bluuebunny/crossref_metadata_2025 huggingface dataset:
A Tribological Study of CrN and TiBN Hard Coatings Deposited on Cobalt Alloys Employed in the Food Industry
Authors: Hidalgo-Badillo Joaquín A. , Hernández-Casco Irma , Herrera Hernández Héctor , Soriano-Vargas Orlando , Contla-Pacheco Alan D. , González Morán Carlos O. , Hernández Jorge Morales , Flores Cuautle José de Jesús Agustín
Published: October 2024

In this work, a comparative study of the tribological performance of two hard coatings, CrN/TiBN, was conducted for research purposes and industrial applications in food products, particularly for food packaging into cans using the double hermetic sealing process. CrN and TiBN coatings were successfully deposited on a base-cobalt metal substrate of a CoCrW commercial alloy using physical vapor deposition by arc evaporation (AEPVD) technology to improve the tribological properties of the commercial alloy, including wear and corrosion resistance, lower coefficient of friction, and overall durability. This research focuses on conducting scratch and abrasion wear resistance tests in dry conditions; specifically, it pursues to evaluate the wear corrosion properties, known as tribocorrosion performance, on CrN/TiBN hard coatings. The experimental results show that the CrN coating (2.9 μm) is slightly thicker than the TiBN coating (2.7 μm), with a 47 N critical load. It also shows a lower coefficient of friction (CoF) in a dry environment, while the TiBN coating showed total detachment and a high coefficient of friction in a dry environment condition. Tribocorrosion testing in brine aqueous solution indicated that CrN coating shows a high friction coefficient with a higher open circuit potential value (Ecorr), and TiBN shows the lowest corrosion potential (Ecorr) and the lowest friction coefficient. This suggests that CrN could provide better corrosion protection for commercial cobalt alloys and improve tool performance during the food canning process in brine environments.
Characterization and Dataset Compilation of Torque–Angle Curve Behavior for M2/M3 Screws
Authors: Pérez-Olguín Iván Juan Carlos , Fernández-Gaxiola Consuelo Catalina , Rodríguez-Picón Luis Alberto , Méndez-González Luis Carlos
Published: October 2024

This research explores the torque–angle behavior of M2/M3 screws in automotive applications, focusing on ensuring component reliability and manufacturing precision within the recommended assembly specification limits. M2/M3 screws, often used in tight spaces, are susceptible to issues like stripped threads and inconsistent torque, which can compromise safety and performance. The study’s primary objective is to develop a comprehensive dataset of torque–angle measurements for these screws, facilitating the analysis of key parameters such as torque-to-seat, torque-to-fail, and process windows. By applying Gaussian curve fitting and Gaussian process regression, the research models and simulates torque behavior to understand torque dynamics in small fasteners and remarks on the potential of statistical methods in torque analysis, offering insights for improving manufacturing practices. As a result, it can be concluded that the proposed stochastics methodologies offer the benefit of fail-to-seat ratio improvement, allow inference, reduce the sample size needed in incoming test studies, and minimize the number of destructive test samples needed.
Evaluation of the Microscanner C3 for Automated Cell Counting in Cerebrospinal Fluid Analysis
Authors: Park Insu , Choi Minkyeong , Lee Eunji , Park Seoyeon , Jang Woong Sik , Lim Chae Seung , Ko Sun-Young
Published: October 2024

Background: Cerebrospinal fluid (CSF) analysis is essential for diagnosing various disorders affecting the central nervous system (CNS). Traditionally, CSF cell count analysis is performed manually using a Neubauer chamber hemocytometer, which is labor-intensive and prone to subjective interpretation. Methods: In this study, we evaluated the analytical and clinical performance of the Microscanner C3, an automated cell counting system, for CSF analysis using artificially prepared samples and 150 clinical CSF samples. Results: The lowest detectable white blood cell (WBC) count was 3.33 cells/µL, and the lowest detectable red blood cell (RBC) count was 3.67 cells/µL. The coefficients of variation (CV%) for the Microscanner C3 were lower than those for the Neubauer chamber at all cell concentrations. The correlation coefficients (R) between the Microscanner C3 and conventional methods were high: 0.9377 for WBCs and 0.9952 for RBCs when compared with the Neubauer chamber, and 0.8782 for WBCs and 0.9759 for RBCs when compared with the flow cytometer. Additionally, the Microscanner C3 showed good agreement with both the Neubauer chamber and flow cytometer in the Passing–Bablok regression analysis and Bland–Altman analysis for WBC count at all concentrations and RBC count at concentrations of 0–1000 cells/µL. Conclusions: The Microscanner C3 proved to be more sensitive, precise, and consistent compared to the conventional hemocytometer. The new system is also compact, convenient, and cost-effective, making it a valuable option for clinical laboratories.
SMOTE-Based Automated PCOS Prediction Using Lightweight Deep Learning Models
Authors: Ahmad Rumman , Maghrabi Lamees A. , Khaja Ishfaq Ahmad , Maghrabi Louai A. , Ahmad Musheer
Published: October 2024

