bluuebunny/crossref_metadata_2025 huggingface dataset:
Visual Odometry in GPS-Denied Zones for Fixed-Wing Unmanned Aerial Vehicle with Reduced Accumulative Error Based on Satellite Imagery
Authors: Mateos-Ramirez Pablo , Gomez-Avila Javier , Villaseñor Carlos , Arana-Daniel Nancy
Published: August 2024

In this paper, we present a method for estimating GPS coordinates from visual information captured by a monocular camera mounted on a fixed-wing tactical Unmanned Aerial Vehicle at high altitudes (up to 3000 m) in GPS-denied zones. The main challenge in visual odometry using aerial images is the computation of the scale due to irregularities in the elevation of the terrain. That is, it is not possible to accurately convert from pixels in the image to meters in space, and the error accumulates. The contribution of this work is a reduction in the accumulated error by comparing the images from the camera with satellite images without requiring the dynamic model of the vehicle. The algorithm has been tested in real-world flight experiments at altitudes above 1000 m and in missions over 17 km. It has been proven that the algorithm prevents an increase in the accumulated error.
Enhancing Firewall Packet Classification through Artificial Neural Networks and Synthetic Minority Over-Sampling Technique: An Innovative Approach with Evaluative Comparison
Authors: Korkmaz Adem , Bulut Selma , Talan Tarık , Kosunalp Selahattin , Iliev Teodor
Published: August 2024

Firewall packet classification is a critical component of network security, demanding precise and reliable methods to ensure optimal functionality. This study introduces an advanced approach that combines Artificial Neural Networks (ANNs) with various data balancing techniques, including the Synthetic Minority Over-sampling Technique (SMOTE), ADASYN, and BorderlineSMOTE, to enhance the classification of firewall packets into four distinct classes: ‘allow’, ‘deny’, ‘drop’, and ‘reset-both’. Initial experiments without data balancing revealed that while the ANN model achieved perfect precision, recall, and F1-Scores for the ‘allow’, ‘deny’, and ‘drop’ classes, it struggled to accurately classify the ‘reset-both’ class. To address this, we applied SMOTE, ADASYN, and BorderlineSMOTE to mitigate class imbalance, which led to significant improvements in overall classification performance. Among the techniques, the ANN combined with BorderlineSMOTE demonstrated superior efficacy, achieving a 97% overall accuracy and consistently high performance across all classes, particularly in the accurate classification of minority classes. In contrast, while SMOTE and ADASYN also improved the model’s performance, the results with BorderlineSMOTE were notably more balanced and reliable. This study provides a comparative analysis with existing machine learning models, highlighting the effectiveness of the proposed approach in firewall packet classification. The synthesized results validate the potential of integrating ANNs with advanced data balancing techniques to enhance the robustness and reliability of network security systems. The findings underscore the importance of addressing class imbalance in machine learning models, particularly in security-critical applications, and offer valuable insights for the design and improvement of future network security infrastructures.
Experimental Investigation of Heat Dissipation of Lithium–Ion Cells and Its Correlation with Internal Resistance
Authors: Peringer Stefan Michael , Kotak Yash , Schweiger Hans-Georg
Published: August 2024

Power loss is a limiting factor for batteries and individual cells. The resulting heat generation due to the power loss leads to reduced battery performance and, thus, lower efficiency. These losses are largely due to the internal resistance of the cells. Therefore, it is important to accurately determine the value of the internal resistance of lithium–ion cells. From the literature, it was found that there are three widely used internal resistance-measurement methods (current step method, direct-energy-loss method, and calorimeter measurement), with negligible research on their comparison demonstrating the most efficient method. Henceforth, to find the most optimal method, this research adopts all three methods on a variety of cell chemistries, including Lithium-ion Manganese Oxide (LMO), Lithium Iron Phosphate (LFP), Nickel Manganese Cobalt (NMC), and Lithium Titanium-Oxide (LTO) for different c-rates (1 C, 2 C, and 3 C), with a wide temperature range (from 0 °C to 40 °C).
Revealing the Protective Dynamics of an Ecologically Engineered Wetland against Acid Mine Drainage: A Case Study in South Africa
Authors: Jansen van Vuuren Mariette , Schoeman Yolandi , Botha Anna-Maria , Oberholster Paul J.
Published: August 2024

