bluuebunny/crossref_metadata_2025 huggingface dataset:
Incidence and Antenatal Detection of Congenital Heart Malformations—Data from a Tertiary Obstetric Romanian Center
Authors: Ciulpan Adrian , Lacatușu Adrian , Pop Liviu Laurenţiu , Paul Corina , Lungeanu Diana , Iacob Daniela , Bernad Brenda-Cristiana , Lascu Ana , Maghet Edida , Arnautu Diana-Aurora , Bernad Elena Silvia
Published: August 2024

Objectives: Congenital heart defects (CHDs) are among the most frequent congenital defects, and they significantly burden the healthcare system due to their high mortality rate and high cost of care for survivors. We aimed to highlight the incidence of CHDs in a tertiary center in Western Romania. Methods: A retrospective study was carried out between 2018 and 2022 at the “Pius Brinzeu” Emergency County Hospital Timisoara. Relevant information about the mothers and the newborns were collected and statistically analyzed. Results: The incidence of CHDs from 2018 to 2022 in our center was 5.3%. Eleven types of malformations were diagnosed postnatally in 541 newborns, with 28.8% of cases having more than one type of CHD. The antenatal detection rate was 28%, with the highest rates for tetralogy of Fallot, hypoplastic left heart syndrome, or significant ventricular septal defects and the lowest for pulmonary stenosis. The lower antenatal detection rate was influenced mainly by incomplete or absent prenatal care. Conclusions: The incidence of CHDs is clearly dependent of a multifactorial approach, and the results highlight this. With an incidence almost 50% lower than reported within the literature and a low rate of prenatal detections, CHDs could be a more of a burden to endure regarding medical treatment. Improvements in patients’ education, prenatal care, and screening programs could improve diagnosis, decrease mortality, and optimize postnatal care.
Utilising Discriminant Function Analysis (DFA) for Classifying Osteoarthritis (OA) Patients and Volunteers Based on Biomarker Concentration
Authors: Coleman Laura Jane , Byrne John L. , Edwards Stuart , O’Hara Rosemary
Published: August 2024

Osteoarthritis (OA) is a degenerative joint disease characterised by the breakdown of cartilage, causing pain, stiffness, and limited movement. Early diagnosis is crucial for effective management but remains challenging due to non-specific early symptoms. This study explores the application of Discriminant Function Analysis (DFA) to classify OA patients and healthy volunteers based on biomarker concentrations of Interleukin-6 (IL-6), Tumour necrosis factor-alpha (TNF-α), and Myeloperoxidase (MPO). DFA was employed to analyse biomarker data from 86 participants (58 patients, 28 volunteers) to evaluate the discriminatory power of these biomarkers in predicting OA. Significant differences were observed in MPO and TNF-α levels between groups, while IL-6 did not show a significant distinction. The iterative classification process improved model assumptions and classification accuracy, achieving a pre-classification accuracy of 71.8%, which adjusted to 57.1% post-classification. The results highlight DFA’s potential in OA diagnosis, suggesting its utility in managing complex data and aiding personalised treatment strategies. The study underscores the need for larger sample sizes and additional biomarkers to enhance diagnostic robustness and provides a foundation for integrating DFA into clinical practice for early OA detection.
Emerging Head and Neck Tumor Targeting Contrast Agents for the Purpose of CT, MRI, and Multimodal Diagnostic Imaging: A Molecular Review
Authors: Neilio Jonathan M. , Ginat Daniel T.
Published: August 2024

Background. The diagnosis and treatment of head and neck tumors present significant challenges due to their infiltrative nature and diagnostic hindrances such as the blood–brain barrier. The intricate anatomy of the head and neck region also complicates the clear identification of tumor boundaries and assessment of tumor characteristics. Aim. This review aims to explore the efficacy of molecular imaging techniques that employ targeted contrast agents in head and neck cancer imaging. Head and neck cancer imaging benefits significantly from the combined advantages of CT and MRI. CT excels in providing swift, high-contrast images, enabling the accurate localization of tumors, while MRI offers superior soft tissue resolution, contributing to the detailed evaluation of tumor morphology in this region of the body. Many of these novel contrast agents have integration of dual-modal, triple-modal, or even dual-tissue targeting imaging, which have expanded the horizons of molecular imaging. Emerging contrast agents for the purpose of MRI and CT also include the widely used standards in imaging such as gadolinium and iodine-based agents, respectively, but with peptide, polypeptide, or polymeric functionalizations. Relevance for patients. For patients, the development and use of these targeted contrast agents have potentially significant implications. They benefit from the enhanced accuracy of tumor detection and characterization, which are critical for effective treatment planning. Additionally, these agents offer improved imaging contrast with the added benefit of reduced toxicity and bioaccumulation. The summarization of preclinical nanoparticle research in this review serves as a valuable resource for scientists and students working towards advancing tumor diagnosis and treatment with targeted contrast agents.
The Radiogenomic Landscape of Clear Cell Renal Cell Carcinoma: Insights into Lipid Metabolism through Evaluation of ADFP Expression
Authors: Greco Federico , Panunzio Andrea , Bernetti Caterina , Tafuri Alessandro , Beomonte Zobel Bruno , Mallio Carlo Augusto
Published: August 2024

