bluuebunny/crossref_metadata_2025 huggingface dataset:
Robot Operating Systems–You Only Look Once Version 5–Fleet Efficient Multi-Scale Attention: An Improved You Only Look Once Version 5-Lite Object Detection Algorithm Based on Efficient Multi-Scale Attention and Bounding Box Regression Combined with Robot Operating Systems
Authors: Wang Haiyan , Shi Zhan , Gao Guiyuan , Li Chuang , Zhao Jian , Xu Zhiwei
Published: August 2024

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models’ ability to generalize. However, the model’s performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPDIoU) is employed to optimize the model, resulting in the ROS–YOLOv5–FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS–YOLOv5–FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities.
Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
Authors: Rosa Ana Carolina , Elomari Youssef , Calderón Alejandro , Mateu Carles , Haddad Assed , Boer Dieter
Published: August 2024

The energy consumption of buildings presents a significant concern, which has led to a demand for materials with better thermal performance. Thermal conductivity (TC), among the most relevant thermal properties, is essential to address this demand. This study introduces a methodology integrating a Multilayer Perceptron (MLP) and a Generative Adversarial Network (GAN) to predict the TC of concrete based on its mass composition and density. Three scenarios using experimental data from published papers and synthetic data are compared and reveal the model’s outstanding performance across training, validation, and test datasets. Notably, the MLP trained on the GAN-augmented dataset outperforms the one with the real dataset, demonstrating remarkable consistency between the model’s predictions and the actual values. Achieving an RMSE of 0.0244 and an R2 of 0.9975, these outcomes can offer precise quantitative information and advance energy-efficient materials.
SARDIMM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
Authors: Kim Haechan , Heo Jinmoo , Lee Seongjoo , Jung Yunho
Published: August 2024

The range-Doppler algorithm (RDA), a key technique for generating synthetic aperture radar (SAR) images, offers high-resolution images but requires significant memory resources and involves complex signal processing. Moreover, the multitude of fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) operations in RDA necessitates high bandwidth and lacks data reuse, leading to bottlenecks. This paper introduces a synthetic aperture radar dual in-line memory module (SARDIMM), which executes RDA operations near memory via near-memory processing (NMP), thereby effectively reducing memory accesses, execution time, and energy consumption. The embedded NMP module in SARDIMM optionally supports a combination of FFT, IFFT, and matched filter operations of the RDA for range and azimuth compression. The operator within the NMP module accelerates the FFT by performing two radix-2 single butterfly operations in parallel. The NMP module was implemented and validated on a Xilinx UltraScale+ field-programmable gate array (FPGA) using Verilog-HDL. The acceleration performance of RDA for images of various sizes was evaluated through a simulator modified with gem5 and DRAMSim3 and achieved a 6.34–6.93× speedup and 41.9–48.2% energy savings.
Research on Intelligent Ventilation System of Metal Mine Based on Real-Time Sensing Airflow Parameters with a Global Scheme
Authors: Chen Yin , Li Zijun , Liu Xin , Tang Wenxuan , Zhang Qilong , Wang Haining , Huang Wei
Published: August 2024

In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety.
Analytical and Experimental Investigation of Windage–Churning Behavior in Spur, Bevel, and Face Gears
Authors: Dai Yu , Yang Caihua , Liu He , Zhu Xiang
Published: August 2024

This paper presents comparable sets of the no-load power loss as a product of windage and churning behaviors of a family of various rotating parts (i.e., disc, spur gear, straight bevel gear, and orthogonal face gear). Experimental measurements were carried out under pure air only and under partial immersion in oil to qualify and quantify the windage and churning effects of no-load power losses of a family of spur, bevel, and face gears along with a representative disc as the baseline. Aiming at exploring the influence of gear teeth on the total no-load power losses, two different theoretical analytical approaches are introduced to account for the churning contributions, by which the total power losses are estimated. Both analytical approaches compare well with the experimental findings. Furthermore, a spatial intersecting cross-axis gear (e.g., straight bevel gear and orthogonal face gear) results in higher no-load power losses than that of a representative disc or a parallel-axes gear. The significance of gear teeth (gear vs. disc) on windage behavior is presented, as well as the gear windage effects on the churning phenomenon in a high-speed splash-lubricated gear.
Multi-Label Emotion Recognition of Korean Speech Data Using Deep Fusion Models
Authors: Park Seoin , Jeon Byeonghoon , Lee Seunghyun , Yoon Janghyeok
Published: August 2024

