bluuebunny/crossref_metadata_2025 huggingface dataset:
Demystifying the Influencing Factors of Construction 4.0 Technology Implementation from a Sustainability Starting Point: Current Trends and Future Research Roadmap
Authors: Zhang Qian , Liu Chang , Zhu Wenhui , Mei Shiqi
Published: February 2024

Given the challenges of innovation and adaptation to change, Construction 4.0 (C4.0) is triggering a revolution within construction and industry firms from automation to a greater level of digitalization. Despite the plethora of advantages and growing research interest in certain aspects of C4.0 technology implementation (C4.0TeIm), previous discourses have been largely fragmented and lack a comprehensive investigation of the factors influencing C4.0TeIm. To this end, this study aims to holistically investigate the influencing factors of C4.0TeIm and propose guidelines for future research directions. Informed by the United Nations twin green and digital transition perspectives, this study initiated its exploration in the background by delving into the potential intersections between C4.0 and sustainability. To achieve the aim, this study (i) reviewed 77 relevant articles and discerned a comprehensive list of factors influencing C4.0TeIm; (ii) outlined and quantified the influence and importance of the identified factors using social network analysis and validated results against the simplified analysis; and (iii) revealed gaps in the literature and proposed a research roadmap directing future research needs. The results show that 60 factors could collectively influence construction firms’ C4.0TeIm; they can be categorized into the external environment, technology competence, organizational factors, project-based factors, and technology challenges. The findings also reveal that further endeavors should emphasize those understudied factors such as “perceived overall organizational performance improvement”, “corporate strategy and management policy”, and “availability of resources”. Practically, the proposed research guidelines provide valuable references to accelerate C4.0TeIm in both academics and the business world and offer strategies for the top management of firms to maximize potential benefits and gain competitiveness.
Research on the Influence of Open Underground Space Entrance Forms on the Microclimate: A Case Study in Xuzhou, China
Authors: Chen Ping , Nie Lufeng , Kang Jinrun , Liu Heng
Published: February 2024

With urban development and renewal, underground space is becoming more utilized. The design and use of open underground public space entrances and exits have become more and more frequent. As a pedestrian passage connecting indoors and outdoors, the wind and thermal environment of open entrances have a great impact on human comfort. This paper investigates the open underground space entrances and exits in Xuzhou. Physical environments such as temperature and wind speed were measured. Through numerical simulation, the influence relationships between the spatial form elements of open entrances and exits and microclimate and thermal comfort were investigated. This study showed that there are four common spatial morphological elements of open entrances and exits. The physiologicafl equivalent temperature (PET) of the outdoor part of the entrance is the highest in summer, and the lowest in winter, and the PET is most affected by the shape of the opening plane and the aspect ratio, which are linearly related. The trends of the spatial morphology elements were not consistent when seeking the optimal situation of PET in summer and winter, respectively. The relationship between the spatial form elements of entrances and PET established in this study provides technical guidance for the design of open entrances, which can help improve environmental quality and enhance human comfort.
Trends, Problems, and Solutions from Point Cloud via Non-Uniform Rational Basis Spline to Building Information Modelling: Bibliometric and Systematic Study
Authors: Stober Dina , Raguz-Lucic Nikolina
Published: February 2024

Building Information Modelling (BIM) has found applications not only in the planning and designing of new buildings but also in project monitoring, record-keeping, and analyses of existing structures. In addition to its primary application, information modelling spreads to other areas, developing solutions for their specific uses. This study engaged a mixed-method literature review according to the extent of the pool of knowledge for two research steps between three concepts—from point cloud to NURBS and then from NURBS to BIM. In the first step, the keywords point cloud and NURBS were analysed using scientometric methods. In the second step, a systematic analysis of the content of works obtained with the keywords NURBS and BIM is presented, extracting problems and proposed solutions for information transfer technology. The results of a quantitative analysis identified major trends, (1) research is distributed in interdisciplinary and multidisciplinary areas and historical (heritage) modelling is the highlighted one, (2) development of technologies for object surveying, and (3) the application of data in different engineering fields, while a qualitative analysis points at problems in (1) model building, (2) interoperability, and (3) automatization. Solutions such as mixed models, multi-layered models, a mix of formats, or bridging elements (semantics, proto-model) are proposed.
Removal of Crystal Violet Dye from Aqueous Solutions through Adsorption onto Activated Carbon Fabrics
Authors: Mulla Batuhan , Ioannou Kyriacos , Kotanidis Gkerman , Ioannidis Ioannis , Constantinides Georgios , Baker Mark , Hinder Steven , Mitterer Christian , Pashalidis Ioannis , Kostoglou Nikolaos , Rebholz Claus
Published: February 2024