Background: The reproductive age of women is particularly vulnerable to the effects of polycystic ovarian syndrome (PCOS). High levels of testosterone and other male hormones are frequent contributors to PCOS. It is believed that miscarriages and ovulation problems are majorly caused by PCOS. A recent study found that 31.3% of Asian women have been afflicted with PCOS. Healing women with life-threatening disorders associated with PCOS requires more research. In prior research, methods have involved autonomously classified PCOS using a number of different machine learning techniques. ML-based approaches involve hand-crafted feature extraction and suffer from low performance issues, which cannot be ignored for the accurate prediction and identification of PCOS. Objective: Hence, predicting PCOS using cutting-edge deep learning methods for automated feature engineering with better performance is the prime focus of this study. Methods: The proposed method suggests three lightweight (LSTM-based, CNN-based, and CNN-LSTM-based) deep learning models, incorporating SMOTE for dataset balancing to obtain a valid performance. Results: The proposed three models tend to offer an accuracy of 92.04%, 96.59%, and 94.31%, an ROC-AUC of 92.0%, 96.6%, and 94.3%, the number of parameters of 6689, 297, and 13285, and a training time of 67.27 s, 10.02 s, and 18.51 s, respectively. In addition, the DeLong test is also performed to compare AUCs to assess the statistical significance of all three models. Among all three models, the SMOTE + CNN models performs better in terms of accuracy, precision, recall, AUC, number of parameters, training time, DeLong’s p-value over the other. Conclusions: Moreover, a performance comparison is also carried out with other state-of-the-art PCOS detection studies and methods, which validates the better performance of the proposed model. Thus, the proposed model provides the greatest performance, which can lead to a reduction in the number of failed pregnancies and help in finding PCOS in the early stages.
Ultrasound Assessment and Self-Perception of Pelvic Floor Muscle Function in Women with Stress Urinary Incontinence in Different Positions
Authors: Krasnopolsky Noa , Ami Noa Ben , Dar Gali
Published: October 2024

Objectives: This study analyzed the effect of different positions on pelvic floor muscle (PFM) function in women with and without stress urinary incontinence (SUI). Methods: This study included women with (n = 17, research group) and without (n = 25, control group) SUI. Using abdominal ultrasound, PFM function (maximum contraction and endurance) was measured in four different positions: lying, sitting, standing and squatting. The level of difficulty perceived by the participants was recorded. Results: In both groups, the best contraction was observed in the standing position and the weakest in the lying position. Women with SUI showed a lower ability to perform PFM contraction. A significant difference was found between the groups in the sitting and standing positions, and it was smaller in the research group. In the research group, the contraction displacement during sitting was 2.68 (1.67) mm versus 4.51 (2.62) mm in the control. The displacement during standing was 6.92 (3.50) mm versus 9.18 (5.05) mm, respectively (p < 0.05). In the research group, 52.9% reported the sitting position as the most difficult compared with only 12% in the control group. Conclusions: Women with SUI have lower PFM function while standing or sitting, but not while lying, than those without SUI. Variations in PFM function across different positions exist. A new protocol for PFM examination should be written with the standing position included.
Improved Discriminability of Severe Lung Injury and Atelectasis in Thoracic Trauma at Low keV Virtual Monoenergetic Images from Photon-Counting Detector CT
Authors: Kaatsch Hanns Leonhard , Völlmecke Maximilian Franz , Becker Benjamin V. , Dillinger Daniel , Kubitscheck Laura , Wöhler Aliona , Schaaf Sebastian , Piechotka Joel , Schreyer Christof , Schwab Robert , Overhoff Daniel , Waldeck Stephan
Published: October 2024

