bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of Au Loading and TiO2 Support on the Catalytic Wet Air Oxidation of Glyphosate over TiO2+Au Catalysts
Authors: Žerjav Gregor , Albreht Alen , Pintar Albin
Published: July 2024

This study aimed to explore the impact of varying amounts of added Au (0.5 to 2 wt.%) and the structural characteristics of anatase TiO2 supports (nanoparticles (TP, SBET = 88 m2/g) and nanorods (TR, SBET = 105 m2/g)) on the catalytic efficiency of TiO2+Au catalysts in eliminating the herbicide glyphosate from aqueous solutions via the catalytic wet air oxidation (CWAO) process. The investigation was conducted using a continuous-flow trickle-bed reactor. Regardless of the TiO2 support and the amount of Au added, the addition of Au has a positive effect on the glyphosate degradation rate. Regarding the amount of Au added, the highest catalytic activity was observed with the TP + 1% Au catalyst, which had a higher Schottky barrier (SB) than the TP + 2% Au catalyst, which helped the charge carriers in the TiO2 conduction band to increase their reduction potential by preventing them from returning to the Au. The role of glyphosate degradation product adsorption on the catalyst surface is crucial for sustaining the long-term catalytic activity of the investigated TiO2+Au materials. This was particularly evident in the case of the TR + 1% Au catalyst, which had the highest glyphosate degradation rate at the beginning of the CWAO experiment, but its catalytic activity then decreased over time due to the adsorption of glyphosate degradation products, which was favoured by the presence of strong acidic sites. In addition, the TR + 1% Au solid had the smallest average Au particle size of all analyzed materials, which were more easily deactivated by the adsorption of glyphosate degradation products. The analysis of the degradation products of glyphosate shows that the oxidation of glyphosate in the liquid phase involves the rupture of C–P and C–N bonds, as amino-methyl-phosphonic acid (AMPA), glyoxylic acid and sarcosine were detected.
Catalytic Applications in the Production of Hydrotreated Vegetable Oil (HVO) as a Renewable Fuel: A Review
Authors: Mussa Nur-Sultan , Toshtay Kainaubek , Capron Mickael
Published: July 2024

The significance and challenges of hydrotreatment processes for vegetable oils have recently become apparent, encompassing various reactions like decarbonylation, decarboxylation, and hydrogenation. Heterogeneous noble or transition metal catalysts play a crucial role in these reactions, offering high selectivity in removing oxygen and yielding desired hydrocarbons. Notably, both sulphided and non-sulphided catalysts exhibit effectiveness, with the latter gaining attention due to health and toxicity concerns associated with sulphiding agents. Nickel-based catalysts, such as NiP and NiC, demonstrate specific properties and tendencies in deoxygenation reactions, while palladium supported on activated carbon catalysts shows superior activity in hydrodeoxygenation. Comparisons between the performances of different catalysts in various hydrotreatment processes underscore the need for tailored approaches. Transition metal phosphides (TMP) emerge as promising catalysts due to their cost-effectiveness and environmental friendliness. Ultimately, there is an ongoing pursuit of efficient catalysts and the importance of further advancements in catalysis for the future of vegetable oil hydrotreatment.
The Multifaceted Interactions of Dictyostelium Atg1 with Mitochondrial Function, Endocytosis, Growth, and Development
Authors: Sen Melodi Gizem , Sanislav Oana , Fisher Paul Robert , Annesley Sarah Jane
Published: July 2024

Autophagy is a degradative recycling process central to the maintenance of homeostasis in all eukaryotes. By ensuring the degradation of damaged mitochondria, it plays a key role in maintaining mitochondrial health and function. Of the highly conserved autophagy proteins, autophagy-related protein 1 (Atg1) is essential to the process. The involvement of these proteins in intracellular signalling pathways, including those involving mitochondrial function, are still being elucidated. Here the role of Atg1 was investigated in the simple model organism Dictyostelium discoideum using an atg1 null mutant and mutants overexpressing or antisense-inhibiting atg1. When evaluated against the well-characterised outcomes of mitochondrial dysfunction in this model, altered atg1 expression resulted in an unconventional set of phenotypic outcomes in growth, endocytosis, multicellular development, and mitochondrial homeostasis. The findings here show that Atg1 is involved in a tightly regulated signal transduction pathway coordinating energy-consuming processes such as cell growth and multicellular development, along with nutrient status and energy production. Furthermore, Atg1’s effects on energy homeostasis indicate a peripheral ancillary role in the mitochondrial signalling network, with effects on energy balance rather than direct effects on electron transport chain function. Further research is required to tease out these complex networks. Nevertheless, this study adds further evidence to the theory that autophagy and mitochondrial signalling are not opposing but rather linked, yet strictly controlled, homeostatic mechanisms.
Production of Porous ZrO2–TiO2 Ceramic Coatings on the Biomedical Ti-6Al-4V Alloy via AC PEO Treatment and Their Effects on the Corrosion Behavior in 0.9% NaCl
Authors: da Silva Aline C. N. , Ribeiro Rafael P. , Rangel Elidiane C. , da Cruz Nilson C. , Correa Diego R. N.
Published: July 2024

