bluuebunny/crossref_metadata_2025 huggingface dataset:
Component Mode Synthesis Based on the Energy Method for Frequency Domain Dynamic Response Analysis of Ballastless Track Structure
Authors: Yang Zhou , Feng Qingsong , Cheng Gong , Zhang Ling
Published: September 2023

Used extensively in structural dynamics analysis, the energy method is advantageous for transforming boundary value problems of differential equations into variational extremum problems. Recent applications include analyzing the wave and vibration characteristics of track structures. However, traditional energy methods for dynamic modeling and analysis of ballastless track structures require obtaining the global stiffness matrix and mass matrix of the entire coupled system, which decreases computational efficiency. The EM-CMS algorithm for frequency domain dynamic response analysis of ballastless track structures has been proposed to address this issue. The core of EM-CMS is the development of model reduction strategies within the framework of the energy method to reduce matrix dimensions and thereby improve computational efficiency. Specifically, the steel spring floating slab track is the focus of this research. Utilizing the energy functional variational method, the modal properties of the rail and floating slab structures are obtained, and truncation is performed. A reduced-order model for the steel spring floating slab track is established by considering the boundary conditions between the rail-floating slab and the floating slab foundation (connected through fastener springs and steel springs, respectively, and considering the springs’ elastic potential energy). Comparatively, the computational efficiency of EM-CMS is approximately ten times greater than that of the energy method. The method’s accuracy is also carefully validated against finite element simulations.
A Novel Improved Dragonfly Algorithm-Based Structural Damage Identification Approach Using Flexibility Assurance Criterion and Trace Lasso
Authors: Chen Zepeng , Zhao Di , Liu Qitian , Zhang Zhiyu , Yu Ling
Published: September 2023

Aiming to enhance the accuracy, stability, and noise robustness of swarm intelligence-based algorithms for structural damage identification (SDI), a novel improved dragonfly algorithm (IDA) is proposed. The IDA integrates the dragonfly algorithm (DA) with three key strategies including enhanced Lévy flight, optimal solution bidirectional search, and greedy preservation. These strategies are introduced to enhance the exploration capability of the original DA and improve the IDA’s capacity to obtain global optima. An objective function is defined using frequency change ratio and flexibility assurance criterion (FAC). Additionally, trace sparse regularization is also incorporated into the objective function since most of the damages in structures tend to be sparsely distributed, so that a sparse result is ensured to improve SDI accuracy. To evaluate the performance of the proposed algorithm, a comparison of the original DA and IDA is conducted using four benchmark functions. The results demonstrate that the proposed algorithm achieves improved convergent rate and accuracy. Furthermore, numerical simulations are performed on a 10-element simply-supported beam and a 31-element planar truss to validate the effectiveness and efficiency of the proposed algorithm in SDI. Significantly, the utilization of IDA instead of DA leads to a substantial reduction in the average calculated relative error for the truly damaged element within the considered damage cases of the simply-supported beam, decreasing from 13.05% to 6.15%. Moreover, an experimental simply-supported beam structure with several assumed damage cases is fabricated in the laboratory. The experimental results further confirm the robustness and capability of the proposed method in real-world SDI applications.
Theoretical and Experimental Research on the Influence and Optimization Method of the 60N Profile in Turnout
Authors: Wang Pu , An Bolun , Ma Junqi , Wang Shuguo , Liu Fengshou
Published: September 2023

This research elucidates the influence of the 60N rail profile on the dynamic interaction between wheel and rail, and the dynamic performance within the turnout. The influence of the 60N profile on the long-term service efficiency of turnouts was scrutinized. Subsequently, an optimization approach for the 60–60N rail combination profile was suggested. The main conclusions are as follows: (1) During the passage in the main direction, the 60N rail profile enhances driving stability and mitigates the progression of wear. However, it causes focalized wheel–rail contact on the rail head and escalates contact stress. (2) During the passage in the branch direction, the 60N rail profile amplifies the lateral dynamic force and rail wear rate in the closure panel, engendering two-point contact and increased contact stress. (3) In the early stage of field service of turnouts, a robust correlation exists between the contact band and the rail profile. In the fragile cross-section of the point rail, the contact band of the 60N rail profile turnout fully occupies the rail head, potentially causing stress concentration on the non-working edge position. By the stable service period, the wheel–rail contact band is mainly influenced by total weight. (4) An optimization method for the 60–60N combination profile was proposed with the corresponding machining tool profile designed. This strategy can avoid the profile transition before and after the turnout, reduce field maintenance workload, enhance running stability of straight passage through the turnout, and preclude fatigue damage in the weak cross-sections of the point rail.
Semidirect product key exchange: The state of play
Authors: Battarbee Christopher , Kahrobaei Delaram , Shahandashti Siamak F.
Published: September 2023