This study investigated the Zaalklapspruit valley bottom wetland in South Africa, an ecologically engineered site influenced by acid mine drainage (AMD) from a defunct coal mine upstream. Conducted in 2022, the research aimed to elucidate the dynamics of contaminant dispersal within this wetland, focusing on the sources, pathways, and receptors of metals and sulfur compounds. The analysis revealed that the wetland’s bottom sediment is rich in organic material, with pH values ranging from 6.05 to 6.59 and low oxidation-reduction potentials reaching −219.67 mV at Site S3. The significant findings included the highest adsorption rates of manganese, contrasted with iron, which was primarily absorbed by the roots of Typha capensis and the algae Klebsormidium acidophilum. The macrophyte rhizospheres were found to host diverse microbiota, including families such as Helicobacteraceae and Hydrogenophilaceae, pivotal in metal and sulfur processing. This study highlighted the complex biogeochemical interactions involving sediment, macrophyte root systems, periphyton, and microbial populations. These interactions demonstrate the efficacy of ecologically engineered wetlands in mitigating the impacts of acid mine drainage, underscoring their potential for environmental remediation. Importantly, the sustainability of such interventions highlights the need for community involvement and acceptance, acknowledging that local support is essential for the long-term success of ecological engineering solutions that address environmental challenges like AMD.
Intrinsic and Extrinsic Factors Associated with Hair Graying (Canities) and Therapeutic Potential of Plant Extracts and Phytochemicals
Authors: Boo Yong Chool
Published: August 2024

This review aims to gain insight into the major causes of hair graying (canities) and how plant-derived extracts and phytochemicals could alleviate this symptom. Research articles on human hair graying were searched and selected using the PubMed, Web of Science, and Google Scholar databases. We first examined the intrinsic and extrinsic factors associated with hair graying, such as the reduced capacity of melanin synthesis and transfer, exhaustion of melanocyte stem cells (MSCs) and melanocytes, genetics and epigenetics, race, gender, family history, aging, oxidative stress, stress hormones, systematic disorders, nutrition, smoking, alcohol consumption, lifestyle, medications, and environmental factors. We also examined various plants and phytochemicals that have shown a potential to interfere with the onset or progression of human hair graying at different levels from in vitro studies to clinical studies: the extract of Polygonum multiflorum and its major components, 2,3,5,4′-tetrahydroxystilbene-2-O-β-D-glucoside and emodin; the extract of Eriodictyon angustifolium and its major flavonoid compounds, hydroxygenkwanin, sterubin, and luteolin; the extracts of Adzuki beans (Vigna angularis), Fuzhuan brick tea (Camellia sinensis), and Gynostemma pentaphyllum; bixin, a carotenoid compound found in Bixa orellana; and rhynchophylline, an alkaloid compound found in certain Uncaria species. Experimental evidence supports the notion that certain plant extracts and phytochemicals could alleviate hair graying by enhancing MSC maintenance or melanocyte function, reducing oxidative stress due to physiological and environmental influences, and managing the secretion and action of stress hormones to an appropriate level. It is suggested that hair graying may be reversible through the following tactical approaches: selective targeting of the p38 mitogen-activated protein kinase (MAPK)–microphthalmia-associated transcription factor (MITF) axis, nuclear factor erythroid 2-related factor 2 (NRF2), or the norepinephrine–β2 adrenergic receptor (β2AR)–protein kinase A (PKA) signaling pathway.
PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring—A Comprehensive Review
Authors: Lapsa Didzis , Janeliukstis Rims , Metshein Margus , Selavo Leo
Published: August 2024

The monitoring of hemodynamic parameters, such as heart rate and blood pressure, provides valuable indications of overall cardiovascular health. It is preferable that such monitoring is non-invasive and in real time via an affordable, compact and small-scale device for maximum convenience. Numerous literature sources have exploited derivations of these parameters from photoplethysmogram (PPG) and electrical bioimpedance (EBI) signal measurements through the use of calculation algorithms of varying complexity. Compared to electrocardiogram (ECG), these measurement techniques have a merit of well-established practices of designing a wearable device that could conveniently be put on a wrist. The current paper provides a comprehensive review on the use of PPG and EBI measurement techniques in the context of hemodynamic parameter monitoring using a wearable device. A special emphasis is placed on the most basic hemodynamic parameter—heart rate—describing different algorithms of heart rate detection and monitoring. The last section provides an overview of commercially available and in-home wearable device technologies based on PPG and EBI measurements, their design challenges, and future prospects.
Large Language Models for Intelligent Transportation: A Review of the State of the Art and Challenges
Authors: Wandelt Sebastian , Zheng Changhong , Wang Shuang , Liu Yucheng , Sun Xiaoqian
Published: August 2024

Large Language Models (LLMs), based on their highly developed ability to comprehend and generate human-like text, promise to revolutionize all aspects of society. These LLMs facilitate complex language understanding, translation, content generation, and problem-solving, enabled by vast historical data processing and fine-tuning. Throughout the past year, with the initial release of ChatGPT to the public, many papers have appeared on how to exploit LLMs for the ways we operate and interact with intelligent transportation systems. In this study, we review more than 130 papers on the subject and group them according to their major contributions into the following five categories: autonomous driving, safety, tourism, traffic, and others. Based on the aggregated proposals and findings in the extant literature, this paper concludes with a set of challenges and research recommendations, hopefully contributing to guide research in this young, yet extremely active research domain.
Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis
Authors: Tutcu Burçin , Kayakuş Mehmet , Terzioğlu Mustafa , Ünal Uyar Güler Ferhan , Talaş Hasan , Yetiz Filiz
Published: August 2024

IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective.
Therapeutic Potential of Water Chestnut Fruit Extract (Trapa bicornis) against Ovariectomy-Induced Climacteric Symptoms in Mice
Authors: Shin Su , Mohibbullah Md. , Kim Ki-Young , Hong Eun-Jin , Choi Jae-Suk , Ku Sae-Kwang
Published: August 2024

Climacteric symptoms, as well as postmenopausal estrogen deficiency, have been associated with many psychological problems and the risk of osteoporosis and heart disease. Therefore, in this study, we aimed to evaluate, for the first time, the dose-dependent effect of water chestnut (WC), also known as Trapa bicornis, a fruit extract, on ovariectomy (OVX)-induced menopause in ICR mice. After bilateral OVX surgery, 200, 100, and 50 mg/kg of WC and 200 mg/kg of pomegranate concentrate powder (PCP) were administered orally for 84 days from 4 weeks after OVX operation. Then, anti-climacteric activities were evaluated in five groups: (1) estrogenic, (2) anti-obesity, (3) hypolipidemic, (4) hepatoprotective, and (5) anti-osteoporosis effects. Different biochemical assays, histopathological and morphological inspections, and mRNA expression findings showed that OVX-induced estrogen deficiency-related AMPK decrease was associated with climacteric symptoms such as obesity, hyperlipidemia, hepatic steatosis, and osteoporosis in ICR mice. However, these climacteric effects were reversed in OVX rats by treating them with WC at a dose relative to the same dose of PCP in OVX-ICR mice (200 and 100 mg/kg). Water chestnut fruit extract demonstrated promise as a complementary treatment for menopausal symptoms, indicating possible uses in the health of women through supplements or prescription drugs.
A Development and Validation of an AI Model for Cardiomegaly Detection in Chest X-rays
Authors: Lee Kang-Hee , Choi Jun-Woo , Park Chun-Oh , Han Dong-Hun , Kang Min-Soo
Published: August 2024

In this study, the development of a deep learning approach for distinguishing cardiomegaly in chest X-ray images and its validation process are presented. Typically, radiologists diagnose cardiomegaly by examining X-ray images. However, their interpretations can vary owing to subjective judgments, and mild cardiomegaly can be missed. For this reason, there is ongoing research into the use of AI-based deep learning algorithms as an adjunct to X-ray interpretation. In this study, radiologists collected 10,000 public images, from which 718 useful images were selected to create a dataset. A DenseNet121 algorithm was then used to develop an AI model for cardiomegaly detection. The results demonstrate an accuracy of 0.95, a recall of 0.91, and an F1 score of 0.94. Additional validation was performed to ensure the accuracy of the cardiomegaly detection model. The validation methods included saliency maps and guided backpropagation, which indicated the significance of the model. In conclusion, this study demonstrates the potential to develop a high-quality AI model with fewer data than previous studies, suggesting its applicability in the medical field.
Multi-Objective Five-Element Cycle Optimization Algorithm Based on Multi-Strategy Fusion for the Bi-Objective Traveling Thief Problem
Authors: Xiang Yue , Guo Jingjing , Jiang Chao , Ma Haibao , Liu Mandan
Published: August 2024

In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.
An Enhanced SL-YOLOv8-Based Lightweight Remote Sensing Detection Algorithm for Identifying Broken Strands in Transmission Lines
Authors: Zhang Xiang , Zhang Jianwei , Jia Xiaoqiang
Published: August 2024

