bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanosignaling via Integrins: Pivotal Players in Liver Fibrosis Progression and Therapy
Authors: Sharip Aigul , Kunz Jeannette
Published: February 2025

Liver fibrosis, a consequence of chronic liver injury, represents a major global health burden and is the leading cause of liver failure, morbidity, and mortality. The pathological hallmark of this condition is excessive extracellular matrix deposition, driven primarily by integrin-mediated mechanotransduction. Integrins, transmembrane heterodimeric proteins that serve as primary ECM receptors, orchestrate complex mechanosignaling networks that regulate the activation, differentiation, and proliferation of hepatic stellate cells and other ECM-secreting myofibroblasts. These mechanical signals create self-reinforcing feedback loops that perpetuate the fibrotic response. Recent advances have provided insight into the roles of specific integrin subtypes in liver fibrosis and revealed their regulation of key downstream effectors—including transforming growth factor beta, focal adhesion kinase, RhoA/Rho-associated, coiled-coil containing protein kinase, and the mechanosensitive Hippo pathway. Understanding these mechanotransduction networks has opened new therapeutic possibilities through pharmacological manipulation of integrin-dependent signaling.
Impact of ZnO Nanostructure Morphology on Electrochemical Sensing Performance for Lead Ion Detection in Real Water Samples
Authors: Sledevskis Eriks , Krasovska Marina , Gerbreders Vjaceslavs , Mihailova Irena , Keviss Jans , Mizers Valdis , Bulanovs Andrejs
Published: February 2025

This study investigated the morphological dependence of ZnO nanostructures, specifically nanotube- and nanorod-based electrodes, on their electrochemical performance for the detection of lead ions (Pb2⁺) in aqueous solutions. The results demonstrate that ZnO nanotubes exhibit significantly enhanced sensitivity compared to nanorods during CV measurements. During SWV measurements, the sensitivity (116.79 mA·mM−1) and a lower limit of detection of 0.0437 μM were determined. The hollow, high-aspect-ratio structure of nanotubes provides a larger active surface area and facilitates better ion accessibility, resulting in superior electron transfer efficiency and catalytic activity. These results underscore the critical role of morphology in optimizing ZnO-based sensors. Analysis of real water samples from various natural reservoirs revealed no detectable lead, while lead was identified exclusively in artificially prepared samples containing water exposed to lead hunting shot. Over a 30-day period, the sensor retained over 95% of its initial performance when stored under vacuum conditions, demonstrating minimal signal degradation. Under ambient conditions, stability loss was attributed to moisture adsorption on the porous nanostructure. The sensor also displayed outstanding reproducibility, with current response variations across multiple probes remaining within 4%. The cost-effective and simple fabrication process of ZnO nanostructures further highlights their potential for scalable production, environmental monitoring, and integration into portable sensing devices.
Fast, Economic, and Improved Nanostructured Polymeric pH Sensor for Agrifood Analysis
Authors: Sainz-Calvo Álvaro Jesús , Sierra-Padilla Alfonso , Bellido-Milla Dolores , Cubillana-Aguilera Laura , García-Guzmán Juan José , Palacios-Santander José María
Published: February 2025

In the present work, a novel and economical pH sensor has been improved through the application of the conducting polymers poly(3,4-ethylendioxythiphene) and poly(sodium 4-styrene sulphonate). Sinusoidal voltages were applied to electrodeposit the different polymers studied for different periods (10, 15, and 20 min) on the electrode surface. The presence of polyaniline and its reversible redox structure have been corroborated by cyclic voltammetry. The working range has been increased from 3–8 to 2–12, obtaining adequate sensibility and linearity. This new sensor presented satisfactory repeatability, reproducibility (RSD < 5%), AND reversibility (pH range 2–12), and excellent selectivity towards H+ in the presence of diverse interferents in agrifood samples. Finally, the sensor was used to measure the pH in several real samples, whose pH values ranged from 2.23 to 11.5, obtaining excellent results. In addition, the values found were very similar to those reported by the gold technique (pH meter), with an error of less than 10% for most of the samples analyzed. In addition, a preliminary survey about measurements in a continuous flow, using a 3D homemade microfluidic cell, was performed with promising results.
Risk and Protective Factors for Obstructive Sleep Apnea Syndrome Throughout Lifespan: From Pregnancy to Adolescence
Authors: Petrongari Duilio , Ciarelli Francesca , Di Filippo Paola , Di Ludovico Armando , Di Pillo Sabrina , Chiarelli Francesco , Pellegrino Giulia Maria , Sferrazza Papa Giuseppe Francesco , Nosetti Luana , Attanasi Marina
Published: February 2025

