bluuebunny/crossref_metadata_2025 huggingface dataset:
Validity of the Enode Sensor and My Jump 3 App for Assessing Countermovement Jump Performance
Authors: Puljić Davor , Karavas Christos , Mandroukas Athanasios , Stafylidis Andreas
Published: December 2024

Countermovement jump (CMJ) performance analysis is vital in sports science for assessing lower-body strength and neuromuscular efficiency. This study evaluated the validity of the Enode Sensor and My Jump 3 App for measuring vertical jump heights, comparing them to those measured using the established Force Plate. Twenty-nine participants performed CMJs measured using each device. Descriptive statistics indicated mean jump heights of 48.4 ± 4.18 cm (for the Enode Sensor), 47.3 ± 4.05 cm (for My Jump 3), and 46.1 ± 4.03 cm (for the Force Plate). Reliability was confirmed via Intraclass Correlation Coefficients (ICCs), with the Enode Sensor at 0.914 and My Jump 3 at 0.968, demonstrating excellent reliability. Bland–Altman analysis showed mean biases of 2.281 cm (for the Enode Sensor) and 1.297 cm (for My Jump 3) against the Force Plate, with limits of agreement suggesting close alignment. Strong positive correlations were observed (for the Enode Sensor, r = 0.972 and for My Jump 3, r = 0.987; p < 0.001), and linear regression analysis produced R2 values of 0.945 and 0.973, respectively, confirming both tools’ accuracy for vertical jump measurement. These findings indicate that although both tools are suitable for CMJ assessment, My Jump 3 demonstrated slightly superior accuracy, underscoring the potential for accessible, reliable performance monitoring in sports contexts.
The Design and Analysis of a Proportional Solenoid with Experimental Validation of Static and Dynamic Behavior
Authors: Düzgün Ercan , Şefkat Gürsel
Published: December 2024

This study presents the design and analysis of a proportional solenoid used in electro-pneumatic brake systems for heavy vehicles. The solenoid was designed using a traditional method, and its static and dynamic characteristics were investigated both theoretically and experimentally. ANSYS 2024 R1 Maxwell was employed for theoretical static analysis, focusing on the effects of the geometric dimension parameters in the fixed and moving pole contact regions on the force–displacement characteristics. The optimal dimensions for proportionality were determined under constraint parameters. The static analysis results provided the magnetization curve data, which were used to create Look-Up Tables for a dynamic model in MATLAB R2024b-Simulink, and this method reduced the simulation time and increased the dynamic simulation accuracy. Following static analysis, a prototype electromagnet was manufactured and tested. The solenoid achieved a constant magnetic force of 45 ± 3 N with a current of 1.3 A over a working range of 1–3 mm. The dynamic model, incorporating data from ANSYS, yielded results that closely matched the experimental findings.
Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
Authors: Xu Yixin , Sun Yanli , Teng Yina , Liu Shanglai , Ji Shiyu , Zou Zhen , Yu Yang
Published: December 2024

Formulating a scientifically sound and efficient approach to allocating carbon quota aligned with the carbon peaking goal is a fundamental theoretical and practical challenge within the context of climate-oriented trading in the power sector. Given the highly irrational allocation of carbon allowances in China’s power sector, as well as the expanding role of renewable energy, it is essential to rationalize the use of green energy in the development of carbon reduction in the power sector. This study addresses the risk of “carbon transfer” within the power industry and develops a predictive model for CO2 emission based on multiple influential factors, thereby proposing a carbon quota distribution scheme adapted to green energy growth. The proposed model employs a hybrid of the gray forecasting model-particle swarm optimization-enhanced back-propagation neural network (GM-PSO-BPNN) for forecasting and allocating the total carbon quota. Assuming consistent total volume control through 2030, carbon quota is distributed to regional power grids in proportion to actual production allocation. Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. With the total national carbon emissions remaining unchanged in 2030, in the end, the regional grids received the following quota values: 873.29 million tons in North China, 522.69 million tons in Northwest China, 194.15 million tons in Northeast China, 1283.16 million tons in East China, 1556.40 million tons in Central China, and 1085.37 million tons in the Southern Power Grid. The power sector can refer to this carbon allowance allocation standard to control carbon emissions in order to meet the industry’s emission reduction standards.
Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors
Authors: Nechyporenko Alina , Frohme Marcus , Strelchuk Yaroslav , Omelchenko Vladyslav , Gargin Vitaliy , Ishchenko Liudmyla , Alekseeva Victoriia
Published: December 2024

This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB “S-40 electric siren” (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min “no-stress” phase, followed by 3 min “stress” phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the “stress “and “no-stress” states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress.
A Case Study of Frequency Analysis of the Contribution of a Single Number Quantity to the Introduction of Rubber Ball Impact Sound and Changes in Assessment Methods
Authors: Goo Hee-Mo , Moon Soon-Seong , Yeon Jun-Oh
Published: December 2024

