bluuebunny/crossref_metadata_2025 huggingface dataset:
The Structural Basis of DL0410, a Novel Multi-Target Candidate Drug for the Treatment of Alzheimer’s Disease
Authors: Liu Ailin , Zhao Jun , Huls Nicholas J. , Zeller Matthias , Wang Lin , Li Tonglei
Published: December 2023

Alzheimer’s disease (AD) is a complicated disease for which there are still no ideal one-target drugs, while multi-target drugs are closer to ideal drugs and will provide new solutions for the clinical treatment of AD. DL0410 is a promising multi-target drug candidate for AD treatment that is not only a significant inhibitor against both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) but also an antagonist of histamine H3 receptor (H3R), and its therapeutic efficacy in treating cognitive dysfunction has been validated in a series of AD-related animal models, including scopolamine-induced mice, D-galactose-induced and Aβ-induced mice, and APP/PS1 and SAMP8 mice. Although the structure of DL0410 has been analyzed using various detection techniques, such as MS and NMR, its three-dimensional crystal structure still requires further confirmation. In this study, the crystal of DL0410 was grown in aqueous solution, and its structure was detected using the X-ray diffraction method. The crystal data, atomic coordinates, bond lengths, angles, and hydrogen bonds of DL0410 were obtained. Its stability was proven by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Based on this study, the molecular docking of DL0410 with AChE, BuChE, and H3R was performed to uncover their interaction mechanisms and explain their bioactivities. This study provides important information for new multi-target drug design and the research and development of new drugs for AD treatment.
Lithium Tetraborate as a Neutron Scintillation Detector: A Review
Authors: Echeverria Elena , McClory John , Samson Lauren , Shene Katherine , Colón Santana Juan A. , Burak Yaroslav , Adamiv Volodymyr , Teslyuk Ihor , Wang Lu , Mei Wai-Ning , Nelson Kyle A. , McGregor Douglas S. , Dowben Peter A. , Ilie Carolina C. , Petrosky James , Dhingra Archit
Published: December 2023

The electronic structure and translucent nature of lithium tetraborate (Li2B4O7) render it promising as a scintillator medium for neutron detection applications. The inherently large neutron capture cross-section due to 10B and 6Li isotopes and the ease with which Li2B4O7 can be enriched with these isotopes, combined with the facile inclusion of rare earth dopants (occupying the Li+ sites), are expected to improve the luminescent properties, as well as the neutron detection efficiency, of Li2B4O7. The electronic structure of both doped and undoped Li2B4O7 were explored, using photoemission and inverse photoemission spectroscopies, optical measurements, and theoretical computational studies such as density functional theory. The scintillation properties are further enhanced because of the wide bandgap, making Li2B4O7 extremely translucent, so that capturing the neutron scintillation output is neither hindered nor diminished. Therefore, in this review, demonstrations of the possible amplification of neutron capture efficiencies, courtesy of rare-earth dopants, along with insights into a significantly large charge production (associated with neutron capture), are presented.
An Investigation into the Diversity of Leeches (Hirudinida) in the Baltic States
Authors: Rutkauskaite-Suciliene Jurgita , Snegiriovaite Justina , Satkauskiene Ingrida
Published: January 2024

The Baltic states—Estonia, Latvia, and Lithuania—are situated on the eastern coast of the Baltic Sea between Russia and the Scandinavian countries. This region is characterized by diverse landscapes and numerous bodies of water, including lakes, peat bogs, and marshes, which serve as natural habitats for aquatic invertebrates, including leeches. Despite the rare and uncertain taxonomic status of leech species in the area, research in the Baltic region has been inadequate. Therefore, this study aims to examine the composition of leech species in the Baltic countries, drawing on both past and current research. The study also seeks to assess the status of Hirudo medicinalis in the Baltic region and discuss the rare, questionable, and potentially present leech species in the Baltic states. A scoping review method was employed, surveying published references, books, and databases. In total, the study found records of 21 leech species in Estonia, 15 in Latvia, and 26 in Lithuania, belonging to the families Glossiphoniidae, Erpobdellidae, Hirudinidae, Haemopidae, and Piscicolidae. Hirudo medicinalis L. is distributed throughout all Baltic countries, but the most recent records on their location are only available for Estonia. The data on a few Piscicolidae species is questionable and requires verification, but further research on Piscicolid leeches can potentially lead to the discovery of rare species.
Application of Speckle Tracking Echocardiography for Evaluating Ventricular Function after Transcatheter Pulmonary Valve Replacement
Authors: Ji Mengmeng , Zhang Li , Gao Lang , Lin Yixia , He Qing , Xie Mingxing , Li Yuman
Published: December 2023