Background: Obstructive sleep apnea syndrome (OSAS) in children is indeed a significant and often underdiagnosed condition. The risk factors for OSAS vary across different stages of life. Objectives: Identifying risk factors early can help in taking preventive measures to reduce the likelihood of developing OSAS, and different life stages may require different interventions. Results: During pregnancy, maternal factors such as obesity, smoking, and genetic predispositions can increase the risk of OSAS, while breastfeeding serves as a protective factor. For children aged 2 to 12, adenotonsillar hypertrophy is the primary cause of airway narrowing, with other contributing factors including obesity, craniofacial abnormalities, and increased nasal resistance. In adolescence, obesity and craniofacial abnormalities remain the main risk factors. Conclusions: By reviewing and understanding these risk factors, healthcare providers can offer more personalized and effective care, ultimately leading to better health outcomes for individuals at all stages of life.
Comparison of High-Resolution Digital Elevation Models for Customizing Hydrological Analysis of Urban Basins: Considerations, Opportunities, and Implications for Stormwater System Design
Authors: Avila-Ruiz Walter , Salazar-Briones Carlos , Ruiz-Gibert José Mizael , Lomelí-Banda Marcelo A. , Saiz-Rodríguez Juan Alejandro
Published: February 2025

Topographical data are essential for hydrological analysis and can be gathered through on-site surveys, UAVs, or remote sensing methods such as Digital Elevation Models (DEMs). These tools are crucial in hydrological studies for accurately modeling basin morphology and surface stream network patterns. Two different DEMs with resolutions of 0.13 m and 5 m were used, as well as tools which carry out urban basin delineation by analyzing their morphometric parameters to process the hydrography of the study area, using three Geographic Information Systems (GIS): ArcGIS, GlobalMapper, and SAGA GIS. Each piece of software uses different algorithms for the pre-processing of DEMs in the calculation of morphometric parameters of the study area. The results showed variations in the quantity of delineated stream networks between the different GIS tools used, even when using the same DEM. Similarly, the morphometric parameters varied between GIS tools and DEMs, which tells us that the tools and topographic data used are important. The stream network generated using ArcGIS and the DEM obtained with UAV offered a more precise description of surface flow behavior in the study area. Concerning ArcGIS, it can be observed that between the resolutions of the INEGI DEM and the UAV DEM, the delimited area of micro-basin 1 presented a minimum difference of 0.03 km2. In contrast, micro-basin 2 had a more significant difference of 0.16 km2. These discrepancies in results are attributed to the different algorithms used by each piece of software and the resolution of each DEM. Although some studies claim to have obtained the same results using different software and algorithms, in this research, different results were obtained, and emphasize the importance of establishing procedural standards, as they can significantly impact the design of stormwater drainage systems. These comparisons will allow decision-makers to consider these aspects to standardize the tools and topographic data used in urban hydrological analyses.
Analysis of the Dynamics of Hydroclimatic Extremes in Urban Areas: The Case of Grand-Nokoué in Benin, West Africa
Authors: Azagoun Vidjinnagni Vinasse Ametooyona , Komi Kossi , Vissin Expédit Wilfrid , Klassou Komi Selom
Published: February 2025

As global warming continues, extremes in key climate parameters will become more frequent. These extremes are one of the main challenges for the sustainability of cities. The aim of this study is to provide a better understanding of the evolution of extremes in precipitation (pcp) and maximum (Tmax) and minimum (Tmin) temperatures in Grand-Nokoué to improve the resilience of the region. To this end, historical daily precipitation and maximum (Tmax) and minimum (Tmin) temperature data from the Cotonou synoptic station were used from 1991 to 2020. First, the extreme events identified using the 99th percentile threshold were used to analyze their annual and monthly frequency. Secondly, a Generalized Extreme Value (GEV) distribution was fitted to the annual maxima with a 95% confidence interval to determine the magnitude of the specific return periods. The parameters of this distribution were estimated using the method of L moments, considering non-stationarity. The results of the study showed significant upward trends in annual precipitation and minimum temperatures, with p-values of 0.04 and 0.001, respectively. Over the past decade, the number of extreme precipitation and Tmin events has exceeded the expected number. The model provides greater confidence for periods ≤ 50 years. Extreme values of three-day accumulations up to 68.21 mm for pcp, 79.38 °C for Tmin and 97.29 °C for Tmax are expected every two years. The results of this study can be used to monitor hydroclimatic hazards in the region.
A Case Study and Concise Literature Review: Adult Patient’s Initial Manifestation of Complicated Acute Otitis Media Presenting as Jugular Foramen Syndrome
Authors: El-Saied Sabri , Ziv Oren , Sapir Aviad , Yafit Daniel , Kaplan Daniel M.
Published: February 2025

