bluuebunny/crossref_metadata_2025 huggingface dataset:
The Study Design of a Double-Action Plate Vacuum Pump
Authors: Medvedskyi Oleksandr , Bleizgys Rolandas , Čėsna Jonas , Domeika Rolandas , Kukharets Savelii , Hrudovij Roman
Published: August 2024

Rotary plate vacuum pumps have become widely used as a source of vacuum for milking systems. The main features of a plate vacuum pump include design simplicity, high efficiency, low cost and adaptability to climatic conditions. A plate vacuum pump requires the improvement of specific performance indicators. This refers to the indicator of specific productivity and specific energy intensity. It is possible to improve the vacuum pump by optimizing the design parameters and technological models of operation. The known studies allow the establishment of rational geometric parameters, the number of plates, the ratio of the main dimensions and eccentricity. However, the problem of reducing the degree of uneven air pumping from the vacuum system needs a scientific solution. The use of a vacuum cylinder in a vacuum line of an increased diameter partially solves the problem of vacuum pressure fluctuations. But such a decision requires additional material costs. In addition, the power of a vacuum pump increases to compensate for the pressure losses. In this study, the authors proposed the design of a double-action plate vacuum pump. It was proven that the simultaneous operation of combined rotors with plates shifted by 45° decreased the degree of air pumping by 7.8%. The research results indicated that the productivity of the developed vacuum pump increased by 13.6%. The drive power increased by 12%, and the specific energy intensity was 20% lower than that of vacuum pumps with similar geometric parameters. The relationship between rational kinematic and design parameters of a double-action vacuum pump was established.
Response Surface Methodology Applied to Cyanobacterial EPS Production: Steps and Statistical Validations
Authors: Rodrigues Filipa , Mendonça Ivana , Faria Marisa , Gomes Ricardo , Pinchetti Juan Luis Gómez , Ferreira Artur , Cordeiro Nereida
Published: August 2024

Understanding the impact of variables involved in soluble-extracellular polymeric substance (S-EPS) production processes is crucial for reducing production costs and enhancing sustainability. Response surface methodology (RSM) provides essential tools that assist in developing predicted interactions among process variables for both industrial and non-industrial applications. The present study offers a simple and systematic demonstration of RSM capabilities, focusing on maximizing efficiency and minimizing production costs of S-EPS produced by Cyanocohniella rudolphia. RSM was employed to (1) design the production setup; (2) fit the collected data into a second-order polynomial model; (3) statistically evaluate the model’s validity and the significance of the involved variables; and (4) identify and optimize production variables to enhance output and reduce costs. Focused on four key variables, each at three levels, RSM designed 25 distinct S-EPS production conditions, each with three replicates. Statistical analysis identified the most significant variables affecting S-EPS production as the culture medium/wet biomass ratio, production days, and nitrogen concentration. The model’s validation demonstrated a strong correlation between the predicted and experimental values, with S-EPS production ranging from 70.46 to 228.65 mg/L and a maximum variation of 11.6%. This study demonstrates the effectiveness of RSM in optimizing S-EPS production, with the developed model showing a strong correlation between the variables and the response. The RSM model offers a promising approach for the bioprocessing industry, enhancing productivity and efficiency, minimizing costs, and leading to sustainable, cost-effective practices.
Experimental Study on the Mechanical Properties of Nano-Silicon-Modified Polyurethane Crack Repair Materials
Authors: Fan Bingsen , Li Xiaolong , Xu Shengjie , Zhong Yanhui , Zhang Bei , Liu Xiaofeng
Published: August 2024

This study aims to solve the problem of dynamic crack repair in concrete. Although conventional polyurethane has good strength, its tensile and shear properties are poor. It was found that nano-silicon had an overall enhancing effect on the mechanical properties of polyurethane; therefore, five sets of tests with different dosages (0%, 2%, 5%, 7.5%, and 10%) were designed. The compressive, tensile, and shear mechanical properties of nano-silicon-modified polyurethanes were tested by compression, tensile, and straight shear tests, and the microscopic appearance of the materials was observed by scanning electron microscopy. The results showed that nano-silicon could enhance the mechanical properties of polyurethane. The best filling effect on polyurethane was achieved at a dosage of 5%, which increased the compressive, tensile, and shear strengths by 29.4%, 257.6%, and 202.1%, respectively, compared with the substrate. The compressive and tensile moduli in the small strain range were enhanced by 268.5% and 511.8%, respectively. After exceeding 5%, the mechanical properties of the materials decreased due to the enhanced nanoparticle agglomeration effect, which led to the appearance of voids inside the materials. The comprehensive analysis shows that nano-silicon can better enhance the mechanical properties of polyurethane with an optimal dosage of 5%, which is stronger relative to other repair materials and does not require time maintenance.
Study on Multi-Objective Optimization of Construction Project Based on Improved Genetic Algorithm and Particle Swarm Optimization
Authors: Hu Weicheng , Zhang Yan , Liu Linya , Zhang Pengfei , Qin Jialiang , Nie Biao
Published: August 2024