Pulmonary regurgitation usually leads to right heart dilatation and eventually right heart dysfunction, which is associated with a poor prognosis. Transcatheter pulmonary valve replacement is a developing treatment for pulmonary valve dysfunction that can take the place of traditional surgery and make up for the shortcomings of a large injury. Echocardiography plays a significant role in assessing ventricular function; however, conventional echocardiographic parameters have several limitations. Speckle tracking echocardiography has been regarded as a more accurate tool for quantifying cardiac function than conventional echocardiography. Therefore, the aim of this review was to summarize the application of speckle tracking echocardiography for evaluating right and left ventricular functions in patients after transcatheter pulmonary valve replacement.
Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT
Authors: Lee Kyu Hong , Lee Ro Woon , Kwon Ye Eun
Published: December 2023

This study evaluates the diagnostic accuracy and clinical utility of two artificial intelligence (AI) techniques: Kakao Brain Artificial Neural Network for Chest X-ray Reading (KARA-CXR), an assistive technology developed using large-scale AI and large language models (LLMs), and ChatGPT, a well-known LLM. The study was conducted to validate the performance of the two technologies in chest X-ray reading and explore their potential applications in the medical imaging diagnosis domain. The study methodology consisted of randomly selecting 2000 chest X-ray images from a single institution’s patient database, and two radiologists evaluated the readings provided by KARA-CXR and ChatGPT. The study used five qualitative factors to evaluate the readings generated by each model: accuracy, false findings, location inaccuracies, count inaccuracies, and hallucinations. Statistical analysis showed that KARA-CXR achieved significantly higher diagnostic accuracy compared to ChatGPT. In the ‘Acceptable’ accuracy category, KARA-CXR was rated at 70.50% and 68.00% by two observers, while ChatGPT achieved 40.50% and 47.00%. Interobserver agreement was moderate for both systems, with KARA at 0.74 and GPT4 at 0.73. For ‘False Findings’, KARA-CXR scored 68.00% and 68.50%, while ChatGPT scored 37.00% for both observers, with high interobserver agreements of 0.96 for KARA and 0.97 for GPT4. In ‘Location Inaccuracy’ and ‘Hallucinations’, KARA-CXR outperformed ChatGPT with significant margins. KARA-CXR demonstrated a non-hallucination rate of 75%, which is significantly higher than ChatGPT’s 38%. The interobserver agreement was high for KARA (0.91) and moderate to high for GPT4 (0.85) in the hallucination category. In conclusion, this study demonstrates the potential of AI and large-scale language models in medical imaging and diagnostics. It also shows that in the chest X-ray domain, KARA-CXR has relatively higher accuracy than ChatGPT.
Effectiveness of Single-Dose Oral Pilocarpine Administration in Patients with Sjögren’s Syndrome
Authors: Komuro Aoi , Yokoi Norihiko , Sotozono Chie , Kinoshita Shigeru
Published: December 2023

In this study, we evaluated the effectiveness of a single-dose oral pilocarpine administration on tear film (TF), as well as dry eye and dry mouth symptoms, in 53 eyes of 27 Sjögren syndrome (SS) patients who were experiencing dry mouth. To evaluate the changes in tear volume, a digital video-meniscometer was used to measure the radius of the lower central tear meniscus curvature (R, mm) of each eye at prior to the administration of 5 mg oral pilocarpine, and at 15 (R:(15)), 30 (R:(30)), and 60 (R:(60)) minutes after administration. The fluorescein breakup time (FBUT, seconds) and ocular and oral dryness symptoms were evaluated before and at 60 min after administration using a visual analogue scale (VAS, mm). A significant increase in R was observed at 15 and 30 min after administration compared to that at prior to administration. FBUT showed significant improvement at 60 min after administration, and the VAS score for ocular and oral dryness symptoms was found to have decreased significantly at 60 min after administration. A single-dose administration of 5 mg oral pilocarpine had a beneficial effect on TF, as well as on ocular and oral dryness symptoms, in patients with SS.
Differentiation of Salivary Gland and Salivary Gland Tumor Tissue via Raman Imaging Combined with Multivariate Data Analysis
Authors: Bassler Miriam , Knoblich Mona , Gerhard-Hartmann Elena , Mukherjee Ashutosh , Youssef Almoatazbellah , Hagen Rudolf , Haug Lukas , Goncalves Miguel , Scherzad Agmal , Stöth Manuel , Ostertag Edwin , Steinke Maria , Brecht Marc , Hackenberg Stephan , Meyer Till
Published: December 2023

Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.
Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning
Authors: Kaur Amandeep , Kaushal Chetna , Sandhu Jasjeet Kaur , Damaševičius Robertas , Thakur Neetika
Published: December 2023

Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings.
Navigation and Robotics in Interventional Oncology: Current Status and Future Roadmap
Authors: Charalampopoulos Georgios , Bale Reto , Filippiadis Dimitrios , Odisio Bruno C. , Wood Bradford , Solbiati Luigi
Published: December 2023

Interventional oncology (IO) is the field of Interventional Radiology that provides minimally invasive procedures under imaging guidance for the diagnosis and treatment of malignant tumors. Sophisticated devices can be utilized to increase standardization, accuracy, outcomes, and “repeatability” in performing percutaneous Interventional Oncology techniques. These technologies can reduce variability, reduce human error, and outperform human hand-to-eye coordination and spatial relations, thus potentially normalizing an otherwise broad diversity of IO techniques, impacting simulation, training, navigation, outcomes, and performance, as well as verification of desired minimum ablation margin or other measures of successful procedures. Stereotactic navigation and robotic systems may yield specific advantages, such as the potential to reduce procedure duration and ionizing radiation exposure during the procedure and, at the same time, increase accuracy. Enhanced accuracy, in turn, is linked to improved outcomes in many clinical scenarios. The present review focuses on the current role of percutaneous navigation systems and robotics in diagnostic and therapeutic Interventional Oncology procedures. The currently available alternatives are presented, including their potential impact on clinical practice as reflected in the peer-reviewed medical literature. A review of such data may inform wiser investment of time and resources toward the most impactful IR/IO applications of robotics and navigation to both standardize and address unmet clinical needs.
Changes in the Perceptions of Oral Symptoms Due to COVID-19 in Korean Adolescents
Authors: Lee Sun-Kyoung , Seong Jeong-Min
Published: December 2023

This study investigated the oral symptoms and subjective health perceptions of Korean adolescents due to COVID-19. Data from the 17th Korea Youth Risk Behavior Survey (KYRBS; 2021) were analyzed, and 54,848 Korean middle and high school students were included in the study. Frequency, cross-tabulation, and logistic regression analyses were performed using IBM SPSS (v. 22.0; IBM Corp., Armonk, NY, USA). Statistical significance was set at p < 0.05. The survey results on subjective health perception showed that 64.8% considered themselves “healthy”, 26.1% rated themselves “moderate” in health, and 9.26% rated themselves “unhealthy.” When asked about brushing their teeth after lunch at school, students answered “no” more frequently than “yes”. Participants provided significantly different responses to questions related to receiving “sealant treatment”, “having broken teeth”, “experiencing tooth pain when eating”, “having throbbing tooth pain”, and “having pain and bleeding in gums”. Regarding the relationship between oral symptoms and subjective health perceptions due to COVID-19, students perceived themselves as healthy when they had no throbbing tooth pain, risk of pain, bleeding in the gums, or symptoms of toothache when eating. Results reveal a relationship between oral symptoms and subjective health perceptions due to COVID-19. Thus, appropriate oral health management for adolescents is needed in response to the COVID-related changes.
Toward the Relational Formulation of Biological Thermodynamics
Authors: Igamberdiev Abir U.
Published: December 2023

Classical thermodynamics employs the state of thermodynamic equilibrium, characterized by maximal disorder of the constituent particles, as the reference frame from which the Second Law is formulated and the definition of entropy is derived. Non-equilibrium thermodynamics analyzes the fluxes of matter and energy that are generated in the course of the general tendency to achieve equilibrium. The systems described by classical and non-equilibrium thermodynamics may be heuristically useful within certain limits, but epistemologically, they have fundamental problems in the application to autopoietic living systems. We discuss here the paradigm defined as a relational biological thermodynamics. The standard to which this refers relates to the biological function operating within the context of particular environment and not to the abstract state of thermodynamic equilibrium. This is defined as the stable non-equilibrium state, following Ervin Bauer. Similar to physics, where abandoning the absolute space-time resulted in the application of non-Euclidean geometry, relational biological thermodynamics leads to revealing the basic iterative structures that are formed as a consequence of the search for an optimal coordinate system by living organisms to maintain stable non-equilibrium. Through this search, the developing system achieves the condition of maximization of its power via synergistic effects.
Estimation of a Simple Structure in a Multidimensional IRT Model Using Structure Regularization
Authors: Shimmura Ryosuke , Suzuki Joe
Published: December 2023