Power transmission lines frequently face threats from lightning strikes, severe storms, and chemical corrosion, which can lead to damage in steel–aluminum-stranded wires, thereby seriously affecting the stability of the power system. Currently, manual inspections are relatively inefficient and high risk, while drone inspections are often limited by complex environments and obstacles. Existing detection algorithms still face difficulties in identifying broken strands. To address these issues, this paper proposes a new method called SL-YOLOv8. This method incorporates an improved You Only Look Once version 8 (YOLOv8) algorithm, specifically designed for online intelligent inspection robots to detect broken strands in transmission lines. Transmission lines are susceptible to lightning strikes, storms, and chemical corrosion, which is leading to the potential failure of steel- and aluminum-stranded lines, and significantly impacting the stability of the power system. Currently, manual inspections come with relatively low efficiency and high risk, and Unmanned Aerial Vehicle (UAV) inspections are hindered by complex situations and obstacles, with current algorithms making it difficult to detect the broken strand lines. This paper proposes SL-YOLOv8, which is a broken transmission line strand detection method for an online intelligent inspection robot combined with an improved You Only Look Once version 8 (YOLOv8). By incorporating the Squeeze-and-Excitation Network version 2 (SENet_v2) into the feature fusion network, the method effectively enhances adaptive feature representation by focusing on and amplifying key information, thereby improving the network’s capability to detect small objects. Additionally, the introduction of the LSKblockAttention module, which combines Large Selective Kernels (LSKs) and the attention mechanism, allows the model to dynamically select and enhance critical features, significantly enhancing detection accuracy and robustness while maintaining model precision. Compared with the original YOLOv8 algorithm, SL-YOLOv8 demonstrates improved precision recognition accuracy in Break-ID-1632 and cable damage datasets. The precision is increased by 3.9% and 2.7%, and the recall is increased by 12.2% and 2.3%, respectively, for the two datasets. The mean average precision (mAP) at the Intersection over Union (IoU) threshold of 0.5 is also increased by 4.9% and 1.2%, showing the SL-YOLOv8’s effectiveness in accurately identifying small objects in complex situations.
A Study of the Corrosion Resistance of 316L Stainless Steel Manufactured by Powder Bed Laser Additive Manufacturing
Authors: Ahuir-Torres Juan Ignacio , Burgess Andrew , Sharp Martin Charles , Öpöz Tahsin Tecelli , Malkeson Sean P. , Falkingham Peter L. , Darlington Robert I. , Tammas-Williams Samuel
Published: August 2024

Commercially available 316L (1.4404) stainless steel is commonly used for industrial filtration due to its combination of good material properties, particularly its corrosion resistance, which is a critical factor for filters in corrosive (e.g., saltwater) environments. Recently, laser powder bed fusion (LPBF) has enabled new more complex and efficient filtration pieces to be manufactured from this material. However, it is critical to know how the corrosion resistance is affected by this manufacturing strategy. Here, the corrosion resistance of LPBF manufactured 316L stainless steel is compared with wrought 316L sheet. The corrosion of the samples in saltwater was assessed with asymmetric electrochemical noise, potentiodynamic polarisation curve, and electrochemical impedance spectroscopy. The samples before and after corrosion were examined with scanning electron microscopy and energy-dispersive spectroscopy. The LPBF samples had higher corrosion resistance than the sheet samples and were more noble. The corrosion resistance of the LPBF sample increased with time, while the wrought sample corrosion resistance reduced over time. The corrosion mechanism of both samples was stable with time, formed of a passive film process and a bared material process. This paper presents the first study about the temporal evolution of the LPBF 316L stainless steel corrosion mechanism.
Evaluation of Spare Parts Support Capacity of Civil Aircrafts Based on Type-2 Hesitant Pythagorean Fuzzy Sets and Improved Technique for Order Preference by Similarity to Ideal Solution
Authors: You Liang , Wang Lili , Lv Xiaofan , Xiang Huachun , Wang Zheng
Published: August 2024

To improve the spare parts support capacity of civil aircrafts and given the actual lack of evaluation methods at present, the evaluation problem of spare parts support capacity was solved in this study by proposing a multi-attribute decision method based on Type-2 hesitant Pythagorean fuzzy sets and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, the basic definitions of Type-2 hesitant Pythagorean fuzzy sets were given, which were further promoted to Type-n hesitant Pythagorean fuzzy sets, and the basic order relation criterion of Type-2 hesitant Pythagorean fuzzy sets was introduced. Second, a complete evaluation system for spare parts supply support capacity was established with the spare parts of civil aircrafts as the study objects, and each evaluation indicator was introduced in detail. Then, the spare parts support solutions were preferentially sorted using the correlation coefficient formula of Type-2 hesitant Pythagorean fuzzy sets and improved TOPSIS. Finally, the reliability and reasonability of the proposed method were verified through an example calculation and comparative analysis. The experimental results indicate that the proposed method can acquire the evaluation results of spare parts support capacity more scientifically and can be referenced by relevant studies.
Food Manufacturing Workers and Structural Causality among Work Environment, Work–Family Conflict, Musculoskeletal Pain, Sleep-Related Problems, and Burnout
Authors: Kim Jun Won , Jeong Byung Yong
Published: August 2024

This study analyzes the association and structural causality among work environment, work–family conflict, musculoskeletal pain, sleep-related problems, and burnout in the food manufacturing industry. This study used the 6th Korean Working Environment Survey data, and 523 food production workers were selected as research subjects. Structural equation modeling showed that work environment and work–family conflict significantly affected musculoskeletal pain. In addition, work–family conflict and musculoskeletal pain affected sleep-related problems, and musculoskeletal pain and sleep-related problems impacted burnout. Furthermore, this research provides practical strategies to deal with musculoskeletal pain, sleep-related issues, and burnout. Burnout is more affected by sleep-related problems than by musculoskeletal pain. Additionally, sleep-related problems seem to be more affected by musculoskeletal pain than by work–family conflict. Meanwhile, musculoskeletal pain is influenced by the work environment rather than by work–family conflict. This result can be used to establish preventive policies for the safety and health of food manufacturing production workers.
Experimental Study and Analysis of Chemical Modification of Coal Surfaces by Viscoelastic Surfactant Fracturing Fluids
Authors: Yang Mengmeng , Gong Shihui , Cheng Yugang
Published: August 2024