Objectives: To evaluate the value of virtual monoenergetic images (VMI) from photon-counting detector CT (PCD-CT) for discriminability of severe lung injury and atelectasis in polytraumatized patients. Materials & Methods: Contrast-enhanced PCD-CT examinations of 20 polytraumatized patients with severe thoracic trauma were included in this retrospective study. Spectral PCD-CT data were reconstructed using a noise-optimized virtual monoenergetic imaging (VMI) algorithm with calculated VMIs ranging from 40 to 120 keV at 10 keV increments. Injury-to-atelectasis contrast-to-noise ratio (CNR) was calculated and compared at each energy level based on CT number measurements in severely injured as well as atelectatic lung areas. Three radiologists assessed subjective discriminability, noise perception, and overall image quality. Results: CT values for atelectasis decreased as photon energy increased from 40 keV to 120 keV (mean Hounsfield units (HU): 69 at 40 keV; 342 at 120 keV), whereas CT values for severe lung injury remained near-constant from 40 keV to 120 keV (mean HU: 42 at 40 keV; 44 at 120 keV) with significant differences at each keV level (p < 0.001). The optimal injury-to-atelectasis CNR was observed at 40 keV in comparison with the remaining energy levels (p < 0.001) except for 50 keV (p > 0.05). In line with this, VMIs at 40 keV were rated best regarding subjective discriminability. VMIs at 60–70 keV, however, provided the highest subjective observer parameters regarding subjective image noise as well as image quality. Conclusions: Discriminability between severely injured and atelectatic lung areas after thoracic trauma can be substantially improved by virtual monoenergetic imaging from PCD-CT with superior contrast and visual discriminability at 40–50 keV.
Feasibility of Double-Deployment Small-Diameter Covered Metallic Stent for Malignant Distal Biliary Obstruction (with Video)
Authors: Nakano Ryota , Shiomi Hideyuki , Okamoto Mamiko , Kawase Yuta , Yoshihara Kohei , Yoshioka Ryota , Kawata Shoki , Yuri Yukihisa , Takashima Tomoyuki , Aizawa Nobuhiro , Ikeda Naoto , Nishimura Takashi , Fukunishi Shinya , Enomoto Hirayuki
Published: October 2024

Background/Objectives: Covered self-expandable metallic stents (CSEMS) are commonly used to treat malignant distal biliary obstructions. A 10-mm CSEMS carries the risk of obstructing the pancreatic and cystic duct orifices by adhering to the bile duct; therefore, postoperative pancreatitis and cholecystitis are reported to occur at a certain frequency. We have adopted a new drainage technique for malignant distal biliary obstruction called ‘‘double-slim SEMS stenting” (DSS), where two small-diameter CSEMS are placed side-by-side. We aimed to compare the efficacy and safety of biliary drainage using DSS with those of conventional CSEMS. Methods: In total, 50 patients who underwent endoscopic biliary drainage for malignant distal biliary obstructions between April 2019 and March 2022 at Hyogo Medical University Hospital were enrolled. Patients were divided into DSS and Conventional groups, and the technical success rate, clinical success rate, adverse events, success rate for reintervention, recurrent biliary obstruction (RBO) rate, and time to RBO (TRBO) were evaluated. Results: There were no significant differences in patient characteristics between the DSS (n = 20) and Conventional groups (n = 30). The technical and clinical success rates were 100% in the DSS group. The incidence of adverse events was not significantly different between the two groups (DSS/Conventional: 10.0% [2/20]/20.0% [6/30]) (p = 0.34). No acute cholecystitis was observed in the DSS group. The incidence rates of RBO were 30% (6/20) and 43% (13/30) in the DSS and Conventional groups, respectively (p = 0.92). The median TRBO in the DSS group was 378 days, while the TRBO in the Conventional group was 195 days (p = 0.03), resulting in significantly longer TRBO in the DSS group. Conclusions: DSS emerges as a viable and safe approach for biliary drainage in malignant distal biliary obstruction, demonstrating a lower incidence of adverse events and longer TRBO compared to conventional CSEMS.
Extracellular Vesicle Characteristics in Local Fluid and Plasma Measured by Nanoparticle Tracking Analysis Can Help Differentiate High-Grade Serous Carcinoma from Benign Ovarian Pathology
Authors: Herzog Maruša , Verdenik Ivan , Černe Katarina , Kobal Borut
Published: October 2024

Background: High-grade serous carcinoma (HGSC) is the most lethal of gynecological cancers in developed countries. It usually presents late with non-specific symptoms and most cases are diagnosed at an advanced stage, with 5-year overall survival being around 40%. Biomarkers for screening and early diagnosis of this aggressive disease are, thus, a research priority. Extracellular vesicles (EVs) that reflect the cell of origin and that can be isolated from local fluid and plasma by minimally invasive liquid biopsy are such promising biomarkers. Besides EV concentration and molecular profile, which have been the main focus of research for many years, recent studies have also called attention to EV size distribution. The aim of our study was to evaluate the potential of EV concentration and size distribution in local fluid and plasma as diagnostic biomarkers for HGSC. Methods: Paired pretreatment ascites and plasma samples from 37 patients with advanced HGSC and paired pretreatment free peritoneal fluid (FPF) and plasma samples from 40 controls with benign ovarian pathology (BOP) were analyzed using nanoparticle tracking analysis (NTA). Results: We observed a significant difference in EV concentration in local fluid, but not in plasma, between HGSC patients and the control group. We also found a significant difference in EV size distribution in both local fluid and plasma between HGSC patients and the control group. The receiver operating characteristics (ROC) curve analysis of EV characteristics showed excellent diagnostic performance for the mode, D10, and D50 in local fluid and acceptable diagnostic performance for EV concentration and mean EV size in local fluid, as well as for the mode and D10 value in plasma. Conclusions: The results of our study show that EV concentration in local fluid and more importantly EV size distribution in both local fluid and plasma are significantly changed in the presence of HGSC. Future research of size-dependent molecular profiling of EVs could help identify novel diagnostic biomarkers for HGSC.
Improving Alzheimer’s Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques
Authors: Alshamlan Hala , Alwassel Arwa , Banafa Atheer , Alsaleem Layan
Published: October 2024

