bluuebunny/crossref_metadata_2025 huggingface dataset:
Targeted Deletion in the Basal Body Protein Talpid3 Leads to Loss of Primary Cilia in Embryonic Stem Cells and Defective Lineage-Specific Differentiation
Authors: Ferguson Ross , Subramanian Vasanta
Published: November 2024

Talpid3 is a basal body protein required for the formation of primary cilia, an organelle involved in signal transduction. Here, we asked if Talpid3 has a role in the regulation of differentiation and/or self-renewal of ES cells and whether cells lacking cilia due to a deletion in Talpid3 can be reprogrammed to induced pluripotent stem (iPS) cells. We show that mouse embryonic limb fibroblasts which lack primary cilia with a targeted deletion in the Talpid3 (Ta3) gene can be efficiently reprogrammed to iPS cells. Furthermore, vector-free Ta3−/− iPS cells retain ES cell features and are able to self-renew. However, both Ta3−/− iPS and ES cells are unable to form visceral endoderm and differentiate poorly into neurons. The observed defects are not a consequence of reprogramming since Ta3−/− ES cells also exhibit this phenotype. Thus, Talpid3 and primary cilia are required for some differentiation events but appear to be dispensable for stem cell self-renewal and reprogramming.
Priorities of the Pediatric Spinal Cord Injury Population: An International Study on Patient-Reported Outcome Measures
Authors: Ríos-León Marta , Onal Bashak , Arango-Lasprilla Juan Carlos , Augutis Marika , Graham Allison , Kelly Erin Hayes , Kontaxakis Antonis , López-Dolado Elisa , Scheel-Sailer Anke , Valiullina Svetlana , PEPSCI Collaboration  , Taylor Julian
Published: November 2024

Background/Objectives: Overall priorities of the international pediatric-onset spinal cord injury (SCI) population are unknown. The purpose was to describe and compare Life and Health (L&H) domain overall priorities of European youth with SCI and their parents and caregivers (P&C). Methods: A survey with a cross-sectional design, prepared by the PEPSCI Collaboration, was conducted in six European countries. In total, 202 participants, including youth with SCI (n = 101) and their P&C (n = 101), were included. Overall priorities were calculated based on unhappiness, importance, and research. Results: The sample included youth aged 8–12 years (30.7%) and 13–25 years (69.3%; 38.6% 13–17-year-olds and 30.7% youth aged 18–25 years), in addition to their P&C. The top three L&H priorities highlighted by P&C of the youth aged 8–12 years were “bladder” function (78%), “leg/foot movement” (77%), or “bowel” function (74%), compared with “leg/foot movement” (79%), “sit-to-stand” (76%), or “walking/ability to move” (75%) reported by P&C of the youth aged 13–25 years. The youth aged 13–25 years considered “leg/foot movement” (68%), “bowel” (66%), or “bladder” function (65%) as priorities. The top 10 priorities highlighted by the youth aged 13–25 years compared to the top 10 priorities rated by P&C were issues related to “personal needs”. Nevertheless, “pressure injuries”, “pain”, “bowel function”, or “mobility in the community” were highlighted as top preferences of priorities for the youth aged 13–25 years compared to their P&C. Conclusions: Adolescents/young adults highlighted health domain priorities compared with their P&C, who equally considered L&H domains. Life domains, which were previously unaddressed, were highlighted by P&C, including “adulthood expectations” and “parenthood expectations”. This survey will promote the involvement of stakeholders for comprehensive rehabilitation management for this population.
Management of Acute Lateral Humeral Condyle Fractures in Children
Authors: Álvarez Muñoz Mónica , García de la Blanca Juan Carlos , Anchía Myriam Vidart , Martí Ciruelos Rafael , Calvo Calvo Sara , Menéndez Crespo María Teresa
Published: November 2024

Pediatric elbow fractures are quite common, accounting for up to 34% of bone fractures in children. Among these, acute lateral humeral condyle (LHC) fractures represent up to 22%. The accurate diagnosis and early treatment of LHC fractures are crucial due to the potential for abnormal growth and significant long-term impacts on joint motion. With the aim of enhancing the understanding of pediatric LHC fracture management among pediatric healthcare practitioners, we present a literature review combined with our technical recommendations based on our experience. Imaging through AP, lateral, and internal oblique X-rays remains the gold standard for diagnosis, although there is increasing focus on non-irradiating techniques, considering the skeletally immature nature of the patients. Several classification systems aid in fracture assessment, each varying in their simplicity, reproducibility, and inter- and intra-observer correlations. The treatment approaches for LHC fractures include conservative management with immobilization for minimally displaced fractures and surgical intervention for displaced fractures. The surgical options encompass closed and open reductions, using Kirschner wires or cannulated screws for fixation. While both methods show favorable outcomes, recent years have seen a growing interest in expanding the traditional indications for closed approaches. After a period of post-surgical immobilization of the limb, rehabilitation care is recommended to assist in the recovery of the range of motion. During the postoperative period, the most frequent complications are bony overgrowth, malunion, and infection, although with highly variable rates, which typically do not result in functional impairment if managed properly. Regular follow-up and monitoring are essential for optimal recovery and minimizing long-term complications.
Study on Microstructure and Corrosion Fatigue Resistance of 14Cr12Ni3Mo2VN Materials Based on the Composite Technology of High-Frequency Induction Quenching and Laser Shock Peening
Authors: Gao Jiashun , Xu Zhilong , Lv Huiting , Yang Zhiling , Yang Guang , Liu Liehua
Published: November 2024