Background: Jugular foramen syndrome (JFS) is a rare condition characterized by the compression or impairment of one or more terminal cranial nerves passing through the jugular foramen. Although malignancies are the primary cause of JFS. Methods: In this report, we present the first documented case of JFS caused by acute otitis media in an adult patient. Results: A 74-year-old woman presented with ear pain, hoarseness, dysphagia, dizziness, tinnitus, and hearing loss. A physical examination revealed a reddish-bulging tympanic membrane, left-sided hearing loss, right uvula deviation, and cranial nerve palsies affecting the ninth and tenth nerves. Imaging studies confirmed temporal bone inflammation, thrombosis of the sigmoid sinus extending into the internal jugular vein, and signs of thrombophlebitis of the jugular vein. The patient underwent a cortical mastoidectomy, sigmoid sinus decompression, and ventilation tube insertion, along with antibiotic, steroid, and anticoagulant therapy. Postoperatively, the patient’s condition improved significantly. Conclusions: This case highlights the importance of considering complicated acute otitis media in the differential diagnosis of neurological abnormalities associated with JFS. A thorough evaluation of the patient’s medical history and radiological imaging can assist in identifying the cause of the symptoms and guide appropriate surgical or conservative treatment. Further research is essential to gain more comprehensive insights into the pathophysiology and therapeutic interventions of JFS affecting the ears.
A Marine Season Metric for Foxe Basin, Nunavut, Canada: Insights into the Evolving Nature of Sea-Ice Breakup and Freeze-Up
Authors: Gough William A.
Published: February 2025

A new marine climate metric, marine season, is introduced for Foxe Basin, Nunavut, Canada capturing the time of the year that the Basin is influenced by open water. The metric is developed with a day-to-day temperature variability framework using the Hall Beach (Sanirajak) climate record (1957–2023). Day-to-day minimum temperature variability provides a clear signal of the marine season. The new metric is compared to the more traditional breakup and freeze-up dates of sea ice that uses a 5/10th sea-ice spatial coverage threshold. While the two metrics are in general agreement, some important differences occur related to the time required for the breakup (full ice coverage to 5/10th sea-ice coverage). The timing from onset of the marine season to 5/10th ice coverage has shortened in time in a statistically significant fashion, indicating a more rapid breakup in recent years. In contrast, the freeze-up period, 5/10th to full sea-ice coverage has increased. The longer ice-free season, as determined by sea-ice data, arises primarily from open water changes in the breakup (shorter) and freeze-up (longer) period timing. These are novel insights that suggest that the basic sea-ice regime, oscillating from a full sea-ice platform and ice-free conditions has not changed, but rather the observed changes are in the nature of the transitions between these two states, breakup and freeze-up.
Influence of Laser Remelting on Creep Resistance in Ti-6Al-4V Alloy with Thermal Barrier Coating
Authors: de Freitas Filipe Estevão , da Silva Roberta Aguiar Luna , Takahashi Renata Jesuina , dos Reis Adriano Gonçalves , Capella Aline Gonçalves , Reis Danieli Aparecida Pereira
Published: February 2025

Ti-6Al-4V alloys with a thermal barrier coating (TBC) have been applied in aeronautical components as turbine blades to provide oxidation resistance and thermal protection, enabling higher operating temperatures and extending component lifespan. Research into TBCs with laser surface modification has investigated improving their mechanical and thermal properties. This study assessed the creep behavior of Ti-6Al-4V alloy with a TBC, where the coating was applied via CO2 laser-remelted plasma spraying. Creep tests were conducted at a constant temperature and a load ranging from 500 to 700 °C at 125 MPa. The microstructure and fractography of the specimens were also investigated. The investigation also included microstructural and fractographic analyses of the specimens. The results indicate that the laser-remelted TBC provided effective thermal protection and increased oxidation resistance, with the stationary creep rate at 600 °C reduced by 50% and the creep rupture life extended by 20%. Observations revealed typical ductile fractures characterized by equiaxed dimples and a homogeneous microstructure with an equiaxed dual-phase (α+β) structure near the fracture zone.
Gen-Optimizer: A Generative AI Framework for Strategic Business Cost Optimization
Authors: Faruqui Nuruzzaman , Raju Nidadavolu Venkat Durga Sai Siva Vara Prasad , Sivakumar Shanmugasundaram , Patel Nikhil , Vengaramkode Bhaskaran Shinoy , Khanam Shapla , Bhuiyan Touhid
Published: February 2025

Strategic cost optimization is a critical challenge for businesses aiming to maintain competitiveness in dynamic markets. This paper introduces Gen-Optimizer, a Generative AI-based framework designed to analyze and optimize business costs through intelligent decision support. The framework employs a transformer-based model with over 140 million parameters, fine-tuned using a diverse dataset of cost-related business scenarios. By leveraging generative capabilities, Gen-Optimizer minimizes inefficiencies, automates cost analysis tasks, and provides actionable insights to decision-makers. The proposed framework achieves exceptional performance metrics, including a prediction accuracy of 93.2%, precision of 93.5%, recall of 93.1%, and an F1-score of 93.3%. The perplexity score of 20.17 demonstrates the model’s superior language understanding and generative abilities. Gen-Optimizer was tested in real-world scenarios, demonstrating its ability to reduce operational costs by 4.11% across key business functions. Furthermore, it aligns with sustainability objectives, promoting resource efficiency and reducing waste. This paper highlights the transformative potential of Generative AI in business cost management, paving the way for scalable, intelligent, and cost-effective solutions.
Management of Peritoneal Metastasis in Patients with Pancreatic Ductal Adenocarcinoma
Authors: Wu Grace , Standring Oliver J. , King Daniel A. , Gholami Sepideh , Devoe Craig E. , Thiels Cornelius A. , Grotz Travis E. , Weiss Matthew J. , Whelan Richard L. , Raoof Mustafa , DePeralta Danielle K.
Published: February 2025

