bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
Authors: Anjum Madiha , Batool Wardah , Saher Raazia , Saeed Sanay Muhammad Umar
Published: October 2024

The analysis of stress in response to videos using electroencephalography (EEG) has emerged as a significant field of research. In this study, we propose a methodology for classifying stress responses to videos using the Database for Emotion Analysis using Physiological Signals (DEAP). EEG signals are preprocessed with resampling and a median filter. We extracted Power Spectral Density (PSD) features from the alpha, beta, delta, and theta bands of the preprocessed EEG. Instances were labeled based on the valence and arousal values provided in the DEAP dataset in response to the presented videos. Four machine learning algorithms, namely Naïve Bayes (NB), Multilayer Perceptron (MLP), Logistic Regression (LR), and Sequential Minimal Optimization (SMO) classifiers, were employed to differentiate between stressed and relaxed states using a 10-fold cross-validation technique. The SMO classifier achieved the highest accuracy of 95.65%. Additionally, statistically significant variations in the alpha band using t-tests suggest that the DEAP dataset video clips can effectively induce stress and relaxation conditions in participants.
Utilization of the Resonance Behavior of a Tendon-Driven Continuum Joint for Periodic Natural Motions in Soft Robotics
Authors: Neumann Oliver , Deutschmann Bastian , Reinecke Jens
Published: October 2024

Continuum joints use structural elastic deformations to enable joint motion, and their intrinsic compliance and inherent mechanical robustness are envisioned for applications in which the robot, the human, and the environment need to be safe during interaction. In particular, the intrinsic compliance makes continuum joints a competitor to soft articulated joints, which require additional integrated spring elements. For soft articulated joints incorporating rigid and soft parts, natural motions have been investigated in robotics research to exploit this energy-efficient motion property for cyclic motions, e.g., locomotion. To the best of the author’s knowledge, there is no robotic system to date that utilizes the natural motion of a continuum joint under periodic excitation. In this paper, the resonant behavior of a tendon-driven continuum joint under periodic excitation of the torsional axis is experimentally investigated in a functional sense. In the experiments, periodic inputs are introduced on the joint side of a tendon driven continuum joint with four tendons. By modulating the pretension of the tendons, both the resonant frequency and the gain can be shifted, from 3 to 4.3 Hz and 2.8 to 1.4, respectively, in the present experimental setup. An application would be the rotation of a humanoid torso, where gait frequencies are synchronized with the resonant frequency of the continuum joint.
Amplitude of Biceps Femoris Activation and Triaxial Acceleration in a 50-Meter Test in Sprinters: Pilot Study
Authors: Martín-Ruiz Julio , Tamarit-Grancha Ignacio , Gallego-Cerveró Clara , Pardo-Ibáñez Alberto , Cordente-Martínez Carlos , Ruiz-Sanchis Laura
Published: October 2024

Objective: To describe the relationship between bilateral electrical activity in the biceps femoris and the variation of triaxial acceleration in three 50 m sprints. Methods: Biceps femoris muscle activation and acceleration in the anterior–posterior, mediolateral, and superior–inferior axes were measured in three 50 m sprints in nine national-level sprinters. Results: There was significant asymmetry between both legs, and the variations between axes were significant between the anterior–posterior with respect to the lateral and superior–inferior, and between the lateral and superior–inferior (p < 0.05). Conclusions: Increased biceps femoris activation during running increases speed regardless of asymmetry in force application. In the maintenance of horizontal velocity, acceleration of the anterior–posterior axis is the most relevant and depends on the flexion-extension muscle actions contained in the lateral axis.
Medium-Duty Road Freight Transport—Investigation of Consumption and Greenhouse Gas Emissions of Battery Electric and Fuel Cell Trucks with Model-Based Predictions Until 2050
Authors: Dollinger Manfred , Fischerauer Gerhard
Published: October 2024

The present work intends to make a scientific contribution to future drive technology in medium-duty road freight transportation that is as objective and fact-based as possible. In cooperation with a medium-sized forwarding company, 1-day transports, previously driven with diesel trucks, were examined. Using a physically based model, which was first validated by comparing simulated CNG drive data with real-world diesel data, the findings were transferred to battery electric trucks (BETs) and fuel cell trucks (FCETs) and extrapolated to 2050 based on expected technological developments. The model makes statements based on the results of the investigated application regarding specific consumption, greenhouse gas (GHG) emissions, consumption shares and recuperation. The CNG combustion technology (ICET-CNG) serves as a reference. BETs in this application have the lowest emission and consumption values: BET2050 will consume a third of the energy and emit a fifth of the GHGs of ICET-CNG2024. The weight of the battery leads to higher consumption values. FCETs have higher fuel consumption due to their longer drive trains. This is partially compensated by their lower weight: FCET2050 will consume 40% of the energy and emit a third of the GHGs of ICET2024. In long-distance traffic, aerodynamic drag is the dominant consumption factor, accounting for 40%, which should be addressed in further truck development. Recuperation extends the range by 3–7%.
Machine Learning Model Trained with Finite Element Modeling Can Predict the Risk of Osteoarthritis: Data from the Osteoarthritis Initiative
Authors: Mononen Mika E. , Liukkonen Mimmi K. , Turunen Mikael J.
Published: October 2024