This study investigated the microstructure, microhardness, and residual compressive stress of 14Cr12Ni3Mo2VN martensitic stainless steel treated with high-frequency induction quenching (HFIQ) and laser shock peening (LSP). Using rotating bending corrosion fatigue testing, the corrosion fatigue performance was analyzed. Results show that a microstructural gradient formed after HFIQ and LSP: the surface layer consisted of nanocrystals, the subsurface layer of short lath martensite, and the core of thick lath martensite. A hardness gradient was introduced, with surface hardness reaching 524 Hv0.1, 163 Hv0.1 higher than the core hardness. A residual compressive stress field was introduced near the surface, with a maximum residual compressive stress of approximately −575 MPa at a depth of 0.1 mm. Corrosion fatigue results indicate that cycle loading times of samples treated with HFIQ and LSP were 2.88, 2.04, and 1.45 times higher than untreated, HFIQ-only, and LSP-only samples, respectively. Transmission electron microscopy (TEM) characterization showed that HFIQ reduced the lath martensite size, while the ultra-high strain rate induced by LSP likely caused dynamic recrystallization, forming numerous sub-boundaries and refining grains, which increased surface hardness. The plastic strain induced by LSP introduced residual compressive stress, counteracting tensile stress and hindering the initiation and propagation of corrosion fatigue cracks.
A Scoping Review on Hazard Recognition and Prevention Using Augmented and Virtual Reality
Authors: Faiz Tauqeer , Tsun Mark Tee Kit , Mahmud Abdullah Al , Sim Kwan Yong
Published: November 2024

Hazard recognition and prevention techniques utilize augmented reality (AR) and virtual reality (VR) technology for immersive training and identification of potential hazards. Currently, no scoping reviews exist specifically on hazard recognition and prevention using AR and VR, which is crucial for forming evidence-based guidelines. This scoping review aimed to identify the use of AR and VR in hazard recognition and prevention, identify research gaps, and provide future recommendations. This Joanna Brings Institute (JBI) approach was used to investigate hazard recognition and prevention with AR and VR interventions. A total of six reliable databases were included: Google Scholar, IEEE Xplore, ACM Digital Library, PubMed, Scopus, and APA PsycNet, focusing on records and publications from the last five years. The review identified 5438 publications, with 22 meeting final inclusion criteria. The major findings include the use of AR and VR for teaching new skills, ensuring health and safety, enhancing realism, developing cognitive skills, improving performance, increasing engagement, and stimulating a sense of presence. Limitations identified include limited or non-diverse sample size, absence of real-world scenarios or contextual learning, inadequate game mechanics, insufficient validation of test results, lack of dynamic game behaviour, insufficient expert inclusion, technology issues, long-term impact assessment, and cost comparisons with traditional approaches. Technological issues have emerged as an additional impediment, limiting the efficacy of AR/VR applications in hazard recognition and prevention. In light of these findings, future research should focus on integrating contextual learning, expert inclusion, sample recruitment, expert validation, long-term impact assessment, and resolving technological challenges to enhance AR/VR applications’ efficacy and real-world viability.
Comparison of Commercial REBCO Tapes Through Flux Pinning Energy
Authors: Khan Masood Rauf , Leo Antonio , Masi Andrea , Angrisani Armenio Achille , Augieri Andrea , Celentano Giuseppe , Galluzzi Armando , Polichetti Massimiliano , Nigro Angela , Grimaldi Gaia
Published: November 2024

This work presents a comparison of different commercial tapes belonging to the second-generation High-Temperature Superconductors (2G HTS) produced by SuNAM Co., Ltd., SuperOx, and Shanghai Superconductors Technology Co., Ltd. (SST) companies. The aim is to investigate pinning mechanisms responsible for best performances, looking at the anisotropy of the irreversibility field and of the flux pinning energy. The irreversibility line states the upper limit of current-carrying capacity, whereas the flux pinning energy explores the ability of material defects to act as weak collectively or strong single vortex pinning centers. All investigated samples have artificial pinning centers (APCs) included in the superconducting matrix: BHO-doped EuBCO for SST, Y2O3 in YBCO for SuperOx, and Gd2O3 particles trapped in GdBCO for SuNAM. Resistive transition curves were measured in high magnetic fields up to 16 T for magnetic field orientations parallel and perpendicular to the tape surface. We found that the anistropy of SST tape shows an overall independence both on temperature and magnetic field, while the other two samples show a more complex behavior. This leads to the conclusion that properly engineered APC optimization in coated conductors can further reduce anisotropy of superconducting properties.
Temperature Effects on Growth Performance, Fecundity and Survival of Hippocampus guttulatus
Authors: Palma Jorge , Correia Miguel , Leitão Francisco , Andrade José Pedro
Published: November 2024

