bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing the Driving Skills of Adolescents with Obstructive Sleep Apnea to Healthy Controls: The Results of a Case-Controlled Observational Study
Authors: Fidler Andrea L. , Zhang Nanhua , Simakajornboon Narong , Epstein Jeffery N. , Kirk Shelley , Beebe Dean W.
Published: September 2023

Auto crashes are a leading cause of death and injury among adolescents. Untreated obstructive sleep apnea (OSA) can cause sleepiness and inattention, which could negatively impact novice drivers, but OSA-related studies have focused on older drivers. This study used a driving simulator to examine whether licensed 16–19-year-old adolescents with OSA have diminished driving skills. Twenty-one adolescents with OSA and twenty-eight without OSA (both confirmed using polysomnography) completed two randomly ordered driving trials in a simulator (with induced distractions versus without). A mixed ANOVA examined the between-subjects effect of the OSA group, the within-subjects effect of the distraction condition, and the group-by-condition interaction effect on the ability to maintain lane position and the frequency of extended eye glances away from the roadway. T-tests were also used to examine group differences in reported sleepiness and inattention during daily life. The distraction task increased extended off-road glances and difficulties maintaining lane position (p < 0.001). However, adolescents with OSA did not display worse eye glance or lane position than controls and there were no significant group-by-condition interactions. Although the groups differed on polysomonographic features, there were also no significant differences in reported sleepiness or inattention. The distraction task negatively impacted both groups of adolescent drivers, but those with OSA did not fare differentially worse. Most adolescents in our study had mild OSA (median obstructive apnea–hypopnea index = 4.4), the most common form in the community. It remains possible that youth with more severe OSA would show increased driving impairment.
An Antimicrobial Marine Cage Surface Modified with Antibacterial Peptides
Authors: Cao Zhimin , Guo Qian
Published: September 2023

Long-term immersion in seawater easily causes surface fouling and affects the marine aquaculture industry. The commonly used method is to apply copper-based coatings on surfaces, however, the release of copper ions will harm marine organisms. Antimicrobial peptides (AMPs) are a substance extracted from organisms that possess environmental friendliness. This study extracted AMPs from traditional Chinese medicine, analyzed their amino acid sequences, and bound them to the surfaces of cage materials based on the strong adhesion of dopamine in weakly alkaline environments. The Fourier transform infrared spectrometer (FTIR) spectrum results showed that the antibacterial peptide was successfully bound to the substrate surface, and the contact angle results demonstrated a significant change in the wettability of the substrate surface. Antibacterial tests were conducted on the surface of the sample using Staphylococcus aureus (S. aureus). The results illustrated that 304 stainless steel (SS) and nylon (PA) surfaces modified by the antibacterial peptide exhibited significant biofilm resistance, with antibacterial adhesion properties reaching 88.68% and 82.61%, respectively, exhibiting the robustness of the antimicrobial efficiency. This study can provide theoretical support for the antifouling performance of the surfaces of marine aquaculture cages.
Correction: Pascuta, M.S.; Vodnar, D.C. Nanocarriers for Sustainable Active Packaging: An Overview during and Post COVID-19. Coatings 2022, 12, 102
Authors: Pascuta Mihaela Stefana , Vodnar Dan Cristian
Published: September 2023

There was an error in the original publication [...]
Sulfonated Pentablock Copolymer with Graphene Oxide for Co2+ Ions Removal: Efficiency, Interaction Mechanisms and Secondary Reaction Products
Authors: Filice Simona , Scuderi Viviana , Zimbone Massimo , Libertino Sebania , La Piana Luana , Farina Roberta Agata , Scalese Silvia
Published: September 2023

In this work, sulfonated pentablock copolymer (s-PBC) and s-PBC mixed with graphene oxide (s-PBC_GO) layers were deposited on polypropylene (PP) fibrous filters and tested as active coatings for the removal of cobalt ions from water using adsorption and filtration processes. Some of the coated filters were treated by UV light irradiation to modify their hydrophilic properties. The filters were characterized, before and after the processes, by energy-dispersive X-ray (EDX) analysis and Fourier transform infrared spectroscopy (FT-IR). The Qt (mg/g) values, defined as the weight ratio between the removed ions and the coating layer, were evaluated. In the case of adsorption processes, the best results for the removal of Co2+ ions were achieved by the s-PBC_GO coating, with a Qt of 37 mg/g compared to 21 mg/g obtained by the s-PBC. This was ascribed to the presence of GO, which contains more favorable sites able to adsorb positive ions from the solution. Vice versa, for filtration processes, the s-PBC coated filters show similar or slightly better results than the s-PBC_GO coated ones. Such differences can be ascribed to the shorter contact time between the solution and the coating layer in the case of filtration, with respect to adsorption processes, thus reducing the chance for the ions to be adsorbed on the GO layers before passing through the filter. A collateral effect, observed in this study and enhanced in the case of UV-treated coatings, is the release of radical oxysulfur species. The mechanisms involved in this effect are discussed and identified as a consequence of the interaction between the coating layers and metal ions. In order to identify the mechanism of oxysulfur radicals formation and considering a water sample closer to real water, the Co2+ ions adsorption experiments were conducted in the presence of a competitive organic contaminant (i.e., methyl orange, MO).
Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection
Authors: Manikandaraja Abishek , Aaby Peter , Pitropakis Nikolaos
Published: September 2023

Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional signature detection, the development of new and novel malware aiming to bypass detection has caused a challenge where models may experience concept drift. However, as new malware samples appear, the detection performance drops. Our work aims to discuss the performance degradation of machine learning-based malware detectors with time, also called concept drift. To achieve this goal, we develop a Python-based framework, namely Rapidrift, capable of analysing the concept drift at a more granular level. We also created two new malware datasets, TRITIUM and INFRENO, from different sources and threat profiles to conduct a deeper analysis of the concept drift problem. To test the effectiveness of Rapidrift, various fundamental methods that could reduce the effects of concept drift were experimentally explored.
Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)
Authors: Lenkala Swetha , Marry Revathi , Gopovaram Susmitha Reddy , Akinci Tahir Cetin , Topsakal Oguzhan
Published: September 2023

Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts and automate many ML processes to create a high-performing ML model. This article explores the use of automated machine learning (AutoML) tools for diagnosing epilepsy using electroencephalogram (EEG) data. The study compares the performance of three different AutoML tools, AutoGluon, Auto-Sklearn, and Amazon Sagemaker, on three different datasets from the UC Irvine ML Repository, Bonn EEG time series dataset, and Zenodo. Performance measures used for evaluation include accuracy, F1 score, recall, and precision. The results show that all three AutoML tools were able to generate high-performing ML models for the diagnosis of epilepsy. The generated ML models perform better when the training dataset is larger in size. Amazon Sagemaker and Auto-Sklearn performed better with smaller datasets. This is the first study to compare several AutoML tools and shows that AutoML tools can be utilized to create well-performing solutions for the diagnosis of epilepsy via processing hard-to-analyze EEG timeseries data.
Carbon–Carbon Composite Metallic Alloy Joints and Corresponding Nanoscale Interfaces, a Short Review: Challenges, Strategies, and Prospects
Authors: Wang Chenyu , Yang Yingguo , Zeng Guangli , Zhou Xingtai , Huang Hefei , Feng Shanglei
Published: September 2023

Brazing of carbon–carbon (C/C) composites with metallic materials currently faces a series of difficulties, such as the poor wettability of metallic materials on the surface, the nanoscale interface bonding of C/C composites and metallic materials, thermal stress problems for these different materials, etc. Especially, the practical problems, including the low joint strength and insufficient reliability, still limit the large-scale practical application of brazing technology for C/C composites and metal materials. Herein, in order to guide the fabrication of high-quality joints, we present a brief discussion on the latest research progress in the joints of C/C composites and metallic materials, including current challenges, solution methods, mechanisms, and future prospects. More importantly, the nanoscale interface in the carbon–carbon composites and metallic alloys is paid very little attention, which has been critically discussed for the first time. Then, we further outline the possible solutions in joint problems of C/C composites and metallic materials, proposing feasible strategies to control the reaction in the brazing process, such as surface treatments, the addition of reinforcing phases, a transition layer sandwiched between the base material and the intermediate layer, etc. These strategies are being envisioned for the first time and further contribute to promoting the converged applications of C/C composites and metallic materials.
In-Clinic versus Hybrid Cancer Rehabilitation Service Delivery during the COVID-19 Pandemic: An Outcome Comparison Study
Authors: Wood Kelley C. , Giri Smith , Kendig Tiffany D. , Pergolotti Mackenzi
Published: September 2023

Diminished health-related quality of life (HRQOL) is common among cancer survivors but often amendable to rehabilitation. However, few access real-world rehabilitation services. Hybrid delivery modes (using a combination of in-clinic and synchronous telehealth visits) became popular during the COVID-19 pandemic and offer a promising solution to improve access beyond the pandemic. However, it is unclear if hybrid delivery has the same impact on patient-reported outcomes and experiences as standard, in-clinic-only delivery. To fill this gap, we performed a retrospective, observational, comparative outcomes study of real-world electronic medical record (EMR) data collected by a national outpatient rehabilitation provider in 2020–2021. Of the cases meeting the inclusion criteria (N = 2611), 60 were seen to via hybrid delivery. The outcomes evaluated pre and post-rehabilitation included PROMIS® global physical health (GPH), global mental health (GMH), physical function (PF), and the ability to participate in social roles and activities (SRA). The patient experience outcomes included the Net Promoter Survey (NPS®) and the Select Medical Patient-Reported Experience Measure (SM-PREM). A linear and logistic regression was used to examine the between-group differences in the PROMIS and SM-PREM scores while controlling for covariates. The hybrid and in-clinic-only cases improved similarly in all PROMIS outcomes (all p < 0.05). The association between the delivery mode and the likelihood of achieving the minimal important change in the PROMIS outcomes was non-significant (all p > 0.05). No between-group differences were observed in the NPS or SM-PREM scores (all p > 0.05). Although more research is needed, this real-world evidence suggests that hybrid rehabilitation care may be equally beneficial for and acceptable to cancer survivors and supports calls to expand access to and reimbursement for telerehabilitation.
Comparison of the 11-Day Adhesive ECG Patch Monitor and 24-h Holter Tests to Assess the Response to Antiarrhythmic Drug Therapy in Paroxysmal Atrial Fibrillation
Authors: Kim Soohyun , Choi Young , Lee Kichang , Kim Sung-Hwan , Kim Hwajung , Shin Sanghoon , Park Soyoon , Oh Yong-Seog
Published: September 2023