The removal of contaminants from aqueous solutions by adsorption onto carbonaceous materials has attracted increasing interest in recent years. In this study, pristine and oxidized activated carbon (AC) fabrics with different surface textures and porosity characteristics were used for the removal of crystal violet (CV) dye from aqueous solutions. Batch adsorption experiments were performed to investigate the CV adsorption performance of the AC fabrics in terms of contact time, temperature, adsorbate concentration and adsorbent amount. Evaluation of the thermodynamic parameters and the adsorption performance of the AC fabrics in ground water and sea water solutions were also carried out. Langmuir isotherm model, pseudo first and pseudo second order kinetics models were utilized to analyze and fit the adsorption data. The introduction of oxygen-based functional groups on the surface of AC fabrics was carried out through a nitric acid treatment. This oxidation process resulted in a significant reduction in the surface area and pore volume, along with a small increase in the average pore size and a significant enhancement in the CV adsorption capacity, indicating that the dye molecules are mainly adsorbed on the external surface of the carbon fabrics. The herein evaluated 428 mg/g adsorption capacity at 55 °C for the oxidized non-woven AC fabric is one of the highest adsorption capacity values reported in the literature for CV removal using AC materials. Thermodynamic studies showed that the adsorption occurs spontaneously and is an endothermic and entropy-driven reaction. Furthermore, pristine and oxidized non-woven AC fabrics displayed more than 90% CV uptake from sea water samples, underlining the great potential these fabrics possess for the removal of dyes from natural/multicomponent waters.
The Wnt Co-Receptor PTK7/Otk and Its Homolog Otk-2 in Neurogenesis and Patterning
Authors: Tan Qian Hui , Otgonbaatar Agimaa , Kaur Prameet , Ga Angelica Faye , Harmston Nathan P. , Tolwinski Nicholas S.
Published: February 2024

Wnt signaling is a highly conserved metazoan pathway that plays a crucial role in cell fate determination and morphogenesis during development. Wnt ligands can induce disparate cellular responses. The exact mechanism behind these different outcomes is not fully understood but may be due to interactions with different receptors on the cell membrane. PTK7/Otk is a transmembrane receptor that is implicated in various developmental and physiological processes including cell polarity, cell migration, and invasion. Here, we examine two roles of Otk-1 and Otk-2 in patterning and neurogenesis. We find that Otk-1 is a positive regulator of signaling and Otk-2 functions as its inhibitor. We propose that PTK7/Otk functions in signaling, cell migration, and polarity contributing to the diversity of cellular responses seen in Wnt-mediated processes.
Corrosion Performance of Epoxy/Sulfur–Selenium Coating on Q235 Steel
Authors: Meng Konglan , Wei Wei , Wei Kunxia , Alexandrov Igor V. , An Xulong , Wang Dandan , Liu Xiangkui
Published: February 2024

Sulfur powder (99.99%) and selenium powder (99.99%) were mixed and heated to approximately 300 °C to obtain an S-Se alloy. It has good flowability at 130 °C and can be applied to Q235 steel to obtain a S-Se coating. Epoxy was used as a filler, and the S-Se alloy was applied as a coating. This combination was utilized to create the composite coatings of epoxy/sulfur–selenium (E/S-Se). To investigate the corrosion resistance of this coating on Q235 steel substrate, we conducted measurements and obtained electrochemical impedance spectra (EIS) and linear polarization curves (LPC). These measurements were performed in a three-electrode cell within an electrochemical workstation using a 3.5 wt.% NaCl aqueous solution. By comparing bare Q235 steel, S-Se, and E/S-Se, the study found that the E/S-Se coating had a higher self-corrosion potential (−0.484 V vs. SCE) and the lowest self-corrosion current density (2.361 × 10−11 A/cm2). The purpose was to simulate the corrosive environment experienced by condensate return pipe walls in petroleum refining equipment. Additionally, experiments were carried out using 0.01 mol/L HCl solution as the corrosion medium at different temperatures (40 °C, 60 °C, 80 °C). The results indicated that the E/S-Se coating exhibited a lower corrosion rate compared to the Q235 steel substrate. Under immersion conditions at 40 °C and 60 °C, no corrosive substances were detected on the surface of the coating. The test results demonstrated that the E/S-Se coating exhibited superior corrosion resistance compared to the Q235 substrate, providing up to 99% protection for the substrate.
Long-Term Comparison of Two- and Three-Dimensional Computed Tomography Analyses of Cranial Bone Defects in Severe Parietal Thinning
Authors: Pallua Johannes Dominikus , Pallua Anton Kasper , Streif Werner , Spiegl Harald , Halder Clemens , Arora Rohit , Schirmer Michael
Published: February 2024