This study aims to explore the relationship between radiological imaging and genomic characteristics in clear cell renal cell carcinoma (ccRCC), focusing on the expression of adipose differentiation-related protein (ADFP) detected through computed tomography (CT). The goal is to establish a radiogenomic lipid profile and understand its association with tumor characteristics. Data from The Cancer Genome Atlas (TCGA) and the Cancer Imaging Archive (TCIA) were utilized to correlate imaging features with adipose differentiation-related protein (ADFP) expression in ccRCC. CT scans assessed various tumor features, including size, composition, margin, necrosis, and growth pattern, alongside measurements of tumoral Hounsfield units (HU) and abdominal adipose tissue compartments. Statistical analyses compared demographics, clinical–pathological features, adipose tissue quantification, and tumoral HU between groups. Among 197 patients, 22.8% exhibited ADFP expression significantly associated with hydronephrosis. Low-grade ccRCC patients expressing ADFP had higher quantities of visceral and subcutaneous adipose tissue and lower tumoral HU values compared to their high-grade counterparts. Similar trends were observed in low-grade ccRCC patients without ADFP expression. ADFP expression in ccRCC correlates with specific imaging features such as hydronephrosis and altered adipose tissue distribution. Low-grade ccRCC patients with ADFP expression display a distinct lipid metabolic profile, emphasizing the relationship between radiological features, genomic expression, and tumor metabolism. These findings suggest potential for personalized diagnostic and therapeutic strategies targeting tumor lipid metabolism.
Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview
Authors: Rozhyna Anastasiia , Somfai Gábor Márk , Atzori Manfredo , DeBuc Delia Cabrera , Saad Amr , Zoellin Jay , Müller Henning
Published: August 2024

Artificial intelligence has transformed medical diagnostic capabilities, particularly through medical image analysis. AI algorithms perform well in detecting abnormalities with a strong performance, enabling computer-aided diagnosis by analyzing the extensive amounts of patient data. The data serve as a foundation upon which algorithms learn and make predictions. Thus, the importance of data cannot be underestimated, and clinically corresponding datasets are required. Many researchers face a lack of medical data due to limited access, privacy concerns, or the absence of available annotations. One of the most widely used diagnostic tools in ophthalmology is Optical Coherence Tomography (OCT). Addressing the data availability issue is crucial for enhancing AI applications in the field of OCT diagnostics. This review aims to provide a comprehensive analysis of all publicly accessible retinal OCT datasets. Our main objective is to compile a list of OCT datasets and their properties, which can serve as an accessible reference, facilitating data curation for medical image analysis tasks. For this review, we searched through the Zenodo repository, Mendeley Data repository, MEDLINE database, and Google Dataset search engine. We systematically evaluated all the identified datasets and found 23 open-access datasets containing OCT images, which significantly vary in terms of size, scope, and ground-truth labels. Our findings indicate the need for improvement in data-sharing practices and standardized documentation. Enhancing the availability and quality of OCT datasets will support the development of AI algorithms and ultimately improve diagnostic capabilities in ophthalmology. By providing a comprehensive list of accessible OCT datasets, this review aims to facilitate better utilization and development of AI in medical image analysis.
Impact of School Nurse on Managing Pediatric Type 1 Diabetes with Technological Devices Support: A Systematic Review
Authors: Cangelosi Giovanni , Mancin Stefano , Morales Palomares Sara , Pantanetti Paola , Quinzi Elisabetta , Debernardi Giulia , Petrelli Fabio
Published: August 2024

Introduction: Type 1 diabetes (T1D) is a disease that primarily occurs in pediatric populations. A school nurse (SN) can provide valuable support in the school setting for minors affected by this condition. Methods: The main objective of this study was to evaluate the impact of nursing care provided to adolescents and children with T1D using technological devices in school. Qualitative and quantitative outcomes considered in the included studies were collected and discussed. A systematic review was conducted in the PubMed, CINAHL, and Scopus databases and reported thought the PRISMA guidelines. Results: Eleven studies were included. The results showed that SNs need to enhance both their skills and organization to effectively manage young patients with T1D using technology. The response of both the pediatric population and their caregivers to the disease management by a SN has been positive. Conclusions: The management of chronic diseases is one of the most urgent public health issues, especially for Western healthcare systems. Proper management of patients with T1D at the school level is definitely an aspect that policymakers and healthcare managers should consider to improve the quality of life of this extremely vulnerable population, particularly those using technological management T1D.
Three Dimensional-Printed Gingivectomy and Tooth Reduction Guides Prior Ceramic Restorations: A Case Report
Authors: Jurado Carlos A. , Villalobos-Tinoco Jose , Lackey Mark A. , Rojas-Rueda Silvia , Robles Manuel , Tsujimoto Akimasa
Published: August 2024