We develop a method for estimating a simple matrix for a multidimensional item response theory model. Our proposed method allows each test item to correspond to a single latent trait, making the results easier to interpret. It also enables clustering of test items based on their corresponding latent traits. The basic idea of our approach is to use the prenet (product-based elastic net) penalty, as proposed in factor analysis. For optimization, we show that combining stochastic EM algorithms, proximal gradient methods, and coordinate descent methods efficiently yields solutions. Furthermore, our numerical experiments demonstrate its effectiveness, especially in cases where the number of test subjects is small, compared to methods using the existing L1 penalty.
Exploring the Impact of Different Leadership Styles on Job Satisfaction among Primary School Teachers in the Achaia Region, Greece
Authors: Panagopoulos Nick , Karamanis Kostas , Anastasiou Sophia
Published: December 2023

This study explores the relationship between the transformational style of primary education Principals and the job satisfaction of their primary teachers in the Achaia region of Greece. A survey of primary school teachers in Greece (n = 411) revealed a strong positive association between transformational leadership and job satisfaction, highlighting the critical role of effective leadership in fostering a positive and supportive work environment for teachers. Pearson correlation analysis demonstrated significant correlations between leadership style and job satisfaction, considering both extrinsic and intrinsic parameters. Demographic factors, such as age and work experience, were associated with variations in satisfaction with the freedom to choose how to work, recognition for work, and promotion prospects. Specifically, older teachers expressed lower satisfaction levels in these areas. Moreover, the study highlighted the substantial impact of leadership style on job satisfaction. Age had a negative correlation with satisfaction with the freedom to choose how to work (r = −0.11, p = 0.019) and satisfaction with recognition for work (r = −0.112, p = 0.019), suggesting that older teachers tend to be less satisfied with these aspects of their jobs. Work experience had a negative correlation with satisfaction with promotion prospects (r = −0.14, p < 0.001), suggesting that more experienced teachers tend to be less satisfied with their prospects for career advancement. Transformational and personalized leadership styles were positively correlated with job satisfaction. Transformational leadership exhibited a strong positive correlation with satisfaction from the administration, colleagues, and working conditions (r = 0.785, p < 0.001). Transactional leadership also exhibits significant positive correlations with these aspects of job satisfaction, albeit to a slightly lesser extent. On the other hand, transactional leadership demonstrated a moderate positive correlation with overall job satisfaction (r = 0.301, p < 0.01), while no significant correlation is observed with satisfaction from the administration, colleagues, and working conditions. Passive laissez-faire leadership, in contrast, revealed negative correlations with all dimensions of job satisfaction, indicating a detrimental impact on the satisfaction levels of primary school teachers. These nuanced findings underscore the importance of considering multiple leadership styles in understanding their varied impacts on job satisfaction among teachers.
The Impacts of the COVID-19 Traffic Light System on Staff in Tertiary Education in New Zealand
Authors: Taylor Lee-Anne , Reid Jodee , Jagroop-Dearing Anita
Published: December 2023

The global impact of the COVID-19 pandemic demanded a swift transition in the usual educational mode of delivery from face to face to online. New Zealand established “a traffic light system” after initial COVID-19 lockdowns, and educational delivery adapted accordingly at a tertiary education provider in Te Pūkenga, Eastern Institute of Technology. This study investigates the ramifications of the traffic light system on this institute’s staff, employing semi-structured interviews and an inductive semantic thematic analysis. The findings reveal a universal impact on staff, characterized by an augmented workload attributed to students’ absences and illnesses. This led to increased support demands of staff for their students’ academic progression. Anxiety, stress, and guilt emerged as prevalent emotions linked to student support. Despite the staff adapting to the mandates, a notable challenge arose from the discord between educational and industry directives, causing confusion among the students. While the study indicates staff resilience in navigating the traffic light changes, it underscores the imperative of recognizing the toll on their wellbeing. The research calls for a proactive consideration of future challenges, emphasizing the importance of safeguarding the mental and emotional health of tertiary education staff amidst potential uncertainties in educational delivery.
Creating Cohesion and Collaboration in Mathematics Classrooms: Implementing Interdepartmental Professional Learning Communities to Support Students with Disabilities
Authors: Pilotti Dawn M. , Harbour Kristin E. , Miller Bridget T. , Larkin Emma K.
Published: December 2023

