bluuebunny/crossref_metadata_2025 huggingface dataset:
Mutation Spectrum Comparison between Benign Breast Lesion Cohort, Unselected Cancer Cohort and High-Risk Breast Cancer Cohort
Authors: Kwong Ava , Ho Cecilia Y. S. , Leung Henry C. M. , Leung Amy W. S. , Au Chun-Hang , Ma Edmond S. K.
Published: September 2024

Mutation study for high-risk breast and ovarian cancer (HBOC) has been extensively studied in patients of different ethnicities. Here we compared the germline mutation rate and mutation spectrum of patients (n = 4341) with benign breast diseases or breast cancers, with and without other risk factors. Three cohorts of Chinese patients were recruited. The first cohort, high-risk cohort (HR, n = 3935) included high-risk breast cancer patients fulfilling high-risk HBOC criteria and who are recruited at our genetics clinic. The second cohort, unselected cancer cohort (CC, n = 307) was from general recruitment of patients with breast cancer at breast surgery clinics. The third cohort, benign breast lesion cohort (NC, n = 99) comprised 99 patients with benign breast diseases such as fibroadenoma, fibroadenomatoid hyperplasia, and intraductal papilloma. Thirty HBOC related genes were sequenced on the above-mentioned patient cohorts. The germline mutation rates of HR, CC, and NC cohort were 11.9%, 6.5%, and 8.1%, respectively. In the CC cohort, 29.3% (90/307) of patients fulfilled the National Comprehensive Cancer Network (NCCN) high-risk genetic test criteria 2022 v.2. The mutation rate for this group of patients was 11.1%, similar to that of the HR cohort, while the mutation rate for those not fulfilling testing criteria was 4.6%, like that of the NC cohort. High penetrance genes (BRCA1/2, CDH1, PALB2, PTEN, and TP53) mutations were only found in the HR (10.6%) and CC (3.3%) cohorts but were not found in the NC cohort. ATM, BRIP1, RAD51C, and RAD51D mutations were identified in all cohorts. RAD51C and RAD51D mutations showed conflicting penetrance. An unexpectedly high mutation rate of total 2% was found in the NC cohort but it was only 0.3% and 0.5% in the HR cohort and CC cohort, respectively. Our results show a clinical need to enhance genetic testing of unselected breast cancer patients to identify the high-risk patients.
Impact of Physical Activity on DNA Methylation Signatures in Breast Cancer Patients: A Systematic Review with Bioinformatic Analysis
Authors: Moulton Chantalle , Lisi Veronica , Silvestri Monica , Ceci Roberta , Grazioli Elisa , Sgrò Paolo , Caporossi Daniela , Dimauro Ivan
Published: September 2024

Breast cancer (BC) continues to significantly impact women worldwide. Numerous studies show that physical activity (PA) significantly enhances the quality of life, aids recovery, and improves survival rates in BC patients. PA’s influence extends to altering DNA methylation patterns on both a global and gene-specific scale, potentially reverting abnormal DNA methylation, associated with carcinogenesis and various pathologies. This review consolidates the findings of the current literature, highlighting PA’s impact on DNA methylation in BC patients. Our systematic analysis indicates that PA may elevate global DNA methylation within tumour tissues. Furthermore, it appears to modify gene-specific promoter methylation across a wide spectrum of genes in various tissues. Through bioinformatic analysis, to investigate the functional enrichment of these affected genes, we identified a predominant enrichment in metabolic pathways, cell cycle regulation, cell cycle checkpoints, mitosis, cellular stress responses, and molecular functions governing diverse binding processes. The Human Protein Atlas corroborates this enrichment, indicating gene functionality across 266 tissues, notably within various breast tissues. This systematic review unveils PA’s capacity to systematically alter DNA methylation patterns across multiple tissues, particularly in BC patients. Emphasising its influence on crucial biological processes and functions, this alteration holds potential for restoring normal cellular functionality and the cell cycle. This reversal of cancer-associated patterns could potentially enhance recovery and improve survival outcomes.
Aberrant SWI/SNF Complex Members Are Predominant in Rare Ovarian Malignancies—Therapeutic Vulnerabilities in Treatment-Resistant Subtypes
Authors: Ma Yue , Field Natisha R. , Xie Tao , Briscas Sarina , Kokinogoulis Emily G. , Skipper Tali S. , Alghalayini Amani , Sarker Farhana A. , Tran Nham , Bowden Nikola A. , Dickson Kristie-Ann , Marsh Deborah J.
Published: September 2024