Parietal thinning was detected in a 72-year-old with recurrent headaches. Quantification of bone loss was performed applying two- and three-dimensional methods using computerized tomographies. Two-dimensional methods provided accurate measurements using single-line analyses of bone thicknesses (2.13 to 1.65 and 1.86 mm on the left and 4.44 to 3.08 and 4.20 mm on the right side), single-point analyses of bone intensities (693 to 375 and 403 on the left and 513 to 393 and 411 Houndsfield Units on the right side) and particle-size analyses of low density areas (16 to 22 and 12 on the left and 18 to 23 and 14 on the right side). Deteriorations between days 0 and 220 followed by bone stability on day 275 were paralleled using the changed volumes of bone defects to 1200 and finally 1133 mm3 on the left side and to 331 and finally 331 mm3 on the right side. Interfolding as measurement of the bones’ shape provided changes to −1.23 and −1.72 mm on the left and to −1.42 and −1.30 mm on the right side. These techniques suggest a stabilizing effect of corticosteroids between days 220 and 275. Reconstruction of computerized tomographies appears justified to allow for quantification of bone loss during long-term follow-up.
Salivary Brain-Derived Neurotrophic Factor and Cortisol Associated with Psychological Alterations in University Students
Authors: Ballestar-Tarín María Luisa , Ibáñez-del Valle Vanessa , Mafla-España Mayra Alejandra , Navarro-Martínez Rut , Cauli Omar
Published: February 2024

Introduction: Recent evidence reported mental health issues in university students such as anxiety and depressive symptoms and poor sleep quality. Decreased plasma brain-derived neurotrophic factor (BDNF) levels have been proposed as a biomarker of depressive symptoms, whereas cortisol levels are an index of energy mobilization and stress and have been linked to sleep quality. Given that salivary biomarkers represent an interesting new field of research, the aim of this cross-sectional study was to evaluate salivary BDNF and cortisol levels in university students to assess whether they have associations with psychological disturbances such as anxiety and depressive symptoms, sleep quality, and stress level. Methods: Salivary BDNF and cortisol levels were measured by specific immunoassays in 70 students whose mental health was also evaluated on the same day through the evaluation of anxiety and depression symptoms (Goldberg scale), sleep quality (Pittsburg Sleep Quality Index and Athens Insomnia Scale), and stress (self-perceived stress scale) and healthy lifestyle habits (alcohol consumption, smoking, regular exercise, and body mass index) were also measured. Multivariate regression analyses were performed in order to identify the strengths of associations between psychological alterations and the concentrations of BDNF, cortisol, and other variables. Results: Salivary BDNF levels were significantly higher in students with more depressive symptoms, whereas no significant differences were found for cortisol levels. When performing the binary logistic regression model, BDNF levels are included as a predictor variable for a high-depressive-symptoms burden (p < 0.05). Students with worse sleep quality on the Pittsburg Scale had higher cortisol levels (p < 0.05). The subdomains of sleep latency and sleep medication were those significantly associated with salivary cortisol levels in logistic regression analyses (OR = 15.150, p = 0.028). Sleep medication only appeared to be related to cortisol levels (OR = 185.142, p = 0.019). Perceived stress levels and anxiety symptoms were not associated with BDNF or cortisol levels. Conclusions: BDNF could play a key role in the pathophysiology of mood-related disorders, and elevation of its peripheral levels could contribute to protecting neurons from the development of mental illness. Higher salivary cortisol levels measured in the morning are accompanied by poorer sleep quality. More research is needed, focusing on salivary biomarkers of disorders related to depressive symptoms and poor sleep quality as a potential tool for the diagnosis and prevention of mental illness.
Could Tumor Necrosis Factor Serve as a Marker for Cardiovascular Risk Factors and Left Ventricular Hypertrophy in Patients with Early-Onset Coronary Artery Disease?
Authors: Białecka Marta , Dziedziejko Violetta , Safranow Krzysztof , Krzystolik Andrzej , Marcinowska Zuzanna , Chlubek Dariusz , Rać Monika
Published: February 2024

Introduction: Tumor necrosis factor (TNF), a pro-inflammatory cytokine, can be produced by cardiomyocytes, leading to metabolic disorders in the myocardium. The objective of this study was to assess the relationship between plasma levels of the TNF cytokine and the presence of known biochemical and clinical risk factors for cardiovascular disease, along with the parameters of cardiac morphology in patients diagnosed with coronary artery disease (CAD) at a young age. Materials and Methods: The study group included 75 men aged up to 50 years and 25 women aged up to 55 years. The plasma TNF concentration was measured by use of the ELISA assay. Echocardiography and electrocardiographic examinations were performed in all patients. Results: We observed positive correlations for TNF with the BMI ratio, weight, waist and hip circumference. We also found negative correlations for TNF with HDL levels and ApoA concentrations, and positive correlations with the ApoB/ApoA1 ratio, Apo B, IL6, LDL and TG concentrations. These results suggest an association between higher plasma TNF concentrations and components of metabolic syndrome, including dyslipidemia. TNF may be a potential risk factor for impaired diastolic function. Conclusions: While TNF may be useful for diagnosing certain risks in CAD patients, the TNF measurement cannot be used as a surrogate test for echocardiography.
Enhancing Cervical Cancer Screening: Review of p16/Ki-67 Dual Staining as a Promising Triage Strategy
Authors: Ouh Yung-Taek , Kim Ho , Yi Kyong , Lee Nak-Woo , Kim Hai-Joong , Min Kyung-Jin
Published: February 2024