Objective: Despite long simulation times, recently developed finite element analysis (FEA) models of knee joints have demonstrated their suitability for predicting individual risk of onset and progression of knee osteoarthritis. Therefore, the objective of this study was to assess the feasibility of machine learning (ML) to replicate outcomes obtained from FEA when simulating mechanical responses and predicting cartilage degeneration within the knee joint. Design: Two ML models based on the Gaussian Process Regression (GPR) algorithms were developed. The first model (GPR1) utilized age, weight, and anatomical joint dimensions as predictor variables to predict tissue mechanical responses and cartilage degeneration based on FEA data. The second model (GPR2) utilized age, weight, height, and gender to predict anatomical joint dimensions, which were then used as inputs in the GPR1 model. Finally, the GPR1 and combined GPR1+GPR2 models were used to investigate the importance of clinical imaging when making personalized predictions for knees from healthy subjects with no history of knee injuries. Results: In the GPR1 model, R2 of 0.9 was exceeded for most of the predicted mechanical parameters. The GPR2 model was able to predict knee shape with R2 of 0.67–0.9. Both GPR1 and combined GPR1+GPR2 models offered equally good performances (AUC = 0.73–0.74) in classifying patients at high risk for the onset and development of knee osteoarthritis. Conclusions: In the future, real-time and easy-to-use GPR models may provide a rapid technology to evaluate mechanical responses within the knee for researchers or clinicians who have no former knowledge of FEA.
Fractal Aspects of Human S100 Protein Structures
Authors: Petreuș David Emanuel , Isvoran Adriana
Published: October 2024

This study analyzes the fractal aspects of the structures of S100 proteins to better understand their structural complexity. We take into account 33 solution structures and 18 crystal structures corresponding to human S100 proteins for the calculation of mass and surface fractal dimensions. The mass fractal dimension value is calculated as Dm = 1.54, confirming the extended conformation of the dimers of these proteins. The mean value of the surface fractal dimension is Ds = 2.35 ± 0.09 when computed using solution structures and Ds = 2.23 ± 0.05 when computed using crystal structures, revealing the surface irregularities of S100 proteins. Changes in surface fractal dimensions have been recorded for S100 proteins due to the changes in the pH of the environment, due to mutations in their sequences that alter how the protein folds, and/or due to their interactions with ions and/or ligands that reflect the structural rearrangements that occur upon binding. These changes can significantly influence the biological activity of the protein, making the fractal dimension of the surface a valuable parameter in studying protein functions, interactions, and potential therapeutic targeting.
Numerical Analyses of Ultrasonic Atomization Utilizing Acoustic Effects of a Beam Diaphragm
Authors: Kondo Atsushi , Matsuura Hiroshi
Published: October 2024

To study mechanisms of jet atomization, a novel method of experimentation utilizing the resonation of diaphragms made from thin steel plates has been previously developed. In the experiments, a diaphragm covered by a film of water emitted acoustic sounds, and jet atomization from the water film was observed. Experiments using diaphragms composed of different materials and fast Fourier transformation analysis of the acoustic sound revealed that jet atomization occurred under limited surface conditions of the diaphragm and a specific range of frequency. In this article, the dynamics of a resonating body composed of the diaphragm and water film were analyzed using the finite element method with a combination of theoretical analyses of surface waves of water, such as the well-known Lang’s equation. The present FEA results, from harmonic response analyses with consideration of viscous damping effect due to interaction between the diaphragm and water film, precisely confirmed the results of FFT analysis previously obtained by the experiment. Specifically, the peak frequency of the frequency response agreed well with the FFT results, and the shift of the peak frequency and attenuation due to the interaction in the analyses corresponded with the difference in surface conditions between the hydrophilic and hydrophobic materials of the diaphragm in the experiments. Our interpretation of the mechanism of jet atomization is expanded by the present numerical results.
Designing Hand Orthoses: Advances and Challenges in Material Extrusion
Authors: Michalec Paweł , Schusser Martin , Weidner Robert , Brandstötter Mathias
Published: October 2024

