bluuebunny/crossref_metadata_2025 huggingface dataset:
Control Level of Type 2 Diabetes Mellitus in the Elderly Is Associated with Polypharmacy, Accompanied Comorbidities, and Various Increased Risks According to the Beers Criteria
Authors: Atak Tel Burcin Meryem , Aktas Gulali , Bilgin Satilmis , Baltaci Sumeyye Buse , Taslamacioglu Duman Tuba
Published: November 2023

Comorbidity rates in the geriatric population have increased because of rising life expectancy; thus, patients have had to use more medications. Type 2 diabetes mellitus, one of the most common diseases, may influence the number of drugs used in geriatric patients. The present study was designed to investigate the association between the level of type 2 DM and polypharmacy. Fifty patients with type 2 diabetes over the age of 65 were included according to the inclusion criteria; 23 were well-controlled and 27 had poorly controlled diabetes. The groups were similar in terms of age, sex, WBC, Hb, Plt, AST, ALT, serum creatinine, fasting glucose, and eGFR levels. Patients with HbA1c values above 7.5 were classified as poorly controlled diabetes patients, and those below were considered well-controlled diabetes patients and were evaluated for inappropriate medication use. The number of medications used daily by the cases (p < 0.001), the number of concomitant diseases (p = 0.001), and the number of increased risks according to the Beers Criteria (p = 0.02) were observed to be high in poorly controlled type 2 diabetes mellitus subjects. HbA1c levels were related to the number of medications (r = 0.4, p = 0.004), comorbidities (r = 0.28, p = 0.04), and the number of increased risks according to the Beers Criteria (r = 0.31, p = 0.014). In conclusion, the number of medications used in patients with poorly controlled type 2 diabetes mellitus was found to be more elevated than in individuals with well-controlled type 2 diabetes mellitus. The HbA1c values varied among patients regarding polypharmacy, comorbidities, and increased risks according to the Beers Criteria.
Evaluation of the Breastfeeding Dynamics of Neonates with Ankyloglossia via a Novel Ultrasonographic Technique
Authors: Alan Arzu , Orhan Ayse Isil , Orhan Kaan
Published: November 2023

To effectively address breastfeeding issues for neonates and mothers, one must understand the physiology of breastfeeding and the anatomical components involved in sucking, swallowing, and respiration. This study compared the tongue position and movement of neonates with tongue ties versus healthy controls during sucking. A new objective ultrasonography diagnostic approach was also introduced for the orofacial region. This retrospective study evaluated B-mode and M-mode ultrasonography images from 30 neonates clinically diagnosed with tongue tie, and a control group of 30 neonates. B-mode ultrasound images were used to examine several characteristics to locate the nipple in the oral cavity during breastfeeding. Anatomic M-mode ultrasound images were used to assess tongue movement during sucking. The nipple moved farther from the intersection of the hard and soft palates during the sucking cycle in the ankyloglossia group than in the control group (p < 0.05). Compared to the control group, neonates with ankyloglossia have a lower capacity to lift the anterior tongue toward the palate when sucking (p < 0.05). There was no significant difference in tongue movement metrics between the two groups (p > 0.05). Our findings were consistent with earlier research. The novel measurement method will offer a new perspective on breastfeeding.
The Comparison of the Selected Parameters of Brain Injury and Interleukins in the CSF in Patients Diagnosed De Novo with RRMS Compared to the Control Group
Authors: Adamczyk Bożena , Morawiec Natalia , Mamak Gabriela , Boczek Sylwia , Brzęk Dominika , Trędota Natalia , Walocha Patryk , Czuba Zenon P. , Błachut Michał , Bartman Wojciech , Adamczyk-Sowa Monika
Published: November 2023

Background: Multiple sclerosis (MS) is a chronic autoimmune disorder affecting the central nervous system (CNS). Due to the different phenotypes of the disease and non-specific symptoms of MS, there is a great need for a validated panel of biomarkers to facilitate the diagnosis, predict disease progression, and evaluate treatment outcomes. Methods: We determined the levels of the parameters of brain injury (NF-H, GPAF, S100B, and UCHL1) and the selected cytokines in the cerebrospinal fluid (CSF) in 101 patients diagnosed de novo with RRMS and 75 healthy controls. All determinations were made using the Bio-Plex method. Results: We found higher levels of NF-H and GFAP in the relapsing-remitting multiple sclerosis (RRMS) group compared to the controls. The concentrations of both molecules were significantly increased in patients with Gd+ lesions on brain MRI. The level of S100B did not differ significantly between the groups. UCHL1 concentrations were higher in the control group. We found some correlations between the selected cytokines, the levels of the parameters of brain injury, and the time from the first symptoms to the diagnosis of MS. Conclusions: The role of the above molecules in MS is promising. However, further research is warranted to define their precise functions.
Classification of Emotional and Immersive Outcomes in the Context of Virtual Reality Scene Interactions
Authors: Daşdemir Yaşar
Published: November 2023