The peritoneum is the second most common site of metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Up to half of all patients that undergo curative-intent resection eventually develop peritoneal metastasis (PM), which accounts for significant morbidity and drives mortality. Despite recent advances in management, PM is associated with very poor prognosis, which is often measured in weeks to months. Clinical manifestations including bowel obstruction, ascites, and urinary obstruction have profound impact on quality of life. Even with relatively advanced disease, PM often remains occult on imaging and thus tend to be underdiagnosed and understudied. Many patients with peritoneal-only PM are excluded from clinical trials because response cannot be measured by standard radiographic criteria. Furthermore, as patients with PM are not eligible for surgical resection and low-volume peritoneal disease is often not amenable to percutaneous biopsy, tissue samples for peritoneal-specific translational studies are limited. Intraperitoneal therapeutics have been proposed as an attractive option for PM, as better penetration of tumor tissue can be achieved with less systemic toxicity compared with intravenous chemotherapy. Heated intraperitoneal chemotherapy (HIPEC), typically combined with cytoreductive surgery (CRS), is an option for select patients with PM from gynecologic or gastrointestinal primary, and for patients with primary peritoneal mesothelioma. However, the incorporation of locoregional therapy for PM in patients with PDAC has been poorly studied given the aggressive nature of pancreatic cancer and overall poor prognosis. With recent advances in existing treatment options, there may be a subset of patients who may derive benefits from locoregional control with cytoreduction and/or intraperitoneal chemotherapy. Critically, additional work is needed to determine PM-favorable clinical and tumoral predictive biomarkers to identify patients who may benefit from a more aggressive approach. We describe the current state of management of patients with peritoneal metastasis from PDAC and review the available data exploring peritoneal-directed therapy with cytoreductive surgery and/or intraperitoneal chemotherapy.
The Role of Autopsy in the Forensic and Clinical Evaluation of Head Trauma and Traumatic Brain Injury in Road Traffic Accidents: A Review of the Literature
Authors: Sacco Matteo Antonio , Verrina Maria Cristina , Raffaele Roberto , Gualtieri Saverio , Tarallo Alessandro Pasquale , Gratteri Santo , Aquila Isabella
Published: February 2025

Road traffic accidents (RTAs) are a leading cause of morbidity and mortality worldwide, frequently resulting in traumatic brain injuries (TBIs), skull fractures, and spinal injuries. This manuscript examines the forensic aspects of head trauma caused by RTAs, focusing on the role of autopsy and imaging in diagnosing and characterizing injuries. Through a systematic review of the literature, the study highlights the mechanisms of injury, including high-speed collisions, whiplash, and pedestrian impacts, and explores their pathological consequences, such as subarachnoid hemorrhage, intracranial hemorrhage, and diffuse axonal injury. The differentiation between traumatic and non-traumatic conditions, such as aneurysmal subarachnoid hemorrhage, is emphasized to ensure accurate clinical and forensic assessments. Advances in imaging technologies, particularly virtopsy, are discussed for their potential in non-invasive documentation and analysis of head injuries, while limitations of this approach are acknowledged. Furthermore, the manuscript underscores the importance of preventive measures, including helmet and seatbelt use, vehicle safety innovations, and improved road design, in reducing the incidence and severity of RTAs. By integrating clinical, forensic, and preventive perspectives, this study provides a comprehensive framework for understanding and addressing the burden of head trauma related to RTAs.
Exploring Factors Contributing to Effective Teaching in Dental Clinical Settings: Perceptions of Dental Students
Authors: Sharka Rayan , Alghamdi Mansour , Almarghlani Ammar , Abed Hassan , Alluqmani Shahad , Alhazmi Ruza , Abuljadayel Jameel
Published: February 2025

Background: Clinical teaching is crucial to dental education as it shapes the identity and professional development of students. However, there is a lack of research regarding the responsibilities of clinical teachers, their professional behavior, pedagogical approaches, and their influence on students’ clinical learning outcomes. This study aims to identify factors that students perceive in clinical teachers and how these factors influence the effectiveness of their clinical education. Methods: This cross-sectional study involved predoctoral and graduate dental students (N = 354). Data were collected using a questionnaire adapted from the Stanford Faculty Development Program (SFDP) scale and open-ended questions. Simple and multiple linear regression analyses were conducted to examine associations and predictive capabilities of the SFDP constructs for clinical teaching effectiveness. Data were analyzed using SPSS version 29. Results: A total of 332 responses were received, with a 94% response rate. Significant positive correlations were found between SFDP constructs and clinical teaching effectiveness. The evaluation construct had the strongest correlation (r = 0.480, p < 0.001), explaining 23.1% of the variance. The communication of clinical goals had the second strongest correlation (r = 0.415, p < 0.001), explaining 17.3%. The lowest correlation was for promotion of understanding and retention (r = 0.332, p < 0.001), explaining 11%. Cronbach’s alpha ranged from 0.607 to 0.783. Conclusions: This study highlighted key factors influencing the effectiveness of clinical teaching, including evaluation quality, clear communication of clinical goals, supportive learning environments, and effective feedback. Clinical teachers’ respect for students also facilitates successful education. More studies are required to explore additional factors across different contexts.
Fostering Equitable Disciplinary Literacy Practices
Authors: Waymouth Heather , Hinchman Kathleen A.
Published: February 2025