Professional learning communities (PLCs) support the growth of educators to improve learning outcomes for all students. Guided by social constructivist and social cognitive theories, this longitudinal participatory action research study explored the implementation of an interdepartmental PLC across Hord’s five dimensions of a successful PLC—supportive and shared leadership, shared beliefs values and vision, collective learning and its application, shared personal practice, and supportive conditions. Additionally, this study explored how engaging with an interdepartmental PLC influenced participants’ collective efficacy. During the PLC, the collective expertise of mathematics teachers, administrators, and additional educational specialists (such as occupational therapists and speech and language pathologists) (n = 13) were leveraged to develop strategies for the improvement of mathematical problem solving among elementary and middle school students with disabilities. Our findings indicate statistically significant increases in the five dimensions of the PLC over time, with qualitative evidence supporting the PLC’s effectiveness. However, our findings revealed no significant increase in participants’ overall collective efficacy, a group’s shared belief that together they can achieve a desired result. Implications for practice and research are discussed.
An Integrated Active Deep Learning Approach for Image Classification from Unlabeled Data with Minimal Supervision
Authors: Abdelwahab Amira , Afifi Ahmed , Salama Mohamed
Published: December 2023

The integration of active learning (AL) and deep learning (DL) presents a promising avenue for enhancing the efficiency and performance of deep learning classifiers. This article introduces an approach that seamlessly integrates AL principles into the training process of DL models to build robust image classifiers. The proposed approach employs a unique methodology to select high-confidence unlabeled data points for immediate labeling, reducing the need for human annotation and minimizing annotation costs. Specifically, by combining uncertainty sampling with the pseudo-labeling of confident data, the proposed approach expands the training set efficiently. The proposed approach uses a hybrid active deep learning model that selects the most informative data points that need labeling based on an uncertainty measure. Then, it iteratively retrains a deep neural network classifier on the newly labeled samples. The model achieves high accuracy with fewer manually labeled samples than traditional supervised deep learning by selecting the most informative samples for labeling and retraining in a loop. Experiments on various image classification datasets demonstrate that the proposed model outperforms conventional approaches in terms of classification accuracy and reduced human annotation requirements. The proposed model achieved accuracy of 98.9% and 99.3% for the Cross-Age Celebrity and Caltech Image datasets compared to the conventional approach, which achieved 92.3% and 74.3%, respectively. In summary, this work presents a promising unified active deep learning approach to minimize the human effort in manually labeling data while maximizing classification accuracy by strategically labeling only the most valuable samples for the model.
Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding
Authors: Kim HyoungJu , Choi Junho
Published: December 2023

Recently, security attacks occurring in edge computing environments have emerged as an important research topic in the field of cybersecurity. Edge computing is a distributed computing technology that expands the existing cloud computing architecture to introduce a new layer, the edge layer, between the cloud layer and the user terminal layer. Edge computing has the advantage of greatly improving the data processing speed and efficiency but, at the same time, is complex, and various new attacks occur frequently. Therefore, for improving the security of edge computing, effective and intelligent security strategies and policies must be established in consideration of a wide range of vulnerabilities. Intelligent security systems, which have recently been studied, provide a way to detect and respond to security threats by integrating the latest technologies, such as machine learning and big data analysis. Intelligent security technology can quickly recognize attack patterns or abnormal behaviors within a large amount of data and continuously respond to new threats through learning. In particular, knowledge-based technologies using ontology or knowledge graph technology play an important role in more deeply understanding the meaning and relationships between of security data and more effectively detecting and responding to complex threats. This study proposed a method for recommending strategies to respond to edge computing security incidents based on the automatic generation and embedding of security knowledge graphs. An EdgeSecurity–BERT model, utilizing the latest security vulnerability data from edge computing, was designed to extract entities and their relational information. Also, a security vulnerability assessment method was proposed to recommend strategies to respond to edge computing security incidents through knowledge graph embedding. In the experiment, the classification accuracy of security news data for common vulnerability and exposure data was approximately 86% on average. In addition, the EdgeSecurityKG applying the security vulnerability similarity improved the Hits@10 performance to identify the correct link, but the MR performance was degraded owing to the increased complexity. In complex areas, such as security, careful evaluation of the model’s performance and data selection are important. The EdgeSecurityKG applying the security vulnerability similarity provides an important advantage in understanding complex security vulnerability relationships.
Toward Ultra-Low Latency SSDs: Analyzing the Impact on Data-Intensive Workloads
Authors: Jo Insoon
Published: December 2023