The constantly evolving technological landscape of the Metaverse has introduced a significant concern: cybersickness (CS). There is growing academic interest in detecting and mitigating these adverse effects within virtual environments (VEs). However, the development of effective methodologies in this field has been hindered by the lack of sufficient benchmark datasets. In pursuit of this objective, we meticulously compiled a comprehensive dataset by analyzing the impact of virtual reality (VR) environments on CS, immersion levels, and EEG-based emotion estimation. Our dataset encompasses both implicit and explicit measurements. Implicit measurements focus on brain signals, while explicit measurements are based on participant questionnaires. These measurements were used to collect data on the extent of cybersickness experienced by participants in VEs. Using statistical methods, we conducted a comparative analysis of CS levels in VEs tailored for specific tasks and their immersion factors. Our findings revealed statistically significant differences between VEs, highlighting crucial factors influencing participant engagement, engrossment, and immersion. Additionally, our study achieved a remarkable classification performance of 96.25% in distinguishing brain oscillations associated with VR scenes using the multi-instance learning method and 95.63% in predicting emotions within the valence-arousal space with four labels. The dataset presented in this study holds great promise for objectively evaluating CS in VR contexts, differentiating between VEs, and providing valuable insights for future research endeavors.
Vertidrome Airside Level of Service: Performance-Based Evaluation of Vertiport Airside Operations
Authors: Schweiger Karolin , Knabe Franz
Published: November 2023

This paper presents the Vertidrome Airside Level of Service (VALoS) framework, a novel performance metric designed to evaluate airside traffic flow operations at vertidromes in the context of Urban Air Mobility (UAM). As the UAM industry rapidly evolves, the need for a comprehensive evaluation framework becomes increasingly important. The VALoS framework provides a performance-based approach to evaluating vertidrome traffic flow performance, considering metrics like average passenger delay, air taxi in-flight delay, and vertidrome punctuality. Unlike existing Level of Service approaches, the VALoS framework unifies the requirements of various stakeholders, the passenger, the air taxi operator, and the vertidrome operator each with their own performance metric and target. It provides a multi-faceted approach covering airside air and ground traffic flows, arrivals and departures, and performance changes during strategic planning and tactical execution phases. The VALoS is evaluated at 15-min intervals while considering changing stakeholder performance targets and operational uncertainties. For the reference use case, the study demonstrates the significant impact of short-term disruptions, while stochastic deviations can be neglected. Higher traffic volumes due to changing demand/capacity ratios result in higher VALoS variability. The VALoS framework, together with a fast-time simulation, provides a versatile method for exploring future vertidrome traffic flows and supporting strategic vertidrome airside planning and integration. This integrated approach is essential for the evolving UAM vertidrome industry; aligning the interests of different stakeholders and promoting sustainable and efficient vertidrome planning and operation.
Pharmacologic Considerations for Antimicrobials and Anticoagulants after Burn Injury
Authors: Ravichandran Pranav , Pruskowski Kaitlin A.
Published: November 2023

Derangements in pharmacokinetics and pharmacodynamics (PK/PD) of burn patients are poorly understood and lacking consistent data. This leads to an absence of consensus regarding pharmacologic management of burn patients, complicating their care. In order to effectively manage burn critical illness, knowledge of pharmacologic parameters and their changes is necessary. It is also imperative that the clinician understands how these changes will affect drug dosing. A common practice is to increase antibiotic dosing and/or frequency; however, this may not be necessary and doses should be adjusted to patient- and drug-specific parameters. Additionally, monitoring assays for antibiotic levels as well as coagulation factors can be useful for adjusting dosages to best treat the patient. This review focuses on alterations in PK/PD as well as other physiologic changes after burn injury, with special reference to care in military and austere settings.
Border Proximity, Ports, and Railways: Analyzing Their Impact on County-Level Economic Dynamics in Hungary, 2001–2020
Authors: Fedorenko Roman , Khmeleva Galina , Kurnikova Marina
Published: November 2023

In this research, our primary objective is to dissect the influence of specific locational elements—proximity to international borders, substantial ports, and significant railway junctions—on the economic vitality of Hungary’s counties from 2001 to 2020. The aim is to reveal how these factors individually contribute to economic disparities and to demonstrate their compounded effect on regional prosperity. This analysis is particularly timely and pertinent as regional inequalities are becoming more pronounced globally, making understanding such disparities crucial for effective policy formulation and regional planning. Utilizing GDP per capita as a fundamental indicator of economic health, we meticulously categorized counties, revealing a clear correlation between these locational advantages and economic performance. We innovatively employed Python to script a unique code, creating a matrix that enriches the presentation of our results, thereby facilitating a more nuanced understanding of these correlations. Our findings are significant in the current socio-economic climate, highlighting the need for tailored strategies considering unique regional attributes. This study is instrumental for policymakers and stakeholders in formulating informed, targeted strategies to harness these locational advantages, fostering balanced development, and narrowing the economic divide within the nation. The actuality of our research lies in its immediate relevance, offering insights critical to current discussions and decisions in regional development planning.
Is Reality in Conflict with Perception? The Impact of Technology-Enhanced Active Learning and Formative Assessment on the Formation of Pre-Service Teachers in the Social Sciences
Authors: Tirado-Olivares Sergio , Cózar-Gutiérrez Ramón , García-Olivares Rebeca , González-Calero José Antonio
Published: November 2023

