bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Multi-level Multi-attention Mechanism Millimeter-Wave Radar and Camera Fusion Method
Authors: Miao Zehua , Li Yinbei , Wu Zizhuo , Yang Jiaqiang , Ma Yuliang 
Published: July 2024

Abstract

In the field of autonomous driving, a commonly employed method to enhance detection accuracy and robustness is the fusion of multi-sensor perception. The fusion of millimeter-wave radar and camera can effectively complement each other, providing sufficient semantic information while ensuring robustness against varying illumination and weather conditions, at a lower cost. In this paper, we focus on the fusion of millimeter-wave radar point cloud features and image features, proposing a multi-level multi-attention feature-level fusion method. By improving the DLA34 backbone network to expand the model's receptive field, we fuse point cloud features at multiple levels with image features and utilize an improved feature pyramid to handle features of both modalities, ensuring good cross-channel information capture capability. Our model leverages the advantages of multi-level multi-attention, achieving an accuracy of 34.3% in the challenging nuScenes dataset, demonstrating promising performance.
Analysis of the Relationship of Age, Gender, Education, Type of Cancer With the Level of Satisfaction and Compliance of One Day Care Chemotherapy Cancer Patients at Dr. Moewardi General Hospital Surakarta
Authors: Hadi Cahyono , Isa Muzakar , Sutrisna EM 
Published: July 2024

Abstract

Background Patient satisfaction is an indicator of a hospital's standards and is a measure of service quality. One day care (ODC) chemotherapy service is one of the service updates at Dr. Moewardi General Hospital facilitates chemotherapy patients to be treated in the recovery room without requiring an inpatient room so that time and costs become more efficient. Cancer patient compliance with chemotherapy requires collaborative efforts between medical personnel providing services and patients regarding the level of compliance with treatment. Cancer patient compliance is relatively low, namely only 50% -70%. One measure of success in providing quality patient care is looking at the satisfaction level of chemotherapy patients and encouraging patients to complete their chemotherapy sessions.Objective To determine the level of satisfaction and compliance of cancer patients with one day care chemotherapy services at Dr. Moewardi General Hospital and its relationship with age, gender, education and type of cancer.Methods This research is descriptive research with a quantitative approach. The sample in this study was 100 patients who met the inclusion and exclusion criteria. The data collection technique used was a questionnaire.Results The satisfaction level of chemotherapy patients assessed from the five main quality dimensions of tangibles, reliability, responsiveness, assurance and empathy was found to be 53% very satisfied, 37% satisfied and 10% quite satisfied. Cancer patient compliance with one day care chemotherapy services has reached 99%. There is a relationship between the level of satisfaction and compliance of cancer patients with one day care chemotherapy services which is significant and has a positive value. The variables age, gender, education and type of cancer do not have a significant relationship with the level of satisfaction and compliance of cancer patients.Conclusion The quality of one day care chemotherapy services at Dr. Moewardi General Hospital is classified as very good in terms of the level of satisfaction and compliance of cancer patients undergoing chemotherapy. Efforts to increase patient satisfaction will have an impact on increasing the level of cancer chemotherapy patient compliance.
Origin-Destination Demand Prediction for Shared Mobility Service Using Fully Convolutional Neural Network
Authors: Phithakkitnukoon Santi , Patanukhom Karn , Demissie Merkebe 
Published: July 2024

Abstract

Emerging on-demand shared mobility services face the difficulty of effectively balancing demand. Influx of these mobility services urges for more precise prediction of origin-destination demand becomes essential and urgent. Our previous work addressed this issue with a Masked Fully Convolutional Network (MFCN) model for short-term pick-up/drop-off demand prediction. In this study, we present a predictive modeling framework designed for short-term origin-destination demand prediction. This framework harnesses the capabilities of Convolutional Neural Networks (CNNs), integrates our previously developed MFCN model, and introduces novel prediction fusion and scaling methodologies. Furthermore, a new loss function is developed and designed to effectively train the model with demand and location information. We evaluated the proposed framework using shared e-scooter trip data from Calgary, Canada. Our evaluation encompasses two prediction scenarios: next-hour and next-24-hour predictions. The performance of our framework is benchmarked against baseline models including the naïve predictor, linear regression, GCN, and variant models. Our model shows the best performance regarding the true positive and F1-score values. The results suggest a high degree of regularity in the daily demand as the next-24-hour predictor performs better than the other scheme. Nonetheless, when a spatial error is considered, the performances of the two prediction schemes are comparable.
Novel Design of Low-Cost Composite Shell and Backfill Tool for Stamping of HSS 590 Sheet Metal
Authors: Athale Madhura , Park Taejoon , Hahnlen Ryan , Pourboghrat Farhang 
Published: July 2024