Accurate assessment of the response to the antiarrhythmic drug (AAD) in atrial fibrillation (AF) is crucial to achieve adequate rhythm control. We evaluated the effectiveness of extended cardiac monitoring using an adhesive ECG patch in the detection of drug-refractory paroxysmal AF. Patients diagnosed with paroxysmal AF and receiving AAD therapy were enrolled. The subjects simultaneously underwent 11-day adhesive ECG patch monitoring and a 24-h Holter test. The primary study outcome was a detection rate of drug-refractory AF or atrial tachycardia (AT) lasting ≥30 s. A total of 59 patients were enrolled and completed the study examinations. AF or AT was detected in 28 (47.5%) patients by an 11-day ECG patch monitor and in 8 (13.6%) patients by a 24-h Holter test (p < 0.001). The 11-day ECG patch monitor identified an additional 20 patients (33.8%) with drug-refractory AF not detected by the 24-h Holter, and as a result, the treatment plan was changed in 11 patients (10 catheter ablations, one medication change). In conclusion, extended cardiac rhythm monitoring using an adhesive ECG patch in patients with paroxysmal AF under AAD therapy led to over a threefold higher detection of drug-refractory AF episodes, compared to the 24-h Holter test.
Detection of Myositis Autoantibodies by Multi-Analytic Immunoassays in a Large Multicenter Cohort of Patients with Definite Idiopathic Inflammatory Myopathies
Authors: Ghirardello Anna , Gatto Mariele , Franco Chiara , Zanatta Elisabetta , Padoan Roberto , Ienna Luana , Gallo Nicoletta , Zen Margherita , Lundberg Ingrid E. , Mahler Michael , Doria Andrea , Iaccarino Luca
Published: September 2023

Background: The usefulness of myositis-specific autoantibodies (MSAs) and myositis-associated autoantibodies (MAAs) for the assessment of idiopathic inflammatory myopathies (IIMs) is acknowledged, but laboratory standardization remains a challenge. We detected MSAs/MAAs by multi-analytic line immunoassay (LIA) and particle-based multi-analyte technology (PMAT) in a multicenter cohort of patients with IIMs. Methods: We tested the sera from 411 patients affected with definite IIM, including 142 polymyositis (PM), 147 dermatomyositis (DM), 19 cancer-associated myositis, and 103 overlap myositis syndrome (OM), and from 269 controls. MSAs/MAAs were determined by 16Ags LIA in all sera, and anti-HMGCR by ELISA in 157/411 IIM sera and 91/269 control sera. The analytical specificity of LIA/HMGCR ELISA was compared with that of PMAT in 89 MSA+ IIM sera. Results: MSAs/MAAs were positive in 307/411 (75%) IIM patients and 65/269 (24%) controls by LIA (Odds Ratio 9.26, 95% CI 6.43–13.13, p < 0.0001). The sensitivity/specificity of individual MSAs/MAAs were: 20%/100% (Jo-1), 3%/99.3% (PL-7), 4%/98.8% (PL-12), 1%/100% (EJ), 0.7%/100% (OJ), 9%/98% (SRP), 5.6%/99.6% (TIF1γ), 4.6%/99.6% (MDA5), 8%/96% (Mi-2), 1.5%/98% (NXP2), 1.7%/100% (SAE1), 4%/92% (Ku), 8.5%/99% (PM/Scl-100), 8%/96% (PM/Scl-75), and 25.5%/79% (Ro52). Anti-HMGCR was found in 8/157 (5%) IIM patients and 0/176 (0%) controls by ELISA (p = 0.007). Concordance between LIA/HMGCR ELISA and PMAT was found in 78/89 (88%) samples. Individual MSAs detected by LIA were associated with IIM subsets: Jo-1 with PM and OM, PL-12 with OM, Mi-2, TIF1γ, and MDA5 with DM, SRP with PM, and PM/Scl-75/100 with OM (p < 0.001 for all). Conclusions: Since MSAs are mostly mutually exclusive, multi-specific antibody profiling seems effective for a targeted clinical-serologic approach to the diagnosis of IIMs.
Evaluation of Bilateral Maxillary Sinus Ectopic Teeth Using CT and Cinematic Rendering—A Case Report
Authors: Baldi Dario , Tramontano Liberatore , Punzo Bruna , Cavaliere Carlo
Published: September 2023