Computer-aided design and computer-aided manufacturing (CAD/CAM) dentistry have significantly changed workflows in recent years. Restorations and devices can now be digitally designed and 3D-printed for dental care purposes. This clinical case report provides straightforward protocols for the digital design and 3D manufacture of gingivectomy and tooth preparation guides. These types of guides improved the gingival architecture of the anterior teeth and provided controllable tooth preparations prior to labial ceramic veneers. Thoughtful clinical evaluation started with listening to the patient’s chief complaint and extra- and intra-oral evaluations. Then a digital wax-up was performed, followed by an intra-oral mock-up, to evaluate the shape of the proposed restorations. After patient acceptance, the clinical procedure started with the gingivectomy and tooth preparation. Hand-crafted porcelain veneers were bonded under rubber dam isolation to avoid any contamination and maximize the bonding protocol. The esthetic and functional demands were fully satisfied. Predictable outcomes can be obtained whenever a meticulous evaluation and execution of all the steps are performed. Three dimensional printing technology allows the fabrication of devices such as gingivectomy and tooth reduction guides that help accomplish the desired results.
UAV Confrontation and Evolutionary Upgrade Based on Multi-Agent Reinforcement Learning
Authors: Deng Xin , Dong Zhaoqi , Ding Jishiyu
Published: August 2024

Unmanned aerial vehicle (UAV) confrontation scenarios play a crucial role in the study of agent behavior selection and decision planning. Multi-agent reinforcement learning (MARL) algorithms serve as a universally effective method guiding agents toward appropriate action strategies. They determine subsequent actions based on the state of the agents and the environmental information that the agents receive. However, traditional MARL settings often result in one party agent consistently outperforming the other party due to superior strategies, or both agents reaching a strategic stalemate with no further improvement. To solve this issue, we propose a semi-static deep deterministic policy gradient algorithm based on MARL. This algorithm employs a centralized training and decentralized execution approach, dynamically adjusting the training intensity based on the comparative strengths and weaknesses of both agents’ strategies. Experimental results show that during the training process, the strategy of the winning team drives the losing team’s strategy to upgrade continuously, and the relationship between the winning team and the losing team keeps changing, thus achieving mutual improvement of the strategies of both teams. The semi-static reinforcement learning algorithm improves the win-loss relationship conversion by 8% and reduces the training time by 40% compared with the traditional reinforcement learning algorithm.
Average Entropy of Gaussian Mixtures
Authors: Joudeh Basheer , Škorić Boris
Published: August 2024

We calculate the average differential entropy of a q-component Gaussian mixture in Rn. For simplicity, all components have covariance matrix σ21, while the means {Wi}i=1q are i.i.d. Gaussian vectors with zero mean and covariance s21. We obtain a series expansion in μ=s2/σ2 for the average differential entropy up to order O(μ2), and we provide a recipe to calculate higher-order terms. Our result provides an analytic approximation with a quantifiable order of magnitude for the error, which is not achieved in previous literature.
Rhizosphere Microbiomes Mediating Abiotic Stress Mitigation for Improved Plant Nutrition
Authors: Adeleke Bartholomew Saanu , Chaudhary Parul , Ayilara Modupe Stella , Ojo Funmilola Mabel , Erinoso Sakiru Morenikeji , Upadhayay Viabhav Kumar , Adeyemo Adeyemi Isaiah , Akinola Saheed Adekunle
Published: August 2024

The extreme conditions linked with abiotic stresses have greatly affected soil and plant health. The diverse biochemical activities occurring in the soil environment have been attributed to shaping the dynamics of plant–soil microbiomes by contributing to microbial lifestyles and enhancing microbial functional properties to boost plant tolerance to abiotic-induced stresses. Soil microbiomes play crucial roles in enhancing plant nutrition and abiotic stress management through diverse mechanisms. With the current insights into the use of engineered soil microbes as single or combined inoculants, their use has contributed to plant fitness and stability under different environmental stress conditions by activating plant defense mechanisms, enzyme production (lowering free radicals resulting in plant oxidative stress), protein regulation, and the production of growth factors. The detection of certain genes involved in the growth factors can underline microbial functions in mitigating plant stress. Hence, the projections for sustainable eco-friendly agriculture with the possible exploration of beneficial rhizosphere microbes to manage the effect of abiotic stress on plant nutrition remain critical points of discussion recently, with prospects for ensuring food security. Therefore, this review focuses on the impacts of soil microbiomes in abiotic stress mitigation for enhancing plant nutrition.
The Role of IT Governance in the Integration of AI in Accounting and Auditing Operations
Authors: Almaqtari Faozi A.
Published: August 2024