Ti and its alloys have been vastly employed in the manufacturing of biomedical implants for orthopedy and dentistry, especially the Ti-6Al-4V alloy (wt%), which is the most-used Ti alloy worldwide. However, the ion release of Al and V in the long term has been related to harmful effects on the human body. In this scenario, surface modification strategies, such as plasma electrolytic oxidation (PEO), have often been performed in Ti alloys to match the clinical needs. This study evaluated the effect of electrical AC parameters on the surface of the commercial Ti-6Al-4V alloy immersed in ZrO2-rich electrolytic solution regarding the chemical, physical, structural, and topographical aspects. Then, the selected PEO-treated samples surpassed the electrochemical test in saline solution. The results indicated that the electrical AC parameters affect the duration and intensity of the oxidative reactions and plasma micro-discharge steps, resulting in porous and thick oxide layers. PEO treatment promoted bio-camouflage of the surface, enriching it with TiO2, ZrO2, and ZrTiO4 compounds and depleting it of Al and V. After screening the PEO-treated samples and their corrosion behavior, the results indicated that the porous ZrO2–TiO2 ceramic coatings in the Ti-6Al-4V alloy can be a viable alternative for the manufacturing of biomedical implants.
Comparison of Fatigue Properties of Porous Polyurethane and Porous Asphalt Mixtures Considering the Influence of Immersion Conditions
Authors: Li Gen , Ye Yali , Zhao Haisheng , Zhang Qi , Zhao Shuqian , Zhuang Chuanyi , Zhang Peiyu
Published: July 2024

As a new binder, polyurethane (PU) is used to overcome the poor durability of porous pavement. This study focuses on the fatigue performance of a porous polyurethane mixture (PPM) under immersion conditions and compares it with a porous asphalt mixture (PAM). The results showed that under the immersion conditions, the initial stiffness of the PPM decreased by approximately 50%. However, the initial stiffness of the PAM was almost unaffected by the immersion conditions. In addition, the PPM exhibited a longer fatigue life under immersion conditions. In comparison, the fatigue life of the PAM decreased. The cracking propagation rate of the PPM decreased under immersion conditions, while the cracking propagation rate of the PAM increased. The fatigue termination condition for the PPM was set to S1 = 0.45S0 (S0 is the initial stiffness, and S1 is the termination stiffness), and that of the PAM was set to S1 = 0.4S0. Compared to the Weibull function, the Chaboche function provides a more accurate description of fatigue damage in the PPM and PAM. From the obtained data, it can be inferred that the fatigue performance of the PPM is far superior to that of the PAM. Therefore, polyurethane has broad application prospects in long-life permeable pavements.
Preparation and Characterization of Melamine–Benzoguanamine–Formaldehyde Resins and Their Flame-Retardant Properties in Impregnated Paper for Low Pressure Laminates
Authors: Çekiç Yusuf , Duyar Halil , Hacıvelioğlu Ferda
Published: July 2024

In this study, Melamine–formaldehyde (MF) resins were subjected to modification with benzoguanamine (BG) to produce MF-BG resins, followed by a comprehensive analysis of their chemical composition using advanced spectroscopic techniques such as Fourier transform infrared (FTIR), 1H-NMR, and 13C-NMR spectroscopy. The flame-retardant characteristics and thermal decomposition behavior of papers impregnated with MF-BG were examined and contrasted with those treated with standard MF. In particular, the optimized MF-BG5-treated paper exhibited a Limiting Oxygen Index (LOI) value exceeding 30%, and analysis using a cone calorimeter indicated a notable decrease in parameters such as the heat-release rate (P-HRR), total heat release (THR), smoke production rate (SPR), and total smoke production (TSP) when compared to papers impregnated with standard MF. The findings from the scanning electron microscopy (SEM) analysis of the residual char following cone calorimeter experiments revealed that the MF-BG5-treated paper exhibited a denser and more uniform char formation. This phenomenon consequently limits the emission of combustion by-products and impedes the spread of flame. This study provides a feasible method for low-pressure laminates with better flame retardancy by using the BG additive up to a limit in MF resin.
Recent Advances in the Development of Thin Films
Authors: Benzarti Zohra , Khalfallah Ali
Published: July 2024

Thin films and coatings are an integral part of modern technology, with applications including solar cells [...]
Study of the Influence of the Change from Methyl to Isopropyl Substituents in 1-(2,4,6-trialkylphenyl)ethanol on the Point Group Symmetry of the 0-D Hydrogen-Bonded Moiety
Authors: Iwanek (nee Wilczkowska) Ewa M. , Gliński Marek
Published: July 2024

