bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalence of Dental Anomalies in Pediatric Patients at King Saud University Dental Hospital, Riyadh, Saudi Arabia—A Radiographic Analysis
Authors: Aldowsari Mannaa K. , Sulimany Ayman M. , Alkhathlan Abdulmajeed , Alfhaed Nawaf , Aldosari Mohammed , Alayed Saud , Bin Saleh Saad , Alshami Abeer A.
Published: December 2024

Objectives: To record the prevalence of dental anomalies in children visiting King Saud Dental Hospital, Riyadh, Saudi Arabia. Materials and Methods: This cross-sectional study involved radiographic examination of children aged 6 to 14 years who visited King Saud Dental Hospital, Riyadh, Saudi Arabia, in the past five years. Four dental interns were trained in three consecutive sessions by a pediatric dentist and evaluated the orthopantomograms of the children. The recorded anomalies were divided into four categories: tooth number, size, position, and general. Descriptive statistics were derived and chi-square tests performed to report prevalence and significance among genders, medical histories, and dental anomalies. The significance level was set at p < 0.05. Results: Of the 1987 radiographs studied, 268 (13.48%) children had dental anomalies. The sample consisted of 51.1% female and 48.5% male children, with a mean age of 11.87 ± 2.1 years. Most study participants had mixed dentition and were healthy. The prevalence of dental anomalies reported in this study was as follows: 4.6% hypodontia, 2.7% ectopic eruption, 1.63% taurodontism, 1.2% infra-occluded molars, 1.1% impacted teeth, 0.75% root dilaceration, and 0.65% supernumerary teeth. Significant correlations were found between gender and ectopic eruption (p = 0.02) and between medical history and hypodontia (p = 0.00), ectopic eruption (p = 0.048), and root dilaceration (p = 0.03). Conclusions: This study showed that hypodontia was the most common dental anomaly, followed by ectopic eruption and taurodontism. The findings of this study may guide dentists in better understanding, diagnosing, and treating dental anomalies in children in Riyadh, Saudi Arabia.
Artificial Intelligence in Fetal and Pediatric Echocardiography
Authors: Wang Alan , Doan Tam T. , Reddy Charitha , Jone Pei-Ni
Published: December 2024

Echocardiography is the main modality in diagnosing acquired and congenital heart disease (CHD) in fetal and pediatric patients. However, operator variability, complex image interpretation, and lack of experienced sonographers and cardiologists in certain regions are the main limitations existing in fetal and pediatric echocardiography. Advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offer significant potential to overcome these challenges by automating image acquisition, image segmentation, CHD detection, and measurements. Despite these promising advancements, challenges such as small number of datasets, algorithm transparency, physician comfort with AI, and accessibility must be addressed to fully integrate AI into practice. This review highlights AI’s current applications, challenges, and future directions in fetal and pediatric echocardiography.
Anticancer Ribosomally Synthesized and Post-Translationally Modified Peptides from Plants: Structures, Therapeutic Potential, and Future Directions
Authors: Hwang Hyeon-Jeong , Nam Youngsang , Jang Chanhee , Kim Eun La , Jang Eun Seo , Lee Yeo Jin , Lee Seoung Rak
Published: December 2024

Cancer remains a significant medical challenge, necessitating the discovery of novel therapeutic agents. Ribosomally synthesized and post-translationally modified peptides (RiPPs) from plants have emerged as a promising source of anticancer compounds, offering unique structural diversity and potent biological activity. This review identifies and discusses cytotoxic RiPPs across various plant families, focusing on their absolute chemical structures and reported cytotoxic activities against cancer cell lines. Notably, plant-derived RiPPs such as rubipodanin A and mallotumides A–C demonstrated low nanomolar IC50 values against multiple cancer cell types, highlighting their therapeutic potential. By integrating traditional ethnobotanical knowledge with modern genomic and bioinformatic approaches, this study underscores the importance of plant RiPPs as a resource for developing innovative cancer treatments. These findings pave the way for further exploration of plant RiPPs, emphasizing their role in addressing the ongoing challenges in oncology and enhancing the repertoire of effective anticancer therapies.
Terahertz Time-Domain Spectroscopy of Substituted Gadolinium Gallium Garnet
Authors: Wang Ke , Xu Junchao , Murakami Hironaru , Yamahara Hiroyasu , Seki Munetoshi , Tabata Hitoshi , Tonouchi Masayoshi
Published: December 2024

Temperature dependence of the lowest frequency transverse optical phonon (TO1) in a single crystal Substituted Gadolinium Gallium Garnet (SGGG, (001)) was studied using terahertz time-domain spectroscopy at temperatures between 80 K and 500 K. The complex dielectric constants were calculated from the optical constants fitting with the Lorentz oscillator model. The results show that the TO1 phonon of SGGG is at 2.5 THz at room temperature, the frequency of the TO1 phonon slightly decreases, and the dumping factor clearly increases with increasing temperature. Additionally, our results demonstrate that even a small substitution can induce a phonon shift, leading to higher absorption and causing a slight degradation in thermal stability. Our work is expected to support the development of magneto-optical and spintronic devices.
Seroprevalence of SARS-CoV-2 Infection Among People Living with HIV in Libreville, Gabon
Authors: Zoa-Assoumou Samira , M’boyis-Kandem Hervé , Saphou-Damon Pelagie , Mouangala Davy Ulrich Leger , Nzengui Guy-Francis , Mbani-Okoumba Marina , Kombila-Koumavor Claudine , Mourembou Gael , Ibrahim Brama , Ndjoyi-Mbiguino Angelique
Published: December 2024