Of the many families of cryptographic schemes proposed to be post-quantum, a relatively unexplored set of examples comes from group-based cryptography. One of the more central schemes from this area is the so-called Semidirect Product Key Exchange (SDPKE), a generalization of Diffie–Hellman Key Exchange that is plausibly post-quantum. In this report we survey the state of the literature relating to SDPKE, providing a high-level discussion of security, as well as a comprehensive overview of the proposed platforms and the main cryptanalytic ideas relevant to each.
A NOVEL DEEP LEARNING METHOD FOR BRAIN TUMOR SEGMENTATION IN MAGNETIC RESONANCE IMAGES BASED ON RESIDUAL UNITS AND MODIFIED U-NET MODEL
Authors: CHEN YUXUAN , CHEN YUNYI , CHEN JIAN , HUANG CHENXI , WANG BIN , CUI XU
Published: September 2023

Brain tumors are among the most deadly forms of cancer, as the brain is a crucial organ for human activity. Early detection and treatment are key to recovery. An expert’s final decision on tumor diagnosis mainly depends on the evaluation of Magnetic Resonance Imaging (MRI) images. However, the traditional manual assessment process is time-consuming, error-prone, and relies on the experience and knowledge of doctors, along with other unstable factors. An automated brain tumor detection system can assist radiologists and internal medicine experts in detecting and diagnosing brain tumors. This study proposes a novel deep learning model that combines residual units with a modified U-Net framework for brain tumor segmentation tasks in brain MR images. In this study, the U-Net-based framework is implemented with a stack of neural units and residual units and uses Leaky Rectified Linear Unit (LReLU) as the model’s activation function. First, neural units are added before the first layer of downsampling and upsampling to enhance feature propagation and reuse. Then, the stacking of residual blocks is applied to achieve deep semantic information extraction for downsampling and pixel classification for upsampling. Finally, a single-layer convolution outputs the predicted segmented images. The experimental results show that the segmentation Dice Similarity Coefficient of this model is 90.79%, and the model demonstrates better segmentation accuracy than other research models.
DOMAIN-ADAPTIVE TSK FUZZY SYSTEM BASED ON MULTISOURCE DATA FUSION FOR EPILEPTIC EEG SIGNAL CLASSIFICATION
Authors: CHENG ZAIHE , ZHOU GUOHUA
Published: September 2023

In recent years, machine learning methods based on epileptic signals have shown good results with brain-computer interfaces (BCIs). With the continuous expansion of their applications, the demand for labeled epileptic signals is increasing. For a large number of data-driven models, such signals are not suitable, as they extend the calibration cycle. Therefore, a new domain-adaptive TSK fuzzy system model based on multisource data fusion (DA-TSK) is proposed. The purpose of DA-TSK is to maintain high classification performance when the amount of labeled data is insufficient. The DA-TSK model not only has a strong learning ability to learn characteristic information from EEG data but is also interpretable, which aids in the understanding of the analytic process of the model for medical purposes. In particular, this model can make full use of a small amount of labeled EEG data in the source domain and target domain through domain adaptation. Therefore, the DA-TSK model can reduce data dependence to a certain extent and improve the generalization performance of the target classifier. Experiments are performed to evaluate the effectiveness of the DA-TSK model on public EEG datasets based on epileptic signals. The DA-TSK model can obtain satisfactory accuracy when the labeled data are insufficient in the target domain.
ANALYSIS OF TRADITIONAL CHINESE MEDICINE PRESCRIPTIONS FOR CEREBRAL STROKE-RELATED DISEASES
Authors: SHAO ZEGUO , HUANG TINGTING , JIANG YANLIN , HAN CHAOYUE , CHEN JIANKUN , JU WENHAO , WANG LI
Published: September 2023

Cerebral stroke, a type of cerebrovascular disease, has become the second leading cause of death globally. It is closely related to many diseases, including hypertension, diabetes, senile dementia, and so forth. As traditional Chinese medicine formulas are increasingly used to treat stroke and its associated diseases, people have begun to use machine learning methods to analyze Chinese medicine prescriptions and summarize their laws. In this study, we collected the data from classic Chinese formulations. Using the Jaccard similarity coefficient method, we calculated the similarity between different prescriptions. We then employed average linkage clustering, categorizing medicine prescriptions for chronic diseases such as diabetes, hypertension, and coronary heart disease into 12 groups. Some of these included “Heart Failure and Warm Kidney Soup”, “Sinus Chamber Junction Syndrome, Quadruple One Depression, Yi Qi, Activating Blood and Feeding Heart Soup”, “Nerves One, Depression One, Tian Ma Hook and Vine Drink”, and “Bawei Antihypertensive Decoction, Anemia Decoction, Hypotension Decoction, Myocardial Live Drink”. We observed that similar prescriptions had more meaningful mutual references. Subsequently, a correlation algorithm was used to analyze the “indications” and “prescription composition”, revealing 11 effective correlation rules. Among these, palpitations were strongly correlated with Astragalus membranaceus, Angelica sinensis, and cassia twig; weakness with Salvia miltiorrhiza, A. membranaceus, and A. sinensis; headaches with Ligusticum wallichii; and vertigo with A. membranaceus. These findings provided a theoretical reference for using traditional Chinese medicine in treating cerebral stroke and associated illnesses.
EFFECTS OF INTERVENTION USING BIOFEEDBACK EQUIPMENT AND A NECK CORRECTION EXERCISE PROGRAM ON BALANCE CONTROL ABILITY, PROPRIOCEPTION AND CRANIOVERTEBRAL ANGLE IN YOUNG ADULTS: A PILOT STUDY
Authors: SEOP LEE HYEON , OH JONG SEON , JIN LEE KYUNG , KIM SEONG-GIL
Published: September 2023