This experiment aimed to determine the temperature limits beyond which seahorse growth and reproduction become suboptimal due to climate change. Four temperatures (16, 20, 24, and 28 °C) were tested to evaluate their effect on juvenile (1–56 days post-parturition (DPP)) and adult (one year old) long-snout seahorses, Hippocampus guttulatus. Additionally, the reproductive performance of adults was observed. Another experiment measured oxygen consumption (MO2) in the same age groups and temperatures. Adults showed significantly higher growth rates at 20 and 24 °C compared to 16 and 28 °C. Adult mortality rates were 0%, 0%, 6.2%, and 62.5% at the respective temperatures. Juvenile growth performance was higher at 20 °C and 24 °C but significantly lower at 16 °C and null at 28 °C, with survival rates of 8%, 62%, 10%, and 0%, respectively. Oxygen consumption increased with temperature, ranging from 106.3 ± 3.1 to 203.3 ± 3.1 μmol O2/g BW/h at 16 °C, and from 127.6 ± 3.5 to 273.3 ± 3.1 μmol O2/g BW/h at 28 °C for adults and 1 DPP juveniles, respectively. The study highlights that juvenile and adult H. guttulatus have narrow thermal boundaries, beyond which reproduction, growth, and survival are seriously affected. Under climate change, the species appears unable to cope, potentially leading to their rapid disappearance.
CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data
Authors: Gück Christian , Roelofs Cyriana M. A. , Faulstich Stefan
Published: November 2024

Early fault detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain-specific public datasets are scarce. Many comparisons of different approaches either use benchmarks composed of data from many different domains, inaccessible data, or one of the few publicly available datasets that lack detailed information about the faults. Moreover, many publications highlight a couple of case studies where fault detection was successful. With this paper, we publish a high quality dataset that contains data from 36 wind turbines across 3 different wind farms as well as the most detailed fault information of any public wind turbine dataset as far as we know. The new dataset contains 89 years worth of real-world operating data of wind turbines, distributed across 44 labeled time frames for anomalies that led up to faults, as well as 51 time series representing normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point. Furthermore, we propose a new scoring method, called CARE (Coverage, Accuracy, Reliability and Earliness), which takes advantage of the information depth that is present in the dataset to identify good early fault detection models for wind turbines. This score considers the anomaly detection performance, the ability to recognize normal behavior properly, and the capability to raise as few false alarms as possible while simultaneously detecting anomalies early.
Analysis of the Porphyrin Peak Shift and Fluorescence Lifetime in Gliomas with Different Tumor Grades, Intratumoral Regions, and Visible Fluorescence Status
Authors: Körner Lisa Irina , Reichert David , Andreana Marco , Unterhuber Angelika , Erkkilae Mikael T. , Makolli Jessica , Kiesel Barbara , Mischkulnig Mario , Rötzer-Pejrimovsky Thomas , Wöhrer Adelheid , Berger Mitchel S. , Leitgeb Rainer , Widhalm Georg
Published: November 2024

Background: 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX (PpIX) fluorescence shows high sensitivity in detecting the tumor core of high-grade gliomas (HGG) but poor sensitivity for tissue of low-grade gliomas (LGG) and the margins of HGG. The characteristic emission peak for PpIX is known to be located at 635 nm. Recently, a second emission peak was described at 620 nm wavelength in LGG and the tumor infiltration zone of HGG. Methods: During surgery, samples from the tumor core and tumor infiltration zone of 43 WHO grade 2–4 gliomas were collected after preoperative 5-ALA administration, and their PpIX emission spectra, as well as fluorescence lifetimes, were determined by ex vivo analysis. Subsequently, the relative PpIX peak contribution (RPPC) was retrieved by calculating the integral of the two bands corresponding to the two emission peaks of PpIX (615–625 nm, 625–635 nm) and correlated with fluorescence lifetimes. Results: The mean RPPC decreased in samples with descending order of WHO grades, non-fluorescing samples, and infiltrative tumor regions, indicating a shift toward the 620 nm peak in porphyrin fluorescence. The porphyrin peak shift across all specimens correlated with lower fluorescence lifetimes (R: 0.854, R-squared: 0.729). Conclusions: The observed peak shift has important implications for fluorescence lifetime analyses since the lifetimes of other porphyrins contribute to the overall decay dynamics. Based on these initial data using fluorescence lifetime, this knowledge is of major importance, especially for detecting tissue from LGG that lack visible fluorescence, to further optimize the visualization of these tumor tissue using this promising imaging modality.
Development and Validation of Quality Indicators for Pulmonary Arterial Hypertension Management in Japan: A Modified Delphi Consensus Study
Authors: Tamura Yuichi , Hosokawa Kazuya , Horimoto Koshin , Ikeda Satoshi , Inami Takumi , Kubota Kayoko , Nakanishi Naohiko , Shirai Yuichiro , Tanabe Nobuhiro , Tsujino Ichizo , Matsubara Hiromi
Published: November 2024

