bluuebunny/crossref_metadata_2025 huggingface dataset:
Alcohol and the Brain–Gut Axis: The Involvement of Microglia and Enteric Glia in the Process of Neuro-Enteric Inflammation
Authors: Khan Mohammed A. S. , Chang Sulie L.
Published: October 2023

Binge or chronic alcohol consumption causes neuroinflammation and leads to alcohol use disorder (AUD). AUD not only affects the central nervous system (CNS) but also leads to pathologies in the peripheral and enteric nervous systems (ENS). Thus, understanding the mechanism of the immune signaling to target the effector molecules in the signaling pathway is necessary to alleviate AUD. Growing evidence shows that excessive alcohol consumption can activate neuroimmune cells, including microglia, and change the status of neurotransmitters, affecting the neuroimmune system. Microglia, like peripheral macrophages, are an integral part of the immune defense and represent the reticuloendothelial system in the CNS. Microglia constantly survey the CNS to scavenge the neuronal debris. These cells also protect parenchymal cells in the brain and spinal cord by repairing nerve circuits to keep the nervous system healthy against infectious and stress-derived agents. In an activated state, they become highly dynamic and mobile and can modulate the levels of neurotransmitters in the CNS. In several ways, microglia, enteric glial cells, and macrophages are similar in terms of causing inflammation. Microglia also express most of the receptors that are constitutively present in macrophages. Several receptors on microglia respond to the inflammatory signals that arise from danger-associated molecular patterns (DAMPs), pathogen-associated molecular patterns (PAMPs), endotoxins (e.g., lipopolysaccharides), and stress-causing molecules (e.g., alcohol). Therefore, this review article presents the latest findings, describing the roles of microglia and enteric glial cells in the brain and gut, respectively, and their association with neurotransmitters, neurotrophic factors, and receptors under the influence of binge and chronic alcohol use, and AUD.
Adaptive Mesh Refinement Strategies for Cost-Effective Eddy-Resolving Transient Simulations of Spray Dryers
Authors: Gutiérrez Suárez Jairo Andrés , Galeano Urueña Carlos Humberto , Gómez Mejía Alexánder
Published: October 2023

The use of adaptive meshing strategies to perform cost-effective transient simulations of spray drying processes is evaluated. These simulations are often computationally expensive, given the large differences between the characteristic times of the central jet and those of the unsteady flow developed at its periphery. Managing the computational cost through the control of the grid resolution by regions is inadequate in many of these applications since the grid resolution requirements change dynamically within the domain. These conditions are related to the unsteady nature of the flow in both the central jet and the flow recirculation zones. Therefore, the application of adaptive mesh refinement (AMR) strategies is recommended. In this paper, general AMR criteria based on relative errors are evaluated by testing three mesh adaptation criteria: velocity gradient, pressure gradient, and vorticity. This evaluation is performed using a low-cost turbulence model with eddy resolution (DDES) in two different types of drying chambers, in which experimental measurements are available. The use of AMR exerts appreciable effects on decreasing computational costs and contributes to the capture of large eddies in critical regions. The present approach provides an appropriate balance between solution accuracy and computational cost. By using a correct AMR configuration, it is possible to obtain results similar to those obtained on a fixed grid but reducing the computational costs by 3 to 5 times.
The 2′,4′-Dichloro-chalcone Inhibits the In Vitro Growth and Pathogenicity of Fusarium tricinctum and Trichothecium roseum by Activating Cyanide-Resistant Respiration
Authors: Zhu Fupeng , Zhu Yan , Zhao Yuanshou , Chen Fu , Sheng Wenjun , Zhang Wei , Wang Pengqing , Deng Jiangwen , Sun Yunyu , Zhang Weibing , Li Yongcai
Published: October 2023

Chalcones are a class of flavonoids possessing antimicrobial properties and have potential for use as coatings of plant products for the control of postharvest diseases. The effects of 2′,4′-dichloro-chalcone on the in vitro growth and in vivo pathogenicity of Fusarium tricinctum and Trichothecium roseum were investigated. First, 1 µM of 2′,4′-dichloro-chalcone strongly inhibited the mycelial growth and conidial production of F. tricinctum (32.3%) and T. roseum (65.2%) in vitro. Meanwhile, the cell membrane permeability was increased by 25% and 22.5% and the accumulation of reactive oxygen species was increased by 41.7 and 65.4%, respectively, of F. tricinctum and T. roseum. This treatment also significantly inhibited the total respiration rate and activated the cyanide-resistant respiratory pathway in both pathogens. The expression level of AOX was enhanced in F. tricinctum and T. roseum by 52.76 and 39.13%, respectively. This treatment also significantly inhibited the expansion of potato dry rot from F. tricinctum (48.6%) and apple rot spot from T. roseum (36.2%). Therefore, 2′,4′-dichloro-chalcone has potential use as an alternative safety method in the control of postharvest diseases by F. tricinctum and T. roseum in agricultural practices.
Stability of Impaired Humoral Immunity HIV-1 Models with Active and Latent Cellular Infections
Authors: AlShamrani Noura H. , Halawani Reham H. , Shammakh Wafa , Elaiw Ahmed M.
Published: October 2023