(1) Objectives: The burden of SARS-CoV-2 infection in people living with HIV (PLHIV) in Gabon is unknown. (2) Methods: We conducted a cross-sectional seroprevalence study of SARS-CoV-2 immunoglobulin (Ig) G/M antibodies in PLHIV in Libreville from April 2022 to April 2023 after the fourth wave of the pandemic. We used the WANTAI SARS-CoV-2 Ab ELISA targeting the SARS-CoV-2 spike, receptor-binding domain. (3) Results: Among 480 samples tested, the seroprevalence of IgG antibodies to SARS-CoV-2 spike protein was 87.5% (95% confidence interval (CI) 77.7–97.3%). History of SARS-CoV-2 diagnosis, ART treatment, and TCD4 lymphocyte count were not found to be associated with the presence of antibodies against SARS-CoV-2 among the study participants. However, having a detectable viral load (p = 0.0001), being vaccinated (COVID-19 vaccine, p = 0.04), and a history of COVID-19 (p < 0.0001) symptoms were associated with a higher risk of having anti-SARS-CoV-2 antibodies. (4) Conclusions: By early 2023, PLHIV in Gabon had high rates of SARS-CoV-2 seropositivity. To our knowledge, this is the first study to determine the seroprevalence of SARS-CoV-2 antibodies in PLHIV in Gabon. This study provides further evidence of anti-SARS-CoV-2 seroconversion in the absence of any vaccination in a particular target population. The surveillance of diseases of global health concern in PLHIV is crucial to estimate population-level exposure and inform public health responses.
Nutritional Counseling During Chemotherapy Treatment: A Systematic Review of Feasibility, Safety, and Efficacy
Authors: James Shalet , Oppermann Alexie , Schotz Kaitlin M. , Minotti Mackenzie M. , Rao Gautam G. , Kleckner Ian R. , Baguley Brenton J. , Kleckner Amber S.
Published: December 2024

Dietary interventions during chemotherapy hold promise for clinical and supportive care outcomes. We systematically investigated the feasibility, safety, and efficacy of nutritional counseling conducted during chemotherapy. Studies prospectively implemented nutrition counseling during chemotherapy. Articles were identified from three databases—EMBASE, Cochrane Library, and SCOPUS—from inception to 1 October 2024. Feasibility, safety, and efficacy of outcome data were extracted. Among 44 publications, 39 studies recruited 98 ± 80 participants (range 15–360); 38/39 (97%) were randomized controlled trials. One-third (31%) were among patients with breast cancer. Interventions were divided into individualized nutritional counseling (n = 21), nutrition counseling plus exercise (n = 13), and nutrient-specific dietary patterns (n = 10). Many had goals to achieve established nutrition guidelines. Feasibility was high based on attendance at counseling sessions, retention, and/or food log analysis. Overall, there were minimal adverse events related to the interventions. Many studies showed between-group differences favoring the intervention group for body weight (8/24, gain or loss, according to goals), nutritional status (8/9), quality of life (3/10 without and 6/9 with exercise), cancer-related fatigue (7/10), chemotherapy tolerance (6/11), and treatment responses (3/13). In conclusion, nutritional interventions were feasible and safe for patients undergoing chemotherapy and demonstrated preliminary efficacy to improve nutritional status, fatigue, chemotherapy tolerance, and other outcomes.
MECP2 Duplication Syndrome: AI-Based Diagnosis, Severity Scale Development and Correlation with Clinical and Molecular Variables
Authors: Vega-Hanna Lourdes , Casas-Alba Dídac , Balsells Sol , Bolasell Mercè , Rubio Patricia , García-García Ana , García-García Oscar , O’Callaghan Mar , Pascual-Alonso Ainhoa , Armstrong Judith ,   MDS Group, Martinez-Monseny Antonio F.
Published: December 2024