The objective of this research was to evaluate the effects of biofeedback equipment with a tilt sensor and a neck correction exercise program on balance control ability, proprioception, and craniovertebral angle (CVA) in young adults. Ten students (M/F, 7/3) aged 20–30 years attending Sunmoon University in Asan-si, South Korea, participated in this study. All subjects participated in three sessions. These sessions consisted of a biofeedback session with a tilt sensor, followed by an exercise session, and a combined session involving biofeedback equipment with a tilt sensor and exercise. Each session takes 30[Formula: see text]min. The sessions were conducted with a one-day interval between each one. Before the start of the experiment, physical characteristics were measured, and proprioception, balance control ability, and CVA were evaluated. The exercise program significantly improved the stability index (SI) in the eyes-closed state. The biofeedback program resulted in improvement in left rotation, and the CVA was significantly improved after all exercise sessions. In conclusion, a neck correction exercise program that actively moves muscles may have a potential positive impact on balance control ability. Biofeedback equipment might aid in enhancing proprioception by preventing forward head posture (FHP).
Quasi-invariance under flows generated by non-linear PDEs
Authors: Löbus Jörg-Uwe
Published: September 2023

The paper is concerned with the change of probability measures [Formula: see text] along non-random probability measure-valued trajectories [Formula: see text], [Formula: see text]. Typically solutions to non-linear partial differential equations (PDEs), modeling spatial development as time progresses, generate such trajectories. Depending on in which direction the map [Formula: see text] does not exit the state space, for [Formula: see text] or for [Formula: see text], the Radon–Nikodym derivative [Formula: see text] is determined. It is also investigated how Fréchet differentiability of the solution map of the PDE can contribute to the existence of this Radon–Nikodym derivative. The first application is a certain Boltzmann type equation. Here, the Fréchet derivative of the solution map is calculated explicitly and quasi-invariance is established. The second application is a PDE related to the asymptotic behavior of a Fleming–Viot type particle system. Here, it is demonstrated how quasi-invariance can be used in order to derive a corresponding integration by parts formula.
Peridynamics–FEM Coupling for Interfacial Delamination Effected by Vertical Crack Density in Thermal Barrier Coatings
Authors: Dong Han , Wang Han , Cai Zhenwei , Wang Weizhe , Liu Yingzheng
Published: September 2023

A coupling model of peridynamics and finite element method is proposed to study the interfacial delamination influenced by vertical crack density in thermal barrier coating (TBC) systems. Specifically, the progressive failure progress under static and fatigue loads in TBCs, including vertical cracks propagation, the evolution of vertical cracks to interfacial cracks, and interfacial delamination, is simulated by the proposed model. The difference between static failure mechanism and fatigue failure mechanism of TBCs is numerically elucidated. The simulated fracture morphology is in good agreement with the experiential observation. In both static and fatigue loads, a higher vertical crack density is found to correspond to a shorter delamination length, and there is no interfacial delamination when the vertical crack density is high enough. The results provide important insight of vertical crack density on interfacial delamination, and the durability of TBCs can be enhanced by ensuring an appropriately high vertical crack density.
Effect of spatial memory on a predator–prey model with herd behavior
Authors: Peng Yahong , Yu Ke , Li Yujing
Published: September 2023

In this paper, we introduce spatial memory into a predator–prey model with herd behavior. Taking memory-based diffusion coefficient and average memory period of predators as control parameters, we obtain the stable conditions of the positive equilibrium of system and prove the existence of Hopf bifurcation. In addition, a double Hopf bifurcation occurs at the intersection of the nonhomogeneous Hopf bifurcation curves, and a spatially nonhomogeneous quasi-periodic pattern can be observed near the double Hopf bifurcation point by numerical simulation.
A stochastic analysis of co-infection model in a finite carrying capacity population
Authors: Ain Qura tul , Wang JinRong
Published: September 2023

The paper focuses on the study of an epidemic model for the evolution of diseases, using stochastic models. We demonstrated the encoding of this intricate model into formalisms suitable for analysis with advanced stochastic model checkers. A co-infection model’s dynamics were modeled as an Ito–Levy stochastic differential equations system, representing a compartmental system shaped by disease complexity. Initially, we established a deterministic system based on presumptions and disease-related traits. Through non-traditional analytical methods, two key asymptotic properties: eradication and continuation in the mean were demonstrated. Section 2 provides a detailed construction of the model. Section 3 results confirm that the outcome is biologically well-behaved. Utilizing simulations, we tested and confirmed the stability of all equilibrium points. The ergodic stationary distribution and extinction conditions of the system are thoroughly analyzed. Investigations were made into the stochastic system’s probability density function, and digital simulations were employed to illustrate the probability density function and systems’ extinction. Although infectious disease control and eradication are major public health goals, global eradication proves challenging. Local disease extinction is possible, but it may reoccur. Extinction is more feasible with a lower [Formula: see text]. Notably, our simulations showed that reducing the [Formula: see text] value significantly increases the likelihood of disease extinction and reduces the probability of future recurrence. Additionally, our study provides insights into the conditions under which a disease can persist or become extinct, contributing to more effective disease control strategies in public health.
Dynamic properties of random linear cocycles
Authors: Lee Manseob , Oh Jumi
Published: September 2023

