bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influences of Indian Monsoon Phases on Aerosol Distribution and Composition over India
Authors: Khan Pathan Imran , Ratnam Devanaboyina Venkata , Prasad Perumal , Saheb Shaik Darga , Jiang Jonathan H. , Basha Ghouse , Kishore Pangaluru , Patil Chanabasanagouda S.
Published: August 2024

This study investigates the impacts of summer monsoon activity on aerosols over the Indian region. We analyze the variability of aerosols during active and break monsoon phases, as well as strong and weak monsoon years, using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Our findings show a clear distinction in aerosol distribution between active and break phases. During active phases, the Aerosol Optical Depth (AOD) and aerosol extinction are lower across the Indian region, while break phases are associated with higher AOD and extinction. Furthermore, we observed a significant increase in AOD over Central India during strong monsoon years, compared to weak monsoon years. Utilizing the vertical feature mask (VFM) data from CALIPSO, we identified polluted dust and dusty marine aerosols as the dominant types during both active/break phases and strong/weak monsoon years. Notably, the contributions of these pollutants are significantly higher during break phases compared to during active phases. Our analysis also reveals a shift in the origin of these aerosol masses. During active phases, the majority originate from the Arabian Sea; in contrast, break phases are associated with a higher contribution from the African region.
Three-Dimensional Posture Estimation of Vehicle Occupants Using Depth and Infrared Images
Authors: Tambwekar Anuj , Park Byoung-Keon D. , Kusari Arpan , Sun Wenbo
Published: August 2024

Pose estimation is crucial for ensuring passenger safety and better user experiences in semi- and fully autonomous vehicles. Traditional methods relying on pose estimation from regular color images face significant challenges due to a lack of three-dimensional (3D) information and the sensitivity to occlusion and lighting conditions. Depth images, which are invariant to lighting issues and provide 3D information about the scene, offer a promising alternative. However, there is a lack of strong work in 3D pose estimation from such images due to the time-consuming process of annotating depth images with 3D postures. In this paper, we present a novel approach to 3D human posture estimation using depth and infrared (IR) images. Our method leverages a three-stage fine-tuning process involving simulation data, approximated data, and a limited set of manually annotated samples. This approach allows us to effectively train a model capable of accurate 3D pose estimation with a median error of under 10 cm across all joints, using fewer than 100 manually annotated samples. To the best of our knowledge, this is the first work focusing on vehicle occupant posture detection utilizing only depth and IR data. Our results demonstrate the feasibility and efficacy of this approach, paving the way for enhanced passenger safety in autonomous vehicle systems.
Vehicle Localization Using Crowdsourced Data Collected on Urban Roads
Authors: Cho Soohyun , Chung Woojin
Published: August 2024

Vehicle localization using mounted sensors is an essential technology for various applications, including autonomous vehicles and road mapping. Achieving high positioning accuracy through the fusion of low-cost sensors is a topic of considerable interest. Recently, applications based on crowdsourced data from a large number of vehicles have received significant attention. Equipping standard vehicles with low-cost onboard sensors offers the advantage of collecting data from multiple drives over extensive road networks at a low operational cost. These vehicle trajectories and road observations can be utilized for traffic surveys, road inspections, and mapping. However, data obtained from low-cost devices are likely to be highly inaccurate. On urban roads, unlike highways, complex road structures and GNSS signal obstructions caused by buildings are common. This study proposes a reliable vehicle localization method using a large amount of crowdsourced data collected from urban roads. The proposed localization method is designed with consideration for the high inaccuracy of the data, the complexity of road structures, and the partial use of high-definition (HD) maps that account for environmental changes. The high inaccuracy of sensor data affects the reliability of localization. Therefore, the proposed method includes a reliability assessment of the localized vehicle poses. The performance of the proposed method was evaluated using data collected from buses operating in Seoul, Korea. The data used for the evaluation were collected 18 months after the creation of the HD maps.
The Status of Environmental Electric Field Detection Technologies: Progress and Perspectives
Authors: Liu Qingsong , Lan Zhaoqing , Guo Wei , Deng Jun , Peng Xiang , Chi Minghe , Li Shunbo
Published: August 2024

The detection of electric fields in the environment has great importance for understanding various natural phenomena, environmental monitoring, and ensuring human safety. This review paper provides an overview of the current state-of-the-art technologies utilized for sensing electric fields in the environment, the challenges encountered, and the diverse applications of this sensing technology. The technology is divided into three categories according to the differences in the physical mechanism: the electro-optic effect-based measurement system, the MEMS-based sensor, and the newly reported quantum effect-based sensors. The principles of the underlying methods are comprehensively introduced, and the tentative applications for each type are discussed. Detailed comparisons of the three different techniques are identified and discussed with regard to the instrument, its sensitivity, and bandwidth. Additionally, the challenges faced in environmental electric field sensing, the potential solutions, and future development directions are addressed.
Therapeutic Exercise Recognition Using a Single UWB Radar with AI-Driven Feature Fusion and ML Techniques in a Real Environment
Authors: Hussain Shahzad , Siddiqui Hafeez Ur Rehman , Saleem Adil Ali , Raza Muhammad Amjad , Iturriaga Josep Alemany , Velarde-Sotres Alvaro , Díez Isabel De la Torre
Published: August 2024