Active learning environments mediated by technologies in which learners assume ownership of their learning and receive daily feedback are gaining in popularity. Nevertheless, variables such as pre-service teachers’ learning gains and their perception towards these educational approaches have been little studied in the field of social science teaching. This quantitative study analyses to what extent (if any) an active methodology such as inquiry-based learning (IBL) supported by a Learning Analytics (LA)-based formative assessment process impacts on the academic achievement attained by 240 prospective teachers and their perception of the quality of the teaching–learning process compared to lecture-based teaching. Results show higher pre-service teachers’ performance in those lessons in which IBL supported by LA were used. However, the class dynamic and the lecturer’s role were rated lower by prospective teachers who used this methodology compared to those who attended lecture-based teaching classes. Results evidence that educational research should not only pay attention to the academic benefits of active methodologies, but also to the necessity of educating (future) teachers about these innovative approaches.
Neurocognitive Profile of Creativity in Improving Academic Performance—A Scoping Review
Authors: Tzachrista Maria , Gkintoni Evgenia , Halkiopoulos Constantinos
Published: November 2023

The present study provides an in-depth review of the neurocognitive aspects of creativity and its association with academic achievement in children. The outcomes of this study indicate a considerable positive relationship between creativity and academic ability, namely in reading, comprehension, and written tasks. Neurocognitive processes, including associative thinking, divergent thinking, executive functions, and predictive representations, play a significant role in shaping the characteristics associated with creativity. Although creativity may lead to potential adverse outcomes, extensive study has firmly established its significance in academic accomplishment. This systematic review includes studies conducted between the years 2016 and 2022. The primary selection criteria employed for the inclusion of articles in the research encompassed the following aspects: articles written in the English language, articles directly relevant to the subject matter of the study, articles featuring a sample population comprising students, displaying regular and typical developmental patterns, articles adopting a descriptive, exploratory, or longitudinal study design. The research promotes the cultivation of creativity within educational settings, emphasizing the utilization of cultural resources, the alignment of teachers’ attitudes with the promotion of creativity, and the comprehension of neurocognitive factors influencing creative processes. There is a significant focus on the necessity for more investigation into the correlation between creativity and academic achievement. Further research is needed to gain a comprehensive understanding of how creativity is incorporated within educational settings to achieve academic performance.
Investigating Metacognitive Strategies and Exam Performance: A Cross-Sectional Survey Research Study
Authors: Kennedy Jolie V. , Arendale David R.
Published: November 2023

This investigation used cross-sectional survey research methods in a high-enrollment undergraduate history course, setting out to examine test performance and metacognitive strategies that subjects self-selected prior to class, during class, and during the exam. This study examined the differences in exam scores between one group of students who self-reported completing specific metacognitive strategies and one group of students who self-reported not completing them. An online survey instrument was used to collect data from 121 students about the frequency of occurrence of specific behaviors. Frequencies and an Independent Samples T-Test were used to analyze metacognitive strategies and exam performance. The results showed the following strategies were statistically significant at the 0.05 alpha level: (1) read or listened to assigned readings and audio files before they were discussed during class; (2) frequently took part in small group discussion at the table during the class session; (3) created outlines for each of the potential essay questions to prepare for the examination; and (4) made an outline of the essay question before beginning to write while taking the exam. Limitations of the study, implications of the results, and recommendations for future research are provided. With the challenges of supporting students to earn higher grades and persist toward graduation, faculty members need to join the rest of the campus to be active agents in supporting students through simple learning strategies and effective student behaviors embedded into their courses. This may require extra time and effort to engage in professional development to learn how to embed practice with metacognitive strategies during class sessions.
STEM Teachers’ Digital Competence: Different Subjects, Different Proficiencies
Authors: Vieira Rui Marques , Tenreiro-Vieira Celina C. , Bem-Haja Pedro , Lucas Margarida
Published: November 2023