This study analyzed the effects of changes in the inter-floor noise assessment system in multi-family housing in Korea on heavy-weight impact sound performance assessment. By comparing the existing pre-approval system with the newly introduced post-verification system, we focused on the effects of the evaluation criteria, impact sources, and frequency band considerations on the single numerical evaluation quantities (L′i,Fmax,AW and L′iA,Fmax) and contribution rates by frequency band. For the analysis, impact sounds were measured using a bang machine used in the existing pre-approval system and a rubber ball used in the post-verification system, and the performance of the floor structure was evaluated in the 1/1 and 1/3 octave bands. As a result, the pre-approval system showed a high contribution rate mainly in the 63 Hz band, but the post-verification system expanded the contribution rate to the mid-low frequency band of 63–160 Hz. In particular, the evaluation method using the A-weighted maximum floor impact sound level (L′iA,Fmax) of the post-verification system was found to reflect the performance in the mid- and high-frequency band of 125–250 Hz more effectively. The post-verification system enables a more accurate evaluation of the performance of high-frequency bands that were overlooked in the existing system, thereby enabling a realistic response to the mitigation of inter-floor noise. Accordingly, construction companies must meet stricter performance standards in floor structure design and resilient materials development, in accordance with the new regulations. For example, designs utilizing sound-absorbing ceiling structures and high-performance resilient materials are expected to be effective in reducing heavy-impact noise. This study provides important basic data for tracking the performance of floor structures according to changes in the system, and selecting key frequency bands for reducing heavy-impact noise. In addition, it emphasizes the need to continuously monitor the performance of multi-family housing constructed under the new system and to derive effective design strategies for solving inter-floor noise problems. In the future, it will be necessary to expand the usability of the results of this study through additional studies targeting more diverse floor plans and floor structures.
Artificial Heart Valve Scaffold Based on Electrospun PCL/PU Three-Layer Composite Fibers
Authors: Gao Xu , Liu Zhong , Zhang Zhiwei , Gao Yuan , Zhang Jun , Long Yunze
Published: December 2024

Abnormal heart valve function is a major cause of heart disease and is often associated with high rates of morbidity and mortality from heart disease. Focusing on the field of valve replacement, tissue-engineered heart valves are becoming popular and attracting more attention. In this work, an artificial heart valve scaffold based on polycaprolactone/polyurethane (PCL/PU) three-layer composite fibers was prepared by 3D printing and electrospinning, including an inner PCL layer, a middle PCL/PU layer, and an outer PCL layer. The obtained valve scaffold had strong circumferential mechanical properties when PCL/PU = 1:2, and its elastic modulus was 14.7 MPa, similar to that of natural valve. The heart valve scaffold had good cytocompatibility, and the final cell survival rate was 99.8% after 14 days of cell culture. The layered structure makes the artificial heart valve more similar to the natural structure of the mammalian heart, which is conducive to cell proliferation and differentiation, and provides a reference solution for interventional treatment of heart valves.
Analysis of Selected IT Tools for Environmental Product Assessment
Authors: Grajewski Damian , Dudkowiak Anna , Wiśniewski Miłosz , Dostatni Ewa
Published: December 2024

This article discusses the subject of the environmental assessment of products based on specific methods (LCA, ReCiPe, and SM2013) and tools (GaBi, SimaPro, and Sustainable Minds). The aim of this work was to analyze selected IT tools for supporting such an assessment. Assumptions and criteria were adopted, based on which specific IT tools were selected for a comparative analysis. Then, an environmental assessment of reference products was carried out using three different IT tools. The structure of the products was prepared in such a way that the parts and subassemblies met two basic requirements. First, they were made of construction materials that are generally available and used in industry. The second requirement was that the technologies for manufacturing these parts included as many frequently used technological processes as possible. This allowed for a comparison of the results of estimated environmental assessments generated by selected IT tools. As a result, the strengths and weaknesses of each of the IT tools were identified.
Flow-Induced Fatigue Damage of Large Francis Turbines Under Multiple Operating Loads
Authors: Liu Pinghu , Huang Xingxing , Yang Tianyu , Wang Zhengwei
Published: December 2024

The Francis runner is a critical component of the Francis turbine generator unit, playing a central role in converting water energy into rotating mechanical energy that drives the generator in hydropower stations. In-depth analyses of the flow characteristics of the Francis runner under various operating conditions and avoiding fatigue damage of the Francis runner are crucial to the reliability and efficiency of hydropower operation. In this paper, the flow dynamics of a large Francis turbine runner are analyzed under three representative loading conditions—low partial load, high partial load, and full load—and the flow-induced stress of the runner is analyzed under these loading conditions. It was found that the maximum static and dynamics stresses of the runner at three representative loading conditions are located at the chamfered surface where the blade trailing edge connects to the runner crown. The maximum static stresses of the Francis runner are 284 MPa, 352 MPa, and 381 MPa at low partial load, high partial load, and full load, respectively, and they are above the allowable stress limits, as half of the yield stress of the runner material of 550 MPa. The peak-to-peak values of runner dynamic stress at low partial load, high partial load, and full load are 15 MPa, 25 MPa, and 14.6 MPa, respectively. The high stress invoked by the unsteady flow under various loading conditions in this runner was the cause of the fatigue breakage of the runner blades. The results of this investigation have important reference values for mitigating fatigue damage in similar Francis runners and optimizing unit operation.
Can Large-Language Models Replace Humans in Agile Effort Estimation? Lessons from a Controlled Experiment
Authors: Pavlič Luka , Saklamaeva Vasilka , Beranič Tina
Published: December 2024