Machine learning (ML) has increasingly been utilized in healthcare to facilitate disease diagnosis and prediction. This study focuses on predicting Alzheimer’s disease (AD) through the development and comparison of ML models using Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) algorithms. Additionally, feature selection techniques including Minimum Redundancy Maximum Relevance (mRMR) and Mutual Information (MI) were employed to enhance the model performance. The research methodology involved training and testing these models on the OASIS-2 dataset, evaluating their predictive accuracies. Notably, LR combined with mRMR achieved the highest accuracy of 99.08% in predicting AD. These findings underscore the efficacy of ML algorithms in AD prediction and highlight the utility of the feature selection methods in improving the model performance. This study contributes to the ongoing efforts to leverage ML for more accurate disease prognosis and underscores the potential of these techniques in advancing clinical decision-making.
Costs of Palliative Care in Oncological and Non-Oncological Patients with Different Types of Ambulatory-Based Attention: Cost-Study Protocol
Authors: Perea-Bello Ana , Trapero-Bertran Marta , Dürsteler Christian
Published: October 2024

Background: Ambulatory-based palliative care is vital to managing oncological and non-oncological patients. Its economic impact on the healthcare and social system has recently begun to be considered significant. It is essential to agree on the cost types, the methodology for approaching and analyzing these costs, and how to determine the burden imposed by this attention on the healthcare and social system. Aim: This study aims to design a study on the economic burden of palliative care (PC) in oncological and non-oncological pathologies in the context of outpatient care (ambulatory-based and home support teams). Methods: A prospective cross-sectional study on the economic burden of ambulatory-based palliative care (ABPC) in three phases is conducted. Phase I: A systematic literature review (SLR) first defines the methodology and data to acquire for costing (results already published). Phase II: The next phase is the piloting of the registration questionnaires for costs/expenses (results already analyzed and presented). Phase III: A cross-sectional study is being conducted to collect data on the direct and indirect costs of ABPC assumed by the healthcare system and patients/caregivers to estimate its economic and social burden (in progress). Discussion: In this study, we create and propose a methodology and extend the approach to the funding of PC in an ambulatory-based context to determine its social cost and provide stakeholders with more information to assign resources more efficiently.
Cost–Utility Analysis of PCSK9 Inhibitors and Quality of Life: A Two-Year Multicenter Non-Randomized Study
Authors: Seijas-Amigo José , Mauriz-Montero Maria José , Suarez-Artime Pedro , Gayoso-Rey Mónica , Reyes-Santías Francisco , Estany-Gestal Ana , Casas-Martínez Antonia , González-Freire Lara , Rodriguez-Vazquez Ana , Pérez-Rodriguez Natalia , Villaverde-Piñeiro Laura , Castro-Rubinos Concepción , Espino-Paisán Esther , Cordova-Arevalo Octavio , Rodriguez-Penas Diego , Cardeso-Paredes Begoña , Ribeiro-Ferreiro Marta , Rodríguez-Mañero Moisés , Cordero Alberto , González-Juanatey José R. ,   MEMOGAL Investigators
Published: October 2024

The primary objective of this study was to conduct a cost–utility analysis of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors in real-world, comparing their use with standard care for managing cardiovascular disease. A multicenter prospective study was conducted across 12 Spanish hospitals from May 2020 to April 2022, involving 158 patients with hypercholesterolemia or atherosclerotic cardiovascular disease. This study assessed health-related quality of life (QoL) using the EQ-5D-3L questionnaire. The cost–utility analysis evaluated the economic impact of PCSK9 inhibitors when used with standard care compared to standard care alone, calculating the incremental cost–effectiveness ratio (ICER). This study included 158 patients with an average age of 61 years, male (66.5%). For patients initiating PCSK9 inhibitors, the treatment cost was EUR 13,633.39, while standard therapy cost EUR 3638.25 over two years. QoL for PCSK9 inhibitors stood at 1.6489 over two years, compared to 1.4548 for standard therapy. The results revealed favorable cost–utility outcomes, with an ICER of EUR 51,427.72. Significant improvements were observed in the domains of mobility, self-care, daily activities, pain/discomfort, and anxiety/depression (p < 0.001). This study presents the first real-world cost–utility analysis of PCSK9 inhibitors, supporting their economic rationale and highlighting their benefits in clinical practice. Healthcare decision-makers can use these results to inform their decisions and reimbursement policies concerning PCSK9 inhibitors. Trial Registration clinicaltrials.gov Identifier: NCT04319081.
Comparative Histological Analysis of Dentine-Derived Tooth Grafts in Maxillary vs Mandibular Socket Preservation: A Retrospective Study of 178 Cases
Authors: Minetti Elio , Gianfreda Francesco , Bollero Patrizio , Annicchiarico Ciro , Daniele Monica , Padula Rossella , Mastrangelo Filiberto
Published: October 2024