Physiotherapy plays a crucial role in the rehabilitation of damaged or defective organs due to injuries or illnesses, often requiring long-term supervision by a physiotherapist in clinical settings or at home. AI-based support systems have been developed to enhance the precision and effectiveness of physiotherapy, particularly during the COVID-19 pandemic. These systems, which include game-based or tele-rehabilitation monitoring using camera-based optical systems like Vicon and Microsoft Kinect, face challenges such as privacy concerns, occlusion, and sensitivity to environmental light. Non-optical sensor alternatives, such as Inertial Movement Units (IMUs), Wi-Fi, ultrasound sensors, and ultrawide band (UWB) radar, have emerged to address these issues. Although IMUs are portable and cost-effective, they suffer from disadvantages like drift over time, limited range, and susceptibility to magnetic interference. In this study, a single UWB radar was utilized to recognize five therapeutic exercises related to the upper limb, performed by 34 male volunteers in a real environment. A novel feature fusion approach was developed to extract distinguishing features for these exercises. Various machine learning methods were applied, with the EnsembleRRGraBoost ensemble method achieving the highest recognition accuracy of 99.45%. The performance of the EnsembleRRGraBoost model was further validated using five-fold cross-validation, maintaining its high accuracy.
Label-Free Biosensor Based on Particle Plasmon Resonance Coupled with Diffraction Grating Waveguide
Authors: Hsu Wei-Ting , Lin Yu-Cheng , Yang Huang-Chin , Barshilia Devesh , Chen Po-Liang , Huang Fu-Chun , Chau Lai-Kwan , Hsieh Wen-Hsin , Chang Guo-En
Published: August 2024

Particle plasmon resonance (PPR), or localized surface plasmon resonance (LSPR), utilizes intrinsic resonance in metal nanoparticles for sensor fabrication. While diffraction grating waveguides monitor bioaffinity adsorption with out-of-plane illumination, integrating them with PPR for biomolecular detection schemes remains underexplored. This study introduces a label-free biosensing platform integrating PPR with a diffraction grating waveguide. Gold nanoparticles are immobilized on a glass slide in contact with a sample, while a UV-assisted embossed diffraction grating is positioned opposite. The setup utilizes diffraction in reflection to detect changes in the environment’s refractive index, indicating biomolecular binding at the gold nanoparticle surface. The positional shift of the diffracted beam, measured with varying refractive indices of sucrose solutions, shows a sensitivity of 0.97 mm/RIU at 8 cm from a position-sensitive detector, highlighting enhanced sensitivity due to PPR–diffraction coupling near the gold nanoparticle surface. Furthermore, the sensor achieved a resolution of 3.1 × 10−4 refractive index unit and a detection limit of 4.4 pM for detection of anti-DNP. The sensitivity of the diffracted spot was confirmed using finite element method (FEM) simulations in COMSOL Multiphysics. This study presents a significant advancement in biosensing technology, offering practical solutions for sensitive, rapid, and label-free biomolecule detection.
Development of an Earth-Field Nuclear Magnetic Resonance Spectrometer: Paving the Way for AI-Enhanced Low-Field Nuclear Magnetic Resonance Technology
Authors: Viciana Eduardo , Martínez-Lao Juan Antonio , López-Lao Emilio , Fernández Ignacio , Arrabal-Campos Francisco Manuel
Published: August 2024

Today, it is difficult to have a high-field nuclear magnetic resonance (NMR) device due to the high cost of its acquisition and maintenance. These high-end machines require significant space and specialist personnel for operation and offer exceptional quality in the acquisition, processing, and other advanced functions associated with detected signals. However, alternative devices are low-field nuclear magnetic resonance devices. They benefit from the elimination of high-tech components that generate static magnetic fields and advanced instruments. Instead, they used magnetic fields induced by ordinary conductors. Another category of spectrometers uses the Earth’s magnetic field, which is simple and economical but limited in use. These devices are called Earth-Field Nuclear Magnetic Resonance (EFNMR) devices. This device is ideal for educational purposes, especially for engineers and those who study nuclear magnetic resonance, such as chemistry or other experimental sciences. Students can observe their internal workings and conduct experiments that complement their education without worrying about damaging equipment. This article provides a detailed explanation of the design and construction of electrical technology devices for the excitation of atomic spin resonance using Earth’s magnetic fields. It covers all necessary stages, from research to analysis, including simulation, assembly, construction of each component, and the development of comprehensive software for spectrometer control.
Multi-Scale Spatio-Temporal Attention Networks for Network-Scale Traffic Learning and Forecasting
Authors: Wu Cong , Ding Hui , Fu Zhongwang , Sun Ning
Published: August 2024