Background: MECP2 duplication syndrome (MDS) (MIM#300260) is a rare X-linked neurodevelopmental disorder. This study aims to (1) develop a specific clinical severity scale, (2) explore its correlation with clinical and molecular variables, and (3) automate diagnosis using the Face2gene platform. Methods: A retrospective study was conducted on genetically confirmed MDS patients who were evaluated at a pediatric hospital between 2012 and 2024. Epidemiological, clinical, and molecular data were collected. A standardized clinical questionnaire was collaboratively developed with input from physicians and parents. Patient photographs were used to train Face2Gene. Results: Thirty-five patients (0–24 years, 30 males) were included. Key features in males comprised intellectual disability (100%), hypotonia (93%), autism spectrum disorder (77%) and developmental regression (52%). Recurrent respiratory infections (79%), dysphagia (73%), constipation (73%) and gastroesophageal reflux (57%) were common. Seizures occurred in 53%, with 33% being treatment-refractory. The Face2Gene algorithm was successfully trained to identify MDS. A specific clinical severity scale (MECPDup) was developed and validated, correlating with the MBA (a scale developed for Rett syndrome). The MECPDup score was significantly higher in males (p < 0.001) and those with early death (p = 0.003). It showed significant positive correlations with age (p < 0.001) and duplication size (p = 0.044). Conclusions: This study expands the understanding of MDS through comprehensive clinical and molecular insights. The integration of AI-based facial recognition technology and the development of the MECPDup severity scale hold promise for enhancing diagnostic accuracy, monitoring disease progression, and evaluating treatment responses in individuals affected by MDS.
Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning
Authors: Liu Hung-Hsien , Chang Chun-Bi , Chen Yi-Sa , Kuo Chang-Fu , Lin Chun-Yu , Ma Cheng-Yu , Wang Li-Jen
Published: December 2024

Background/Objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients. Methods: In this retrospective study, a deep learning model is developed, based on aortic computed tomography angiography (CTA) scans of 498 patients using training, validation and test sets of 398, 50 and 50 patients, respectively. An independent test set of 316 patients is used to validate and evaluate its performance. Results: Our model comprises two components. The first one is an objection detection model, which can identify the aorta from CTA. The second one is a dissection classification model, which can automatically detect the presence of aortic dissection and determine its type based on Stanford classification. Overall, the sensitivity and specificity for Type A AD were 0.969 and 0.982, for Type B AD were 0.946 and 0.996 and for normal cases were 0.988 and 1.000, respectively. The average processing time per CTA scan was 7.9 ± 2.8 s. (mean ± standard deviation). Conclusions: This deep learning automatic model can accurately and quickly detect type A AD patients, and could serve as an imaging triage in an emergency setting and facilitate early intervention and surgery to decrease the mortality rates of type A AD patients.
Predictive Modeling of Long-Term Care Needs in Traumatic Brain Injury Patients Using Machine Learning
Authors: Nyam Tee-Tau Eric , Tu Kuan-Chi , Chen Nai-Ching , Wang Che-Chuan , Liu Chung-Feng , Kuo Ching-Lung , Liao Jen-Chieh
Published: December 2024

Background: Traumatic brain injury (TBI) research often focuses on mortality rates or functional recovery, yet the critical need for long-term care among patients dependent on institutional or Respiratory Care Ward (RCW) support remains underexplored. This study aims to address this gap by employing machine learning techniques to develop and validate predictive models that analyze the prognosis of this patient population. Method: Retrospective data from electronic medical records at Chi Mei Medical Center, encompassing 2020 TBI patients admitted to the ICU between January 2016 and December 2021, were collected. A total of 44 features were included, utilizing four machine learning models and various feature combinations based on clinical significance and Spearman correlation coefficients. Predictive performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated with the DeLong test and SHAP (SHapley Additive exPlanations) analysis. Result: Notably, 236 patients (11.68%) were transferred to long-term care centers. XGBoost with 27 features achieved the highest AUC (0.823), followed by Random Forest with 11 features (0.817), and LightGBM with 44 features (0.813). The DeLong test revealed no significant differences among the best predictive models under various feature combinations. SHAP analysis illustrated a similar distribution of feature importance for the top 11 features in XGBoost, with 27 features, and Random Forest with 11 features. Conclusions: Random Forest, with an 11-feature combination, provided clinically meaningful predictive capability, offering early insights into long-term care trends for TBI patients. This model supports proactive planning for institutional or RCW resources, addressing a critical yet often overlooked aspect of TBI care.
Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights
Authors: Sazak Halenur , Kotan Muhammed
Published: December 2024

Background/Objectives: Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification using the newly proposed YOLOv10 and YOLOv11 models, with a specific focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets in microscopic images as a preliminary step of the complete blood count (CBC). Methods: The Blood Cell Count Detection (BCCD) dataset was enriched using data augmentation techniques to improve model robustness and diversity. Extensive experiments were performed, including complete weight initialization, advanced optimization strategies, and meticulous hyperparameter tuning for the YOLOv11 architecture. Results: The YOLOv11-l model achieved an overall mean Average Precision (mAP) of 93.8%, reflecting its robust accuracy across multiple blood cell types. Conclusions: The findings underscore the efficacy of the YOLOv11 architecture in automating blood cell classification with high precision, demonstrating its potential to enhance hematological analyses and support clinical diagnosis.
Early Prediction of Abdominal Aortic Aneurysm Rupture Risk Using Numerical Biomechanical Analysis
Authors: Grassl Kristina , Gasser Thomas C. , Enzmann Florian K. , Gratl Alexandra , Klocker Josef , Wippel David , Walcher David C. , Gizewski Elke R. , Wipper Sabine H.
Published: December 2024