This research aims to formulate and analyze two mathematical models describing the within-host dynamics of human immunodeficiency virus type-1 (HIV-1) in case of impaired humoral immunity. These models consist of five compartments, including healthy CD4+ T cells, (HIV-1)-latently infected cells, (HIV-1)-actively infected cells, HIV-1 particles, and B-cells. We make the assumption that healthy cells can become infected when exposed to: (i) HIV-1 particles resulting from viral infection (VI), (ii) (HIV-1)-latently infected cells due to latent cellular infection (CI), and (iii) (HIV-1)-actively infected cells due to active CI. In the second model, we introduce distributed time-delays. For each of these systems, we demonstrate the non-negativity and boundedness of the solutions, calculate the basic reproductive number, identify all possible equilibrium states, and establish the global asymptotic stability of these equilibria. We employ the Lyapunov method in combination with LaSalle’s invariance principle to investigate the global stability of these equilibrium points. Theoretical findings are subsequently validated through numerical simulations. Additionally, we explore the impact of B-cell impairment, time-delays, and CI on HIV-1 dynamics. Our results indicate that weakened immunity significantly contributes to disease progression. Furthermore, the presence of time-delays can markedly decrease the basic reproductive number, thereby suppressing HIV-1 replication. Conversely, the existence of latent CI spread increases the basic reproductive number, intensifying the progression of HIV-1. Consequently, neglecting latent CI spread in the HIV-1 dynamics model can lead to an underestimation of the basic reproductive number, potentially resulting in inaccurate or insufficient drug therapies for eradicating HIV-1 from the body. These findings offer valuable insights that can enhance the understanding of HIV-1 dynamics within a host.
Investigation of the Evolution of Schmid Factors (SF) in 316 Stainless Steel during In Situ Plastic Deformation
Authors: Wan Xiaofeng , Wang Jin , Li Penghou , Chen Jianguo , Wang Xiao
Published: October 2023

The Schmid factor (SF) is a critical parameter in crystal plasticity research that is often used to evaluate the level of difficulty in activating the slip systems within a grain. The evolution process and change mechanism of SF in 316 austenitic stainless steel during plastic deformation were investigated in this paper by using the in situ electron backscatter diffraction (EBSD) technique. The results showed that the average Schmid factor of global grains was highest in the original state, but after stretching, multiple rotation paths appeared in the grain, and the SF presented a monotonically decreased tendency with the increase in plastic strain degree. Numerical computation revealed that the decrease of SF was mainly governed by the change in φ angle, i.e., the angle between loading direction and slip plane normal increased inside the grains after the lattice rotation, which caused the slip plane to move parallel to the loading direction. The higher φ, the lower its cosine, which corresponds to low shear stress acting on the slip plane and could increase the difficulty of crystal slip.
A Comparison Study of Roseolumiflavin Solvates: Structural and Energetic Perspective on Their Stability
Authors: Haj Hassani Sohi Takin , Maass Felix , Czekelius Constantin , Vasylyeva Vera
Published: October 2023

Roseolumiflavin is a deep red microcrystalline derivative of isoalloxazine that exhibits a weak photophysical activity in the solid state. In aqueous as well as in acidic solution of formic or acetic acid, respectively, it tends to form solvates. Herein, we present a set of binary and ternary roseolumiflavin solvates including one hydrate and a solvate hydrate. The impact of the solvent on solvate formation along with an in-depth structural analysis was investigated. Calculations of the lattice energies provide insight into the phase stability of the evaluated systems showing an energetic benefit for all solvates with values up to −395.82 kJ/mol. The total interaction energies between molecules calculated via Crystal Explorer further identified cofacial π···π stacks to be the most strongly bonding fragments in the crystal lattices for all systems except the formic acid solvate, followed by remarkably weaker hydrogen-bonded arrangements. The energetic contributions of single intermolecular interactions within the fragments are evaluated by an atoms-in-molecules approach. It is shown that physicochemical properties, such as thermal stability, can be tuned depending on the incorporated solvent molecules despite a high decomposition temperature of the chromophore.
A Dataset of Non-Indigenous and Native Fish of the Volga and Kama Rivers (European Russia)
Authors: Karabanov Dmitry P. , Pavlov Dmitry D. , Dgebuadze Yury Y. , Bazarov Mikhail I. , Borovikova Elena A. , Gerasimov Yuriy V. , Kodukhova Yulia V. , Mikheev Pavel B. , Nikitin Eduard V. , Opaleva Tatyana L. , Severov Yuri A. , Sabitova Rimma Z. , Smirnov Alexey K. , Solomatin Yury I. , Stolbunov Igor A. , Tsvetkov Alexander I. , Vlasenko Stanislav A. , Voroshilova Irina S. , Zhong Wenjun , Zhang Xiaowei , Kotov Alexey A.
Published: October 2023