Accurate and timely forecasting of traffic on local road networks is crucial for deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and non-linear temporal dynamics influenced by changing road conditions. This paper introduces the spatio-temporal network embedding (STNE) model, a novel deep learning framework tailored for learning and forecasting graph-structured traffic data over extended input sequences. Unlike traditional convolutional neural networks (CNNs), the model employs graph convolutional networks (GCNs) to capture the spatial characteristics of local road network topologies. Moreover, the segmentation of very long input traffic data into multiple sub-sequences, based on significant temporal properties such as closeness, periodicity, and trend, is performed. Multi-dimensional long short-term memory neural networks (MDLSTM) are utilized to flexibly access multi-dimensional context. Experimental results demonstrate that the STNE model surpasses state-of-the-art traffic forecasting benchmarks on two large-scale real-world traffic datasets.
Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data
Authors: Othman Ghada Ben , Ynineb Amani R. , Yumuk Erhan , Farbakhsh Hamed , Muresan Cristina , Birs Isabela Roxana , De Raeve Alexandra , Copot Cosmin , Ionescu Clara M. , Copot Dana
Published: August 2024

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient η using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast η values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter η, achieving an R2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast η with an R2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.
Study on Temperature Response of Rubberized Concrete Pavement Based on Fiber Bragg Grating Testing Technology
Authors: Zhang Gaojun , Zhang Gaowang , Yuan Jie , Su Manman
Published: August 2024

The temperature response of pavement is not only crucial for assessing the internal stresses within pavement structures but is also an essential parameter in pavement design. Investigating the temperature response of rubberized concrete pavements (RCP) can support the construction of large-scale rubber concrete pavements. This study constructed a pavement monitoring system based on fiber Bragg grating technology to investigate the temperature distribution, temperature strain, temperature effects, and temperature stress of RCP. The results show that the daily temperature–time history curves of concrete pavement exhibit a significant asymmetry, with the heating phase accounting for only one-third of the curve. The temperature at the middle of RCP is 1.8 °C higher than that of ordinary concrete pavement (OCP). The temperature distribution along the thickness of the pavement follows a “spindle-shaped” pattern, with higher temperatures in the center and lower temperatures at the ends. Additionally, the addition of rubber aggregates increases the temperature strain in the pavements, makes the temperature–strain hysteresis effect more pronounced, and increases the curvature of the pavement slab. However, the daily stress range at the bottom of RCP is approximately 0.7 times that of OCP.
A Smith Predictor Modified with a Pseudo Feedforward Control for the Charge-Coupled Device-Based Optoelectronic Tracking System
Authors: Deng Keran , Tan Juan , Chen Piao , Zhang Shige , Wang Ke , Luo Yong
Published: August 2024

In the high-precision optoelectronic tracking system (OTS) based on a charge-coupled device (CCD), the boresight error extracted from the tracking image contains an undeniable delay, which directly limits the control bandwidth of visual tracking. High bandwidth means high response speed and tracking accuracy. Generally, a model-based delay compensation control method called the Smith predictor is utilized to separate time delay from the closed loop to promote the control bandwidth. However, due to the existence of errors between the established model and the real object, the improvement in the bandwidth is still limited to ensure system stability, resulting in insufficient tracking performance. In this paper, to solve the problem, a Smith predictor modified with pseudo feedforward control for the OTS is proposed. The experimental results demonstrate that the proposed method achieves significant improvements in tracking performance, reducing the maximum residual error at 1 Hz from 365 arcseconds (using the classic Smith predictor) to 283 arcseconds, a 22.5% improvement. Across the main frequency band (0.2 Hz to 2 Hz), the residual errors were consistently lower using the proposed method.
A Unified Knowledge Model for Managing Smart City/IoT Platform Entities for Multitenant Scenarios
Authors: Bellini Pierfrancesco , Bologna Daniele , Nesi Paolo , Pantaleo Gianni
Published: August 2024