As speech is the most natural way for humans to express emotions, studies on Speech Emotion Recognition (SER) have been conducted in various ways However, there are some areas for improvement in previous SER studies: (1) while some studies have performed multi-label classification, almost none have specifically utilized Korean speech data; (2) most studies have not utilized multiple features in combination for emotion recognition. Therefore, this study proposes deep fusion models for multi-label emotion classification using Korean speech data and follows four steps: (1) preprocessing speech data labeled with Sadness, Happiness, Neutral, Anger, and Disgust; (2) applying data augmentation to address the data imbalance and extracting speech features, including the Log-mel spectrogram, Mel-Frequency Cepstral Coefficients (MFCCs), and Voice Quality Features; (3) constructing models using deep fusion architectures; and (4) validating the performance of the constructed models. The experimental results demonstrated that the proposed model, which utilizes the Log-mel spectrogram and MFCCs with a fusion of Vision-Transformer and 1D Convolutional Neural Network–Long Short-Term Memory, achieved the highest average binary accuracy of 71.2% for multi-label classification, outperforming other baseline models. Consequently, this study anticipates that the proposed model will find application based on Korean speech, specifically mental healthcare and smart service systems.
Application Potential of a Dew-Point Cooling Tower in Selected Energy Intensive Applications in Temperate Climate
Authors: Pandelidis Demis , Matuszczak Mikołaj , Krowicki Paweł , Poskart Bartosz , Iskierka Grzegorz , Worek William , Cetin Sabri
Published: August 2024

In the article, the application potential of the dew-point cooling tower (DPCT) in selected energy-intensive applications in temperate climates was analyzed and discussed. The applications selected for analysis are power generation with natural gas turbines and chilled water air conditioning systems. The study is based on a mathematical model derived from a modified ε-NTU model. The model was validated against experimental results and showed satisfactory agreement with the experimental data. DPCT was compared with a typical cooling tower limited by the wet-bulb temperature (wet-bulb cooling tower, WBCT). The simulation results showed that DPCT is able to provide significant energy savings in energy-intensive applications; therefore, its application potential in temperate climates can be considered justified. In the case of gas turbines, DPCT was able to generate 2 to 10 percentage points more capacity than operating on outdoor air and 1.8 to 5 percentage points more than operating with WBCT. In the case of air conditioning systems, the system equipped with DPCT achieved EERs (energy efficiency ratios) higher by 1 to 7.2 compared to dry cooling and by 0.3 to 5.1 compared to systems equipped with WBCT. The annual energy savings obtained by the system with DPCT were 14.7 MWh compared to WBCT and 30 MWh compared to dry cooling.
A Novel Approach to Energy Management with Power Quality Enhancement in Hydrogen Based Microgrids through Numerical Simulation
Authors: Qamar Hafiz Ghulam Murtza , Guo Xiaoqiang , Ghith Ehab , Tlija Mehdi
Published: August 2024

A hydrogen-based microgrid (MG) is an energy system that uses hydrogen as a primary energy carrier within a localized grid. Numerous alternative approaches and concepts are found concerning the management of renewable energy systems. This study proposes a novel approach to assess the energy management system (EMS) and optimal hydrogen-based Energy Storage Systems (HBESS) at minimal total cost, employing particle swarm optimization (PSO) and fuzzy control in stand-alone microgrids. Together, these methods effectively address control and management challenges within hybrid microgrids (HMGs). This has been proposed to enhance energy management and to improve power quality. The findings reveal that PSO is the most advantageous and efficient approach. Its utilization proves instrumental in reducing costs, boosting reliability, and optimizing operational schedules within HMGs. Furthermore, the power profile holds considerable importance in this study, significantly enhancing system reliability and stability. This study has achieved an impressive 6.147% improvement in cost-effectiveness compared to traditional methods. This has been put into practice and validated through implementation within a MATLAB (9.13.0 (R2022b))/Simulink framework.
1D-CLANet: A Novel Network for NLoS Classification in UWB Indoor Positioning System
Authors: Wang Qiu , Chen Mingsong , Liu Jiajie , Lin Yongcheng , Li Kai , Yan Xin , Zhang Chizhou
Published: August 2024

