bluuebunny/crossref_metadata_2025 huggingface dataset:
Regulation of Mitochondrial Respiration by Hydrogen Sulfide
Authors: Huang Dandan , Jing Guangqin , Zhu Shuhua
Published: August 2023

Hydrogen sulfide (H2S), the third gasotransmitter, has positive roles in animals and plants. Mitochondria are the source and the target of H2S and the regulatory hub in metabolism, stress, and disease. Mitochondrial bioenergetics is a vital process that produces ATP and provides energy to support the physiological and biochemical processes. H2S regulates mitochondrial bioenergetic functions and mitochondrial oxidative phosphorylation. The article summarizes the recent knowledge of the chemical and biological characteristics, the mitochondrial biosynthesis of H2S, and the regulatory effects of H2S on the tricarboxylic acid cycle and the mitochondrial respiratory chain complexes. The roles of H2S on the tricarboxylic acid cycle and mitochondrial respiratory complexes in mammals have been widely studied. The biological function of H2S is now a hot topic in plants. Mitochondria are also vital organelles regulating plant processes. The regulation of H2S in plant mitochondrial functions is gaining more and more attention. This paper mainly summarizes the current knowledge on the regulatory effects of H2S on the tricarboxylic acid cycle (TCA) and the mitochondrial respiratory chain. A study of the roles of H2S in mitochondrial respiration in plants to elucidate the botanical function of H2S in plants would be highly desirable.
The Unfolded Protein Response: A Double-Edged Sword for Brain Health
Authors: Gebert Magdalena , Sławski Jakub , Kalinowski Leszek , Collawn James F. , Bartoszewski Rafal
Published: August 2023

Efficient brain function requires as much as 20% of the total oxygen intake to support normal neuronal cell function. This level of oxygen usage, however, leads to the generation of free radicals, and thus can lead to oxidative stress and potentially to age-related cognitive decay and even neurodegenerative diseases. The regulation of this system requires a complex monitoring network to maintain proper oxygen homeostasis. Furthermore, the high content of mitochondria in the brain has elevated glucose demands, and thus requires a normal redox balance. Maintaining this is mediated by adaptive stress response pathways that permit cells to survive oxidative stress and to minimize cellular damage. These stress pathways rely on the proper function of the endoplasmic reticulum (ER) and the activation of the unfolded protein response (UPR), a cellular pathway responsible for normal ER function and cell survival. Interestingly, the UPR has two opposing signaling pathways, one that promotes cell survival and one that induces apoptosis. In this narrative review, we discuss the opposing roles of the UPR signaling pathways and how a better understanding of these stress pathways could potentially allow for the development of effective strategies to prevent age-related cognitive decay as well as treat neurodegenerative diseases.
Neuroprotective Potential of Pyranocoumarins from Angelica gigas Nakai on Glutamate-Induced Hippocampal Cell Death
Authors: Tran Nguyen Khoi Song , Trinh Tuy An , Pyo Jaesung , Kim Chang Geon , Park Jae Gyu , Kang Ki Sung
Published: August 2023

Chronic neurodegenerative diseases are typically associated with oxidative stress conditions leading to neuronal cell death. We aimed to investigate the neuroprotective effect of three pyranocoumarins (decursin, decursinol angelate, and decursinol) targeting oxidative stress factors. Decursin (also known as dehydro-8-prenylnaringenin) is a prenylated coumarin compound consisting of a coumarin ring system with a prenyl group attached to one of the carbons in the ring. As a secondary metabolite of plants, pyranocoumarin decursin from Angelica gigas Nakai presented protective effects against glutamate-induced oxidative stress in HT22, a murine hippocampal neuronal cell line. Decursinol (DOH) is a metabolite of decursin, sharing same coumarin ring system but a slightly different chemical structure with the prenyl group replaced by a hydroxyl group (-OH). In our findings, DOH was ineffective while decursin was, suggesting that this prenyl structure may be important for compound absorption and neuroprotection. By diminishing the accumulation of intracellular reactive oxygen species as well as stimulating the expression of HO-1, decursin triggers the self-protection system in neuronal cells. Additionally, decursin also revealed an anti-apoptotic effect by inhibiting chromatin condensation and reducing the forming of annexin-V-positive cells.
Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation
Authors: Li Yongjian , Li He , Fan Dazhao , Li Zhixin , Ji Song
Published: August 2023

Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U2-Net network was constructed using multiscale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U2-Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network’s ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U2-Net network feature extraction and enhanced the model accuracy and generalization ability.
Experimental Research into the Repair of High Temperature Damage to Cement Mortar Samples Using Microbial Mineralization Technology
Authors: Zhao Teng , Du Hongxiu , Wang Linhao
Published: August 2023