Abstract

Sheet metal stamping uses hardened steel tools, due to their advantages in hardness, resistance to deformation, and resistance to abrasion. However, these tools have limitations when it comes to prototype production volumes, due to the high cost and time required for tool fabrication. Forming tools fabricated with polymers using additive manufacturing (AM) offer an inexpensive alternative suitable for low volume production and prototyping. For successful implementation of polymer AM tooling in sheet metal forming, tool cost, tool life, and part accuracy are important metrics. A novel composite tool design concept consisting of two distinct components – an outer polymer AM shell, and inner backfill – to make up the composite tool is proposed. Experimental and numerical investigation of stamping of high strength steel sheets with the new tool design is presented. It is shown that the new composite tool design concept improves performance and is more economical compared with fully dense or solid AM polymer tools.
Developing EFL Preservice Teachers' Oral Communicative Competence and Autonomy Utilizing Literature Circles and Process Drama Design
Authors: Nasr Nesma , El-Naggar Bahaa Eldin El-Sayed 
Published: July 2024

Abstract

This research investigated the impact of literature circles and process drama design on developing EFL preservice teachers’ oral communicative competence, strategic and sociolinguistic competence, and autonomy. Employing a quasi-experimental one-group pretest-posttest design with 32 junior EFL preservice teachers, the study revealed significant improvements post-intervention. Data were collected using a communicative competence test and rubric, and a learner autonomy scale. Paired sample t-tests in SPSS indicated a significant difference at the 0.01 level in mean scores favoring post-intervention, highlighting enhanced levels of both communicative competence learner autonomy. The intervention fostered a shift from teacher-centered to student-centered approaches, providing opportunities for independent learning and collaborative discussions. During the literature circles phase, students took on roles such as discussion director, literary luminary, connector, and summarizer, which developed autonomy. Participants monitored their learning, interacted in groups, exchanged roles, and evaluated the learning process through literature circles’ evaluative forms. This phase also enhanced strategic competence, enabling students to speak without hesitation using paraphrasing, description, antonyms, and synonyms. In the process drama phase, students' sociolinguistic competence improved as they engaged in performing role-playing, story dramatization, improvisation, human slideshow, and tableaux after reading stories. These activities facilitated oral engagement and reflection, resulting in improved expression characterized by continuous, confident, and appropriately intoned speech. The findings underscored the positive effect of the design on teaching English literature novels, emphasizing student-centered and collaborative methods while prioritizing learning processes over outcomes. This research significantly contributed to developing active, orally competent, and autonomous EFL learners.
Security enhancement of remote user authentication schemes based on biometrics
Authors: Kim Hyunseok 
Published: July 2024

Abstract

Remote authentication has been extensively studied over the past few decades, with password-based authentication being a common approach since Lamport's 1981 proposal of a password-based remote authentication scheme. Despite numerous advancements, including the introduction of biometric and smart card-based schemes by Li and Hwang, as well as Chen et al.'s claims of robustness against various attacks, these protocols continue to exhibit vulnerabilities. These weaknesses include susceptibility to attacks such as replay, man-in-the-middle, user impersonation, and offline password guessing, among others. In this study, we conduct a comprehensive analysis of several existing biometric-based authentication protocols, identifying critical vulnerabilities and areas for improvement. To address these issues, we propose a novel authentication protocol that leverages the biometrics of mobile devices. Our protocol incorporates a collision-free one-way hash function to enhance security. We conduct a thorough security analysis of the proposed protocol using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, alongside both formal and informal security evaluations. The results of these analyses indicate that our proposed scheme significantly improves security by effectively mitigating common attacks that have compromised previous protocols. Additionally, our protocol demonstrates superior computational efficiency, making it practical for real-world applications. By addressing the security flaws inherent in existing protocols and optimizing for performance, our scheme provides a robust and efficient solution for secure remote authentication using mobile device biometrics.
Predictive modeling of ICU-AW inflammatory factors based on machine learning
Authors: Guo Yuanyuan , Shan Wenpeng , Xiang Jie 
Published: July 2024