The steric hindrance in molecules of 1-(2,4,6-trimethylphenyl)ethanone and 1-(2,4,6-triisopropylphenyl)ethanone were shown to substantially differentiate the options of synthesis of the respective alcohols. The former was obtained with a yield of 12% with a mild reducing agent, i.e., NaBH4, as well as in vapor phase transfer hydrogenation (22% yield at 673 K) over MgO, whereas the latter was not formed at all under those conditions. The only agent that was able to reduce both ketones was LiAlH4. The single crystals of the two alcohols were obtained and their structures were determined. The symmetry of the 0-D hydrogen-bonded networks of molecules in these crystals was analyzed. It was shown that the methyl substituent allows the molecules to form hexameric rings, whereas the isopropyl-substituted molecules formed tetrameric ones. In both cases, there were two types of rings in the cell, but four types of molecules forming tetramers and only three types of molecules in the hexamers. These structures were compared to similar structures formed by other molecules found in the Cambridge Structural Database via hydrogen bonding. Moreover, the single crystal of 1-(2,4,6-triisopropylphenyl)ethanone was obtained to explain if either the hydrogen bonding or the presence of isopropyl groups influences the angles in the molecules.
Atypical 18F-FDG PET-CT Findings in a Rare Case of Primary Hepatic Leiomyosarcoma
Authors: Cheon Miju , Yi Hyunkyung , Ha Joo Young , Kim Min A
Published: July 2024

The primary hepatic leiomyosarcoma is a rare malignant tumor arising from the smooth muscle cells in the hepatic vessels, bile ducts, and ligamentum teres. It is considered a subtype of hepatic sarcomas. We report awkward 18F-FDG PET-CT findings of a primary hepatic leiomyosarcoma masquerading as a benign hepatic tumor, which were confirmed by histopathological and immunohistochemical examinations in a 78-year-old woman.
The Role of Social Determinants in Diagnosis Timing for Fetal Care Center-Eligible Conditions: A Scoping Review
Authors: Wilpers Abigail B. , Eichhorn Barbara , Batten Janene , Francis Katie , Powne Amy B. , Jumale Shukri , Hansen Kara , Kohari Katherine , Lorch Scott A.
Published: July 2024

Timely identification of fetal conditions enables comprehensive evaluation, counseling, postnatal planning, and prenatal treatments. This study assessed the existing evidence on how social determinants of health (SDOH) influence diagnosis timing of fetal conditions appropriate for care in fetal care centers (FCCs). Eligible studies were conducted in the U.S. and published in English after 1999. We employed the Healthy People 2020 SDOH framework to categorize and analyze data from 16 studies, where 86% focused solely on congenital heart disease (CHD). Studies primarily focused on individual-level SDOH, with only 36% addressing structural-level factors. A total of 31 distinct indicators of SDOH were identified, with 68% being unique to individual studies. Indicators often varied in definition and specificity. Three studies covered all five SDOH categories in the Healthy People 2020 Framework. Studies revealed varying and often conflicting associations with SDOH indicators, with race and ethnicity being the most explored (100%), followed by socioeconomic status (69%), maternal age (57%), residence (43%), and structural factors (29%). Our findings highlight the need for more comprehensive research, including conditions beyond CHD, and the establishment of consensus on indicators of SDOH. Such efforts are necessary to gain a deeper understanding of the underlying factors driving disparities in fetal diagnosis and treatment.
Predictive Modeling of COVID-19 Readmissions: Insights from Machine Learning and Deep Learning Approaches
Authors: Loo Wei Kit , Voon Wingates , Suhaimi Anwar , Teh Cindy Shuan Ju , Tee Yee Kai , Hum Yan Chai , Hasikin Khairunnisa , Teo Kareen , Ong Hang Cheng , Lai Khin Wee
Published: July 2024

This project employs artificial intelligence, including machine learning and deep learning, to assess COVID-19 readmission risk in Malaysia. It offers tools to mitigate healthcare resource strain and enhance patient outcomes. This study outlines a methodology for classifying COVID-19 readmissions. It starts with dataset description and pre-processing, while the data balancing was computed through Random Oversampling, Borderline SMOTE, and Adaptive Synthetic Sampling. Nine machine learning and ten deep learning techniques are applied, with five-fold cross-validation for evaluation. Optuna is used for hyperparameter selection, while the consistency in training hyperparameters is maintained. Evaluation metrics encompass accuracy, AUC, and training/inference times. Results were based on stratified five-fold cross-validation and different data-balancing methods. Notably, CatBoost consistently excelled in accuracy and AUC across all tables. Using ROS, CatBoost achieved the highest accuracy (0.9882 ± 0.0020) with an AUC of 1.0000 ± 0.0000. CatBoost maintained its superiority in BSMOTE and ADASYN as well. Deep learning approaches performed well, with SAINT leading in ROS and TabNet leading in BSMOTE and ADASYN. Decision Tree ensembles like Random Forest and XGBoost consistently showed strong performance.
Demirjian’s and Cameriere’s Methods for the Assessment of Dental Age Estimation in Children from a Southern Brazilian City
Authors: Carelli Julia , da Silva Gabriela Sabrina , Gomes Mariana Vegini , Vilalba Thais , Baratto-Filho Flares , Armando Brancher João , Beisel-Memmert Svenja , Kirschneck Christian , Lopes Celia Maria Condeixa de França , Moro Alexandre , Küchler Erika Calvano
Published: July 2024