The continuous deepening of coal-seam extraction has sharply increased both gas pressure and content. The use of viscoelastic surfactant fracturing fluids (VESFFs) has been demonstrated to effectively improve coal-seam permeability and mitigate the occurrence of gas disasters. After injection into coal, VESFFs interact with the coal and affect its surface characteristics. In this study, to characterize changes in zeta potential, oxygen-containing functional groups, and the microcrystalline structure of hard and soft coal surfaces under the influence of VESFFs with different formulations, zeta potential measurements and Fourier-transform infrared and Raman spectroscopies were performed. The VESFFs enhanced the electrostatic repulsion between the pore wall and coal particles, which is favorable for the removal of coal particles from hard and soft coal surfaces. The combination of cationic with zwitterionic viscoelastic surfactants (VESs) in the VESFFs exposed more hydrophilic functional groups on the surfaces of hard and soft coal, increasing wettability and affecting nanometer pores. A VESFF based on anionic and zwitterionic VESs as the primary agents could enhance the extension of the aromatic layer (La) of the aromatic crystal nuclei and reduce the interlayer spacing (d002) of hard and soft coal, thereby increasing the volume of micropores. This research offers theoretical guidance for optimizing the primary components of VESFFs and elucidates the mechanism through which VESFFs act on nanopores in coal from a microscopic perspective.
Hermite Quartic Splines for Smoothing and Sampling a Roughing Curvilinear Spiral Toolpath
Authors: Leroy Cédric , Lavernhe Sylvain , Rivière-Lorphèvre Édouard
Published: August 2024

From an industrial point of view, the milling of 2.5D cavities is a frequent operation, consuming time and presenting optimization potential, especially through a judicious choice of the tool trajectory. Among the different types of trajectories, some have a general spiral-like aspect and can potentially offer a reduced machining time. They are called curvilinear trajectories and are obtained by interpolation between structure curves, which are the numerical solutions of a partial differential equation. In this case, the machine tool will connect points, and the trajectory will be made up of small segments. While these trajectories exhibit all the necessary qualities on a macroscopic level for rapid tool movement, the tangential discontinuities at a microscopic scale, inherent in the discretization, significantly increase the machining time. This article proposes a method to reparameterize the structure curves of the curvilinear spiral with a set of C2 connected Hermit quartic spline patches. This creates a smooth toolpath that can be machined at an average feedrate closer to the programmed one and will, de facto, reduce the machining time. This article shows that the proposed method increases on two representative geometries of cavities and toolpath quality indicators, and reduces the milling time from 10% to 18% as compared to the PDE curvilinear spiral generation method proposed by Bieterman and Sandström. In addition, the proposed method is suitable for any non-convex pocket, with or without island(s).
An Assessment of the Mechanical Deformation Behavior of Three Different Clear Aligner Materials: A Digital Image Correlation Analysis
Authors: Olteanu Nicolae Daniel , Szuhanek Camelia , Rosu Sorana Nicoleta , Chitariu Dragos Florin , Seghedin Nicolae , Panaite Tinela , Casalean Amalia , Zetu Irina Nicoleta
Published: August 2024

Introduction: The present study aimed to investigate the deformation behavior of three different clear aligner systems, CA® Pro+ Clear Aligner (Scheu Dental, Iserlohn, Germany), Taglus Premium (Taglus Company, Mumbai, India), and Spark Trugen (Ormco Corp., Orange, CA, USA), under compression testing, using the digital image correlation (DIC) technique. Materials and Methods: A total of 15 patients were treated with each of the three aligner systems, resulting in 45 sets of aligners. Each aligner set was fixed on the 3D-printed dental arches and then in an articulator. Then, the samples were subjected to occlusal forces using a purpose-built test stand to allow for controlled force application and precise displacement determination. The DIC technique was used for capturing the deformation behavior, providing detailed strain and displacement fields. Statistical analysis was performed using one-way ANOVA and Bonferroni post hoc tests with a significance of 0.05. Results: The results indicate that the Spark system exhibited the most substantial rigid displacement. Furthermore, the elastic deformation values of the Spark and Taglus systems were significantly higher than those of the CA Pro+ system (p > 0.05). Conclusions: The Spark Trugen clear aligner system demonstrated a lower stability to rigid displacement and elastic deformation under compression testing compared to the Scheu CA® Pro+ Clear Aligner and Taglus Premium. All three tested clear aligner systems showed an increased resistance to elastic displacement and rigid deformation in the mandibular arch.
Natural Lime–Cork Mortar for the Seismic and Energetic Retrofit of Infill Walls: Design, Materials, and Method
Authors: Buda Rocco , Pucinotti Raffaele
Published: August 2024