Ultra-Wideband (UWB) technology is crucial for indoor localization systems due to its high accuracy and robustness in multipath environments. However, Non-Line-of-Sight (NLoS) conditions can cause UWB signal distortion, significantly reducing positioning accuracy. Thus, distinguishing between NLoS and LoS scenarios and mitigating positioning errors is crucial for enhancing UWB system performance. This research proposes a novel 1D-ConvLSTM-Attention network (1D-CLANet) for extracting UWB temporal channel impulse response (CIR) features and identifying NLoS scenarios. The model combines the convolutional neural network (CNN) and Long Short-Term memory (LSTM) architectures to extract temporal CIR features and introduces the Squeeze-and-Excitation (SE) attention mechanism to enhance critical features. Integrating SE attention with LSTM outputs boosts the model’s ability to differentiate between various NLoS categories. Experimental results show that the proposed 1D-CLANet with SE attention achieves superior performance in differentiating multiple NLoS scenarios with limited computational resources, attaining an accuracy of 95.58%. It outperforms other attention mechanisms and the version of 1D-CLANet without attention. Compared to advanced methods, the SE-enhanced 1D-CLANet significantly improves the ability to distinguish between LoS and similar NLoS scenarios, such as human obstructions, enhancing overall recognition accuracy in complex environments.
Application of Central Composite Design in the Drilling Process of Carbon Fiber-Reinforced Polymer Composite
Authors: Arhamnamazi Seyyedabbas , Aymerich Francesco , Buonadonna Pasquale , El Mehtedi Mohamad , Taheri Hossein
Published: August 2024

Composite materials are utilized in various industries due to their advantageous properties. Drilling is a crucial process for joining these materials to construct the structures. During the drilling of composite materials, several types of defect can occur, with delamination being the most prevalent. Delamination adversely effects the properties of the drilled hole and diminishes the quality of the final structure. Thrust force is a key parameter used to monitor the drilling process; a higher thrust force increases the likelihood of defects, particularly delamination, in the drilled area. In this article, a central composite design is applied to the drilling process of carbon fiber-reinforced polymer (CFRP) composites, focusing on parameters such as rotational speed, feed rate, and the angle between the composite layer sequences. The objective is to minimize delamination factors and thrust force. The effect of drilling parameters on the responses is analyzed independently. The results indicate that the derived models can predict the thrust force and delamination factors in the drilling of CFRP composites.
Enhancing Task-Oriented Dialogue Modeling through Coreference-Enhanced Contrastive Pre-Training
Authors: Huang Yi , Chen Si , Chen Yaqin , Feng Junlan , Deng Chao
Published: August 2024

Pre-trained language models (PLMs) are proficient at understanding context in plain text but often struggle with the nuanced linguistics of task-oriented dialogues. The information exchanges in dialogues and the dynamic role-shifting of speakers contribute to complex coreference and interlinking phenomena across multi-turn interactions. To address these challenges, we propose Coreference-Enhanced Contrastive Pre-training (CECPT), an innovative pre-training framework specifically designed to enhance dialogue modeling. CECPT utilizes unsupervised dialogue datasets to capture both semantic richness and structural coherence. Our experimental results demonstrate that the CECPT model significantly outperforms established baselines in three critical applications: intent recognition, dialogue act prediction, and dialogue state tracking. These findings suggest that CECPT is more adept at following the information flow within dialogues and accurately linking statuses to their respective references.
A Framework for Multi-Physics Modeling, Design Optimization and Uncertainty Quantification of Fast-Spectrum Liquid-Fueled Molten-Salt Reactors
Authors: Holler David , Bhaskar Sandesh , Delipei Grigirios , Avramova Maria , Ivanov Kostadin
Published: August 2024

The analysis of liquid-fueled molten-salt reactors (LFMSRs) during steady state, operational transients and accident scenarios requires addressing unique reactor multi-physics challenges with coupling between thermal hydraulics, neutronics, inventory control and species distribution phenomena. This work utilizes the General Nuclear Field Operation and Manipulation (GeN-Foam) code to perform coupled thermal-hydraulics and neutronics calculations of an LFMSR design. A framework is proposed as part of this study to perform modeling, design optimization, and uncertainty quantification. The framework aims to establish a protocol for the studies and analyses of LFMSR which can later be expanded to other advanced reactor concepts too. The Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) statistical analysis tool was successfully coupled with GeN-Foam to perform uncertainty quantification studies. The uncertainties were propagated through the input design parameters, and the output uncertainties were characterized using statistical analysis and Spearman rank correlation coefficients. Three analyses are performed (namely, scalar, functional, and three-dimensional analyses) to understand the impact of input uncertainty propagation on temperature and velocity predictions. Preliminary three-dimensional reactor analysis showed that the thermal expansion coefficient, heat transfer coefficient, and specific heat of the fuel salt are the crucial input parameters that influence the temperature and velocity predictions inside the LFMSR system.
Enhancing Continuous Usage Intention in E-Commerce Marketplace Platforms: The Effects of Service Quality, Customer Satisfaction, and Trust
Authors: Kim Jongnam , Yum Kyeongmin
Published: August 2024