Objective: We aimed to predict patient-specific rupture risks and growth behaviors in abdominal aortic aneurysm (AAA) patients using biomechanical evaluation with finite element analysis to establish an additional AAA repair threshold besides diameter and sex. Methods: A total of 1219 patients treated between 2005 and 2024 (conservative and repaired AAAs) were screened for a pseudo-prospective single-center study. A total of 15 ruptured (rAAA) vs. 15 non-ruptured AAAs (control group) were matched for pre-rupture imaging (first rAAA) and the initial post-rupture imaging (second rAAA) with two images in the asymptomatic control group (first and second control). The matching criteria were as follows: aneurysm diameter, sex, and time period between imagings. The biomechanical properties were analyzed with the finite element method (A4clinicsRE, Vascops GmbH, Graz, Austria). Results: Both groups had the same median aortic diameter of 5.5 cm in the first imaging but had significantly different aneurysm progressions with 6.9 cm (5.5–9.4 cm) in the second rAAA vs. 6.0 cm (5.1–7.3 cm) in the second control group (p = 0.006). The first rAAA, compared to the first control, showed significantly a higher peak wall stress (PWS) (211.8 kPa vs. 180.5 kPa, p = 0.029) and luminal diameter (43.5 mm vs. 35.3 mm; p = 0.016). The second rAAA, compared to the matched second control, showed a significantly higher PWS (281.9 kPa vs. 187.4 kPa, p = 0.002), luminal diameter (58.3 mm vs. 39.7 mm; p = 0.007), PWRR (0.78 vs. 0.49, p = 0.014) and RRED (79.8 vs. 56.5, p = 0.014). The rAAA group showed over-proportional averages, over the observation time, and an increase in PWS (nearly 10× faster in rAAA) and luminal diameter (nearly 4× faster in rAAA) per month. Conclusions: The finite element analysis of biomechanical properties could be used for the early prediction of an increased rupture risk in AAA patients. This was confirmed by matched imaging analyses before and after AAA rupture. Further multicenter data are needed to support these findings.
A Digital Approach for a Complete Rehabilitation with Fixed and Removable Prostheses: A Technical Procedure
Authors: Lefrançois Etienne , Delanoue Victor , Morice Samuel , Ravalec Xavier , Desclos-Theveniau Marie
Published: December 2024

Background: The present article describes a step-by-step maximally digitalized workflow protocol with computer-aided design and computer-aided manufacturing (CAD/CAM) in partial-arch edentulous patients rehabilitated with fixed dental prostheses and removable partial dentures (FDPs and RPDs). Methods: Facial digitalization, intraoral scans, and functional mandibular movement recordings were used to create a 4D virtual patient on commercially available CAD software. The fixed components including post-and-cores, both metal–ceramic with extra-coronal attachment and monolithic zirconia crowns, and the RPDs were manufactured by computer numerical controlled direct milling. Results: This innovative digital approach using the virtual patient and the superimposition of interim RPDs fitted in the mouth has been used to provide fixed and removable rehabilitation to the patient without clinical complications with 2 years of follow-up. Conclusions: Within the limitations of this report, the developed combined prosthesis fabrication technique allowed optimization of the production by decreasing the clinical steps and laboratory procedures in partial-arch edentulous rehabilitated with FDPs and RPDs.
Model Selection from Multiple Model Families in Species Distribution Modeling Using Minimum Message Length
Authors: Wen Zihao , Dowe David L.
Published: December 2024

Species distribution modeling is fundamental to biodiversity, evolution, conservation science, and the study of invasive species. Given environmental data and species distribution data, model selection techniques are frequently used to help identify relevant features. Existing studies aim to find the relevant features by selecting the best models using different criteria, and they deem the predictors in the best models as the relevant features. However, they mostly consider only a given model family, making them vulnerable to model family misspecification. To address this issue, this paper introduces the Bayesian information-theoretic minimum message length (MML) principle to species distribution model selection. In particular, we provide a framework that allows the message length of models from multiple model families to be calculated and compared, and by doing so, the model selection is both accurate and robust against model family misspecification and data aggregation. To find the relevant features efficiently, we further develop a novel search algorithm that does not require calculating the message length for all possible subsets of features. Experimental results demonstrate that our proposed method outperforms competing methods by selecting the best models on both artificial and real-world datasets. More specifically, there was one test on artificial data that all methods got wrong. On the other 10 tests on artificial data, the MML method got everything correct, but the alternative methods all failed on a variety of tests. Our real-world data pertained to two plant species from Barro Colorado Island, Panama. Compared to the alternative methods, for both the plant species, the MML method selects the simplest model while also having the overall best predictions.
Coding Readiness Assessment: A Measure of Computational Thinking for Preschoolers
Authors: Relkin Emily , Doss Christopher , Jones Victoria L. , Pane John F.
Published: December 2024