Abstract

Background
ICU-acquired weakness (ICU-AW) is a common complication among ICU patients, and we used machine learning techniques to construct an ICU-AW inflammatory factor prediction model to predict the risk of disease development and reduce the incidence of ICU-AW.
Methods
The Least Absolute Shrinkage and Selection Operator (LASSO) technique was used to screen key variables related to ICU-AW. Eleven indicators, such as the presence of sepsis, glucocorticoids(GC), neuromuscular blocking agents(NBAs), length of ICU stay, the Acute Physiology and Chronic Health Evaluation Score II (APACHE II), as well as the levels of albumin(ALB), lactate(LAC), glucose(GLU), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-10 (IL-10), were used as variables to establish the prediction model. We divided the data into a dataset that included inflammatory factors and a dataset that excluded inflammatory factors. Separately, 70% of the participants in both datasets were used as the training set, and 30% of the participants were used as the test set. Three machine learning methods, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB), were used in the 70% participant training set to construct six different models, which were validated and evaluated in the remaining 30% participants as the test set. The optimal model was visualized for prediction using nomograms.
Results
The logistic regression model including the inflammatory factor demonstrated excellent performance on the test set with the area under the curve (AUC) of 82.1% and the best calibration curve fit, outperforming the other five models. The optimal model is represented visually in nomograms.
Conclusion
This study used easily accessible clinical characteristics and laboratory data that can help early clinical recognition of ICU-AW. inflammatory factors IL-1β, IL-6, and IL-10 have high predictive value for ICU-AW.
Trial registration
The trial was registered at the Chinese Clinical Trial Registry with the registration number: ChiCTR2300077968.
Exploring Gender Differences in Falls and Mobility Patterns Among Older Adults in Lebanon
Authors: Khatib Sarah El , Malham Carmela Bou , Sacre Hala , Rassy Rachelle , Hajj Aline , Salameh Pascale , Saadeh Danielle 
Published: July 2024

Abstract

Introduction:
Falls represent a significant public health concern affecting the elderly, yet epidemiological studies within Lebanese institutions are scarce. This study aimed to explore gender differences in falls and mobility patterns and assess causes, potential risks, and protective factors against falls among institutionalized elderly in the Chouf district, Lebanon.
Methods
This cross-sectional pilot study involved an elderly healthcare institution. Data were collected using an anonymous, custom-designed questionnaire and patient medical records. Fall risk was evaluated with the Tinetti Balance and Gait Assessment, the Timed Up and Go Test, the Morse Fall Assessment, and other tools. Relative risks were estimated using odds ratios (OR), and logistic regression was stratified by gender to control confounders and assess the associations between fall episodes and potential risk and protective factors.
Results
This study included 300 institutionalized elderly aged 65 years and older, with 57.7% females. The mean age was 79.27 ± 7.61. Among the participants, 197 (65.7%) reported at least one fall in the past twelve months, with a higher prevalence among women compared to men (69.9% vs. 59.8%). Elderly women exhibited higher levels of depression and consumed more sleeping pills and mood-enhancing medications, which were positively associated with increased falls (p = 0.003). Among elderly men, unsteady walking was positively associated with the history of falls (p = 0.004).
Conclusion
This study found a higher incidence of falls among institutionalized women, possibly associated with the intake of sleeping pills and mood stabilizers. These findings have positive implications for future research in Lebanon and neighboring countries, with the potential to inform preventive measures to reduce falls among the elderly.
Be Aware of Your Text Messages: Fraud Attempts Identification Based on Semantic Sequential Learning for Financial Transactions through Mobile Services in Bangladesh
Authors: Khushbu Sharun Akter , Jaigirdar Fariha Tasmin , Anwar Adnan , Tuhin Ohidujjaman 
Published: July 2024

Abstract

The growth of mobile financial services over the last decade is well accepted by the people of Bangladesh. However, although reported here and there, a complete understanding of illegal payments and fraudulent transactions is still missing. This paper studies one of the popular and experienced financial transaction services of Bangladesh, Bkash, to identify fraudulent text messages through the service. We propose an improved classification method for fraudulent messages through feature extraction and a hybrid deep learning (DL) approach with ML, which combines a convolutional neural network with sequential learning, referred to as the customized Bkash fraud semantic sequential learning (CBF-SSL) model. Seven machine-learning classifiers and five deep-learning sequential models are experimentally evaluated within the hybrid classifier. The performance of the fraud detection system is assessed using the loss function and confusion matrix on a dataset of 500 Bengali text messages. The performance evaluation demonstrates that the hybrid model of convolution neural network (CNN) using long short-term memory (LSTM) outperforms all other classifiers with the lowest error 1 rate of 0.2532%, the highest F1-measure of 75.00% with the highest test accuracy. The proposed text features in combination with CNN and LSTM prove to be highly effective in detecting fraudulent messages.
A novel Federated Learning method with Domain Adaptation and Model Selection for Intrusion Detection
Authors: Li Xiaoyu , Su Kai , Liu Jingjing , Wu Weifei , Bao Liying 
Published: July 2024