Experiments such as microbial activation culture, subculture selection, and fire damage repair of cement mortar specimens were conducted to investigate the repairing effect of Sporosarcina pasteurii as a repair agent for fire-damaged cracks in cement mortar specimens. In addition, multi-scale parameters such as compressive strength and chloride ion migration coefficient of cement mortar specimens before and after restoration were compared. The effect of microbial mineralization technology on the repair of fire-damaged cracks in cement mortar specimens was investigated, and the microstructure and mineral composition of the products were analyzed. The results showed that the strong alkaline environment in the cracks of the cement mortar specimens after a high temperature of 500 °C inhibited the activity of bacteria and weakened the mineralization effect; the compressive strength of the repaired cement mortar specimens was 22.8% higher than that of the unrepaired fire-damaged specimens; the compressive strength of the repaired cement mortar reached 78.2% of the strength of the original cement mortar specimen without high temperature; after restoration, the chloride ion penetration resistance of the cement mortar specimens decreased by about 16.9% compared with that before restoration.
Inpainting in Discrete Sobolev Spaces: Structural Information for Uncertainty Reduction
Authors: Seracini Marco , Brown Stephen R.
Published: August 2023

In this article, we introduce a new mathematical functional whose minimization determines the quality of the solution for the exemplar-based inpainting-by-patch problem. The new functional expression includes finite difference terms in a similar fashion to what happens in the theoretical Sobolev spaces: its use reduces the uncertainty in the choice of the most suitable values for each point to inpaint. Moreover, we introduce a probabilistic model by which we prove that the usual principal directions, generally employed for continuous problems, are not enough to achieve consistent reconstructions in the discrete inpainting asset. Finally, we formalize a new priority index and new rules for its dynamic update. The quality of the reconstructions, achieved using a reduced neighborhood size of more than 95% with respect to the current state-of-the-art algorithms based on the same inpainting approach, further provides the experimental validation of the method.
Linear Project-Scheduling Optimization Considering a Reverse Construction Scenario
Authors: Yu Ze , Wang Chuxin , Zhao Yuanyuan , Hu Zhiyuan , Tang Yuanjie
Published: August 2023

The linear scheduling method (LSM) for optimization in linear projects has been the focus of numerous academic studies over the years. However, research on incorporating reverse construction activities and other practical scenarios, such as flexible activity–section–crew assignment demands, in linear project-scheduling optimization remains insufficient. This has further spurred research on practical scenario-based linear project-scheduling optimization. We performed an analysis of a description method system within the LSM framework, outlining the spatiotemporal logical relationship in scenarios involving reverse activities. A scheduling optimization model incorporating a flexible constraint system, capable of handling multisection–multicrew, multicrew–multimode, and multicrew–multiconstruction direction scenarios is developed to enhance the practicability of linear project-scheduling optimization. Additionally, an advanced grey wolf optimization (GWO) algorithm is devised and validated through a real-world case study. The case study provides insights into the impact of reverse construction activities on the linear project-scheduling problem, specifically in the dimension of project duration.
Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences
Authors: Fan Yunlong , Li Bin , Sataer Yikemaiti , Gao Miao , Shi Chuanqi , Cao Siyi , Gao Zhiqiang
Published: August 2023

Most natural-language-processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works addressed the issue with the intuition of decomposing complex sentences and linking simple ones, such as rhetorical-structure-theory (RST)-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple sentence decomposition (SSD), etc. However, these works are not applicable for semantic parsing such as abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments with semantic relations and unavailabilities to preserve the original semantics. Following the same intuition and avoiding the deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), for capturing clausal structures of complex sentences, based on the linguistic research of clause hierarchy. With the HCA framework, we annotated a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decomposed HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more-silver annotations. In evaluating the proposed models on our manually annotated HCA dataset, the performances of clause segmentation and parsing resulted in 91.3% F1-scores and 88.5% Parseval scores, respectively. Due to the same model architectures employed, the performance differences of the clause/discourse segmentation and parsing subtasks was reflected in our HCA corpus and compared discourse corpora, where our sentences contained more segment units and fewer interrelations than those in the compared corpora.
Spacecraft Attitude Stabilization Control with Fault-Tolerant Capability via a Mixed Learning Algorithm
Authors: Wang Jihe , Jia Qingxian , Yu Dan
Published: August 2023