The chronological age estimation of living individuals is a crucial part of forensic practice and clinical practice, such as in orthodontic treatment. It is well-known that methods for age estimation in living children should be tested on different populations. Ethnic affiliations in Brazil are divided into several major groups depending on the region, with the south of Brazil being known for its German immigration. (1) Background: This study aimed to evaluate the correlation between chronological age and dental age using Demirjian’s method and Cameriere’s method in a group of children from Joinville, South Brazil to investigate if both methods can be used to estimate dental age in this population. (2) Methods: The sample consisted of 229 panoramic radiographs (119 were males and were 110 females) from Brazilian children (ages ranging from 6 to 12 years). The chronological age at the time of the panoramic radiographic exam was calculated for each child. The dental age was estimated according to Demirjian’s method and Cameriere’s method. All continuous data were tested for normality by using the Shapiro–Wilk test. The Pearson correlation coefficient test was applied. An alpha of 5% (p < 0.05) was used for all analyses. (3) Results: The mean chronological age was 8.75 years. According to Demirjian’s method, the mean dental age was 9.3 years, while according to Cameriere’s method, the mean dental age was 8.66 years. A strong correlation between chronological age and dental age according to Demirjian (r = 0.776 and p < 0.0001) and Cameriere (r = 0.735 and p < 0.0001) was observed for both genders. (4) Conclusions: Both methods presented a good correlation with chronological age in the studied population and could be used to assess dental age in this population.
Line-Field Confocal Optical Coherence Tomography for the Diagnosis of Skin Tumors: A Systematic Review and Meta-Analysis
Authors: Razi Shazli , Kuo Yen-Hong , Pathak Gaurav , Agarwal Priya , Horgan Arianna , Parikh Prachi , Deshmukh Farah , Rao Babar K.
Published: July 2024

A line-field confocal optical coherence tomography (LC-OCT) combines confocal microscopy and optical coherence tomography into a single, rapid, easy-to-use device. This meta-analysis was performed to determine the reliability of LC-OCT for diagnosing malignant skin tumors. PubMed, EMBASE, Web of Science databases, and the Cochrane Library were searched for research studies in the English language from inception till December 2023. To assess quality and the risk of bias, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used. The sensitivity and specificity of each study were calculated. The bivariate summary sensitivity and specificity were calculated using the linear mixed model. Five studies with 904 reported per lesion analyses in our study; the specificity and sensitivity ranged from 67% to 97% and 72% to 92%, respectively. The pooled specificity and sensitivity were 91% (95% CI: 76–97%) and 86.9% (95% CI: 81.8–90.8%), respectively. The summary sensitivity and specificity from the bivariate approach are 86.9% (95% CI: 81.8–90.8%) and 91.1% (95% CI: 76.7–97.0%), respectively. The area under the curve is 0.914. LC-OCT shows great sensitivity and specificity in diagnosing malignant skin tumors. However, due to the limited number of studies included in our meta-analysis, it is premature to elucidate the true potential of LC-OCT.
Differentiation of Acute Internal Carotid Artery Occlusion Etiology on Computed Tomography Angiography: Diagnostic Tree for Preparing Endovascular Treatment
Authors: Kim Bo Kyu , Kim Byungjun , You Sung-Hye
Published: July 2024

Background and Purpose: This study aimed to identify the imaging characteristics and discriminate the etiology of acute internal carotid artery occlusion (ICAO) on computed tomography angiography (CTA) in patients with acute ischemic stroke. Materials and Methods: We retrospectively evaluated consecutive patients who underwent endovascular thrombectomy for acute ICAO. Contrast filling of the extracranial ICA in preprocedural CTA was considered apparent ICAO. Non-contrast filling of the extracranial ICA was evaluated according to the contrast-filled lumen configuration, lumen margin and location, Hounsfield units of the non-attenuating segment, and presence of calcification or an intimal flap. Digital subtraction angiography findings were the reference standard for ICAO etiology and the occlusion site. A diagnostic tree was derived using significant variables according to pseudo-occlusion, atherosclerotic vascular disease (ASVD), thrombotic occlusion, and dissection. Results: A total of 114 patients showed apparent ICAO (n = 21), pseudo-occlusion (n = 51), ASVD (n = 27), thrombotic occlusion (n = 9), or dissection (n = 6). Most pseudo-occlusions (50/51, 98.0%) showed dependent locations with ill-defined contrast column margins and classic flame or beak shapes. The most common occlusion site of pseudo-occlusion was the petro-cavernous ICA (n = 32, 62.7%). Apparent ICAO mainly appeared in cases with occlusion distal to the posterior communicating artery orifice. ASVD showed beak or blunt shapes in the presence of low-density plaques or dense calcifications. Dissection revealed flame- or beak-shaped appearances with circumscribed margins. Thrombotic occlusions tended to appear blunt-shaped. The decision-tree model showed a 92.5% overall accuracy. Conclusions: CTA characteristics may help diagnose ICAO etiology. We provide a simple and easy decision-making model to inform endovascular thrombectomy.
Clinical Trial Validation of Automated Segmentation and Scoring of Pulmonary Cysts in Thoracic CT Scans
Authors: Baral Aneesha , Lee Simone , Hussaini Farah , Matthew Brianna , Lebron Alfredo , Wang Muyang , Hsu Li-Yueh , Moss Joel , Wen Han
Published: July 2024