The intricate structure of human hands requires personalized orthotic treatments, especially with the growing aging population’s demand for accessible care. While traditional orthoses are effective, they face challenges of cost, customization time, and accessibility. Additive manufacturing, particularly material extrusion (MEX) techniques, can effectively address challenges in orthotic device production by enabling automated, complex, and cost-effective solutions. This work aims to provide engineers with a comprehensive set of design considerations for developing hand orthoses using MEX technology, focusing on applying design for additive manufacturing principles, to enhance rehabilitation outcomes. This objective is achieved by establishing design requirements for hand orthoses, reviewing design choices and methodologies across conventional and state-of-the-art MEX-based devices, and proposing an innovative approach to orthotic design. Hand orthosis design requirements were gathered through workshops with occupational therapists and categorized into engineer-, medical-, and patient-specific needs. A review of 3D-printed hand orthoses using MEX analyzes various design approaches, providing insights into existing solutions. The study introduces a modular design concept aimed at improving rehabilitation by enhancing customizability and functionality. It highlights the potential of MEX for creating personalized, cost-effective orthoses and offers recommendations for future research, to optimize designs and improve patient outcomes.
Calculation of Trusses System in MATLAB—Multibody
Authors: Ondočko Štefan , Svetlík Jozef , Jánoš Rudolf , Semjon Ján , Dovica Miroslav
Published: October 2024

This article discusses the software tool (Simscape—Multibody program of MATLAB) primarily intended for dynamic and kinematic processes with practical applications in static calculations. Currently, there are few published scientific works utilizing this tool for tasks like basic static calculations of truss systems. We were interested in comparing the calculation using the tools we use in our work and research activities for theoretical calculation; the potential reliance on simulations in the future could help to avoid the necessity of complex theoretical calculations, which can be time-consuming and prone to errors. Despite the fact that the structure may appear simple, in practice, there may not always be time for a verification calculation in the theoretical field (proper model creation, inclusion of all conditions, etc.). The beam system is intentionally both externally and internally statically indeterminate. For this reason, it is logically necessary to also consider deformation conditions. The achieved results were interesting in terms of accuracy compared to SOLIDWORKS, which was used for computation verification. Through very simple optimization, we were able to further increase the calculation accuracy without complicating other parameters.
Reduced-Order Modeling for Dynamic System Identification with Lumped and Distributed Parameters via Receptance Coupling Using Frequency-Based Substructuring (FBS)
Authors: Hamedi Behzad , Taheri Saied
Published: October 2024

Paper presents an effective technique for developing reduced-order models to predict the dynamic responses of systems using the receptance coupling and frequency-based substructuring (RCFBS) method. The proposed approach is particularly suited for reconfigurable dynamic systems across various applications, like cars, robots, mechanical machineries, and aerospace structures. The methodology focuses on determining the overall system receptance matrix by coupling the receptance matrices (FRFs) of individual subsystems in a disassembled configuration. Two case studies, one with distributed parameters and the other with lumped parameters, are used to illustrate the application of this approach. The first case involves coupling three substructures with flexible components under fixed–fixed boundary conditions, while the second case examines the coupling of subsystems characterized by multiple masses, springs, and dampers, with various internal and connection degrees of freedom. The accuracy of the proposed method is validated against a numerical finite element analysis (FEA), direct methods, and a modal analysis. The results demonstrate the reliability of RCFBS in predicting dynamic responses for reconfigurable systems, offering an efficient framework for reduced-order modeling by focusing on critical points of interest without the need to account for detailed modeling with numerous degrees of freedom.
Accounting Method and Indicators of Multilevel CO2 Emissions Based on Cost During Construction of Shield Tunnels
Authors: Zheng Shengdi , Xie Xiongyao , Zhou Biao
Published: October 2024

With the rapid expansion of shield tunnel projects in China, precise accounting of CO2 emissions throughout the entire process is essential for advancing green and low-carbon construction practices. This paper introduces an innovative CO2 accounting methodology utilizing a cost-based carbon emission coding system. It adopts a multilevel approach to carbon emission accounting, aligned with this coding system, which facilitates a detailed examination of carbon emission ratios and characteristics across various construction techniques. The analysis includes six typical shield tunnel projects varying in diameter, focusing on sub-projects to scrutinize CO2 emissions and establish specific indicators. The findings indicate that CO2 emissions per 10,000 yuan of investment, approximately 30.25% (or 3025 kgCO2e/10,000 yuan), are more consistent than those per unit of length. Moreover, the study highlights differing CO2 emission trends among sub-projects compared to whole tunnel projects, assessing emission indicators and distribution patterns in four sub-projects: shield excavation, segmental lining, internal structure, and tunnel reinforcement. From these findings, the paper suggests more precise and tailored strategies for CO2 reduction. This research provides a theoretical basis for future construction planning and carbon management strategies.
Preliminary Study on the Bending Behavior of Solid Timber Beams Reinforced with Basalt Fiber-Reinforced Polymer Bars
Authors: Dygas Justyna , Bakalarz Michał Marcin , Kossakowski Paweł Grzegorz
Published: October 2024