Background: Quality indicators (QIs) are used to standardize care and improve outcomes in patients with pulmonary arterial hypertension (PAH). It is important that QIs are validated within specific healthcare contexts. Therefore, this study aimed to validate QIs for PAH management in Japan using a modified Delphi consensus method. Methods: QI candidates were identified from published European QIs and clinical practice guidelines. An expert panel of 11 PAH specialists from diverse Japanese institutions anonymously rated the 36 initial QI candidates in two rounds using a nine-point appropriateness scale. Results: In the first round, 35 QIs received a median score of ≥7 points. A panel discussion was held between rounds to address the single low-scored QI, biomarker modifications, and invasive examinations, resulting in 36 modified QIs. In the second round, all modified QIs received median scores of ≥7 points and were judged to be valid as the final Japanese set of QIs. Conclusions: The findings of this study validated a set of QIs for PAH management tailored to the Japanese healthcare context. These QIs can be used to standardize care, identify areas for improvement, and ultimately enhance outcomes for Japanese patients with PAH.
Design and Development of Portable Body Composition Analyzer for Children
Authors: Rashmi Richa , Umapathy Snekhalatha , Alhajlah Omar , Almutairi Fadiyah , Aslam Shabnam Mohamed
Published: November 2024

Objectives: The aim of this study was (i) to design and develop a portable BCA device for measuring body composition parameters such as body weight, body fat (BF) %, total body water (TBW), fat-free mass (FFM), muscle mass (MM), and bone mass (BM); (ii) to validate the developed portable BCA with the Tanita MC 980 MA BCA device. Methods: For this current study, two hundred healthy and obese subjects, whose ages ranged from 8 to 12 years (8.4 ± 1.7), were considered. Results: The highest percentage difference between the two study groups was found to be in BFat (50.39%), followed by body mass index (BMI) (41.73 kg), FFM (38.32 kg), and MM (37.89 kg), and this was found to be statistically significant. The results obtained from the designed prototype of the body composition analyzer were validated using Tanita MC 980MA BCA. The overall error% was calculated as ±3% for measuring the different body composition parameters. Conclusions: Due to its low standard error and high overall accuracy, the BCA prototype demonstrates the potential to be a dependable instrument for evaluating and tracking the body composition of children.
Urban Air Mobility Communications and Networking: Recent Advances, Techniques, and Challenges
Authors: Arafat Muhammad Yeasir , Pan Sungbum
Published: November 2024

Over the past few years, our traditional ground-based transportation system has encountered various challenges, including overuse, traffic congestion, growing urban populations, high infrastructure costs, and disorganization. Unmanned aerial vehicles, commonly referred to as drones, have significantly impacted aerial communication in both the academic and industrial sectors. Therefore, researchers and scientists from the aviation and automotive industries have collaborated to create an innovative air transport system that solves traditional transport problems. In the coming years, urban air mobility (UAM) is expected to become an emerging air transportation system that enables on-demand air travel. UAM is also anticipated to offer more environmentally friendly, cost-effective, and faster modes of transportation than ground-based alternatives. Owing to the unique characteristics of personal air vehicles, ensuring reliable communication and maintaining proper safety and security, air traffic management, collision detection, path planning, and highly accurate localization and navigation have become increasingly complex. This article provides an extensive literature review of recent technologies to address the challenges UAM faces. First, we present UAM communication requirements in terms of coverage, data rate, latency, spectrum efficiency, networking, and computing capabilities. Subsequently, we identify the potential key technological enablers to meet these requirements and overcome their challenges. Finally, we discuss open research issues, challenges, and future research directions for UAM deployment.
Exploring the Thermodynamic Uncertainty Constant: Insights from a Quasi-Ideal Nano-Gas Model
Authors: Sonnino Giorgio
Published: November 2024

In previous work, we investigated thermodynamic processes in systems at the mesoscopic level where traditional thermodynamic descriptions (macroscopic or microscopic) may not be fully adequate. The key result is that entropy in such systems does not change continuously, as in macroscopic systems, but rather in discrete steps characterized by the quantization constant β. This quantization reflects the underlying discrete nature of the collision process in low-dimensional systems and the essential role played by thermodynamic fluctuations at this scale. Thermodynamic variables conjugate to the forces, along with Glansdorff–Prigogine’s dissipative variable can be discretized, enabling a mesoscopic-scale formulation of canonical commutation rules (CCRs). In this framework, measurements correspond to determining the eigenvalues of operators associated with key thermodynamic quantities. This work investigates the quantization parameter β in the CCRs using a nano-gas model analyzed through classical statistical physics. Our findings suggest that β is not an unknown fundamental constant. Instead, it emerges as the minimum achievable value derived from optimizing the uncertainty relation within the framework of our model. The expression for β is determined in terms of the ratio χ, which provides a dimensionless number that reflects the relative scales of volume and mass between entities at the Bohr (atomic level) and the molecular scales. This latter parameter quantifies the relative influence of quantum effects versus classical dynamics in a given scattering process.
RNA World with Inhibitors
Authors: Synak Jaroslaw , Rybarczyk Agnieszka , Kasprzak Marta , Blazewicz Jacek
Published: November 2024