A burgeoning amount of research has recently been published to foster equitable disciplinary literacy practices in secondary schools. The literature review included in this paper examined this recent scholarship with a multistep qualitative analysis of 31 studies published between 2019 and 2024 to explore how these studies conceptualized and actualized attention to equity in discipline literacy instruction. The analysis revealed three overlapping themes reflecting the studies’ conceptualizations of how to address equity with disciplinary literacy. These included equity as access to disciplinary instruction, equity as scaffolding literacy for disciplinary learning, and equity as engaging in locally and culturally relevant inquiry. The Results Section summarizes how the studies reviewed addressed these themes. Studies situated disciplinary literacy in many types of instructional contexts, including subject-area classrooms and segregated settings for students with learning differences, suggesting a notion of equity as making disciplinary literacy instruction available to all students. Most disciplinary literacy instruction supported students’ reading, discussion, writing, and argumentation to report on results of disciplinary study, suggesting that equitable disciplinary literacy instruction was provided as needed support with a focus on students engaging in disciplinary studies. A few studies addressed, instead, school and classroom literacy culture or a focus on achievement that seemed to inhibit students’ ability to benefit from disciplinary literacy efforts. More than half of the studies reviewed focused on teachers’ orchestration of compelling, culturally relevant student inquiry, suggesting equitable disciplinary literacy instruction as needing to be relevant to students. Only a few studies delineated what such instruction would need to look like to build student independence over time and to include inquiry into multiple perspectives toward key disciplinary ideas. The paper ends with a summary, critique, and conclusion that encourages further long-term studies involving the schoolwide use of equitable disciplinary literacy approaches.
Harnessing Generative AI for Text Analysis of California Autonomous Vehicle Crashes OL316 (2014–2024)
Authors: El-Yabroudi Mohammad , Pothuguntla Sri Harsha , Ghadi Athar , Muniandi Balakumar
Published: February 2025

Autonomous vehicles (AVs) are expected to eventually replace traditional vehicles that require human drivers. In recent years, several AV manufacturers have begun on-road testing to validate the safety of these vehicles. California is one of the few states to permit such testing, regulating it through a permit system. To ensure transparency and public awareness, the state mandates that any licensed AV manufacturer conducting on-road tests report crashes involving AVs. This must be conducted using a standardized format known as OL316, a requirement that has been in place since late 2014. While previous research has explored AV crash data, most studies have focused on specific timeframes without covering the entire period since 2014. Moreover, converting the data from PDFs to machine-readable formats has often been a manual process, and the description text field in reports has rarely been fully analyzed. This article presents a comprehensive, machine-readable dataset of AV crashes from 2014 to September 2024, along with publicly available parsing code to streamline future data analysis. Additionally, we provide an updated statistical analysis of AV crashes during this period. Furthermore, we leverage Generative AI (GenAI) to analyze the description text field of the OL316 reports. This analysis identifies common crash scenarios, contributing factors, and additional insights into moderate and major incidents. The final dataset comprises 728 crash entries. Notably, only 2% of the crashes were categorized as major, while 14% were classified as moderate. Furthermore, 43% of the crashes occurred while the AV was stationary, whereas 55% took place while the AV was in motion. Our GenAI analysis indicates that, in many instances, human drivers of non-autonomous vehicles were at fault. Common causes include rear-end collisions due to insufficient following distances, traffic violations such as running red lights or stop signs, and reckless behaviors like lane boundary violations or speeding.
Coils Optimisation to Avoid Parasitic Capacitance Effects in an Inductive Wireless Charger for Underwater Vehicles
Authors: Casaucao Inmaculada , Linares Alejandro , Triviño Alicia
Published: February 2025

The integration of inductive charging technology in electric vehicles has aroused the interest of researchers in recent years. Specifically, one of the growing areas is wireless charging in Autonomous Underwater Vehicles (AUVs). In this environment, the effects of seawater in wireless power transmission should be carefully studied. Specifically, one of the effects that should be analysed is the appearance of parasitic capacitances (Ce) between the power coils due to the high conductivity of seawater. The parasitic capacitance, together with the power converters switching losses and the resistive and inductive losses, can lead to a drop in efficiency during the charging process. The main objective of this contribution is to find the optimal solution to avoid the effects of Ce during the coils design, thus simplifying the process and making it equivalent to an air-based solution. To do so, different design criteria have been defined with a comparative analysis among different topologies proposed. Specifically, we have studied the variations of voltage, current, and efficiency caused by the Ce. Additionally, a comparison between Series-Series (SS) and LCC–Series (LCC–S) compensation systems has been considered, studying the system efficiency and maximum current values found on the circuit. The results of these studies have been verified through experimental validations, where the design and implementation of the elements that constitute the inductive charger have been performed. This validation has demonstrated the possibility of neglecting the effects of Ce by optimising the coil’s design.
Evaluation and Explanation of Post Quality Based on a Multimodal, Multilevel, and Multi-Scope Focused Fusion Mechanism
Authors: Guo Xiaoxu , Cao Han , Cui Yachao , Zhao Haiyan
Published: February 2025