The purpose of this work is to test the effectiveness of strengthening timber structures by means of composite bars. This article presents the results of preliminary tests carried out on solid beams made of fir wood. The test specimens, which are classified as strength class C24, had dimensions of 7 × 17 × 330 cm. Beams were reinforced with 8 mm diameter basalt fiber-reinforced polymer (BFRP) bars. The bars were glued into grooved channels along the bottom surface. Epoxy resin was used as an adhesive. The strength tests were conducted in accordance with the requirements of EN 408+A1:2012. The four-point bending scheme was adopted. The tests were conducted in the following two series: unreinforced beams (A) and beams reinforced with composites (B). Five elements were tested in each series. The reinforcement resulted in an average increase in the bending moment value of 8.41%. The deflection value at failure increased by 19.77%. The work also includes an analysis of the failure mode and a ductility analysis. Further tests should be carried out using a higher reinforcement ratio. A higher reinforcement ratio should make the presented reinforcement configuration more effective.
HGTMFS: A Hypergraph Transformer Framework for Multimodal Summarization
Authors: Lu Ming , Lu Xinxi , Zhang Xiaoming
Published: October 2024

Multimodal summarization, a rapidly evolving field within multimodal learning, focuses on generating cohesive summaries by integrating information from diverse modalities, such as text and images. Unlike traditional unimodal summarization, multimodal summarization presents unique challenges, particularly in capturing fine-grained interactions between modalities. Current models often fail to account for complex cross-modal interactions, leading to suboptimal performance and an over-reliance on one modality. To address these issues, we propose a novel framework, hypergraph transformer-based multimodal summarization (HGTMFS), designed to model high-order relationships across modalities. HGTMFS constructs a hypergraph that incorporates both textual and visual nodes and leverages transformer mechanisms to propagate information within the hypergraph. This approach enables the efficient exchange of multimodal data and improves the integration of fine-grained semantic relationships. Experimental results on several benchmark datasets demonstrate that HGTMFS outperforms state-of-the-art methods in multimodal summarization.
Determining Rock Joint Peak Shear Strength Based on GA-BP Neural Network Method
Authors: Zhu Chuangwei , Guo Baohua , Zhang Zhezhe , Zhong Pengbo , Lu He , Sigama Anthony
Published: October 2024

The peak shear strength of a rock joint is an important indicator in rock engineering, such as mining and sloping. Therefore, direct shear tests were conducted using an RDS-200 rock direct shear apparatus, and the related data such as normal stress, roughness, size, normal loading rate, basic friction angle, and JCS were collected. A peak shear strength prediction model for rock joints was established, by which a predicted rock joint peak shear strength can be obtained by inputting the influencing factors. Firstly, the study used the correlation analysis method to find out the correlation coefficient between the above factors and rock joint peak shear strength to provide a reference for factor selection of the peak shear strength prediction model. Then, the JRC-JCS model and four established GA-BP neural network models were studied to identify the most valuable rock joint peak shear strength prediction method. The GA-BP neural network models used a genetic algorithm to optimize the BP neural network with different input factors to predict rock joint peak shear strength, after dividing the selected data into 80% training set and 20% test set. The results show that the error of the JRC-JCS model is a little bigger, with a value of 11.2%, while the errors of the established GA-BP neural network models are smaller than 6%, which indicates that the four established GA-BP neural network models can well fit the relationship between the peak shear strength and selected input factors. Additionally, increasing the factor number of the input layer can effectively improve the prediction accuracy of the GA-BP neural network models, and the prediction accuracy of the GA-BP neural network models will be higher if factors that have higher correlation with the output results are used as input factors.
An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning
Authors: Min Wei , Mo Liping , Yin Biao , Li Shan
Published: October 2024

This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms.
Irregular Eccentric Wellbore Cementing: An Equivalent Circulation Density Calculation and Influencing Factors Analysis
Authors: Yang Fujie , Sun Jinfei , Luo Hanlin , Shi Yue , Zhang Jingyan , Li Zaoyuan
Published: October 2024

In the field of cement, if the formation cannot be given sufficient pressure to maintain stability during construction, pressure control failure may occur, leading to the leakage of liquids and gasses from the formation to the wellbore. In addition, irregular wellbore diameter and casing eccentricity are important factors that are easily overlooked and affect the prediction of ECD (Equivalent Circulation Density) calculation. This results in major accidents and ecological disasters, further impacting the global environment. This study focuses on a well in the eastern oilfields of China, and based on a rheological experiment of high temperature and high pressure, an irregular eccentric wellbore model is established according to the measured wellbore diameter and eccentricity data to calculate the ECD of the whole cementing process. Then, a data set is constructed and analyzed using the random forest method to quantitatively evaluate influencing factors such as displacement, rheology, density, and eccentricity on the bottomhole and wellbore ECD. Results find that the density of cement slurry and drilling fluid has the most significant impact on the maximum ECD, with the impact reaching 0.3142 and 0.2902, respectively, and the main factors that affect the minimum ECD are the density and rheological changes in the drilling fluid, reaching 0.7014 and 0.2846. These research findings will contribute to the precise control of wellbore pressure during cementing operations, further ensuring the safety of cementing operations, and laying a technical foundation for the automation and intelligentization of subsequent cementing operations.
The Non-Linear Impact of Industry 4.0 on Carbon Emissions in China’s Logistics Sector
Authors: Li Qingwen , Tang Waifan , Li Zhaobin , Lam Suikei
Published: October 2024

