bluuebunny/crossref_metadata_2025 huggingface dataset:
Operational Protocol for Prosthetically Guided Reconstruction of Edentulous Sites Using a Custom-Made Technique
Authors: Brauner Edoardo , Laudoni Federico , Terribile Lucia , Bellizzi Andrea , Amelina Giulia , De Angelis Francesca , Pranno Nicola , Di Carlo Stefano
Published: December 2024

The success of dental implant placement and prosthetic reconstruction relies on sufficient bone volume at the implant site. Contemporary implantology integrates prosthetic planning with implant placement, emphasizing the importance of pre-established prosthetic goals. Guided Bone Regeneration (GBR) techniques have become crucial for bone reconstruction. This study explores the use of custom-made GBR membranes, designed through CAD-CAM technology, in 10 patients with severe bone defects. Patients underwent meticulous preoperative planning, in which digital diagnostic wax-ups and RealGUIDE™ software for implant positioning were used. The custom-made membranes, which were designed based on the individual defects, exhibited successful adaptation. Surgical phases involved membrane placement, bone grafting, and subsequent implant placement. Clinical–radiographic follow-ups demonstrated favorable bone gain outcomes, which enabled prosthetic rehabilitation. Despite membrane exposure occurring in 30% of cases, it did not compromise overall success. The study underscores the potential of custom-made GBR membranes in achieving prosthetic goals, though challenges like exposure and the need for a second surgery require further investigations. The results indicate a promising alternative for predictable bone regeneration in complex cases, shedding light on the prosthetically guided approach for optimal functional and aesthetic outcomes.
Recent Advances in Tunable External Cavity Diode Lasers
Authors: Wang Yan , Song Yue
Published: December 2024

A narrow linewidth tunable laser source is a critical component in various fields, including laser radar, quantum information, coherent communication, and precise measurement. Tunable external cavity diode lasers (ECDLs) demonstrate excellent performance, such as narrow linewidth, wide tunable range, and low threshold current, making them increasingly versatile and widely applicable. This article provides an overview of the fundamental structures and recent advancements in external cavity semiconductor lasers. In particular, we discuss external cavity semiconductor lasers based on quantum well and quantum dot gain chips. The structure of the gain chip significantly influences laser’s performance. External cavity quantum well laser has a narrower linewidth, higher power, and better mode stability. Conversely, external cavity quantum dot laser provides a wider tunable range and a remarkably lower threshold current. Furthermore, dual-wavelength external cavity tunable diode lasers are gaining importance in applications such as optical switching and terahertz radiation generation. With the continuous optimization of chips and external cavity structures, external cavity diode lasers are increasingly recognized as promising light sources with narrow linewidth and wide tunability, opening up broader application prospects.
Accurate Needle Localization in the Image Frames of Ultrasound Videos
Authors: Daoud Mohammad I. , Khraiwesh Samira , Alazrai Rami , Ali Mostafa Z. , Zayadeen Adnan , Qaadan Sahar , Alhaddad Rafiq Ibrahim
Published: December 2024

Ultrasound imaging provides real-time guidance during needle interventions, but localizing the needle in ultrasound videos remains a challenging task. This paper introduces a novel machine learning-based method to localize the needle in ultrasound videos. The method comprises three phases for analyzing the image frames of the ultrasound video and localizing the needle in each image frame. The first phase aims to extract features that quantify the speckle variations associated with needle insertion, the edges that match the needle orientation, and the pixel intensity statistics of the ultrasound image. The features are analyzed using a machine learning classifier to generate a quantitative image that characterizes the pixels associated with the needle. In the second phase, the quantitative image is processed to identify the region of interest (ROI) that contains the needle. In the third phase, the ROI is processed using a custom-made Ranklet transform to accurately estimate the needle trajectory. Moreover, the needle tip is identified using a sliding window approach that analyzes the speckle variations along the needle trajectory. The performance of the proposed method was evaluated by localizing the needle in ex vivo and in vivo ultrasound videos. The results show that the proposed method was able to localize the needle with failure rates of 0%. The angular, axis, and tip errors computed for the ex vivo ultrasound videos are within the ranges of 0.3–0.7°, 0.2–0.7 mm, and 0.4–0.8 mm, respectively. Additionally, the angular, axis, and tip errors computed for the in vivo ultrasound videos are within the ranges of 0.2–1.0°, 0.3–1.0 mm, and 0.3–1.1 mm, respectively. A key advantage of the proposed method is the ability to achieve accurate localization of the needle without altering the clinical workflow of the intervention.
Analytical and Computational Fluid Dynamics Methods for Determining the Torque and Power of a Vertical-Axis Wind Turbine with a Carousel Rotor
Authors: Lisowski Filip , Augustyn Marcin
Published: December 2024