Construction projects require concurrent consideration of the three major objectives of construction period, cost, and quality. To address the multi-objective optimization issues of construction projects, mathematical models of construction period, quality, and cost are established, respectively, and multi-objective optimization models are constructed for different construction objectives. A hybrid optimization method combining an improved genetic algorithm (GA) with a time-varying mutation rate and a particle swarm algorithm (PSO) is proposed to optimize construction projects, which overcomes the shortcomings of the original GA and improves the global optimality and stability of results. Various construction projects were considered, and different construction objectives were analyzed individually. Finally, an uncertainty analysis is developed for the proposed GA-PSO algorithm and compared with GA and PSO. The results indicate that the proposed hybrid approach outperforms the PSO and GA algorithms in providing a better and more stable multi-objective optimized construction solution, with performance improvements of 4.3–8.5% and volatility reductions of 37.5–64.4%. This provides a reference for the optimal design of wind farms, buildings, and other construction projects.
A PLC-Embedded Implementation of a Modified Takagi–Sugeno–Kang-Based MPC to Control a Pressure Swing Adsorption Process
Authors: Mendes Teófilo Paiva Guimarães , Ribeiro Ana Mafalda , Schnitman Leizer , Nogueira Idelfonso B. R.
Published: August 2024

The paper presents a case study that applies a model predictive control (MPC) approach in a Micro850 programmable logic controller (PLC) to a laboratory pressure swing adsorption (PSA) process used for separating gas mixtures of CO2 and CH4. PLC is an industrial hardware characterized by its robustness to hazardous environments and limited computational capacities, which poses computational challenges for MPC implementation. This paper’s main contribution is the application of the modified Takagi–Sugeno–Kang-based MPC (MTSK-MPC) algorithm to this PSA unit, which provides features to investigate and implement feasible MPC designs in PLCs. The investigation consists of a sensitivity analysis of how some design parameters influence the PLC memory and the MPC implementation and a comparative evaluation of the computational processing from different MPC algorithms and simulations. The comparison comprises software-in-the-loop simulations with three algorithms in the PC: an implicit MPC, an explicit MPC, and the MTSK-MPC. Additionally, it includes a hardware-in-the-loop simulation with the implemented MTSK-MPC in Micro850. The results show that the MPC algorithms achieve close performance, tracking setpoint changes and rejecting output disturbances, with the MTSK-MPC presenting the lower processing time among the MPCs in the PC. The study concludes that the implementation of MTSK-MPC in the Micro850 is feasible.
Sensitivity Analysis of Injection Mass Flow to the Inlet Orifice Radius of a GDI Injector Nozzle
Authors: Li Xinhai , Guo Jian , Shen Bing , Zhang Peijie
Published: August 2024

It is more difficult to accurately control the cycle fuel mass of a gasoline engine as the injection pressure increases, and the difference in the injection mass flow will become intense due to the machining error of GDI injector structural parameters. In order to guarantee the uniform cycle fuel mass of the GDI injector, this paper investigates the effect of the inlet orifice on the injection mass flow and the sensitivity of inner-flow characteristics in the GDI injector nozzle to the inlet orifice radius by the single factor analysis method. The results show that the local resistance of the injector nozzle will decrease and the discharge coefficient will rise due to the increase in the inlet orifice radius; the sensitivity of cycle fuel mass to the inlet orifice radius is most strong at r = 0.02 mm, and its accuracy should be improved when grinding the chamfer of the nozzle inlet; the sensitivity coefficient of the inlet orifice radius will rise up, and the machining accuracy of the inlet orifice radius should be improved as there is an increase in injection pressure.
Green Roof Systems within the Framework of a Circular Economy: A Scoping Review
Authors: Cervantes-Nájera Ana Laura , Martínez-Rodríguez María-Concepción , Campos-Villegas Lorena Elizabeth , Bello-Yañez Xochitl Virginia , Brenneisen Stephan
Published: August 2024