Cervical cancer, primarily caused by high-risk human papillomavirus (HR-HPV) types 16 and 18, is a major global health concern. Persistent HR-HPV infection can progress from reversible precancerous lesions to invasive cervical cancer, which is driven by the oncogenic activity of human papillomavirus (HPV) genes, particularly E6 and E7. Traditional screening methods, including cytology and HPV testing, have limited sensitivity and specificity. This review explores the application of p16/Ki-67 dual-staining cytology for cervical cancer screening. This advanced immunocytochemical method allows for simultaneously detecting p16 and Ki-67 proteins within cervical epithelial cells, offering a more specific approach for triaging HPV-positive women. Dual staining and traditional methods are compared, demonstrating their high sensitivity and negative predictive value but low specificity. The increased sensitivity of dual staining results in higher detection rates of CIN2+ lesions, which is crucial for preventing cervical cancer progression. However, its low specificity may lead to increased false-positive results and unnecessary biopsies. The implications of integrating dual staining into contemporary screening strategies, particularly considering the evolving landscape of HPV vaccination and changes in HPV genotype prevalence, are also discussed. New guidelines and further research are necessary to elucidate the long-term effects of integrating dual staining into screening protocols.
Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review
Authors: Hermosilla Pamela , Soto Ricardo , Vega Emanuel , Suazo Cristian , Ponce Jefté
Published: February 2024

In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.
Successful Implementation of HITOC and HIPEC in the Management of Advanced Ovarian Carcinoma with Pleural and Peritoneal Carcinomatosis
Authors: Moldovan Bogdan , Saon Codin Theodor , Adam Iris-Iuliana , Pisica Radu-Mihai , Silaghi Vlad Teodor , Untaru Vlad , Stoica Doly , Crisan Madalina , Popianas Andreea , Pescaru Florentina , Zolog Adriana , Vecerzan Liliana
Published: February 2024

This case report details the application and outcomes of a novel therapeutic approach involving hyperthermic intraperitoneal chemotherapy (HIPEC) and hyperthermic intrathoracic chemotherapy (HITOC) in a single patient diagnosed with advanced ovarian neoplasm. The treatment protocol included pleural cytoreductive surgery (CRS) and HITOC followed by a second surgical intervention consisting of peritoneal CRS and HIPEC. HIPEC targeted the intraperitoneal space with heated chemotherapy, while HITOC extended the thermal perfusion to the thoracic cavity. The patient has shown significant progression in disease-free survival over one year and eight months of observation, demonstrating lower recurrence rates and an overall survival outcome exceeding expectations based on conventional therapy outcomes. The combined modality demonstrated a manageable toxicity profile, with no significant increase in peri- or postoperative complications observed.
Noninvasive Models to Assess Liver Inflammation and Fibrosis in Chronic HBV Infected Patients with Normal or Mildly Elevated Alanine Transaminase Levels: Which One Is Most Suitable?
Authors: Ma Shasha , Zhou Lian , Lin Shutao , Li Mingna , Luo Jing , Chen Lubiao
Published: February 2024

The prevalence of substantial inflammation or fibrosis in treatment-naïve patients with chronic hepatitis B (CHB) and normal alanine transaminase (ALT) levels is high. A retrospective analysis was conducted on 559 consecutive patients with hepatitis B virus infection, who underwent liver biopsy, to investigate the value of noninvasive models based on routine serum markers for evaluating liver histology in CHB patients with normal or mildly elevated ALT levels and to provide treatment guidance. After comparing 55 models, we identified the top three models that exhibited excellent performance. The APGA model, based on the area under the receiver operating characteristic curve (AUROC), demonstrated a superior ability to evaluate significant (AUROC = 0.750) and advanced fibrosis (AUROC = 0.832) and demonstrated a good performance in assessing liver inflammation (AUROCs = 0.779 and 0.874 for stages G ≥ 2 and G ≥ 3, respectively). APGA also exhibited significant correlations with liver inflammation and fibrosis stage (correlation coefficients, 0.452 and 0.405, respectively (p < 0.001)). When the patients were stratified into groups based on HBeAg status and ALT level, APGA consistently outperformed the other 54 models. The other top two models, GAPI and XIE, also outperformed models based on other chronic hepatitis diseases. APGA may be the most suitable option for detecting liver fibrosis and inflammation in Chinese patients with CHB.
Conference Report: The FutuRE oF MinimalLy InvasivE GI and Capsule DiagnosTics (REFLECT) Nyborg, Denmark, October 2023
Authors: Selnes Ola , Thorndal Camilla , Hansen Lea Østergaard , Eskemose Sebastian Radic , Koulaouzidis Anastasios
Published: February 2024

The gastrointestinal (GI) tract, particularly the small bowel (SB), can be challenging for novel investigation tools [...]
Unraveling Chronic Cardiovascular and Kidney Disorder through the Butterfly Effect
Authors: Bedo Dimitri , Beaudrey Thomas , Florens Nans
Published: February 2024