Effort estimation is critical in software engineering to assess the resources needed for development tasks and to enable realistic commitments in agile iterations. This study investigates whether generative AI tools, which are transforming various aspects of software development, can improve effort estimation efficiency. A controlled experiment was conducted in which development teams upgraded an existing information system, with the experimental group using the generative-AI-based tool GitLab Duo for estimation and the control group using conventional methods (e.g., planning poker or analogy-based planning). Results show that while generative-AI-based estimation tools achieved only 16% accuracy—currently insufficient for industry standards—they offered valuable support for task breakdown and iteration planning. Participants noted that a combination of conventional methods and AI-based tools could offer enhanced accuracy and efficiency in future planning.
Mechanical Behavior of Hollow Corrugated Sandwich Cylinders Under Inner Pressure Loading
Authors: Zhang Li , Wu Junan , Chen Changfa , Cui Hao , Yang Yongliang , Guo Rui , Zhou Hao
Published: December 2024

Taking pressure-bearing equipment as a prototype, the mechanical behavior of hollow corrugated sandwich cylinders under inner pressure loading was investigated. Considering the hollow cylindrical shell with finite length, the elastic closed-form solutions of hollow corrugated sandwich cylinders under inner pressure loading were presented. The optimization for minimum weight was performed. The stress distribution characteristics of the structures with different topological parameters were discussed. It can be found that an inflection point of the structure loading efficiency exists during an increase in the pressure, providing that under lower inner pressure loading, the loading efficiency of hollow corrugated sandwich structures is not invariably superior to homogeneous structures, yet while subjected to higher inner pressures, the sandwich structures exhibit a significant advantage in load-bearing efficiency.
The Deep Deterministic Policy Gradient Algorithm Based on RIS Technology in a Coal Mine Tunnel
Authors: Wang Shuqi , Wang Fengjiao , Zhang Wei , Wang Yaqi
Published: December 2024

Reconfigurable Intelligent Surface (RIS) technology relies on its reconfigurable electromagnetic properties and offers an efficient solution for enhancing signal quality in coal mine communications. RIS technology significantly enhances signal coverage and transmission quality in complex, confined environments. This paper proposes a channel propagation optimization scheme for coal mine RIS communication systems, using the Deep Deterministic Policy Gradient (DDPG) algorithm. By jointly optimizing base station power allocation and RIS phase shift, this paper comparatively analyzes RIS reflection performance under ideal and non-ideal conditions, focusing on its impact on system propagation rates. A comparison of system stability and convergence rates among the DDPG, A3C, and DQN algorithms reveals that, under the DDPG optimization scheme, the average link rate reaches 6.6 bps/Hz with ideal RIS reflection and 4.6 bps/Hz with non-ideal conditions when the base station transmit power is defined as 38 dBm. Furthermore, increasing the number of RIS units from 8 to 32 results in a system link rate improvement from 5 bps/Hz to 6.8 bps/Hz. The research results provide new design ideas for optimizing coal mine RIS communication systems and open up new solutions for the use of artificial intelligence in complex coal mine tunnel environments.
Balneotherapy (Mud-Bath Therapy) with a Peloid Enriched with Rosmarinic Acid Enhances Clinical Outcomes and Innate Immune Benefits in Elderly Patients with Osteoarthritis: A Pilot Study
Authors: Ortega-Collazos Eduardo , Hinchado María Dolores , Otero Eduardo , López-Jurado Casimiro Fermín , Gálvez Isabel , Legido José Luis , Muñoz-Torrero Juan Francisco Sánchez , Ortega Eduardo , Torres-Piles Silvia
Published: December 2024

Osteoarthritis (OA) is a common chronic condition that causes pain and disability, particularly in the elderly, resulting in significant limitations on mobility and overall quality of life. Balneotherapy using peloids (mud therapy) is an effective, non-pharmacological treatment for OA that improves symptoms and function. This pilot study aimed to assess whether a controlled-matured peloid, supplemented with rosmarinic acid (RosA), could enhance clinical outcomes, functional status, and immune response in OA patients. The study involved 42 elderly OA patients (mean age 70), comparing a 10-day balneotherapy cycle using either a RosA-fortified or non-fortified peloid. The effects on pain (Visual Analogue Scale), functional status (WOMAC, knee flexion/extension), quality of life (EUROQOL), and innate immune response (neutrophil phagocytic and microbicidal activity) were evaluated. Both treatments resulted in significant improvements in pain (by approximately 60%), function, and quality of life, but the RosA-fortified peloid led to greater benefits, particularly in the anxiety/depression dimension of the EUROQOL questionnaire and in enhancing neutrophil immune responsiveness. These findings suggest that RosA supplementation may further improve the therapeutic effects of mud therapy for OA management.
Development of a Virtual Reality-Based Environment for Telerehabilitation
Authors: Covaciu Florin , Vaida Calin , Gherman Bogdan , Pisla Adrian , Tucan Paul , Pisla Doina
Published: December 2024