SWI/SNF (SWItch/Sucrose Non-Fermentable) is the most frequently mutated chromatin-remodelling complex in human malignancy, with over 20% of tumours having a mutation in a SWI/SNF complex member. Mutations in specific SWI/SNF complex members are characteristic of rare chemoresistant ovarian cancer histopathological subtypes. Somatic mutations in ARID1A, encoding one of the mutually exclusive DNA-binding subunits of SWI/SNF, occur in 42–67% of ovarian clear cell carcinomas (OCCC). The concomitant somatic or germline mutation and epigenetic silencing of the mutually exclusive ATPase subunits SMARCA4 and SMARCA2, respectively, occurs in Small cell carcinoma of the ovary, hypercalcaemic type (SCCOHT), with SMARCA4 mutation reported in 69–100% of SCCOHT cases and SMARCA2 silencing seen 86–100% of the time. Somatic ARID1A mutations also occur in endometrioid ovarian cancer (EnOC), as well as in the chronic benign condition endometriosis, possibly as precursors to the development of the endometriosis-associated cancers OCCC and EnOC. Mutation of the ARID1A paralogue ARID1B can also occur in both OCCC and SCCOHT. Mutations in other SWI/SNF complex members, including SMARCA2, SMARCB1 and SMARCC1, occur rarely in either OCCC or SCCOHT. Abrogated SWI/SNF raises opportunities for pharmacological inhibition, including the use of DNA damage repair inhibitors, kinase and epigenetic inhibitors, as well as immune checkpoint blockade.
Beyond the Beat: Understanding Inherited Risk and Therapeutic Opportunities in Cardiovascular Diseases with Emphasis on Inherited Cardiomyopathies and Inherited Arrhythmic Syndromes
Authors: Krsek Antea , Baticic Lara , Sotosek Vlatka
Published: September 2024

Over the past three decades, significant progress has been made in elucidating the intricate connection between genetic predispositions and cardiovascular diseases (CVDs). Through extensive investigation, numerous genetic variants linked to various cardiovascular conditions have been discovered, shedding crucial light on the underlying biological mechanisms and pathways. These discoveries have not only revolutionized risk assessment for patients but have also paved the way for personalized treatment strategies, allowing healthcare providers to tailor interventions according to individual genetic profiles. Furthermore, genetic testing has facilitated cascade screening, enabling the early identification and intervention of potential cardiovascular issues among at-risk biological family members. This review aims to comprehensively summarize the current state of knowledge regarding inherited risk and novel insights from human genome and epigenome research, as well as therapeutic opportunities in CVDs with special emphasis on inherited cardiomyopathies and inherited arrhythmic syndromes. The newest translational trials for CVDs and pharmaceutical approaches are discussed, including gene therapy options for heart failure and cardiomyopathies.
Heterogeneous Photo-Fenton Degradation of Azo Dyes over a Magnetite-Based Catalyst: Kinetic and Thermodynamic Studies
Authors: Ribeiro Jackson Anderson S. , Alves Júlia F. , Salgado Bruno César B. , Oliveira Alcineia C. , Araújo Rinaldo S. , Rodríguez-Castellón Enrique
Published: September 2024

Textile wastewater containing dyes poses significant environmental hazards. Advanced oxidative processes, especially the heterogeneous photo-Fenton process, are effective in degrading a wide range of contaminants due to high conversion rates and ease of catalyst recovery. This study evaluates the heterogeneous photodegradation of the azo dyes Acid Red 18 (AR18), Acid Red 66 (AR66), and Orange 2 (OR2) using magnetite as a catalyst. The magnetic catalyst was synthesized via a hydrothermal process at 150 °C. Experiments were conducted at room temperature, investigating the effect of catalyst dosage, pH, and initial concentrations of H2O2 and AR18 dye. Kinetic and thermodynamic studies were performed at 25, 40, and 60 °C for the three azo dyes (AR18, AR66, and OR2) and the effect of the dye structures on the degradation efficiency was investigated. At 25 °C for 0.33 mmolL−1 of dye at pH 3.0, using 1.4 gL−1 of the catalyst and 60 mgL−1 of H2O2 under UV radiation of 16.7 mWcm−2, the catalyst showed 62.3% degradation for AR18, 79.6% for AR66, and 83.8% for OR2 in 180 min of reaction. The oxidation of azo dyes under these conditions is spontaneous and endothermic. The pseudo-first-order kinetic constants indicated a strong temperature dependence with an order of reactivity of the type OR2 > AR66 > AR18, which is associated with the molecular size, steric hindrance, aromatic conjugation, electrostatic repulsion, and nature of the acid–base interactions on the catalytic surface.
The Role of Catalysts in Life Cycle Assessment Applied to Biogas Reforming
Authors: Nogales-Delgado Sergio , González González Juan Félix
Published: September 2024

The real implementation of biogas reforming at an industrial scale to obtain interesting products (like hydrogen or syngas) is a developing research field where multidisciplinary teams are continuously adding improvements and innovative technologies. These works can contribute to the proliferation of green technologies where the circular economy and sustainability are key points. To assess the sustainability of these processes, there are different tools like life cycle assessment (LCA), which involves a complete procedure where even small details count to consider a certain technology sustainable or not. The aim of this work was to review works where LCA is applied to different aspects of biogas reforming, focusing on the role of catalysts, which are essential to improve the efficiency of a certain process but can also contribute to its environmental impact. In conclusion, catalysts have an influence on LCA through the improvement of catalytic performance and the impact of their production, whereas other aspects related to biogas or methane reforming could equally affect their catalytic durability or reusability, with a subsequent effect on LCA. Further research about this subject is required, as this is a continuously changing technology with plenty of possibilities, in order to homogenize this research field.
The Development of a Novel Transient Signal Analysis: A Wavelet Transform Approach
Authors: Gómez-Luna Eduardo , Cuadros-Orta Dixon E. , Candelo-Becerra John E. , Vasquez Juan C.
Published: September 2024