STEM—science, technology, engineering, and mathematics—and STEM literacy have emerged as one (of many) concerted efforts aiming to provide the different competences required for future generations to adapt to an evolving world. Despite lacking a comprehensive framework for STEM, this concept has been studied under different perspectives, one of which is the digital competences of teachers. This study focuses on the digital competence and proficiency of 20,935 teachers, distributed across the following subjects: mathematics and natural sciences, physics and chemistry, biology and geology, and mathematics. It uses DigCompEdu as a conceptual framework to describe teachers’ use of digital technologies to teach STEM subjects. The results show that biology and geology teachers achieved significantly higher digital proficiency scores when compared to teachers from the other three subjects. Physics and chemistry teachers also scores significantly higher than mathematics and natural sciences teachers. The results show the existence of significant positive correlations among all competence areas for the four STEM subjects. In conclusion, Portuguese teachers presented different levels of digital competence and perceived themselves as differently prepared for integrating digital technologies. A potential implication of this study is the need for teacher education about digital competences and a focus on producing teachers capable of dealing with STEM in their future classroom teaching and learning.
Accounting Students in the Role of Equal-Status Team Teacher for the Purpose of Knowledge and Competency Development
Authors: Pollock Marchantia , Coetzee Stephen A. , Schmulian Astrid
Published: November 2023

This study investigates the impact of engaging accounting students in a team-teaching role on their knowledge and competency development in a higher education setting. The research quantifies the knowledge gains from this learning-by-team-teaching intervention and explores students’ experiences with this intervention through survey data. The findings suggest that engaging students in a team-teaching role, specifically a sequential equal-status team-teaching role incorporating interactive teaching styles, significantly enhances knowledge development, particularly among lower-performing students. Students reported a largely positive experience across all performance levels, attributing their growth to improved knowledge, teamwork, and communication skills provided by the intervention. The study recognizes the benefits derived from the team-based design of the intervention, such as enhanced social constructivist knowledge development. Overall, this study contributes to the existing body of knowledge on learning-by-teaching strategies. It emphasizes the potential of engaging students in a team-teaching role to enhance their academic performance and the development of key professional competencies.
“I Am on Top!”: An Interactive Intervention Program to Promote Self-Regulation Processes in the Prevention of the Use of Doping in Sports High Schools
Authors: Galli Federica , Chirico Andrea , Codella Roberto , Zandonai Thomas , Deplano Vindice , De Maria Alessandra , Palombi Tommaso , Gotti Daniel , Alivernini Fabio , Mallia Luca , Zelli Arnaldo , Lucidi Fabio
Published: November 2023

The use of substances to improve sports performance is a widespread phenomenon among adolescents. Several anti-doping programs have been developed, mainly based on knowledge-based evidence. The main aim of the present study was to implement an anti-doping intervention in sports high school students, based on a psychological framework, such as Socio-Cognitive Theory, through the development of a Serious Game (SG), i.e., digital learning based on the game. The experimental design included an intervention group (n = 167; F = 37.7%; Meanage = 17.5 years; SD = 0.58) and a control group (n = 112; F = 42%; Meanage = 17.6; SD = 1). Both of the groups completed the same questionnaire on two different occasions (i.e., time 1 and time 2) for measuring doping intention, self-regulatory efficacy to resist social pressure for the use of substances, moral disengagement, and doping knowledge. Data were analyzed through repeated measures of Group X Time ANOVA, demonstrating some degree of efficacy of the intervention, in particular in terms of the decrease in doping intention and the strengthening of doping knowledge. Moreover, the study demonstrated that the score obtained during the implementation of the SG could partially represent a coherent measure of the participants’ beliefs regarding doping. These results could be considered a starting point for future research to better develop technological anti-doping interventions.
RCA-GAN: An Improved Image Denoising Algorithm Based on Generative Adversarial Networks
Authors: Wang Yuming , Luo Shuaili , Ma Liyun , Huang Min
Published: November 2023

Image denoising, as an essential component of image pre-processing, effectively reduces noise interference to enhance image quality, a factor of considerable research importance. Traditional denoising methods often lead to the blurring of image details and a lack of realism at the image edges. To deal with these issues, we propose an image denoising algorithm named Residual structure and Cooperative Attention mechanism based on Generative Adversarial Networks (RCA-GAN). This algorithm proficiently reduces noise while focusing on preserving image texture details. To maximize feature extraction, this model first employs residual learning within a portion of the generator’s backbone, conducting extensive multi-dimensional feature extraction to preserve a greater amount of image details. Secondly, it introduces a simple yet efficient cooperative attention module to enhance the representation capacity of edge and texture features, further enhancing the preservation of intricate image details. Finally, this paper constructs a novel loss function—the Multimodal Loss Function—for the network training process. The experimental results were evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) as evaluation metrics. The experimental results demonstrate that the proposed RCA-GAN image denoising algorithm has increased the average PSNR from 24.71 dB to 33.76 dB, achieving a 36.6% improvement. Additionally, the average SSIM value has risen from 0.8451 to 0.9503, indicating a 12.4% enhancement. It achieves superior visual outcomes, showcasing the ability to preserve image texture details to a greater extent and excel in edge preservation and noise suppression.
Design of an Online Programming Platform and a Study on Learners’ Testing Ability
Authors: Hsueh Nien-Lin , Lai Lien-Chi , Tseng Wei-Hsiang
Published: November 2023