Fish in the Volga-Kama River System (the largest river system in Europe) are important as a crucial food source for local populations; fish have the highest trophic level among hydrobionts. The purpose of this research is to describe the diversity of non-indigenous and native fish in the Volga and Kama Rivers, in the European part of Russia. This dataset encompasses data from June 2001 to September 2021 and comprises 1888 records (36,376 individual observations) for littoral and pelagic habitats from 143 sampling sites, representing 52 species from 42 genera in 22 families. The dataset has a Darwin Core standard format and has been fully released in the Global Biodiversity Information Facility (GBIF) under CC-BY 4.0 International license. The data are validated with several international databases such as FishBase, Eschmeyer’s Catalog of Fishes, the Barcode of Life Data System, and the SAS.Planet geoinformations system. Newly established populations have been found for several species belonging to the following Actinopteri families: Alosidae, Anguillidae, Cichlidae, Ehiravidae, Gobiidae, Odontobutidae, Syngnathidae, and Xenocyprididae. Therefore, this dataset can be used in the particular taxon species distribution analysis, which are especially important for non-indigenous species.
Comparative Analysis of the Efficacy of Different Regimens of 12 Months Rifaximin-Alfa Therapy in Patients with Liver Cirrhosis and Minimal Hepatic Encephalopathy
Authors: Bakulin Igor G. , Ivanova Kristina N. , Eremina Elena Y. , Marchenko Natalya V.
Published: October 2023

It is a matter of current interest which rifaximin-α regimens in patients with liver cirrhosis and minimal hepatic encephalopathy are the most efficient. Study objective: to evaluate the effect of various rifaximin-α regimens for 12 months on clinical and laboratory parameters and quality of life in patients with liver cirrhosis and minimal hepatic encephalopathy. Methods. It was a multicenter, prospective, open-label, observational study that included 288 patients with liver cirrhosis and minimal hepatic encephalopathy of both sexes over the age of 18 years, who were prescribed a 12-month course of treatment with rifaximin-α in accordance with the product label. Statistical analysis was performed in the population of patients who completed all visits according to the protocol (n = 258). Retrospectively, the patients were divided into two subgroups: subgroup 1 (continuous course)—patients who received the study drug for a year and the number of days of administration was 360 days (n = 41); subgroup 2 (cyclic course)—patients who received the study drug during the year for less than 360 days (n = 217). At each of the 4 visits, the quality of life was assessed using the CLDQ questionnaire, the time to perform the number connection test, the severity of symptoms associated with hepatic encephalopathy, and laboratory parameters. Results. During the 12-month observation period, an increase in the total score on the CLDQ quality of life questionnaire in patients with chronic liver diseases was revealed, which indicates an improvement in the quality of life of patients receiving rifaximin-α therapy. When patients were divided into subgroups depending on the duration of therapy, some benefits of continuous rifaximin-α therapy were noted in the more pronounced dynamics of decrease in the time to perform the number connection test, and in decreased severity of the following symptoms associated with hepatic encephalopathy: impaired concentration and memory, cognitive impairment, and decreased performance. Laboratory findings showed positive dynamics in both subgroups. Conclusion. A continuous rifaximin-α regimen in patients with liver cirrhosis and minimal hepatic encephalopathy for 12 months was superior to cyclic use with a more pronounced effect on the quality of life of patients and on the symptoms associated with hepatic encephalopathy.
Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images
Authors: Rao P. Kiran , Chatterjee Subarna , Janardhan M. , Nagaraju K. , Khan Surbhi Bhatia , Almusharraf Ahlam , Alharbe Abdullah I.
Published: October 2023

Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely and effective intervention. Although neural networks have shown great promise in disease prediction, their computational demands have limited their practicality in clinical settings. This study introduces a novel methodology, the UNet-PWP architecture, tailored explicitly for kidney tumor segmentation, designed to optimize resource utilization and overcome computational complexity constraints. A key novelty in our approach is the application of adaptive partitioning, which deconstructs the intricate UNet architecture into smaller submodels. This partitioning strategy reduces computational requirements and enhances the model’s efficiency in processing kidney tumor images. Additionally, we augment the UNet’s depth by incorporating pre-trained weights, therefore significantly boosting its capacity to handle intricate and detailed segmentation tasks. Furthermore, we employ weight-pruning techniques to eliminate redundant zero-weighted parameters, further streamlining the UNet-PWP model without compromising its performance. To rigorously assess the effectiveness of our proposed UNet-PWP model, we conducted a comparative evaluation alongside the DeepLab V3+ model, both trained on the “KiTs 19, 21, and 23” kidney tumor dataset. Our results are optimistic, with the UNet-PWP model achieving an exceptional accuracy rate of 97.01% on both the training and test datasets, surpassing the DeepLab V3+ model in performance. Furthermore, to ensure our model’s results are easily understandable and explainable. We included a fusion of the attention and Grad-CAM XAI methods. This approach provides valuable insights into the decision-making process of our model and the regions of interest that affect its predictions. In the medical field, this interpretability aspect is crucial for healthcare professionals to trust and comprehend the model’s reasoning.
Assessing the Tool-Use Learning Process in Persons with Profound Intellectual and Multiple Disabilities
Authors: Modh Christina , Öhrvall Ann-Marie , Nilsson Lisbeth
Published: October 2023