This paper presents the results of experimental, analytical, and numerical studies on determining the driving torque and power of a vertical-axis wind turbine (VAWT) with planetary blade motion forced by a carousel rotor. First, experimental studies in the wind tunnel laboratory were conducted to determine the tip speed ratio λ for the real-scale wind turbine model under self-starting conditions. Then, an analytical kinematic model of the turbine was developed. Finally, computational fluid dynamics (CFD) analysis was conducted to verify the analytical approach and examine aerodynamic interferences between particular turbine blades. The main objective of the study was to verify the accuracy of the simplified analytical approach to calculating the driving torque and turbine power compared to the numerical results based on 2D analysis using computational fluid dynamics. The obtained results showed good agreement considering the modeling of the motion of the three dual-coherent blades of the wind turbine. Comparing the analytical and CFD approaches, the error in determining the average driving torque and the average turbine power was about 1%. An additional objective of the study was to use the developed analytical method to calculate the starting torque and demonstrate the main advantage of the carousel wind rotor, which is its higher starting torque compared to the H-type Darrieus rotor.
Sulfonated Polyethersulfone Membranes for Brackish Water Desalination: Fabrication, Characterization, and Electrodialysis Performance Evaluation
Authors: Chen Li , Deemer Eva M. , Li XiuJun , Walker W. Shane
Published: December 2024

The widespread application of electrodialysis is constrained by the high cost of ion exchange membranes, necessitating the development of affordable alternatives. This study focuses on the fabrication and performance evaluation of cation exchange membranes made from polyethersulfone (PES) and sulfonated polyethersulfone (sPES). Membranes were synthesized through phase inversion with varying solvent evaporation times, using N-Methyl-2-Pyrrolidone (NMP) as the solvent. The structural and functional modifications were confirmed using FTIR, XPS, and AFM techniques. Performance tests identified optimal electrodialysis results for PES membranes with a 3 h solvent evaporation time and for sPES membranes with a 1 h evaporation time. Under varying operational conditions, including applied voltage, flow rates, and feed solutions, sPES membranes demonstrated superior performance, underscoring their potential for cost-effective brackish water desalination applications.
Potential Flood Risk Scenario and Its Effects on Landscape Composition Using Hydraulic Modeling (HEC-RAS) in Boğaçay Sub-Basin/Türkiye
Authors: Selim Serdar , Kahraman Emine , Selim Ceren , Olgun Rifat , Karakuş Nihat , Önen Erhan , Çoşlu Mesut , Ardahanlıoğlu Zeynep R. , Çakır Mert , Çinar İsmail
Published: December 2024

Flooding, one of the most destructive and recurrent natural catastrophes, causes severe loss of life and property. The destructive effect of floods has increased with climate change and unplanned urbanization. To prevent this devastation and find solutions to potential flooding, it is important to improve engineering, ecological, hydrological, and hydrogeological precautions, as well as potential flood simulations. Using hydraulic models to perform flood simulations is a common and successful approach globally. In this study, HEC-RAS (1D) was used to simulate three different flood scenarios on the Boğaçay sub-basin in Antalya, the most important tourism destination of Türkiye. Flood scenarios were developed based on the data of the floods that occurred in the region in 2003 and 2006, with measured flow rates of 1899.9 m3/s and 1450 m3/s, respectively, and with the maximum flow rate (2408 m3/s) determined by the relevant ministry. Then, the landscape composition of the region at the sub-basin scale was determined and the impacts of flood scenarios on the landscape composition around the riverbed were evaluated. The results of the analysis show that the water height will increase, ranging from 1.4 m to 3.6 m, and the landscape composition of the region will be significantly affected by this increase in water height in the three different flood scenarios. Especially in the part where the river meets the sea, 580.74 ha of urban settlement is estimated to be damaged by flooding, according to the worst-case scenario. Finally, the study will guide decision-makers to take the necessary measures under the relevant scenarios.
Image Analysis of the Influence of the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) on the Mars Environmental Dynamics Analyzer at Extremely Low Reynolds Number
Authors: Rodríguez-Sevillano Ángel Antonio , Casati-Calzada María Jesús , Bardera-Mora Rafael , Matías-García Juan Carlos , Barroso-Barderas Estela , Fernández-Rivero Emilio
Published: December 2024

This study analyzes the influence of the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) on the Mars Environmental Dynamics Analyzer (MEDA) station located on board the Perseverance rover (Mars 2020). A novel visualization methodology was developed using a hydrodynamic towing tank and 3D-printed models created through additive manufacturing. This experimental approach, not previously applied in this context, proved to be a cost-effective alternative for studying thermal interactions while providing accurate preliminary insights into the behavior of thermal plumes under Martian-like conditions. Key factors such as the extremely low Reynolds number, an increasing temperature of the model, and atmospheric properties similar to those in Mars were incorporated. The findings suggest that the MMRTG’s thermal plume may significantly influence MEDA’s performance due to the plume’s height and its interaction with the surrounding environment.
A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection
Authors: Sarıateş Murat , Özbay Erdal
Published: December 2024