Online judge (OJ) systems are essential in programming education as they efficiently evaluate learners’ programming skills and reduce instructor workload. However, these systems often overlook the importance of software-testing concepts. To address this gap, we developed a system called Pytutor that integrates software-testing concepts to assess learners’ programming abilities and proficiency by exploring students’ test cases and learning behaviors. Drawing on software engineering theory and practical techniques, test capabilities are evaluated by analyzing the code coverage and mutation testing of Defining Test Cases. Since our experiment is conducted in an online environment, we can collect students’ learning behaviors and further analyze the relationship between software engineering abilities and learning behaviors. We also analyzed the differences in programming and testing abilities between computer science majors and non-computer-science majors. Our findings suggest that better testing abilities may contribute to the improvement in programming abilities, whereas in the current Taiwanese education context, computer science majors do not necessarily have better testing abilities. This result provides suggestions for us to strengthen software-testing education no matter which type of students it is targeted at.
Frame Duplication Forgery Detection in Surveillance Video Sequences Using Textural Features
Authors: Li Li , Lu Jianfeng , Zhang Shanqing , Mohaisen Linda , Emam Mahmoud
Published: November 2023

Frame duplication forgery is the most common inter-frame video forgery type to alter the contents of digital video sequences. It can be used for removing or duplicating some events within the same video sequences. Most of the existing frame duplication forgery detection methods fail to detect highly similar frames in the surveillance videos. In this paper, we propose a frame duplication forgery detection method based on textural feature analysis of video frames for digital video sequences. Firstly, we compute the single-level 2-D wavelet decomposition for each frame in the forged video sequences. Secondly, textural features of each frame are extracted using the Gray Level of the Co-Occurrence Matrix (GLCM). Four second-order statistical descriptors, Contrast, Correlation, Energy, and Homogeneity, are computed for the extracted textural features of GLCM. Furthermore, we calculate four statistical features from each frame (standard deviation, entropy, Root-Mean-Square RMS, and variance). Finally, the combination of GLCM’s parameters and the other statistical features are then used to detect and localize the duplicated frames in the video sequences using the correlation between features. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art (SOTA) methods in terms of Precision, Recall, and F1Score rates. Furthermore, the use of statistical features combined with GLCM features improves the performance of frame duplication forgery detection.
Bluetooth 5.0 Suitability Assessment for Emergency Response within Fire Environments
Authors: Black Brendan , Rafferty Joseph , Santos Jose , Ennis Andrew , Perry Philip , McKee Maurice
Published: November 2023

Natural disasters, such as wildfires, can cause widespread devastation. Future-proofing infrastructure, such as buildings and bridges, through technological advancements is crucial to minimize their impact. Fires in disasters often stem from damaged fuel lines and electrical equipment, such as the 2018 California wildfire caused by a power line fault. To enhance safety, IoT applications can continuously monitor the health of emergency personnel. Using Bluetooth 5.0 and wearables in mesh networks, these apps can alert others about an individual’s location during emergencies. However, fire can disrupt wireless networks. This study assesses Bluetooth 5.0’s performance in transmitting signals in fire conditions. It examined received signal strength indicator (RSSI) values in a front open-fire chamber using both Peer-to-Peer (P2P) and mesh networks. The experiment considered three transmission heights of 0.61, 1.22, and 1.83 m and two distances of 11.13 m and 1.52 m. The study demonstrated successful signal transmission with a maximum loss of only 2 dB when transmitting through the fire. This research underscores the potential for reliable communication in fire-prone environments, improving safety during natural disasters.
Multimodal Global Trajectory Planner for Autonomous Underwater Vehicles
Authors: Kot Rafał
Published: November 2023

The underwater environment introduces many limitations that must be faced when designing an autonomous underwater vehicle (AUV). One of the most important issues is developing an effective vehicle movement control and mission planning system. This article presents a global trajectory planning system based on a multimodal approach. The trajectory of the vehicle’s movement has been divided into segments between introduced waypoints and calculated in parallel by advanced path planning methods: modified A* method, artificial potential field (APF), genetic algorithm (GA), particle swarm optimisation (PSO), and rapidly-exploring random tree (RRT). The shortest paths in each planned segment are selected and combined to give the resulting trajectory. A comparison of the results obtained by the proposed approach with the path calculated by each method individually confirms the increase in the system’s effectiveness by ensuring a shorter trajectory and improving the system’s reliability. Expressing the final trajectory in the form of geographical coordinates with a specific arrival time allows the implementation of calculation results in mission planning for autonomous underwater vehicles used commercially and in the military, as well as for autonomous surface vehicles (ASVs) equipped with trajectory tracking control systems.
A Comprehensive Study on the Role of Machine Learning in 5G Security: Challenges, Technologies, and Solutions
Authors: Fakhouri Hussam N. , Alawadi Sadi , Awaysheh Feras M. , Hani Imad Bani , Alkhalaileh Mohannad , Hamad Faten
Published: November 2023