In this paper, we extend the expansivity, pseudo trajectory tracing property and hyperbolicity of linear dynamical systems for the random view point. We show that to a random linear cocycle [Formula: see text] it is expansive if and only if it has the generalized pseudo trajectory tracing property. Moreover, we show that [Formula: see text] is topologically stable if and only if it is structurally stable.
Wiener index and graph energy of zero divisor graph for commutative rings
Authors: Rayer Clement Johnson , Jeyaraj Ravi Sankar
Published: September 2023

Let [Formula: see text] be commutative ring and [Formula: see text] be the set of all non-zero zero divisors of [Formula: see text]. Then [Formula: see text] is said to be the zero divisor graph if and only if [Formula: see text] where [Formula: see text] and [Formula: see text]. Graph energy [Formula: see text] is defined as the sum of the absolute eigenvalues of the adjacency matrix [Formula: see text], then [Formula: see text]. Wiener index [Formula: see text] is defined as the sum of all distance between pairs of vertices [Formula: see text] and [Formula: see text], then [Formula: see text]. In this paper, we compute the graph energy from the adjacency matrix of the zero divisor graph and the Wiener index from the zero divisor graph associated with commutative rings.
Photothermal insulation mechanism of submicron ITO hollow particles in PVDF film
Authors: Li Hedong , Shen Peihu , He Zizheng , Xu Yang , Wang Minjia
Published: September 2023

In this study, submicron Sn[Formula: see text]-doped In2O3 hollow particles (indium-tin oxide (ITO)) with a lyrium shell-like surface were synthesized via solvothermal method. The appropriate addition of these particles into polyvinylidene fluoride (PVDF) films exhibits high visible light transmission and effective UV/IR blocking properties, surpassing those of ITO nanoparticles. This can be attributed to the reduced absorption of UV/IR radiation by the hollow ITO particles, as well as their higher diffuse reflectivity and thermal insulation.
China’s Policy Toward Afghanistan and Its Impact on India
Authors: Askari Muhammad Usman , Khalid Talha
Published: September 2023

The South Asia subcontinent is witnessing new security and political dynamics after the United States withdrew its troops in mid-2021. Even as the Taliban-led government is stepping up engagement with the outside world to earn wider diplomatic recognition and exhibit its governing credentials, Beijing and New Delhi are adjusting their regional policies in a bid to acquire more influence in a changing geopolitical landscape. Assuming nation states to be rational actors, this paper uses the stag hunt game to analyze the potential maximum payoffs for Beijing, Kabul, and New Delhi, respectively, if they could make the right strategic calculations about their short-term interests and long-term goals. Even though the subcontinent is not immune to the effect of great power rivalry that is unfolding on the world stage, regional stakeholders could still choose to cooperate on issues of common concern while limiting the fallout of competition and confrontation on other more contentious challenges.
Discovering Latent Macroeconomic Effects in Peer-to-Peer Lending Data
Authors: Foo Jessica Yi-Hui , Lim Lek-Heng , Wong Ken Sze-Wai
Published: September 2023

Peer-to-peer (P2P) lending is a fast growing financial technology (FinTech) trend that is displacing traditional retail banking. Studies on P2P lending have focused on predicting individual interest rates or default probabilities. However, the relationship between aggregated P2P interest rates and the general economy will be of interest to investors and borrowers as the P2P credit market matures. We show that the variation in P2P interest rates across grade types is determined by three macroeconomic latent factors formed by Canonical Correlation Analysis (CCA)—macro default, investor uncertainty, and the fundamental value of the market. However, the variation in P2P interest rates across term types cannot be explained by the general economy.
An Ensemble Approach for Prioritizing Antivirals Against COVID-19 via Heterogeneous Network Inference-Based Inductive Matrix Completion
Authors: Aruna A. S. , Remesh Babu K. R. , Deepthi K.
Published: September 2023