Green roofs are nature-based solutions with multiple benefits for the urban environment, but they have specific polymeric components in their structure that cause a carbon footprint. This scoping review highlights the technological advance in reusing or recycling alternative materials in green roof structures worldwide, making them a part of the circular economy (CE), and the search strategies to promote them to find whether there is a relation between them. The data were collected using the Web of Science from 2012 to 2022; we also used the Bibliometrix package to analyse research development by country and the development of the topic by author’s keywords. We selected 68 open-access articles from twenty-four countries and reviewed the grey literature from the top five productive countries on this topic. Our findings show that CE strategies are performed nationally, while the green roof promotion ones are concentrated at the city level. The theme co-occurrence analysis showed that the substrate is the primary layer used with alternative materials, and the three materials most applied for it are rubber, biochar, and hydrogel. It was found that the primary purpose of optimising green roofs with alternative materials is to increase the ecosystem services they provide and indirectly function within the circular economy; still, there is a gap in estimating the environmental impact of doing this as a potential CE activity.
Sensing-Efficient Transmit Beamforming for ISAC with MIMO Radar and MU-MIMO Communication
Authors: Liu Huimin , Li Yong , Cheng Wei , Dong Limeng , Yan Beiming
Published: August 2024

We focus on an integrated sensing and communication (ISAC) system—a single platform equipped with multiple antennas transmitting a waveform to detect targets and communicate with downlink users. Due to spectrum sharing between multiple-input–multiple-output (MIMO) radar and multiuser MIMO (MU-MIMO) communication, beamforming is becoming increasingly important as a technique that enables the creation of directional beams. In this paper, we propose a novel joint transmit beamforming design scheme that employs a beam pattern approximation strategy for radar sensing and utilizes rate-splitting for multiuser communication offering advanced interference management strategies. The optimization problems are formulated from both radar-centric and trade-off viewpoints. First, we propose a radar-centric beamforming scheme to achieve sensing efficiency through beam pattern approximation, while requiring the fairness signal-to-interference-plus-noise ratio (SINR) to be higher than a given threshold to guarantee a minimal level of communication quality, while the obtained performance for the communication system is limited in this scheme. To address this problem, we propose a beamforming design scheme from a trade-off viewpoint that flexibly optimizes both sensing and communication performances with a regularization parameter. Finally, we propose a partial rate-splitting-based beamforming design method aimed at maximizing the effective sensing power, with the constraint of a minimal sum rate for downlink users. Numerical results are provided to assess the effectiveness of all proposed schemes.
Accelerated Atmospheric to Hydrological Spread of Drought in the Yangtze River Basin under Climate
Authors: Zhang Chengyuan , Han Zhiming , Wang Shuo , Wang Jiankun , Cui Chenfeng , Liu Junrong
Published: August 2024

Persistent droughts pose a threat to agricultural production, and the changing environment worsens the risk of drought exposure. Understanding the propagation of drought in changing environments and assessing possible impact factors can help in the early detection of drought, guiding agricultural production practices. The current study cannot reflect the propagation status of drought to the total terrestrial hydrological drought, so this work creatively investigated the atmospheric to hydrological drought propagation time in the Yangtze River Basin under the dynamic and static perspectives based on the Standardized Precipitation Evapotranspiration Index and the Terrestrial Water Storage Anomalous Drought Index, fine-tuned the time scale to the seasonal scale, and explored the contributing capacity of the variable interactions. The results show that: (1) under the dynamic perspective, while the propagation time is decreasing in the annual scale, the spring season shows the opposite trend; and (2) large variability exists in the timing of drought propagation at spatial scales, with elevation playing the most important influential role, and bivariate interactions contributing stronger explanations compared to single variables. This study highlights the importance of considering the impact of variable interactions and contributes to our understanding of the response of secondary droughts to upper-level droughts, providing valuable insights into the propagation of droughts to total terrestrial hydrologic drought.
A Simple Path to the Small Perturbation Method for Scattering from Slightly Rough Dielectric Surfaces
Authors: Iodice Antonio , Imperatore Pasquale
Published: August 2024

We propose a perturbative method to compute electromagnetic scattering from slightly rough dielectric surfaces, which leads to the same result as the usual Small Perturbation Method (SPM) in a surprisingly simple way. The proposed method is based on three pillars: the volumetric perturbative approach, the reciprocity theorem, and a proper approximation of the electric field within the perturbation volume, that we name Internal Field Approximation (IFA). The proposed new mathematical derivation of the SPM turns out to be much simpler and more concise than the classical one. In addition, being based on a volumetric perturbation approach, it has the potential of dealing in future with surface and volume scattering within a unitary framework, which is useful in modelling scattering from, e.g., vegetated soil, snow-covered terrain, and inhomogeneous soils. Therefore, although the presented result is mainly theoretical, it can have important applications in remote sensing.
A Multi-Scale Mask Convolution-Based Blind-Spot Network for Hyperspectral Anomaly Detection
Authors: Yang Zhiwei , Zhao Rui , Meng Xiangchao , Yang Gang , Sun Weiwei , Zhang Shenfu , Li Jinghui
Published: August 2024