The paper presents an innovative virtual reality (VR)-based environment for personalized telerehabilitation programs. This environment integrates a parallel robotic structure designed for the lower limb rehabilitation of patients with neuromotor disabilities and a virtual patient. The robotic structure is controlled via a user interface (UI) that communicates with the VR environment via the TCP/IP protocol. The robotic structure can also be operated using two controllers that communicate with a VR headset via the Bluetooth protocol. Through these two controllers, the therapist demonstrates to the patient various exercises that the robotic system can perform. With the right-hand controller, the therapist guides exercises for the hip and knee, while the left-hand controller manages ankle exercises. The therapist remotely designs a rehabilitation plan for patients at home, defining exercises, interacting with the rehabilitation robot in real-time via the VR headset and the two controllers, and initiating therapy sessions. The user interface allows monitoring of patient progress through video feedback, electromyography (EMG) sensors, and session recording.
FedSeq: Personalized Federated Learning via Sequential Layer Expansion in Representation Learning
Authors: Jang Jae Won , Choi Bong Jun
Published: December 2024

Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, especially in IoT scenarios where devices have varying capabilities and data heterogeneity exists among IoT clients, appropriate personalization methods are necessary. In this paper, this work aims to address this heterogeneity using a form of parameter decoupling known as representation learning. Representation learning divides deep learning models into ‘base’ and ‘head’ components. The base component, capturing common features across all clients, is shared with the server, while the head component, capturing unique features specific to individual clients, remains local. This work proposes a new representation learning-based approach, named FedSeq, that suggests decoupling the entire deep learning model into more densely divided parts with the application of suitable scheduling methods, which can benefit not only data heterogeneity but also class heterogeneity. FedSeq has two different layer scheduling approaches, namely forward (Vanilla) and backward (Anti), in the context of data and class heterogeneity among clients. Our experimental results show that FedSeq, when compared to existing personalized federated learning algorithms, achieves increased accuracy, especially under challenging conditions, while reducing computation costs. The study introduces a novel personalized federated learning approach that integrates sequential layer expansion and dynamic scheduling methods, demonstrating a 7.31% improvement in classification accuracy on the CIFAR-100 dataset and a 4.1% improvement on the Tiny-ImageNet dataset compared to existing methods, while also reducing computation costs by up to 15%. Furthermore, Anti Scheduling achieves a computational efficiency improvement of 3.91% compared to FedAvg and 3.06% compared to FedBABU, while Vanilla Scheduling achieves a significant efficiency improvement of 63.93% compared to FedAvg and 63.61% compared to FedBABU.
Evaluating a Solar–Biogas Hybrid Renewable Power Plant by Heating the Anaerobic Digester Using the Rejected Heat of Rankine Cycle in Idlib, Syria
Authors: Alhijazi Ayman Abdul Karim , Alloush Ahmad Firas , Almasri Radwan A.
Published: December 2024

This research includes modeling and studying the performance improvement of a hybrid renewable energy power plant using the modeling software Greenius in Idlib, Syria. The system consists of solar parabolic trough collectors and an anaerobic digester for generating biogas. This study included a practical experiment for generating biogas using five identical digesters operating at five different temperatures. The raw material was a mixture of 81% food waste and 19% human waste, and average temperatures were as follows: 49.6, 45.9, 43.5, 37.5, and 33.2 °C. Modeling operations were conducted for each case, as well as for the case corresponding to the highest growth rate of methanogenic bacteria theoretically. The modeling processes were conducted at 11 different values for the storage capacity from Full Load Hours (FLHs) 0 to 10 and by varying the solar multiple factor (SM) from 1 to 8. This study showed that when operating as a net solar plant, the lowest value for the cost of produced electricity (LCOE) was 0.1785 EUR/kWh at FLHs = 5 h and SM = 2, while the annual electricity production was 25.21 GWh. The maximum annual electricity production was 48.66 GWh, achieved at FLHs = 10 h, SM = 8, and the LCOE = 0.2896 EUR/kWh. It is possible to obtain annual electrical energy of 39.7 GWh, which was about 82% of the maximum possible annual production, at a cost of LCOE = 0.1864 EUR/kWh, which is less than 5% higher than the lowest possible cost. When operating as a hybrid plant with an annual capacity factor of 1 (full load), it is discovered that the lowest value of energy produced is in the third scenario at tAD = 43.52 °C and tc = 63.5 °C, with FLHs = 0 h and SM = 1, with the LCOE = 0.1283 EUR/kWh.
Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
Authors: Vaida Calin , Rus Gabriela , Tucan Paul , Machado José , Pisla Adrian , Zima Ionut , Birlescu Iosif , Pisla Doina
Published: December 2024