In cystic lung diseases such as lymphangioleiomyomatosis (LAM), a CT-based cyst score that measures the percentage of the lung volume occupied by cysts is a common index of the cyst burden in the lungs. Although the current semi-automatic measurement of the cyst score is well established, it is susceptible to human operator variabilities. We recently developed a fully automatic method incorporating adaptive features in place of manual adjustments. In this clinical study, the automatic method is validated against the standard method in several aspects. These include the agreement between the cyst scores of the two methods, the agreement of each method with independent tests of pulmonary function, and the temporal consistency of the measurements in the consecutive visits of the same patients. We found that the automatic method agreed with the standard method as well as the agreement between two trained operators running the same standard method; both methods obtained the same level of correlation with laboratory pulmonary function tests; the automated method had better temporal consistency than the standard method (p < 0.0001). The study indicates that the automatic method could replace the standard method and provide better consistency in assessing the extent of cystic changes in the lungs of patients.
Positive Psychology and Strengths-Based Interventions (SBIs): Implications for Uses with Special Student Populations (Special Needs, P-3, Gifted)
Authors: Mueller Christian E. , Jiang Xu , Winsor Denise L.
Published: July 2024

In 2000, Seligman and Csikszentmihalyi published the highly influential “Positive Psychology: An Introduction” in American Psychologist, and with it, they advocated for a strengths-based approach for helping people live a “good life”. Building on that work, positive psychology was further conceptualized as consisting of meta-psychological and practical aspects. The meta-psychological constitutes the aims or “grand vision” of what positive psychology stands for, whereas the practical constitutes the “nuts-and-bolts” elements that should be focused on in developing interventions. In the present commentary, we build on this previous work to propose a “positive psychology pragmatic framework” to develop strengths-based interventions (SBIs) for use with special student populations; in this case, students with special needs and P-3 and gifted students. Relatedly, we review the empirical and theoretical/conceptual literature relevant to the use of SBIs within each subpopulation as well as discuss some characteristics that make each subpopulation unique. Consequently, to the highest degree possible, we advocate for integrating intrapersonal, interpersonal, contextual/environmental, systemic, and developmental factors in developing SBIs and we make specific recommendations for future research through the lens of this proposed framework.
Writing Strategies for Elementary Multilingual Writers: A Systematic Review
Authors: Lewis Bethany P.
Published: July 2024

Because of the Common Core State Standards (2010), all elementary students across the United States, including multilingual learners, must learn to compose written texts in opinion, informational, and narrative genres. By nature, writing also plays many diverse roles in educational settings, and advanced writing skills are needed for students to reach interdisciplinary success. Specifically, writing can be incorporated into content-area learning in subjects such as science, social studies, and math to support genre-based writing development. While advanced writing can be challenging for any student to master, multilingual students may need additional support to develop these crucial composition skills because of the complexities of learning two languages simultaneously. Furthermore, while many educators understand that multilingual students have distinct literacy needs, not all teachers are prepared to meet these specific writing instructional demands. This article presents the results of a systematic review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. This study investigated literacy-based empirical research used to support multilingual learners since the inception of the Common Core (2010–2023) genre-based writing standards. In characterizing effective writing supports, seven themes emerged: (a) the role of the teacher, (b) writing mentor texts, (c) the creation of multimodal texts, (d) writing scaffolds, (e) authenticity, (f) specific teacher instruction, and (g) multilingual language approaches. While this body of research is literacy-focused, these findings provide elementary teachers with specific strategies to support developing multilingual writers when implemented into traditional literacy spaces or content-area instruction.
Virtual Reality in Fluid Power Education: Impact on Students’ Perceived Learning Experience and Engagement
Authors: Azzam Israa , El Breidi Khalil , Breidi Farid , Mousas Christos
Published: July 2024

The significance of practical experience and visualization in the fluid power discipline, highly tied to students’ success, requires integrating immersive pedagogical tools for enhanced course delivery, offering real-life industry simulation. This study investigates the impact of using virtual reality (VR) technology as an instructional tool on the learning and engagement of 48 mechanical engineering technology (MET) students registered in the MET: 230 Fluid Power course at Purdue University. An interactive VR module on hydraulic grippers was developed utilizing the constructivist learning theory for MET: 230 labs, enabling MET students to explore light- and heavy-duty gripper designs and operation through assembly, disassembly, and testing in a virtual construction environment. A survey consisting of a Likert scale and short-answer questions was designed based on the study’s objective to evaluate the students’ engagement and perceived attitude toward the module. Statistical and natural language processing (NLP) analyses were conducted on the students’ responses. The statistical analysis results revealed that 97% of the students expressed increased excitement, over 90% reported higher engagement, and 87% found the VR lab realistic and practical. The NLP analysis highlighted positive themes such as “engagement”, “valuable experience”, “hands-on learning”, and “understanding”, with over 80% of students endorsing these sentiments. These findings will contribute to future efforts aimed at improving fluid power learning through immersive digital reality technologies, while also exploring alternative approaches for individuals encountering challenges with such technologies.
Recent Advances in Computer Vision: Technologies and Applications
Authors: Gao Mingliang , Zou Guofeng , Li Yun , Guo Xiangyu
Published: July 2024