Existing methods of hyperspectral anomaly detection still face several challenges: (1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; (2) the ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; and (3) current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind-spot network based on multi-scale blind-spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial–spectral joint module and a background feature attention mechanism to enhance the interaction between spatial–spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that combines pixel shuffle down-sampling (PD) with spatial spectral joint screening. This approach addresses anomalous identity mapping and enables finite-scale mask convolution for better detection of targets at various scales. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the-art methods.
A Survey on IoT Application Architectures
Authors: Dauda Abdulkadir , Flauzac Olivier , Nolot Florent
Published: August 2024

The proliferation of the IoT has led to the development of diverse application architectures to optimize IoT systems’ deployment, operation, and maintenance. This survey provides a comprehensive overview of the existing IoT application architectures, highlighting their key features, strengths, and limitations. The architectures are categorized based on their deployment models, such as cloud, edge, and fog computing approaches, each offering distinct advantages regarding scalability, latency, and resource efficiency. Cloud architectures leverage centralized data processing and storage capabilities to support large-scale IoT applications but often suffer from high latency and bandwidth constraints. Edge architectures mitigate these issues by bringing computation closer to the data source, enhancing real-time processing, and reducing network congestion. Fog architectures combine the strengths of both cloud and edge paradigms, offering a balanced solution for complex IoT environments. This survey also examines emerging trends and technologies in IoT application management, such as the solutions provided by the major IoT service providers like Intel, AWS, Microsoft Azure, and GCP. Through this study, the survey identifies latency, privacy, and deployment difficulties as key areas for future research. It highlights the need to advance IoT Edge architectures to reduce network traffic, improve data privacy, and enhance interoperability by developing multi-application and multi-protocol edge gateways for efficient IoT application management.
Revolutionizing Grain and Particle Size Measurement in Metals: The Role of Sensor-Assisted Metallographic Image Analysis
Authors: Shirsat Tushar , Bairagi Vinayak , Buchade Amar , Boonchieng Ekkarat
Published: August 2024

Metallographic image analysis is vital in the field of metal science due to its potential to automate the sensing process for grain and particle size estimation. To ensure the good quality and reliability of metal products, analysis of the integrity of metallic components is required. In contemporary manufacturing processes, microscopic analysis is a crucial step, mainly when complex systems like gearboxes, turbines, or engines are assembled using various components from multiple suppliers. A final product’s quality, durability, and lifespan are determined via the quality analysis of properties of a material with proper tolerances. A flaw in a single component can cause the breakdown of the entire finished product. To ensure the good quality of a material, micro-structural analysis is necessary, which includes the routine measurement of inclusions. The particle and grain sizes of particulate samples are the most crucial physical characteristics of metals. Their measurement is routinely conducted across various industries, and they are frequently considered essential parameters in the creation of many products. This paper discusses the role of sensors in enhancing the accuracy and efficiency of metallographic image analysis, as well as the challenges and limitations associated with this technology. The paper also highlights the potential applications of sensor-assisted metallographic image analysis in various industries, such as aerospace, automotive, and construction. The paper concludes by identifying future research directions for this emerging field, including the development of more sophisticated algorithms for grain and particle size estimation, the integration of multiple sensors for more accurate measurements, and the exploration of new sensing modalities for metallographic image analysis.
Optimal Path Planning Algorithm with Built-In Velocity Profiling for Collaborative Robot
Authors: Szczepanski Rafal , Erwinski Krystian , Tejer Mateusz , Daab Dominika
Published: August 2024

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm.
U-TAG: Electromagnetic Wireless Sensing System for Robotic Hand Pre-Grasping
Authors: Gharibi Armin , Costa Filippo , Genovesi Simone
Published: August 2024

In order to perform complex manipulation and grasp tasks, robotic hands require sensors that can handle increasingly demanding functionality and degrees of freedom. This research paper proposes a radiofrequency sensor that uses a wireless connection between a probe and a tag. A compact and low-profile antenna is mounted on the hand and functions as a probe to read a printed passive resonator on the plastic object being targeted, operating within a pre-touch sensing range. The grasping strategy consists of four stages that involve planar alignment in up-to-down and left-to-right directions between the probe and tag, the search for an appropriate distance from the object, and rotational (angular) alignment. The real and imaginary components of the probe-input impedance are analyzed for different orientation strategies and positioning between the resonator on the object and the probe. These data are used to deduce the orientation of the hand relative to the target object and to determine the optimal position for grasping.
ANN-Based Bridge Support Fixity Quantification Using Thermal Response Data from Real-Time Wireless Sensing
Authors: Bhandari Prakash , Jang Shinae , Malla Ramesh B. , Han Song
Published: August 2024