The issue of active attitude fault-tolerant stabilization control for spacecrafts subject to actuator faults, inertia uncertainty, and external disturbances is investigated in this paper. To robustly and accurately reconstruct actuator faults, a novel mixed learning observer (MLO) is explored by combining the iterative learning algorithm and the repetitive learning algorithm. Moreover, to guarantee robust spacecraft attitude fault-tolerant stabilization, by synthesizing the mixed learning algorithm with the sliding mode controller, a novel mixed learning sliding-mode controller (MLSMC) is designed based on the separation principle, in which the mixed learning algorithm is used to update composite disturbances online, including fault errors, inertia uncertainty, and external disturbances. Finally, a numerical example is provided to demonstrate the effectiveness and superiority of our proposed spacecraft attitude fault-tolerant stabilization control approach.
Penetration Performance of Steel Cylinders in Sand Foundations
Authors: Xu Weiqiang , Ruan Huaining , Jiang Pengming
Published: August 2023

In order to study the effect of wall thickness on the penetration process of steel cylinders, we carried out physical tests and numerical simulations of the penetration process of steel cylinders with different wall thicknesses in sandy soil foundations, focusing on analyzing the penetration force and soil uplift during the penetration process of steel cylinders. The results show that the coupled Eulerian-Lagrangian (CEL) approach can be used to simulate the penetration process of steel cylinders. During the penetration of steel cylinders with different wall thicknesses, the relationship between the penetration force and penetration depth can be approximated by a quadratic function. The larger the wall thickness is, the larger the plug volume inside the steel cylinder during penetration. The research results can provide support for the design and construction of steel cylinders.
Regional Traffic Event Detection Using Data Crowdsourcing
Authors: Kim Yuna , Song Sangho , Lee Hyeonbyeong , Choi Dojin , Lim Jongtae , Bok Kyoungsoo , Yoo Jaesoo
Published: August 2023

Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data for detecting events that influence traffic. The proposed technique uses various machine-learning methods to accurately identify events and location information. Therefore, it can resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Performance evaluations also demonstrated its validity and effectiveness.
Morphology and Maturity Status and Their Relationship with Stroke Steadiness in Young Sprint Paddlers
Authors: Abellán-Aynés Oriol , Alacid Fernando , López-Plaza Daniel
Published: August 2023

Elite kayakers are characterized by robust somatotypes and a high degree of technical skills acquired from early ages along the biological maturity process. This study aimed to analyze the interdependence of anthropometric attributes, maturity status, and new stroke kinematics variables such as long- and short-term stroke steadiness in young sprint paddlers. Twenty-two elite young male canoeists, aged 13.5 ± 0.4 years, volunteered for the investigation. A battery of anthropometric measurements was obtained for each individual while stroke kinematics were determined by lateral recordings in an all-out 200 m test. Frame-to-frame analysis of consecutive strokes allowed the determination of short-term and long-term steadiness. Significant and positive correlations (p < 0.05) between maturity status, canoeing experience, and stroke steadiness were identified, especially in long-term steadiness (r = 0.60–0.83). In addition, upper-body dimensions in the chest and arms exhibited strong associations with both short- and long-term steadiness (p < 0.05). These findings suggest that biological maturity, but particularly experience, are key factors for a higher stroke efficiency. Therefore, early identification of morphological characteristics and maturity status along with specific practice in canoeing should be taken into consideration for ideal talent development in young canoeists.
A Low-Complexity Accurate Ranging Algorithm for a Switch Machine Working Component Based on the Mask RCNN
Authors: Wei Lili , Kong Lingkai , Liu Zhigang , Yang Zhenglong , Zhang Hua
Published: August 2023

According to the intelligent development needs of railway operation and maintenance, turnout maintenance also needs an efficient and intelligent means of detection. It is the main method used to measure the access depth of static contact manually. In order to change the disadvantages of the low efficiency and strong subjectivity of traditional schemes, a low-complexity accurate ranging algorithm of the Mask RCNN is proposed to measure the on–off working parts. Firstly, the Mask RCNN and an interactive iterative method are used to segment the region of interest accurately twice. Secondly, the graph distortion is corrected according to the vertex mapping principle. Finally, the accurate actual distance is calculated through fitting the linear distance transformation equation. Through the secondary segmentation and correction algorithm, the accurate calculation of a small target is completed. The experimental results show that the algorithm can accurately measure the distance of different working parts; the average processing time is 0.8 s/amplitude and the measurement error is ±1 mm.
TruMuzic: A Deep Learning and Data Provenance-Based Approach to Evaluating the Authenticity of Music
Authors: Gurjar Kuldeep , Moon Yang-Sae , Abuhmed Tamer
Published: August 2023