Computer vision plays a pivotal role in modern society, which transforms fields such as healthcare, transportation, entertainment, and manufacturing by enabling machines to interpret and understand visual information, revolutionizing industries, and enhancing daily life [...]
Enhancing Autonomous Driving Robot Systems with Edge Computing and LDM Platforms
Authors: Moon Jeongmin , Hong Dongwon , Kim Jungseok , Kim Suhong , Woo Soomin , Choi Hyeongju , Moon Changjoo
Published: July 2024

The efficient operation and interaction of autonomous robots play crucial roles in various fields, e.g., security, environmental monitoring, and disaster response. For these purposes, processing large volumes of sensor data and sharing data between robots is essential; however, processing such large data in an on-device environment for robots results in substantial computational resource demands, causing high battery consumption and heat issues. Thus, this study addresses challenges related to processing large volumes of sensor data and the lack of dynamic object information sharing among autonomous robots and other mobility systems. To this end, we propose an Edge-Driving Robotics Platform (EDRP) and a Local Dynamic Map Platform (LDMP) based on 5G mobile edge computing and Kubernetes. The proposed EDRP implements the functions of autonomous robots based on a microservice architecture and offloads these functions to an edge cloud computing environment. The LDMP collects and shares information about dynamic objects based on the ETSI TR 103 324 standard, ensuring cooperation among robots in a cluster and compatibility with various Cooperative-Intelligent Transport System (C-ITS) components. The feasibility of operating a large-scale autonomous robot offloading system was verified in experimental scenarios involving robot autonomy, dynamic object collection, and distribution by integrating real-world robots with an edge computing–based offloading platform. Experimental results confirmed the potential of dynamic object collection and dynamic object information sharing with C-ITS environment components based on LDMP.
Violence Detection Using Wi-Fi and 5G/6G Sensing Technologies: A Review
Authors: Kannan Aieswarya , Kouzani Abbas Z.
Published: July 2024

Violence, a pervasive societal concern, demands innovative approaches for its early detection and prevention. This review paper explores the intersection of violence detection and wireless fidelity (Wi-Fi), alongside fifth-generation (5G) and sixth-generation (6G) mobile technologies. Wi-Fi sensing, initially employed for human activity detection, has also demonstrated versatility across a number of other important applications. The significance of leveraging Wi-Fi sensing for violence detection is investigated, underscoring its ability to enhance security protocols and minimise response time. Moreover, through the development and use of machine learning algorithms to analyse and interpret intricate channel state information (CSI) features, the accuracy of violence detection can be improved. Furthermore, this investigation delves into the rapidly developing domain of mobile sensing, examining its contribution to the advancement of violence detection functionalities. The potential convergence of 5G and forthcoming 6G sensing technologies increases the effectiveness of violence detection. Through an analysis of Wi-Fi and mobile sensing technologies, this review paper highlights the transformative capacity that their integration may have on approaches to violence prevention and response.
Impact of H-Related Chemical Bonds on Physical Properties of SiNx:H Films Deposited via Plasma-Enhanced Chemical Vapor Deposition
Authors: Ning Jianping , Tang Zhen , Chen Lunqian , Li Bowen , Wu Qidi , Sun Yue , Zhou Dayu
Published: July 2024

SiNx:H film deposition via plasma-enhanced chemical vapor deposition has been widely used in semiconductor devices. However, the relationship between the chemical bonds and the physical and chemical properties has rarely been studied for films deposited using tools in terms of the actual volume production. In this study, we investigated the effects of the deposition conditions on the H-related chemical bonding, physical and chemical properties, yield, and quality of SiNx:H films used as passivation layers at the 28 nm technology node. The radiofrequency (RF) power, electrode plate spacing, temperature, chamber pressure, and SiH4:NH3 gas flow ratio were selected as the deposition parameters. The results show a clear relationship between the H-related chemical bonds and the examined film properties. The difference in the refractive index (RI) and breakdown field (EB) of the SiNx:H films is mainly attributed to the change in the Si–H:N–H ratio. As the Si–H:N–H ratio increased, the RI and EB showed linear growth and exponential downward trends, respectively. In addition, compared with the Si–H:N–H ratio, the total Si–H and N–H contents had a greater impact on the wet etching rates of the SiNx:H films, but the stress was not entirely dependent on the total Si–H and N–H contents. Notably, excessive electrode plate spacing can lead to a significant undesired increase in the non-uniformity and surface roughness of SiNx:H films. This study provides industry-level processing guidance for the development of advanced silicon nitride film deposition technology.
Multimodal Hateful Meme Classification Based on Transfer Learning and a Cross-Mask Mechanism
Authors: Wu Fan , Chen Guolian , Cao Junkuo , Yan Yuhan , Li Zhongneng
Published: July 2024