(1) Background: In recent years, there has been a growing interest in tooth-derived materials as valuable alternatives to synthetic biomaterials for preventing alveolar ridge dimensional changes. This study aimed to evaluate the histological and clinical differences between alveolar ridge preservation procedures in the maxilla and mandible using demineralized dentin treated with Tooth Transformer®. (2) Methods: A total of 178 patients in good general health were enrolled, with 187 post-extractive sockets lacking buccal and/or palatal bone walls. Alveolar socket preservation procedures and histological evaluations were performed. The sites were divided into two groups: Group A (99 mandibular samples) and Group B (108 maxillary samples). After 5 months (±1 month), single bone biopsies were performed for histologic and histomorphometric analysis. (3) Results: Clinical outcomes demonstrated a good healing of hard and soft tissues with an effective maintenance of bone architecture in both groups. Histomorphometric analysis revealed a total bone volume of 50.33% (±14.86) in Group A compared to 43.53% (±12.73) in Group B. The vital new bone volume was 40.59% (±19.90) in Group A versus 29.70% (±17.68) in Group B, with residual graft dentin material volume at 7.95% (±9.85) in Group A compared to 6.75% (±9.62) in Group B. (4) Conclusions: These results indicate that tooth-derived material supports hard tissue reconstruction by following the structure of the surrounding bone tissue. A 6.8% difference observed between the maxilla and mandible reflects the inherent disparities in natural bone structures in these regions. This suggests that the bone regeneration process after tooth extraction adheres to an anatomical functional pattern that reflects the specific bone characteristics of each area, thus contributing to the preservation of the morphology and functionality of the surrounding bone tissue.
3D-Printable Biopolymers for Socket Preservation Technique: Soft Tissues Response: A Pilot Randomised Clinical Study
Authors: De Angelis Nicola , Pesce Paolo , Poedjiastoeti Wiwiek , Suwandi Trijani , Tjandrawinata Rosalina , Bagnasco Francesco , Menini Maria
Published: October 2024

Background: The aim of the present parallel clinical study is to evaluate the efficacy of 3D-printed biopolymers compounded with osteoconductive material (beta-tricalcium phosphate and hydroxyapatite) for soft tissue closure after tooth extraction. Materials and Methods: this study followed the CONSORT reporting guidelines; 39 patients were treated with socket preservation using 3D-printed biopolymers and randomly divided into 3 groups (Test 1, Test 2, and Control). All cases were treated without flap elevation, careful cleaning and debridement of the sites, and then randomly sealed as follows: In T1, with a 3D-printed disk of poli-D-lactic acid with 10% of hydroxyapatite; in T2, using a 3D-printed disk of poli-ε caprolactone with 20% of β-tricalcium phosphate; and in T3, the socket was left open to heal. At baseline (extraction time) and 6 weeks after extraction, the rate of exposure was evaluated and stratified according to the site (anterior, posterior). Results: No dropouts were observed during the 6 weeks follow-up. All sites underwent uneventful healing with no complications. For posterior teeth, Test 1 and Test 2 showed full healing of the soft tissues with a reduction of the exposed area from 46.5 ± 8.25 mm2 to 0.6 ± 0.84 mm2 and from 47.1 ± 8.67 mm2 to 0.6 ± 0.7 mm2, respectively. The Control group exhibited a reduction from 45.6 ± 7.25 mm2 to 1.2 ± 0.9 mm2. Both Tests 1 and 2, when compared to the Control group, showed statistically significant better healing (p < 0.05). Anterior teeth showed a complete closure of the socket 4 weeks after the extraction with no noticeable differences between Test and Control. Conclusions: Both materials used in this study showed evidence to achieve the purpose. Ethical Guidelines: written informed consent was obtained from the participants of the study, as requested by the Ethics Committee for Health Research Faculty of Dentistry, Universitas Trisakti, with the following number: 641/S3/KEPK/FKG/5/2023.
Prototype for Multi-UAV Monitoring–Control System Using WebRTC
Authors: Kilic Fatih , Hassan Mainul , Hardt Wolfram
Published: October 2024