Smart city/IoT frameworks are becoming more complex for the needs regarding multi-tenancy, data streams, real-time event-driven processing, data, and visual analytics. The infrastructures also need to support multiple organizations and optimizations in terms of data, processes/services, and tools cross-exploited by multiple applications and developers. In this paper, we addressed these needs to provide platform operators and developers effective models and tools to: (i) identify the causes of problems and dysfunctions at their inception; (ii) identify references to data, processes, and APIs to add/develop new scenarios in the infrastructure, minimizing effort; (iii) monitor resources and the work performed by developers to exploit the complex multi-application platform. To this end, we developed a semantic unified knowledge model, UKM, and a number of tools for its implementation and exploitation. The UKM, with its inferences, allows to browse and extract information from complex relationships among entities. The proposed solution has been designed, implemented, and validated in the context of the open source Snap4City.org platform and applied in many geographical areas with 18 organizations, 40 cities, thousands of operators and developers, and free trials to keep platform complexity under control, as in the interconnected scenarios of the Herit-Data Interreg Project, which is a lighthouse project of the European Commission.
Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness
Authors: Valle Cortés Sheila M. , Pérez Morales Jaileene , Nieves Plaza Mariely , Maldonado Darielys , Tevenal Baez Swizel M. , Negrón Blas Marc A. , Lazcano Etchebarne Cayetana , Feliciano José , Ruiz Deyá Gilberto , Santa Rosario Juan C. , Santiago Cardona Pedro
Published: August 2024

Prostate cancer (PCa) poses a significant challenge because of the difficulty in identifying aggressive tumors, leading to overtreatment and missed personalized therapies. Although only 8% of cases progress beyond the prostate, the accurate prediction of aggressiveness remains crucial. Thus, this study focused on studying retinoblastoma phosphorylated at Serine 249 (Phospho-Rb S249), N-cadherin, β-catenin, and E-cadherin as biomarkers for identifying aggressive PCa using a logistic regression model and a classification and regression tree (CART). Using immunohistochemistry (IHC), we targeted the expression of these biomarkers in PCa tissues and correlated their expression with clinicopathological data of the tumor. The results showed a negative correlation between E-cadherin and β-catenin with aggressive tumor behavior, whereas Phospho-Rb S249 and N-cadherin positively correlated with increased tumor aggressiveness. Furthermore, patients were stratified based on Gleason scores and E-cadherin staining patterns to evaluate their capability for early identification of aggressive PCa. Our findings suggest that the classification tree is the most effective method for measuring the utility of these biomarkers in clinical practice, incorporating β-catenin, tumor grade, and Gleason grade as relevant determinants for identifying patients with Gleason scores ≥ 4 + 3. This study could potentially benefit patients with aggressive PCa by enabling early disease detection and closer monitoring.
The Influence of Fibres on the Properties and Sustainability of Oil-Impacted Concrete
Authors: Aljuaydi Fahad , Abousnina Rajab , Alajarmeh Omar , Alajmi Abdalrahman
Published: August 2024

There are significant environmental and health consequences associated with oil-contaminated sand due to its toxic and persistent nature. The impacts include disrupted ecosystems with harm to plants and animals and contamination of water sources, requiring immediate and sustained remediation. Using oil-contaminated sand in construction addresses waste management and promotes sustainability by reducing waste, protecting the environment, saving energy, and driving innovation. This study investigates the impact of crude oil-contaminated sand on concrete’s physical and mechanical characteristics. It focuses on assessing the impact of incorporating four different fibres (Forta Ferro PP, ReoShore 45 PP, glass, and steel fibres) and finding the optimal quantity (0.1, 0.2, 0.3, 0.4 or 0.5%) to improve the physical and mechanical properties of concrete prepared with sand contaminated by crude oil. The impact of crude oil on the bond strength between fibres and concrete was examined. Additionally, the effect of crude oil on heat flow and cumulative heat was analysed. The results demonstrated that increasing oil content decreases concrete density and compressive strength. Nevertheless, the findings indicated that sand contaminated with 10% oil is suitable for low-strength concrete applications. Incorporating 0.1% of Forta Ferro PP, glass, and ReoShore 45 PP fibres had a negligible impact on the mechanical properties of concrete contaminated with 10% oil. Comparatively, steel fibres enhanced the concrete’s compressive strength by 30% at 0.1%, and the flexural strength improved by 9.6% at 0.5%. Concrete with a 10% crude oil content reinforced with steel fibres hinders fracture stabilisation and load transfer, making it suitable as a sustainable material for low-strength civil engineering applications.
Designing Sustainable Drainage Systems as a Tool to Deal with Heavy Rainfall—Case Study of Urmia City, Iran
Authors: Anvigh Reza Mehdizadeh , Silva José Figueiredo , Macedo Joaquim
Published: August 2024