Stroke, amyotrophic lateral sclerosis (ALS), and Parkinson’s disease are some of the conditions that can lead to neuromotor disabilities requiring rehabilitation. To address the socio-economic burden that is amplified by the rapidly increasing elderly population, traditional rehabilitation techniques have recently been complemented by technological advancements, particularly Robot-Assisted Therapy (RAT). RAT enhances motor learning by improving both accuracy and consistency. This study proposes an innovative rehabilitation system that combines serious gaming and augmented reality (AR) with the LegUp parallel robot, developed for the spatial rehabilitation of the hip, knee, and ankle in bed-ridden patients. The system aims to improve patient outcomes and actively involve patients in their therapy. Electro-goniometers and a HoloLens 2 device are used to provide immediate feedback about the position of the patient’s joints, forming the basis of an interactive game in which the patient moves their leg to reach various targets. Two game modes were developed, each targeting different aspects of neuromotor rehabilitation, such as coordination, strength, and flexibility. Preliminary findings suggest that combining RAT with augmented reality-based serious gaming can increase patient motivation and engagement. Furthermore, the personalized and interactive nature of the therapy holds the potential to improve rehabilitation outcomes by fostering sustained engagement and effort.
Validity and Feasibility of the Seated Medicine Ball Throw and Unilateral Shot-Put Tests in Assessing Upper Extremity Function in Rotator-Cuff-Related Shoulder Pain
Authors: Linkovski Michal , Lewis Jeremy , Sarig Bahat Hilla
Published: December 2024

Background: Rotator cuff-related shoulder pain (RCRSP) is a common musculoskeletal condition characterized by pain, functional disability, reduced mobility, and weakness. There is a need for valid functional tests that can measure shoulder strength and power without exacerbating pain. The Seated Medicine Ball Throw (SMBT) and Unilateral Shot-Put Test (ULSPT) are throwing tests that use a weighted ball in a seated position, measuring throwing distance (m). This study aimed to evaluate the feasibility, discriminative validity, and convergent validity of these tests in individuals with RCRSP. Methods: This cross-sectional study included 64 participants: 30 with RCRSP and 34 asymptomatic controls. Participants completed the QuickDASH and Fear-Avoidance Beliefs Questionnaire (FABQ). Pain was assessed using a 10 cm visual analog scale (VAS) at multiple time points. The SMBT and ULSPT were performed using a 2 kg ball, with throwing distance calculated as the average of three trials. Active shoulder range of motion (AROM) and grip strength were also measured. A two-way mixed-model repeated-measures ANOVA was conducted to examine group effects, with post hoc analyses performed where relevant. Pearson correlations explored associations among outcome measures. Results: The RCRSP group presented with persistent moderate shoulder pain (mean duration = 6.33 ± 5.7 months, VAS = 5.03 ± 1.99 cm, QuickDASH = 26.2 ± 10.54). Pain did not significantly increase after throwing (VAS change = 0.5 ± 1.6 cm, P = 0.4), supporting the tests’ feasibility. ULSPT demonstrated significant differences between the RCRSP and control groups for both symptomatic (2.03 ± 0.81 m) and asymptomatic shoulders (2.04 ± 0.8 m) compared with controls (2.51 ± 0.93 m, P < 0.01). SMBT showed a trend toward group differences (P = 0.05). RCRSP participants showed reduced AROM (166.2 ± 10° vs. 175.1 ± 8.2°) but similar grip strength compared to controls. ULSPT strongly correlated with SMBT (r = 0.92–0.94, P < 0.0001). Both throwing tests correlated moderately with grip strength (r = 0.61–0.81, P < 0.05) and showed mild to moderate correlations with disability, pain, and fear-avoidance measures (r = 0.26–0.48, P < 0.05). Conclusions: The ULSPT demonstrated good discriminative validity in differentiating individuals with RCRSP from controls, while the SMBT showed a trend toward discrimination. Both tests were feasible to administer without significantly exacerbating pain. The strong correlation between ULSPT and SMBT, along with their associations with established measures, supports their potential as functional assessments of upper extremity performance in RCRSP.
A Comprehensive Review on Uncertainty and Risk Modeling Techniques and Their Applications in Power Systems
Authors: Afzali Peyman , Hosseini Seyed Amir , Peyghami Saeed
Published: December 2024