Most unmanned aerial vehicle (UAV) ground control station (GCS) solutions today are either web-based or native applications, primarily designed to support a single UAV. In this paper, our research aims to provide an open, universal framework intended for rapid prototyping, addressing these objectives by developing a Web Real-Time Communication (WebRTC)-based multi-UAV monitoring and control system for applications such as automated power line inspection (APOLI). The APOLI project focuses on identifying damage and faults in power line insulators through real-time image processing, video streaming, and flight data monitoring. The implementation is divided into three main parts. First, we configure UAVs for hardware-accelerated streaming using the GStreamer framework on the NVIDIA Jetson Nano companion board. Second, we develop the server-side application to receive hardware-encoded video feeds from the UAVs by utilizing a WebRTC media server. Lastly, we develop a web application that facilitates communication between clients and the server, allowing users with different authorization levels to access video feeds and control the UAVs. The system supports three user types: pilot/admin, inspector, and customer. Our research aims to leverage the WebRTC media server framework to develop a web-based GCS solution capable of managing multiple UAVs with low latency. The proposed solution enables real-time video streaming and flight data collection from multiple UAVs to a server, which is displayed in a web application interface hosted on the GCS. This approach ensures efficient inspection for applications like APOLI while prioritizing UAV safety during critical scenarios. Another advantage of the solution is its integration compatibility with platforms such as cloud services and native applications, as well as the modularity of the plugin-based architecture offered by the Janus WebRTC server for future development.
An Adaptive Task Planning Method for UAVC Task Layer: DSTCA
Authors: Duan Ting , Li Qun , Zhou Xin , Li Xiaobo
Published: October 2024

With the rapid development of digital intelligence, drones can provide many conveniences for people’s lives, especially in executing rescue missions in special areas. When executing rescue missions in remote areas, communication cannot be fully covered. Therefore, to improve the online adaptability of the task chain link in task planning with a complex system structure as the background, a distributed source-task-capability allocation (DSTCA) problem was constructed. The first task chain coordination mechanism scheme was proposed, and a DSTCA architecture based on the task chain coordination mechanism was constructed to achieve the online adaptability of the swarm. At the same time, the existing algorithms cannot achieve this idea, and the DSTCA-CBBA algorithm based on CNP is proposed. The efficiency change, agent score, and time three indicators are evaluated through specific cases. In response to sudden changes in nodes in the task chain link, the maximum spanning tree algorithm is used to reconstruct the task chain link in a short time, thereby completing the mission task assigned to the drone entity. Meanwhile, the experimental results also prove the effectiveness of the proposed algorithm.
Evaluation of the Impact of the Internet of Things on Postal Service Efficiency in Slovakia
Authors: Baláž Martin , Vaculík Juraj , Corejova Tatiana
Published: October 2024

This paper investigates the impact of technological advancements, particularly the integration of the Internet of Things (IoT), on the efficiency and productivity of the postal service industry in Slovakia. By employing the Cobb–Douglas production function, we analyze the relationship between key inputs—capital and labor—and output for six major postal service providers: Slovak Post, Packeta, DHL Express, DPD, TNT Express, and GLS. Using data from 2017 to 2021, this study quantifies the elasticities of capital and labor to assess how IoT adoption influences operational performance. The regression analysis reveals significant variations in input contributions across companies, highlighting the diverse effects of IoT integration. Findings suggest that while some companies benefit more from capital investments in technology, others rely heavily on labor efficiency. These insights offer valuable implications for policymakers and industry stakeholders aiming to optimize resource allocation and enhance productivity through technological innovation.
Dynamics of Human Fertility, Environmental Pollution, and Socio-Economic Factors in Aral Sea Basin
Authors: Saidmamatov Olimjon , Sobirov Yuldoshboy , Makhmudov Sardorbek , Marty Peter , Yusupova Shahnoza , Ibadullaev Ergash , Toshnazarova Dilnavoz
Published: October 2024