Heavy rainfall, a natural phenomenon reinforced by climate change and global warming, can cause severe social, economic, and safety impacts. Due to the impact of climate change and global warming, heavy rainfall events have become more frequent and intense in recent years, underscoring the urgent need to develop robust stormwater management systems that can prevent related social, economic, and safety issues. This is of greater importance in developing countries. The present study identified areas in Urmia City, Iran, that require stormwater management to develop a comprehensive understanding of the hydrological processes within the study area and to prevent the subsequent effects of heavy rainfall. For this purpose, a combination of the watershed modeling system (WMS) and stormwater management model (SWMM) was employed. Also, three possible scenarios that could be implemented to address the issue of water flow in the medium were proposed. Results indicated that the scenario involving the application of a vegetative swale was the most promising solution. Overall, the results of the present study offer a valuable framework for decision-makers in regions facing heavy rainfalls to effectively manage and minimize the adverse impacts of such events.
The Adoption of Digital Technologies by Small and Medium-Sized Enterprises for Sustainability and Value Creation in Pakistan: The Application of a Two-Staged Hybrid SEM-ANN Approach
Authors: Soomro Raheem Bux , Memon Sanam Gul , Dahri Nisar Ahmed , Al-Rahmi Waleed Mugahed , Aldriwish Khalid , A. Salameh Anas , Al-Adwan Ahmad Samed , Saleem Atif
Published: August 2024

Digital technologies have revolutionized the business field, offering significant opportunities for small and medium-sized enterprises (SMEs) to enhance sustainability and value creation. This study investigates the impact of digital technology adoption on economic and social value creation, as well as SME performance. Specifically, it examines how social media applications, big data analytics, IoT applications, blockchain applications, and AI-enabled applications influence economic and social value within SMEs. We employed a hybrid approach integrating Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) techniques using SmartPLs 4.0 Application; this research analyzes these relationships. For our analysis, data were collected from 305 SME managers operating in Upper Sindh, Pakistan, specifically from major cities like Sukkur, Larkana, Shikarpur, Jacobabad, and Khairpur. The findings reveal that social media applications, big data analytics, IoT applications, and blockchain applications significantly contribute to both economic and social value creation for SMEs. Conversely, AI-enabled applications show no significant impact on value creation. Importantly, economic and social value creation positively correlates with enhanced SME performance. This study enriches our understanding of how digital technologies influence SMEs in Pakistan, particularly in enhancing economic and social value creation. Through advanced methodologies and rigorous analysis, it bridges theory with practical applications in SMEs’ digital transformation.
Pea Pod Valorization: Exploring the Influence of Biomass/Water Ratio, Particle Size, Stirring, and Catalysts on Chemical Platforms and Biochar Production
Authors: Galvis Sandoval Daniel Esteban , Lozano Pérez Alejandra Sophia , Guerrero Fajardo Carlos Alberto
Published: August 2024

This study delves into the valorization of pea pod waste using hydrothermal processes, focusing on optimizing key parameters such as temperature, biomass-to-water ratio, particle size, and catalyst influence. Noteworthy findings include the significant impact of temperature variations on product yields, with 180 °C favoring sugars, HMF, and furfural, while 220 °C and 260 °C lead to distinct platform chemical productions. The utilization of a 1:20 biomass-to-water ratio consistently enhances yields by 10%, underscoring its importance in promoting efficient hydrolysis without excessive product degradation. Furthermore, the investigation into particle size reveals that smaller dimensions, particularly 1 mm particles, improved heat and mass transfer, reduced diffusion barriers, and enhanced digestibility, ultimately boosting overall efficiency in platform chemical production. Moreover, the study sheds light on the role of catalysts in the hydrothermal processes, showcasing the differential impact of acid and basic catalysts on product yields. Acid catalysts demonstrate a notable increase of up to 135.5% in the production of platform chemicals, emphasizing their crucial role in enhancing reaction efficiency. The complex relationship between agitation, temperature, and product formation is elucidated, with experiments revealing varying outcomes based on the presence or absence of agitation at different temperatures. These findings provide valuable insights into optimizing pea pod waste valorization, offering a pathway towards sustainable and efficient conversion of agricultural residues into valuable platform chemicals.
Mechanisms of Charged Particle Motion during Capture by Charged Droplets in Marine Diesel Exhaust
Authors: Yang Lin , Zhou Peilin , Wang Haibin , Mei Ning , Yuan Han
Published: August 2024

Marine diesel engine emissions of particulate matter pose a significant threat to the environment and human health, necessitating the development of new technologies for their reduction. Among all of these clean and sustainable methods, electrohydrodynamic methods, particularly the use of charged droplets in a wet electrostatic scrubber, offer a viable solution for effectively eliminating particulate matter. This study investigates the basic mechanism of wet electrostatic scrubbers, which involves spraying charged droplets to capture small-sized particles and applying electrostatic forces to act on the oppositely charged particles. In this paper, a mathematical model is presented to describe the process of particle motion when being captured by droplets under the effect of an electrostatic field. Additionally, a co-simulation with ANSYS Fluent and MATLAB is conducted to simulate this process. The mathematical calculation results and co-simulation results are basically consistent, and the average error between these two results is 1.5%. Then, the effect of features, such as electrostatic field strength, particle size, and droplet size, on the particle capture process is analyzed. This study mainly discusses the relationship between the electrostatic field, gas flow field, and particle, motion and proposed a theoretical model and numerical simulation method to describe this mechanism relationship. The research findings provide important evidence for the efficient control of ship pollutants, which is of significant importance for the sustainable development of the international shipping industry.
Energy Management Strategy of Fuel Cell Commercial Vehicles Based on Adaptive Rules
Authors: Tao Shiyou , Peng Zhaohui , Zheng Weiguang
Published: August 2024