Chronic Cardiovascular and Kidney Disorder (CCKD) represents a growing challenge in healthcare, characterized by the complex interplay between heart and kidney diseases. This manuscript delves into the “butterfly effect” in CCKD, a phenomenon in which acute injuries in one organ lead to progressive dysfunction in the other. Through extensive review, we explore the pathophysiology underlying this effect, emphasizing the roles of acute kidney injury (AKI) and heart failure (HF) in exacerbating each other. We highlight emerging therapies, such as renin–angiotensin–aldosterone system (RAAS) inhibitors, SGLT2 inhibitors, and GLP1 agonists, that show promise in mitigating the progression of CCKD. Additionally, we discuss novel therapeutic targets, including Galectin-3 inhibition and IL33/ST2 pathway modulation, and their potential in altering the course of CCKD. Our comprehensive analysis underscores the importance of recognizing and treating the intertwined nature of cardiac and renal dysfunctions, paving the way for more effective management strategies for this multifaceted syndrome.
Possible Etiological Factors for the Development of Molar Incisor Hypomineralization (MIH) in Austrian Children
Authors: Altner Sarra , Milutinovic Ivan , Bekes Katrin
Published: February 2024

Background: Molar incisor hypomineralization (MIH) is a developmental enamel defect that primarily affects the first permanent molars and sometimes the incisors. Its increasing prevalence worldwide has raised clinical concerns, yet its exact cause remains unknown. This study aimed to assess potential factors influencing MIH development by analyzing the medical history of children aged 6 to 12 years using a questionnaire. Methods: This study included 100 children aged 6–12 years diagnosed with MIH during dental examination, and 100 age-matched children in the non-MIH (healthy) group from the Department of Pediatric Dentistry, University Clinic of Dentistry, Medical University of Vienna. The parents of the participants completed a two-page questionnaire regarding possible etiological factors of MIH. Results: The data analysis involved 100 children with MIH (mean age 8.5; ±1.3; 52% female) and 100 children in the healthy group (mean age 9.2; ±1.3; 42% female). The optimized binary logistic regression analysis revealed a significant association between MIH development and cesarean-section delivery (OR = 3; CI = [1.5–6.2]) and sixth disease (roseola) (OR = 3.5; CI = [1.5–8.0]). Conclusions: This study suggests that cesarean-section delivery and sixth disease (roseola) might increase the likelihood of MIH development in children.
A Deep Learning Approach of Intrusion Detection and Tracking with UAV-Based 360° Camera and 3-Axis Gimbal
Authors: Xu Yao , Liu Yunxiao , Li Han , Wang Liangxiu , Ai Jianliang
Published: February 2024

Intrusion detection is often used in scenarios such as airports and essential facilities. Based on UAVs equipped with optical payloads, intrusion detection from an aerial perspective can be realized. However, due to the limited field of view of the camera, it is difficult to achieve large-scale continuous tracking of intrusion targets. In this study, we proposed an intrusion target detection and tracking method based on the fusion of a 360° panoramic camera and a 3-axis gimbal, and designed a detection model covering five types of intrusion targets. During the research process, the multi-rotor UAV platform was built. Then, based on a field flight test, 3043 flight images taken by a 360° panoramic camera and a 3-axis gimbal in various environments were collected, and an intrusion data set was produced. Subsequently, considering the applicability of the YOLO model in intrusion target detection, this paper proposes an improved YOLOv5s-360ID model based on the original YOLOv5-s model. This model improved and optimized the anchor box of the YOLOv5-s model according to the characteristics of the intrusion target. It used the K-Means++ clustering algorithm to regain the anchor box that matches the small target detection task. It also introduced the EIoU loss function to replace the original CIoU loss function. The target bounding box regression loss function made the intrusion target detection model more efficient while ensuring high detection accuracy. The performance of the UAV platform was assessed using the detection model to complete the test flight verification in an actual scene. The experimental results showed that the mean average precision (mAP) of the YOLOv5s-360ID was 75.2%, which is better than the original YOLOv5-s model of 72.4%, and the real-time detection frame rate of the intrusion detection was 31 FPS, which validated the real-time performance of the detection model. The gimbal tracking control algorithm for intrusion targets is also validated. The experimental results demonstrate that the system can enhance intrusion targets’ detection and tracking range.
Performance Analysis of UAV RF/FSO Co-Operative Communication Network with Co-Channel Interference
Authors: Song Xinkang , Zhao Shanghong , Wang Xiang , Li Xin , Tian Qin
Published: February 2024