Background: Accurate and reliable classification models play a major role in clinical decision-making processes for prostate cancer (PCa) diagnosis. However, existing methods often demonstrate limited performance, particularly when applied to small datasets and binary classification problems. Objectives: This study aims to design a fine-tuned deep learning (DL) model capable of classifying PCa MRI images with high accuracy and to evaluate its performance by comparing it with various DL architectures. Methods: In this study, a basic convolutional neural network (CNN) model was developed and subsequently optimized using techniques such as L2 regularization, Tanh activation, dropout, and early stopping to enhance its performance. Additionally, a pyramid-type CNN architecture was designed to simultaneously evaluate both fine details and broader structures by combining low- and high-resolution information through feature maps extracted from different CNN layers. This approach enabled the model to learn complex features more effectively. For performance comparison, the developed fine-tuned enhanced pyramid network (FT-EPN) model was benchmarked against models such as Vgg16, Vgg19, Resnet50, InceptionV3, Densenet121, and Xception, which were trained using transfer learning (TL) techniques. It was also compared to next-generation models such as vision transformer (ViT) and MaxViT-v2. Results: The developed fine-tuned model achieved an accuracy rate of 96.77%, outperforming pre-trained TL models and next-generation models like ViT and MaxViT-v2. Among the TL models, Vgg19 achieved the highest accuracy rate at 92.74%. In comparison, ViT achieved an accuracy of 93.55%, while MaxViT-v2 achieved an accuracy of 95.16%. Conclusions: This study presents an optimized FT-EPN model to enhance the performance of DL models for PCa classification, offering a reference solution for future research. This model provides significant advantages in terms of classification accuracy and simplicity and has been evaluated as an effective solution in clinical applications.
Enhancing Supercapacitor Simulation Accuracy Through a Novel Hybrid Modeling Approach
Authors: Lemian Diana , Bode Florin Ioan , Lapusan Ciprian
Published: December 2024

In the last 2 decades, supercapacitors have become an important component in the hybrid energy-storage system, in combination with batteries. The resulting system offers higher performance in comparison to classic energy storage systems due to the complementary advantages offered by the two components. Accurate modeling of supercapacitors and batteries is an important step in the development process of hybrid energy storage systems. The presence of proper models has a direct impact on the optimal design of the energy management strategies and in enhancing the lifespan and performance of the system. In this paper, a new approach for modeling methods for supercapacitors, named Hybrid Modeling Approach for Supercapacitors (HyMASC), is proposed. The proposed method uses an equivalent RC circuit model to capture the dynamics of the supercapacitor and introduces a hybrid method to optimally calculate the model parameters. The validation of HyMASC is done using two supercapacitors with different capacitances. The obtained numerical models were simulated in Matlab for different scenarios, and their results and performance were compared with other methods from the literature. It was observed that the proposed method offers good results with better performance, thereby contributing to the advancement in the field of supercapacitor modeling techniques.
Collision Frequency and Energy Transfer Rate in e–He Scattering
Authors: Seitkozhanov Yeldos , Dzhumagulova Karlygash , Shalenov Erik , Jumagulov Murat
Published: December 2024

Using the optical interaction potential between an electron and a helium atom, we have calculated the momentum-transfer cross-section, collision frequency, and energy transfer rate during elastic electron–helium scattering, focusing on energies up to the ionization threshold of helium (24.6 eV). The interaction potential includes static, polarization, and exchange contributions, accurately representing the scattering process in this range. The optical potential method is well-suited for this analysis, as it effectively reduces the complexity of multiparticle interactions while maintaining the essential physics of elastic scattering. The calculated collision frequency as a function of energy exhibits a distinct maximum near 5 eV, consistent with experimental observations, which has not been captured in earlier theoretical studies. The energy transfer rate, derived using the effective collision frequency, demonstrates efficient energy exchange at low electron energies, with a gradual decline as the energy approaches the ionization threshold. These findings offer critical insights into plasma processes in the diverter region of tokamaks, where helium atoms play a significant role, and contribute to modeling energy transport properties such as electron mobility and temperature equilibrium. The results can serve as a valuable reference for plasma simulations and fusion research applications.
Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
Authors: Sun Zhe , Liang Bin , Liu Shengyao , Liu Zhansheng
Published: December 2024