Abstract

At present, the intrusion detection data of a single organization is insufficient, the intrusion detection data of each organization cannot meet the conditions of independent and identical distribution, and the distribution of each organization in different geographical locations will also cause the transmission difficulties of data and data leakage problems, which poses a huge challenge to the existing intrusion detection methods based on machine learning. To remedy the problem, a novel Federated Learning Algorithm with Domain Adaptation and Model Selection for Intrusion Detection (FEDTLDAM) is proposed in this paper. FEDTLDAM uses the proposed transfer deep learning model under the federation learning framework to train the local learning model on each organization's local intrusion detection data (source domain) and the global model of the public server (target), and t the designed local model selection method was used to select the local model. Only the local model parameters that meet the conditions are uploaded to the public cloud server to share the knowledge of each organization model, improve the intrusion detection effect of the target model, and ensure the security and privacy of the data of each organization. The domain adaptation strategy of the transfer deep learning model not only considers the difference of distribution between marginal probability and conditional probability, but also utilized the designed weighted method to measure the importance of the above two distribution differences to improve the model learning effect. The model selection method reduces the influence of bad local models, reduces the communication overhead, and improves the global model detection performance. The proposed algorithm FEDTLDAM is verified by experiments on three intrusion detection datasets ISCX2012, NSL-KDD and CICIDS2017, and the results show that compared with the benchmark algorithms, the proposed method has significantly improved the detection accuracy, training efficiency and other key performance indicators. In addition, FEDTLDAM also has good ability of generalization and data privacy protection, and significant application potential in the field of network security.
Integrating Tagged Neutron Inspection with Explainable AI for Threat Material Identification
Authors: Shahabinejad Hadi , Sudac Davorin , Nad Karlo , Espagnon Isabelle , Foy Clotilde de Sainte , Perot Bertrand , Carasco Cedric , Sardet Alix , Friedmann Edwin , Poli Jean Philippe , Delgado Jessica , Pino Felix , Moretto Sandra , Mer Christine , Sannie Guillaume , Obhodas Jasmina 
Published: July 2024

Abstract

Here we present an innovative approach for detecting threat materials within a sealed container by integrating tagged fast neutron activation analysis with Explainable Artificial Intelligence (XAI). Two AI models, a Feed-Forward Neural Network (FFNN) and a Convolutional Neural Network (CNN), were developed to analyze the emitted gamma rays to identify materials like explosives and drugs based on depth profiles of carbon, nitrogen, and oxygen concentrations. XAI was applied to make the models' decision-making process transparent. The method is adaptable to various spectrometric analyses. We demonstrate its effectiveness using data obtained by the Rapidly Relocatable Tagged Neutron Inspection System (RRTNIS), which is irreplaceable for inspecting sealed cargo containers, despite challenges such as variable material placement, background noise, and shielding effects. Our approach successfully locates and categorizes threat materials, both alone and within surrounding materials, at various locations within sealed cargo containers.
DenseNet Structured Neural Network-based Chaos Prediction for Stepped-chamber Tapered Air Bearing System
Authors: Wang Cheng-Chi , Kuo Ping-Huan , Oshima Masahide , Hong Song-Wei 
Published: July 2024

Abstract

Stepped-chamber tapered air bearing (STAB) systems are advanced bearing arrangements designed for applications requiring superior stability at high rotational speeds, along with precise control and stiffness. These systems generate multidirectional supporting forces and offer higher stiffness, making them ideal for high-performance mechanical designs. Despite their advantages, STAB systems can exhibit chaotic motion due to factors like nonlinear pressure distributions, gas imbalances, and design flaws. To understand and control non-periodic motions, a comprehensive analysis is essential. Analytical methods, including dynamic trajectories, spectral responses, bifurcation diagrams, Poincaré maps, and Lyapunov indices, are employed. The study reveals that chaotic phenomena are sensitive to eccentricity and bearing number variations. The interplay of these factors is thoroughly examined. To address chaotic behavior, a DenseNet-structured neural network (DSNN) is developed. This computational framework predicts chaos by evaluating the maximum Lyapunov index of the STAB system. Comparative assessments demonstrate the superior predictive efficacy of the DSNN model, making it a robust tool for anticipating and managing chaotic behavior in STAB systems. These findings enhance our understanding of dynamic intricacies in STAB systems and provide practical design guidelines for industrial applications prioritizing precision, rotational speed, and stiffness.
Stereodynamics effects on van der Waals molecule formation through three-body recombination
Authors: Wang Yu , Pérez-Ríos Jesús 
Published: July 2024

Abstract

In this work, we present a study on the role of atomic configurations in forming van der Waals molecules through three-body recombination. Fixing the angle between the two momenta associated with the Jacobi vectors, we calculate the reaction probability for a specific configuration. In this way, we elucidate the nature of the total reaction probability in reactions X + He + He$\rightarrow$XHe + He, which is essential for understanding van der Waals molecules formation in molecular beams and buffer gas cells.