The digitalization of music has led to increased availability of music globally, and this spread has further raised the possibility of plagiarism. Numerous methods have been proposed to analyze the similarity between two pieces of music. However, these traditional methods are either focused on good processing speed at the expense of accuracy or they are not able to properly identify the correct features and the related feature weights needed for achieving accurate comparison results. Therefore, to overcome these issues, we introduce a novel model for detecting plagiarism between two given pieces of music. The model does this with a focus on the accuracy of the similarity comparison. In this paper, we make the following three contributions. First, we propose the use of provenance data along with musical data to improve the accuracy of the model’s similarity comparison results. Second, we propose a deep learning-based method to classify the similarity level of a given pair of songs. Finally, using linear regression, we find the optimized weights of extracted features following the ground truth data provided by music experts. We used the main dataset, containing 3800 pieces of music, to evaluate the proposed method’s accuracy; we also developed several additional datasets with their own established ground truths. The experimental results show that our method, which we call ‘TruMuzic’, improves the overall accuracy of music similarity comparison by 10% compared to the other state-of-the-art methods from recent literature.
Ex Situ and In Situ Artificial Thermo-Aging Study of the Natural Degradation of Bombyx mori Silk Fibroin
Authors: Koperska Monika A. , Bagniuk Jacek , Zaitz-Olsza Małgorzata M. , Gassowska Katarzyna , Pawcenis Dominika , Sitarz Maciej , Bulska Ewa , Profic-Paczkowska Joanna
Published: August 2023

This study investigates the degradation mechanism of silk fibroin through Fourier-transformed infrared spectroscopy (FTIR) analysis. The secondary structure of silk fibroin-based materials is monitored using FTIR, and various estimators are calculated to assess the impact of degradation conditions and aging time. The oxidation estimator shows consistent growth, indicating peptide bond oxidation from the early stages of artificial aging, regardless of the conditions. The environment influences the hydrolysis estimator, with water introduction leading to significant changes. The crystallinity estimator reflects the overall degradation level, affected by oxidation and hydrolysis. XRD and FTIR analysis of historical silk banners up to 500 years old demonstrate a decrease in crystallinity and an increase in hydrolysis and oxidation. The presence of water accelerates the oxidation process, while crystallinity changes are primarily driven by oxidation. Fibroin degradation affects both antiparallel and parallel regions, with water playing a crucial role in accelerating hydrolysis and causing structural shifts. This study enhances our understanding of silk fibroin degradation and provides valuable insights for preserving historical silk artifacts.
The Opportunities and Challenges for the Rising Star of Soft Robots
Authors: Dong Yue , Li Bing
Published: August 2023

Robots tend to be designed to replace human beings, so as to efficiently finish some tasks in a repeatable or dangerous situation, bring us huge economic value and good services [...]
The Long and Winding Road to Cardiac Regeneration
Authors: Sacco Anna Maria , Castaldo Clotilde , Di Meglio Franca Di , Nurzynska Daria , Palermi Stefano , Spera Rocco , Gnasso Rossana , Zinno Giorgio , Romano Veronica , Belviso Immacolata
Published: August 2023

Cardiac regeneration is a critical endeavor in the treatment of heart diseases, aimed at repairing and enhancing the structure and function of damaged myocardium. This review offers a comprehensive overview of current advancements and strategies in cardiac regeneration, with a specific focus on regenerative medicine and tissue engineering-based approaches. Stem cell-based therapies, which involve the utilization of adult stem cells and pluripotent stem cells hold immense potential for replenishing lost cardiomyocytes and facilitating cardiac tissue repair and regeneration. Tissue engineering also plays a prominent role employing synthetic or natural biomaterials, engineering cardiac patches and grafts with suitable properties, and fabricating upscale bioreactors to create functional constructs for cardiac recovery. These constructs can be transplanted into the heart to provide mechanical support and facilitate tissue healing. Additionally, the production of organoids and chips that accurately replicate the structure and function of the whole organ is an area of extensive research. Despite significant progress, several challenges persist in the field of cardiac regeneration. These include enhancing cell survival and engraftment, achieving proper vascularization, and ensuring the long-term functionality of engineered constructs. Overcoming these obstacles and offering effective therapies to restore cardiac function could improve the quality of life for individuals with heart diseases.
Big Data and AI-Driven Product Design: A Survey
Authors: Quan Huafeng , Li Shaobo , Zeng Changchang , Wei Hongjing , Hu Jianjun
Published: August 2023

As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.
Statistical Algorithms with Phase-Sensitive Detection for the Number of Hovers and S Turns in Aircraft Flights
Authors: Chen Moli , Wei Xunkai , Wang Hao , Jiang Zhenhe
Published: August 2023