This paper presents a new method for the analysis of transient signals in the frequency domain based on the Continuous Wavelet Transform (CWT). The proposed case study involves test signals measured from an electronic switch considering open and close operations. The source is connected to inductive, resistive, and capacitive loads. Resonance behaviors are introduced and compared with the Discrete Fourier Transform (DFT). Multiple factors, such as reliability, repeatability, high noise attenuation, and the smoothing of the analyzed spectrum, are considered in this study. This proposed study highlights the effectiveness of CWT in signal processing, especially in obtaining a detailed spectrum that reveals the behavior of electrical circuits. Resonance behaviors were analyzed, demonstrating that the signal processing performed by CWT is better for spectrum analysis than DFT. This study shows the potential of CWT to analyze transient electrical signals, specifically for identifying and characterizing the behavior of load connections and disconnections.
Detection of Bus Driver Mobile Phone Usage Using Kolmogorov-Arnold Networks
Authors: Hollósi János , Ballagi Áron , Kovács Gábor , Fischer Szabolcs , Nagy Viktor
Published: September 2024

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific task, a unique dataset was constructed consisting of images of bus drivers in two scenarios: driving without phone interaction and driving while on a phone call. This dataset provides the basis for the current research. Different KAN-based networks were developed for custom action recognition tailored to the nuanced task of identifying drivers holding phones. The system’s performance was evaluated against convolutional neural network-based solutions, and differences in accuracy and robustness were observed. The aim was to propose an appropriate solution for professional Driver Monitoring Systems (DMS) in research and development and to investigate the efficiency of KAN solutions for this specific sub-task. The implications of this work extend beyond enforcement, providing a foundational technology for automating monitoring and improving safety protocols in the commercial and public transport sectors. In conclusion, this study demonstrates the efficacy of KAN network layers in neural network designs for driver monitoring applications.
Multi-Objective Optimization for the Forming Quality of a CeO2/Al6061 Alloy as an Aluminum–Air Battery Anode Manufactured via Selective Laser Melting
Authors: Peng Guangpan , Niu Chenhao , Geng Yuankun , Duan Weipeng , Cao Shu
Published: September 2024

To improve the discharge performance of aluminum–air batteries, CeO2/Al6061 composites were prepared as an anode using selective laser melting (SLM). Response surface methodology (RSM) was employed, and the test results were linearly fitted. A prediction model for the forming quality of the composite anode was established, and the reliability of the model and the interaction between process parameters were explored based on variance analysis and significance testing. On this basis, with corrosion potential, self-corrosion rate, and discharge voltage as optimization objectives, the optimal solution set of the SLM forming CeO2/Al6061 anode process parameter was solved through a genetic algorithm, and experimental verification was conducted. The results indicate that the optimal process range for the forming quality and various properties of composite materials is laser power of 265~285 W, scanning speed of 985~1025 mm/s, and scanning spacing of 0.116~0.140 mm. The optimized process parameters were selected for reliability testing, and the errors were all within 3.0%, verifying the accuracy and reliability of the model.
Occurrence of Novel and Legacy Per/Polyfluoroalkyl Substances (PFASs) in Scopoli’s Shearwater (Calonectris diomedea) Feathers
Authors: Trypidaki Eirini , Gudmundsen Silje Marie Bøe , Karris Georgios , Xirouchakis Stavros , Gonzalez Susana V. , Zhang Junjie , Jaspers Veerle L. B. , Ciesielski Tomasz Maciej , Tsangaris Catherine , Asimakopoulos Alexandros G.
Published: September 2024

Per- and polyfluoroalkyl substances (PFASs) are contaminants of great concern due to their ubiquitous environmental occurrence in the environment and their potential adverse effects on organisms. There is currently limited information regarding the occurrence of PFASs in Scopoli’s Shearwater (Calonectris diomedea). In this study, two feather samples per bird were obtained from 26 adults on Strofades colony (Ionian Sea/Greece) during the early phase of the chick-rearing period (late July 2019). The samples consisted of barbs and barbules of the primary feathers, P1 and P10, reflecting pollution pressures at the time and the place of feather growth, i.e., at the species’ breeding and wintering grounds for P1 and P10, respectively. There were 25 PFAS detected in the feathers, with detection rates ranging from 2% (perfluorododecanoic acid—PFDoDA; perfluorohexane sulfonate—PFHxS; 9-chlorohexadecafluoro-3-oxanonane-1-sulfonate—9Cl-PF3ONS; 2,3,3,3-tetrafluoro-2-(1,1,2,2,3,3,3-heptafluoropropoxy)propanoate—Gen-X) to 98% (sodium 1H,1H,2H,2H-perfluorooctane sulfonate; 6:2 FTSA). ∑PFAS ranged from 25.93 ng/g to 426.86 ng/g of feather sample. The highest mean concentration (109.10 ng/g feather) was reported for perfluorononanoic acid (PFNA). No significant differences in PFAS concentrations with high detection rate (>20%) were found according to the sex of the birds. PFAS concentrations with a detection rate > 20% in the P1 vs. P10 feathers of Scopoli’s Shearwater adults were not significantly different, reflecting the fact that breeding grounds in the Mediterranean and wintering grounds in the Atlantic seem to be contaminated with similar PFASs levels, even though some compounds showed regional trends.
Effectiveness and Factors Influencing Success of Transcanalicular Laser-Assisted Endoscopic Dacryocystorhinostomy: Cohort Study
Authors: Różycki Radosław , Skrzypiec Łukasz , Ulaszewska Katarzyna , Gąsior Jakub S. , Wasyluk Jaromir
Published: September 2024