The increasing integration of renewable energy sources (RESs) into power systems has introduced new complexities due to the inherent variability and uncertainty of these energy sources. In addition to the uncertainty in RES generation, the demand-side load of power systems is also subject to fluctuations, further complicating system operations. Addressing these challenges requires effective modeling and assessment techniques to quantify and mitigate the risks associated with system uncertainties. This paper evaluates the impact of various uncertainty modeling techniques on power system reliability with wind farm integration. Furthermore, this paper reviews the state of the art of the various uncertainty and risk modeling techniques in power systems. Through a detailed case study, the performance of these techniques in modeling uncertainties of wind speeds is analyzed. Based on the results, the integration of wind turbines improves the system’s overall reliability when there is a reduction in conventional power plants (CPPs)’ generation, which are dispatchable energy sources providing a stable and flexible supply. However, the generation of wind farms is associated with uncertainty. The results show Monte Carlo simulation combined with the K-Means method is consistently a more accurate uncertainty model for wind speeds, closely aligning with real-case scenarios, compared to other methods such as Markov Chain Monte Carlo (MCMC), robust optimization (RO), and information-gap decision theory (IGDT).
Applying Game Theory to Teach the PERT Scheduling Method
Authors: Forcael Eric , Contreras Carla , Francesconi Caroll , Baesler Felipe
Published: December 2024

This research focuses on applying Game Theory to teach the PERT scheduling method to civil engineering students. The work was carried out with students from the Construction Planning and Scheduling course, who were given face-to-face classes on Game Theory applied to teaching the PERT method. This proposal considers that gamification plays a role in user engagement, while Game Theory contributes to data analysis and decision strategies. It began with an initial pre-test evaluation consisting of questions classified by items and evaluation criteria to teach the PERT method using Game Theory, followed by a post-test and a quantitative evaluation that allowed to know the perception and interests of the students. Using Game Theory principles in teaching the PERT method demonstrated improvements in students. In addition, the results presented statistically significant differences in favor of using Game Theory, which would allow it to be proposed as a viable option for teaching PERT and other scheduling methods.
Multi-Scale Information Collaborative Management Method in Hierarchical Construction Projects Based on Building Information Modeling
Authors: Zhao Xuefeng , Yan Wenkai , Xia Yu , Sun Zhe , Li Mengxuan , Bao Yan , Guo Qiankun
Published: December 2024

Building Information Modeling (BIM) has been widely adopted in the Architecture, Engineering, and Construction (AEC) industry for information modeling and project-level based on information collected from multiple sources. Unfortunately, multi-scale building information is often organized in different formats according to different data management protocols, making it a challenge to extract the data needed for effective decision-making by different project participants. This study proposed a method for the effective exchanges of multi-scale information through BIM, This method includes establishing an information management protocol between microscale and mesoscale through format conversion, standard unification, and contract constraint, and establishing an information management protocol between mesoscale and macroscale through model analysis, data integration, and institutional guarantee. The three paths of model, data, and management are used to link up the collaborative management of information in the three dimensions of individual, group, and region. The authors validated the proposed method using a case study of a project in Xiong’an New District in China as an example. Results show that the proposed method could help with the transmission and utilization of multi-scale building information using BIM for effective project management, which in turn enables the urban planners to have overall control over the information of urban construction projects.
Re-Understanding the Sedimentary Environment of the Wufeng–Longmaxi Shales in the Sichuan Basin
Authors: Mao Xiaoping , Chen Xiurong , Yang Fan , Li Shuxian , Li Zhen , Yang Yuexing
Published: December 2024

The current understanding of organic matter enrichment in marine shales remains highly controversial. Most scholars argue that deeper water environments and warmer climates facilitate the enrichment of organic matter. However, this perspective contradicts the principles of carbon sequestration in environmental science, necessitating a more in-depth discussion of its underlying mechanisms. This article utilizes the Wufeng–Longmaxi shales in the Sichuan Basin as a case study and integrates the primary productivity and carbon sequestration patterns observed in modern water bodies to analyze the processes governing organic matter enrichment in shales. The findings indicate that the Wufeng–Longmaxi shales in the Sichuan Basin did not form in a deep-water shelf environment during a period of large-scale transgression; rather, they developed in a highly enclosed shallow-water environment during a regressive phase. The proximity to ancient land correlates with higher organic matter abundance and gas production, suggesting that the mineralization model closely resembles that of coal, thereby highlighting the significance of proximity to land and the supply of terrigenous materials. It can be concluded that the depositional environment of organic-matter-rich marine shales is characterized by four key attributes: a shallow water depth, proximity to land (with a supply of terrestrial materials), a high enclosure, and a cold climate.
Two-Dimensional Scanning of Circularly Polarized Beams via Array-Fed Fabry–Perot Cavity Antennas
Authors: Madji Mikhail , Negri Edoardo , Fuscaldo Walter , Comite Davide , Galli Alessandro , Burghignoli Paolo
Published: December 2024