The unmanned aerial vehicle (UAV) communication network has emerged as a promising paradigm capable of independent operation and as a relay to enhance communication coverage and efficiency. However, densely distributed terrestrial base stations with shared communication frequencies inevitably generate co-channel interference (CCI). The interference effect can be effectively eliminated by implementing free-space optical (FSO) communication in the UAV communication network. This paper proposes a solution for the UAV communication network to address interference effectively, specifically by employing a hybrid millimeter-wave radio frequency (RF)/FSO communication system. The RF links serve as the primary means of communication, while the FSO links act as a backup means of communication in the case of CCI. The exact outage probability (OP) and average symbol error rate (SER) expressions are derived for the hybrid RF/FSO communication network. The decision to switch between them depends on the signal-to-interference-plus-noise ratio (SINR). Furthermore, the SINR switching threshold value, which satisfies the target SER, has been calculated numerically for the proposed model. Simulation results indicate that the proposed network notably enhances the OP and attains a signal-to-noise ratio gain of approximately 4.6 dB in the average SER, particularly in scenarios where the RF links are subjected to severe interference or adverse weather conditions, as opposed to a pure RF communication network.
Genetic-Algorithm-Aided Deep Reinforcement Learning for Multi-Agent Drone Delivery
Authors: Tarhan Farabi Ahmed , Ure Nazım Kemal
Published: February 2024

The popularity of commercial unmanned aerial vehicles has drawn great attention from the e-commerce industry due to their suitability for last-mile delivery. However, the organization of multiple aerial vehicles efficiently for delivery within limitations and uncertainties is still a problem. The main challenge of planning is scalability, since the planning space grows exponentially to the number of agents, and it is not efficient to let human-level supervisors structure the problem for large-scale settings. Algorithms based on Deep Q-Networks had unprecedented success in solving decision-making problems. Extension of these algorithms to multi-agent problems is limited due to scalability issues. This work proposes an approach that improves the performance of Deep Q-Networks on multi-agent delivery by drone problems by utilizing state decompositions for lowering the problem complexity, Curriculum Learning for handling the exploration complexity, and Genetic Algorithms for searching efficient packet-drone matching across the combinatorial solution space. The performance of the proposed method is shown in a multi-agent delivery by drone problem that has 10 agents and ≈1077 state–action pairs. Comparative simulation results are provided to demonstrate the merit of the proposed method. The proposed Genetic-Algorithm-aided multi-agent DRL outperformed the rest in terms of scalability and convergent behavior.
Historical and Current Landscapes of Autonomous Quadrotor Control: An Early-Career Researchers’ Guide
Authors: Asignacion Abner , Satoshi Suzuki
Published: February 2024

The rising demand for autonomous quadrotor flights across diverse applications has led to the introduction of novel control strategies, resulting in several comparative analyses and comprehensive reviews. However, existing reviews lack a comparative analysis of experimental results from published papers, resulting in verbosity. Additionally, publications featuring comparative studies often demonstrate biased comparisons by either selecting suboptimal methodologies or fine-tuning their own methods to gain an advantageous position. This review analyzes the experimental results of leading publications to identify current trends and gaps in quadrotor tracking control research. Furthermore, the analysis, accomplished through historical insights, data-driven analyses, and performance-based comparisons of published studies, distinguishes itself by objectively identifying leading controllers that have achieved outstanding performance and actual deployment across diverse applications. Crafted with the aim of assisting early-career researchers and students in gaining a comprehensive understanding, the review’s ultimate goal is to empower them to make meaningful contributions toward advancing quadrotor control technology. Lastly, this study identifies three gaps in result presentation, impeding effective comparison and decelerating progress. Currently, advanced control methodologies empower quadrotors to achieve a remarkable flight precision of 1 cm and attain flight speeds of up to 30 m/s.
Multivariate Stochastic Volatility Modeling via Integrated Nested Laplace Approximations: A Multifactor Extension
Authors: Nacinben João Pedro Coli de Souza Monteneri , Laurini Márcio
Published: February 2024

This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally burdensome or inefficient as the dataset size and problem complexity increase. Furthermore, issues related to chain convergence can also arise. In light of these challenges, this research aims to establish a computationally efficient approach for estimating multivariate stochastic volatility models. We propose a multifactor formulation estimated using the INLA methodology, enabling an approach that leverages sparse linear algebra and parallelization techniques. To evaluate the effectiveness of our proposed model, we conduct in-sample and out-of-sample empirical analyses of stock market index return series. Furthermore, we provide a comparative analysis with models estimated using MCMC, demonstrating the computational efficiency and goodness of fit improvements achieved with our approach.
Assessment of Teacher Motivation, Psychometric Properties of the Work Tasks Motivation Scale for Teachers (WTMST) in Spanish Teachers
Authors: Criado-Del Rey Julia , Portela-Pino Iago , Domínguez-Alonso José , Pino-Juste Margarita
Published: February 2024

The lack of motivation among teachers is of increasing concern. Consequently, identifying motivations for their teaching performance can help to improve the quality and effectiveness of educational systems. The aim of this article was to translate the Work Tasks Motivation Scale for Teachers (WTMST) into Spanish, an instrument aimed at exploring teachers’ motivation and analyzing its psychometric properties. A non-probabilistic sample of 369 teachers (71.3% women) with an age measurement of 44.93 years (SD = 9.58) was used. Exploratory factor analysis and confirmatory factor analysis were applied. The AFE suggested a first solution of four factors that explained 80.57% of the variance in the model. Likewise, a new factor analysis was generated with the extraction of a fixed number of factors indicated in the theoretical review, explaining 86.20% of the total variance. It was concluded that the WTMST is a scale that has evidence of validity and reliability, and that can be considered a valuable contribution to the evaluation of teacher motivation regarding specific tasks in Spanish-speaking populations.
Review of Industry 4.0 from the Perspective of Automation and Supervision Systems: Definitions, Architectures and Recent Trends
Authors: Folgado Francisco , Calderón David , González Isaías , Calderón Antonio
Published: February 2024