The aim of the study was to explore inter-rater reliability and rater experiences of applying the universal instrument Assessment of Learning Process (ALP) with persons with profound intellectual and multiple disabilities. The study used a mixed-methods design. Inter-rater reliability was tested by eight clinical raters who assessed 23 video clips of people learning to use a powered AKKA mobility platform with a line-follower system. The raters were four occupational therapists, two special education teachers, and two speech-language therapists. The qualitative Think-aloud method explored three of the raters experiences of applying the ALP instrument. The inter-rater reliability test of the universal ALP instrument showed a moderate linear weighted kappa value (0.45). All eight raters’ estimated the degree of difficulty or confidence for each assessment. Content analysis of Think-aloud data elucidated three of the raters experiences of observing performance characteristics and interpreting tool-use understanding. The reported inter-rater reliability of the universal ALP instrument can be considered acceptable for an observational study involving persons with profound intellectual and multiple disabilities. Thereby, the results suggest that the ALP can be applied to this population. This is important as assessing a person’s phase in the learning process guides the choice of ALP-facilitating strategies supporting progress in tool-use learning, which in turn may expand a person’s activity repertoire and participation.
Cold Anaphylaxis in Children: Italian Case Series and Review of the Literature
Authors: Tomei Leonardo , Saretta Francesca , Arasi Stefania , Sarti Lucrezia , Licari Amelia , Giovannini Mattia , Barni Simona , Liccioli Giulia , Tallarico Valeria , Piccorossi Alessandra , Caffarelli Carlo , Novembre Elio , Mori Francesca
Published: October 2023

Chronic urticaria (CU) is one of the most common skin disorders worldwide. Among the inducible subgroup of CU, cold urticaria (ColdU) can affect both children and adults and is the only type associated with the risk of anaphylaxis without cofactors. In the scientific literature, data about cold anaphylaxis (ColdA) are poor, especially at pediatric age, and little is known about risk factors associated with the onset of systemic reactions and about the criteria for prescribing adrenaline auto-injectors (AAIs) in these patients. We describe the clinical characteristics and management of a case series of 21 patients with a history of ColdA, and we compare them with the pediatric case reports and case series published so far. On the basis of the scientific literature and of our case series of patients, we suggest that AAI should be prescribed to all high-risk patients: those with urticaria caused by cold-water immersion, oropharyngeal reactions, and with a previous history of systemic symptoms or anaphylaxis.
Accuracy of Intraoral Scanner for Recording Completely Edentulous Arches—A Systematic Review
Authors: Srivastava Gunjan , Padhiary Subrat Kumar , Mohanty Neeta , Molinero-Mourelle Pedro , Chebib Najla
Published: October 2023

Scanning edentulous arches during complete denture fabrication is a crucial step; however, the quality of the resulting digital scan is still questionable. The purpose of this study is to systematically review studies (both clinical and in vitro) and determine whether intraoral scanners have clinically acceptable accuracy when recording completely edentulous arches for the fabrication of removable complete dentures. An electronic search in medical databases like PubMed, Scopus, and Web of Science (WOS), using a combination of relevant keywords, retrieved 334 articles. After full-text evaluation, twelve articles fulfilled the inclusion criteria for this review (eight clinical studies and four in vitro studies). A quality analysis of the included studies was carried out using the QUADAS-2 tool. The accuracy values varied between different intraoral scanners. Different regions of the edentulous arches showed differences in trueness and precision values in both in vitro and clinical studies. Peripheral borders, the inner seal, and poorly traceable structures like the soft palate showed maximum discrepancies. The accuracy of intraoral scanners in recording clear anatomic landmarks like hard tissues with attached mucosa was comparable to conventional edentulous arch impressions. However, higher discrepancies were recorded when digitizing mobile and poorly traceable structures. Intraoral scanners can be used to digitize denture-bearing areas, but the interpretation of the peripheral border and the soft palate should be carefully carried out.
Health and Well-Being in Higher Education: Student Perception of an Australian University
Authors: Siddiqui Nazlee , Maxwell Hazel , Agaliotis Maria
Published: October 2023