The global spread of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated in Wuhan in December 2019, created a massive health crisis, and disrupted the world economy. Much research has been conducted to discover drugs, develop vaccines, and find repurposable drugs against the disease. Computational drug repurposing, the process of determining new uses for approved drugs through computational techniques, becomes an effective solution to fight the COVID-19 pandemic. This study aims to investigate and prioritize potential drugs against SARS-CoV-2 through an integrated network-based approach. We propose an ensemble approach based on network inference and inductive matrix completion (NIMCVDA) for virus–drug association prediction to identify antivirals against COVID-19. We constructed a heterogeneous drug–virus network using intra-similarities of virus genomic sequences and drug chemical structures and existing associations between viruses and drugs. A network inference method is used to infer missing drug–virus edges. Based on this, existing drug–virus association matrix is reconstructed. Finally, more accurate association scores between drugs and viruses are computed using the inductive matrix completion algorithm. The proposed method achieved an AUC of 0.9020 on five-fold cross-validation and 0.8786 on leave-one-out cross-validation. We compared the performance of the model with related approaches. In addition, we carried out case studies on the top-predicted drugs and implemented our model with other datasets to verify prediction performance. Our work prioritized repurposable drugs to battle with COVID-19 epidemic. The cross-validation results and case studies illustrate that the top-predicted drugs are strong candidates for further biological tests.
Computational Study on D-π-A-Based Electron Donating and Withdrawing Effect of Metal-Free Organic Dye Sensitizers for Efficient Dye-Sensitized Solar Cells
Authors: Arunkumar A. , Anbarasan P. M.
Published: September 2023

A new generation of metal-free organic dyes with a range of donor (D1) and acceptors (A1-A3) were designed and examined for dye-sensitized solar cells (DSSCs) based on (3a) dye as a literature. Triphenylamine (TPA), thiophene ([Formula: see text] and 2-cyanoacrylic acid groups each perform the roles of an acceptor (A), donor (D) and spacer in order to produce a D-[Formula: see text]-A system. To investigate the intramolecular charge transfer (ICT), electronic distribution, ultra-violet visible (UV–Vis) absorption wavelengths, molecular electrostatic potential (MEP) and photovoltaic (PV) parameters of the D1 and A1–A3 molecules, density functional theory (DFT) and time-dependent DFT (TD-DFT) were used. The classification of the tunable donor D1 and A1–A3 determines the PV performance of the dye molecules. Results show that the A2 dye replacement group increases the performance of PV cells via red-shifting absorption spectra. Also, when compared to 3a, A2 dye have lower energy gap ([Formula: see text] and superior UV–Vis spectra that cover the full visible range. These results demonstrate the viability of molecular tailoring as an approach to improve D-[Formula: see text]-A sensitizer proposal for efficient DSSCs fabrication.
Non-contact Monitoring of Fatigue Driving Using FMCW Millimeter Wave Radar
Authors: Chen Honghong , Han Xinyu , Hao Zhanjun , Yan Hao , Yang Jie
Published: September 2023

Fatigue driving is the leading cause of severe traffic accidents, which is considered as an important point of the research. Although a precise definition of fatigue is lacking, it is possible to detect the physiological characteristics of the human body to determine whether a person is fatigued, such as head shaking, yawning, and a significant drop in breathing. In our study, fatigue actions were collected first, and then the different micro-Doppler characteristics produced by human activity were used to classify and recognize the fatigue action using the fine-tuning convolution neural network (FT-CNN) model. The collected signals in the breathing mode were preprocessed to judge whether the person was fatigued according to the estimated value of the respiratory rate. Data in different environments were collected to verify the proposed method. Our results showed that the accuracy of fatigue detection can reach 91.8% in the laboratory environment and 87.3% in realistic scenarios.
A Friend in Need Is a Friend Indeed: Investigating the Quality of Training Data from Peers for Auto-generating Empathetic Textual Responses to Non-Sensitive Posts in a Cohort of College Students
Authors: Sharma Ravi , Mirzakhalov Jamshidbek , Bharti Pratool , Goyal Raj , Schmidt Trine , Chellappan Sriram
Published: September 2023

Towards providing personalized care, digital mental-wellness apps today ask questions to learn about subjects. However, not all subjects using these apps will have mood problems; thus, they do not need follow-up questions. In this study, we investigate an alternate mechanism to handle such non-sensitive posts (i.e., those not indicating mood problems) in college settings. To do so, we generate and use training data provided by a cohort of peer college students so that responses to non-sensitive posts are contextual, emotionally aware, and empathetic while also being terminal (not asking follow-up questions). Using data from a real mental-wellness app used by students, we identify that AI models trained with our peer-provided dataset generate desirable responses to non-sensitive posts, while models trained with state-of-the-art (Facebook’s) Empathetic Dataset yields responses that ask many follow-up questions, hence giving a perception of being intrusive. We believe that mental wellness apps today must not assume that any subject using these apps has mood problems. Perceptions of intrusiveness (i.e., apps asking many questions) must be a factor in design. We also believe that peer students can provide rich and reliable training datasets for college mental wellness apps, a topic that is not yet explored.
Multimodal Religiously Hateful Social Media Memes Classification Based on Textual and Image Data
Authors: Hamza Ameer , Javed Abdul Rehman , Iqbal Farkhund , Yasin Amanullah , Srivastava Gautam , Połap Dawid , Gadekallu Thippa Reddy , Jalil Zunera
Published: September 2023