Fuel cell vehicles have been widely used in the commercial vehicle field due to their advantages of high efficiency, non-pollution and long range. In order to further improve the fuel economy of fuel cell commercial vehicles under complex working conditions, this paper proposes an adaptive rule-based energy management strategy for fuel cell commercial vehicles. First, the nine typical working conditions of commercial vehicles are classified into three categories of low speed, medium speed and high speed by principal component analysis and the K-means algorithm. Then, the crawfish optimization algorithm is used to optimize the back propagation neural network recognizer to improve the recognition accuracy and optimize the rule-based energy management strategy under the three working conditions to obtain the optimal threshold. Finally, under WTVC and combined conditions, the optimized recognizer is used to identify the conditions in real time and call the optimal rule threshold, and the sliding average filter is used to filter the fuel cell output power in real time, which finally realizes the adaptive control. The simulation results show that compared with the conventional rule-based energy management strategy, the number of fuel cell start–stops is reduced. The equivalent hydrogen consumption is reduced by 7.04% and 4.76%, respectively.
Implementation Models of RECs in Public-Private Partnerships: The Distribution of Risks and Benefits among the Participants in the Operation
Authors: Martiniello Laura , Presciutti Andrea
Published: August 2024

The aim of this work is to study possible models for the development of renewable energy communities (RECs) in public private partnership (PPP), aimed at supporting the transition of energy consumption of Italian public administrations towards renewable sources and the sharing of any energy-economic benefits with the community of reference. In particular, the research work analyses the correct balance of interests of the main protagonists of the operation: public concessionary authorities, private Concessionaire and the energy community as the subject that aggregates the interests of the reference community. Moreover, the work aims to highlight the main advantages and risks for each of the actors involved and examine in depth the system of rules and incentives that enhance the actors’ interest in these operations. The economic–financial balance of PPP operations has been investigated in order to quantify the residual economic benefits, after the amortization of the investments and the absorption of management costs. The results highlight that the sustainability of the investments for REC establishment is achieved only under certain conditions with rather modest returns and, in some cases, with the necessity of public capital contributions.
Analysis of the Choice of Cement in Construction and Its Impact on Comfort in Togo
Authors: Tossim Magnouréwa Josiane , Tombar Parfait Altolnan , Banakinao Sinko , Mavunda Célestin Adeito , Sondou Tchakouni , Aholou Cyprien Coffi , Ayité Yawovi Mawuénya Xolali Dany
Published: August 2024

The cement industry, a key player in globalization and urbanization, has replaced traditional shelters with modern buildings, raising environmental concerns. This study examines the use of cement in construction in Togo, its impact on residents’ thermal comfort, and adaptation strategies for sustainable construction and well-being. The research was conducted in Lomé, Atakpamé, and Kara through documentary research, photography, household surveys, interviews, and temperature recordings, involving 646 households and nine interviews. Findings reveal a high use of cement due to availability, ease of use, strength, affordability, and aesthetics. Cement houses are generally less comfortable than earthen houses, particularly in hot climates. Lomé and Kara experience higher discomfort, while Atakpamé has lower discomfort despite cement dominance. Thermal comfort varies more during the day. Residents adopt strategies like sleeping outside, wearing light clothing, installing awnings and false ceilings, planting trees, staying hydrated, taking cool showers, and ventilating rooms.
Emerging Technologies, Sustainable Engineering and Cybersecurity in the Digital Age
Authors: Wynn Martin , Bechkoum Kamal
Published: August 2024

This Special Issue includes papers on various aspects of emerging technologies and cybersecurity, and the theme of sustainable engineering is also interwoven within some of these articles [...]
Digital Transformation of Enterprises to Enhance Sustainability: How Does the Reputation of Digital Applications Influence the Attributes of E-Commerce
Authors: Zaheer Muhammad Asif , Anjum Temoor , Díaz Tautiva Julián Andrés , Heidler Petra
Published: August 2024

Driven by the increase in mobile applications, this study tests a conceptual model which links antecedents of Food delivery applications (FDApps) and their effect on electronic trust (eTrust) and electronic loyalty (eLoyal). By adopting a quantitative approach, we employed a self-administered questionnaire distributed among 501 regular users of FDApps. Partial least squares path modeling was used to test seven hypotheses including direct, moderating, and mediating effects. The findings reveal that eTrust significantly contributes to fostering eLoyal. Additionally, eTrust mediates the relationships between electronic privacy (ePriv), electronic security (eSec), electronic payment (ePay), usability (ULY), electronic innovativeness (eInnov), and eLoyal. Moreover, reputation (RTN) has a positive moderating effect on the relationship between eTrust and eLoyal. The research explores the core elements linked to FDApps, with the goal of improving their business and marketing strategies while increasing consumer satisfaction, thereby boosting repurchase intentions. Therefore, it provides FDApps with insights on the required elements linked to a higher eLoyal.
Unlocking Sustainable Economic Development in Saudi Arabia through the Coffee Industry
Authors: Al Shammre Abdullah Sultan
Published: August 2024