The health and well-being of university students is a priority agenda, given the need to advance health in the university system and the United Nations Sustainability Development Goal regarding quality and inclusive education. However, current literature lacks adequate insights regarding health and well-being considerations. This study investigated students’ perceptions concerning how university students experience health and well-being. The study is underpinned using the biopsychosocial model of health and was conducted via a cross-sectional student survey with quantitative and open-ended questions at an Australian university in 2021. A hierarchical regression model with 625 respondents indicated health and well-being are significantly influenced by mental (t-value = 15.7, p < 0.001), physical (t-value = 9.48, p < 0.001), university learning (t-value = 5.16, p < 0.001), and economic (t-value = 4.78, p < 0.001) domains regardless of the demographic and study characteristics of students. Students’ perception of their health and well-being varied according to student age, the college of study, and whether they were an international student. Both the quantitative and qualitative findings supported that the mental, physical, university learning, and economic domains of students’ health and well-being are interdependent. There is a case for a proactive, continuous, inclusive, and holistic health and well-being approach to support student success in higher education.
Even-Order Taylor Approximation-Based Feature Refinement and Dynamic Aggregation Model for Video Object Detection
Authors: Chen Liule , Li Jianqiang , Li Yunyu , Zhao Qing
Published: October 2023

Video object detection (VOD) is a sophisticated visual task. It is a consensus that is used to find effective supportive information from correlation frames to boost the performance of the model in VOD tasks. In this paper, we not only improve the method of finding supportive information from correlation frames but also strengthen the quality of the features extracted from the correlation frames to further strengthen the fusion of correlation frames so that the model can achieve better performance. The feature refinement module FRM in our model refines the features through the key–value encoding dictionary based on the even-order Taylor series, and the refined features are used to guide the fusion of features at different stages. In the stage of correlation frame fusion, the generative MLP is applied in the feature aggregation module DFAM to fuse the refined features extracted from the correlation frames. Experiments adequately demonstrate the effectiveness of our proposed approach. Our YOLOX-based model can achieve 83.3% AP50 on the ImageNet VID dataset.
NAVIBox: Real-Time Vehicle–Pedestrian Risk Prediction System in an Edge Vision Environment
Authors: Lee Hyejin , Cho Haechan , Noh Byeongjoon , Yeo Hwasoo
Published: October 2023

This study introduces a novel system, termed NAVIBox, designed to proactively identify vehicle–pedestrian risks using vision sensors deployed within edge computing devices in the field. NAVIBox consolidates all operational components into a single unit, resembling an intelligent CCTV system, and is built upon four core pipelines: motioned-video capture, object detection and tracking, trajectory refinement, and predictive risk recognition and warning decision. The operation begins with the capture of motioned video through a frame difference approach. Road users are subsequently detected, and their trajectories are determined using a deep learning-based lightweight object detection model, in conjunction with the Centroid tracker. In the trajectory refinement stage, the system converts the perspective of the original image into a top view and conducts grid segmentation to capture road users’ behaviors precisely. Lastly, vehicle–pedestrian risks are predicted by analyzing these extracted behaviors, and alert signals are promptly dispatched to drivers and pedestrians when risks are anticipated. The feasibility and practicality of the proposed system have been verified through implementation and testing in real-world test sites within Sejong City, South Korea. This systematic approach presents a comprehensive solution to proactively identify and address vehicle–pedestrian risks, enhancing safety and efficiency in urban environments.
Comparative Study of Sentinel-1-Focused and Simulated SAR Images Using LiDAR Point Cloud Modeling for Coastal Areas
Authors: Jia Haowen , Yang Pengju , Wu Rui
Published: October 2023

Aiming at SAR imaging for large coastal scenes, a comprehensive comparative study is performed based on Sentinel-1 raw data, SAR imaging simulation, and Google Maps. A parallel Range-Doppler (RD) algorithm is developed and applied to focus Sentinel-1 raw data for large coastal scenes, and the focused SAR image is compared with the multi-look-processed SAR image using SNAP 9.0.0 software, as well as the corresponding areas of Google Maps. A scheme is proposed to convert the LiDAR point cloud data of the coast into a 3D coastal area digital elevation model (DEM), and a tailored 3D model suitable for RaySAR simulator is obtained after statistical outlier removal (SOR) denoising and down-sampling processing. Comparison results show good agreements, which verify the effectiveness of the parallel RD algorithm as well as the backward ray-tracing-based RaySAR simulator, which serves as a powerful SAR imaging tool due to its high efficiency and flexibility. The cosine similarity between the RaySAR-simulated SAR image and Google Maps achieves 0.93, while cosine similarity reaches 0.85 between Sentinel-1 SAR-focused images with our parallel RD algorithm and multi-look SAR image processed using SNAP software. This article can provide valuable assistance for SAR system performance evaluation, SAR imaging algorithm improvement, and remote sensing applications.
Large-Scale β-Ga2O3 Trench MOS-Type Schottky Barrier Diodes with 1.02 Ideality Factor and 0.72 V Turn-On Voltage
Authors: He Hao , Zhou Xinlong , Liu Yinchi , Liu Wenjing , Yang Jining , Zhang Hao , Xie Genran , Liu Wenjun
Published: October 2023