IT governance is a framework that manages the efficient use of information technology within an organization, focusing on strategic alignment, risk management, resource management, performance measurement, compliance, and value delivery. This study investigates the role of IT governance in integrating artificial intelligence (AI) in accounting and auditing operations. Data were collected from 228 participants from Saudi Arabia using a combination of convenience sampling and snowball sampling methods. The collected data were then analyzed using structural equation modeling. Unexpectedly, the results demonstrate that AI, big data analytics, cloud computing, and deep learning technologies significantly enhance accounting and auditing functions’ efficiency and decision-making capabilities, leading to improved financial reporting and audit processes. The results highlight that IT governance plays a crucial role in managing the complexities of AI integration, aligning business strategies with AI-enabled technologies, and facilitating these advancements. This research fills a gap in previous research and adds significantly to the academic literature by improving the understanding of integrating AI into accounting and auditing processes. It builds on existing theoretical frameworks by investigating the role of IT governance in promoting AI adoption. The findings provide valuable insights for accounting and auditing experts, IT specialists, and organizational leaders. The study provides practical insights on deploying AI-driven technology in organizations to enhance auditing procedures and financial reporting. In a societal context, it highlights the broader implications of AI on transparency, accountability, and trust in financial reporting. Finally, the study offers practitioners, policymakers, and scholars valuable insights on leveraging AI advancements to optimize accounting and auditing operations. It highlights IT governance as an essential tool for effectively integrating AI technologies in accounting and auditing operations. However, successful implementation encounters significant organizational challenges like organizational support, training, data sovereignty, and regulatory compliance.
The Effect of Regular and Innovative Control Devices on Cultivating Creativity in a Game Creating Course in Primary School
Authors: Chen Chien-Yu , Su Shih-Wen , Yuan Shyan-Ming
Published: August 2024

The development of creativity plays a decisive role in the future development of human life as it stimulates divergent thinking and grants the critical ability to innovate and solve problems. Therefore, the question of how to encourage students’ creativity has also attracted the attention of related research in various fields. Our study used the Scratch visual programming tool to allow students to create their own digital games. We used different technological devices as external stimuli during the research process to stimulate students’ creative ideas. We provided four control devices: a keyboard and mouse, PicoBoard, a 65-inch touch screen, and a Wii remote control for 92 fifth-grade students in four S-shaped classes. After a 12-week experimental process, students designed their original Scratch game. The results of the study found that differences in device properties correspond to different items of creativity: devices highly relevant to students’ life experiences improve the flexibility and elaboration of creativity. Innovative controls promote creative originality. The freedom of device control can increase the fluency of creativity. Therefore, providing control devices with different properties will allow teachers to establish learning environments that foster creativity. Finally, we speculate on the impact of other control devices on creativity based on the research results for future reference.
Foresight Methodologies in Responsible GenAI Education: Insights from the Intermedia-Lab at Complutense University Madrid
Authors: Azcárate Asunción López-Varela
Published: August 2024

This study, conducted at Complutense Intermedia-Lab, employs a dual approach to explore university students’ use of Generative AI (GenAI), combining a survey with foresight methodologies (Sci-fi prototyping). The initial survey gathers baseline data on students’ experiences, attitudes, and concerns regarding GenAI, providing a comprehensive understanding of current practices among university students in Spain. This empirical foundation informs subsequent Sci-fi prototyping sessions, where students creatively envision future scenarios, fostering futurist thinking and deeper engagement. By integrating principles of Responsible Research and Innovation (RRI), this approach facilitates a nuanced exploration of GenAI’s potential impacts on education. The incorporation of both quantitative data collection and qualitative foresight methods in this study serves to navigate challenges and level opportunities of promoting the ethical and inclusive incorporation of GenAI in Higher Education, ensuring that future innovations align with societal values and needs.
Evaluating Recent Advances in Affective Intelligent Tutoring Systems: A Scoping Review of Educational Impacts and Future Prospects
Authors: Fernández-Herrero Jorge
Published: August 2024

Affective intelligent tutoring systems (ATSs) are gaining recognition for their role in personalized learning through adaptive automated education based on students’ affective states. This scoping review evaluates recent advancements and the educational impact of ATSs, following PRISMA guidelines for article selection and analysis. A structured search of the Web of Science (WoS) and Scopus databases resulted in 30 studies covering 27 distinct ATSs. These studies assess the effectiveness of ATSs in meeting learners’ emotional and cognitive needs. This review examines the technical and pedagogical aspects of ATSs, focusing on how emotional recognition technologies are used to customize educational content and feedback, enhancing learning experiences. The primary characteristics of the selected studies are described, emphasizing key technical features and their implications for educational outcomes. The discussion highlights the importance of emotional intelligence in educational environments and the potential of ATSs to improve learning processes. This review identifies gaps in the current research and suggests future directions, including broader implementation across diverse educational settings and deeper integration of affective data to refine system responsiveness. Future research should investigate the integration of advanced natural dialogue modules and generative AI to create more sophisticated interfaces, underscoring the role of affective adaptation in educational technology.
Advanced Electrochemical Detection of Tetrabromobisphenol A and Hexabromocyclododecane via Modified Carbon Electrodes with Inorganic Nanoparticles: A Short Review
Authors: Jayaprakash Gururaj Kudur , Mohanty Kaustubha
Published: August 2024