The rapid expansion of civil infrastructure in China underscores the critical need for advanced solutions to ensure the structural health of aging bridges. This study introduces a novel data and knowledge-driven digital twin modeling (DK-DTM) framework designed to enhance the safe and efficient operation and maintenance (O&M) of bridges. Such a system should be capable of (1) monitoring structural dynamics in real time, (2) capturing spatiotemporal details and changes (e.g., defects and deformations), (3) analyzing structure deterioration patterns, (4) predicting structure failure risks, and (5) generating optimal maintenance and repair actions for ensuring structural safety. Previous studies have developed advanced sensing techniques and robust artificial intelligence algorithms for capturing and analyzing bridge health conditions. However, most existing techniques and algorithms heavily rely on high-quality data, which are difficult to obtain during bridge O&M. This raises the critical question of how to incorporate expert knowledge together with data-driven tools to establish a trustworthy DT for bridge O&M. This study presents the DK-DTM framework, which uniquely integrates multi-source data collection, spatiotemporal modeling, and expert knowledge reasoning. By combining these components, the framework supports smart structural health assessments of bridges, enabling comprehensive monitoring, prediction, and decision-making for efficient maintenance. The spatial and temporal models provide real-time data, while the expert knowledge model functions as an automated evaluation tool for structural health assessment. The results demonstrate that the proposed DK-DTM framework significantly enhances the accuracy and efficiency of O&M processes for aging bridges, addressing key gaps in existing digital twin methodologies.
Electric Ferry Fleet Peak Charging Power Schedule Optimization Considering the Timetable and Daily Energy Profile
Authors: Peša Tomislav , Krčum Maja , Kero Grgo , Šoda Joško
Published: December 2024

Decarbonization of shipping is a legal obligation imposed by the International Maritime Organization (IMO). The ferry port and daily operations located near or in urban zones negatively impact the nearby environment. The electrification of ferries contributes to reducing the negative environmental impact. The available electrical infrastructure in ports often does not meet daily needs. The ferry fleet’s sailing schedule creates a non-periodic daily energy profile to determine the energy needs of the shore connection. The proposed research aims to optimize the daily electric ferry fleet peak charging power schedule process using particle swarm optimization and a greedy algorithm. A four-stage model has been proposed, consisting of the initialization of the ferry fleet’s daily energy profile, initial population generation with input constraints, optimization, and the creation of the modified daily energy load diagram. Robustness and validation of the proposed model were investigated and proven for energy profiles with and without optimization. For the proposed charging schedule, the study results show a reduction in peak power of 24%. By optimizing the charging process, peak charging power has been reduced without needing an additional energy storage system.
Investigation of Pre-Pulse Effects on Ultrashort-Pulse Laser Interaction with Structured Targets
Authors: Kim Artem , Dey Indranuj , Bespaly Alexander , Komm Pavel , Shaham Assaf , Papeer Jenya , Botton Mordechai , Zigler Arie
Published: December 2024

The role of pre-plasma in the efficient generation of protons by intense laser-matter interaction from structured targets is investigated. Optimal energy coupling between laser and plasma is found by varying the fluence and arrival time of an independently controllable ultrashort pre-pulse with respect to the main interaction pulse. The coupling is evaluated based on the energy of the accelerated protons. The accelerated proton energy is maximized at optimal pre-pulse delay and fluence conditions. Plasma emission spectrum and Particle-in-Cell simulations provide a possible explanation of the obtained experiment results.
Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
Authors: Wen Yalin , Ke Wei , Sheng Hao
Published: December 2024

Video location prediction for handwritten digits presents unique challenges in computer vision due to the complex spatiotemporal dependencies and the need to maintain digit legibility across predicted frames, while existing deep learning-based video prediction models have shown promise, they often struggle with preserving local details and typically achieve clear predictions for only a limited number of frames. In this paper, we present a novel video location prediction model based on Convolutional Gated Recurrent Units (ConvGRU) that specifically addresses these challenges in the context of handwritten digit sequences. Our approach introduces three key innovations. Firstly, we introduce a specialized decoupling model using modified Generative Adversarial Networks (GANs) that effectively separates background and foreground information, significantly improving prediction accuracy. Secondly, we introduce an enhanced ConvGRU architecture that replaces traditional linear operations with convolutional operations in the gating mechanism, substantially reducing spatiotemporal information loss. Finally, we introduce an optimized parameter-tuning strategy that ensures continuous feature transmission while maintaining computational efficiency. Extensive experiments on both the MNIST dataset and custom mobile datasets demonstrate the effectiveness of our approach. Our model achieves a structural similarity index of 0.913 between predicted and actual sequences, surpassing current state-of-the-art methods by 1.2%. Furthermore, we demonstrate superior performance in long-term prediction stability, with consistent accuracy maintained across extended sequences. Notably, our model reduces training time by 9.5% compared to existing approaches while maintaining higher prediction accuracy. These results establish new benchmarks for handwritten digit video prediction and provide practical solutions for real-world applications in digital education, document processing, and real-time handwriting recognition systems.
Acute Effects of Drop Jumps on Lower Limb Stiffness and Mechanical and Kinematic Parameters During High-Speed Treadmill Running
Authors: Pappas Panagiotis , Stavridis Ioannis , Paradisis Giorgos
Published: December 2024