Ectopic teeth in the maxillary sinus are a rare finding and pose a diagnostic challenge due to their unusual location and clinical management. A 28-year-old man presented with complaints of discomfort and pressure in the maxillary sinus region. A CT scan and cinematic rendering revealed the presence of ectopic teeth in the maxillary sinus bilaterally. The use of cinematic rendering provided a more detailed and accurate visualization of the ectopic teeth and surrounding anatomical structures. A CT scan is the primary imaging modality used for the diagnosis and visualization of ectopic teeth in the maxillary sinus. In addition, the use of cinematic rendering can improve diagnostic accuracy and reduce the need for further imaging studies. The use of CT and cinematic rendering can help in the diagnosis and visualization of ectopic teeth in the maxillary sinus, aiding in the planning of surgical interventions.
Relationship of the Morphology and Size of Sella Turcica with Dental Anomalies and Skeletal Malocclusions
Authors: Mortezai Omid , Rahimi Haniyeh , Tofangchiha Maryam , Radfar Sina , Ranjbaran Mehdi , Pagnoni Francesco , Reda Rodolfo , Testarelli Luca
Published: September 2023

This study aimed to assess the relationship of the morphology and size of the sella turcica (ST) with dental anomalies and skeletal malocclusions. This cross-sectional study was conducted on records of fixed orthodontic patients treated between 2013 and 2022. Cephalometric analysis was performed to determine the anteroposterior and vertical skeletal patterns. Preoperative panoramic radiographs and lateral cephalograms, intraoral photographs, and primary dental casts of patients were used to detect dental anomalies. Gender, sagittal and vertical skeletal patterns, dental anomalies, and dimensions (length, depth, and diameter), and morphology of the ST were all recorded according to the lateral cephalograms of patients. Data were analyzed using independent t-test, one-way and two-way ANOVA, Chi-square test, and log rank test (alpha = 0.05). The depth and diameter of the ST had no significant correlation with gender (p > 0.05); however, the length of the ST was significantly longer in males than females (p < 0.05). The morphology of the ST had a significant correlation with gender (p < 0.05). The ST morphology had a significant correlation with the anteroposterior skeletal pattern, microdontia, and tooth impaction as well (p < 0.05). The present results revealed a significant correlation of the ST morphology with the anteroposterior skeletal pattern, microdontia, and tooth impaction.
Aplastic Internal Carotid Artery: A Potentially Catastrophic Vascular Anomaly
Authors: Bosse Benjamin L. , Wilkinson Geoffrey , Anderson Zoe N. , Babu Jay , Rajesh Riyaa , Rangaswamy Rajesh , Raghuram Karthikram
Published: September 2023

Congenital absence of an internal carotid artery (ICA) is a rare vascular anomaly and occurs in less than 0.01% of the population. We report a case of aplastic internal carotid artery in a 34-year-old female. The patient presented to the emergency department with complaints of new-onset involuntary swaying-like movement of her right arm. Brain magnetic resonance imaging showed multifocal tiny areas of acute infarcts in the bilateral frontal, parietal, and left occipital lobes in the watershed distribution. There was no visualization of the flow of the intracranial left internal carotid artery. Follow-up CTA of the head and neck showed a congenital absence of the left internal carotid artery with no evidence of arterial dissection, occlusion, or aneurysm. Obstruction of the internal carotid artery has significant consequences for patients. This effect is amplified if the disruption occurs in the sole anterior blood supply to the parenchyma of the brain, as in this case. In our patient care, imaging was vital to the detection and subsequent treatment with anticoagulation to avoid further cerebral complications, and the patient will now have a better understanding of the increased lifetime risk of further events.
Electrocardiographic Characteristics, Identification, and Management of Frequent Premature Ventricular Contractions
Authors: Tsiachris Dimitris , Botis Michail , Doundoulakis Ioannis , Bartsioka Lamprini Iro , Tsioufis Panagiotis , Kordalis Athanasios , Antoniou Christos-Konstantinos , Tsioufis Konstantinos , Gatzoulis Konstantinos A.
Published: September 2023

Premature ventricular complexes (PVCs) are frequently encountered in clinical practice. The association of PVCs with adverse cardiovascular outcomes is well established in the context of structural heart disease, yet not so much in the absence of structural heart disease. However, cardiac magnetic resonance (CMR) seems to contribute prognostically in the latter subgroup. PVC-induced myocardial dysfunction refers to the impairment of ventricular function due to PVCs and is mostly associated with a PVC burden > 10%. Surface 12-lead ECG has long been used to localize the anatomic site of origin and multiple algorithms have been developed to differentiate between right ventricular and left ventricular outflow tract (RVOT and LVOT, respectively) origin. Novel algorithms include alternative ECG lead configurations and, lately, sophisticated artificial intelligence methods have been utilized to determine the origins of outflow tract arrhythmias. The decision to therapeutically address PVCs should be made upon the presence of symptoms or the development of PVC-induced myocardial dysfunction. Therapeutic modalities include pharmacological therapy (I-C antiarrhythmic drugs and beta blockers), as well as catheter ablation, which has demonstrated superior efficacy and safety.
Large-Sized Multirotor Design: Accurate Modeling with Aerodynamics and Optimization for Rotor Tilt Angle
Authors: Xie Anhuan , Yan Xufei , Liang Weisheng , Zhu Shiqiang , Chen Zheng
Published: September 2023

Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and reduce physical trial and error, a detailed dynamic model is firstly established, with an accurate aerodynamic model. In addition, the center of gravity (CoG) offset and actuator dynamics are also well considered, which are usually ignored in small-sized multirotors. To improve the endurance and maneuverability of large-sized multirotors, which is the key concern in real applications, a two-loop optimization method for rotor tilt angle design is proposed based on the mathematical model established previously. Its inner loop solves the dynamic equilibrium points to relax the complex dynamic constraints caused by aerodynamics in the overall optimization problem, which improves the solution efficiency. The ideal design results can be obtained through the offline process, which greatly reduces the difficulties of physical trial and error. Finally, various experiments are carried out to demonstrate the accuracy of the established model and the effectiveness of the optimization method.
Lossless Transformations and Excess Risk Bounds in Statistical Inference
Authors: Györfi László , Linder Tamás , Walk Harro
Published: September 2023

We study the excess minimum risk in statistical inference, defined as the difference between the minimum expected loss when estimating a random variable from an observed feature vector and the minimum expected loss when estimating the same random variable from a transformation (statistic) of the feature vector. After characterizing lossless transformations, i.e., transformations for which the excess risk is zero for all loss functions, we construct a partitioning test statistic for the hypothesis that a given transformation is lossless, and we show that for i.i.d. data the test is strongly consistent. More generally, we develop information-theoretic upper bounds on the excess risk that uniformly hold over fairly general classes of loss functions. Based on these bounds, we introduce the notion of a δ-lossless transformation and give sufficient conditions for a given transformation to be universally δ-lossless. Applications to classification, nonparametric regression, portfolio strategies, information bottlenecks, and deep learning are also surveyed.
Physical Layer Security: Channel Sounding Results for the Multi-Antenna Wiretap Channel
Authors: Harman Daniel , Knapp Karl , Sweat Tyler , Lundrigan Philip , Rice Michael , Harrison Willie
Published: September 2023

Many physical-layer security works in the literature rely on purely theoretical work or simulated results to establish the value of physical-layer security in securing communications. We consider the secrecy capacity of a wireless Gaussian wiretap channel using channel sounding measurements to analyze the potential for secure communication in a real-world scenario. A multi-input, multi-output, multi-eavesdropper (MIMOME) system is deployed using orthogonal frequency division multiplexing (OFDM) over an 802.11n wireless network. Channel state information (CSI) measurements were taken in an indoor environment to analyze time-varying scenarios and spatial variations. It is shown that secrecy capacity is highly affected by environmental changes, such as foot traffic, network congestion, and propagation characteristics of the physical environment. We also present a numerical method for calculating MIMOME secrecy capacity in general and comment on the use of OFDM with regard to calculating secrecy capacity.
LPN: Label-Enhanced Prototypical Network for Legal Judgment Prediction
Authors: Chen Junyi , Han Yingjie , Zhou Xiabing , Zan Hongying , Zhou Qinglei
Published: September 2023

As one of the most critical tasks in legal artificial intelligence, legal judgment prediction (LJP) has garnered growing attention, especially in the civil law system. However, current methods often overlook the challenge of imbalanced label distributions, treating each label with equal importance, which can lead the model to be biased toward labels with high frequency. In this paper, we propose a label-enhanced prototypical network (LPN) suitable for LJP, that adopts a strategy of uniform encoding and separate decoding. Specifically, LPN adopts a multi-scale convolutional neural network to uniformly encode case factual description to capture long-distance features of the document. At the decoding end, a prototypical network incorporating label semantic features is used to guide the learning of prototype representations of high-frequency and low-frequency labels, respectively. At the same time, we also propose a prototype-prototype loss to optimize the prototypical representation. We conduct extensive experiments on two real datasets and show that our proposed method effectively improves the performance of LJP, with an average F1 of 1.23% and 1.13% higher than the state-of-the-art model on two subtasks, respectively.
Adaptive Fast Image Encryption Algorithm Based on Three-Dimensional Chaotic System
Authors: Wang Yiming , Leng Xiangxin , Zhang Chenkai , Du Baoxiang
Published: September 2023

This paper introduces a novel three-dimensional chaotic system that exhibits diverse dynamic behaviors as parameters vary, including phase trajectory offset behaviors and expansion–contraction phenomena. This model encompasses a broad chaotic range and proves suitable for integration within image encryption. Building upon this chaotic system, the study devised a fast image encryption algorithm with an adaptive mechanism, capable of autonomously determining optimal encryption strategies to enhance algorithm security. In pursuit of heightened encryption speed, an FPGA-based chaotic sequence generator was developed for the image encryption algorithm, leveraging the proposed chaotic system. Furthermore, a more efficient scrambling algorithm was devised. Experimental results underscore the superior performance of this algorithm in terms of both encryption duration and security.
Exploring the Impact of Front-Line Employees’ Extra Role Behavior on Student Loyalty and Retention in a Semi-Government University in the UAE
Authors: Alshamsi Aisha , Al-Hawari Mohammad Ahmad , Aderibigbe Semiyu Adejare , Omar Maher , Alotaibi Emran
Published: September 2023