The escalating concern over environmental pollutants, particularly brominated flame retardants (BFRs), demands sophisticated detection methodologies for compounds like Tetrabromobisphenol A (TBBPA) and Hexabromocyclododecane (HBCD). Amidst these challenges, advancements in electrochemical detection have notably focused on the integration of inorganic modifiers within carbon electrodes. Inorganic nanoparticles, known for their catalytic and surface-enhancing properties, play a pivotal role in augmenting the sensitivity and selectivity of electrode-based detection systems. These modifiers, encompassing materials such as graphene, CeO2 nanocubes, and metal-organic frameworks, among others, have revolutionized the capabilities of carbon-based electrodes in accurately identifying specific BFRs.
An Autonomous Cooperative Navigation Approach for Multiple Unmanned Ground Vehicles in a Variable Communication Environment
Authors: Lin Xudong , Huang Mengxing
Published: August 2024

Robots assist emergency responders by collecting critical information remotely. Deploying multiple cooperative unmanned ground vehicles (UGVs) for a response can reduce the response time, improve situational awareness, and minimize costs. Reliable communication is critical for multiple UGVs for environmental response because multiple robots need to share information for cooperative navigation and data collection. In this work, we investigate a control policy for optimal communication among multiple UGVs and base stations (BSs). A multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to update the control policy for the maximum signal-to-interference ratio. The UGVs communicate with both the fixed BSs and a mobile BS. The proposed control policy can navigate the UGVs and mobile BS to optimize communication and signal strength. Finally, a genetic algorithm (GA) is proposed to optimize the hyperparameters of the MADDPG-based training. Simulation results demonstrate the computational efficiency and robustness of the GA-based MADDPG algorithm for the control of multiple UGVs.
Comment on Sakli, H. Cylindrical Waveguide on Ferrite Substrate Controlled by Externally Applied Magnetic Field. Electronics 2021, 10, 474
Authors: Moradi Afshin
Published: August 2024

Recently, Sakli investigated the propagation of electromagnetic waves in metallic cylindrical waveguides filled with longitudinally magnetized ferrite, focusing on TEz (transverse electric) and TMz (transverse magnetic) modes relative to the z-axis. This commentary highlights that the proposed system generally cannot support the propagation of the TEz and TMz waves, rendering the main results derived by Sakli invalid.
Power Pylon Type Identification and Characteristic Parameter Calculation from Airborne LiDAR Data
Authors: Zu Shengxuan , Wang Linong , Wu Shaocheng , Wang Guanjian , Song Bin
Published: August 2024

Reconstructing three-dimensional (3D) models of power equipment plays an increasingly important role in advancing digital twin power grids. To reconstruct a high-precision model, it is crucial to accurately obtain the pylon type and its necessary parameter information before modeling. This study proposes an improved method for identifying pylon types based on similarity measurement and a linearly transformed dataset. It begins by simplifying the identification of point clouds using the pylon shape curve. Subsequently, the resemblance between the curve and those curves within the dataset is evaluated using a similarity measurement to determine the pylon type. A novel method is proposed for calculating the characteristic parameters of the pylon point clouds. The horizontal and vertical distribution characteristics of the pylon point clouds are analyzed to identify key segmentation positions based on their types. Feature points are derived from key segmentation positions to calculate the characteristic parameters. Finally, the pylon 3D models are reconstructed on the basis of the calculated values. The experimental results showed that, compared with other similarity measurements, the Hausdorff distance had the best effect as a similarity measurement using the linearly transformed dataset, with an overall evaluation F-score of 86.4%. The maximum relative error of the calculated pylon parameters did not exceed 5%, affirming the feasibility of the algorithm.
Toward Safer Roads: Predicting the Severity of Traffic Accidents in Montreal Using Machine Learning
Authors: Muktar Bappa , Fono Vincent
Published: August 2024