Multimodal hateful social media meme detection is an important and challenging problem in the vision-language domain. Recent studies show high accuracy for such multimodal tasks due to datasets that provide better joint multimodal embedding to narrow the semantic gap. Religiously hateful meme detection is not extensively explored among published datasets. While there is a need for higher accuracy on religiously hateful memes, deep learning–based models often suffer from inductive bias. This issue is addressed in this work with the following contributions. First, a religiously hateful memes dataset is created and published publicly to advance hateful religious memes detection research. Over 2000 meme images are collected with their corresponding text. The proposed approach compares and fine-tunes VisualBERT pre-trained on the Conceptual Caption (CC) dataset for the downstream classification task. We also extend the dataset with the Facebook hateful memes dataset. We extract visual features using ResNeXT-152 Aggregated Residual Transformations–based Masked Regions with Convolutional Neural Networks (R-CNN) and Bidirectional Encoder Representations from Transformers (BERT) uncased for textual encoding for the early fusion model. We use the primary evaluation metric of an Area Under the Operator Characters Curve (AUROC) to measure model separability. Results show that the proposed approach has a higher AUROC score of 78%, proving the model’s higher separability performance and an accuracy of 70%. It shows comparatively superior performance considering dataset size and against ensemble-based machine learning approaches.
Fact-checking Vietnamese Information Using Knowledge Graph, Datalog, and KG-BERT
Authors: Duong Huong T. , Ho Van H. , Do Phuc
Published: September 2023

In the era of digital information, ensuring the accuracy and reliability of information is crucial, making fact-checking a vital process. Currently, English fact-checking has thrived due to various language processing tools and ample datasets. However, the same cannot be said for Vietnamese fact-checking, which faces significant challenges due to the lack of such resources. To address these challenges, we propose a model for checking Vietnamese facts by synthesizing three popular technologies: Knowledge Graph (KG), Datalog, and KG-BERT. The KG serves as the foundation for the fact-checking process, containing a dataset of Vietnamese information. Datalog, a logical programming language, is used with inference rules to complete the knowledge within the Vietnamese KG. KG-BERT, a Deep Learning (DL) model, is then trained on this KG to rapidly and accurately classify information that needs fact-checking. Furthermore, to put Vietnamese complex sentences into the fact-checking model, we present a solution for extracting triples from these sentences. This approach also contributes significantly to the ease of constructing foundational datasets for the Vietnamese KG. To evaluate the model's performance, we create a Vietnamese dataset comprising 130,190 samples to populate the KG. Using Datalog, we enrich this graph with additional knowledge. The KG is then utilized to train the KG-BERT model, achieving an impressive accuracy of 95%. Our proposed solution shows great promise for fact-checking Vietnamese information and has the potential to contribute to the development of fact-checking tools and techniques for other languages. Overall, this research makes a significant contribution to the field of data science by providing an accurate solution for fact-checking information in Vietnamese language contexts.
Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and Analysis
Authors: Andrade Ermeson , Machida Fumio
Published: September 2023

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.
AMULET: a Mutation Language Enabling Automatic Enrichment of SysML Models
Authors: Sultan Bastien , Frénot Léon , Apvrille Ludovic , Jaillon Philippe , Coudert Sophie
Published: September 2023

SysML models are widely used for designing and analyzing complex systems. Model-based design methods often require successive modifications of the models, whether for incrementally refining the design (e.g. in agile development methods) or for testing different design options. Such modifications, or mutations, are also used in mutation-based testing approaches. However, the definition of mutation operators can be a complex issue and applying them to models is sometimes performed by hand: this is time consuming and error prone. The paper addresses this issue thanks to the introduction of AMULET, the first mutation language for SysML. AMULET encompasses the modifications targeting SysML block and state-machine diagrams, and is supported by a compiler the paper presents. This compiler is integrated in TTool, an open-source SysML toolkit, enabling the full support of design methods including model design, mutation and verification tasks in a unique toolkit. The paper also introduces two case-studies providing concrete examples of AMULET use for modeling vulnerabilities and cyber attacks, and highlighting the benefits of AMULET for SysML mutations.
Attention-guided Multi-modality Interaction Network for RGB-D Salient Object Detection
Authors: Wang Ruimin , Wang Fasheng , Su Yiming , Sun Jing , Sun Fuming , Li Haojie
Published: September 2023

The past decade has witnessed great progress in RGB-D salient object detection (SOD). However, there are two bottlenecks that limit its further development. The first one is low-quality depth maps. Most existing methods directly use raw depth maps to perform detection, but low-quality depth images can bring negative impacts to the detection performance. Hence, it is not desirable to utilize depth maps indiscriminately. The other one is how to effectively predict salient maps with clear boundary and complete salient region. To address these problems, an Attention-Guided Multi-Modality Interaction Network (AMINet) is proposed. First, we propose a new quality enhancement strategy for unreliable depth images, named
D
epth
E
nhancement
M
odule (
DEM
). With respect to the second issue, we propose
C
ross-
M
odality
A
ttention
M
odule (
CMAM
) to rapidly locate salient region. The
B
oundary-
A
ware
M
odule (
BAM
) is designed to utilize high-level feature to guide the low-level feature generation in a top-down way to make up for the dilution of the boundary. To further improve the accuracy, we propose
A
trous
R
efined
B
lock (
ARB
) to adaptively compensate for the shortcoming of atrous convolution. By integrating these interactive modules, features from depth and RGB streams can be refined efficiently, which consequently boosts the detection performance. Experimental results demonstrate the proposed AMINet exceeds state-of-the-art (SOTA) methods on several public RGB-D datasets.
Bi2WO6/Ti3C2 MXene Quantum Dots Heterostructure-Based Label-Free Photoelectrochemical Aptamer Sensor for Ultrasensitive Kanamycin Monitoring
Authors: Liu Chuxing , Ye Cui
Published: September 2023