In this paper, we present an array-fed Fabry–Perot cavity antenna (FPCA) based on a partially reflecting sheet (PRS) capable of generating a circularly polarized (CP), highly directive, far-field radiation pattern in the 27–28.5 GHz frequency range. The PRS, the cavity, and the array of feeders serve to different purposes in this original structure. The PRS is engineered to produce a circular polarization from a linearly polarized source placed inside the cavity. The cavity is optimized to obtain a directive conical beam from the dipole-like pattern of the simple source, and allows for a frequency scan of the beam along the elevation plane. The array of feeders is designed to obtain a pencil beam whose azimuthal pointing direction can be controlled by properly phasing the sources. The radiation performance is studied with a specific application of the reciprocity theorem in a full-wave solver along with the pattern multiplication principle. A number of array-pattern configurations in terms of operation frequency and phase shift are investigated and presented to show the potential of the proposed solution in terms of design flexibility and radiation performance.
Preliminary Studies on the Use of an Electrical Method to Assess the Quality of Honey and Distinguish Its Botanical Origin
Authors: Wilczyńska Aleksandra , Banach Joanna , Żak Natalia , Grzywińska-Rąpca Małgorzata
Published: December 2024

This study aimed to determine the possibility of deploying an innovative electrical method and to establish the usefulness of conductivity and dielectric parameters for assessing the quality of Polish honeys, as well as for distinguishing their botanical origin. An attempt was also made to determine which standard physicochemical parameter could be replaced by conductivity and dielectric parameters. The experimental material consisted of seven varieties of honey (linden, rapeseed, buckwheat, goldenrod, phacelia, multifloral, acacia), obtained from beekeepers from northern Poland. Their quality was assessed based on their physicochemical parameters, biological activity, and color. Electrical parameters were measured using a measuring system consisting of an LCR meter, and own-construction sensor. Conductivity (Z, G) and dielectric (Cs, Cp) parameters were measured. Statistical analysis of the results of measurements of electrical parameters of the seven types of honey tested allowed classifying them in terms of their conductivity properties into two groups of single-flower honeys and one group of multi-flower honeys. This proves the feasibility of identifying their botanical origin using the electrical method, which is characterized by non-invasiveness, measurement speed, and high sensitivity. The usefulness of parameters Z and G in replacing quality parameters was confirmed mainly for single-flower honeys: buckwheat, linden, rapeseed, and phacelia.
Research on the Method for Solving the Safety Factor of Rock Slope Based on Deformation Monitoring Warning Threshold
Authors: Wei Xiaoyan , Zhang Xiuli
Published: December 2024

In view of the fact that field monitoring information can more intuitively and accurately reflect the stability state of slopes, this paper takes the warning threshold of slope deformation rate monitoring as the slope stability evaluation standard, and puts forward a method for solving the safety coefficient of rocky slopes. The discrete element method (3DEC), which is suitable for rocky slopes, is selected as the numerical calculation tool, the convergence criterion of its strength reduction method is modified to the slope deformation rate threshold, and the method is realized by the bifurcation method through its built-in FISH programming language. The results of the classical case show that, by selecting the slope deformation rate threshold in the appropriate interval, the results of this paper’s method are very close to those of the finite unit stress method and the limit equilibrium method, verifying the reliability of this paper’s method. Further, the method of this paper is applied to an open-pit mine slope project, based on the slope deformation on-site monitoring data and through the time series prediction method to determine the slope deformation rate warning threshold, using the threshold as an evaluation criterion to solve the slope’s coefficient of safety. The calculation results show that the slope’s coefficient of safety in natural working conditions is 1.086, being basically stable. However, with continuous rainfall, the slope’s body gradually becomes saturated, the slope’s coefficient of safety is reduced to 0.987, and the slope’s safety is reduced to 0.987. After continuous rainfall and gradual saturation of the slope, the coefficient of safety decreases to 0.987, resulting in destabilization and destruction, which is consistent with the site conditions.
Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
Authors: Zbarcea Andrei , Tudose Cătălin
Published: December 2024

Modern software application development imposes standards regarding high performance, scalability, and minimal system latency. Multi-threading asynchronous programming is one of the standard solutions proposed by the industry for achieving such objectives. However, the recent introduction of the reactive programming interface in Java presents a potential alternative approach for addressing such challenges, promising performance improvements while minimizing resource utilization. The research examines the migration process from the asynchronous paradigm to the reactive paradigm, highlighting the implications, benefits, and challenges resulting from this transition. To this end, the architecture, technologies, and design of a support application are presented, outlining the practical aspects of this experimental process while closely monitoring the phased migration. The results are examined in terms of functional equivalence, testing, and comparative analysis of response times, resource utilization, and throughput, as well as the cases where the reactive paradigm proves to be a solution worth considering. Across multiple scenarios, the reactive paradigm demonstrated advantages such as up to 12% reduction in memory usage, 56% faster 90th percentile response times, and a 33% increase in throughput under high-concurrency conditions. However, the results also reveal cases, such as data-intensive scenarios, where asynchronous programming outperforms reactive approaches. Additionally, possible directions for further research and development are presented. This paper not only investigates the design and implementation process but also sets a foundation for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.
Radiotherapy for Rectal Cancer and Radiation-Induced Malignancies from Epidemiological and Dosimetric Data
Authors: Kachris Stefanos , Mazonakis Michalis
Published: December 2024