Traffic accidents are among the most common causes of death worldwide. According to statistics from the World Health Organization (WHO), 50 million people are involved in traffic accidents every year. Canada, particularly Montreal, is not immune to this problem. Data from the Société de l’Assurance Automobile du Québec (SAAQ) show that there were 392 deaths on Québec roads in 2022, 38 of them related to the city of Montreal. This value represents an increase of 29.3% for the city of Montreal compared with the average for the years 2017 to 2021. In this context, it is important to take concrete measures to improve traffic safety in the city of Montreal. In this article, we present a web-based solution based on machine learning that predicts the severity of traffic accidents in Montreal. This solution uses a dataset of traffic accidents that occurred in Montreal between 2012 and 2021. By predicting the severity of accidents, our approach aims to identify key factors that influence whether an accident is serious or not. Understanding these factors can help authorities implement targeted interventions to prevent severe accidents and allocate resources more effectively during emergency responses. Classification algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF), and Gradient Boosting (GB) were used to develop the prediction model. Performance metrics such as precision, recall, F1 score, and accuracy were used to evaluate the prediction model. The performance analysis shows an excellent accuracy of 96% for the prediction model based on the XGBoost classifier. The other models (CatBoost, RF, GB) achieved 95%, 93%, and 89% accuracy, respectively. The prediction model based on the XGBoost classifier was deployed using a client–server web application managed by Swagger-UI, Angular, and the Flask Python framework. This study makes significant contributions to the field by employing an ensemble of supervised machine learning algorithms, achieving a high prediction accuracy, and developing a real-time prediction web application. This application enables quicker and more effective responses from emergency services, potentially reducing the impact of severe accidents and improving overall traffic safety.
Compact Absorptive Microstrip Bandpass Filter with Adjustable Bandwidth and Phase
Authors: Li Lei , Shen Zhongxiang , Wu Wen
Published: August 2024

In this communication, a compact absorptive bandpass filter (ABPF) characterized by adjustable bandwidth and phase, low group delay (GD), a high absorptive ratio and low insertion loss (IL) is proposed. The presented ABPF consists of a bandpass section that is made of a quarter-wavelength coupled line and four stubs that are basically a lumped resistor in series with a short-circuited quarter-wavelength transmission line. The stubs not only perform the function of absorption but also have the advantage of adjustable bandwidth and phase. To verify the design concept and analysis formulas, an ABPF operating at 2.4 GHz is fabricated and measured; its simulated and measured results are in good agreement.
Evolution of Antenna Radiation Parameters for Air-to-Plasma Transition
Authors: Miś Tomasz Aleksander
Published: August 2024

This paper presents the description of antenna parameters related to its radiation/reception capabilities influenced by the plasma parameters in the environment surrounding the antenna, complementing the existing works on the antenna parameters (e.g., the impedance or currents). The parameters considered are the radiation zones’ radiuses (inductive, Fresnel, Fraunhofer), scalloping and directivity; a method of transformation of the air/vacuum-measured radiation/reception pattern to the pattern expected for given plasmatic conditions is also considered. Three different simplified plasma conditions are taken into account (different electron densities: 1.4 × 1012 m−3, 4 × 1011 m−3 and 108 m−3), with varying antenna length (1 m, 10 m, 100 m) and signal propagation mode (classic-ionospheric, whistler and Alfvén). The findings show that the presented antenna parameters and its radiation/reception pattern are heavily dependent on the plasma conditions. These findings can be used to form additional requirements and constraints for the mechanical design of new instrumentation for space weather measurements on board spacecraft (e.g., moving the antennas away from the spacecraft in order not to alter their radiation/reception patterns or not to measure the plasma around the spacecraft) or more accurate data processing from existing space weather satellites, allowing, for example, a more precise triangulation of the signal source or its spectral power regarding the actual performance of the antennas submerged in plasma.
A Resource-Based Dynamic Pricing and Forced Forwarding Incentive Algorithm in Socially Aware Networking
Authors: Zhang Xuemin , Li Yuan , Xiong Zenggang , Liu Yanchao , Wang Shihui , Hou Delin
Published: August 2024

In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node’s resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes’ message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency.
Efficient Speech Signal Dimensionality Reduction Using Complex-Valued Techniques
Authors: Ko Sungkyun , Park Minho
Published: August 2024

In this study, we propose the CVMFCC-DR (Complex-Valued Mel-Frequency Cepstral Coefficients Dimensionality Reduction) algorithm as an efficient method for reducing the dimensionality of speech signals. By utilizing the complex-valued MFCC technique, which considers both real and imaginary components, our algorithm enables dimensionality reduction without information loss while decreasing computational costs. The efficacy of the proposed algorithm is validated through experiments which demonstrate its effectiveness in building a speech recognition model using a complex-valued neural network. Additionally, a complex-valued softmax interpretation method for complex numbers is introduced. The experimental results indicate that the approach yields enhanced performance compared to traditional MFCC-based techniques, thereby highlighting its potential in the field of speech recognition.
A Single-Stage Electronic Lighting Driver Circuit Utilizing SiC Schottky Diodes for Supplying a Deep Ultraviolet LED Disinfection and Sterilization Lamp
Authors: Cheng Chun-An , Chang Chien-Hsuan , Cheng Hung-Liang , Chang En-Chih , Lan Long-Fu , Hou Sheng-Hong , Lin Cheng-Kuan
Published: August 2024