Laser dacryocystorhinostomy (LDCR) is a surgical procedure designed to treat obstructions in the lacrimal duct system, which can cause excessive tearing, infections, and discomfort. This technique involves creating a new passage for tear drainage, thereby restoring normal tear flow and alleviating symptoms associated with duct obstruction. A retrospective study was conducted on 48 patients who underwent the LDCR procedure, amounting to 56 eyes. The primary outcome measured was the anatomical success rate, defined as the restoration of duct patency. Patients were examined postoperatively from 6 months to 3.5 years. The LDCR method demonstrated a 95% success rate, encompassing both anatomical and functional outcomes. The procedure’s effectiveness was determined by achieving a patent osteotomy and resolving symptoms. Anatomical success was measured by the creation of a viable drainage pathway, while functional success pertained to the resolution of symptoms such as epiphora. The efficacy of the procedure was found to be independent of both age and gender. Among patients with successful anatomical outcomes, there was a statistically significant improvement in their Munk scores. The LDCR method is highly effective in treating lacrimal duct obstruction. These findings highlight the importance of the Munk score as a predictive indicator of procedural success in LDCR.
Free-Running Cardiac and Respiratory Motion-Resolved Imaging: A Paradigm Shift for Managing Motion in Cardiac MRI?
Authors: Holtackers Robert J. , Stuber Matthias
Published: September 2024

Cardiac magnetic resonance imaging (MRI) is widely used for non-invasive assessment of cardiac morphology, function, and tissue characteristics due to its exquisite soft-tissue contrast. However, it remains time-consuming and requires proficiency, making it costly and limiting its widespread use. Traditional cardiac MRI is inefficient as signal acquisition is often limited to specific cardiac phases and requires complex view planning, parameter adjustments, and management of both respiratory and cardiac motion. Recent efforts have aimed to make cardiac MRI more efficient and accessible. Among these innovations, the free-running framework enables 5D whole-heart imaging without the need for an electrocardiogram signal, respiratory breath-holding, or complex planning. It uses a fully self-gated approach to extract cardiac and respiratory signals directly from the acquired image data, allowing for more efficient coverage in time and space without the need for electrocardiogram gating, triggering, navigators, or breath-holds. This review provides a comprehensive overview of the free-running framework, detailing its history, concepts, recent improvements, and clinical applications.
A Semi-Supervised Transformer-Based Deep Learning Framework for Automated Tooth Segmentation and Identification on Panoramic Radiographs
Authors: Hao Jing , Wong Lun M. , Shan Zhiyi , Ai Qi Yong H. , Shi Xieqi , Tsoi James Kit Hon , Hung Kuo Feng
Published: September 2024

Automated tooth segmentation and identification on dental radiographs are crucial steps in establishing digital dental workflows. While deep learning networks have been developed for these tasks, their performance has been inferior in partially edentulous individuals. This study proposes a novel semi-supervised Transformer-based framework (SemiTNet), specifically designed to improve tooth segmentation and identification performance on panoramic radiographs, particularly in partially edentulous cases, and establish an open-source dataset to serve as a unified benchmark. A total of 16,317 panoramic radiographs (1589 labeled and 14,728 unlabeled images) were collected from various datasets to create a large-scale dataset (TSI15k). The labeled images were divided into training and test sets at a 7:1 ratio, while the unlabeled images were used for semi-supervised learning. The SemiTNet was developed using a semi-supervised learning method with a label-guided teacher–student knowledge distillation strategy, incorporating a Transformer-based architecture. The performance of SemiTNet was evaluated on the test set using the intersection over union (IoU), Dice coefficient, precision, recall, and F1 score, and compared with five state-of-the-art networks. Paired t-tests were performed to compare the evaluation metrics between SemiTNet and the other networks. SemiTNet outperformed other networks, achieving the highest accuracy for tooth segmentation and identification, while requiring minimal model size. SemiTNet’s performance was near-perfect for fully dentate individuals (all metrics over 99.69%) and excellent for partially edentulous individuals (all metrics over 93%). In edentulous cases, SemiTNet obtained statistically significantly higher tooth identification performance than all other networks. The proposed SemiTNet outperformed previous high-complexity, state-of-the-art networks, particularly in partially edentulous cases. The established open-source TSI15k dataset could serve as a unified benchmark for future studies.
Correction: Stämmler et al. Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C. Diagnostics 2021, 11, 2291
Authors: Stämmler Frank , Grassi Marcello , Meeusen Jeffrey W. , Lieske John C. , Dasari Surendra , Dubourg Laurence , Lemoine Sandrine , Ehrich Jochen , Schiffer Eric
Published: September 2024