This study aimed to investigate the post-activation performance enhancement effects of drop jumps (DJs) on lower limb stiffness [leg stiffness (Kleg); vertical stiffness (Kvert)] and the related mechanical [maximal ground reaction force (Fmax), vertical displacement of the centre of mass (Δy), and lower limb length (ΔL)] and kinematic parameters [step length (SL), step frequency (SF), flight time (FT), and contact time (CT)] during high-speed treadmill running. For this purpose, 18 male physical education students performed 10 s running bouts on a treadmill at a speed of 6.67 m·s−1 in an experimental condition (EC) or in a control condition (CC). During the EC, following a 5 min treadmill running warm-up at 2.22 m·s−1, the participants were tested pre and 3 min post five DJs, while during the CC, the participants did not perform DJs. The study results revealed that EC significantly increased Fmax, Δy, SL, and FT, while SF decreased after performing drop jumps (mean differences: 0.026 ± 0.007 kN, p = 0.003; 0.001 ± 0.001 m, p = 0.025; 0.034 ± 0.013 m, p = 0.019; 0.05 ± 0.001 s, p = 0.005 and −0.057 ± 0.023 Hz, p = 0.026, respectively). Kleg and Kvert showed no significant differences (p > 0.05) following the preconditioning exercise. The findings suggest that a plyometric stimulus of low volume and a short recovery period prior to high-speed treadmill running may be beneficial to acutely improve running mechanical and kinematic parameters without affecting lower limb stiffness.
Effect of Camera Choice on Image-Classification Inference
Authors: Brown Jason , Nguyen Andy , Raj Nawin
Published: December 2024

The field of image classification using Convolutional Neural Networks (CNNs) to predict the principal object in an image has seen many recent innovations. One aspect that has not been extensively explored is the effect of the camera employed to acquire images for inference. We investigate this by capturing comparable images of five drinking vessels using six cameras in various scenarios. We examine the classification ranking of object classes when these images are input to an independently pretrained Resnet-18 model based on the ImageNet-1k dataset. We find that the camera used can affect the top prediction of object class, particularly in scenarios with a more complex background. This is the case even when the cameras have similar fields of view. We also introduce a metric called selectivity, defined as the mean absolute difference between prediction probabilities of similar relevant object classes (such as cups and mugs). We show that the effect of the camera is largest when the selectivity of the pretrained model between these object classes is small. The effect of camera choice is also demonstrated quantitatively by examining Cohen’s Kappa (κ) statistic. Finally, we make recommendations on mitigating the effect of the camera on image-classification inference.
Comparative Metabolic Profiling of Different Raphanus sativus Cultivars and Their Antioxidant and Anti-Inflammatory Activities
Authors: Kim Jae Hyeon , Kim Heeju , Park Minkuk , Park Byeongjun , Park Beomjune , Park Han Yong , Lee Gakyung , Yu Jae Sik
Published: December 2024

This study investigates the phytochemical composition of the aerial parts of three radish cultivars: KR 391, KR CheongHong, and KR 407, named RL1, RL2, and RL3, respectively, using UPLC–Orbitrap–MS to profile the secondary metabolites. The secondary metabolites were characterized to provide a comprehensive overview of their diversity and bioactivity. Multivariate analysis revealed cultivar-specific variations in metabolite profiles and tentatively identified 22 metabolites, which include 17 flavonoids and five phenolics. The extracts of the radish cultivars were tested for their cytotoxicity, antioxidant activity measured with a DPPH assay, and anti-inflammatory activity measured by NO production. The findings highlight the pharmaceutical potential of radish aerial parts and provide insights into the relationship between metabolite composition and biological activity, contributing to their value in health-promoting applications.
Study on the Multi-Hazard Responses of Transmission Tower-Line Systems Under Fire and Wind Loads Using ABAQUS
Authors: He Xiwei , Ma Huichao , Zhang Shibo , Wang Wenming , Zhang Lijuan
Published: December 2024

Transmission lines are usually located outdoors and are subjected to wind loads year-round. When a fire occurs, transmission towers are exposed to the combined effects of fire and wind loads. This paper investigates the impact of high temperatures on the bearing capacity of transmission tower-line systems under wind load and explores the effects of uneven horizontal spacing distribution and changes in the elevation of the target tower on the bearing capacity of the tower-line system. The failure criteria for transmission tower components at high temperatures were determined by considering the constitutive relationship of steel at ambient temperature and the variation patterns in material strength and elastic modulus with temperature. A finite element model of the transmission tower-line system was established using ABAQUS (2023). This paper studied the effects of temperature, uneven horizontal spacing distribution, and changes in the elevation of the target tower on the response of the transmission tower-line system by comparing collapse-resisting wind speeds and collapse processes under various conditions. The research indicates that the load-bearing capacity of the transmission tower-line system decreases as temperature increases. When the temperature exceeds 400 °C, the collapse-resisting wind speed of the transmission tower drops sharply. At temperatures above 600 °C, the transmission tower may collapse even at the annual average wind speed. In addition, the uneven horizontal spacing distribution and changes in the elevation of the target tower have an adverse effect on the stability of the transmission tower-line system. It is recommended to choose steel materials with higher fire resistance or apply fire-resistant coatings to existing steel, and to avoid extremely uneven spacing distributions and excessively high target tower elevations.
Evaluating the Role of Interactive Encouragement Prompts for Parents in Parent–Child Stress Management
Authors: Wang Pinhao , Huang Lening , Dai Guang , Li Jing , Hu Jun , Barakova Emilia , Yao Cheng , Ying Fangtian
Published: December 2024