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge failures are associated with support or joint damages, indicating the importance of bridge support. Indeed, bridge support affects the performance of both the substructure and superstructure by maintaining the load path and allowing certain movements to mitigate thermal and other stresses. The support deterioration leads to a change in fixity in the superstructure, compromising the bridge’s integrity and safety. Hence, a reliable method to determine support fixity level is essential to detecting bearing health and enhancing the accuracy of the bridge health monitoring system. However, such research is lacking because of its complexity. In this study, we developed a support fixity quantification method based on thermal responses using an Artificial Neural Network (ANN) model. A finite element (FE) model of a representative highway bridge is used to derive thermal displacement data under different bearing stiffnesses, superstructure damage, and thermal loading. The thermal displacement behavior of the bridge under different support fixity conditions is presented, and the model is trained on the simulated response. The performance of the developed FE model and ANN was validated with field monitoring data collected from two in-service bridges in Connecticut using a real-time Wireless Sensor Network (WSN). Finally, the support stiffnesses of both bridges were predicted using the ANN model for validation.
A Review on Soft Error Correcting Techniques of Aerospace-Grade Static RAM-Based Field-Programmable Gate Arrays
Authors: Wang Weihang , Li Xuewu , Chen Lei , Sun Huabo , Zhang Fan
Published: August 2024

Aerospace-grade SRAM-based field-programmable gate arrays (FPGAs) used in space applications are highly susceptible to single event effects, leading to soft errors in FPGAs. Additionally, as FPGAs scale up, the difficulty of correcting soft errors also increases. This paper proposes that performing soft error sensitivity analysis on FPGAs can help target the more sensitive areas for detection and correction, thereby improving the efficiency of soft error repair. Firstly, in accordance with the dual-layer architecture of SRAM-based FPGAs, methods for the soft error sensitivity analysis of FPGA application layer resources and configuration bitstreams are reviewed. Subsequently, based on the analysis results, it also covers corresponding application layer memory scrubbing and configuration scrubbing techniques. A prospective look at emerging soft error mitigation technologies is discussed at the end of this review, supporting the development of highly reliable aerospace-grade SRAM-based FPGAs.
Object Detection and Information Perception by Fusing YOLO-SCG and Point Cloud Clustering
Authors: Liu Chunyang , Zhao Zhixin , Zhou Yifei , Ma Lin , Sui Xin , Huang Yan , Yang Xiaokang , Ma Xiqiang
Published: August 2024

Robots need to sense information about the external environment before moving, which helps them to recognize and understand their surroundings so that they can plan safe and effective paths and avoid obstacles. Conventional algorithms using a single sensor cannot obtain enough information and lack real-time capabilities. To solve these problems, we propose an information perception algorithm with vision as the core and the fusion of LiDAR. Regarding vision, we propose the YOLO-SCG model, which is able to detect objects faster and more accurately. When processing point clouds, we integrate the detection results of vision for local clustering, improving both the processing speed of the point cloud and the detection effectiveness. Experiments verify that our proposed YOLO-SCG algorithm improves accuracy by 4.06% and detection speed by 7.81% compared to YOLOv9, and our algorithm excels in distinguishing different objects in the clustering of point clouds.
User-Centered Evaluation of the Wearable Walker Lower Limb Exoskeleton; Preliminary Assessment Based on the Experience Protocol
Authors: Camardella Cristian , Lippi Vittorio , Porcini Francesco , Bassani Giulia , Lencioni Lucia , Mauer Christoph , Haverkamp Christian , Avizzano Carlo Alberto , Frisoli Antonio , Filippeschi Alessandro
Published: August 2024

Using lower limb exoskeletons provides potential advantages in terms of productivity and safety associated with reduced stress. However, complex issues in human–robot interactions are still open, such as the physiological effects of exoskeletons and the impact on the user’s subjective experience. In this work, an innovative exoskeleton, the Wearable Walker, is assessed using the EXPERIENCE benchmarking protocol from the EUROBENCH project. The Wearable Walker is a lower-limb exoskeleton that enhances human abilities, such as carrying loads. The device uses a unique control approach called Blend Control that provides smooth assistance torques. It operates two models simultaneously, one in the case in which the left foot is grounded and another for the grounded right foot. These models generate assistive torques combined to provide continuous and smooth overall assistance, preventing any abrupt changes in torque due to model switching. The EXPERIENCE protocol consists of walking on flat ground while gathering physiological signals, such as heart rate, its variability, respiration rate, and galvanic skin response, and completing a questionnaire. The test was performed with five healthy subjects. The scope of the present study is twofold: to evaluate the specific exoskeleton and its current control system to gain insight into possible improvements and to present a case study for a formal and replicable benchmarking of wearable robots.
Lung Involvement in Systemic Sclerosis—From Pathogenesis to Prediction
Authors: Kaouri Issa El , Bakopoulou Konstantina , Padjen Ivan , Lazarov Velik , Sdralis Paraskevas Panagiotis , Velikova Tsvetelina , Shumnalieva Russka
Published: August 2024