This study aims to investigate the relationship between front-line employees’ (FLEs) extra-role behavior (ERB) and its influence on student satisfaction (SS), student loyalty (SL), and university image (UI), while also examining the moderating effect of student cynicism (SC). Drawing on the service profit chain model, this research proposes a positive relationship between ERB and SS, SL, and UI, with SC acting as a moderator for the relationship between ERB and SS. Using a survey research design, the study was conducted at a semi-government university in the United Arab Emirates (UAE) during a service encounter between FLEs and students, involving 251 students as respondents. Structural Equation Modeling (SEM) was used to analyze the data collected. The findings demonstrated a significant positive relationship between FLE ERB and SS, SL, and UI. Additionally, the study revealed that SC moderates the relationship between FLE ERB and SS, suggesting that highly cynical students are less responsive to employee extra-role behavior. These findings have significant implications for university management and policymakers. By understanding the impact of FLE ERB on student outcomes and the moderating effect of SC, universities can develop and strengthen targeted strategies to enhance SS, SL, and UI.
Practice of Game Development Project-Based Learning Classes for Improving Disaster Management
Authors: Arakawa Toshiya , Yamabe Shigeyuki , Suzuki Takahiro
Published: September 2023

It is necessary to discuss from various angles how to make the younger generation, who will be responsible for future society, aware of disasters and how to cope with them. Therefore, we designed a project-based learning class, “Media Design Project III · IV”, in which students are asked to create a game to raise their awareness of disaster prevention. This class aims to improve disaster awareness, especially tsunami evacuation, and to improve abilities that students will need in the future, i.e., time management and problem-solving, especially after they begin work. It was found that the game can increase tsunami evaluation awareness. Therefore, the projects presented here might become a new method for education, especially disaster education and career education, with games focused on disasters potentially being a new tool for raising awareness about disasters.
Seamless Connections: Harnessing Machine Learning for MAC Optimization in Home Area Networks
Authors: Khan Bilal Muhammad , Kadri Muhammad Bilal
Published: September 2023

The latest technologies and communication protocols are arousing a keen interest in automation, in which the field of home area networks is the most prominent area to work upon toward solving the issues and challenges faced by wireless home area networks regarding adaptability, reliability, cost, throughput, efficiency, and scalability. However, managing the immense number of communication devices on the premises of a smart home is a challenging task. Moreover, the Internet of Things (IoT) is an emerging global trend with billions of smart devices to be connected in the near future resulting in a huge amount of diversified data. The continuous expansion of the IoT network causes complications and vulnerabilities due to its dynamic nature and heterogeneous traffic. In the applications of IoT, the wireless sensor network (WSN) plays a major role, and to take benefits from WSN, medium access control (MAC) is the primary protocol to optimize, which helps in allocating resources to a huge number of devices in the smart home environment. Furthermore, artificial intelligence is highly demanded to enhance the efficiency of existing systems and IoT applications. Therefore, the purpose of this research paper is to achieve an optimized medium access control protocol through machine learning. The machine learning classifier, e.g., random forest (RF) and linear regression model, is adopted for predicting the features of home area networks. The proposed technique is helpful and could overcome the demerits of existing protocols in relation to scalability, throughput, access delay, and reliability and help in achieving an autonomous home area network (HAN).
Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering
Authors: Yang Geng , Li Qin , Yun Yu , Lei Yu , You Jane
Published: September 2023

Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only captures the pairwise relationships between samples, and cannot fully explore the higher-order information and complex structure among multiple sample points. Additionally, most existing methods do not make full use of the complementary information and spatial structure contained in multi-view data, which is crucial to clustering results. We propose a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach to overcome these limitations. Specifically, the proposed method fully considers the relationship between multiple sample points and utilizes hypergraph-induced hyper-Laplacian matrices to preserve the high-order geometrical structure in data. Based on the principle of complementarity and consistency between views, this method simultaneously learns indicator matrices of all views and harnesses the tensor Schatten p-norm to extract both complementary information and low-rank spatial structure within these views. Furthermore, we introduce an auto-weighted strategy to address the discrepancy between singular values, enhancing the robustness and stability of the algorithm. Detailed experimental results on various datasets demonstrate that our approach surpasses existing state-of-the-art semi-supervised multi-view clustering methods.
FPQNet: Fully Pipelined and Quantized CNN for Ultra-Low Latency Image Classification on FPGAs Using OpenCAPI
Authors: Ji Mengfei , Al-Ars Zaid , Hofstee Peter , Chang Yuchun , Zhang Baolin
Published: September 2023