Compared to maximum state parameters, such as maximum altitude and Mach, the number of hovers and S turns can be used as process parameters representing the complexity of military aircraft maneuvers when classifying big flight mission data to compile flight load spectra for structures. This study developed intelligent statistical algorithms based on yaw angle data from flight parameters such as the number of hovers and S turns. Using the median-crossing de-redundant function of Phase-Sensitive Detection (PSD) and analyzing the characteristics of 360° hovering flight parameters, a statistical algorithm for the number of hovers during a flight profile is presented. Using the split-half function of PSD, a triangle layering algorithm based on the yaw angle signal was developed to count the number of S turns during a flight profile, where the signal of each sublayer is segmented into median-crossing intervals to eliminate the redundant median-crossing marks from the previous layer. Compared with artificial means, the statistical results of the flight example showed that the developed intelligent algorithms are effective.
Spatial Distribution of Nutrient Loads Based on Mineral Fertilizers Applied to Crops: Case Study of the Lobo Basin in Côte d’Ivoire (West Africa)
Authors: Koua Tanoh Jean-Jacques , Jeong Jaehak , Alemayehu Tadesse Abitew , Dhanesh Yeganantham , Srinivasan Raghavan
Published: August 2023

Eutrophication in the Lobo watershed remains a major problem. The work carried out has focused on chemical and biological analyses in the lake or in its immediate environment: they did not sufficiently take into account the diffuse transfer of nutrients over the entire watershed. This study aims to assess the nutrient (N and P) loads in the Lobo watershed, an agricultural area, to understand the spatio-temporal impacts of land management practices on eutrophication. The methodology uses two steps: streamflow calibration and nutrient (N and P) estimation using the Soil and Water Assessment Tool (SWAT) watershed model. Thus, the nutrient inputs were estimated based on the levels of N and P in every kilogram of Nitrogen-phosphorus-Potassium (NPK) type fertilizers applied by farmers. The average quantities of N and P applied to the crops were 47.24 kg ha−1 and 21.25 kg ha−1. Results show a good performance on flow calibration as evidenced using evaluation criteria R2, Nash–Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS) of 0.63, 0.62, and −8.1, respectively. The yields of inorganic N and soluble P varied from 0 to 0.049 kg ha−1 and from 0 to 0.31 kg ha−1. These results show that the crops’ inorganic nitrogen requirements were higher than the demands for soluble phosphorus. Simulations relating to the organic N transfer revealed values ranging from 0.2 to 5 kg ha−1, while the transport of organic phosphorus was estimated to vary from 0.3 to 1.3 kg ha−1.
Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt
Authors: Megahed Hanaa A. , Abdo Amira M. , AbdelRahman Mohamed A. E. , Scopa Antonio , Hegazy Mohammed N.
Published: August 2023

The occurrence of flash floods is a natural yet unavoidable occurrence over time. In addition to harming people, property, and resources, it also undermines a country’s economy. This paper attempts to identify areas of flood vulnerability using a frequency ratio approach. The frequency ratio (FR) model was used to produce flood prediction maps for New Cairo City, Egypt. Using field data and remote sensing data, 143 spatial flooded point sites were mapped to build a flood inventory map. The primary driving criteria for flash floods were determined to be elevation, slope, aspect, Land Use Land Cover (LULC), lithology, stream distance, stream density, topographic wetness index (TWI), surface runoff, and terrain ruggedness index (TRI), in that order of importance. A flood susceptibility map (FSM) has been created using the FR model, which combines geographical flooded sites and environmental variables. Our findings from FSM, roughly a fifth of the city is very highly susceptible to flooding (19.32%), while the remaining 40.09% and 13.14% of the study area rank very low and low risk, respectively. The receiver operating characteristic curve (ROC) technique was also used to validate the FSM, and the resulting results showed an area under the curve (AUC) of 90.11%. In conclusion, decision makers can employ models to extract and generate flood risk maps in order to better understand the effects of flash floods and to create alternative measures to prevent this hazard in similar regions. The results of this study will aid planners and decision makers in developing some likely actions to reduce floods vulnerability in this area.
Fatigue Crack Growth Studies under Mixed-Mode Loading in AISI 316 Stainless Steel
Authors: Alshoaibi Abdulnaser M. , Bashiri Abdullateef H.
Published: August 2023