Systemic sclerosis (SSc) is a rare, multifactorial autoimmune disease characterized by widespread vascular damage and fibrosis. Pulmonary involvement is a significant manifestation of SSc, contributing to considerable morbidity and mortality. Therefore, identifying reliable biomarkers is of the utmost importance. This review explores emerging biomarkers to enhance diagnostic accuracy, prognostic assessment, and disease monitoring in SSc lung involvement. We discuss recent findings in immunological biomarkers, inflammatory indicators, and other parameters that can function as potential diagnostic and prognostic tools. A comprehensive understanding of these biomarkers could result in earlier and more accurate detection of pulmonary complications in SSc, aiding in timely intervention. Furthermore, we explore the advances in disease monitoring through innovative biomarkers, focusing on their roles in disease activity and treatment response. Integrating these novel biomarkers into current clinical practice and therapeutic protocols through clinical trials can revolutionize the management of SSc-related lung disease, ultimately improving patient outcomes and quality of life.
Applying a Holistic Injury Prevention Approach to Elite Triathletes
Authors: Arévalo-Chico Héctor , Sellés-Pérez Sergio , Cejuela Roberto
Published: August 2024

(1) Background: Studies on injury prevention programs are lacking for triathletes. The aim of the present study was to describe the results of a holistic (injury) training prevention program (HITP), based on training load control and strength training, in elite triathletes. (2) Methods: The study was conducted over 2021–2023 and involved 18 males and 10 females from the same training group. The HITP itself included various methods of fatigue monitoring, strength training focused on the prevention of overuse injuries (OIs), cycling skills training, and recovery strategies. The total number and type of injuries that were sustained, subsequent training/competition absence time, and injury incidence were determined. (3) Results: Twenty-four injuries were recorded over all three seasons, i.e., 0.65 injuries per 1000 h of training and competition exposure. Fourteen injuries were traumatic injuries (TIs) and ten were OIs. Of the OIs, four were of minimal severity, two were mild, three were moderate, and one was severe (accounting for 1–3, 4–7, 8–28, and >28 days of training absenteeism, respectively). A total of 46.4% of the participants did not present any type of injury and 71,4% did not incur any OIs. Average absenteeism was 17.3 days per injury. (4) Conclusions: The HITP design and implementation resulted in low OI and severe injury incidence. Due to their unpredictable nature, the number of TIs was not reduced. The TIs were suffered more frequently by men. Women are more likely to suffer from OIs, so it is particularly important to prevent OIs in women.
A Comprehensive Research Agenda for Integrating Ecological Principles into the Transportation Sector
Authors: Corazza Maria Vittoria
Published: August 2024

The paper examines the integration of novel Transportation Ecology principles into transit operations, aiming to address the environmental impacts associated with surface services in urban areas and with the purpose of creating a comprehensive agenda for integrating ecological principles into transit planning and management. The research problem is to quantify the tangible benefits for transit operators, particularly in the context of mitigating wildlife-vehicle collisions and improving overall operational efficiency as a motivator for transit managers to adopt Transportation Ecology principles. The study design, after analyzing the regulatory requirements, implements scenario-based methodology, utilizing data from an average Italian bus fleet to estimate potential monetary savings and benefits. Key parameters, such as maintenance costs, insurance premiums, and collision-related expenses, are analyzed to provide a realistic assessment of the economic advantages of implementing Transportation Ecology measures. The findings reveal that significant cost reductions can be achieved by minimizing accidents involving wildlife, alongside other operational improvements. The scenario demonstrates that even a small fleet, when adopting these principles, can yield substantial financial benefits, thereby making a compelling case for broader implementation. The paper concludes that while the qualitative nature of the analysis necessitates conservative estimates, the results underscore the value of incorporating ecological considerations into transit planning and management. These insights are vital for transit operators and policymakers seeking to balance environmental sustainability with operational profitability and protect urban ecosystems. This also implies the need for a more holistic and interdisciplinary approach to transportation planning and management.
An Approach to Analyzing Migration Flow and House Price Data: Exploring Spatial Linkages between Flow, Price, Distance, Age at the Local Level
Authors: Le To Quyen , Doan Le-Vinh-Lam , Le Hoang Nam
Published: August 2024

This paper is intended to elucidate the efficacy of employing a Geographic Information Systems (GIS) methodology to investigate the spatial interrelations among migration flows, housing prices, age demographics, and geographical distances. Utilizing data on migration flows and housing prices from Greater Manchester, UK, in 2011, in this study, we performed statistical analyses and generated a suite of visual mappings to illustrate the spatial dynamics between migration patterns, distances, age brackets, and the segmentation of migration destinations by housing price tiers. The findings reveal pronounced spatial associations among migration destinations, underscored by the interplay of housing price levels with age categories, distance intervals, or the origination points tied to specific housing price ranges. This GIS-based analytical framework offers policy-makers and urban planners a powerful tool for discerning distinct migration trends and locales, thereby facilitating more informed decision-making processes.
Driving Factors of Shading Adjustment Behavior Based on Social Cognitive Theory and Structural Equation Modeling
Authors: Yuan Jinyu , Yao Jian , Zheng Rongyue
Published: August 2024