During the evolution of the RNA World, compartments, which were fragments of space surrounded by a primitive lipid membrane, had to have emerged. These led eventually to the formation of modern cellular membranes. Inside these compartments, another process had to take place—switching from RNA to DNA as a primary storage of genetic information. The latter part needed a handful of enzymes for the DNA to be able to perform its function. A natural question arises, i.e., how the concentration of all vital molecules could have been kept in check without modern cellular mechanisms. The authors propose a theory on how it could have worked during early stages, using only short RNA molecules, which could have emerged spontaneously. The hypothesis was analysed mathematically and tested against different scenarios by using computer simulations.
Women in Transition: The Dynamic Effects of Inward FDI on Female Employment in the Economy and Across Sectors
Authors: Ghazalian Pascal L.
Published: November 2024

This paper examines the effects of inward Foreign Direct Investment (FDI) on the female employment rate in the economy and the share of female employment across sectors. The empirical analysis is implemented through the Generalized Method of Moments (GMM) System estimator for dynamic panel models using different empirical specifications and FDI openness indicators. The main results show that the overall effects of inward FDI on the national female employment rate are not statistically significant. However, they reveal that inward FDI has promoted the share of female employment in the service sector and has led to decreases in the share of female employment in agriculture. The FDI effects on the share of female employment in the industrial sector are found to be statistically insignificant. These results are generally supported when running the empirical analysis through alternative FDI openness indicators. Also, supplementary analysis reveals some variations in the magnitude of these effects over different national income categories. The findings in this paper emphasize FDI’s gendered influences in the labour market. They are consistent with the prevalence of macroeconomic channels through which inward FDI impacts female employment across sectors, and they encompass the underlying implications of various counteracting microeconomic factors.
Navigating AI Integration in Career and Technical Education: Diffusion Challenges, Opportunities, and Decisions
Authors: Sun Jeffrey C. , Pratt Taylor L.
Published: November 2024

This review paper explores the integration of artificial intelligence (AI) in career and technical education (CTE). CTE is an educational domain often overlooked in discussions about teaching and learning and notably omitted in the extant literature about AI’s application in educational settings. Although much of the existing literature focuses on AI in K-12 and higher education, CTE faces distinct challenges and opportunities in both education and the application of AI because CTE programming is more hands-on and industry-connected. This paper, grounded in Diffusion of Innovations theory, examines AI tool adoption processes among CTE educators by analyzing both barriers and opportunities. Key findings suggest that while AI offers significant benefits, its adoption is hindered by systemic factors. This paper contributes to the literature by highlighting the importance of contextualizing AI adoption within the distinct pedagogical practices and industry partnerships of CTE. It emphasizes the need for targeted strategies that address CTE-specific challenges, including robust infrastructure, equitable resource distribution, and fostering a culture of innovation among educators. The implications of this work underscore AI’s potential to bridge the gap between education and workforce demands, positioning CTE programs as critical sites for preparing students for the next phase of workforce under Industry 5.0.
Mathematics Discourse in Secondary Teacher Candidates’ Lessons: A Mixed Methods Analysis
Authors: Schmidt Ashley , Rakes Christopher R. , Bush Sarah B. , Ronau Robert N. , Soni Siddhi , Fisher Molly , Amick Lisa , Viera Julian , Safi Farshid
Published: November 2024

Meaningful discourse in the mathematics classroom involves creating a learning community that empowers students to articulate their reasoning and make sense of the contributions of their peers while advancing the learning of mathematics for the entire class. This study assessed the degree to which secondary mathematics (Grades 7–12) teacher candidates incorporated discourse into their lessons and the factors influencing their decisions. An explanatory mixed methods design was used, in which data were collected sequentially. Lesson videos were analyzed, followed by interviews of teacher candidates with high-discourse lessons. This study found that participants showed significant growth in their use of effective teaching practices from the beginning of the semester to the end of the semester, including mathematics discourse. The interviews revealed four contributing factors: intentional effort, learning experiences, professional relationships, and pedagogical knowledge. Understanding the experiences of teacher candidates during their own learning of mathematics, their mathematical identities, and beliefs about mathematics could help generate knowledge regarding the implementation of mathematics discourse and other reform-based practices in teacher instruction.
A Novel Multi-Task Self-Supervised Transfer Learning Framework for Cross-Machine Rolling Bearing Fault Diagnosis
Authors: Zhao Lujia , He Yuling , Dai Derui , Wang Xiaolong , Bai Honghua , Huang Weiling
Published: November 2024