In the original publication [...]
A Justice-Oriented Conceptual and Analytical Framework for Decolonising and Desecularising the Field of Educational Technology
Authors: Adam Taskeen
Published: September 2024

Education systems globally are increasingly being shaped by the logics, assumptions and pedagogical underpinnings of educational technology (EdTech) products, services, programmes, policies, and systems. These often promote rationalistic, secular, universal, objectivist, (post)modernist, written, behaviourist, and individualistic ways of being, marginalising religious, spiritual, oral, subjective, critical, and communitarian ways of being. Given that technological ways of being have been propagated globally, these logics are no longer predominantly promoted by those in the Global North, but by techno-solutionists globally, although the core-to-periphery flows of ideology and funding are still prominent. This article develops a conceptual and analytical framework for decolonising and desecularising the field of EdTech. Concepts are drawn from various discourses: the desecularisation of knowledge to set the ontological framing; embodied cognition to set the epistemological framing; and social justice and decolonial discourses to set the axiological framing. From this, the article develops the Dimensions of Human Injustice Analytical Framework—covering material, ontological and epistemic, and (geo)political injustices—to assist policymakers, educators, EdTech developers, and international development practitioners in identifying and confronting coloniality in their EdTech. Acknowledging the complexity and contentions within decolonial thought, this article does not claim a unified stance on achieving justice but aims to offer a tool for deconstructing and questioning injustices.
“Home Is the Mouth of a Shark”: Trauma and the Needs of Students from Refugee Backgrounds from the Perspective of Boundary Spanning Refugee Resettlement Workers
Authors: González-Doğan Shyla , Soyer Gonca , Bayhan Siddika Asik , Yazici Mualla , Akçay Meryem , Harb Jordan , Ramadan Awad Mekkawi , Turan Adnan
Published: September 2024

This research project examines the experiences of four individuals working in refugee resettlement and their perspectives on the educational needs of refugee students, given their role as boundary spanners. They also discuss the need for teachers to encourage students to use the Funds of Knowledge in the classroom. Participants discuss their varied experience as professionals in refugee resettlement, with two being refugees and one being an immigrant. This paper (1) explores the stress associated with relocation, (2) delves into the mental health issues faced by individuals from refugee backgrounds, emphasizing trauma, (3) analyzes how inefficiencies and lack of support within the public school system contribute to a stressful and ineffective experience for all parties involved, and (4) highlights the importance of human connection. The implications for future research are also discussed with particular attention paid to the need to actively work with other stakeholders.
Re-Evaluating Components of Classical Educational Theories in AI-Enhanced Learning: An Empirical Study on Student Engagement
Authors: Bognár László , Ágoston György , Bacsa-Bán Anetta , Fauszt Tibor , Gubán Gyula , Joós Antal , Juhász Levente Zsolt , Kocsó Edina , Kovács Endre , Maczó Edit , Mihálovicsné Kollár Anita Irén , Strauber Györgyi
Published: September 2024

The primary goal of this research was to empirically identify and validate the factors influencing student engagement in a learning environment where AI-based chat tools, such as ChatGPT or other large language models (LLMs), are intensively integrated into the curriculum and teaching–learning process. Traditional educational theories provide a robust framework for understanding diverse dimensions of student engagement, but the integration of AI-based tools offers new personalized learning experiences, immediate feedback, and resource accessibility that necessitate a contemporary exploration of these foundational concepts. Exploratory Factor Analysis (EFA) was utilized to uncover the underlying factor structure within a large set of variables, and Confirmatory Factor Analysis (CFA) was employed to verify the factor structure identified by EFA. Four new factors have been identified: “Academic Self-Efficacy and Preparedness”, “Autonomy and Resource Utilization”, “Interest and Engagement”, and “Self-Regulation and Goal Setting.” Based on these factors, a new engagement measuring scale has been developed to comprehensively assess student engagement in AI-enhanced learning environments.
Enhancing QoS in Multicore Systems with Heterogeneous Memory Configurations
Authors: Kim Jesung , Park Hoorin , Hong Jeongkyu
Published: September 2024