Conducting business nowadays has become much more challenging. Sustainability is a crucial goal for all industries, including the coffee sector, which has emerged as a robust trend in the GCC (Gulf Cooperation Council), establishing Saudi Arabia as one of the pioneers in developing specialty coffee in the Gulf region. Hence, in an attempt to reveal the determinants of sustainability, this study examined the effect of collective individual capabilities (i.e., commitments, communication, connections, and coordination) on entrepreneurial resilience and the subsequent effect on economic, social, and environmental firm performance. This study used a cross-sectional design to collect quantitative data from 213 respondents using non-probability-based convenient sampling. Data were analyzed using SEM-PLS. Findings revealed that commitment has a significant positive effect on entrepreneurial resilience. On the other hand, resilience showed significant positive effects on the economic, social, and environmental performance of coffee shops in Saudi Arabia. Results further confirm a significant mediating effect of entrepreneurial resilience between commitments and economic, social, and environmental performance. Several key insights and policy implications have been drawn from the findings.
Corporate Greenwashing Unexpectedly Caused by the Green Credit Policy: A Comparison between Environmental Sustainability Information Disclosure and Actual Environmental Protection Investment from China’s Listed Companies
Authors: Cao Chaoyu , Chen Qibo , Zhu Lili
Published: August 2024

This paper employs a continuous difference-in-differences model to compare environmental sustainability information disclosure with the real environmental investment amount of listed companies in China before and after the implementation of the Green Credit Guidelines, and it investigates the influence of Green Credit Policy on corporate environmental sustainability information greenwashing. The results show that the Green Credit Policy unexpectedly leads to the greenwashing of environmental sustainability information of listed companies. After the implementation of the Green Credit Policy, the amount of positive environmental information disclosed by listed companies has increased faster than that of corporate environmental investment. Further research on the intermediary effect shows that the reason why the Green Credit Policy leads to greenwashing is that the Green Credit Policy greatly reduces the availability of bank credit for enterprises on the restricted list, and these enterprises will relax the financing constraints caused by the policy in the future and thus adopt the greenwashing behavior. Heterogeneity analysis shows that these effects become smaller in state-owned enterprises and enterprises with large commercial financing scales, as they are less affected by financing constraints of the Green Credit Policy. The policy goal of the Green Credit Policy is to limit the financing of polluting projects and promote the sustainable development of enterprises. However, by comparing the corporate environmental sustainability information disclosure and the actual amount of environmental investment, this paper finds that the Green Credit Policy has unexpectedly led to corporate greenwashing. This result goes against the policy goal of sustainable development. Commercial banks should be more cautious about the environmental sustainability information voluntarily provided by enterprises to correct these distorted results.
Mapping and Assessing Urban Agriculture in São Paulo: Tackling Socio-Economic and Environmental Issues through Nature-Based Solutions
Authors: Bennedetti Luiza Vigne , Ronchi Silvia , Lamano Ferreira Maurício , Lemes de Oliveira Fabiano
Published: August 2024

Inefficient urban transformations pose multifaceted challenges. In this context, urban agriculture (UA) can address environmental and socio-economic issues. Despite recent UA policies in São Paulo, the lack of data hampers comprehensive evaluations, highlighting the need for robust indicators to enhance environmental sustainability. This article assesses São Paulo’s UA potential using an Ecosystem Services (ES)-based approach, combining ES spatial mapping and a producer survey at 49 UA sites. Results show natural habitats and anthropic areas within urbanised land, especially in the south, with high habitat quality scores. Nine ES were identified: food supply, commercialisation, income generation, waste mediation, lifecycle maintenance, soil formation, leisure/social, well-being, and education. Utilising mixed methods, this study provides innovative insights into ES related to UA in São Paulo, offering valuable input for urban policies and planning.
Assessment and Prediction of Health and Agricultural Impact from Combined PM2.5 and O3 Pollution in China
Authors: Luan Ying , Guo Xiurui , Chen Dongsheng , Yao Chang , Tian Peixia , Xue Lirong
Published: August 2024