In recent years, intelligent methods based on transfer learning have achieved significant research results in the field of rolling bearing fault diagnosis. However, most studies focus on the transfer diagnosis scenario under different working conditions of the same machine. The transfer fault diagnosis methods used for different machines have problems such as low recognition accuracy and unstable performance. Therefore, a novel multi-task self-supervised transfer learning framework (MTSTLF) is proposed for cross-machine rolling bearing fault diagnosis. The proposed method is trained using a multi-task learning paradigm, which includes three self-supervised learning tasks and one fault diagnosis task. First, three different scales of masking methods are designed to generate masked vibration data based on the periodicity and intrinsic information of the rolling bearing vibration signals. Through self-supervised learning, the attention to the intrinsic features of data in different health conditions is enhanced, thereby improving the model’s feature expression capability. Secondly, a multi-perspective feature transfer method is proposed for completing cross-machine fault diagnosis tasks. By integrating two types of metrics, probability distribution and geometric similarity, the method focuses on transferable fault diagnosis knowledge from different perspectives, thereby enhancing the transfer learning ability and accomplishing cross-machine fault diagnosis of rolling bearings. Two experimental cases are carried out to evaluate the effectiveness of the proposed method. Results suggest that the proposed method is effective for cross-machine rolling bearing fault diagnosis.
Deep Reinforcement Learning Recommendation System Algorithm Based on Multi-Level Attention Mechanisms
Authors: Wang Gaopeng , Ding Jingyi , Hu Fanlin
Published: November 2024

Traditional recommendation systems, which rely on static user profiles and historical interaction data, frequently face difficulties in adapting to the rapid changes in user preferences that are typical of dynamic environments. In contrast, recommendation algorithms based on deep reinforcement learning are capable of dynamically adjusting their strategies to accommodate real-time fluctuations in user preferences. However, current deep reinforcement learning recommendation algorithms encounter several challenges, including the oversight of item features associated with high long-term rewards that reflect users’ enduring interests, as well as a lack of significant relevance between user attributes and item characteristics. This leads to an inadequate extraction of personalized information. To address these issues, this study presents a novel recommendation system known as the Multi-Level Hierarchical Attention Mechanism Deep Reinforcement Recommendation (MHDRR), which is fundamentally grounded in a multi-layer attention mechanism. This mechanism consists of a local attention layer, a global attention layer, and a Transformer layer, allowing for a detailed analysis of individual attributes and interactions within short-term preferred items, while also exploring users’ long-term interests. This methodology promotes a comprehensive understanding of users’ immediate and enduring preferences, thereby improving the overall effectiveness of the system over time. Experimental results obtained from three publicly available datasets validate the effectiveness of the proposed model.
Ring: A Lightweight and Versatile Cross-Platform Dynamic Programming Language Developed Using Visual Programming
Authors: Fayed Mahmoud Samir , Alohali Yousef A.
Published: November 2024

New programming languages are often designed to keep up with technological advancements and project requirements while also learning from previous attempts and introducing more powerful expression mechanisms. However, most existing dynamic programming languages rely on English keywords and lack features that facilitate easy translation of language syntax. Additionally, maintaining multiple implementations of the same language for different platforms, such as desktops and microcontrollers, can lead to inconsistencies and fragmented features. Furthermore, they usually do not use visual programming to fully implement the compiler and virtual machine. In this research paper, we introduce Ring—a dynamically-typed language with a lightweight implementation. However, it boasts several advantages, including a rich and versatile standard library and direct support for classes and object-oriented programming. The Ring language offers customization features. For instance, it allows easy modification of the language syntax multiple times, enabling programming by writing code using Arabic, English, or other keywords. Additionally, the language permits the creation of domain-specific languages through new features that extend object-oriented programming, allowing for specialized languages resembling CSS or Supernova. In the era of the Internet of Things, instead of creating another language implementation to support microcontrollers, the same Ring implementation allows us to create projects and applications for desktops, the web, WebAssembly, Android, or Raspberry Pi Pico. The Ring Compiler and Virtual Machine are designed using the PWCT Visual Programming language based on ANSI C. The visual implementation is composed of 18,945 components that generate 24,743 lines of code, which increases the abstraction level by approximately 23.5% and hides unnecessary details.
Applying Large Language Model to User Experience Testing
Authors: Hsueh Nien-Lin , Lin Hsuen-Jen , Lai Lien-Chi
Published: November 2024