Assessing the quality of multimodal posts is a challenging task that involves using multimodal data to evaluate the quality of posts’ responses to discussion topics. Providing evaluations and explanations plays a crucial role in promoting students’ individualized development. However, existing research on post quality faces the following challenges: (1) Most evaluation methods are classification tasks that lack explanations and guidance. (2) There is a lack of a fusion mechanism that focuses on each modality’s information, is multidimensional, and operates at multiple levels. Based on these challenges, we propose the task of multimodal post quality assessment and explanation (MPQAE), aiming to leverage multimodal data to accurately evaluate and explain the quality of posts. To address this task, we introduce a Multimodal multi-level fusion model for post quality assessment with explanations (MMFPQ). The model uses multimodal topic–post pair data for fusion across multiple scopes and levels, attending to the information from each modality during the fusion process, thereby acquiring multi-level relational information to promote the generation of post quality evaluations, explanations, and guidance. We conducted comparative experiments on two newly created datasets for post quality assessment and explanation, as well as on the public dataset. The results demonstrate that our model outperforms the baseline models used for comparison. Furthermore, our model exceeds the best-performing baseline model, TEAM(mulTisource sEmantic grAph-based Multimodal sarcasm explanation scheme), by 10.32% and 13.72% on the Art Literature dataset in terms of METEOR and BLEU-4, respectively.
Optimization Model-Based Robust Method and Performance Evaluation of GNSS/INS Integrated Navigation for Urban Scenes
Authors: Chai Dashuai , Song Shijie , Wang Kunlin , Bi Jingxue , Zhang Yunlong , Ning Yipeng , Yan Ruijie
Published: February 2025

The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal interruptions in occluded environments, which lead to unreliable parameter estimations in GNSS/INS based on filter models. To address this issue, in this paper, a GNSS/INS combination model based on factor graph optimization (FGO) is investigated and the robustness of this optimization model is evaluated in comparison to the traditional extended Kalman filter (EKF) model and robust Kalman filter (RKF) model. In this paper, both high- and low-accuracy GNSS/INS combination data are used and the two sets of urban scene data are collected using high- and low-precision consumer-grade inertial guidance systems and an in-vehicle setup. The experimental results demonstrate that the position, velocity, and attitude estimates obtained using the GNSS/INS and the FGO model are superior to those obtained using the traditional EKF and robust EKF methods. In the simulated scenarios involving gross interference and GNSS signal loss, the FGO model achieves optimal results. The maximum improvement rates of the position, velocity, and attitude estimates are 81.1%, 73.8%, and 75.1% compared to the EKF method and 79.8%, 72.1%, and 57.1% compared to the RKF method, respectively.
Deployment of TinyML-Based Stress Classification Using Computational Constrained Health Wearable
Authors: Abu-Samah Asma , Ghaffa Dalilah , Abdullah Nor Fadzilah , Kamal Noorfazila , Nordin Rosdiadee , Dela Cruz Jennifer C. , Magwili Glenn V. , Mercado Reginald Juan
Published: February 2025

Stress has become a common mental health issue in modern society, causing individuals to experience acute behavioral changes. Exposure to prolonged stress without proper prevention and treatment may cause severe damage to one’s physiological and psychological health. Researchers around the world have been working to find and create solutions for early stress detection using machine learning (ML). This paper investigates the possibility of utilizing Tiny Machine Learning (TinyML) in developing a wearable device, comparable to a smartwatch, that is equipped with both physiological and psychological data detection system to enable edge computing and give immediate feedback for stress prediction. The main challenge of this study was to fit a trained ML model into the microcontroller’s limited memory without compromising the model’s accuracy. A TinyML-based framework using a Raspberry Pi Pico RP2040 on a customized board equipped with several health sensors was proposed to predict stress levels by utilizing accelerations, body temperature, heart rate, and electrodermal activity from a public health dataset. Moreover, a few selected machine learning models underwent hyperparameter tuning before a porting library was used to translate them from Python to C/C++ for deployment. This approach led to an optimized XGBoost model with 86.0% accuracy and only 1.12 MB in size, hence perfectly fitting into the 2 MB constraint of RP2040. The prediction of stress on the edge device was then tested and validated using a separate sub-dataset. This trained model on TinyML can also be used to obtain an immediate reading from the calibrated health sensors for real-time stress predictions.
Improving Location Recommendations Based on LBSN Data Through Data Preprocessing
Authors: Bembenik Robert , Orzoł Mateusz , Maciąg Piotr
Published: February 2025