Preoperative or postoperative radiation therapy is broadly employed in patients with rectal carcinoma. Radiotherapy directs high-energy beams of ionizing radiation toward the tumor area to destroy cancer cells. High radiation doses are needed for cell killing. The radiation exposure of the healthy tissues/organs may lead to carcinogenesis. This study describes the evolving role of radiotherapy in rectal cancer management. The present report also reviews epidemiological and dosimetric studies related to the radiation-induced second malignancies from pelvic radiotherapy. Some epidemiological studies have concluded that the second-cancer risk in patients subjected to radiation therapy does not increase compared to unexposed rectal cancer patients. Other researchers found an elevated or a marginally increased probability for second-cancer induction. Dosimetric studies reported cancer risk estimates for critical organs or tissues in the near and far periphery of the treatment volume. Useful information about the effect of the treatment parameters such as the irradiation technique, photon beam energy, and fractionation schedule on the organ-specific second-cancer risk was derived from the dose data analysis. The knowledge of these effects is needed for the selection of the optimal treatment parameters enabling a reduction in the resultant risk of carcinogenesis.
Distributed Formation Control for Underactuated, Unmanned Surface Vehicles with Uncertainties and Disturbances
Authors: Huang Wenbin , Zheng Yuxin , Zhang Lei , Li Yanhao , Chen Xi
Published: December 2024

This paper investigates the distributed formation control problem of underactuated unmanned surface vehicles (UUSVs) with uncertainties and disturbances and proposes a novel distributed formation controller. The proposed controller redefines the dynamic and kinematic models for each UUSV, which reduces the complexity of the underactuated controller design. Dynamic surface control (DSC) is employed to eliminate the repeated derivatives of the virtual control law, which is crucial for the generation of real-time control signals. The proposed controller integrates neural network approximation with MLP-based adaptive laws to enhance the model’s resistance to disturbances. Then, an auxiliary adaptive law is designed for each UUSV to obtain a continuous controller under the compensation of approximate errors and disturbances. The results demonstrate that the controller achieves the desired goals for the formation control, and all control signals are guaranteed to be semi-global uniformly ultimately bounded (SGUUB). The final simulation results thoroughly prove the effectiveness of the theoretical results.
Adaptive Path Planning for UAV-Based Pollution Sampling
Authors: Kosior Mateusz , Przystałka Piotr , Panfil Wawrzyniec
Published: December 2024

Unmanned Aerial Vehicles (UAVs) continue to gain popularity in applications such as military reconnaissance, environmental monitoring in remote locations, and package delivery. High-Altitude Long-Endurance (HALE) UAVs can remain airborne for extended periods, enabling air pollution measurements to be conducted across a wide range of altitudes, from a few hundred meters above ground level to the lower stratosphere. However, the challenges posed by dynamic environmental conditions and strict energy limitations necessitate the use of adaptive path planning algorithms that account for UAV and environmental models. To address these challenges, we propose a two-tier Adaptive Path Planner (APP), which comprises a Global Path Planner (GPP) and a Local Path Planner (LPP). The GPP, operating offline, generates obstacle-free, energy-efficient paths that adhere to the UAV’s kinematic constraints, while the LPP dynamically recalculates alternative routes in real time when obstacles arise. The APP leverages a novel data-driven environmental model, integrating terrain, wind, airspace, and measurement maps. Extensive Model-in-the-Loop testing was conducted to evaluate various single-objective optimization algorithms for the GPP. Subsequently, the APP was successfully validated in simulation scenarios inspired by real-world pollution monitoring missions conducted in Poland and the Arctic. Additionally, the proposed approach was tested under real-world conditions, demonstrating significant application potential. A comparative analysis of the generated paths demonstrated that the APP effectively replaces human operators. Further testing confirmed the APP’s capability for adaptive re-planning during mission execution.
Analyzing Student Behavioral Patterns in MOOCs Using Hidden Markov Models in Distance Education
Authors: Verykios Vassilios S. , Alachiotis Nikolaos S. , Paxinou Evgenia , Feretzakis Georgios
Published: December 2024

The log files of Massive Open Online Courses (MOOCs) reveal useful information that can help interpret student behavior. In this study, we focus on student performance based on their access to course resources and the grades they achieve. We define states as the Moodle resources and quiz grades for each student ID, considering participation in resources such as wikis and forums. We use efficient Hidden Markov Models to interpret the abundance of information provided in the Moodle log files. The transitions among certain resources for each student or groups of students are determined as behaviors. Other studies employ Machine Learning and Pattern Classification algorithms to recognize these behaviors. As an example, we visualize these transitions for individual learners. Additionally, we have created row and column charts to present our findings in a comprehensible manner. For implementing the proposed methodology, we use the R programming language. The dataset that we use was obtained from Kaggle and pertains to a MOOC of 4037 students.