Quality of service (QoS) has evolved to ensure performance across various computing environments, focusing on data bandwidth, response time, throughput, and stability. Traditional QoS schemes primarily target DRAM-based homogeneous memory systems, exposing limitations when applied to diverse memory configurations. Moreover, the emergence of nonvolatile memories (NVMs) has made achieving QoS even more challenging due to their differing characteristics. While QoS schemes have been proposed for DRAM-based memory systems or hybrid memory systems combining DRAM and a single NVM type, there is a lack of research on QoS techniques for memory systems that incorporate multiple types of NVM simultaneously. Ensuring QoS in these heterogeneous memory environments is challenging due to significant differences in memory characteristics. In this paper, we propose a novel technique, dynamic affinity-based resource pairing (DARP), designed to enhance QoS in multicore heterogeneous memory systems. The proposed approach dynamically monitors the memory access patterns of applications and leverages the specific read/write characteristics of NVM devices. Detailed information from monitoring is used to optimally allocate memory data to the most suitable memory devices, ensuring stable memory response times and mitigating bottlenecks. Extensive experiments validate the efficiency and scalability of DARP across various workloads and heterogeneous memory configurations, including memory systems with multiple types of NVM. The results show that our technique significantly outperforms state-of-the-art QoS methods in terms of memory response time consistency and overall QoS in heterogeneous memory environments. DARP achieved a memory response time variability of 74.4% in six different memory configurations compared to the baseline on average, demonstrating its high scalability and effectiveness in enhancing QoS across various heterogeneous memory systems.
Hybrid Transformer and Convolution for Image Compressed Sensing
Authors: Nan Ruili , Sun Guiling , Zheng Bowen , Zhang Pengchen
Published: September 2024

In recent years, deep unfolding networks (DUNs) have received widespread attention in the field of compressed sensing (CS) reconstruction due to their good interpretability and strong mapping capabilities. However, existing DUNs often improve the reconstruction effect at the expense of a large number of parameters, and there is the problem of information loss in long-distance feature transmission. Based on the above problems, we propose an unfolded network architecture that mixes Transformer and large kernel convolution to achieve sparse sampling and reconstruction of natural images, namely, a reconstruction network based on Transformer and convolution (TCR-Net). The Transformer framework has the inherent ability to capture global context through a self-attention mechanism, which can effectively solve the challenge of long-range dependence on features. TCR-Net is an end-to-end two-stage architecture. First, a data-driven pre-trained encoder is used to complete the sparse representation and basic feature extraction of image information. Second, a new attention mechanism is introduced to replace the self-attention mechanism in Transformer, and a hybrid Transformer and convolution module based on optimization-inspired is designed. Its iterative process leads to the unfolding framework, which approximates the original image stage by stage. Experimental results show that TCR-Net outperforms existing state-of-the-art CS methods while maintaining fast computational speed. Specifically, when the CS ratio is 0.10, the average PSNR on the test set used in this paper is improved by at least 0.8%, the average SSIM is improved by at least 1.5%, and the processing speed is higher than 70FPS. These quantitative results show that our method has high computational efficiency while ensuring high-quality image restoration.
Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey
Authors: Moosavi Sajad , Farajzadeh-Zanjani Maryam , Razavi-Far Roozbeh , Palade Vasile , Saif Mehrdad
Published: September 2024

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.
Multimodal Driver Condition Monitoring System Operating in the Far-Infrared Spectrum
Authors: Knapik Mateusz , Cyganek Bogusław , Balon Tomasz
Published: September 2024

Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in driver behavior. Analyzing driver states using visible spectrum imaging is particularly challenging under low-light conditions, such as at night. Additionally, relying on a single behavioral indicator often fails to provide a comprehensive assessment of the driver’s condition. To address these challenges, we propose a system that operates exclusively in the far-infrared spectrum, enabling the detection of critical features such as yawning, head drooping, and head pose estimation regardless of the lighting scenario. It integrates a channel fusion module to assess the driver’s state more accurately and is underpinned by our custom-developed and annotated datasets, along with a modified deep neural network designed for facial feature detection in the thermal spectrum. Furthermore, we introduce two fusion modules for synthesizing detection events into a coherent assessment of the driver’s state: one based on a simple state machine and another that combines a modality encoder with a large language model. This latter approach allows for the generation of responses to queries beyond the system’s explicit training. Experimental evaluations demonstrate the system’s high accuracy in detecting and responding to signs of driver fatigue and distraction.
Integrating Occupant Behaviour into Urban-Building Energy Modelling: A Review of Current Practices and Challenges
Authors: Banfi Alessia , Ferrando Martina , Li Peixian , Shi Xing , Causone Francesco
Published: September 2024

Urban-Building Energy Modelling (UBEM) tools play a crucial role in analysing and optimizing energy use within cities. Among the available approaches, the bottom-up physics-based one is the most versatile for urban development and management applications. However, their accuracy is often limited by the inability to capture the dynamic impact of occupants’ presence and actions (i.e., Occupant Behaviour) on building energy use patterns. While recent research has explored advanced Occupant Behaviour (OB) modelling techniques that incorporate stochasticity and contextual influences, current UBEM practices primarily rely on static occupant profiles, due to limitations in the software itself. This paper addresses this topic by conducting a thorough literature review to examine existing OB modelling techniques, data sources, key features and detailed information that could enhance UBEM simulations. Furthermore, the flexibility of available UBEM tools for integrating advanced OB models will be assessed, along with the identification of areas for improvement. The findings of this review are intended to guide researchers and tool developers towards creating more robust and occupant-centric urban energy simulations.
Comparison of the Energy Efficiency of Fixed and Tracking Home Photovoltaic Systems in Northern Poland
Authors: Listewnik Karol Jakub , Nowak Tomasz
Published: September 2024