β-Ga2O3 Schottky barrier diodes (SBDs) suffer from the electric field crowding and barrier height lowering effect, resulting in a low breakdown voltage (BV) and high reverse leakage current. Here, we developed β-Ga2O3 trench MOS-type Schottky barrier diodes (TMSBDs) on β-Ga2O3 single-crystal substrates with halide vapor phase epitaxial layers based on ultraviolet lithography and dry etching. The 1/C2−V  plots are deflected at 2.24 V, which is caused by the complete depletion in the mesa region of the TMSBDs. A close-to-unity ideality factor of 1.02 and a low turn-on voltage of 0.72 V are obtained. This is due to the low interface trap density in the metal/semiconductor interface of TMSBDs, as confirmed by the current–voltage (I–V) hysteresis measurements. The specific on-resistance calculated with the actual Schottky contact area increases as the area ratio (AR) increases because of the current spreading phenomenon. Furthermore, the reverse leakage current of the TMSBDs is smaller and the BV is increased by 120 V compared with the regular SBD. This work paves the way for further improving the overall performance of β-Ga2O3 TMSBDs.
A Novel Approach for Improving the Security of IoT–Medical Data Systems Using an Enhanced Dynamic Bayesian Network
Authors: Almaiah Mohammed Amin , Yelisetti Sandeep , Arya Leena , Babu Christopher Nelson Kennedy , Kaliappan Kumaresan , Vellaisamy Pandimurugan , Hajjej Fahima , Alkdour Tayseer
Published: October 2023

IoT (Internet of Things) devices are increasingly being used in healthcare to collect and transmit patient data, which can improve patient outcomes and reduce costs. However, this also creates new challenges for data security and privacy. Thus, the major demand for secure and efficient data-sharing solutions has prompted significant attention due to the increasing volume of shared sensor data. Leveraging a data-fusion-based paradigm within the realm of IoT-protected healthcare systems enabled the collection and analysis of patient data from diverse sources, encompassing medical devices, electronic health records (EHRs), and wearables. This innovative approach holds the potential to yield immediate benefits in terms of enhancing patient care, including more precise diagnoses and treatment plans. It empowers healthcare professionals to devise personalized treatment regimens by amalgamating data from multiple origins. Moreover, it has the capacity to alleviate financial burdens, elevate healthcare outcomes, and augment patient satisfaction. Furthermore, this concept extends to fortifying patient records against unauthorized access and potential misuse. In this study, we propose a novel approach for secure transmission of healthcare data, amalgamating the improved context-aware data-fusion method with an emotional-intelligence-inspired enhanced dynamic Bayesian network (EDBN). The findings indicated that F1 score, accuracy, precision, recall, and ROC-AUC score using DCNN were 89.3%, 87.4%, 91.4%, 92.1%, and 0.56, respectively, which was second-highest to the proposed method. On the other hand, the F1 score, accuracy, precision, recall, and ROC-AUC scores of FRCNN and CNN were low in accuracy at 83.2% and 84.3%, respectively. Our experimental investigation demonstrated superior performance compared with existing methods, as evidenced by various performance metrics, including recall, precision, F measures, and accuracy.
Design and Implementation of Enabling SQL–Query Processing for Ethereum-Based Blockchain Systems
Authors: Han Jongbeen , Seo Yunhyeong , Lee Sangjin , Kim Sunggon , Son Yongseok
Published: October 2023

A blockchain is designed to establish consistent and reliable agreements in an untrusted and decentralized environment. In addition, the blockchain enables transaction processing and the creation of smart contracts. It empowers end users to execute contracts without any intermediate entities. However, there are some issues when it comes to retrieving information, such as the state and history of smart contracts and regular transactions in the blockchain. For example, in a smart contract, user-defined data structures can be used to recall the state of the smart contract for a range query, which can decrease the general performance. In addition, an external database can be required to retrieve regular transactions for range queries, which increases management costs. To achieve this, we propose a new scheme that enables SQL query operations to retrieve a smart contract and regular transaction information within the blockchain system. To achieve this, we combine an embedded relational database with an Ethereum-based blockchain system to provide the SQL query. It enables range queries on smart contracts without requiring user-defined data structures and decreases management costs for regular transactions without any external database. We implement the proposed blockchain system on quorum, which is an Ethereum-based blockchain system. Also, we evaluate the proposed system using a synthetic benchmark. The performance of retrieving smart contract data is improved by up to approximately 22×, with low memory usage compared with the existing system. Moreover, the proposed system demonstrates a similar search performance to the existing system, even when considering external databases in regular transactions.
Augmenting Beam Alignment for mmWave Communication Systems via Channel Attention
Authors: Kim Jihyung , Kim Junghyun
Published: October 2023