Research on occupant behavior is a crucial aspect of building energy-saving research. Among them, the shading adjustment behavior in buildings, which occurs frequently during building usage, significantly impacts building energy consumption. Due to the randomness of shading adjustment behavior and the complexity of its motivations, interdisciplinary research is required in this field. In order to better analyze the driving factors of shading adjustment behavior and thus provide a reference for promoting building energy-saving technologies and strategies, this paper employs novel methods for research. We establish a structural equation model based on social cognitive theory, then design a questionnaire and collect data. We utilize structural equation modeling to examine the interrelationships between different dimensions. We ultimately determine the connections between different driving factors of shading adjustment behavior. The results show that whether in the behavior of activating or deactivating shading, environmental factors have a positive correlation with personal factors, and both environmental and personal factors significantly influence shading adjustment behavior. Furthermore, within environmental factors, social environmental factors also significantly affect shading adjustment behavior. Lastly, comparing the behaviors of activating and deactivating shading, the impacts of light and thermal environmental factors on shading adjustment behavior show certain differences.
Is It Necessarily Better for More Commuters to Share a Vehicle?
Authors: Wang Zhen , Chen Haiyun , Zhu Ting , Huo Jiazhen
Published: August 2024

Increasing private car ownership has congested urban roads and made parking more difficult, especially during the morning commute. Carpooling offers a new way to address these problems. This paper studies the dynamic departure patterns for both regular and carpooling vehicles with parking space constraints in the morning commute without the carpool lane. The results suggest that as the parking fee of the central cluster increases, the earliest time for the two types of vehicles to enter the central cluster is delayed. The increase in the proportion of regular vehicles delays the earliest time for carpooling vehicles to enter the central cluster. More commuters sharing a vehicle in the morning commute is not necessarily better. Only a reasonable level of carpooling can reduce the peak time and unnecessary time consumption on the road and effectively promote the reduction in parking fees, commuters’ travel costs, and other societal transportation costs. This research gives practical guidance and suggestions on formulating a reasonable parking fee and controlling a reasonable carpooling level.
A Review of the Most Concerning Chemical Contaminants in Drinking Water for Human Health
Authors: Jurczynski Yasemin , Passos Robson , Campos Luiza C.
Published: August 2024

Chemical contaminants in drinking water, including arsenic, nitrate, and fluoride, pose significant health risks, particularly in low-income countries with inadequate water management infrastructure. This study aims to identify the most hazardous chemical contaminants, evaluate global drinking water quality, and assess health impacts based on a comprehensive literature review guided by the PRISMA method. The findings revealed that arsenic concentrations in Romania, Pakistan, and India exceed the WHO and USEPA safety thresholds, with maximum levels reaching 130.3 µg/L. Nitrate levels in India and Morocco were found to be as high as 844 mg/L and 270.1 mg/L, respectively, far surpassing safety standards. Fluoride contamination in Pakistan reached 30 mg/L, well above the recommended limits. These contaminants are primarily sourced from industrial effluents, agricultural runoff, and improper waste disposal. The study highlights significant regional disparities, with 67% of reports from low-income countries and 88% of contamination cases linked to groundwater sources. The results underscore the urgent need for improved monitoring, stricter regulations, and effective management strategies to mitigate health risks, particularly in vulnerable populations such as infants and children. Governments and international bodies must prioritise addressing chemical contamination to protect public health.
Smart Mobility in German-Speaking Cities and Sarajevo: Differences, Challenges, Opportunities, and Lessons for Implementation Success
Authors: Situm Mario , Sorrentino Giuseppe , Mangafić Jasmina , Lazović-Pita Lejla
Published: August 2024

As urbanization increases, cities face challenges related to sustainability and mobility. This study, conducted through interviews in March and April 2023, investigates the implementation of smart mobility solutions in German-speaking cities (Austria, Germany, and Switzerland) and Sarajevo, Bosnia and Herzegovina, through a comparative analysis of stakeholder perspectives. Using semi-structured interviews with 25 experts, we explored the opportunities and challenges associated with smart mobility in these distinct socio-economic contexts. The findings reveal significant differences in technological advancement, infrastructural support, and financial resources, providing valuable insights for policymakers and urban planners. This study contributes to the existing literature by bridging the gap between developed and developing regions, offering practical recommendations for achieving sustainable urban transportation systems.
Improving the Regional Science and Technology Innovation Ability under the Background of Data Element Marketization: Insights from the Yunnan Province
Authors: Wang Jinli , Feng Libo
Published: August 2024