Recently, a new type of lighting source, deep ultraviolet light-emitting diode (LED), has appeared in the markets of space purification and surface sterilization. In this paper, a new type of electronic lighting driver for supplying a deep-ultraviolet LED sterilization lamp is proposed and developed. The main circuit combines a buck converter and a flyback converter into a single-stage single-switch buck-flyback AC-DC power converter with power factor correction. In addition, the proposed electronic lighting driver leverages a wide bandgap SiC Schottky diode as the output diode to lower the power diode losses and recycles the energy stored in the leakage inductance of the transformer in order to improve the circuit efficiency. The magnetizing inductor inside the presented AC-DC power converter is designed to operate in discontinuous conduction mode (DCM), which naturally enables power factor correction (PFC). A single-stage prototype driver with a power rating of 3.6 W (90 V/40 mA) was developed and implemented for providing a deep ultraviolet LED disinfection and sterilization lamp. Experimental results show that the measured power factor (PF) is greater than 0.9 and the measured total harmonic distortion (THD) of the input current is less than 18% at an input utility voltage of 110 V. Furthermore, the measured output voltage ripple factor is less than 1% and the output current ripple factor is less than 4%. In addition, the proposed single-stage electronic lighting driver for supplying a deep ultraviolet LED disinfection and sterilization lamp achieves high circuit efficiency (greater than 90%), low circuit component count, and low circuit cost.
Blockchain and Homomorphic Encryption for Data Security and Statistical Privacy
Authors: Raj Rahul , Kurt Peker Yeşem , Mutlu Zeynep Delal
Published: August 2024

This study proposes a blockchain-based system that utilizes fully homomorphic encryption to provide data security and statistical privacy when data are shared with third parties for analysis or research purposes. The proposed system not only provides security of data in transit, at rest, and in use but also assures privacy and computational integrity for simple statistical computations. This is achieved by leveraging the attributes of the blockchain technology, which provides availability and data integrity, combined with homomorphic encryption, which provides confidentiality of data in use. The computations are performed on smart contracts residing on the blockchain, providing computational integrity. The proposed system is implemented on the Zama blockchain and performs statistical operations including mean, median, and variance on encrypted data. The results indicate that it is possible to perform fully homomorphic computations on the blockchain. Even though current computing limitations on the blockchain do not allow running the system for large data sets, the technology is available, and with advancements toward more efficient homomorphic operations on blockchains, the proposed system will provide an ultimate solution for providing the much-desired security properties in applications, including data and statistical privacy, confidentiality, and integrity at rest, in transit, and in use.
Modelling Analysis of Channel Assembling in CRNs Based on Priority Scheduling Strategy with Reserved Queue
Authors: Xu Qianyu , Li Suoping , Gaber Jaafar , Han Yuzhou
Published: August 2024

In cognitive radio networks, channel assembling allows secondary users (SUs) to expand network capacity and improve spectrum utilization. Scheduling strategies only based on heterogeneous service classification cannot guarantee the delivery priority of vital elastic services in special scenarios such as emergency rescue. Therefore, a priority scheduling strategy with reserved queue (Ps-rq) is proposed in this work. A static factor is defined to classify SUs into elastic services and real-time services based on message type, while a dynamic factor is defined to differentiate high-priority elastic services based on information validity, message correlation and message size. The high-priority users in the interrupted elastic services are placed in the reserved queue to ensure its services. Accordingly, the scheduling algorithm and the dynamic channel access process is presented. A continuous-time Markov chain analysis is conducted and all possible transition states, trigger events, transition rates and transition conditions of the system starting from a general state are derived. Furthermore, evaluation indexes of system performance are obtained. Study cases and simulation results prove that the proposed strategy can enhance network capacity, reduce blocking probability and forced termination probability for secondary users, and notably enhance the performance of high-priority elastic services. In addition, we analyze the characteristics of Ps-rq through a comprehensive comparison with four other schemes. The experiment proves that the Ps-rq strategy can effectively improve the service quality of the vital elastic services on the basis of providing fair scheduling.
Improving Attack Graph Visual Syntax Configurations
Authors: Sherzhanov Askhat , Atlam Hany F. , Azad Muhammad Ajmal , Lallie Harjinder Singh
Published: August 2024