Convolutional neural networks (CNNs) are to be effective in many application domains, especially in the computer vision area. In order to achieve lower latency CNN processing, and reduce power consumption, developers are experimenting with using FPGAs to accelerate CNN processing in several applications. Current FPGA CNN accelerators usually use the same acceleration approaches as GPUs, where operations from different network layers are mapped to the same hardware units working in a multiplexed manner. This will result in high flexibility in implementing different types of CNNs; however, this will degrade the latency that accelerators can achieve. Alternatively, we can reduce the latency of the accelerator by pipelining the processing of consecutive layers, at the expense of more FPGA resources. The continued increase in hardware resources available in FPGAs makes such implementations feasible for latency-critical application domains. In this paper, we present FPQNet, a fully pipelined and quantized CNN FPGA implementation that is channel-parallel, layer-pipelined, and network-parallel, to decrease latency and increase throughput, combined with quantization methods to optimize hardware utilization. In addition, we optimize this hardware architecture for the HDMI timing standard to avoid extra hardware utilization. This makes it possible for the accelerator to handle video datasets. We present prototypes of the FPQNet CNN network implementations on an Alpha Data 9H7 FPGA, connected with an OpenCAPI interface, to demonstrate architecture capabilities. Results show that with a 250 MHz clock frequency, an optimized LeNet-5 design is able to achieve latencies as low as 9.32 µs with an accuracy of 98.8% on the MNIST dataset, making it feasible for utilization in high frame rate video processing applications. With 10 hardware kernels working concurrently, the throughput is as high as 1108 GOPs. The methods in this paper are suitable for many other CNNs. Our analysis shows that the latency of AlexNet, ZFNet, OverFeat-Fast, and OverFeat-Accurate can be as low as 69.27, 66.95, 182.98, and 132.6 µs, using the architecture introduced in this paper, respectively.
Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
Authors: Zhao Kunxia , Liu Yan , Hu Kui
Published: September 2023

The grey wolf optimization (GWO) algorithm is a new nature-inspired meta-heuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. In this paper, the GWO algorithm is improved to overcome previous shortcomings of being easily trapped in local optima and having a low convergence rate. The proposed enhancement of the GWO algorithm utilizes logistic-tent double mapping to generate initialized populations, which enhances its global search capability and convergence rate. This improvement is called the nonlinear chaotic grey wolf optimization (NCGWO) algorithm. The performance of the NCGWO algorithm was evaluated with four representative benchmark functions. Then, the NCGWO algorithm was applied to perform an optimal pattern synthesis of linear array antennas (LAAs) using two distinct approaches: optimizing the amplitudes of the antenna currents while preserving uniform spacing and optimizing the positions of the antennas while assuming uniform excitation. To validate the effectiveness of the proposed approach, the results obtained by the NCGWO algorithm were compared with those obtained by other intelligent algorithms. Additionally, the NCGWO algorithm was applied to a more complex planar antenna array to further validate its performance. Our results demonstrate that the NCGWO algorithm exhibits superior performance regarding electromagnetic optimization problems compared to widely recognized algorithms.
A Deep GMDH Neural-Network-Based Robust Fault Detection Method for Active Distribution Networks
Authors: Çelik Özgür , Farkhani Jalal Sahebkar , Lashab Abderezak , Guerrero Josep M. , Vasquez Juan C. , Chen Zhe , Bak Claus Leth
Published: September 2023

The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active distribution networks (ADN) in the case of short-circuit faults. To overcome these challenges, the accurate detection of faults in a reasonable fraction of time appears as a critical issue in distribution networks. Machine learning techniques are capable of generating efficient analytical expressions that can be strong candidates in terms of reliable and robust fault detection for several operating scenarios of ADNs. This paper proposes a deep group method of data handling (GMDH) neural network based on a non-pilot protection method for the protection of an ADN. The developed method is independent of the DG capacity and achieves accurate fault detection under load variations, disturbances, and different high-impedance faults (HIFs). To verify the improvements, a test system based on a real distribution network that includes three generators with a capacity of 6 MW is utilized. The extensive simulations of the power network are performed using DIgSILENT Power Factory and MATLAB software. The obtained results reveal that a mean absolute percentage error (MAPE) of 3.51% for the GMDH-network-based protection system is accomplished thanks to formulation via optimized algorithms, without requiring the utilization of any feature selection techniques. The proposed method has a high-speed operation of around 20 ms for the detection of faults, while the conventional OC relay performance is in the blinding mode in the worst situations for faults with HIFs.
Towards Social Understanding of Energy Storage Systems—A Perspective
Authors: Sibilla Maurizio , Kurul Esra
Published: September 2023