The accurate prediction of the next location in a sequence is highly beneficial for users of mobile applications. In this study, we investigate how various data preprocessing techniques affect the performance of location recommendation systems. We utilize datasets from Foursquare and Twitter, incorporating users’ historical check-ins. Key preprocessing steps include filtering datasets to users with common features, analyzing user location preferences, varying sequence lengths and location categories, and integrating time-of-day information. Our findings reveal that proper data preprocessing significantly enhances the accuracy of recommendations by addressing key challenges such as data sparsity and user heterogeneity. Specifically, tailoring datasets to individual user attributes improves model personalization, while restructuring category hierarchies balances precision and diversity in the recommendations that are given. Integrating temporal data further refines the predictions that are made by accounting for time-based user behavior. Recommendations are generated using recurrent neural networks (RNNs) and hidden Markov models (HMMs), with the experimental results showing up to 20% improvement in the precision of personalized models compared to global ones.
Smart Contract for Relay Verification Collaboration Rewarding in NOMA Wireless Communication Networks
Authors: Sileikis Vidas , Wang Wei
Published: February 2025

Future generations of wireless networks at high-frequency spectrum suffer from limited coverage and Non-Line- of-Sight signal blockage, challenging emerging applications, such as smart industries and intelligent automation systems. Collaborative and cooperative communications with smart relays via Non-Orthogonal Multiple Access (NOMA) could be a breakthrough solution to this challenge. This paper presents a blockchain-integrated framework for NOMA wireless communication systems that incentivizes cooperation among users serving as relays. By leveraging Ethereum-based smart contracts, we introduce a Service Verification Contract featuring a Proof of Quality of Experience (PQoE) mechanism. The contract uses trust scores, weighted verifications, and dynamic validation thresholds to ensure honest behavior and deter malicious activities. The simulation results show that honest participants gradually increase their trust scores and require fewer verifications, while malicious verifiers lose influence over repeated rounds. Our findings indicate that combining trust-based incentives with a decentralized ledger can effectively promote reliable data-relaying services and streamline payment processes in collaborative and smart wireless networking systems.
Multi-Risk Factor and Knowledge Entropy Framework for Alternating Current Arc Fault Detection
Authors: Hu Pochen , Kong Zhengmin , Huang Tao , Ding Li
Published: February 2025

This study addresses the significant challenges associated with detecting series AC arc faults, particularly in the context of diverse load types, coupled features, and the superimposed characteristics of arc signals. To overcome these complexities, a novel AC arc detection methodology is proposed, which leverages the construction of multiple risk factors. Specifically, the approach introduces three innovative risk factors: the abnormal distribution risk factor, the harmonic energy risk factor, and the abnormal pulse risk factor (collectively referred to as AHA). These factors are designed to extract the distinct characteristics of AC arc faults across varying operational scenarios. Furthermore, an expert knowledge-driven fusion framework based on information entropy (KE) is developed to integrate these risk factors, enhancing the robustness and precision of the detection process. Experimental validation conducted in low-voltage electrical environments demonstrates that the proposed AHA-KE model achieves high detection accuracy, effectively addressing the inherent challenges of arc fault detection in such settings.
Improved EfficientNet Architecture for Multi-Grade Brain Tumor Detection
Authors: Ishaq Ahmad , Ullah Fath U Min , Hamandawana Prince , Cho Da-Jung , Chung Tae-Sun
Published: February 2025

Accurate detection and diagnosis of brain tumors at early stages is significant for effective treatment. While numerous methods have been developed for tumor detection and classification, several rely on traditional techniques, often resulting in suboptimal performance. In contrast, AI-based deep learning techniques have shown promising results, consistently achieving high accuracy across various tumor types while maintaining model interpretability. Inspired by these advancements, this paper introduces an improved variant of EfficientNet for multi-grade brain tumor detection and classification, addressing the gap between performance and explainability. Our approach extends the capabilities of EfficientNet to classify four tumor types: glioma, meningioma, pituitary tumor, and non-tumor. For enhanced explainability, we incorporate gradient-weighted class activation mapping (Grad-CAM) to improve model interpretability. The input MRI images undergo data augmentation before being passed through the feature extraction phase, where the underlying tumor patterns are learned. Our model achieves an average accuracy of 98.6%, surpassing other state-of-the-art methods on standard datasets while maintaining a substantially reduced parameter count. Furthermore, the explainable AI (XAI) analysis demonstrates the model’s ability to focus on relevant tumor regions, enhancing its interpretability. This accurate and interpretable model for brain tumor classification has the potential to significantly aid clinical decision-making in neuro-oncology.
A New Technology-Adaptable Design for High-Endurance EEPROM
Authors: Zhang Hong , Wu Chang , Zhang David Wei , Chen Guiling
Published: February 2025