The maturation of internet usage environments has elevated User Experience (UX) to a critical factor in system success. However, traditional manual UX testing methods are hampered by subjectivity and lack of standardization, resulting in time-consuming and costly processes. This study explores the potential of Large Language Models (LLMs) to address these challenges by developing an automated UX testing tool. Our innovative approach integrates the Rapi web recording tool to capture user interaction data with the analytical capabilities of LLMs, utilizing Nielsen’s usability heuristics as evaluation criteria. This methodology aims to significantly reduce the initial costs associated with UX testing while maintaining assessment quality. To validate the tool’s efficacy, we conducted a case study featuring a tennis-themed course reservation system. The system incorporated multiple scenarios per page, allowing users to perform tasks based on predefined goals. We employed our automated UX testing tool to evaluate screenshots and interaction logs from user sessions. Concurrently, we invited participants to test the system and complete UX questionnaires based on their experiences. Comparative analysis revealed that varying prompts in the automated UX testing tool yielded different outcomes, particularly in detecting interface elements. Notably, our tool demonstrated superior capability in identifying issues aligned with Nielsen’s usability principles compared to participant evaluations. This research contributes to the field of UX evaluation by leveraging advanced language models and established usability heuristics. Our findings suggest that LLM-based automated UX testing tools can offer more consistent and comprehensive assessments.
A Lightweight Transmission Line Foreign Object Detection Algorithm Incorporating Adaptive Weight Pooling
Authors: Hao Junbo , Yan Guangying , Wang Lidong , Pei Honglan , Xiao Xu , Zhang Baifu
Published: November 2024

Aerial photography using unmanned aerial vehicles (UAVs) to detect foreign objects is an important method to ensure the safety of transmission lines. However, existing detection algorithms often encounter challenges in complex environments, including limited recognition capability and high computational demands. To address these issues, this paper proposes YOLO-LAF, a lightweight foreign object detection algorithm that is based on YOLOv8n and incorporates an innovative adaptive weight pooling technique. The proposed method introduces a novel adaptive weight pooling module within the backbone network to enhance feature extraction for detecting foreign objects on transmission lines. Additionally, a multi-scale detection strategy is designed to integrate the FasterBlock and EMA modules. This combination enables the model to effectively capture both global and local image features through cross-channel interactions, thereby reducing misdetection and omission rates. Furthermore, a C2f-SCConv module is introduced in the neck network to streamline the model by eliminating redundant features, thus improving computational efficiency. Experimental results demonstrate that YOLO-LAF achieves average accuracies of 91.2% and 85.3% on the Southern Power Grid and RailFOD23 datasets, respectively, outperforming the original YOLOv8n algorithm by 2.6% and 1.8%. Moreover, YOLO-LAF reduces the number of parameters by 23.5% and 14.8% and the computational costs by 19.9% and 24.8%, respectively. These improvements demonstrate the superior detection performance of YOLO-LAF compared to other mainstream detection algorithms.
Fuzzy Neural Network for Detecting Anomalies in Blockchain Transactions
Authors: Apiecionek Łukasz , Karbowski Paweł
Published: November 2024

This publication focuses on the use of the artificial intelligence for detecting anomalies, especially in the blockchain network. The research methodology includes the selection of anomalies to be detected and the processing of blockchain data. Various artificial intelligence methods were implemented for anomaly detection as part of the tests, and one new solution—a Fuzzy Neural Network—was presented. The findings indicate the possibility of detecting selected anomalies in the blockchain using artificial intelligence, which is of significant importance for the security of this technology. The conclusions present a discussion on limitations, future research prospects, and guidelines for future work.
A Hyper-Parameter Optimizer Algorithm Based on Conditional Opposition Local-Based Learning Forbidden Redundant Indexes Adaptive Artificial Bee Colony Applied to Regularized Extreme Learning Machine
Authors: Vasquez-Iglesias Philip , Pizarro Amelia E. , Zabala-Blanco David , Fuentes-Concha Juan , Ahumada-Garcia Roberto , Laroze David , Gonzalez Paulo
Published: November 2024

Finding the best configuration of a neural network’s hyper-parameters may take too long to be feasible using an exhaustive search, especially when the cardinality of the search space has a big combinatorial number of possible solutions with various hyper-parameters. This problem is aggravated when we also need to optimize the parameters of the neural network, such as the weight of the hidden neurons and biases. Extreme learning machines (ELMs) are part of the random weights neural network family, in which parameters are randomly initialized, and the solution, unlike gradient-descent-based algorithms, can be found analytically. This ability is especially useful for metaheuristic analysis due to its reduced training times allowing a faster optimization process, but the problem of finding the best hyper-parameter configuration is still remaining. In this paper, we propose a modification of the artificial bee colony (ABC) metaheuristic to act as parameterizers for a regularized ELM, incorporating three methods: an adaptive mechanism for ABC to balance exploration (global search) and exploitation (local search), an adaptation of the opposition-based learning technique called opposition local-based learning (OLBL) to strengthen exploitation, and a record of access to the search space called forbidden redundant indexes (FRI) that allow us to avoid redundant calculations and track the explored percentage of the search space. We set ten parameterizations applying different combinations of the proposed methods, limiting them to explore up to approximately 10% of the search space, with results over 98% compared to the maximum performance obtained in the exhaustive search in binary and multiclass datasets. The results demonstrate a promising use of these parameterizations to optimize the hyper-parameters of the R-ELM in datasets with different characteristics in cases where computational efficiency is required, with the possibility of extending its use to other problems with similar characteristics with minor modifications, such as the parameterization of support vector machines, digital image filters, and other neural networks, among others.
Mechanism and Control Strategies for Current Sharing in Multi-Chip Parallel Automotive Power Modules
Authors: Jiang Yuqi , Li Xuehan , Ma Kun
Published: November 2024