Coding and computational thinking (CT) are important skills to include in early childhood education. Measuring these vital skills in preschool-age children can be challenging. We modified the TechCheck-PreK assessment, an unplugged measure of CT for 3-to-5-year-old children, to increase reliability and to add foundational coding concepts. We created the Coding Readiness Assessment (CRA) from a subset of nine TechCheck-PreK items and twelve new items that assess additional CT and coding readiness constructs. In an initial feasibility study of the CRA, teachers observed impulsive responses by children. We mitigated this by implementing a brief delay between the appearance of the question and the timeframe in which children could respond. In a subsequent randomized control trial, the CRA was administered 1637 times by Head Start educators. The assessment took an average of 9.8 min to administer. CRA scores were normally distributed and increased on average as a function of age. Girls scored slightly higher than boys, although the difference was not significant when age and race were taken into account. The CRA showed acceptable levels of reliability in terms of internal consistency (α = 0.78) and test–retest reliability (r = 0.65). Results from a 3PL indicate that the CRA has suitable levels of difficulty and skill level discrimination for 3-to-5-year-olds. The 3PL guessing parameter was 0.28, indicating that steps to reduce impulsive responses were successful. We conclude that the CRA has suitable properties for assessing preschool-age children’s CT skills and coding readiness.
Stretched at Both Ends: Pressure on Student Services and the Impact on Academic Staff at UK Universities
Authors: Hughes Gareth , Priestley Michael , Spanner Leigh
Published: December 2024

The role and responsibility of universities in supporting student mental health has been the subject of high-profile legal debate. Drawing on a thematic analysis of twelve semi-structured focus groups conducted during the Student Minds UK University Mental Health Charter consultations, this paper elucidates the experiences, perceptions, and practices of 75 staff working within student services to support student mental health, with the aim of clarifying the implications for role responsibilities within a whole university approach. Participants described being ‘stretched at both ends’ in response to a significant and ongoing increase both in overall demand and complexity of presentation, further compounded by capacity challenges in public mental health services. Despite the care and commitment of staff, these conditions compromise the effectiveness, safety, and accessibility of university services. As a result, students increasingly present with mental health challenges in academic settings, multiplying risk for themselves, their peers, academic staff, and their universities, whilst negatively impacting the learning process. Thus, precisely as sectoral debate around UK universities’ legal duty of care intensifies, the role and responsibility of university services and academic staff in relation to other institutional and external stakeholders is becoming increasingly indeterminate. Taken together, the findings demonstrate the imperative of clearer conceptualisation and investment in student services alongside closer working relationships with academic staff to ensure student success and safety, and to meet the principles of good practice in the University Mental Health Charter, as advocated by UK government.
Advances in Image Processing, Artificial Intelligence, and Intelligent Robotics
Authors: Tadic Vladimir , Odry Peter
Published: December 2024

In recent years, scientists and engineers have been striving to make digital image processing as efficient as the human visual system [...]
Adaptive Embedded Flexible Tensor Singular Spectrum Decomposition
Authors: Ma Huaicheng , Li Jingran , Huang Jinfeng , Wang Ruijian , Ge Rui , Zhang Feibin
Published: December 2024

To address the difficulty in extracting fault features from dual-channel signals, this work proposes a multichannel signal fusion processing method based on Flexible Tensor Singular Spectrum Decomposition (FTSSD) with adaptive embedding dimension selection. Firstly, the optimal embedding dimension of the trajectory tensor is adaptively determined using the proposed Trajectory Dimension Ratio (TDR) index. Once the optimal embedding dimension is obtained, the multichannel signals are represented as an optimal trajectory tensor. Then, FTSSD is employed to decompose the tensor and extract feature component signals. Moreover, by setting a residual threshold or maximum number of components to control the iterative process, the precision and rationality of the decomposition are ensured. Finally, all component signals are reconstructed, and their waveforms and spectra are comprehensively analyzed. The experimental results demonstrate that the proposed adaptive embedding FTSSD algorithm achieves a high accuracy and robustness in multichannel signal decomposition and feature extraction, making it suitable for the multicomponent analysis of complex dynamic signals such as mechanical fault diagnosis and vibration analysis.
FaCoCo-RED: A Fast Response Congestion Control Mechanism for Constrained Application Protocol
Authors: Suwannapong Chanwit , Atsawaraungsuk Sarutte , Somsuk Kritsanapong , Chaichitwanidchakol Pitsanu
Published: December 2024

The rapid growth of the Internet of Things (IoT) has contributed to significant challenges in dealing with congestion within IoT communications due to high packet error rates, latency, and interference in networks. With an emphasis on the Constrained Application Protocol (CoAP), the present study aims to propose the design and development of a novel congestion control mechanism, namely, Fast Response Congestion Control—Random Early Detection, abbreviated as FaCoCo-RED, along with performance analysis and comparison of congestion management efficacy between FaCoCo-RED and Default CoAP Congestion Control (Default CoAP CC) under a Cooja simulator on the Contiki OS platform. The findings from both experiment and performance analysis, which were based on statistical testing, showed that, under medium-scale to large-scale node networks across all traffic scenarios in this study, FaCoCo-RED significantly outperformed Default CoAP CC. The improvement can be seen in such metrics as average throughput, packet loss, response time, settling time, and retransmission timeout values (RTOs). The experimental findings also showed that FaCoCo-RED can perform effectively within the IoT networks, thus potentially enhancing the reliability and scalability of CoAP for large-scale and more complex IoT applications in the future.
BEV Semantic Map Reconstruction for Self-Driving Cars with the Multi-Head Attention Mechanism
Authors: Liao Yi-Cheng , Tsai Jichiang , Chien Hsuan-Ying
Published: December 2024