Fifth-generation (5G) mobile networks have already marked their presence globally, revolutionizing entertainment, business, healthcare, and other domains. While this leap forward brings numerous advantages in speed and connectivity, it also poses new challenges for security protocols. Machine learning (ML) and deep learning (DL) have been employed to augment traditional security measures, promising to mitigate risks and vulnerabilities. This paper conducts an exhaustive study to assess ML and DL algorithms’ role and effectiveness within the 5G security landscape. Also, it offers a profound dissection of the 5G network’s security paradigm, particularly emphasizing the transformative role of ML and DL as enabling security tools. This study starts by examining the unique architecture of 5G and its inherent vulnerabilities, contrasting them with emerging threat vectors. Next, we conduct a detailed analysis of the network’s underlying segments, such as network slicing, Massive Machine-Type Communications (mMTC), and edge computing, revealing their associated security challenges. By scrutinizing current security protocols and international regulatory impositions, this paper delineates the existing 5G security landscape. Finally, we outline the capabilities of ML and DL in redefining 5G security. We detail their application in enhancing anomaly detection, fortifying predictive security measures, and strengthening intrusion prevention strategies. This research sheds light on the present-day 5G security challenges and offers a visionary perspective, highlighting the intersection of advanced computational methods and future 5G security.
Fault Mechanism and Improvement in the Augmented Railgun Excitation Circuit
Authors: Cao Genrong , Xiang Hongjun , Qiao Zhiming , Liang Chunyan , Yuan Xichao
Published: November 2023

The augmented electromagnetic railgun has demonstrated more potent electromagnetic force, a low excitation current, and less rail thermal damage, showing good potential in heavy load launch. However, the augmented railgun’s load characteristics differ from the conventional double-rail railgun. In the augmented railgun launching experiment, it was found that the fly-wheel diode was damaged, and the capacitive power supply could not discharge fully, which led to residual energy and lower energy utilization. This paper began with the characteristics of the sequential trigger circuit, followed by the causes of the above faults, as well as the corresponding solutions. Based on the conventional excitation circuit, fault detection experiments were carried out first, and the fault mechanism was clarified. Furthermore, according to the solutions proposed, the conventional excitation circuit was improved so that it can work normally when a heavy inductance was loaded. Finally, the sequential trigger experiment was carried out again with the same circuit parameters. The research results showed that the fly-wheel diode could work normally after the circuit was improved, and the problem of residual energy could also be solved effectively. Moreover, the output performance was almost unaffected.
Automated Facial Emotion Recognition Using the Pelican Optimization Algorithm with a Deep Convolutional Neural Network
Authors: Alonazi Mohammed , Alshahrani Hala J. , Alotaibi Faiz Abdullah , Maray Mohammed , Alghamdi Mohammed , Sayed Ahmed
Published: November 2023

Facial emotion recognition (FER) stands as a pivotal artificial intelligence (AI)-driven technology that exploits the capabilities of computer-vision techniques for decoding and comprehending emotional expressions displayed on human faces. With the use of machine-learning (ML) models, specifically deep neural networks (DNN), FER empowers the automatic detection and classification of a broad spectrum of emotions, encompassing surprise, happiness, sadness, anger, and more. Challenges in FER include handling variations in lighting, poses, and facial expressions, as well as ensuring that the model generalizes well to various emotions and populations. This study introduces an automated facial emotion recognition using the pelican optimization algorithm with a deep convolutional neural network (AFER-POADCNN) model. The primary objective of the AFER-POADCNN model lies in the automatic recognition and classification of facial emotions. To accomplish this, the AFER-POADCNN model exploits the median-filtering (MF) approach to remove the noise present in it. Furthermore, the capsule-network (CapsNet) approach can be applied to the feature-extraction process, allowing the model to capture intricate facial expressions and nuances. To optimize the CapsNet model’s performance, hyperparameter tuning is undertaken with the aid of the pelican optimization algorithm (POA). This ensures that the model is finely tuned to detect a wide array of emotions and generalizes effectively across diverse populations and scenarios. Finally, the detection and classification of different kinds of facial emotions take place using a bidirectional long short-term memory (BiLSTM) network. The simulation analysis of the AFER-POADCNN system is tested on a benchmark FER dataset. The comparative result analysis showed the better performance of the AFER-POADCNN algorithm over existing models, with a maximum accuracy of 99.05%.
Robust Optimization over Time Problems—Characterization and Literature Review
Authors: Novoa-Hernández Pavel , Puris Amilkar , Pelta David A.
Published: November 2023

