low-cost, easily synthesized oral drugs for rapid deployment during outbreaks is crucial. Broad-spectrum therapeutics, or pan-antivirals, are designed to target multiple viral pathogens simultaneously by focusing on shared molecular features, such as common metal cofactors or conserved residues in viral catalytic domains. This study introduces a new generation of potent sartans, known as bisartans, engineered in our laboratories with negative charges from carboxylate or tetrazolate groups. These anionic tetrazoles interact strongly with cationic arginine residues or metal cations (e.g., Zn2+) within viral and host target sites, including the SARS-CoV-2 ACE2 receptor, influenza H1N1 neuraminidases, and the RSV fusion protein. Using virtual ligand docking and molecular dynamics, we investigated how bisartans and their analogs bind to these viral receptors, potentially blocking infection through a pan-antiviral mechanism. Bisartan, ACC519TT, demonstrated stable and high-affinity docking to key catalytic domains of the SARS-CoV-2 NSP3, H1N1 neuraminidase, and RSV fusion protein, outperforming FDA-approved drugs like Paxlovid and oseltamivir. It also showed strong binding to the arginine-rich furin cleavage sites S1/S2 and S2′, suggesting interference with SARS-CoV-2’s spike protein cleavage. The results highlight the potential of tetrazole-based bisartans as promising candidates for developing broad-spectrum antiviral therapies.