Recent seismic events have prompted research into innovative and sustainable materials for strengthening and repairing obsolete and vulnerable buildings. These earthquakes have exposed the high seismic vulnerability of existing reinforced concrete (RC) buildings, particularly in secondary structural elements like infill walls. In addition to structural issues, these buildings often face significant energy deficiencies, such as thermal bridges, due to inadequate insulation. Traditionally, structural and energy improvements for residential buildings are addressed separately with different methods and protocols. This preliminary study is part of a broader research initiative at the University of Reggio Calabria (Italy), aiming to design an innovative fiber-reinforced plaster using natural, sustainable, and locally produced materials to enhance the energy and structural performance of existing wall infills. The study investigates two plaster matrices made of natural hydraulic lime and silica sand, with 15% and 30% cork granules added. Mechanical and thermophysical tests on multiple specimens were conducted to evaluate their suitability for seismic and energy retrofitting of infill walls. Results indicate that adding cork reduces mechanical strength by approximately 42% at a 30% cork content without compromising its use in seismic retrofitting. Thermophysical tests show improved thermal performance with a higher cork content. These findings suggest that the lime–cork mixture at 30% is effective, offering excellent ductility and serving as a promising alternative to traditional cementitious plaster systems. The next experimental phase will test matrices with varying percentages of gorse fiber.
CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
Authors: Pham Ngoc-Lan , Ta Quoc-Bao , Kim Jeong-Tae
Published: August 2024

This study presents a convolutional neural network (CNN) deep learning approach for identifying damage in submerged structure-foundation systems using vibration data. Firstly, foundation damage in a lab-scale caisson-foundation system is simulated to measure time-history responses. Singular value decomposition (SVD) responses are derived from the time-history responses. Secondly, the 1-D CNN deep learning model is trained using both the time-history responses and SVD responses. Finally, the trained CNN models are implemented to evaluate the foundation damage under conditions of noise contamination and partially untrained data. The experimental results demonstrate the effectiveness of CNN models for damage identification and highlight the comparative strengths of time-history and SVD data. The CNN model trained using SVD data outperforms the other model when under noise contamination conditions, while the CNN model trained using time-history data maintains better accuracy in partially untrained data conditions. Integrating both types of data enhances the accuracy of damage classification.
Flexural Fatigue Behavior of Prestressed High-Performance Concrete Bridges with Double Mineral Fine Powder Admixture: An Experimental Study
Authors: Zhang Zhiguo , Li Haoran
Published: August 2024

High-performance concrete (HPC) is commonly used in the main structures of bridges. HPC is widely applied in the main structures of bridges, yet some skepticism remains with integrating fly ash and mineral powder as admixtures into prestressed HPC bridges. To address this, this study conducted scaled-model experiments to analyze the flexural fatigue behavior of prestressed HPC bridges with double-mineral fine powder admixtures (PB-DA). This study derives the similarity criteria for a simply supported beam bridge under a concentrated load based on similarity theory. Subsequently, in following these criteria, a 30 m long actual bridge is scaled down to a 6 m PB-DA at a 1:5 scale. For this scaled PB-DA, the concentrated load is reduced to 1/25 of the actual bridge, while the strain remains the same as in the actual bridge. The double-mineral fine powder admixture (D-A) was produced and used to fabricate PB-DA by mixing fly ash and mineral powder. Five PB-DAs were constructed, with C50 and C80 concrete strength grades, and admixture ranges from 10% to 32%. Sinusoidal half-wave constant stress amplitude loading at 5 Hz frequency was applied, with 2 million fatigue loading cycles. After fatigue loading, a continuously increasing static load was applied until the PB-DA failed. The experimental results show that the upper part of the PB-DA is compressed, and the lower part is in tension. The PB-DA strain distribution from top to bottom generally conforms to the plane section assumption. During 2 million fatigue loading cycles, 200,000 cycles mark the beam strain and stiffness evolution boundary. Below 200,000 cycles, the PB-DA strain rapidly increases, and flexural stiffness quickly decreases. Beyond 200,000 cycles, the rate of increase in strain and the rate of decrease in flexural stiffness significantly slow down. After fatigue loading, the PB-DA displacement increases exponentially under a continuously increasing static load. The crack distribution is uniform across all PB-DA, with the cracks being sparsest at a 30% admixture. A comprehensive analysis shows that all PB-DAs demonstrate good flexural fatigue behavior. Notably, when D-A content reaches 30%, strain increases, but reductions in flexural stiffness and damage in PB-DA significantly decrease. This paper’s conclusions provide a reference for applying D-A at PB-DA.
Chemical Substance Exposure of Some Cleaning Workers in Korea: Focusing on Inhalation Exposure
Authors: Ki Nosung , Shin Saemi , Choi Jihyung , Shim Sanghyo , Byeon Sanghoon
Published: August 2024