Robust optimization over time (ROOT) is a relatively recent topic in the field of dynamic evolutionary optimization (EDO). The goal of ROOT problems is to find the optimal solution for several environments at the same time. Although significant contributions to ROOT have been published in the past, it is not clear to what extent progress has been made in terms of the type of problem addressed. In particular, we believe that there is confusion regarding what it actually means to solve a ROOT problem. To overcome these limitations, the objective of this paper is twofold. On the one hand, to provide a characterization framework of ROOT problems in terms of their most relevant features, and on the other hand, to organize existing contributions according to it. As a result, from an initial set of 186 studies, the characterization framework was applied to 35 of them, allowing to identification of some important gaps and proposing new research opportunities. We have also experimentally addressed the effect of available information on ROOT problems, concluding that there is indeed a significant impact on the performance of the algorithm and that the proposed classification is appropriate to characterize the complexity of ROOT problems. To help identify further research opportunities, we have implemented an interactive dashboard with the results of the review conducted, which is available online.
Detection of Fittings Based on the Dynamic Graph CNN and U-Net Embedded with Bi-Level Routing Attention
Authors: Xie Zhihui , Fu Min , Liu Xuefeng
Published: November 2023

Accurate detection of power fittings is crucial for identifying defects or faults in these components, which is essential for assessing the safety and stability of the power system. However, the accuracy of fittings detection is affected by a complex background, small target sizes, and overlapping fittings in the images. To address these challenges, a fittings detection method based on the dynamic graph convolutional neural network (DGCNN) and U-shaped network (U-Net) is proposed, which combines three-dimensional detection with two-dimensional object detection. Firstly, the bi-level routing attention mechanism is incorporated into the lightweight U-Net network to enhance feature extraction for detecting the fittings boundary. Secondly, pseudo-point cloud data are synthesized by transforming the depth map generated by the Lite-Mono algorithm and its corresponding RGB fittings image. The DGCNN algorithm is then employed to extract obscured fittings features, contributing to the final refinement of the results. This process helps alleviate the issue of occlusions among targets and further enhances the precision of fittings detection. Finally, the proposed method is evaluated using a custom dataset of fittings, and comparative studies are conducted. The experimental results illustrate the promising potential of the proposed approach in enhancing features and extracting information from fittings images.
Digital Calibration of Input Offset Voltage and Its Implementation in FDDA Circuits
Authors: Maljar David , Sovcik Michal , Potocny Miroslav , Ondica Robert , Arbet Daniel , Stopjakova Viera
Published: November 2023

This article deals with the calibration method of analog integrated circuits (ICs) designed in CMOS nanotechnology. A brief analysis of various methods and techniques (e.g., fuse trimming, chopper stabilization, auto-zero technique, etc.) for calibration of a specific IC’s parameter is given, leading to motivation for this research that is focused on the digital calibration. Then, the principle and overall design of the calibration subcircuit, which was generally used to calibrate the input offset voltage VIN_OFF of the operational amplifier (OPAMP). The essence of this work is verification of the proposed digital calibration algorithm for minimization the VIN_OFF of a bulk-driven fully differential difference amplifier (FDDA) with the power supply voltage VDD = 0.4 V. Evaluation of ASIC prototyped chip samples with silicon-proved results has been done. This evaluation contains comparison of selected parameters and characteristics obtained from both simulations and measurements of non-calibrated and calibrated FDDA configurations.
Building Trust in Microelectronics: A Comprehensive Review of Current Techniques and Adoption Challenges
Authors: Nyako Kwame , Devkota Suman , Li Frank , Borra Vamsi
Published: November 2023

The field of microelectronics has experienced extensive integration into various aspects of our everyday lives, evident via its utilization across a wide range of devices such as cellphones, airplanes, computers, wristwatches, and other similar technologies. Microelectronics are vital to the healthcare and defense industries, making them vulnerable to counterfeit products. Currently, the complicated global microelectronics supply chain involves the production of varied components in multiple places, resulting in tremendous risk. In this scenario, it is possible for hostile or adversarial actors to exploit the situation by intentionally introducing counterfeit components. This hostile behavior could steal data or use these components as remote kill switches. To address these problems, enormous resources are being committed to research, innovation, and development to build trust in microelectronics. This research study provides a thorough analysis of the taxonomy associated with prominent attack, detection, and avoidance models in the realm of counterfeit microelectronics. This research aims to improve our understanding of dependable microelectronics. Prevention strategies like Physical Unclonable Functions (PUFs) and machine learning (ML), and detection methods like aging-based fingerprints are reviewed in this study. Finally, we underscore the significance of interdisciplinary cooperation, commitment to norms, and proactive methods.
Load Modulation Feedback in Adaptive Matching Networks for Low-Coupling Wireless Power Transfer Systems
Authors: Bertozzi Michele , Catania Alessandro , Bandini Gabriele , Strangio Sebastiano , Iannaccone Giuseppe
Published: November 2023