This study investigates the impact of Industry 4.0 on carbon emissions within China’s logistics sector, focusing on its potential to support global carbon reduction goals. Utilizing panel data from 30 Chinese provinces between 2011 and 2019, this research employed an Industry 4.0 index alongside IPCC guidelines to estimate logistics emissions. Through fixed effects and dynamic panel models, this study indicated an inverted U-shaped relationship among the development of Industry 4.0 and carbon emission management. Industries move to emitting more pollution to provide their required needs to all but once technologies advance, drastic decreases in combustion and emissions take place as the industries move more towards conservation and the integration of information continues to improve. This study investigates the non-linear impact of Industry 4.0 on carbon emissions in China’s logistics sector. Utilizing panel data from 30 Chinese provinces between 2011 and 2019, the research employs a dynamic panel model to quantify the relationship between Industry 4.0 adoption and carbon emissions. The findings reveal an inverted U-shaped relationship, with emissions initially increasing by 10% at lower levels of technology adoption before decreasing by 15% as the Industry 4.0 index reached higher levels. This study provides empirical evidence supporting the strategic deployment of Industry 4.0 technologies to achieve sustainable logistics growth. Key policy implications and future research directions are discussed.
One-Step Magneton Sputtering of Crystalline Cu-Doped TiO2 Coatings: Characterization and Antibacterial Activity
Authors: Nikolova Maria P. , Yousefi Sadegh , Handzhiyski Yordan , Apostolova Margarita D.
Published: October 2024

Early biofilm formation could be inhibited by applying a thin biocompatible copper coating to reduce periprosthetic infections. In this study, we deposited crystalline Cu-doped TiO2 films using one-step DC magnetron sputtering in an oxygen atmosphere on a biased Ti6Al4V alloy without external heating. The bias voltage varied from −25 V to −100 V, and the resultant substrate temperature was measured. The deposited coatings were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), microhardness, scratch and hydrophilicity tests, potentiodynamic polarization measurements, and antibacterial assays against S. aureus and E. coli. The findings demonstrated that when a higher negative bias is applied, the substrate temperature drops, and the anatase to rutile transformation is initiated without indicating obvious Cu-containing phases. The SEM images of the films showed spherical agglomerates with homogeneously distributed Cu with decreasing Cu content as the bias value increased. Higher bias results in the grain refinement of the thinning coatings with more lattice microstrain and more defects, together with an increase in water contact angles and hardness values. Samples biased at −75 V exhibited the highest adhesive strength between coatings and substrate, whereas the specimen biased at −50 V demonstrated higher corrosion resistance. Cu-containing TiO2 coatings with pure anatase phase composition and Cu concentrations of 2.62 wt.% demonstrated excellent bactericidal activity against both S. aureus and E. coli. The layers containing 2.34 wt.% Cu exhibited very good antibacterial properties against S. aureus, only. According to these findings, the produced copper-doped TiO2 coatings have high bactericidal qualities in vitro and may be used to prepare orthopaedic and dental implants in the future.
The Optimisation of Ultrasound-Assisted Extraction for the Polyphenols Content and Antioxidant Activity on Sanguisorba officinalis L. Aerial Parts Using Response Surface Methodology
Authors: Muzykiewicz-Szymańska Anna , Kucharska Edyta , Pełech Robert , Nowak Anna , Jakubczyk Karolina , Kucharski Łukasz
Published: October 2024

The aim of this study was to optimise ultrasound-assisted extraction (UAE) of the herb Sanguisorba officinalis L. in terms of the antioxidant activity (DPPH and FRAP method) and total polyphenol content (TPC). Optimisation was performed using the response surface methodology (RSM) with a third-degree (33) Central Composite Design (CCD) approach. The RSM was applied to obtain the optimal combination of (1) raw material content (2.25–7.5 g raw material/100 mL of solvent), (2) ethanol concentration (20–60% v/v), and (3) extraction time (1–15 min). The optimal conditions for the extraction of polyphenols and antioxidant potential were a raw material content of 7.5 g/100 mL of solvent (solid/solvent ratio 13.3 mL/g), an ethanol concentration of 47% v/v, and an extraction time of 10 min. At these optimal extraction parameters, the maximum extraction of polyphenols and antioxidant activity obtained experimentally was found to be very close to its predicted value and was 12.9 mmol Trolox/L (DPPH method), 19.4 mmol FeSO4/L (FRAP method), and 2.1 g GA/L (TPC). The mathematical model developed was found to fit with the experimental data on the antioxidant potential and polyphenol extraction. The n-octanol/water partition coefficient of the optimised extract was used to determine their lipophilicity. Our studies have shown that the optimised extract is highly hydrophilic (log P < 0). Optimal parameters can be used for the industrial extraction of the S. officinalis herb for the needs of, among others, the pharmaceutical or cosmetic industry.
Impact of UV Light Exposure During Printing on Thermomechanical Properties of 3D-Printed Polyurethane-Based Orthodontic Aligners
Authors: Šimunović Luka , Marić Antun Jakob , Bačić Ivana , Haramina Tatjana , Meštrović Senka
Published: October 2024