This study aimed to prevent health damage caused by chemical exposure among cleaning workers who use cleaning agents and disinfectants in facility management and kitchen areas. We analyzed 5 years of measurement data (2016–2020) for cleaning workers across various industries in Korea, and conducted an exposure survey and health risk assessment for the two most frequently measured substances (i.e., 2-butoxyethanol and sodium hydroxide) and representative substances generated by their combined use (i.e., chlorine and chloroform). The findings indicate that when chlorine was generated by mixing hypochlorite-based disinfectants (e.g., bleach) with acidic cleaners containing substances such as nitric, hydrochloric, or citric acid, the risk index for chlorine gas (based on the 95th percentile exposure values) was 5.65 in the facility management cleaning industry, exceeding the acceptable threshold of 1. Because of the high usage and exposure frequency of cleaning and disinfecting agents and the common practice of mixing multiple products to enhance cleaning efficacy, it is necessary to manage hazardous risk factors by providing education on appropriate working methods, supplying personal protective equipment, and installing ventilation systems for these workers. Further research on the health risk assessment of combined chemical use is needed.
Machine-Learning-Based Path Loss Prediction for Vehicle-to-Vehicle Communication in Highway Environments
Authors: Sagir Nugman , Tugcu Zeynep Hasirci
Published: August 2024

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R2 values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems.
Improved Subsidence Assessment for More Reliable Excavation Activity in Tehran
Authors: Dashti Mahdi , Virág Zoltán
Published: August 2024

This paper presents a particular tunneling method, the new Austrian tunneling method (NATM), which plays an important role in reducing subsidence of the surface and damage to structures in urban areas. It has a wide range of applications in shallow tunneling projects all over the world. In this study, numerical modeling of the third-line Metro tunnel in Tehran, which is designed and stabilized by the NATM, is under discussion. The foregoing tunnel is excavated manually with a one-meter advancing step. In this project, the constructors use a lattice girder and spray concrete with 31 cm thickness as the initial lining. A suitable numerical software for this modeling is PLAXIS 3D Tunnel, which allows high-resolution finite element modeling (FEM) of the studied object. The performance of this method is investigated and compared with that of other NATMs. The numerical modeling yielded a value of 30.01 mm for earth subsidence in the most damaged area of the settlement, which was confirmed with a dramatically low difference by earth surface monitoring. Moreover, this tunnel was drilled and excavated using various methods, among which the least settlement was obtained by the proposed method. The results are promising, and they indicate that tunneling with this method should continue to be used to expand the subway line in the city.
The Effect of Organic and Amino Acid Biostimulants on Actinidia deliciosa ‘Hayward’ Cultivation: Evaluation of Growth, Metabolism, and Kiwifruit Postharvest Performance
Authors: Papantzikos Vasileios , Stournaras Vasileios , Mpeza Paraskevi , Patakioutas Georgios
Published: August 2024

The commercial value of kiwifruit is determined mostly by its nutritional composition and antioxidant content. The enrichment of these traits in an era where climate change affects kiwi growth conditions is worth investigating via the application of biostimulants that enhance plant growth. In this work, we evaluated the effects of three commercial biostimulants on the metabolism and growth characteristics of the Actinidia deliciosa cultivar ‘Hayward’: (I) a humic and fulvic-based biostimulant, (II) a glycine–betaine–proline-based biostimulant, and (III) a vegetal amino acid-based biostimulant. A biostimulant-free treatment was used as a control. In the two-year experiment (2022 and 2023), the growth characteristics of kiwi trees were measured, such as stem length, the number of leaves, leaf area, and fresh and dry biomass at the end of each growing season. The leaves’ total chlorophyll, total phenolics, and proline content were detected during the two seasons in order to obtain more insights into plant metabolism. Κiwifruit qualities and antioxidant traits, such as total soluble solids, titratable acidity, firmness, fresh and dry biomass, DPPH, and ascorbic acid (vitamin C), were recorded during the postharvest life for each season. Data analysis illustrated the beneficial impact of some biostimulants on several of the previously mentioned parameters, such as antioxidant kiwifruit traits, especially in the case of glycine–betaine–proline-based acid-based biostimulants.
Small Airways Dysfunction and Lung Hyperinflation in Long COVID-19 Patients as Potential Mechanisms of Persistent Dyspnoea
Authors: Vontetsianos Angelos , Chynkiamis Nikolaos , Anagnostopoulou Christina , Lekka Christiana , Zaneli Stavrina , Anagnostopoulos Nektarios , Rovina Nikoleta , Kampolis Christos F. , Papaioannou Andriana I. , Kaltsakas Georgios , Vogiatzis Ioannis , Stratakos Grigorios , Bakakos Petros , Koulouris Nikolaos
Published: August 2024