This paper explores the use of load modulation feedback (LMF) in adaptive matching networks (MN) for low-coupling inductive wireless power transfer systems, with an emphasis on its use in implantable medical devices. After deriving the handy expressions of link efficiency and modulation depth in the case of LMF in the case of loose coupling, a brief overview of the most common capacitive resonance networks is presented. In particular, the MN employing two capacitors in Series–Parallel and in Parallel–Series configurations allow adaptivity with a wide range of load conditions. Then, the authors describe an effective design procedure of an adaptive matching network with LMF for an inductive wireless power transfer system, exploring the trade-off between power efficiency and modulation depth. Analytical and electrical simulations show that the proposed simple modulation strategy can successfully achieve high power transfer efficiency while maintaining steady back telemetry under varying loading conditions.
Weakly-Supervised Video Anomaly Detection with MTDA-Net
Authors: Wu Huixin , Yang Mengfan , Wei Fupeng , Shi Ge , Jiang Wei , Qiao Yaqiong , Dong Hangcheng
Published: November 2023

Weakly supervised anomalous behavior detection is a popular area at present. Compared to semi-supervised anomalous behavior detection, weakly-supervised learning both eliminates the need to crop videos and solves the problem of semi-supervised learning’s difficulty in handling long videos. Previous work has used graph convolution or self-attention mechanisms to model temporal relationships. However, these methods tend to model temporal relationships at a single scale and lack consideration of the aggregation problem for different temporal relationships. In this paper, we propose a weakly supervised anomaly detection framework, MTDA-Net, with emphasis on modeling different temporal relationships and enhanced semantic discrimination. To this end, we construct a new plug-and-play module, MTDA, which uses three branches, Multi-headed Attention (MHA), Temporal Shift (TS), and Dilated Aggregation (DA), to extract different temporal sequences. Specifically, the MHA branch can globally model the video information and project the features into different semantic spaces to enhance the expressiveness and discrimination of the features. The DA branch extracts temporal information of different scales via dilated convolution and captures the temporal features of local regions in the video. The TS branch can fuse the features of adjacent frames on a local scale and enhance the information flow. MTDA-Net can learn the temporal relationships between video segments on different branches and learn powerful video representations based on these relationships. The experimental results on the XD-Violence dataset show that MTDA-Net can significantly improve the detection accuracy of abnormal behaviors.
Attention-Guided HDR Reconstruction for Enhancing Smart City Applications
Authors: Chen Yung-Yao , Hsia Chih-Hsien , Jhong Sin-Ye , Lai Chin-Feng
Published: November 2023

In the context of smart city development, video surveillance serves as a critical component for maintaining public safety and operational efficiency. However, traditional surveillance systems are often constrained by a limited dynamic range, leading to the loss of essential image details. To address this limitation, this paper introduces HDRFormer, an innovative framework designed to enhance high dynamic range (HDR) image quality in edge–cloud-based video surveillance systems. Leveraging advanced deep learning algorithms and Internet of Things (IoT) technology, HDRFormer employs a unique architecture comprising a feature extraction module (FEM) and a weighted attention module (WAM). The FEM leverages a transformer-based hierarchical structure to adeptly capture multi-scale image information. In addition, the guided filters are utilized to steer the network, thereby enhancing the structural integrity of the images. On the other hand, the WAM focuses on reconstructing saturated areas, improving the perceptual quality of the images, and rendering the reconstructed HDR images with naturalness and color saturation. Extensive experiments on multiple HDR image reconstruction datasets demonstrate HDRFormer’s substantial improvements, achieving up to a 2.7 dB increase in the peak signal-to-noise ratio (PSNR) and an enhancement of 0.09 in the structural similarity (SSIM) compared to existing methods. In addition, the framework exhibits outstanding performance in multi-scale structural similarity (MS-SSIM) and HDR visual difference predictor (HDR-VDP2.2). The proposed method not only outperforms the existing HDR reconstruction techniques but also offers better generalization capabilities, laying a robust foundation for future applications in smart cities.
Intelligent Eye-Tracker-Based Methods for Detection of Deception: A Survey
Authors: Celniak Weronika , Słapczyńska Dominika , Pająk Anna , Przybyło Jaromir , Augustyniak Piotr
Published: November 2023

Over the last few years, a large number of studies have been conducted on the monitoring of human behavior remaining beyond conscious control. One area of application for such monitoring systems is lie detection. The most popular method currently used for this purpose is polygraph examination, which has proven its usefulness in the field and in laboratories, but it is not without its drawbacks. Technological advances in data acquisition and automated analysis have ensured that contactless tools are in high demand in security fields like airport screening or pre-employment procedures. As a result, there has been a shift in interest away from traditional polygraph examinations toward the analysis of facial expressions, voice, and speech patterns, as well as eye-tracking signals to detect deceptive behavior. In this paper, we focus on the last aspect, offer a comprehensive overview of two distinct lie detection methodologies based on eye tracking, and examine the commonly used oculomotor feature analysis. Furthermore, we explore current research directions and their results within the context of their potential applications in the field of forensics. We also highlight future research prospects, suggesting the utilization of eye tracking and scan path interpretation methodologies as a potential fully functional alternative for the conventional polygraph in the future. These considerations refer to legal and ethical issues related to the use of new technology to detect lies.