Parental involvement is crucial for children’s stress management, and co-regulation of stress can have a positive effect. To facilitate parental involvement in children’s stress management in learning, we proposed an embodied connected system, which provides stress detection, stress information feedback, and encouragement prompts, aiming to help parents better understand and engage in children’s stress-regulation process. This article focuses on the impact of interactive encouragement prompts provided to parents on children’s stress management. The within-group experiment was used to collect stress data and scales from 36 parent–child groups during a controlled learning experiment, and semi-structured interviews were conducted with parents and children. The results indicate that the encouragement prompts provided to the parents enhance the effectiveness of stress relief in children facilitated by parental involvement. In particular, the psychological stress was reduced, and the communication between parents and children became more effective. In addition, active parental involvement and timely encouragement prompts can improve children’s stress-coping abilities, providing an interactive intervention approach for learning stress management.
Geological Influences on Wine Quality: Analyzing Nebbiolo Grapes from Northern Italy
Authors: Santagostini Laura , Guglielmi Vittoria
Published: December 2024

This study investigates the critical relationship between soil characteristics, trace element concentrations in Nebbiolo grapes, and the resulting wine quality, emphasizing the importance of terroir in winemaking. Italy, particularly the regions of Piedmont, Lombardy, and the Aosta Valley, is home to Nebbiolo, a prestigious grape variety known for its depth and aging potential in wines like Barolo and Barbaresco. The research focuses on seventeen grape and wine samples, highlighting how soil mineral composition could affect grape composition and wine characteristics. The analysis employed ICP-AES (inductively coupled plasma atomic emission spectrometry) to measure trace elements such as Al, Ba, and Mn, linking their concentrations to the soil’s geological properties. Elements were categorized into three groups based on their origins—natural soil contributions (Al, Ba, Li, Mn, Mo, Sr, Ti), those influenced by production cycles (Ca, Mg, K, Cu, Zn, Fe), and artificial sources (Co, Cr, Ni, V)—asserting that the first group serves as the most reliable indicators for tracing wines back to their vineyard origins. By establishing a chemical fingerprint for Nebbiolo wines, this research aims to enhance their authenticity and market value while providing insights into the intricate interplay between soil, grape varietals, and winemaking practices and contemporary challenges like climate change and evolving market demands.
Enhancing Camera Source Identification: A Rapid Algorithm with Enhanced Discriminative Power
Authors: Lai Zhimao , Cheng Lijuan , Feng Renhai
Published: December 2024

Digital image source identification primarily focuses on analyzing and detecting the machine imprints or camera fingerprints left by imaging devices during the imaging process to trace the origin of digital images. The development of a swift search algorithm is crucial for the effective implementation of camera source identification. Despite its importance, this domain has witnessed limited research, with existing studies predominantly focusing on search efficiency while neglecting robustness, which is essential. In practical scenarios, query images often suffer from poor signal quality due to noise, and the variability in fingerprint quality across different sources presents a significant challenge. Conventional brute-force search algorithms (BFSAs) prove largely ineffective under these conditions because they lack the necessary robustness. This paper addresses the issues in digital image source identification by proposing a rapid fingerprint search algorithm based on global information. The algorithm innovatively introduces a search priority queue (SPQ), which analyzes the global correlation between the query fingerprint and all reference fingerprints in the database to construct a comprehensive priority ranking, thereby achieving the efficient retrieval of matching fingerprints. Compared to the traditional brute-force search algorithm (BFSA), our method significantly reduces computational complexity in large-scale databases, optimizing from O(nN) to O(nlogN), where n is the length of the fingerprint, and N is the number of fingerprints in the database. Additionally, the algorithm demonstrates strong robustness to noise, maintaining a high matching accuracy rate even when image quality is poor and noise interference is significant. Experimental results show that in a database containing fingerprints from 70 cameras, our algorithm is 50% faster in average search time than BFSA, and its matching accuracy rate exceeds 90% under various noise levels. This method not only improves the efficiency and accuracy of digital image source identification but also provides strong technical support for handling large-scale image data, with broad application prospects in fields such as copyright protection and forensic evidence.
Advances in the Study of Bioactive Compounds and Nutraceutical Properties of Goji Berry (Lycium barbarum L.)
Authors: Shi Xin , Wang Xiaojing , Zheng Yuhong , Fu Li
Published: December 2024