The beamforming technique has attracted considerable attention in wireless communication due to its various advantages such as interference reduction and improved wireless resource efficiency. However, the beam alignment between transmitting and receiving devices, which is fundamentally required for the beamforming, poses a significant challenge due to the continuous variability of the wireless channel. Recently, a deep learning-based technique has been proposed to predict narrow beam indices by measuring wide beams. However, there is room for improvement in the performance of the neural network architecture employed in this technique. Therefore, we suggest a novel deep learning model architecture that incorporates a channel attention module for beam training. The simulation results show a significant enhancement in performance with our scheme compared to both a state-of-the-art approach and other existing methods across all scenarios. Particularly, we confirm that even when reducing the number of wide beams used for measurement by approximately 50%, our proposed approach achieves a performance close to that of the state-of-the-art scheme.
The Markov Concept of the Energy Efficiency Assessment of the Edge Computing Infrastructure Peripheral Server Functioning over Time
Authors: Kovtun Viacheslav , Altameem Torki , Al-Maitah Mohammed , Kempa Wojciech
Published: October 2023

The article is devoted to the research of the peripheral server energy consumption managing process defined based on the threshold policy by manipulating the values of the characteristic parameters of the arithmetic-logical complex of the latter. The research object is formalized by a Markov queue model with a single-threshold control scheme for the intensity of accepted requests service. A characteristic feature of the life cycle of a peripheral server is the non-stationary mode of operation in terms of energy consumption, due to the need to periodically poll the controlled sensor network and process the received data in real-time. To take into account this circumstance, the intensities of transitions in the heterogeneous birth-and-death Markov process of the created model are interpreted as non-random periodic functions of time. The resulting mathematical apparatus is summarized in the metric that allows us to estimate the target peripheral server’s energy consumption both at a specific moment and for a censored time interval (this distinguishes the obtained result from existing analogs).
VNF Migration in Digital Twin Network for NFV Environment
Authors: Hu Ying , Min Guanbo , Li Jianyong , Li Zhigang , Cai Zengyu , Zhang Jie
Published: October 2023

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtual Network Functions (VNFs), adapting services to the complex and dynamic network environment to enhance network performance. However, VNF migration and energy consumption pose significant challenges due to the dynamic nature of the physical network. In order to maximize the acceptance rate of Service Function Chain Requests (SFCR), and reduce VNF migration and energy consumption as much as possible, we summarize several related factors such as the node hosting state, link hosting state, energy consumption, migrated nodes, and whether the mapping is successful. We define the Markov decision process by considering the factors mentioned above. Next, we design the VNF migration algorithm utilizing actor–critic models, graph convolution networks, and LSTM networks. In order to reduce the risk of trial and error during training and prediction in deep reinforcement learning scenarios, we designed a network architecture based on a digital twin (DT). In simulation experiments, compared with the FF algorithm that greedily selects the first available node, our AC_GCN algorithm significantly improves the acceptance rate of SFC requests by 2.9 times more than the FF algorithm in small topology experiments, and 27 times more than the FF algorithm in large topology experiments. Compared with the deep reinforcement learning (DRL) algorithm, which does not consider all the above factors together, for the small topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of request acceptance rate by 13%, underperforms compared to the DRL algorithm in terms of energy consumption by 3.8%, and underperforms compared to the DRL algorithm in terms of the number of migrated nodes for 22%; for the large topology experiment, our AC_GCN algorithm outperforms the DRL algorithm in terms of the request acceptance rate by 7.7%, outperforms the DRL algorithm in terms of energy consumption by 0.4%, and outperforms the DRL algorithm in terms of the number of migrated nodes by 1.6%.
Machine Learning for Energy-Efficient Fluid Bed Dryer Pharmaceutical Machines
Authors: Barriga Roberto , Romero Miquel , Hassan Houcine
Published: October 2023

The pharmaceutical industry is facing significant economic challenges due to measures aimed at containing healthcare costs and evolving healthcare regulations. In this context, pharmaceutical laboratories seek to extend the lifespan of their machinery, particularly fluid bed dryers, which play a crucial role in the drug production process. Older fluid bed dryers, lacking advanced sensors for real-time temperature optimization, rely on fixed-time deterministic approaches controlled by operators. To address these limitations, a groundbreaking approach taking into account Exploration Data Analysis (EDA) and a Catboost machine-learning model is presented. This research aims to analyze and enhance a drug production process on a large scale, showcasing how AI algorithms can revolutionize the manufacturing industry. The Catboost model effectively reduces preheating phase time, resulting in significant energy savings. By continuously monitoring critical parameters, a paradigm shift from the conventional fixed-time models is achieved. It has been shown that the model is able to predict on average a reduction of 50.45% of the preheating process duration and up to 59.68% in some cases. Likewise, the energy consumption of the fluid bed dryer for the preheating process could be reduced on average by 50.48% and up to 59.76%, which would result on average in around 3.120 kWh energy consumption savings per year.
Investigations into the Influence of Matrix Dimensions and Number of Iterations on the Percolation Phenomenon for Direct Current
Authors: Zukowski Pawel , Okal Paweł , Kierczynski Konrad , Rogalski Przemyslaw , Borucki Sebastian , Kunicki Michał , Koltunowicz Tomasz N.
Published: October 2023