Renewable, decentralised, and citizen-centred energy paradigms have emerged as feasible and reliable alternatives to the traditional centralised fossil-based infrastructure. In this scenario, energy storage systems (ESSs) are enabling technologies to boost the stability and flexibility of the power grid in the short-to-medium term, allowing local communities to envision energy autonomy in the medium term. Traditionally, ESSs have been installed in individual households for their own benefit. However, new storage paradigms focusing on building clusters and district scale have illustrated the need to revise the role of ESSs and to pay close attention to the social factors, while devising implementation strategies for scaling up these new energy infrastructural models. This study reviews recent research trends (2021–2023), proposing three integrated social pillars for the implementation of ESSs: (i) multi-dimensional geographical and institutional scales of ESSs; (ii) social components of spatial and temporal flexibility of ESSs; and (iii) co-creation approaches to devising ESS implementation strategies. These pillars point out the necessary social factors for the implementation of ESSs at scale, highlighting future research perspectives to operationalise such factors, with a particular focus on the importance of citizens’ perception, participation, and collaboration, which are critical for maximising the benefit of sharing and exchanging renewable energy locally. Development of flexible and agile digital platforms that facilitate the co-creation of adaptable socio-technical solutions to adopting ESSs is proposed. The need to tailor these solutions to suit the stakeholders’ capabilities is emphasized.
Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems
Authors: Yuvaraj Thangaraj , Suresh Thirukoilur Dhandapani , Ananthi Christy Arokiasamy , Babu Thanikanti Sudhakar , Nastasi Benedetto
Published: September 2023

The research presented in this article aims at the modelling and optimization of hydrogen-fuel-cell-based distributed generation (HFC-DG) to minimize the effect of electric vehicle charging stations (EVCSs) in a radial distribution system (RDS). The key objective of this work is to address various challenges that arise from the integration of EVCSs, including increased power demand, voltage fluctuations, and voltage stability. To accomplish this objective, the study utilizes a novel spotted hyena optimizer algorithm (SHOA) to simultaneously optimize the placement of HFC-DG units and EVCSs. The main goal is to mitigate real power loss resulting from the additional power demand of EVCSs in the IEEE 33-bus RDS. Furthermore, the research also investigates the influence of HFC-DG and EVCSs on the reliability of the power system. Reliability is crucial for all stakeholders, particularly electricity consumers. Therefore, the study thoroughly examines how the integration of HFC-DG and EVCSs influences system reliability. The optimized solutions obtained from the SHOA and other algorithms are carefully analyzed to assess their effectiveness in minimizing power loss and improving reliability indices. Comparative analysis is conducted with varying load factors to estimate the performance of the presented optimization approach. The results prove the benefits of the optimization methodology in terms of reducing power loss and improvising the reliability of the RDS. By utilizing HFC-DG and EVCSs, optimized through the SHOA and other algorithms, the research contributes to mitigating power loss caused by EVCS power demand and improving overall system reliability. Overall, this research addresses the challenges associated with integrating EVCSs into distribution systems and proposes a novel optimization approach using HFC-DG. The findings highlight the potential benefits of this approach in terms of minimizing power loss, enhancing reliability, and optimizing distribution system operations in the context of increasing EV adoption.
Evaluating the Operation of a Full-Scale Sequencing Batch Reactor–Reverse Osmosis–Evaporation System Used to Treat Landfill Leachates: Removal of Pollutants, Energy Consumption and Greenhouse Gas Emissions
Authors: Tsompanoglou Konstantinos , Koutsou Olga P. , Stasinakis Athanasios S.
Published: September 2023

Limited information is available in the literature regarding the energy consumption and the greenhouse gases emitted during landfill leachates treatment. A full-scale landfill leachates treatment system that included primary sedimentation, biological treatment in sequencing batch reactors, reverse osmosis and mechanical vapor recompression evaporation was monitored and evaluated for the removal of major pollutants, energy consumption and greenhouse gas emissions. Samples were taken during a period of two years from different points of the system, while the actual power consumption was calculated considering the available mechanical equipment and the hours of operation. The quantities of greenhouse gases emitted were estimated using appropriate equations and based on the operational characteristics of the system. According to chemical analyses, biological treatment resulted in partial removal of COD and total nitrogen, while the removal of BOD5 and NH4-N was significant, reaching 90 and 98%, respectively. Use of reverse osmosis increased the removal of all pollutants, satisfying the requirements of the legislation on wastewater discharge into the environment. Power consumption was calculated to be 35.3 KWhr per m3 of treated leachate, while mechanical vapor recompression evaporation was responsible for 60.5% of the total energy required. The contribution of other processes to energy consumption was as follows, in decreasing order: sequencing batch reactors > reverse osmosis > primary treatment. The roots blower vacuum pump used for mechanical vapor recompression evaporation, and the blowers providing air to the sequencing batch reactors, were the most energy-intensive pieces of apparatus, contributing 44.2% and 11.3% of the required energy, respectively. The quantity of greenhouse gases emitted was estimated to be 27.7 Kg CO2eq per m3 of treated leachates. Among the different processes used, biological treatment and mechanical vapor recompression evaporation contributed to 45.7% and 44.1% of the total emissions, respectively. The findings of this study reveal that an integrated landfill leachate treatment system that combines biological treatment and reverse osmosis can assure the protection of the aquatic environment by producing high-quality effluent; however, further research should be conducted regarding the sustainable management of reverse osmosis concentrate. Mechanical vapor recompression evaporation contributes significantly to the environmental footprint of the landfill leachates treatment system due to both high energy consumption and elevated emissions of greenhouse gases.