This review examines the nutritional composition, bioactive compounds, and potential health benefits of goji berries (Lycium barbarum L.). Goji berries contain significant amounts of carbohydrates (46–87% dry weight), proteins (5.3–14.3% dry weight), and dietary fiber (3.63–16 g/100 g fresh weight). They are rich in micronutrients, including vitamin C (2.39–48.94 mg/100 g fresh weight) and potassium (434–1460 mg/100 g fresh weight). The berries’ unique polysaccharides, particularly Lycium barbarum polysaccharides (LBPs), exhibit molecular weights ranging from 10 to 2300 kDa. Flavonoids, such as quercetin and rutin (1.0–1.3 mg/g dry weight), and carotenoids, especially zeaxanthin (0.5–1.2 mg/g dry weight), contribute to the berries’ antioxidant properties. In vitro and animal studies have demonstrated various health benefits, including antioxidant, anti-inflammatory, and immunomodulatory effects. However, more human clinical trials are needed to confirm these findings. The review also highlights the impact of geographical origin, cultivation practices, and processing methods on nutrient composition, emphasizing the need for standardization in research and commercial applications. Future research should focus on the bioavailability, metabolism, and potential synergistic effects of goji berry compounds.
Analysis of the Matching Media Effects by Microwave Field Distribution Simulations for the Cylindrically Layered Human Arm Model
Authors: Yelkenci Tanju
Published: December 2024

In this study, a method is presented to determine the matching media parameters that maximize the electromagnetic energy penetrating into the human arm modeled as a radially stratified cylinder. In this context, first, the electromagnetic scattering problem related to the layered cylindrical model in question was solved analytically using cylindrical harmonics. Then, based on this solution, a frequency-dependent functional in terms of the electromagnetic parameters of the matching medium was defined, and the parameters that minimize this functional were determined through the graphs of this functional. In this functional, which depends on the permittivity, conductivity and frequency of the matching medium, one parameter was kept constant at every turn while the other two parameters were optimized. The accuracy of the approach was demonstrated by calculating the electric field amplitudes inside and outside the layers for the parameters determined by the proposed method. The numerical results given in this context demonstrate that if a matching medium is used, the penetrating field increases between 1.3 to 13.96 times compared to the case where the matching medium is absent.
CMM Influence Factors and Uncertainty Associated with Length Measurement
Authors: Forbes Alistair
Published: December 2024

This paper is concerned with coordinate measuring machine (CMM) uncertainty evaluation, in particular, the uncertainties associated with point clouds and distances derived from the point cloud. The uncertainty evaluation approach is model-based following the principles of the Guide to the Expression of Uncertainty in Measurement and the law of the propagation of uncertainty. The paper considers a range of CMM influence factors and derives an explicit dependence for the point cloud data coordinates on the influence factors, allowing uncertainties associated with the influence factors to be propagated through to point cloud uncertainties. The paper describes the use of Gaussian processes to model kinematic and probing errors using a small number of statistical hyper-parameters. These models permit an explicit statement of the uncertainty associated with point clouds and length measurement, enabling the latter to be compared directly with a statement of the maximum permissible error in length measurement. The uncertainty evaluation methodology is direct in that it requires no optimisation nor Monte Carlo simulations.
Special Issue on Advanced Technologies in Seismic Design, Assessment and Retrofitting
Authors: Morfidis Konstantinos , Kostinakis Konstantinos
Published: December 2024

The seismic design of new structures, as well as the assessment of seismic vulnerability and the retrofitting or rehabilitation of existing structures, are two important research fields in civil engineering [...]
Prediction of Truck Fuel Consumption Based on Crossformer-LSTM Characteristic Distillation
Authors: Du Kai , Shi Qingqing , Song Jingni , Chen Dan , Liu Weiyu
Published: December 2024

With the increasing number of heavy-duty trucks and their high fuel consumption characteristics, reducing fuel costs has become a primary challenge for the freight industry. Consequently, accurately predicting fuel consumption for heavy-duty trucks is crucial. However, existing fuel consumption prediction models still face challenges in terms of prediction accuracy. To address this issue, a model named Cross-LSTM Multi-Feature Distillation (CLMFD) is proposed. The CLMFD model employs the Crossformer model and the LSTM model as teacher and student models, respectively, utilizing multi-layer intermediate features for distillation. Fuel consumption data from a vehicular networking system was used in this study. Initially, the raw data were preprocessed by segmenting it into two-kilometer intervals, calculating sample features, and handling outliers using box plots. Feature selection was then performed using XGBoost. Subsequently, the CLMFD model was applied to predict fuel consumption. Experimental results demonstrate that the CLMFD model significantly outperforms baseline models in prediction performance. Ablation studies further indicate that the CLMFD model effectively integrates the strengths of both the Crossformer and LSTM, exhibiting superior predictive performance. Finally, predictions on data with varying masking rates show that the CLMFD model demonstrates robust performance. These findings validate the reliability and practicality of the CLMFD model, providing strong support for future research in fuel consumption prediction.
Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis
Authors: Walczyna Tomasz , Jankowski Damian , Piotrowski Zbigniew
Published: December 2024