High-endurance EEPROM is widely used for data storage with a data maintenance requirement when powered off. A major factor in EEPROM design is its lifetime cycle. However, state-of-the-art high-endurance designs are based on an IDM (integrated device manufacturer), which is very expensive and unfeasible for fabless design houses. In this article, we propose an innovative technology-adaptable high-endurance EEPROM design that is suitable for fabless design houses, with a much lower manufacturing cost. The key part of our design is a new charge pump with a precise voltage step increase control to reduce the high voltage damage to memory cells and to increase the EEPROM’s endurance. A temperature-insensitive clamping device is also used to alleviate the voltage fluctuation problem. Our method is adaptable to advanced fabrication processes and, thus, applicable for fabless designs with lower costs. Our test on a 0.13 µm commercial fabrication technology shows 4 million erase/write cycles, which is on a par with the state-of-the-art IDM supplier STMicroelectronics.
The Intrinsic Mechanism and Suppression Strategy of Transient Current Imbalance Among Parallel Converters
Authors: Fu Mingjie , Cai Huafeng , Lin Xinchun
Published: February 2025

Due to the difficulty in achieving a high-power output with a single converter, parallel converters are widely used in high-power applications. However, inconsistency in the output voltage feedback coefficients of individual converters and the associated dispersion in parallel systems often lead to unbalanced current sharing during transient processes, such as load disturbances. This imbalance can result in certain converters being overloaded during transients, leading to premature shutdown. Subsequently, the load on the remaining converters increases, further aggravating the imbalance and triggering additional shutdowns in a domino effect, which may, ultimately, cause the entire parallel converter system to shut down. To address this issue, this study focuses on parallel phase-shift full-bridge systems, analyzing the intrinsic mechanism by which feedback coefficient dispersion affects transient current sharing. A droop control strategy with improved transient virtual impedance is proposed to enhance the current sharing during transients. A simulation and experimental results demonstrate that the proposed strategy significantly improves current sharing during transient processes, effectively enhancing the dynamic performance and reliability of the system.
A Comprehensive Numerical Analysis of a 2.45 GHz Energy Harvesting Rectenna System and a Proposal for a Figure of Merit for Rectenna Systems
Authors: Koubar Gabriel , Haddad Fayrouz , Gadacha Amine , Sadek Sawsan , Rahajandraibe Wenceslas
Published: February 2025

This work presents a numerical analysis of a 2.45 GHz full-wave bridge rectifier for RF (radio frequency) energy harvesting under low-power input conditions, and a guideline for developing a figure of merit (FOM) for RF energy harvester rectennas by relying on data science techniques, laying the foundation for a universally accepted FOM. The performance of the full-wave bridge rectifier, using two types of Schottky diodes, HSMS2850 and SMS7630, was evaluated at −5 and −15 dBm, with the diodes achieving maximum power conversion efficiencies (PCEs) of 57% and 33%, respectively, and reflection coefficient S11 values below −30 dB. A printed circuit board was designed to prepare for future laboratory measurements offering insights into real-world performance. Additionally, a double-voltage rectifier was simulated, achieving PCE values of 41% and 66% at similar input power levels; furthermore, various CMOS-based rectifier topologies reached PCE values of 69% at −5 dBm and 43.6% at −26 dBm.
Optimal Control Study of CCM SITO Buck Converter Based on Objective Holographic Feedback
Authors: Li Jiyong , Dong Hao , Chen Peiwen , Zhou Pengcheng
Published: February 2025

With the rapid development of new energy sources, distributed power supplies are widely used in DC microgrid systems. The DC–DC converter, as the hub for transmitting energy between the distributed power supply and the DC bus, plays an important role in the stability of the whole system performance. Due to the complexity of the actual working environment, the DC bus voltage is often affected by uncertainties such as fluctuations of distributed power supply and random changes in load, so the reliability of DC–DC converters is increasingly required, and it is difficult for traditional linear controllers to ensure that a DC–DC converter operates stably over a wide range. In order to solve this problem, this paper takes the Single-Inductor Triple-Output (SITO) Buck converter, which represents the multi-input and multi-output system, as the research object, analyzes the respective operating characteristics and control difficulties, and proposes Objective Holographic Feedback Nonlinear Control (OHFNC) to improve the stability of the research system. The optimal control based on objective holographic feedback is proposed to address the cross-influence factors between the multiple output branches of the Buck converter and the inability of accurate feedback linearization. Finally, the validity of the model and theoretical analysis is verified by simulation and experimental results.
A 512 KBytes Highly Reliable and High-Speed Embedded NOR Flash Memory
Authors: Jiang Yinuo , Wang Zhexian , Yang Guangjun , Du Tao
Published: February 2025

With the increasing requirements for chip data storage capabilities in fields such as automotive electronics and the Internet of Things, Flash memory is becoming more and more widely used. This paper presents a 512 KBytes Flash memory array with high reliability, high-speed reading, and high noise immunity. By regarding one bit of the dual-bit NORD structure as a dummy bit, we simplify the operation mode and obtain a wider cell current window. Meanwhile, this paper minimized the influence of supply voltage fluctuation on the comparison between cell current and reference current through the optimization of the sense amplifier circuit. We tested whether this array depicts a high-endurance performance under 25 °C and 85 °C, as well as high-speed reading up to 18 ns. This enhanced Flash memory is expected to bring inspiration for achieving high reliability and endurance in the automotive field under harsh operating conditions.