The relevance of the article’s results lies in presenting the actual energy yields of PV panels of various generations and types of installations. The aim of the article is to provide answers about the effective operation of three different photovoltaic systems: a stationary off-grid system operated for several years, a stationary on-grid system, and a system mounted on trackers. A stationary on-grid system was used as the reference system, taking its area and energy yield as the reference point. The assessment was made on the basis of energy and cost efficiency analysis using the comparative method. The obtained results were compared to the results of other PV systems whose parameters were obtained from literature analysis. The analysis showed significant differences in adapting them to different sunlight conditions. The results confirmed the validity of using fixed PV panels (Installation II) in the short term and the advantage of the PV panel tracking system (Installation III) in the long term. The results also confirm that Installation I, despite its eight years of operation time, shows a relatively small decrease in efficiency, which confirms the validity of the long-term operation of the PV installation.
Feasibility Study on Production of Slush Hydrogen Based on Liquid and Solid Phase for Long Term Storage
Authors: Park Sungho , Lee Changhyeong , Chung Sohmyung , Hwang Seonghyeon , Lim Jongwoong , Chang DaeJun
Published: September 2024

To achieve net-zero objectives, the expansion of renewable energy sources is anticipated to be accompanied by an increased use of carbon-free fuels, such as hydrogen. Internationally, there are proposals for transporting hydrogen by synthesizing it into carriers like ammonia or Liquid Organic Hydrogen Carriers (LOHCs). However, considering the energy consumption required for hydrogenation and dehydrogenation processes and the need for high-purity hydrogen production, the development of liquid hydrogen transportation technologies is becoming increasingly important. Liquid hydrogen, with a density approximately one-sixth that of liquid natural gas and a boiling point roughly 90 K lower, poses significant challenges in suppressing and managing boil-off gas during transportation. Slush hydrogen, a mixture of liquid and solid phases, offers potential benefits. with an approximate 15% increase in density and an 18% increase in thermal capacity compared to liquid hydrogen. The latent heat of fusion of solid hydrogen effectively suppresses boil-off gas (BOG), and the increased density can reduce transportation costs. This study experimentally validated the long-duration storage and transportation concept of slush hydrogen by adapting NASA’s (National Aeronautics and Space Administration) proposed IRAS (Integrated Refrigeration and Storage) technology for compact and mobile tanks. Slush hydrogen was successfully produced by reaching the triple point of hydrogen, resulting in a composition of 47% solid and 53% liquid, with a density of approximately 80.9 kg/m3. Most importantly, methodologies were presented to observe and measure whether the hydrogen was indeed in the slush state and to determine its density. Additionally, CFD (Computational Fluid Dynamics) analysis was performed using solid hydrogen properties, and the results were compared with experimental values. Notably, this analytical technique can be utilized in designing large-capacity tanks for storing slush hydrogen.
Assessing the Features of PV System’s Data and the Soiling Effects on PV System’s Performance Based on the Field Data
Authors: Al Humairi Ali , El Asri Hayat , Al Hemyari Zuhair A. , Jung Peter
Published: September 2024

This paper assesses the features/characteristics of a photovoltaic system’s data, investigates the relationship between the soiling and solar panel performance, and leverages real-world data obtained from a solar site in Shams Solar Facility located at the German University of Technology in Oman. Through an experimental approach, different parameters were scrutinized to unravel the dynamics at play. Due to the lack of studies on how to assess the features of a PV System’s data, and in order to model the PV System’s data, extensive analyses were conducted based on a big dataset containing 36,851 observations of each parameter (environmental factors) of the study. In addition, diverse environmental factors, operational conditions, and the collected data were analyzed by various mathematical/statistical measures, and inferential statistical measures were applied to obtain accurate and significant results that explain the level of each parameter (environmental factors), and are developed to examine the features/characteristics and performance of PV Systems and reveal the influence of soiling accumulation on the energy output. The research findings do not only deepen the understanding of the features of PV Systems data and the impact of soiling on solar panels, but also underscore the significance of considering geographical and climatic variations. This research contributes significantly to advancing knowledge within the realm of solar energy systems and provides actionable insights for optimizing the performance and reliability of PV installations in real-world settings. The discussion, conclusions, limitations, and future directions have been discussed.
The Distribution of Climate Comfort Duration for Forest Therapy Has Temporal and Regional Heterogeneity in Xinjiang
Authors: Zhu Shuxin , Wang Ruifeng , Wang Qiya , Shao Su , Lin Hai , Lei Ting , Wang Qingchun , Cui Guofa
Published: September 2024