One of the worst natural, economic, and social catastrophes caused by human activity is the Aral Sea crisis in Central Asia. The Aral Sea’s desiccation, which has an impact on the region’s overall sustainable development, human welfare, security, and survival, is what led to the problem. This study assesses the effects of economic expansion, population ageing, life expectancy, internet usage, and greenhouse gas emissions on the fertility rate in the countries that made up the Aral Sea basin between 1990 and 2021. Several econometric techniques were used in this study, including Pooled OLS (Ordinary Least Squares) with the Driscoll–Kraay estimating method, FMOLS (Fully Modified Ordinary Least Square), and DOLS (Dynamic Ordinary Least Square). Additionally, we used the Hurlin and Dumitrescu non-cause tests to verify the causal links between the variables. The empirical findings verify that a decrease in the fertility rate among women in the nations surrounding the Aral Sea occurs when the population of a certain age (women aged 15–64 as a percentage of the total population) grows and life expectancy rises. Greenhouse gas emissions (GHGs) also have an adverse effect on reproductive rates. Conversely, the region’s fertility rate may rise as a result of increased internet usage and economic growth. Furthermore, this study indicates that certain variables—aside from greenhouse gas emissions (GHGs)—have a causal relationship with the fertility rate.
An Innovative STEAM-Based Method for Teaching Cycloidal Curves in Engineering Higher Education
Authors: Szilágyi Szilvia , Körei Attila , Vaičiulyté Ingrida
Published: October 2024

Robotics offers innovative possibilities at all levels of education, which should be considered when planning the teaching process for certain engineering mathematics topics in higher education. This paper introduces a new teaching–learning technique that utilizes STEAM-based methods to explore cycloidal curves for Computer Science Engineering BSc students. Traditional frontal teaching has been enhanced with methods addressing the generational needs of students, including problem-based learning, STEAM integration, and project-based learning. We developed a methodological model that merges traditional teaching advantages with practical modern approaches suitable for Generation Z. The four-stage model for learning cycloidal curves employs various didactical approaches, utilizing different learning techniques at each stage to create an engaging and effective learning experience. A vital component of this model is the use of Desmos dynamic geometry software to create animations alongside educational robotics to aid visualization. We conducted quantitative studies with 98 first-year Computer Science Engineering students using a quasi-experimental research design to evaluate the new teaching technique’s effectiveness. Results from the Mann–Whitney U test indicated that the experimental group significantly outperformed the control group. Additionally, the Kruskal–Wallis test confirmed that the four-stage model for learning cycloidal curves enhances learning achievement for all students, regardless of their prior knowledge.
University Teachers’ Views on the Adoption and Integration of Generative AI Tools for Student Assessment in Higher Education
Authors: Khlaif Zuheir N. , Ayyoub Abedalkarim , Hamamra Bilal , Bensalem Elias , Mitwally Mohamed A. A. , Ayyoub Ahmad , Hattab Muayad K. , Shadid Fadi
Published: October 2024

This study examines the factors that may impact the adoption of generative artificial intelligence (Gen AI) tools for students’ assessment in tertiary education from the perspective of early-adopter instructors in the Middle East. It utilized a self-administered online survey and the Unified Theory of Acceptance and Use of Technology (UTAUT) model to collect data from 358 faculty members from different countries in the Middle East. The Smart PLS software 4 was used to analyze the data. The findings of this study revealed that educators developed new strategies to integrate Gen AI into assessment and used a systematic approach to develop assignments. Moreover, the study demonstrated the importance of developing institutional policies for the integration of Gen AI in education, as a driver factor influencing the use of Gen AI in assessments. Additionally, the research identified significant factors, namely performance expectancy, effort expectancy, social influences, and hedonic motivation, shaping educators’ behavioral intentions and actual use of Gen AI tools to assess students’ performance. The findings reveal both the potential advantages of Gen AI, namely enhanced student engagement and reduced instructor workloads, and challenges, including concerns over academic integrity and the possible negative impact on students’ writing and thinking skills. This study emphasizes the significance of targeted professional development and ethical criteria for the proper integration of Gen AI in educational assessment.
Information Extraction of Aviation Accident Causation Knowledge Graph: An LLM-Based Approach
Authors: Chen Lu , Xu Jihui , Wu Tianyu , Liu Jie
Published: October 2024

Summarizing the causation of aviation accidents is conducive to enhancing aviation safety. The knowledge graph of aviation accident causation, constructed based on aviation accident reports, can assist in analyzing the causes of aviation accidents. With the continuous development of artificial intelligence technology, leveraging large language models for information extraction and knowledge graph construction has demonstrated significant advantages. This paper proposes an information extraction method for aviation accident causation based on Claude-prompt, which relies on the large-scale pre-trained language model Claude 3.5. Through prompt engineering, combined with a few-shot learning strategy and a self-judgment mechanism, this method achieves automatic extraction of accident-cause entities and their relationships. Experimental results indicate that this approach effectively improves the accuracy of information extraction, overcoming the limitations of traditional methods in terms of accuracy and efficiency in processing complex texts. It provides strong support for subsequently constructing a structured knowledge graph of aviation accident causation and conducting causation analysis of aviation accidents.
An Adaptive Controller with Disturbance Observer for Underwater Vehicle Manipulator Systems
Authors: Zheng Xinhui , Wang Yuliang , Tian Qiyan , Zhang Qifeng , Wang Xiaohui , Xu Wenbo , Wang Guodong , Yang Xuejiao , Sun Yuze
Published: October 2024