The recent trend in hyper-scale computing has shown that applications are getting hungrier for low, consistent latency for predictably fast service. In accordance with this change, storage vendors have innovated their SSDs to keep ultra-low latencies while delivering high IOPS. Even though these SSDs can provide significant value to a wide variety of workloads, their practical impact on performance has not been discussed much. In this context, our study aims to empirically examine the impact of emerging ultra-low latency SSDs on workload performance, particularly in comparison to the latest conventional SSDs. We conduct benchmarking using a diverse set of data-intensive workloads to comprehensively assess the performance of both ultra-low latency SSDs and conventional SSDs. Our research provides an in-depth performance analysis of the examined SSDs, shedding light on their comparative strengths and weaknesses. The results of our study contribute valuable insights into the practical implications of adopting ultra-low latency SSDs across various workloads, guiding future decisions in storage technology.
Explainability and Evaluation of Vision Transformers: An In-Depth Experimental Study
Authors: Stassin Sédrick , Corduant Valentin , Mahmoudi Sidi Ahmed , Siebert Xavier
Published: December 2023

In the era of artificial intelligence (AI), the deployment of intelligent systems for autonomous decision making has surged across diverse fields. However, the widespread adoption of AI technology is hindered by the risks associated with ceding control to autonomous systems, particularly in critical domains. Explainable artificial intelligence (XAI) has emerged as a critical subdomain fostering human understanding and trust. It addresses the opacity of complex models such as vision transformers (ViTs), which have gained prominence lately. With the expanding landscape of XAI methods, selecting the most effective method remains an open question, due to the lack of a ground-truth label for explainability. To avoid subjective human judgment, numerous metrics have been developed, with each aiming to fulfill certain properties required for a valid explanation. This study conducts a detailed evaluation of various XAI methods applied to the ViT architecture, thereby exploring metrics criteria like faithfulness, coherence, robustness, and complexity. We especially study the metric convergence, correlation, discriminative power, and inference time of both XAI methods and metrics. Contrary to expectations, the metrics of each criterion reveal minimal convergence and correlation. This study not only challenges the conventional practice of metric-based ranking of XAI methods but also underscores the dependence of explanations on the experimental environment, thereby presenting crucial considerations for the future development and adoption of XAI methods in real-world applications.
Determination of Anchor Drop Sequence during Vessel Anchoring Operations Based on Expert Knowledge Base and Hydrometeorological Conditions
Authors: Wnorowski Jakub , Łebkowski Andrzej
Published: December 2023

Presently, the most common technique for maintaining a ship’s location is dynamic positioning, which uses a series of thrusters to hold its position. This method is resilient to moderate hydro-meteorological conditions, eliminating the need for extensive preliminary steps before initiating positioning operations. An alternative approach involves station keeping using a set of anchors, where thrusters are not employed, necessitating careful planning of the anchorage in light of hydro-meteorological conditions. Presently, in vessels using this anchoring method, the captain determines the order of anchor drops, taking into account the prevailing weather conditions, the ship’s maneuvering abilities, and vessel capability plots. This article introduces a novel algorithm that uses sensor-acquired weather data and a cognitive knowledge base to establish the best sequence for anchor drops. This innovation represents a significant stride towards the automation of the anchoring process. By using the anchorage planning algorithm presented in this publication, it has been possible to reduce the time required for anchor deployment by about 52%, due to the preparation of the anchor deployment strategy in port. A reduction in energy consumption of about 8% was also achieved.
A Hardware-Simplified Soft-Start Scheme for Current-Fed Full-Bridge DC-DC Converter
Authors: Li Shupeng , Li Zhenbin , Meng Weiqi , He Jinwei
Published: December 2023

At present, the current-fed full-bridge DC-DC converter still faces two inherent defects, namely, the soft-start issue during the start-up process and the voltage spike issue in normal working conditions. In existing research, a large number of attempts have been reported to overcome these two inherent issues. However, the existing solutions are all based on both redundant soft-start circuits and snubber circuits, and two different circuits are required. As a result, the cost of the current-fed full-bridge DC-DC converter is high, especially because the soft-start circuits are only used during the start-up process. So, to reduce device redundancy, a hardware-simplified soft-start scheme is proposed in this paper. In the proposed scheme, the snubber circuit is reused for the soft-start process based on topology reconfiguration, avoiding additional soft-start circuits to reduce the cost of this DC-DC converter. In this proposed method, the current-fed full-bridge DC-DC converter is operated as the voltage-fed full-bridge DC-DC converter by always turning on the snubber switch during the start-up process. Then, the output capacitor can be charged by slowly increasing the output voltage of the H bridge. Then, when the output voltage is high enough, the current-fed full-bridge DC-DC converter is again operated in the current-fed mode to boost the voltage. Subsequently, the converter characters and control strategy were examined and analyzed. The experimental results verify the feasibility of the proposed hardware-simplified soft-start scheme for the current-fed full-bridge DC-DC converter.
Multi-Spatio-Temporal Convolutional Neural Network for Short-Term Metro Passenger Flow Prediction
Authors: Lu Ye , Zheng Changjiang , Zheng Shukang , Ma Junze , Wu Zhilong , Wu Fei , Shen Yang
Published: December 2023