Combined PM2.5 and O3 pollution in China has caused negative health impacts on residents and reduced crop yields. The quantitative assessment and prediction of these impacts could provide a scientific basis for policy development. This study assessed the nationwide premature mortality, health effects, and crop damage attributable to PM2.5 and O3 pollution in 2019, and projected the associated health and agricultural losses under a business-as-usual (BAU) scenario for 2025. The economic benefits of improving air quality under different policy scenarios, including the 14th Five-Year Plan (FFP), Secondary Standard Limit (SSL), and Primary Standard Limit (PSL), were also explored. The results showed PM2.5 pollution in 2019 resulted in 246,000 all-cause premature deaths and the economic health loss was RMB 196.509 billion. Similarly, O3 pollution caused 186,300 premature deaths and the economic health loss was RMB 155.807 billion. O3 pollution has led to a loss of 28.5241 million tonnes of crop production and an economic loss of RMB 62.268 billion. Compared with 2019, the avoidable premature deaths from PM2.5 under different scenarios in 2025 were 50,600, 43,000, and 200,300 cases, respectively, exceeding the number of avoided premature deaths from O3 pollution. Compared with the BAU, reducing PM2.5 under different scenarios could generate economic benefits of RMB 70.178 billion, RMB 60.916 billion, and RMB 229.268 billion. Furthermore, the FFP scenario outperformed the SSL in mitigating winter wheat production losses caused by O3 pollution. These results provide important scientific support for the development and evaluation of future comprehensive pollution control measures for PM2.5 and O3.
Symmetries of the Electromagnetic Turbulence in a Tokamak Edge
Authors: Montani Giovanni , Moretti Fabio
Published: August 2024

We construct the low-frequency formulation of the turbulence characterizing the plasma in a Tokamak edge. Under rather natural assumptions, we demonstrate that, even in the presence of poloidal magnetic fluctuations, it is possible to deal with a reduced model for turbulence dynamics. This model relies on a single equation for the electric potential from which all the physical turbulent properties can be calculated. The main result of the present analysis concerns the existence of a specific Fourier branch for the dynamics which demonstrate the attractive character of the two-dimensional turbulence with respect to non-axisymmetric fluctuations. The peculiar nature of this instability, affecting the non-axially symmetric modes, is discussed in some detail by recovering two different physical regimes.
Evaluation Method of the Impact of Twin Shield Tunneling Construction on Elevated Bridges: Case Study
Authors: Huang Junzhou , Liu Jizhixian , Guo Kai , Yang Shan , Lu Yani , Wang Ying , Wu Cai
Published: August 2024

In urban metro construction, shield tunneling often needs to pass through building and bridge pile foundations, potentially affecting the stability of existing structures. Therefore, accurately assessing the impact of shield tunneling on bridges and buildings is crucial. This study presents a comprehensive prediction method combining numerical simulation and empirical formulas, taking the underpass project of the Shijiazhuang–Wuhan High-Speed Railway Bridge by Zhengzhou Metro Line 5 as a case study. Three-dimensional numerical model calculations were performed using finite element software to analyze the displacement and stress changes of buildings and tunnel structures at different construction stages, revealing the deformation patterns of buildings adjacent to the tunnel during shield tunneling. In particular, the ground settlement caused by twin-tunnel excavation was compared with Peck’s empirical formula to verify the reliability of the numerical simulation. The results show that twin-tunnel excavation exacerbates the horizontal displacement, uplift, and settlement of the ground, with maximum deformation rates increasing by 7.10%, 20%, and 11.4%, respectively. Comparing the ground deformation results of Peck’s empirical formulas with numerical calculations revealed similar trends in the settlement curves, with a maximum deviation of 6.67%. It can be concluded that using Peck’s empirical formula to calculate ground deformation characteristics complements the limitations of numerical simulations, making the assessment results more reliable. The findings of this study demonstrate that integrating numerical simulation with empirical formulas significantly enhances the reliability of deformation predictions in complex tunneling scenarios. This research not only offers a comprehensive safety assessment method for shield tunneling construction but also provides valuable guidance for the design and construction of similar projects, serving as a theoretical reference for future engineering endeavors.
Systematic Approach to Academic Teamwork as Basis of Effective Cooperation
Authors: Holubčík Martin , Soviar Jakub , Rechtorík Miroslav
Published: August 2024

A systematic approach to teamwork is important for clarifying the behavior of actors in a cooperative environment. The cooperative game as system steps is most often shown in teamwork. The aim of this article is to present the results of this research, in which two rounds of an experiment were carried out and then an interview was held to clarify the results. We pointed out academic teamwork. This article focuses on the research of team cooperation under specified conditions of an experiment on students, which were divided into organized and unorganized teams and assigned two different tasks. Within this research, team cooperation, quality of team results, and conflicts in teams were observed. Detailed results are presented in this article. The results of this research mainly present the different quality of team outputs, which is caused by the different abilities of the members of individual groups and not by the different form of team formation. The main result is also that knowledge and training on how to work together in a team had a positive impact on team results. From a managerial point of view, we see room for establishing an exact strategic procedure in the form of cooperation moves for a positive result of team cooperation in the business environment we are presenting.