Dynamic control of underwater vehicle manipulator systems (UVMSs) is the key part of underwater intervention tasks. In this paper, we propose an adaptive controller with a disturbance observer that mainly consists of two parts: the first part is the adaptive control law that estimates the changes in the center of mass (COM) and the center of buoyancy (COB) of the vehicle, and the second part is the nonlinear disturbance observer that estimates the external disturbance and model uncertainties. To attenuate the overestimation problem, a damping term is introduced to the adaptive law. The stability of the proposed method is proven on the basis of Lyapunov theory. We develop three scenarios on the Simurv platform and illustrate the effectiveness of the proposed method with a short response time and high tracking performance.
DAFE-MSGAT: Dual-Attention Feature Extraction and Multi-Scale Graph Attention Network for Polyphonic Piano Transcription
Authors: Cao Rui , Liang Zushuang , Yan Zheng , Liu Bing
Published: October 2024

Automatic music transcription (AMT) aims to convert raw audio signals into symbolic music. This is a highly challenging task in the fields of signal processing and artificial intelligence, and it holds significant application value in music information retrieval (MIR). Existing methods based on convolutional neural networks (CNNs) often fall short in capturing the time-frequency characteristics of audio signals and tend to overlook the interdependencies between notes when processing polyphonic piano with multiple simultaneous notes. To address these issues, we propose a dual attention feature extraction and multi-scale graph attention network (DAFE-MSGAT). Specifically, we design a dual attention feature extraction module (DAFE) to enhance the frequency and time-domain features of the audio signal, and we utilize a long short-term memory network (LSTM) to capture the temporal features within the audio signal. We introduce a multi-scale graph attention network (MSGAT), which leverages the various implicit relationships between notes to enhance the interaction between different notes. Experimental results demonstrate that our model achieves high accuracy in detecting the onset and offset of notes on public datasets. In both frame-level and note-level metrics, DAFE-MSGAT achieves performance comparable to the state-of-the-art methods, showcasing exceptional transcription capabilities.
An Adaptive Sliding Mode Control Using a Novel Adaptive Law Based on Quasi-Convex Functions and Average Sliding Variables for Robot Manipulators
Authors: Seo Dong Hee , Lee Jin Woong , An Hyuk Mo , Lee Seok Young
Published: October 2024

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance.
Leveraging Digital Twins and Intrusion Detection Systems for Enhanced Security in IoT-Based Smart City Infrastructures
Authors: El-Hajj Mohammed
Published: October 2024

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber–physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high Central Processing Unit (CPU) usage during SYN and ACK flood attacks and persistent memory usage after Network Mapper (NMAP) scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber–physical systems.
Unsupervised Learning for Lateral-Movement-Based Threat Mitigation in Active Directory Attack Graphs
Authors: Herranz-Oliveros David , Tejedor-Romero Marino , Gimenez-Guzman Jose Manuel , Cruz-Piris Luis
Published: October 2024

Cybersecurity threats, particularly those involving lateral movement within networks, pose significant risks to critical infrastructures such as Microsoft Active Directory. This study addresses the need for effective defense mechanisms that minimize network disruption while preventing attackers from reaching key assets. Modeling Active Directory networks as a graph in which the nodes represent the network components and the edges represent the logical interactions between them, we use centrality metrics to derive the impact of hardening nodes in terms of constraining the progression of attacks. We propose using Unsupervised Learning techniques, specifically density-based clustering algorithms, to identify those nodes given the information provided by their metrics. Our approach includes simulating attack paths using a snowball model, enabling us to analytically evaluate the impact of hardening on delaying Domain Administration compromise. We tested our methodology on both real and synthetic Active Directory graphs, demonstrating that it can significantly slow down the propagation of threats from reaching the Domain Administration across the studied scenarios. Additionally, we explore the potential of these techniques to enable flexible selection of the number of nodes to secure. Our findings suggest that the proposed methods significantly enhance the resilience of Active Directory environments against targeted cyber-attacks.
Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
Authors: Karahan Sümeye Nur , Kalaycıoğlu Aykut
Published: October 2024

In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding in MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the Tikhonov regularization parameter, as well as the step size and search direction coefficients of the conjugate gradient (CG) method, as trainable parameters within the deep learning framework. This enables dynamic adjustments based on varying channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach across a range of MIMO configurations and channel conditions, consistently achieving lower bit error rate (BER) and normalized minimum mean square error (NMSE) compared to well-known techniques like DetNet and CG. The proposed method has superior performance over CG and other model-based methods, especially with a small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations, modulation orders, and different channel conditions.
Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data
Authors: Choi YuRim , Joe Inwhee
Published: October 2024

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future.