Background: Reticulation, ground glass opacities and post-infection bronchiectasis are present three months following hospitalisation in patients recovering from SARS-CoV-2 infection and are associated with the severity of acute infection. However, scarce data exist on small airways impairment and lung hyperinflation in patients with long COVID-19. Aim: To evaluate small airways function and lung hyperinflation in previously hospitalised patients with long COVID-19 and their association with post-COVID-19 breathlessness. Methods: In total, 33 patients (mean ± SD, 53 ± 11 years) with long COVID-19 were recruited 149 ± 90 days following hospital discharge. Pulmonary function tests were performed and lung hyperinflation was defined as RV/TLC ≥ 40%. Small airways function was evaluated by measuring the closing volume (CV) and closing capacity (CC) using the single-breath nitrogen washout technique (SBN2W). Results: CC was 115 ± 28% pred. and open capacity (OC) was 90 ± 19. CC was abnormal in 13 patients (39%), CV in 2 patients (6.1%) and OC in 9 patients (27%). Lung hyperinflation was present in 15 patients, whilst the mean mMRC score was 2.2 ± 1.0. Lung hyperinflation was associated with CC (r = 0.772, p = 0.001), OC (r = 0.895, p = 0.001) and mMRC (r = 0.444, p = 0.010). Conclusions: Long COVID-19 patients present with small airways dysfunction and lung hyperinflation, which is associated with persistent dyspnoea, following hospitalisation.
Switched Observer Design for a Class of Non-Linear Systems
Authors: Yupanqui Tello Ivan Francisco , Coutinho Daniel , Mendoza Rabanal Renzo Martín
Published: August 2024

This paper is concerned with the switched observer design for a class of systems subject to locally Lipschitz non-linearities. By performing a suitable description of the estimation error dynamics into a linear parameter varying (LPV) system representation, sufficient conditions for the existence of a switching output injection gain are proposed such that the asymptotic stability of the estimation error is guaranteed. These conditions can be conveniently expressed by means of linear matrix inequalities (LMIs), which are easily computationally tractable. A numerical example is provided to show the favorable performance achieved by the proposed observer, which can be applied to a large class of non-linear systems.
Model for Global Quality Management System in System of Systems
Authors: Agmon Noga , Kordova Sigal
Published: August 2024

This study inaugurates an innovative field of research for Global Quality Management System (G-QMS) in System of Systems (SoS), integrating emerging and rapidly evolving disciplines of QMS, SoS Globalization, and Systems approaches, chiefly Systems Thinking. This manuscript introduces, for the first time, a conceptual model for G-QMS in sectors of SoS, developed from an extensive field study conducted in real SoS global organizations, employing the Grounded Theory methodology. We found that this model can be described by two separate supra entities, despite their extensive interrelationships. This manuscript focuses on the first supra entity, which constitutes the foundation for understanding the second supra entity. The model pertaining to the first supra entity, named G-QMS of G-Organization in Sectors of SoS, is introduced through a detailed description of its structural principles. Additionally, a detailed description of its complementary aspects and elements is provided, which condenses these principles into a complete conceptual model picture. This field of research is highly significant for such organizations. These organizations typically maintain leading and advanced quality bodies, especially in comparison to the broader industry. Therefore, the G-QMS model developed through this research can offer substantial contributions to these organizations, but also to all other global organizations.
Automated Identification of Cylindrical Cells for Enhanced State of Health Assessment in Lithium-Ion Battery Reuse
Authors: de la Iglesia Alejandro H. , Lobato Alejano Fernando , de la Iglesia Daniel H. , Chinchilla Corbacho Carlos , López Rivero Alfonso J.
Published: August 2024

Lithium-ion batteries are pervasive in contemporary life, providing power for a vast array of devices, including smartphones and electric vehicles. With the projected sale of millions of electric vehicles globally by 2022 and over a million electric vehicles in Europe alone in the first quarter of 2023, the necessity of securing a sustainable supply of lithium-ion batteries has reached a critical point. As the demand for electric vehicles and renewable energy storage (ESS) systems increases, so too does the necessity to address the shortage of lithium batteries and implement effective recycling and recovery practices. A considerable number of electric vehicle batteries will reach the end of their useful life in the near future, resulting in a significant increase in the number of used batteries. It is of paramount importance to accurately identify the manufacturer and model of cylindrical batteries to ascertain their State of Health (SoH) and guarantee their efficient reuse. This study focuses on the automation of the identification of cylindrical cells through optical character recognition (OCR) and the analysis of the external color of the cell and the anode morphology based on computer vision techniques. This is a novel work in the current limited literature, which aims to bridge the gap between industrialized lithium-ion cell recovery processes and an automated SoH calculation. Accurate battery identification optimizes battery reuse, reduces manufacturing costs and mitigates environmental impact. The results of the work are promising, achieving 90% accuracy in the identification of 18,650 cylindrical cells.