The objective of this study is to examine the behavior of fatigue crack growth (FCG) in the mixed mode (I/II) of the AISI 316 austenitic stainless steel alloy, considering mode mixity angles of 30°, 45°, and 60°. This particular alloy is widely used in the marine industry and various structural components because of its exceptional properties, such as high corrosion resistance, good formability, weldability, and high-temperature strength. By investigating the crack growth behavior, the study seeks to provide insights into the material’s durability and potential for long-term use in demanding applications. To analyze fatigue crack growth behavior using linear elastic fracture mechanics (LEFM), this study utilizes compact tension shear (CTS) specimens with varying loading angles. The CTS specimens provide an accurate simulation of real-world loading conditions by allowing for the application of various loading configurations, resulting in mixed-mode loading. The ANSYS Mechanical APDL 19.2 software, which includes advanced features such as separating, morphing, and adaptive remeshing technologies (SMART), was utilized in this study to precisely model the path of crack propagation, evaluate the associated fatigue life, and determine stress intensity factors. Through comparison with experimental data, it was confirmed that the loading angle had a significant impact on both the fatigue crack growth paths and the fatigue life cycles. The stress-intensity factor predictions from numerical models were compared to analytical data. Interestingly, it was observed that the maximum shear stress and von Mises stresses occurred when the loading angle was 45 degrees, which is considered a pure shear loading condition. The comparison shows consistent results, indicating that the simulation accurately captures the behavior of the AISI 316 austenitic stainless steel alloy under mixed-mode loading conditions.
Assessment of Thermal Performance of Phase-Change Material-Based Multilayer Protective Clothing Exposed to Contact and Radiant Heat
Authors: Renard Morgan , Machnowski Waldemar , Puszkarz Adam K.
Published: August 2023

The research presented in this article concerns the thermal properties of multilayer protective clothing, specifically, the impact of phase-change material (PCM) incorporation on the occurring heat transfer. Multilayer textile assemblies with PCM inserts (macrocapsules containing n-octadecane) and reference assemblies with PP inserts (macrogranules from polypropylene) with very similar geometry and the same textile layers were tested. The spatial geometry of tested assemblies was examined using high-resolution X-ray microtomography (micro-CT). The heating process of the assemblies was examined under the conditions of exposure to contact heat (using thermography) and radiant heat (using a copper plate calorimeter, according to EN ISO 6942). PCM-containing assemblies achieved a temperature rise of 12 °C in a longer period than the reference assemblies; for the contact heat method, the time was longer by 11 and 14 min, and for the radiant heat method by 1.7 and 2.1 min.
Using Public Landslide Inventories for Landslide Susceptibility Assessment at the Basin Scale: Application to the Torto River Basin (Central-Northern Sicily, Italy)
Authors: Martinello Chiara , Mercurio Claudio , Cappadonia Chiara , Bellomo Viviana , Conte Andrea , Mineo Giampiero , Di Frisco Giulia , Azzara Grazia , Bufalini Margherita , Materazzi Marco , Rotigliano Edoardo
Published: August 2023

In statistical landslide susceptibility evaluation, the quality of the model and its prediction image heavily depends on the quality of the landslide inventories used for calibration. However, regional-scale inventories made available by public territorial administrations are typically affected by an unknown grade of incompleteness and mapping inaccuracy. In this research, a procedure is proposed for verifying and solving such limits by applying a two-step susceptibility modeling procedure. In the Torto River basin (central-northern Sicily, Italy), using an available regional landslide inventory (267 slide and 78 flow cases), two SUFRA_1 models were first prepared and used to assign a landslide susceptibility level to each slope unit (SLU) in which the study area was partitioned. For each of the four susceptibility classes that were obtained, 30% of the mapping units were randomly selected and their stable/unstable status was checked by remote analysis. The new, increased inventories were finally used to recalibrate two SUFRA_2 models. The prediction skills of the SUFRA_1 and SUFRA_2 models were then compared by testing their accuracy in matching landslide distribution in a test sub-basin where a high-resolution systematic inventory had been prepared. According to the results, the strong limits of the SUFRA_1 models (sensitivity: 0.67 and 0.57 for slide and flow, respectively) were largely solved by the SUFRA_2 model (sensitivity: 1 for both slide and flow), suggesting the proposed procedure as a possibly suitable modeling strategy for regional susceptibility studies.
Leveraging Language Models for Inpatient Diagnosis Coding
Authors: Suvirat Kerdkiat , Tanasanchonnakul Detphop , Chairat Sawrawit , Chaichulee Sitthichok
Published: August 2023