Hateful memes are malicious and biased sentiment information widely spread on the internet. Detecting hateful memes differs from traditional multimodal tasks because, in conventional tasks, visual and textual information align semantically. However, the challenge in detecting hateful memes lies in their unique multimodal nature, where images and text in memes may be weak or unrelated, requiring models to understand the content and perform multimodal reasoning. To address this issue, we introduce a multimodal fine-grained hateful memes detection model named “TCAM”. The model leverages advanced encoding techniques from TweetEval and CLIP and introduces enhanced Cross-Attention and Cross-Mask Mechanisms (CAM) in the feature fusion stage to improve multimodal correlations. It effectively embeds fine-grained features of data and image descriptions into the model through transfer learning. This paper uses the Area Under the Receiver Operating Characteristic Curve (AUROC) as the primary metric to evaluate the model’s discriminatory ability. This approach achieved an AUROC score of 0.8362 and an accuracy score of 0.764 on the Facebook Hateful Memes Challenge (FHMC) dataset, confirming its high discriminatory capability. The TCAM model demonstrates relatively superior performance compared to ensemble machine learning methods.
Building a Sustainable Future: A Three-Stage Risk Management Model for High-Permeability Power Grid Engineering
Authors: Wu Weijie , Li Dongwei , Sun Hui , Li Yixin , Zhang Yining , Zhao Mingrui
Published: July 2024

Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and engineering frequently involve multiple risk considerations. In this study, we constructed a three-stage comprehensive risk management model of HPGE, which can help to overcome the issues of redundant risk indicators, imprecise risk assessment techniques, and irrational risk warning models in existing studies. First, we use the fuzzy Delphi model to identify the key risk indicators of HPGE. Then, the Bayesian best–worst method (Bayesian BWM) is adopted, as well as the measurement alternatives and ranking according to the compromise solution (MARCOS) approach, to evaluate the comprehensive risks of projects; these methods are proven to have more reliable weighting results and a larger sample separation through comparative analysis. Finally, we established an early warning risk model on the basis of the non-compensation principle, which can help prevent the issue of actual risk warning outcomes from being obscured by some indicators. The results show that the construction of the new power system and clean energy consumption policy are the key risk factors affecting HPGE. It was found that four projects are in an extremely high-risk warning state, five are in a relatively high-risk warning state, and one is in a medium-risk warning state. Therefore, it is necessary to strengthen the risk prevention of HPGE and to develop a reasonable closed-loop risk control mechanism.
An Identification Method for Mixed Coal Vitrinite Components Based on An Improved DeepLabv3+ Network
Authors: Wang Fujie , Li Fanfan , Sun Wei , Song Xiaozhong , Lu Huishan
Published: July 2024

To address the high complexity and low accuracy issues of traditional methods in mixed coal vitrinite identification, this paper proposes a method based on an improved DeepLabv3+ network. First, MobileNetV2 is used as the backbone network to reduce the number of parameters. Second, an atrous convolution layer with a dilation rate of 24 is added to the ASPP (atrous spatial pyramid pooling) module to further increase the receptive field. Meanwhile, a CBAM (convolutional block attention module) attention mechanism with a channel multiplier of 8 is introduced at the output part of the ASPP module to better filter out important semantic features. Then, a corrective convolution module is added to the network’s output to ensure the consistency of each channel’s output feature map for each type of vitrinite. Finally, images of 14 single vitrinite components are used as training samples for network training, and a validation set is used for identification testing. The results show that the improved DeepLabv3+ achieves 6.14% and 3.68% improvements in MIOU (mean intersection over union) and MPA (mean pixel accuracy), respectively, compared to the original DeepLabv3+; 12% and 5.3% improvements compared to U-Net; 9.26% and 4.73% improvements compared to PSPNet with ResNet as the backbone; 5.4% and 9.34% improvements compared to PSPNet with MobileNetV2 as the backbone; and 6.46% and 9.05% improvements compared to HRNet. Additionally, the improved ASPP module increases MIOU and MPA by 3.23% and 1.93%, respectively, compared to the original module. The CBAM attention mechanism with a channel multiplier of 8 improves MIOU and MPA by 1.97% and 1.72%, respectively, compared to the original channel multiplier of 16. The data indicate that the proposed identification method significantly improves recognition accuracy and can be effectively applied to mixed coal vitrinite identification.
Real-Time Energy Management Strategy for Fuel Cell Vehicles Based on DP and Rule Extraction
Authors: Liu Yanwei , Wang Mingda , Tan Jialuo , Ye Jie , Liang Jiansheng
Published: July 2024

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy.
Numerical Simulation of Geothermal Energy Development at Mount Meager and Its Impact on In Situ Thermal Stress
Authors: Chai Yutong , Chen Zhuoheng , Yin Shunde
Published: July 2024