Environmental perception is crucial for safe autonomous driving, enabling accurate analysis of the vehicle’s surroundings. While 3D LiDAR is traditionally used for 3D environment reconstruction, its high cost and complexity present challenges. In contrast, camera-based cross-view frameworks can offer a cost-effective alternative. Hence, this manuscript proposes a new cross-view model to extract mapping features from camera images and then transfer them to a Bird’s-Eye View (BEV) map. Particularly, a multi-head attention mechanism in the decoder architecture generates the final semantic map. Each camera learns embedding information corresponding to its position and angle within the BEV map. Cross-view attention fuses information from different perspectives to predict top-down map features enriched with spatial information. The multi-head attention mechanism then globally performs dependency matches, enhancing long-range information and capturing latent relationships between features. Transposed convolution replaces traditional upsampling methods, avoiding high similarities of local features and facilitating semantic segmentation inference of the BEV map. Finally, we conduct numerous simulation experiments to verify the performance of our cross-view model.
NRDPA: Review-Aware Neural Recommendation with Dynamic Personalized Attention
Authors: Sun Qinghao , Li Ziyang , Yu Jiong , Li Xue , Wang Xin
Published: December 2024

Review-based recommendation can utilize user and item features extracted from review text to alleviate the problems of data sparsity and poor interpretability. However, most existing methods focus on static modeling of user personality and item attributes while ignoring the dynamic changes of user and item features. Therefore, this paper proposes a neural recommendation method with dynamic personalized attention (NRDPA). First, this method captures the changes in user behavior at the word level and review level and models the personalized features of users and items by dynamically highlighting key words and important reviews. Second, the method considers information interaction in the process of user and item modeling and adjusts the feature representations of the interacting parties according to the user’s preferences for different items. Finally, experiments on five public datasets from Amazon demonstrate that the proposed NRDPA model has superior performance, with improvements of up to 10% in MSE and 6.3% in MAE compared to state-of-the-art models.
Adaptive Control Strategy of Parallel Virtual Synchronizer of Wind–Solar–Storage Microgrid Based on Neural Network
Authors: Wang Hui , Zhang Zhuanqin , Sun Li , Yang Mao , Sun Peng , Wang Jinxin
Published: December 2024

In order to solve the problem that the impedance of each line of the parallel system of the wind–solar–storage virtual synchronous machine (VSG) is inconsistent, resulting in the power circulation between the parallel VSG, a multi-parameter collaborative adaptive control strategy for the parallel virtual synchronizers of a wind–solar–storage microgrid based on a neural network was proposed. Firstly, the topology of the virtual synchronous machine parallel system of the wind–solar–storage microgrid was built, and the VSG was analyzed. Then, the neural network algorithm was used to realize the adaptive adjustment of each parameter of VSG, which improves the uneven power distribution and the influence of circulation. Next, the parameters of multiple parallel VSG control systems were configured. Finally, MATLAB2021a/Simulink was used to model the system, and the VSG capacity under different scale conditions was simulated and analyzed. The simulation results show that when the capacity ratio of VSG1 and VSG2 is 1:1, the active power output is 9000 W, and the reactive power output is 7500 Var, which realizes accurate distribution, and when the capacity ratio of VSG1 and VSG2 is 2:1, the output values of active power and reactive power are 12,000 W/6000 W and 10,000 Var/5000 Var, and the output is carried out according to the ratio of 2:1, which shows that the control strategy can effectively improve the power allocation accuracy, suppressing circulation.
Mining Nuanced Weibo Sentiment with Hierarchical Graph Modeling and Self-Supervised Learning
Authors: Wang Chuyang , Konpang Jessada , Sirikham Adisorn , Tian Shasha
Published: December 2024

Weibo sentiment analysis has gained prominence, particularly during the COVID-19 pandemic, as a means to monitor public emotions and detect emerging mental health trends. However, challenges arise from Weibo’s informal language, nuanced expressions, and stylistic features unique to social media, which complicate the accurate interpretation of sentiments. Existing models often fall short, relying on text-based methods that inadequately capture the rich emotional texture of Weibo posts, and are constrained by single loss functions that limit emotional depth. To address these limitations, we propose a novel framework incorporating a sentiment graph and self-supervised learning. Our approach introduces a “sentiment graph” that leverages both word-to-post and post-to-post relational connections, allowing the model to capture fine-grained sentiment cues and context-dependent meanings. Enhanced by a gated mechanism within the graph, our model selectively filters emotional signals based on intensity and relevance, improving its sensitivity to subtle variations such as sarcasm. Additionally, a self-supervised objective enables the model to generalize beyond labeled data, capturing latent emotional structures within the graph. Through this integration of sentiment graph and self-supervised learning, our approach advances Weibo sentiment analysis, offering a robust method for understanding the complex emotional landscape of social media.
Modeling and Estimation of the Pitch Angle for a Levitating Cart in a UAV Magnetic Catapult Under Stationary Conditions
Authors: Ładyżyńska-Kozdraś Edyta , Czaja Bartosz , Czubaj Sławomir , Tracz Jan , Sibilska-Mroziewicz Anna , Baranowski Leszek
Published: December 2024