Climatic comfortability serves as a crucial factor in tourism decision making; however, there remains a gap in evaluating the climate comfort conditions specifically for forest therapy. We developed a new index—Forest Therapy Climate Comfort Index (FTCCI)—to evaluate the climate comfort conditions for forest therapy by integrating the Temperature (T), Temperature and Humidity Index (THI), and Wind Efficiency Index (WEI). A total of 26 potential forest therapy bases were selected from the protected areas in Xinjiang and divided into five clusters: Aksu cluster, Hami cluster, Altai cluster, Ili and its surrounding cluster, and Urumqi and its surrounding cluster. Based on the monthly observation data from 25 surface meteorological stations in Xinjiang, spanning from 1994 to 2023, employing the Co-Kriging interpolation method, we explored the spatial–temporal variation in FTCCI from June to September and made clear the climate comfort duration across 26 bases in Xinjiang. The results indicated that (1) The variation in T, THI, and WEI in 26 bases demonstrated a consistent pattern of temporal variation. July emerged as the optimal month, followed closely by August, with most indices in both months falling within the comfort level. Conversely, September proved to be the least favorable month due to frigid conditions and discomfort for the human body, whereas June’s sensation was slightly more tolerable. (2) The distribution of T, THI, and WEI showed regional heterogeneity. The Urumqi and its surrounding cluster displayed the most favorable conditions for forest therapy, whereas the Aksu cluster showed the poorest performance. (3) There were differences in both FTCCI and climate comfort duration among various clusters in Xinjiang. Overall, excluding Tomur Peak and Nalati (July and August), the remaining 24 bases offered ideal climate comfort conditions for forest therapy from mid to late June through August. Notably, the bases in Urumqi and its surrounding cluster had the longest climate comfort duration, ranging from 3.5 to 4 months. Therefore, reliance on the unique climate, resource, and geographical condition of each base is crucial in creating special forest therapy products that cater to the diverse health needs of tourists.
Design of Vibration and Noise Reduction for Ultra-Thin Cemented Carbide Circular Saw Blades in Woodworking Based on Multi-Objective Optimization
Authors: Jia Na , Guo Lei , Wang Ruisen , Liu Jiuqing
Published: September 2024

Cemented carbide circular saw blades are widely used for wood cutting, but they often suffer from vibration and noise issues. This study presents a multi-objective optimization method that integrates ANSYS and MATLAB to optimize the design of noise reduction slots in circular saw blades. A mathematical model was developed to correlate the emitted sound power with the overall vibration intensity. A multi-objective optimization model was then formulated to map the slot shape parameters to the deformation, equivalent stress, and vibration intensity during sawing. The ABAQUS thermal–mechanical coupling analysis was used to determine the sawing force and temperature field. The NSGA-II algorithm was applied on the ANSYS–MATLAB platform to iteratively compute slot shape parameters and conduct optimization searches for a globally optimal solution. Circular saw blades were fabricated based on the optimization results, and experimental results showed a significant reduction in sawing noise by 2.4 dB to 3.0 dB on average. The noise reduction effect within the specified frequency range closely agreed with the simulation results, validating the method’s efficiency. This study provides a feasible and cost-effective solution to the multi-objective optimization design problem of noise reduction slots for circular saw blades.
Solid-State Fermentation of Grain-Derived By-Products by Aspergillus kawachii and Rhizopus oryzae: Preparation and Evaluation of Anti-Allergic Activity
Authors: Huang Chung-Hsiung , Liao Yu-Ming , Tsai Guo-Jane
Published: September 2024

Grain processing produces many by-products, including wheat bran, wheat germ and rice bran, which are rich in carbohydrates, proteins and trace elements. In this study, these grain-derived by-products were used as raw materials to conduct solid-state fermentation using mixed strains of Aspergillus kawachii and Rhizopus oryzae, and the potential immunomodulatory and anti-allergic properties of fermented product were evaluated. Solid-state fermentation of a grain by-product mixture, consisting of rice bran, wheat bran, and wheat germ in a 2:1:1 weight ratio, using both A. kawachii L1 and R. oryzae L1 at 26 °C for 5 days, significantly increased the total phenolic, flavonoid, and amino acid contents. The anti-allergic activity of aqueous extract of the fermented product was evaluated in murine models of food allergy and delayed-type hypersensitivity. Oral administration of the fermented product extract (100–200 mg/kg) notably alleviated allergic symptoms such as diarrhea and histopathological changes in the intestines. Moreover, the extract effectively reduced allergen-specific serum antibodies, suppressed splenic cytokine secretion, and mitigated tissue edema and inflammation induced by allergens. Importantly, the extract induced the production of IL-10 and TGF-β, which are well-known cytokines primarily secreted by regulatory T cells. These results underscore the promising immunomodulatory effects of A. kawachii and R. oryzae fermented grain product, suggesting their potential as functional foods or additives for managing allergic disorders, with implications for future therapeutic and dietary applications.
Graph Attention Networks: A Comprehensive Review of Methods and Applications
Authors: Vrahatis Aristidis G. , Lazaros Konstantinos , Kotsiantis Sotiris
Published: September 2024

Real-world problems often exhibit complex relationships and dependencies, which can be effectively captured by graph learning systems. Graph attention networks (GATs) have emerged as a powerful and versatile framework in this direction, inspiring numerous extensions and applications in several areas. In this review, we present a thorough examination of GATs, covering both diverse approaches and a wide range of applications. We examine the principal GAT-based categories, including Global Attention Networks, Multi-Layer Architectures, graph-embedding techniques, Spatial Approaches, and Variational Models. Furthermore, we delve into the diverse applications of GATs in various systems such as recommendation systems, image analysis, medical domain, sentiment analysis, and anomaly detection. This review seeks to act as a navigational reference for researchers and practitioners aiming to emphasize the capabilities and prospects of GATs.