Accurate short-term prediction of metro passenger flow can offer significant assistance in optimizing train schedules, reducing congestion during peak times, and improving the service level of the metro system. Currently, most models do not fully utilize the high-resolution data aggregated by automatic fare collection systems. Therefore, we propose a model, called MST-GRT, that aggregates multi-time granularity data and considers multi-graph structures. Firstly, we analyze the correlation between metro passenger flow sequences at different time granularities and establish a principle for extracting the spatiotemporal correlation of data at different time granularities using the multi-graph neural network. Subsequently, we use residual blocks to construct a deep convolutional neural network to aggregate the data of different time granularities from small to large, obtaining multi-channel feature maps of multi-time granularity. To process the multi-channel feature maps, we use 2D dilated causal convolution to reconstruct the TCN (Temporal Convolutional Network) to compress the channel number of the feature maps and extract the time dependency of the data, and finally output the results through a fully connected layer. The experimental results demonstrate that our model outperforms the baseline models on the Hangzhou Metro smart-card data set.
Predicting Software Defects in Hybrid MPI and OpenMP Parallel Programs Using Machine Learning
Authors: Althiban Amani S. , Alharbi Hajar M. , Al Khuzayem Lama A. , Eassa Fathy Elbouraey
Published: December 2023

High-performance computing (HPC) and its supercomputers are essential for solving the most difficult issues in many scientific computing domains. The proliferation of computational resources utilized by HPC systems has resulted in an increase in the associated error rates. As such, modern HPC systems promote a hybrid programming style that integrates the message-passing interface (MPI) and open multi-processing (OpenMP). However, this integration often leads to complex defects, such as deadlocks and race conditions, that are challenging to detect and resolve. This paper presents a novel approach: using machine learning algorithms to predict defects in C++-based systems by employing hybrid MPI and OpenMP models. We focus on employing a balanced dataset to enhance prediction accuracy and reliability. Our study highlights the effectiveness of the support vector machine (SVM) classifier, enhanced with term frequency (TF) and recursive feature elimination (RFE) techniques, which demonstrates superior accuracy and performance in defect prediction when compared to other classifiers. This research contributes significantly to the field by providing a robust method for early defect detection in hybrid programming environments, thereby reducing development time, costs and improving the overall reliability of HPC systems.
Effects of the Hyperparameters on CNNs for MDD Classification Using Resting-State EEG
Authors: Yang Chia-Yen , Lee Hsin-Min
Published: December 2023

To monitor patients with depression, objective diagnostic tools that apply biosignals and exhibit high repeatability and efficiency should be developed. Although different models can help automatically learn discriminative features, inappropriate adoption of input forms and network structures may cause performance degradation. Accordingly, the aim of this study was to systematically evaluate the effects of convolutional neural network (CNN) architectures when using two common electroencephalography (EEG) inputs on the classification of major depressive disorder (MDD). EEG data for 21 patients with MDD and 21 healthy controls were obtained from an open-source database. Five hyperparameters (i.e., number of convolutional layers, filter size, pooling type, hidden size, and batch size) were then evaluated. Finally, Grad-CAM and saliency map were applied to visualize the trained models. When raw EEG signals were employed, optimal performance and efficiency were achieved as more convolutional layers and max pooling were used. Furthermore, when mixed features were employed, a larger hidden layer and smaller batch size were optimal. Compared with other complex networks, this configuration involves a relatively small number of layers and less training time but a relatively high accuracy. Thus, high accuracy (>99%) can be achieved in MDD classification by using an appropriate combination in a simple model.
Management of Household-Generated Construction and Demolition Waste: Circularity Principles and the Attitude of Latvian Residents
Authors: Mavlutova Inese , Atstaja Dzintra , Gusta Sandra , Hermanis Janis
Published: December 2023