The paper presents a method for modeling and estimating the orientation of a launch cart in the magnetic suspension system of an innovative UAV catapult. The catapult consists of stationary tracks lined with neodymium magnets, generating a trough-shaped magnetic field. The cart levitates above the tracks, supported by four containers housing high-temperature YBCO superconductors cooled with liquid nitrogen. The Meissner effect, characterized by the expulsion of magnetic fields from superconductors, ensures stable hovering of the cart. The main research challenge was to determine the cart’s orientation relative to the tracks, with a focus on the pitch angle, which is critical for collision-free operation and system efficiency. A dedicated measurement stand equipped with Hall sensors and Time-of-Flight (ToF) distance sensors was developed. Hall sensors mounted on the cart’s supports captured magnetic field data at specific points. To model the tracks, the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology was employed—a structured framework consisting of six stages; from problem understanding and data preparation to model evaluation and deployment. This approach guided the analysis of data-driven models and facilitated accurate pitch angle estimation. Evaluation metrics, including mean squared error (MSE), were used to identify and select the optimal models. The final model achieved an MSE of 0.084°, demonstrating its effectiveness for precise orientation control.
A High Reliability Ferroresonant Inverter with Improved Efficiency and Wider Input Voltage
Authors: Theodorakis Antony G. , Korakianitis Nikolaos S. , Vokas Georgios A. , Ioannidis George Ch. , Kaminaris Stavros D.
Published: December 2024

In this research, a ferroresonant (SATFORMER) inverter, optimized in terms of power efficiency, noise reduction, and reliability, is analyzed and described. This modified SATFORMER inverter can be used as a standalone inverter. In the proposed topology, the variable DC input is converted to an AC square wave by a thyristor-based modified current source inverter and additionally applied to primary winding, which is divided into four sub-windings, that are wound on the saturable core portion, via six relays with one change-over (CO) contact. The proposed ferroresonant inverter is innovative, to the best of our knowledge. A method for reducing the power losses of the proposed inverter is described and analyzed. A 150 VA model of a constant voltage transformer (ferroresonant transformer) is tested in order to experimentally investigate its basic characteristics. The circuit and simulated results of the modified current source inverter are presented in detail. The experimental results of our 150 VA model are presented. A precise term is introduced to describe the operation of the converter’s magnetic components. The occurring phenomenon is thoroughly explained, and a meaningful, perceptive, and newly incorporated term is introduced to provide a clearer understanding of the phenomenon.
Effect of Temperature on Kerogen Transformation and Hydrocarbon Generation in Bazhenov Formation (Western Siberia, Russia) Rocks During Hydrous Pyrolysis
Authors: Savostin Grigorii G. , Tikhonova Margarita S. , Kalmykov Anton G. , Volkov Dmitry S. , Kostyshina Mariia O. , Vidishcheva Olesya N. , Makhnutina Mariia L. , Kotochkova Yuliya A. , Grigorenko Tatiana V. , Proskurnin Mikhail A.
Published: December 2024

The aim of this investigation is to determine the role of temperature in hydrocarbon generation in Bazhenov formation source rocks, as hydrothermal processes in situ are supposed to be the main contributor to kerogen transformation in Western Siberia. Laboratory modeling of organic matter isothermal transformation in powdered and extracted samples by hydrous pyrolysis at 300 °C, 325 °C, 350 °C, 375 °C, and 400 °C for 24 h was performed. Heated samples were analyzed via pyrolysis, and hydrocarbons were investigated by GC and GC-MS. Results show that more than 90% of the kerogen generation potential was utilized. The amounts of liquid and gaseous products reached 82 and 126 mg of hydrocarbon/g TOC, respectively. The volume of generated gas increased while the maximum production of liquid decreased with temperatures over 350 °C. The biomarker composition of liquid products at different temperatures was similar. The characteristics of kerogen and the product composition had a high correlation with the parameters for samples naturally formed in situ under an elevated heat flow. More subtle chemical analyses are required to determine the effects of kerogen’s chemical structure and the rock mineral composition on oil and gas generation.
Online Measurement Method for Circuit Breaker Mechanical–Time Characteristics Based on Transient Voltage and Current Signal Feature Extraction
Authors: Weng Liting , Xia Jianhua , Zhang Zhaochuang , Wang Jingang , Chen Lin , Zi Yingbo , Ma Lingyi , Zhang Xingyu , Zhang Fan , Zhao Pengcheng
Published: December 2024