Industry 4.0 is a new paradigm that is transforming the industrial scenario. It has generated a large amount of scientific studies, commercial equipment and, above all, high expectations. Nevertheless, there is no single definition or general agreement on its implications, specifically in the field of automation and supervision systems. In this paper, a review of the Industry 4.0 concept, with equivalent terms, enabling technologies and reference architectures for its implementation, is presented. It will be shown that this paradigm results from the confluence and integration of both existing and disruptive technologies. Furthermore, the most relevant trends in industrial automation and supervision systems are covered, highlighting the convergence of traditional equipment and those characterized by the Internet of Things (IoT). This paper is intended to serve as a reference document as well as a guide for the design and deployment of automation and supervision systems framed in Industry 4.0.
Research on Implementation of a PWM Generation Algorithm for Train Stationary Stopping Frequency
Authors: Han Euntaek , Park Changsik , Kim Ikjae , Shin Dongkyoo
Published: February 2024

In industrial electronic equipment or communication equipment, a reference clock should be generated for stable operation of the equipment, which requires precise and stable reference frequency generation. As a method for generating this reference frequency, an analog method called PLL (phase locked-loop) has been devised and widely used. However, in order to make a more precise and stable reference frequency simple and economical, a DDS (direct digital synthesizer) has been developed. In this paper, we propose a stable and accurate method to generate a low frequency of the PWM method via pure logic circuit configuration without a microprocessor for digital reference frequency generation. Depending on the electronic communication equipment, the required reference frequency varies from a low frequency to a very high frequency. The reference frequency synthesis required in these frequency bands has been studied in various ways, but in industries such as railways, the low-frequency band based on the DDS method is used. In particular, it is very important to operate without a single operating error or failure in order to obtain information for stopping the train. Therefore, it is necessary to design a pure logic method that excludes a stored program type processor that minimizes the possibility of temporary interruption due to disturbance such as surge or high voltage. Therefore, through this study, the algorithm is implemented so that the duty ratio is output at 50:50, the circuit is configured so that two target frequencies are generated at the same time, and the performance is verified by generating the low-frequency band used for stopping the railway train. It was confirmed that the accuracy and stability were improved compared to the analog method used for stopping the railway train, and it was verified that the frequency resolution was superior to the similar results obtained in the digital frequency synthesis field so far.
Simulation Study of 4H-SiC Low Turn-Off Loss and Snapback-Free Reverse-Conducting Gate Turn-Off Thyristor with N-Float Structure
Authors: Wu Chengcheng , Li Juntao , Li Zhiqiang , Zhang Lin , Zhou Kun , Deng Xiaochuan
Published: February 2024

In this study, a novel integrated 4H-SiC reverse-conducting gate turn-off thyristor (GTO) featuring an N-type floating (NF) structure is proposed. The proposed NF-structured 4H-SiC GTO outperforms conventional reverse-conducting GTOs in forward conduction, effectively eliminating the snapback phenomenon. This is achieved by increasing lateral resistance above the P-injector and modifying the electron current path during early turn-on. NF structures with a doping concentration of 2 × 1014 cm−3 and thicknesses exceeding 4 μm have been indicated to successfully eliminate the snapback phenomenon. Moreover, the anode-shorted structure enhances the GTO’s breakdown voltage and concurrently reduces turn-off losses by 85% at low current densities.
Development of Non-Invasive Ventilator for Homecare and Patient Monitoring System
Authors: Menniti Michele , Laganà Filippo , Oliva Giuseppe , Bianco Maria , Fiorillo Antonino S. , Pullano Salvatore A.
Published: February 2024

Recently, the incidence of, and interest in, respiratory diseases has been amplified by severe acute respiratory syndrome coronavirus (SARS-CoV-2) and other respiratory diseases with a high prevalence. Most of these diseases require mechanical ventilation for homecare and clinical therapy. Herein, we propose a portable and non-invasive mechanical fan (NIV) for home and clinical applications. The NIV’s core is a turbine for airflow generation, which can provide and monitor a positive two-level pressure of up to approximately 500 lpm at 50 cmH2O according to the inspiration/expiration phase. After calibration, the proposed NIV can precisely set the airflow with a pressure between 4 cmH2O and 20 cmH2O, providing a versatile device that can be used for continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP). The airflow is generated by a turbine monitored using a mass flow sensor. The whole NIV is monitored with a 16 MHz clock microcontroller. An analog-to-digital converter is used as the input for analog signals, while a digital-to-analog converter is used to drive the turbine. I2C protocol signals are used to manage the display. Moreover, a Wi-Fi system is interfaced for the transmission/reception of clinical and technical information via a smartphone, achieving a remote-controlled NIV.
An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System
Authors: Zhou Xuan
Published: February 2024