This study aims to investigate to what extent the construction and demolition waste generated by households is managed by the principles of circularity and to identify the main influencing factors in the behavior of households regarding the circularity-based management of construction waste in Latvia. The current research presents principles of circularity of household-generated waste based on a systematic literature review, and the data obtained from a survey were analyzed using both descriptive and inferential statistics. This study clarifies the circular economy rationale for construction and demolition waste (CDW) management in Latvia and proposes further development to promote the achievement of sustainable development goals and increased energy efficiency. The results reveal that the observance of circular economy principles in construction and demolition waste management among Latvian households does not correspond to good circular economy practices due to attitudes toward environmental issues, expenses, and logistics; thus, compliance with these principles and legislation as well as closer cooperation between municipalities and households can promote significant economic benefits.
Microgrid Protection Coordination Considering Clustering and Metaheuristic Optimization
Authors: Santos-Ramos Javier E. , Saldarriaga-Zuluaga Sergio D. , López-Lezama Jesús M. , Muñoz-Galeano Nicolás , Villa-Acevedo Walter M.
Published: December 2023

This paper addresses the protection coordination problem of microgrids combining unsupervised learning techniques, metaheuristic optimization and non-standard characteristics of directional over-current relays (DOCRs). Microgrids may operate under different topologies or operative scenarios. In this case, clustering techniques such as K-means, balanced iterative reducing and clustering using hierarchies (BIRCH), Gaussian mixture, and hierarchical clustering were implemented to classify the operational scenarios of the microgrid. Such scenarios were previously defined according to the type of generation in operation and the topology of the network. Then, four metaheuristic techniques, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO), and Artificial Bee Colony (ABC) were used to solve the coordination problem of every cluster of operative scenarios. Furthermore, non-standard characteristics of DOCRs were also used. The number of clusters was limited to the maximum number of setting setting groups within commercial DOCRs. In the optimization model, each relay is evaluated based on three optimization variables, namely: time multiplier setting (TMS), the upper limit of the plug setting multiplier (PSM), and the standard characteristic curve (SCC). The effectiveness of the proposed approach is demonstrated through various tests conducted on a benchmark test microgrid.
Off-Gassing and Oxygen Depletion in Headspaces of Solid Biofuels Produced from Forest Residue Biomass
Authors: Warmiński Kazimierz , Jankowska Klaudia Anna , Bęś Agnieszka , Stolarski Mariusz Jerzy
Published: December 2023

As living standards improve worldwide, the demand for energy increases. However, climate changes and decreasing fossil fuel deposits have increased interest in renewable energy sources, including pellets produced from forest residues. This study aimed to compare changes in concentration of gases (CO, CO2, O2, volatile organic compounds—VOCs) in enclosed headspaces above pellets produced from deciduous (oak OA, birch BI) and coniferous (pine PI, spruce SP) dendromass and selected types of commercial pellets during their storage. The experiment measured the concentration of gas released from the pellets in storage daily for 14 days. The highest mean CO concentration was found for PI pellets (1194 ppm), and the lowest was for OA (63.3 ppm). Likewise, the highest CO2 concentration was noted for PI pellets (4650 ppm), and the lowest was for BI (1279 ppm). The largest VOC amount was released in the headspace above PI (88.8 ppm), and the smallest was above BI (4.6 ppm). The oxygen concentration was the lowest as measured for PI (minimum 16.1% v/v) and for SP (19.3% v/v). The threshold limit value (8 h) for CO was exceeded for all the pellets under analysis and, in the case of CO2, only for PI after day 10 of incubation. The study findings are extremely important from a scientific (but mainly from a practical) perspective because of the safety of storing and transporting wood pellets. The knowledge of autooxidation processes in those biofuels can help organize their logistics and storage and result in proper warehouse ventilation and monitoring of noxious gases.
Sensitivity Analysis and Multi-Objective Optimization of Skylight Design in the Early Design Stage
Authors: Fang Yuan , Cho Soolyeon , Wang Yanyu , He Luya
Published: December 2023

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution.
OLTC Fault detection Based on Acoustic Emission and Supported by Machine Learning
Authors: Cichoń Andrzej , Włodarz Michał
Published: December 2023

Power transformers are an essential part of the power grid. They have a relatively low rate of failure, but removing the consequences is costly when it occurs. One of the elements of power transformers that are often the reason for shutting down the unit is the on-load tap changer (OLTC). Many methods have been developed to assess the technical condition of OLTCs. However, they require the transformer to be taken out of service for the duration of the diagnostics, or they do not enable precise diagnostics. Acoustic emission (AE) signals are widely used in industrial diagnostics. The generated signals are difficult to interpret for complex systems, so artificial intelligence tools are becoming more widely used to simplify the diagnostic process. This article presents the results of research on the possibility of creating an online OLTC diagnostics method based on AE signals. An extensive measurement database containing many frequently occurring OLTC defects was created for this research. A method of feature extraction from AE signals based on wavelet decomposition was developed. Several machine learning models were created to select the most effective one for classifying OLTC defects. The presented method achieved 96% efficiency in OLTC defect classification.