The Meager Mountain Geothermal Project stands as one of the pioneering geothermal energy initiatives in its early stages of resource development. Despite its abundant geothermal heat resources, no prior studies have systematically evaluated the potential of implementing coaxial borehole heat exchangers on site. This study addresses this research gap by presenting a comprehensive heat transfer model for an underground closed-loop geothermal system utilizing a single coaxial well. Finite element analysis incorporated fluid and solid heat transfer, as well as solid mechanics. The results obtained facilitated the construction of the temperature and thermal stress profiles induced by the cooling effects resulting from years of heat extraction. After 25 years of operation, the outlet temperature has reached approximately 74 °C, and the maximum radial tensile thermal stress amounts to ~47 MPa. Furthermore, the analysis demonstrates that higher fluid velocities contribute to more perturbed temperature and stress distributions. The study attained maximum thermal and electric power outputs of 208 kW and 17 kW, respectively. This research also underscores the significant impact of geothermal gradient and well length on BHE design, with longer wells yielding more power, especially at higher injection velocities.
Variable-Length Pendulum-Based Mechatronic Systems for Energy Harvesting: A Review of Dynamic Models
Authors: Yakubu Godiya , Olejnik Paweł , Adisa Ademola B.
Published: July 2024

The ability to power low-power devices and sensors has drawn a great deal of interest to energy harvesting from ambient vibrations. The application of variable-length pendulum systems in conjunction with piezoelectric or electromagnetic energy-harvesting devices is examined in this thorough analysis. Because of their changeable length, such pendulums may effectively convert mechanical vibrations into electrical energy. This study covers these energy-harvesting systems’ basic theories, design concerns, modeling methods, and performance optimization strategies. This article reviews several studies that look at dynamic models, the effects of damping coefficients, device designs, and excitation parameters on energy output. The advantages and disadvantages of piezoelectric and electromagnetic coupling techniques are demonstrated by comparative research. This review also looks at technical advances and future research prospects in variable-length, pendulum-based energy harvesting. An expanded model for an energy harvester based on a variable-length pendulum derived from the modified, swinging Atwood machine is more specifically presented. This model’s numerical simulations, estimated current and voltage outputs, and produced power from the electromagnetic and piezoelectric devices integrated at various points in a 4-DOF variable-length pendulum model all indicate encouraging results. This necessitates extra study, changes, and optimizations to improve the usefulness of the proposed model. Finally, important dynamic models on developing variable-length, pendulum-based energy harvesters for usage in a range of applications to create sustainable energy are summarized.
Hybrid PVP/Battery/Fuel Cell Wireless Charging Stations Using High-Frequency Optimized Inverter Technology for Electric Vehicles
Authors: Baccouche Gaith , Chehab Mohamed Haikel , Ben Salah Chokri , Tlija Mehdi , Rabhi Abdelhamid
Published: July 2024

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options.
A Novel Voltage-Abnormal Cell Detection Method for Lithium-Ion Battery Mass Production Based on Data-Driven Model with Multi-Source Time Series Data
Authors: Wang Xiang , He Jianjun , Huang Fuxin , Liu Zhenjie , Deng Aibin , Long Rihui
Published: July 2024

Before leaving the factory, lithium-ion battery (LIB) cells are screened to exclude voltage-abnormal cells, which can increase the fault rate, troubleshooting difficulty, and degrade pack performance. However, the time interval to obtain the detection results through the existing voltage-abnormal cell method is too long, which can seriously affect production efficiency and delay shipment, especially in the mass production of LIBs when facing a large number of time-critical orders. In this paper, we propose a data-driven voltage-abnormal cell detection method, using a fast model with simple architecture, which can detect voltage-abnormal cells based on the multi-source time series data of the LIB without a time interval. Firstly, our method transforms the different source data of a cell into a multi-source time series data representation and utilizes a recurrent-based data embedding to model the relation within it. Then, a simplified MobileNet is used to extract hidden feature from the embedded data. Finally, we detect the voltage-abnormal cells according to the hidden feature with a cell classification head. The experiment results show that the accuracy and average running time of our model on the voltage-abnormal cell detection task is 95.42% and 0.0509 ms per sample, which is a considerable improvement over existing methods.
Design and Simulation of Adiabatic–Damping Dual–Function Strut for LH2 Storage Tank
Authors: Qiu Yinan , Xiao Jianwei , Ma Xinglong , Xu Yuanyuan , Kang Huifang
Published: July 2024

In the process of the on–board transportation of liquid hydrogen storage and transportation tanks, apart from considering the support strength and adiabatic performance, it is imperative to take into account the vibration characteristics of the carrying platform. The present work introduces a versatile support structure comprising a damping module and a ball contact insulation structure, enabling effective isolation of external vibrations while simultaneously providing support and insulation. The first step involves describing the principle of a flexible support structure and designing the mechanical structure. Subsequently, a damping analysis is conducted based on dynamic theory to establish the relationship between the spring and damping. Finally, the structural parameters of the dual–function strut are determined, followed by simulation of heat transfer performance. The results demonstrate that the dual–function strut exhibits exceptional vibration damping performance by reducing the amplitude of external vibrations greater than 5 Hz to less than 6%. Moreover, owing to the compact linear diameter spring structure of the vibration damping module and its ball contact effect, the thermal resistance of the dual–function strut is significantly enhanced, resulting in a mere heat leakage of only 22 W/m2 in a single rod section.