Medical coding plays an essential role in medical billing, health resource planning, clinical research and quality assessment. Automated coding systems offer promising solutions to streamline the coding process, improve accuracy and reduce the burden on medical coders. To date, there has been limited research focusing on inpatient diagnosis coding using an extensive comprehensive dataset and encompassing the full ICD-10 code sets. In this study, we investigate the use of language models for coding inpatient diagnoses and examine their performance using an institutional dataset comprising 230,645 inpatient admissions and 8677 diagnosis codes spanning over a six-year period. A total of three language models, including two general-purpose models and a domain-specific model, were evaluated and compared. The results show competitive performance among the models, with the domain-specific model achieving the highest micro-averaged F1 score of 0.7821 and the highest mean average precision of 0.8097. Model performance varied by disease and condition, with diagnosis codes with larger sample sizes producing better results. The rarity of certain diseases and conditions posed challenges to accurate coding. The results also indicated the potential difficulties of the model with long clinical documents. Our models demonstrated the ability to capture relevant associations between diagnoses. This study advances the understanding of language models for inpatient diagnosis coding and provides insights into the extent to which the models can be used.
An Integrated Testbed for Power System Cyber-Physical Operations Training
Authors: Chamana Manohar , Bhatta Rabindra , Schmitt Konrad , Shrestha Rajendra , Bayne Stephen
Published: August 2023

The increased adoption of information and communication technology for smart grid applications will require innovative cyber–physical system (CPS) testbeds to support research and education in the field. Groundbreaking CPS testbeds with realistic and scalable platforms have progressively gained interest in recent years, with electric power flowing in the physical layer and information flowing in the network layer. However, CPSs are critical infrastructures and not designed for testing or direct training, as any misbehaving in an actual system operation could cause a catastrophic impact on its operation. Based on that, it is not easy to efficiently train professionals in CPSs. Aiming to support the advancement and encourage the training of industry professionals, this paper proposes and develops a complete testbed using a real-time simulator, protection and automation devices, and a supervisory control and data acquisition (SCADA) system. The testbed replicated the performance of smart grids, and the main potential cyber threats that electric grids may face. Different case scenarios include a distribution system protection study, a denial of service (DoS) attack, a jamming attack, a network packet manipulation attack, a sensor data manipulation attack, a false trip command attack, etc. The system’s performance before and after the cyberattacks are studied using packet-sniffing tools and a network packet analyzer. The impact on the grid is analyzed using metrics such as voltage oscillation, frequency deviation, and loss of active power generation. Moreover, the complex interdependencies between the cyber and physical domains are discussed in detail, providing insightful guidelines for key features and design decisions for future smart grid testbeds.
A Multimodal Deep Learning Model Using Text, Image, and Code Data for Improving Issue Classification Tasks
Authors: Kwak Changwon , Jung Pilsu , Lee Seonah
Published: August 2023

Issue reports are valuable resources for the continuous maintenance and improvement of software. Managing issue reports requires a significant effort from developers. To address this problem, many researchers have proposed automated techniques for classifying issue reports. However, those techniques fall short of yielding reasonable classification accuracy. We notice that those techniques rely on text-based unimodal models. In this paper, we propose a novel multimodal model-based classification technique to use heterogeneous information in issue reports for issue classification. The proposed technique combines information from text, images, and code of issue reports. To evaluate the proposed technique, we conduct experiments with four different projects. The experiments compare the performance of the proposed technique with text-based unimodal models. Our experimental results show that the proposed technique achieves a 5.07% to 14.12% higher F1-score than the text-based unimodal models. Our findings demonstrate that utilizing heterogeneous data of issue reports helps improve the performance of issue classification.
Numerical Investigation of Existing Tunnel Deformation Induced by Basement Excavation Considering the Unloading Ratio
Authors: Liu Junxiu , Huang Xuhui , Li Kai , Dai Yuntong , Ma Wei
Published: August 2023

Basement excavation may induce deformations of the adjacent tunnels. The response of existing tunnels to basement excavation considering the critical unloading ratio is rarely studied. In this study, a three-dimensional numerical model is established to investigate basement–tunnel interaction. Then, the numerical model is validated by simulating the centrifuge model test. Thereafter, the influences of basement geometry and tunnel location relative to the basement on the vertical deformation of the tunnel are studied. The results show that the vertical deformation of the tunnel increases linearly with the unloading ratio, which describes the degree of excavation depth above the tunnel. But there exists a critical unloading ratio of 0.6, beyond which the vertical deformation of the tunnel increases significantly. On this basis, an empirical model is proposed to predict the vertical deformation of the tunnel considering the unloading ratio.
Dynamic Bending Model Describing the Generation of Negative Stiffness by Buckled Beams: Qualitative Analysis and Experimental Verification
Authors: Fossat Pascal , Kothakota Madhurima , Ichchou Mohamed , Bareille Olivier
Published: August 2023