Aim: Polyurethane-based aligners, created through photoinitiated free-radical polymerization, have been the subject of numerous studies focusing solely on their mechanical properties. In contrast, we investigate their thermomechanical properties, which are crucial for their efficacy. This paper aims to investigate the effects of different UV light exposure durations on the complex modulus of elasticity, tan delta, glass transition temperature, and the degree of conversion (DC). Methods: Aligners were printed using Tera Harz TC-85 and NextDent Ortho Flex resin with specific exposure times (2, 2.4, 3, 4, and 4.5 s for Tera Harz; 5, 6, 7, and 8 s for NextDent) and processed per manufacturer guidelines. The degree of conversion was analyzed using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy, while Dynamic Mechanical Analysis (DMA) characterized the mechanical properties (complex modulus and tan delta) and the glass transition. Results: Tera Harz TC-85 showed a higher degree of conversion (90.29–94.54%), suggesting fewer residual monomers, which is potentially healthier for patients. However, its lower glass transition temperature (35.60–38.74 °C) might cause it to become rubbery in the mouth. NextDent Orto Flex, with a higher storage modulus (641.85–794.55 MPa) and Tg (49.36–50.98 °C), offers greater rigidity and stability at higher temperatures (greater than temperature in the oral cavity), ideal for orthodontic forces, though its lower degree of conversion raises health concerns. Conclusions: Tera Harz TC 85 generally achieves higher DC and more stable polymerization across different UV exposure times than NextDent Orto Flex. Optimal polymerization times significantly impact both the mechanical and thermal properties of these dental resins, with NextDent showing optimal properties at 7 s and Tera Harz benefiting from both very short and extended exposure times.
Life Cycle Assessment of Extraction of Cellulose from Date Palm Biomass Using Natural Deep Eutectic Solvent (NaDES) via Microwave-Assisted Process
Authors: Alanazi Yousef M. , Yin Chun-Yang , Ragib Abdullah Al , El-Harbawi Mohanad , Sivapragasam Magaret
Published: October 2024

This study investigates the extraction of cellulose from Saudi Arabia-based date palm biomass utilizing a natural deep eutectic solvent (NaDES) integrated with a microwave-assisted process. A comprehensive life cycle assessment (LCA) was conducted in accordance with the ISO 14040 standard, encompassing four key stages: goal and scope definition, life cycle inventory analysis (LCI), life cycle impact assessment (LCIA) and interpretation. The analysis was confined to a gate-to-gate boundary in which two impact assessment methods, namely, ReCiPe Midpoint (H) 2016 and ILCD 2011 Midpoint, were used to assess the environmental impacts. The OpenLCA software (version 2.1.1) with the European Life Cycle Database 3.2 (ELCD 3.2) was used in the study. The ReCiPe method identified impact categories such as fossil resource scarcity, terrestrial ecotoxicity, freshwater ecotoxicity, water consumption, human carcinogenic toxicity and marine ecotoxicity. Conversely, the ILCD method identified freshwater ecotoxicity, water resource depletion, mineral, fossil and resource depletion, human toxicity and cancer effects. The results indicate that freshwater ecotoxicity presents the most substantial environmental impact across both assessment methods, surpassing other categories. Fossil resource scarcity, even though originally appearing impactful, demonstrated a relatively lower normalized score compared to freshwater ecotoxicity. Terrestrial ecotoxicity and water consumption were found to be negligible in their impact. Our findings provide important insights into sustainable material science and waste management, affording potential applications for biomass utilization in the Gulf region.
YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO
Authors: Wang Feilong , Yang Xiaobing , Wei Juan
Published: October 2024

Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based on the YOLOv7 framework. YOLO-ESL integrates the ELAN-SA module, designed to enhance feature extraction, with the LGA module, which improves feature fusion. The ELAN-SA module optimizes the flexibility and efficiency of small object feature extraction, while the LGA module effectively integrates multi-scale features through local and global attention mechanisms. Additionally, the CIOUNMS algorithm addresses the issue of target loss in cases of high overlap, improving boundary box filtering. Evaluated on the VOC2012 pedestrian dataset, YOLO-ESL achieved an accuracy of 93.7%, surpassing the baseline model by 3.0%. Compared to existing methods, this model not only demonstrates strong performance in handling occluded and small object detection but also remarkable robustness and efficiency.
Progress of Capillary Flow-Related Hydrovoltaic Technology: Mechanisms and Device Applications
Authors: Zhang Wenluan , Liu Runru Tristan , Huang Yumin
Published: October 2024

Capillary flow-related hydrovoltaic technology is an emerging research field for sustainable electricity generation. Despite great progress in the last decade, the mechanisms behind electricity generation remain unclear. In this review, we provide an overview of the current proposed mechanisms for electricity generation induced by water evaporation and moisture absorption. We explore key mechanisms, including streaming potential, ion concentration gradient, microbial electricity, ionovoltaic effect, pseudo-streaming, evaporating potential, and upstream proton diffusion. Each offers distinct insights and faces specific challenges that require further study. Unlike previous reviews, we focus specifically on the detailed mechanistic understanding of capillary flow-related electricity generation and highlight the interplay of different mechanisms. Additionally, we identify critical gaps in current research, particularly the need for empirical validation through advanced characterization techniques, such as spectroscopy, microscopy, and electrochemical analysis. Moreover, we discuss the practical applications of capillary flow-related hydrovoltaic technology in energy harvesting systems and self-powered sensors, highlighting its potential to convert water evaporation and environmental moisture into sustainable energy. We believe this review can serve as a starting point for further efforts aimed at addressing these challenges, thus paving the way for the commercialization of this technology and its contribution to sustainable development goals.
Design of a Lower Limb Prosthesis for Ballet Practice
Authors: Pérez Blanca Monge , Alía García Cristina , Cano-Moreno Juan David
Published: October 2024

Ballet is a discipline that combines art and sport in a harmonious way. It is a practice that has high physical and mental demands to achieve the proper body precision. During this activity, numerous muscles, including those in the legs, need to be exercised. Therefore, individuals who have lost part of their lower limb due to amputation face numerous significant challenges when it comes to practicing ballet. Throughout this article, the key aspects that influence the design of a lower limb prosthesis specifically adapted for ballet practice will be analyzed. New materials will be explored with the goal of designing an optimal model that ensures maximum performance and comfort for the users. Additionally, the prosthesis will be customized using 3D-printing technology, and a prototype will be presented. This study will merge biomechanics, ergonomics, and design. Its goal is to find a solution that improves the quality of life for lower limb amputees whose passion is ballet. The aim is to overcome physical and emotional barriers and provide a way to reintegrate amputee dancers into the world of dance. It is important to highlight the novelty of this work: combining different disciplines to provide a solution for individuals who engage in dance as a hobby rather than professionally. The proposed methodology aims to enable users with disabilities to access a personalized, complex, and potentially costly product.
CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
Authors: Yook Dongsuk , Han Geonhee , Chang Hyung-Pil , Yoo In-Chul
Published: October 2024

Voice conversion (VC) refers to the technique of modifying one speaker’s voice to mimic another’s while retaining the original linguistic content. This technology finds its applications in fields such as speech synthesis, accent modification, medicine, security, privacy, and entertainment. Among the various deep generative models used for voice conversion, including variational autoencoders (VAEs) and generative adversarial networks (GANs), diffusion models (DMs) have recently gained attention as promising methods due to their training stability and strong performance in data generation. Nevertheless, traditional DMs focus mainly on learning reconstruction paths like VAEs, rather than conversion paths as GANs do, thereby restricting the quality of the converted speech. To overcome this limitation and enhance voice conversion performance, we propose a cycle-consistent diffusion (CycleDiffusion) model, which comprises two DMs: one for converting the source speaker’s voice to the target speaker’s voice and the other for converting it back to the source speaker’s voice. By employing two DMs and enforcing a cycle consistency loss, the CycleDiffusion model effectively learns both reconstruction and conversion paths, producing high-quality converted speech. The effectiveness of the proposed model in voice conversion is validated through experiments using the VCTK (Voice Cloning Toolkit) dataset.
Node Importance Evaluation of Urban Rail Transit Based on Signaling System Failure: A Case Study of the Nanjing Metro
Authors: Hu Junhong , Yang Mingshu , Zhen Yunzhu , Fu Wenling
Published: October 2024