Multi-chip parallel power modules are highly favored in applications requiring high capacity and high switching frequency. However, the dynamic current imbalance between parallel chips caused by asymmetric layouts limits the available capacity. This paper presents a method to optimize dynamic current distribution by adjusting the lengths and connection points of bond wires. For the first time, a response surface model and nonlinear constraint optimization algorithm are introduced, along with parameter analysis based on finite element methods, to establish the response surface models for the parasitic inductance of bond wires and DBC (direct bonded copper). By leveraging the optimization goals for parasitic inductance and the analytical expressions of all response surfaces, the dynamic current sharing issue was transformed into a nonlinear constrained optimization problem. The solution to this optimization problem identified the optimal connection points for the bond wires, enhancing dynamic current sharing performance. Simulations and experiments were conducted, revealing that the optimized automotive-grade module exhibited a significant reduction in current differences between parallel branches, from 41.7% to 5.03% compared with the original design. This indicated that the proposed optimization scheme for adjusting bond wire connection points could significantly mitigate current disparities, thereby markedly improving current distribution uniformity.
Enhancing Building-Integrated Photovoltaic Power Forecasting with a Hybrid Conditional Generative Adversarial Network Framework
Authors: Choi Dong Ha , Li Wei , Zomaya Albert Y.
Published: November 2024

This paper presents a novel framework that integrates Conditional Generative Adversarial Networks (CGANs) and TimeGAN to generate synthetic Building-Integrated Photovoltaic (BIPV) power data, addressing the challenge of data scarcity in this domain. By incorporating time-related attributes as conditioning information, our method ensures the preservation of chronological order and enhances data fidelity. A tailored learning scheme is implemented to capture the unique characteristics of solar power generation, particularly during sunrise and sunset. Comprehensive evaluations demonstrate the framework’s effectiveness in generating high-quality synthetic data, evidenced by a 79.58% improvement in the discriminative score and a 13.46% improvement in the predictive score compared to TimeGAN. Moreover, integrating the synthetic data into forecasting models resulted in up to 23.56% improvement in mean absolute error (MAE) for BIPV power generation predictions. These results highlight the potential of our framework to enhance prediction accuracy and optimize data utilization in renewable energy applications.
Methodology for Optimal Design of Active Fluid Film Bearings Considering Their Power Losses, Stability and Controllability: Theory and Experiment
Authors: Shutin Denis , Fetisov Alexander , Litovchenko Maksim , Rodichev Aleksey , Kazakov Yuri , Savin Leonid
Published: November 2024

This study addresses the problem of the automated synthesis of active fluid film bearings optimized for their adjustable design for new generations of turbomachines. The developed methodology proposes a criterion describing the ability of a bearing’s mechanical design to effectively implement control actions along with its energy efficiency and stability properties considered in a solved multi-objective optimization problem. The design process of actively lubricated journal bearings was investigated in the context of the proposed approach. A multi-objective optimization problem was solved with heuristic algorithms. An analysis of the results obtained with the MOGA and MOPSO algorithm revealed their shortcomings emerging in such problems. The MOPSO algorithm was improved to expand the range and uniformity of the distribution of solutions in the resulting Pareto set and to speed up calculations. Four bearing configurations with significantly different properties were selected from the obtained set of solutions, manufactured and experimentally tested, showing the good agreement between the actual parameters and those set during the design procedure. The results substantiate the applicability of the proposed theoretical and computational tools for designing active fluid film bearings with pre-specified properties to meet the comprehensive requirements of the energy efficiency, reliability and service life of turbomachines.
Optimal Coordination of Directional Overcurrent Relays in Microgrids Considering European and North American Curves
Authors: Serna-Montoya León F. , Saldarriaga-Zuluaga Sergio D. , López-Lezama Jesús M. , Muñoz-Galeano Nicolás
Published: November 2024

Protecting AC microgrids (MGs) is a challenging task due to their dual operating modes—grid-connected and islanded—which cause sudden variations in fault currents. Traditional protection methods may no longer ensure network security. This paper presents a novel approach to protection coordination in AC MGs using non-standard features of directional over-current relays (DOCRs). Three key optimization variables are considered: Time Multiplier Setting (TMS), the plug setting multiplier’s (PSM) maximum limit, and the standard characteristic curve (SCC). The proposed model is formulated as a mixed-integer nonlinear programming problem and solved using four metaheuristic techniques: the genetic algorithm (GA), Imperialist Competitive Algorithm (ICA), Harmonic Search (HS), and Firefly Algorithm (FA). Tests on a benchmark IEC MG with distributed generation and various operating modes demonstrate that this approach reduces coordination times compared to existing methods. This paper’s main contributions are threefold: (1) introducing a methodology for assessing the optimal performance of different standard curves in MG protection; (2) utilizing non-standard characteristics for optimal coordination of DOCRs; and (3) enabling the selection of curves from both North American and European standards. This approach improves trip time performance across multiple operating modes and topologies, enhancing the reliability and efficiency of MG protection systems.