With the booming rollout of 5G communication, abundant new technologies have been proposed for quality of service requirements. In terms of the betterment in transmission coverage, mobile edge caching (MEC) has shown potential in reducing the transmission outage. The performance of MEC, meanwhile, can be promisingly enhanced by reconfigurable intelligent surfaces (RIS). Under this context, we explore a system comprising a small base-station (SBS) with limited cache capacity, two users, and one RIS. The SBS transmits the contents from the cache or fetches them from the remote backhaul hub to communicate with users through directional and possibly reflective channels. In this point-to-multipoint connection, non-orthogonal multiple access (NOMA) is applied, improving the capacity of the system. To minimize the outage probability, we first propose a caching policy from entropy perspective, based on which we investigate the beamforming and power allocation problem. The issue, however, is non-convex and involves multi-dimensional optimization. To address this, we introduce an efficient block successive upper-bound minimization algorithm, grounded in Gershgorin’s circle theorem. This algorithm aims to find the globally optimal solution for power allocation and RIS beamformer, considering both the channel condition and content popularity. Numerical studies are performed to verify the effectiveness of the proposed algorithm.
Enhancing Heterogeneous Network Performance: Advanced Content Popularity Prediction and Efficient Caching
Authors: Sun Zhiyao , Chen Guifen
Published: February 2024

With the popularity of smart devices and the growth of high-bandwidth applications, the wireless industry is facing an increased surge in data traffic. This challenge highlights the limitations of traditional edge-caching solutions, especially in terms of content-caching effectiveness and network-communication latency. To address this problem, we investigated efficient caching strategies in heterogeneous network environments. The caching decision process becomes more complex due to the heterogeneity of the network environment, as well as due to the diversity of user behaviors and content requests. To address the problem of increased system latency due to the dynamically changing nature of content popularity and limited cache capacity, we propose a novel content placement strategy, the long-short-term-memory–content-population-prediction model, to capture the correlation of request patterns between different contents and the periodicity in the time domain, in order to improve the accuracy of the prediction of content popularity. Then, to address the heterogeneity of heterogeneous network environments, we propose an efficient content delivery strategy: the multi-intelligent critical collaborative caching policy. This strategy models the edge-caching problem in heterogeneous scenarios as a Markov decision process using multi-base-station-environment information. In order to fully utilize the multi-intelligence information, we have improved the actor–critic approach by integrating the attention mechanism into a neural network. Whereas the actor network is responsible for making decisions based on local information, the critic network evaluates and enhances the actor’s performance. We conducted extensive simulations, and the results showed that the Long Short Term Memory content population prediction model was more advantageous, in terms of content-popularity-prediction accuracy, with a 28.61% improvement in prediction error, compared to several other existing methods. The proposed multi-intelligence actor–critic collaborative caching policy algorithm improved the cache-hit-rate metric by up to 32.3% and reduced the system latency by 1.6%, demonstrating the feasibility and effectiveness of the algorithm.
Capacitor Voltage Balancing Control of MMC Sub-Module Based on Neural Network Prediction
Authors: Xi Qingbo , Tian Yizhi , Fan Yanfang
Published: February 2024

The issue of sub-module (SM) capacitor voltage unbalance is a hot topic in the current research into the modular multilevel converter (MMC). An excellent strategy comprises mitigating the SM capacitor voltage imbalance by adjusting the SM on time. The traditional capacitor voltage balancing control regulates the speed to maintain accuracy. A unique SM capacitor voltage balancing control strategy is presented in this paper and is based on conventional capacitor voltage balance management and neural network prediction. Firstly, the SM capacitor voltage and arm current are speculated by operating the time series forecasting technique in real time, considering the dynamic changes in the SM capacitor voltage and arm current. Secondly, the SM capacitor voltage distinction between the actual and theoretical value is determined, and a deviation’s mixed Gaussian distribution is established to estimate its compensation voltage. Thirdly, the SM triggering sequence is anticipated by using the neural network along with the pilot values of the SM capacitor voltage, arm current, and the offset compensation value, and the control is executed. Finally, a three-phase, six-leg, eight-module, nine-level MMC model is built to verify the feasibility of the suggested approach.
Keyword Data Analysis Using Generative Models Based on Statistics and Machine Learning Algorithms
Authors: Jun Sunghae
Published: February 2024

For text big data analysis, we preprocessed text data and constructed a document–keyword matrix. The elements of this matrix represent the frequencies of keywords occurring in a document. The matrix has a zero-inflation problem because many elements are zero values. Also, in the process of preprocessing, the data size of the document–keyword matrix is reduced. However, various machine learning algorithms require a large amount of data, so to solve the problems of data shortage and zero inflation, we propose the use of generative models based on statistics and machine learning. In our experimental tests, we compared the performance of the models using simulation and practical data sets. Thus, we verified the validity and contribution of our research for keyword data analysis.