As Kanamycin (KAN) is an amino-glycoside antibiotic and has seriously negative effects towards human health, it is urgent to develop an effective way for KAN monitoring. Herein, Bi2WO6/Ti3C2 MXene quantum dots (Bi2WO6/Ti3C2 QDs) heterostructure was synthesized by electrostatically driven assembly and hydrothermal method. On the basis of a Bi2WO6/Ti3C2 QDs heterostructure, a novel aptamer-based anodic photoelectrochemical (PEC) sensing platform was constructed. Through the formation of Au–S bond, the mercapto-group modified aptamers were immobilized as a recognition unit for KAN. Under visible light irradiation, this PEC platform exhibits excellent signal-on photocurrent in the presence of KAN. A log-linear response is observed over a KAN concentration range from 0.01 nM to 50 nM, with a detection limit of 0.005 nM. Moreover, this aptamer-sensor can accurately detect KAN in animal product samples, with recoveries ranging from 94.2% to 101.3%. Overall, this work demonstrates the considerable potential of component reconstitution in PEC biosensors for real sample analysis, providing a new perspective on PEC sensor development.
TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross‐Task Consistency
Authors: Zhang Dan , Zheng Rui , Luosang Gadeng , Yang Pei
Published: September 2023

This paper addresses the task of semantic segmentation in computer vision, aiming to achieve precise pixel‐wise classification. We investigate the joint training of models for semantic edge detection and semantic segmentation, which has shown a promise. However, implicit cross‐task consistency learning in multitask networks is limited. To address this, we propose a novel “decoupled cross‐task consistency loss” that explicitly enhances cross‐task consistency. Our semantic segmentation network, TriangleNet, achieves a substantial 2.88% improvement over the Baseline in mean Intersection over Union (mIoU) on the Cityscapes test set. Notably, TriangleNet operates at 77.4% mIoU/46.2 FPS on Cityscapes, showcasing real‐time inference capabilities at full resolution. With multiscale inference, performance is further enhanced to 77.8%. Furthermore, TriangleNet consistently outperforms the Baseline on the FloodNet dataset, demonstrating its robust generalization capabilities. The proposed method underscores the significance of multitask learning and explicit cross‐task consistency enhancement for advancing semantic segmentation and highlights the potential of multitasking in real‐time semantic segmentation.
Impact of Position in Family on Suicide: A Case-Control Psychological Autopsy Study among Chinese Rural Youths
Authors: Zhou Qin , Li Ning , Zhang Jie , Fan Libo
Published: September 2023

Introduction. Suicide remains an important public health issue in China. Existing literature on the relationship between individual-familial variables and suicide risk mainly focused on family socioeconomic status, and few studies analyzed the effect of position in family on suicide. In this study, we aimed to explore the association between position in family and suicide among Chinese rural youths. Methods. We conducted a case-control psychological autopsy study. The data collection yielded 392 suicide cases aged 15-34 years and 416 community living controls within the same age range. Personal position in family was assessed by the question “How do you evaluate his/her position in the family?” and categorized as high, general, and low to reflect the relative position in family. Logistic regression models were conducted to explore the association between position in family and suicide risk. Results. The results showed that compared with females (males) having a high position in family, females (males) with a low position in family were 7.1 (9.1) times more likely to commit suicide (

p
<
0.01

). Mental disorders, social support, and coping strain were potentially important mediating factors linking position in family to suicide, with certain heterogeneity among males and females. Low coping strain played the most important role in underlying the association between a low position in family and suicide for both females and males, accounting for 55% (28%) of the contribution to the total effect for females (males). Subgroup analysis revealed that low position in family had more significant impacts on suicide risk among married youths and those with low education levels. Conclusion. The effect of position in family on suicide should receive greater consideration when predicting suicide in rural China. Possible mechanisms underlying the effect of position in family on suicide include mental status, social support, and coping strain.
Study on Solidification Characteristics of Granular Coal Gangue: Fine Sand Paste-Cemented Filling Materials
Authors: Deng Daiqiang , Wang Runze , Cao Guodong , Liang Yihua , Fan Jinkuan , Ma Yunfan , Gao Yu
Published: September 2023