Assessing the importance of nodes in urban rail transit systems helps enhance their ability to respond to emergencies and improve reliability in view of the fact that most of the existing methods for evaluating the importance of rail transit nodes ignore the disturbance effect of signaling system failures and are unable to objectively identify critical stations in specific disturbance scenarios. Therefore, this paper proposed a method for evaluating the importance of urban rail transit nodes in signaling system failure scenarios. The method was based on the research background of the signaling system failure that occurs most frequently and analyzed the network failure mechanism after the occurrence of a disturbance. The node importance evaluation indices were selected from the network topology and network operation performance in two aspects. The variation coefficient–VIKOR method was employed to comprehensively assess the significance of urban rail transit stations during signaling system failures. The Nanjing Metro network was also used as an example to evaluate the importance of network stations. The results showed that under the attack method of signaling system failure, most ECC and interlocking stations experienced significantly higher network performance losses compared to the original attack method, and a few interchange stations showed smaller performance losses. The critical stations identified based on the proposed method are mainly distributed in the passenger flow backbone of the Nanjing Metro and were constructed in the early stage; of these, 85% are ECC stations or interlocking stations, which are easily neglected in daily management, in contrast to interchange stations with heavy passenger flow. The results of this study can provide an important reference for the stable operation and sustainable construction of urban rail transit.
Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
Authors: Chai Hongbao , Wu Jianguo , Zhang Lei , Zhao Yanlin , Cai Kangxu
Published: October 2024

This document introduces a novel equivalent algorithm for forecasting gas content within deep coal seams, which is subject to constraints stemming from the advancements and precision achieved in well and roadway engineering endeavors. This algorithm meticulously acknowledges that coal seam gas content comprises three fundamental components: the inherent gas emission rate of the equivalent stratum, the residual gas content retained within the coal seam itself, and the influence imparted by the gas content within the coal seam. Furthermore, the approach thoroughly considers variations in the level of porosity development within the coal seam and its surrounding rock formations, as well as the occurrence of gas within these structures. The equivalent layer is classified into two distinct groups: the sandstone zone and the clay zone. The sandstone zone utilizes pertinent parameters pertaining to fine sandstone, whereas the clay zone distinguishes between clay rock and thick mudstone. The influencing factor considerations solely encompass natural elements, such as the coal seam’s occurrence and geological structure. The residual gas content employs either existing measured parameters or acknowledged experimental parameters specific to the coal seam. Based on this predictive approach, an intelligent auxiliary software (V1.0) for mine gas forecasting was devised. The software calculates the gas content of deep coal seams within the mine at intervals of 100 m × 100 m, subsequently fitting the contour lines of gas content across the entire area. The gas content predictions derived from this equivalent algorithm demonstrate robust adaptability to variations in gas content caused by construction activities, and the prediction results exhibit an acceptable level of error on-site. Notably, the prediction process is not constrained by the progress of tunnel engineering, ensuring that the prediction outcomes can accurately represent the distribution characteristics of deep coal seam gas content. After a year of application, the prediction results have consistently met on-site requirements, providing a scientific foundation for the implementation of effective gas prevention and control measures in the mining area. Furthermore, this approach can effectively guide the formulation of medium- and long-term gas prevention and control plans for mines.
Physiological Response and Sports Injury Risk Relevant Biomechanics in Endurance Obstacle Course Races
Authors: Rey-Mota Jorge , Álvarez David Martín-Caro , Onate-Figuérez Ana , Yañez-Sepúlveda Rodrigo , Clemente-Suárez Vicente Javier
Published: October 2024

Obstacle course races (OCR) have experienced significant growth in recent years, with millions of participants worldwide. However, there is limited research on the specific physiological demands and injury prevention strategies required for these events. This study aimed to analyze the physiological responses and injury risks in participants of a 5 km (Sprint) and 13 km (Super) OCR. Sixty-eight participants were assessed for cortical arousal, leg strength, isometric handgrip strength, blood lactate, heart rate, blood oxygen saturation, body temperature, urine composition, spirometry values, hamstring flexibility, lower limb stability, foot biomechanics, and scapular kinematics, one hour before and immediately after the races. The results showed a significant decrease in leg strength (Sprint: r = −0.56, p < 0.01; Super: r = −0.54, p = 0.01) and urine pH (Sprint: r = −0.70, p = 0.03; Super: r = −0.67, p = 0.01) in both distances, with increases in urine colour, protein, and glucose (Sprint: p < 0.04). In the 13 km race, lower limb stability decreased significantly post-race (r = −0.53, p = 0.01). Positive correlations were found between performance and pre-race handgrip strength (Sprint: r = 0.71, p = 0.001; Super: r = 0.72, p = 0.01) and spirometry values (FVC, FEF 25–75%, FEV1) (Sprint: r = 0.52, p = 0.031; Super: r = 0.48, p = 0.035). Thermoregulation capacity, reflected in a higher pre-race body temperature and lower post-race body temperature, also correlated with improved performance (r = 0.49, p = 0.046). Injury risk increased post-race, with a significant decline in lower limb stability (p < 0.05). These findings highlight the importance of targeted training programs, focusing on grip strength, leg strength, respiratory muscle training, and hydration strategies to optimize performance and reduce injury risk in OCR athletes.