This work addresses the dynamic modeling of a negative stiffness absorber consisting of an assembly of curved beams. Design rules are derived from the orders of magnitude of stiffness and elastic energy stored by the negative stiffness elements. Although static and dynamic performances are widely documented using equivalent spring–mass system equations of motion, this paper presents a modeling approach based on beam dynamics to predict the behavior by incorporating the generation of negative stiffness with prestressed Euler beams. The static behavior is first recalled to feed the dynamic beam model with realistic orders of magnitude. The latter is derived from the beam balance instead of the spring–mass system and aims at solving the beam problem, which encompasses more realistic phenomena compared to introducing the equivalent stiffness in the spring–mass equation of motion. The consistency of the beam modeling is confirmed by comparison with available models in the literature and finite element simulations. A mock-up is built in which beam-type components are 3D-printed. Axial loading is introduced on the curved beams to evaluate its influence on the response of the isolator, and the observed softening trend complies with the theoretical predictions.
Effects of Vehicle Speed on Vehicle-Induced Dynamic Behaviors of a Concrete Bridge with Smooth and Rough Road Surfaces
Authors: Dai Li , Cui Mi-Da , Zhu Ze-Wen , Li Yi , Qiu Jiang-Rui , Cheng Xiao-Xiang
Published: August 2023

According to a previous study, a concrete bridge bearing vehicles traveling at lower speeds suffers from more severe apparent damage compared to one bearing vehicles traveling at higher speeds. The authors of the study subjectively inferred that the observed phenomenon is due to different vehicle load-holding durations for different vehicle speeds. However, this interpretation is not true for bridges with a smooth road surface. Based on an engineering case study of Renyihe Bridge (a concrete rigid-frame continuous highway bridge with spans of 80 m + 4 × 145 m + 80 m), this article reveals via numerical simulations that with the increase in road surface roughness, the resonant responses of the bridge are significantly amplified for cases of low vehicle speed, which can well explain the phenomenon observed by the aforementioned study. Field experiments undertaken on Renyihe Bridge further reveal the related mechanism. These experiments reveal that the frequency of the vehicle excitation for a bridge with sufficient road surface roughness might be closer to the low-order natural frequencies of a bridge with a decrease in vehicle speed. Therefore, the resonant responses are supposed to be more significantly amplified in cases of low vehicle speed after an increase in road surface roughness.
Unlocking Everyday Wisdom: Enhancing Machine Comprehension with Script Knowledge Integration
Authors: Zhou Zhihao , Yue Tianwei , Liang Chen , Bai Xiaoyu , Chen Dachi , Hetang Congrui , Wang Wenping
Published: August 2023

Harnessing commonsense knowledge poses a significant challenge for machine comprehension systems. This paper primarily focuses on incorporating a specific subset of commonsense knowledge, namely, script knowledge. Script knowledge is about sequences of actions that are typically performed by individuals in everyday life. Our experiments were centered around the MCScript dataset, which was the basis of the SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. As a baseline, we utilized our Three-Way Attentive Networks (TriANs) framework to model the interactions among passages, questions, and answers. Building upon the TriAN, we proposed to: (1) integrate a pre-trained language model to capture script knowledge; (2) introduce multi-layer attention to facilitate multi-hop reasoning; and (3) incorporate positional embeddings to enhance the model’s capacity for event-ordering reasoning. In this paper, we present our proposed methods and prove their efficacy in improving script knowledge integration and reasoning.
Teleportation-Based Error Correction Protocol of Time–Frequency Qubit States
Authors: Fabre Nicolas
Published: August 2023

We present a linear optical protocol for teleporting and correcting both temporal and frequency errors in two time–frequency qubit states. The first state is the frequency (or time-of-arrival) cat qubit, which is a single photon in a superposition of two frequencies (or time-of-arrival), while the second is the time–frequency Gottesman–Kitaev–Preskill (GKP) state, which is a single photon with a frequency comb structure. The proposed optical scheme could be valuable for reducing the error rate in quantum communication protocols involving one of these qubits.
A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition
Authors: Mao Yu , Cheng Yuxuan , Shi Chunyu
Published: August 2023

In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, traditional recommendation methods only rely on user rating data for jobs and provide recommendation services for recruiters and candidates through simple information matching. This simple recommendation strategy not only causes a lot of information waste but also cannot effectively utilize the multi-source heterogeneous data information in the field of job recruitment. Therefore, this paper proposes a job recommendation model based on users’ attention levels and tensor decomposition for specific recruitment positions. This model puts forward assumptions based on browsing time for the special behaviors and habits of users in the field of job recruitment, defines corresponding label values for different interactive behaviors, and establishes a grading method based on the attention of job seekers, thus constructing a three-dimensional tensor of “job seeker user-position-attention layered”. Then, a recommendation model is constructed by decomposing the three-dimensional tensor. The effectiveness of the model is verified by comparative experiments with other recommendation algorithms.