Under the background of the marketization of data elements, enhancing regional scientific and technological innovation capabilities has become the main driving force for urban sustainable development. It contributes to the sustainable development of the region. Based on the data of the Yunnan statistical yearbook, we built a regional science and technology innovation index system from three dimensions of science and technology innovation input, output, and benefit. Firstly, the entropy method is used to obtain the weight of each index. Secondly, TOPSIS is used to evaluate the 36 index data of each prefecture. Finally, the scores and rankings of the scientific and technological innovation ability of each prefecture are obtained. The research can effectively find the advantages and disadvantages of scientific and technological innovation in the Yunnan Province, provide data support and decision support for regional economic development, and play an important role in promoting the level of technological innovation in modern industries. At the same time, research projects can help various regions allocate various technological innovation resources reasonably, enhance their original innovation capabilities, lead the development of strategic emerging industries and future industries, and accelerate the formation of new quality productivity.
Post-Trauma Fetal Care Using Computational Analysis in Prenatal Surgical Guidance
Authors: Ashkezari Atieh Dehghani , Bekbolatova Molly , Mayer Jonathan , Devine Timothy , Nio Kusuma , Chan-Akeley Rosalyn , Toma Milan
Published: August 2024

The purpose of this research is to explore the biomechanical consequences of maternal injuries on fetal movements. Additionally, the research aims to comprehend the relationship between these injuries and fetal movement within the amniotic sac and to understand the extent to which the amniotic fluid can provide protection during severe injuries. The focus is on the potential impact these injuries could have on surgical procedures and preventative strategies. Using advanced computational simulations, the study investigates how various maternal injuries can influence the behavior of amniotic fluid and the subsequent stress exerted on fetal development. The findings suggest that maternal injuries can induce stress, primarily affecting the posterior regions of the fetus and the umbilical cord, depending on the boundary and initial conditions. This stress is associated with fetal displacement within the amniotic sac. While the amniotic fluid provides a certain level of protection, its limitations become apparent during severe injuries. These insights have implications for the field of surgery, particularly fetal procedures. They underscore the need for improved protective measures and the development of personalized obstetric and neonatal care strategies. Moreover, the study highlights the potential of computational simulations in aiding surgeons. These simulations can provide a more accurate understanding of the critical areas to focus on during surgical procedures, thereby enhancing the precision and safety of these operations.
C5 Palsy after Cervical Disc Arthroplasty: Two Case Reports and Literature Review
Authors: Carballo Cuello César , Flores Milan Gabriel , Baerga Nicolas , Greenberg Mark , Alikhani Puya , Hayman Erik
Published: August 2024

Cervical disc arthroplasty (CDA), also known as cervical artificial disc replacement, has become an alternative for the treatment of cervical spondylosis with radicular or myelopathic symptoms. However, there is limited literature regarding its complications and outcomes. We present two cases that developed C5 nerve palsy (C5P) following two-level CDA. Both patients presented with C5P in a delayed fashion with 2/5 and 1/5 deltoid weakness on MMT, respectively. Postoperative imaging did not demonstrate any spinal cord compression and symptoms resolved (5/5 on MMT) with conservative management in both cases. To our knowledge, these represent the first cases of delayed C5P after cervical arthroplasty reported in the literature. We performed a literature review to further enhance our knowledge regarding CDA. By understanding its pathophysiology and response to treatment, these cases can serve as a guide for spine surgeons and improve their future outcomes.
Enhancing NSGA-II Algorithm through Hybrid Strategy for Optimizing Maize Water and Fertilizer Irrigation Simulation
Authors: Du Jinyang , Liu Renyun , Cheng Du , Wang Xu , Zhang Tong , Yu Fanhua
Published: August 2024

In optimization problems, the principle of symmetry provides important guidance. This article introduces an enhanced NSGA-II algorithm, termed NDE-NSGA-II, designed for addressing multi-objective optimization problems. The approach employs Tent mapping for population initialization, thereby augmenting its search capability. During the offspring generation process, a hybrid local search strategy is implemented to augment the population’s exploration capabilities. It is crucial to highlight that in elite selection, norm selection and average distance elimination strategies are adopted to strengthen the selection mechanism of the population. This not only enhances diversity but also ensures convergence, thereby improving overall performance. The effectiveness of the proposed NDE-NSGA-II is comprehensively evaluated across various benchmark functions with distinct true Pareto frontier shapes. The results consistently demonstrate that the NDE-NSGA-II method presented in this paper surpasses the performance metrics of the other five methods. Lastly, the algorithm is integrated with the DSSAT model to optimize maize irrigation and fertilization scheduling, confirming the effectiveness of the improved algorithm.