This article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologies to enhance anomaly detection performance. A comparative analysis of autoencoders, Variational Autoencoders, and their modified counterparts was conducted within a tailored experimental environment designed to simulate real-world scenarios. The results demonstrate that these models, when fine-tuned, achieve significant improvements in detection accuracy, specificity, and sensitivity while maintaining computational efficiency. The findings underscore the importance of lightweight, practical models and the integration of streamlined training processes in developing effective anomaly detection systems. This study provides valuable insights into advancing machine learning methods for real-world applications and sets the stage for further refinement of autoencoder-based approaches.
Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study
Authors: Grobler-Dębska Katarzyna , Mularczyk Rafał , Gawęda Bartłomiej , Kucharska Edyta
Published: December 2024

Corporate budget planning involves forecasting expenses and revenues to support strategic goals, resource allocation, and supply chain coordination. Regular updates to forecasts and collaboration across organizational levels ensure adaptability to changing business conditions. Long-term sales forecasts form the foundation for budgeting, guiding resource allocation and enhancing financial efficiency. The budgeting process in organizations is complex and requires data from various operational areas, which is collected over a representative period. Key inputs include quantitative sales data, direct costs indirect costs, and historical revenues and profitability, which are often sourced from ERP systems. While ERP systems typically provide tools for basic budgeting, they lack advanced capabilities for forecasting and simulation. We proposed a solution, which includes dynamic demand forecasting based on time series methods such as Build-in method in Power BI (which is ETS—exponential smoothing), linear regression, XGBoost, ARIMA and flexible product groupings, which are simulations for cost changes. The case study concerns a manufacturing company in the mass customization industry. The solution is designed to be intuitive and easily implemented in the business.
Impact of Varying Amplitudes of Upward and Downward Motion on the Torque/Force Generation, Canal-Centering Ability, and Cyclic Fatigue Resistance of Nickel–Titanium Reciprocating Instrument
Authors: Hirano Keiko , Kimura Shunsuke , Maki Keiichiro , Omori Satoshi , Ebihara Arata , Okiji Takashi
Published: December 2024

The optimal amplitude of pecking motion during nickel–titanium rotary root canal instrumentation remains unclear. This study evaluated how different pecking amplitudes influence the force/torque generation, canal-centering ability, and cyclic fatigue resistance of the Reciproc Blue R25 instrument. Thirty J-shaped resin canals were instrumented with Reciproc Blue using automated instrumentation and a torque/force sensing device with an up-and-down speed of 50 mm/min and varying pecking amplitudes: Group S (1 s apically, 0.5 s coronally), Group M (2 s apically, 1 s coronally), and Group L (4 s apically, 2 s coronally) (n = 10 each). Force/torque were recorded, and canal-centering ratios were determined. In a dynamic cyclic fatigue test, the Reciproc Blue instruments were rotated in a curved metal canal with 1 mm or 3 mm back-and-forth motions (n = 10 each). Group L exhibited significantly greater maximum downward force and cumulative upward (screw-in) force than Group S (p < 0.05), but torque was not significantly different across groups. Groups M and L exhibited a significantly smaller canal-centering ratio than Group S (p < 0.05). Pecking at 3 mm led to a significantly longer time to fracture and shorter fragment lengths than 1 mm pecking (p < 0.05). In the Reciproc Blue instrumentation, higher pecking amplitude improved canal-centering ability and cyclic fatigue resistance while increasing upward and downward forces.
Enhanced Magnetic Properties of Co1−xMnxFe2O4 Nanoparticles
Authors: Szatmari Adam , Bortnic Rareș , Atanasov Roman , Barbu-Tudoran Lucian , Nekvapil Fran , Dudric Roxana , Tetean Romulus
Published: December 2024

Co1−xMnxFe2O4 nanoparticles (0 ≤ x ≤ 1) have been prepared via the hydrothermal method. The prepared samples were studied using X-ray diffraction measurements (XRD), transmission electron microscopy (TEM), Raman spectroscopy, and magnetic measurements. All studied samples were found to be single phases and to have a cubic Fd-3m structure. The average crystalline sizes are between 7.8 and 15 nm. EDS analysis confirmed the presence of cobalt, manganese, iron, and oxygen in all prepared samples. It was found by Raman spectroscopy that Fe3+ would be placed on octahedral sites while Fe2+ would, in turn, be displaced to tetrahedral sites while Mn ions will be placed on both sites. Both Mn2+ and Mn4+ are present in studied ferrites. The experimental saturation magnetizations for doped samples are much higher when compared with previous reports, reaching values between 3.71 and 6.7 μB/f.u. The doping with Mn in nanocrystalline cobalt ferrite enhanced the magnetic properties due to changes in the cation distribution between the two sublattices. The higher magnetic moments are explained by the presence of Mn4+ ions located preferentially on tetrahedral sites while Mn2+ prefer octahedral sites, and by the high quality and crystallinity of our samples the nanoparticles being almost monodomain. Large values of the coercive field were found at 4.2 K while the hysteresis is almost absent in all investigated samples at room temperature.