Three groups of filling materials with different mix proportions were prepared. PO42.5 grade cement was selected as the binding material, mechanized granular coal gangue and fine sand were used as the filling aggregate, and then the specimens were curing at room temperature and 95% moisture. Through the uniaxial compression test, the influence of gangue–sand ratio, the mass fraction, and curing age on the mechanical properties of filling materials was analyzed. The microstructure analysis of SEM was conducted to explore the internal mechanism of the strength difference of filling materials. The results show that the gangue–sand ratio dramatically influences the uniaxial compressive strength. The strength increases first and then decreases with the gangue–sand ratio increase. The mass fraction of filling paste is positively correlated with specimen strength. When the sand–cement ratio, gangue–sand ratio, and the mass fraction remain unchanged, the longer the curing age, the greater the uniaxial compressive strength of specimens. The specimens with a sand–cement ratio of 3.5 : 1, a gangue–sand ratio of 5 : 5, and a mass fraction of 86% reach the maximum value of 11.03 MPa at a curing age of 10 days. It can be seen that when the gangue–sand ratio is 5 : 5, the filling material has the best mechanical properties, so it can be recommended as the optimal mix proportion for goaf filling. Thus, it provides a solid technical guarantee for the treatment of mine surface collapse disasters and the utilization of bulk coal gangue resources.
tRNA-Uridine Aminocarboxypropyltransferase DTW Domain Containing 2 Suppresses Colon Adenocarcinoma Progression
Authors: Qian Yun , Li Yu-Jiang , Fu Yi-Wei , Liu Cui-Xia , Wang Juan , Yang Bin
Published: September 2023

Background. DTW Domain Containing 2 (DTWD2) is a newly identified transfer RNA-uridine aminocarboxypropyltransferase. Dysregulated expression of DTWD1 has been reported in several malignancies, nevertheless, the role of DTWD2 in cancers remains completely unknown. Here, we aimed to initially investigate the expression and role of DTWD2 in colon adenocarcinoma. Methods. We first evaluated the transcription and mRNA levels of DTWD2 using data from The Cancer Genome Atlas. Besides, we tested its mRNA and protein expression in our enrolled retrospective cohort. Univariate and multivariate analyses were conducted to assess its prognostic value. Cellular experiments and xenografts were also performed to validate the role of DTWD2 in colon cancer progression. Results. DTWD2 was downregulated in colon adenocarcinoma and associated with poor prognosis. Lymph node metastasis, distant metastasis, and advanced tumor stage are all characterized by lower DTWD2 levels. Furthermore, Cox regression analysis demonstrated that DTWD2 is a novel independent prognostic factor for colon cancer patients. Finally, cellular and xenograft data demonstrated that silencing DTWD2 significantly enhanced colon cancer growth. Conclusion. Low expression of DTWD2 may be a potential molecular marker for poor prognosis in colon cancer.
Emergency Removal of a Proximal Tracheal Foreign Body by Tracheotomy in a Dog and a Cat
Authors: Itoh Teruo , Kojimoto Atsuko , Shii Hiroki
Published: September 2023

There have been few reports of emergency cases of proximal tracheal foreign bodies in dogs and cats. Here, we report a dog and a cat that underwent an emergency tracheotomy for a foreign body in the proximal trachea. Case 1 was a dog with respiratory arrest caused by a large stone in the proximal trachea. The stone was immediately removed via tracheotomy without anesthesia. After intubation and ventilation under anesthesia, hypoxia persisted but improved after aspiration of 100 mL of bloody fluid from the lower trachea. Case 2 was a cat with dyspnea because of a proximal tracheal stone and increased radiopacity in the right lung. The stone was removed via tracheotomy after mask induction of anesthesia, followed by intubation and incision closure. Radiographs immediately after extubation showed worsened right lung atelectasis, alleviated by reintubation and positive pressure ventilation. Both patients recovered completely after surgery. An emergency tracheotomy may be indicated for a large foreign body in the proximal trachea. Additionally, concurrent conditions in the lower respiratory tract should be addressed.
An Efficient Aircraft Conflict Detection and Resolution Method Based on an Improved Reinforcement Learning Framework
Authors: Xu Qiucheng , Chen Zhangqi , Li Fangfang , Shen Zhiyuan , Wei Wenbin
Published: September 2023

With the steady increase of air traffic column, an auxiliary decision tool is required to compensate the operation redundancy deficiency of more sectors of air traffic control. To solve the problem of nonconflict high-density departure and arrival traffic flow, this method is expected to rapidly establish and maintain safe separation with more flexible changing strategies for aircraft heading and speed. This paper proposes an improved reinforcement learning framework to achieve conflict detection and resolution. The proposed framework includes the first development of an air traffic flow model based on a multiagent Markov decision process. The goal reward function was then maximized by improved Monte-Carlo tree search combined with an upper confidence bound tree. Three simulation scenarios were designed for illustrating the improvements of the proposed algorithm, with the results indicating that the algorithm could establish and maintain safe separation between 20 agents in the simplified hexagon-shaped airspace of Huadong, China. Furthermore, the proposed method was demonstrated to reduce the number of conflicts between aircraft agents by up to 26.32% compared to previous research.