The good mechanical characteristics of circuit breakers are essential to ensuring their safe and stable operation. The characteristic signals at the points of initial opening and closing contain abundant information about the mechanical–time characteristics of the circuit breaker. Existing online measurement methods for circuit breaker mechanical–time characteristics typically rely on monitoring the secondary current signals of the opening and closing coils, or the signals such as vibration, pressure, and angular displacement that are generated during the opening and closing of moving and stationary contacts. Unlike these conventional approaches, this paper proposes an online measurement method for circuit breaker mechanical–time characteristics based on feature extraction from transient voltage and current signals in the primary circuit. By extracting features from the transient voltage and current signals in the primary circuit, this method obtains mechanical–time characteristics such as the points of initial opening and closing. This method is convenient and stable, enabling the online detection of the mechanical characteristics of the circuit breaker. Finally, a comparison and analysis of the online measurement results with the offline measurement results were conducted. The analysis shows that the calculation error of the opening and closing times is within 0.2 ms. This online detection method has a high measurement accuracy and provides a new approach for the online measurement of the mechanical characteristics of circuit breakers.
Chemical Profile and Antibacterial Activity of Vitis vinifera L. cv Graciano Pomace Extracts Obtained by Green Supercritical CO2 Extraction Method Against Multidrug-Resistant Escherichia coli Strains
Authors: Fernández-Pérez Rocío , Ayuso Silvia , Moreta Cristina , Saiz-Abajo María-José , Gastón-Lorente Miguel , Ruiz-Larrea Fernanda , Tenorio Carmen
Published: December 2024

The objectives of this study were to obtain and characterise polyphenolic extracts from red grape pomace of Vitis vinifera L. cv Graciano via conventional solvent extraction (SE) and green supercritical fluid extraction (SFE) and to evaluate their in vitro antibacterial activity against susceptible and multidrug-resistant Escherichia coli strains of intestinal origin. The SE and SFE methods were optimised, and ultra-performance liquid chromatography/mass spectrometry (UPLC/QqQ-MS/MS) analysis revealed 38 phenolic compounds in the SE sample, with anthocyanins being the predominant polyphenols, and 21 phenolic compounds in the SFE samples, among which hydroxybenzoic acids and flavonols were the predominant compounds. The SE and SFE samples showed antibacterial activity against both antibiotic-susceptible and -resistant E. coli strains, and minimal inhibitory concentration values were in the range of 1–4 mg/mL. The activity was bacteriostatic in all cases, and it was shown that a higher content of total polyphenols correlated with a higher antibacterial activity of the extracts. This study shows that red grape pomace of Vitis vinifera L. cv. Graciano is a rich source of bioactive phenolic compounds that can become an important feedstock for additives and other upgraded products of biotechnological interest, which can help to modulate intestinal microbiota and combat bacterial antibiotic resistance.
The Properties of Damaged Starch Granules: The Relationship Between Granule Structure and Water–Starch Polymer Interactions
Authors: Teobaldi Andrés Gustavo , Carrillo Parra Esteban Josué , Barrera Gabriela Noel , Ribotta Pablo Daniel
Published: December 2024

The morphology of wheat starch granules with different damaged starch (DS) content was analyzed using a particle size analyzer and scanning electron microscopy (SEM); the granular structure was studied using FT-IR spectroscopy and X-ray diffraction (XRD); and the granule–water interaction was evaluated by thermogravimetric analysis (TGA) and dynamic vapor sorption (DVS). The increase in the level of DS shifted the population of B-type granules towards larger particle diameters and shifted the population of A-type granules towards smaller particle diameters. The appearance of the surface of the starch-damaged granules was rough and flaky (SEM images). Crystallinity reductions were related to higher mechanical damage levels of the granular structure (FT-IR and XRD). Higher DS increased the liquid-water absorption capacity of the granules. Higher DS was associated with increments in less-bound water proportions and reductions in more strongly bound water proportions and related to reductions in the evaporation temperature of these water populations (TGA analyses). Concerning DVS data, the results suggested that the driving force for water–monolayer attachment to the starch granules decreased as DS increased. Therefore, it was suggested that the changes in granule structure led to a weaker water–starch polymer chain interactions due to the increase in DS. The results contribute to a better understanding of the influence of mechanical damage on the starch granular structure, which could be related to the rheological and thermal behavior of starch-based systems with different DS.
Highly Sensitive Lateral Flow Immunodetection of the Insecticide Imidacloprid in Fruits and Berries Reached by Indirect Antibody–Label Coupling
Authors: Barshevskaya Lyubov V. , Zvereva Elena A. , Zherdev Anatoly V. , Dzantiev Boris B.
Published: December 2024

A highly sensitive lateral flow immunoassay (LFIA) for imidacloprid, a widely used neonicotinoid insecticide, has been developed. The LFIA realizes the indirect coupling of anti-imidacloprid antibodies and gold nanoparticle (GNP) labels directly in the course of the assay. For this purpose, the common GNPs conjugate with anti-imidacloprid antibodies and are changed into a combination of non-modified, anti-imidacloprid antibodies, and the GNPs conjugate with anti-species antibodies. The given approach provides the possibility of selecting independent concentrations of GNPs and anti-imidacloprid antibodies to obtain the influence of minimal imidacloprid concentrations in the samples on the formation of detected, labeled immune complexes. A comparative study of imidacloprid LFIAs with common and indirect antibody–label coupling was implemented. The second variant reduced the limit of detection (LOD) of imidacloprid 20 times, reaching 0.2 ng/mL and 0.002 ng/mL for visual and instrumental detection, respectively, thus surpassing the existing LFIAs for imidacloprid. The developed highly sensitive LFIA was tested for imidacloprid detection in freshly squeezed fruits and berries without any additional sample preparation. The imidacloprids revealed were in the range of 75–97% for grape, 75–85% for orange, and 86–97% for apple samples. The time of the testing was 15 min.