The paper presents studies of the site percolation phenomenon for square matrixes with dimensions L = 55, 101 and 151 using the Monte Carlo computer simulation method. The number of iterations for each matrix was 5 × 106. An in-depth analysis of the test results using the metrological approach consisting of determining the uncertainty of estimating the results of iterations with statistical methods was performed. It was established that the statistical distribution of the percolation threshold value is a normal distribution. The coefficients of determination for the simulation results in approximations of the percolation threshold using the normal distribution for the number of iterations 5 × 106 are 0.9984, 0.9990 and 0.9993 for matrixes with dimensions 55, 101 and 151, respectively. The average value of the percolation threshold for relatively small numbers of iterations varies in a small range. For large numbers of iterations, this value stabilises and practically does not depend on the dimensions of the matrix. The value of the standard deviation of the percolation threshold for small numbers of iterations also fluctuates to a small extent. For a large number of iterations, the standard deviation values reach a steady state. Along with the increase in the dimensions of the matrix, there is a clear decrease in the value of the standard deviation. Its value is about 0.0243, about 0.01 and about 0.012 for matrixes with dimensions 55, 101 and 151 for the number of iterations 5 × 106. The mean values of the percolation threshold and the uncertainty of its determination are (0.5927046 ± 1.1 × 10−5), (0.5927072 ± 7.13 × 10−6) and (0.5927135 ± 5.33 × 10−6), respectively. It was found that the application of the metrological approach to the analysis of the percolation phenomenon simulation results allowed for the development of a new method of optimizing the determination and reducing the uncertainty of the percolation threshold estimation. It consists in selecting the dimensions of the matrix and the number of iterations in order to obtain the assumed uncertainty in determining the percolation threshold. Such a procedure can be used to simulate the percolation phenomenon and to estimate the value of the percolation threshold and its uncertainty in matrices with other matrix shapes than square ones.
Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach
Authors: Shu Tong , Joo Young Hoon
Published: October 2023

A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines’ affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible.
Forest Supply Chain for Bioenergy: An Approach for Biomass Study in the Framework of a Circular Bioeconomy
Authors: Manrique Silvina M. , Subelza Carolina R. , Toro María Antonia , Quintero Bertel Quelbis R. , Tauro Raúl J.
Published: October 2023

To ensure the long-term viability of a circular bioeconomy based on native forests, it is crucial to enhance our understanding and overcome existing disparities in knowledge and application throughout the entire value chain of forest products. The objective of this article is to contribute towards this goal and facilitate the proper management of forest biomass. Firstly, a methodology is proposed for the study of biomass throughout the native forest value chain, identifying the main steps, criteria, and variables to consider. This approach is evaluated through a case study in Argentina, where over 2370 tons of biomass are wasted annually. A series of strategies for analyzing the most suitable uses and applications for this biomass are examined. Finally, some key approaches for the promotion of a circular and sustainable forest bioeconomy are identified. While it is true that there is still a long way to go before small rural economies can make a more efficient and comprehensive use of their resources (potentially including small biorefineries) with appropriate cascade use schemes, moving towards biomass energy use constitutes a practical and concrete alternative today. This proposal provides tools for accelerating this necessary ecological and energy transition.
The Effect of Foam-Recycle on Ammonium Removal by Aerobic Denitrification Using Alcaligenes faecalis No. 4
Authors: Lee Kwanyong , Joo Hung-Soo
Published: October 2023

Aerobic denitrifier Alcaligenes faecalis No. 4 removes ammonium-nitrogen to nitrogen gas via denitrification in a single aerobic condition. In our previous studies, factors such as ammonium removal rate, denitrification ratio, and cell growth were tested in various conditions. The removal pathway from ammonium to nitrogen gas still needs to be determined in detail. To clarify this pathway of Alcaligenes faecalis No. 4, we in this study investigated the effects of several factors on ammonium removal, such as foam-recycle, initial pH, initial ammonium concentration, and airflow rate. Denitrification ratio was improved by up to 23% through foam-recycle. The improvement of the denitrification ratio was resulted by the higher enzyme activity of hydroxylamine oxidoreductase (HAO) in the produced foam, which was about 28 times higher than that in the culture broth (i.e., without foam-recycle). The stripped ammonia was significantly high (above pH 9). The initial ammonium concentration and airflow rate also influenced the denitrification ratio.