As technology advances and cyber threats become increasingly sophisticated, the task of recognising and understanding malicious activities becomes more complex. This persistent issue is widely acknowledged and extensively documented within the cybersecurity community. Attack modelling techniques (AMTs), such as attack graphs, have emerged as valuable tools in aiding cyberattack perception. These visualisation tools offer crucial insights into the complex relationships between various components within a system or network, shedding light on potential attack paths and vulnerabilities. This paper proposes an attack graph visual syntax method to improve cyberattack perception among experts and non-experts. The proposed approach was developed to streamline complexity and enhance clarity, thus augmenting the interpretability for users by enhancing visual structural components, such as hue, chromaticity, and line parameters. The proposed attack graph (pag) was empirically evaluated against the adapted attack graph (aag) presented in the literature. The empirical evaluation (n = 83) was conducted through a 3 × 2 × 2 factorial design and two-way analysis of variance (ANOVA) with repeated measures. The participants were classified according to their respective background cohorts into expert and non-expert (expert n = 37, non-expert n = 46) and then grouped into two groups: proposed attack graph (pag) and adapted attack graph (aag) (pag n = 41, aag n = 42). The empirical results demonstrated that while the proposed attack graph (pag) implemented various visual modifications such as brighter hues, denser line structures, and varied shapes, these enhancements did not significantly improve the perception of cyberattacks among individuals who lack expertise in the field, including corporate executives. Moreover, the use of variables such as colour, tone, and line width/density/structure did not help objects in the graph be distinguished more effectively. This paper provides significant insights into the impact of visual enhancements on cyberattack perception, highlighting that visual enhancements alone may not be sufficient to improve cyberattack perception for individuals lacking expertise in the field.
Enhancing Swine Wastewater Treatment: A Sustainable and Systematic Approach through Optimized Chemical Oxygen Demand/Sulfate Mass Ratio in Attached-Growth Anaerobic Bioreactor
Authors: Lamssali Mehdi , Mantripragada Shobha , Deng Dongyang , Zhang Lifeng
Published: August 2024

The swine industry generates millions of gallons (thousands of cubic meters) of wastewater every day, posing significant environmental risk due to high concentrations of organics and nutrients. This study aims to investigate the effectiveness of attached-growth anaerobic bioreactors for treating swine wastewater by utilizing sulfate-reducing bacteria, focusing on the impact of chemical oxygen demand (COD)/sulfate mass ratios on organics degradation. A series of lab-scale anaerobic bioreactors were employed to treat swine wastewater for a 14-day period. The study evaluated changes in pH, acidity, alkalinity, COD, sulfate, and various nutrients along with total suspended solids (TSS) and volatile suspended solids (VSS) before and after treatment. At a COD/sulfate mass ratio of 2:1, the bioreactors achieved optimum removal efficiencies of 80% for TSS, 83% for VSS, 86–88% for COD, 82–87% for sulfate, 73% for sulfide, and 73% for sulfite. The nutrient removal efficiency was 67% for nitrate and 72% for nitrite. The acidity and alkalinity were effectively controlled, with alkalinity values reaching up to 2161 ± 92.5 mg/L and pH within the range of 7–7.24. The findings demonstrated that anaerobic bioreactor at a COD/sulfate mass ratio of 2:1 significantly enhanced the degradation of organic matter coupling with sulfate reduction in swine wastewater, providing an efficient and sustainable treatment method.
Effect of Forestry Carbon Offset Policy on Sharing the Pressure of Emission Reduction: Findings from China
Authors: Sun Rui , He Dayi , Yan Jingjing
Published: August 2024

The forestry carbon offset policy has been implemented for a short time, and in order to study its optimization mechanism and pressure-sharing emission reduction effect, this paper applies the directional distance function to calculate the marginal abatement cost of each province/city based on the panel data of 30 provinces/cities in China from 2000 to 2020. Then, we utilize the synthetic control method to analyze the forestry carbon offset policy by taking Beijing, Guangdong, and Fujian as a natural experiment. Finally, placebo tests and differences-in-differences tests were used to verify the experiment’s effectiveness. The study has the following results. (1) The forestry carbon offset policy is a Pareto improvement after integrating multiple benefits. The proportion of FCS offset should be increased, and government subsidies should be reduced when carbon quotas are tightened, followed by the gradual inclusion of more industries and enterprises in the scope of mandatory emission reductions. (2) The impact of forestry carbon offset policy on sharing the abatement pressure has regional heterogeneity, which is affected mainly by geographical location, economic level, and industrial structure. It can be obtained that the forestry carbon offset policy has shared the pressure for emission reductions in Guangdong and Fujian. This study provides a theoretical basis for promoting forestry carbon offset policies and their coupling with other carbon-reduction policies.
Online Identification of Beer Fermentation Phases
Authors: Buonocore Daniele , Ciavolino Giuseppe , Dello Iacono Salvatore , Liguori Consolatina
Published: August 2024

Over the last two decades, the craft beer industry has significantly developed with the emergence of thousands of microbreweries all over the world. These are mostly small companies that cannot afford the cost of the process monitoring systems that are usually embedded in the machinery used by industrial breweries, but they need to monitor and control the production process in order to guarantee a constant quality of beer. The development of low-cost systems for monitoring the production process would help microbreweries obtain the desired product quality consistency. In this paper, the authors propose a low-cost system for the real-time identification of the different phases of the alcoholic fermentation of beer. The first results prove the ability of the proposed system to monitor the fermentation and to detect anomalies in the process promptly.