E-commerce marketplace platforms have evolved into integral digital intermediaries that shape online transactions in competitive environments. Companies continuously endeavor to improve e-service quality, customer satisfaction, and e-trust to gain a competitive advantage. This study aimed to identify the relationships between e-service quality, customer satisfaction, e-trust, and continuous usage intention in e-commerce marketplace platforms. Moreover, this study examined the roles of customer satisfaction and e-trust as mediators. We estimated nine hypothesized relationships using a structural equation modeling technique. Data from 311 users were used in the data analysis. The results are as follows: First, e-service quality significantly and positively affects customer satisfaction, e-trust, and continuous usage intention. Second, customer satisfaction has a significant and positive impact on e-trust and continuous usage intention. Third, e-trust has a significant and positive impact on continuous usage intention. Finally, both customer satisfaction and e-trust serve as significant mediating factors in the relationship between e-service quality and continuous usage intention. These insights hold strategic importance for e-commerce marketplace platform operators, allowing them to formulate service strategies and policies tailored to enhance user experience, foster trust, and drive continued usage, thereby strengthening their market position and ensuring sustained success.
Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
Authors: Xu Zhaofei , Gao Jie , Wang Xianghui , Kang Chong
Published: August 2024

With the widespread application of infrared technology in military, security, medical, and other fields, the demand for high-definition infrared images has been increasing. However, the complexity of the noise introduced during the imaging process and high acquisition costs limit the scope of research on super-resolution algorithms for infrared images, particularly when compared to the visible light domain. Furthermore, the lack of high-quality infrared image datasets poses challenges in algorithm design and evaluation. To address these challenges, this paper proposes an optimized super-resolution algorithm for infrared images. Firstly, we construct an infrared image super-resolution dataset, which serves as a robust foundation for algorithm design and rigorous evaluation. Secondly, in the degradation process, we introduce a gate mechanism and random shuffle to enrich the degradation space and more comprehensively simulate the real-world degradation of infrared images. We train an RRDBNet super-resolution generator integrating the aforementioned degradation model. Additionally, we incorporate spatially correlative loss to leverage spatial–structural information, thereby enhancing detail preservation and reconstruction in the super-resolution algorithm. Through experiments and evaluations, our method achieved considerable performance improvements in the infrared image super-resolution task. Compared to traditional methods, our method was able to better restore the details and clarity of infrared images.
Cu(II) Biosorption and Synthesis of CuO Nanoparticles by Staphylococcus epidermidis CECT 4183: Evaluation of the Biocidal Effect
Authors: Muñoz Antonio J. , Espínola Francisco , Moya Manuel , Martín Celia , Ruiz Encarnación
Published: August 2024

Copper contamination of natural waters is a global problem that affects ecosystems and public health, yet this metal is an essential micronutrient and has important applications. The efficacy of Staphylococcus epidermidis CECT 4183 as a Cu(II) biosorbent in synthetic solutions and its potential ability to synthesize CuO nanoparticles (CuO-NPs) from its cellular extract was investigated. In addition, the biocidal potential of the nanoparticles was evaluated against five microorganisms. Using response surface methodology, the optimal operating conditions were determined to be biomass dose, 0.2 g/L, and pH 5.5. Equilibrium tests were performed, and biosorption isotherms were obtained for four models with a maximum biosorption capacity of 48.14 mg/g for the Langmuir model. Different spectroscopic and microscopic techniques were used to determine the mechanisms involved in the biosorption process, which was dominated by surface physicochemical interactions with strong involvement of methyl, methylene, carbonyl, amino, and phosphate groups. The techniques also allowed for characterizing the obtained nanoparticles, which had a quasi-spherical morphology and an average size of 14 nm. Finally, biocidal tests showed that the CuO-NPs had a good inhibitory capacity for the microorganisms tested, with minimum inhibitory concentrations (MIC) between 62.5 and 500 µg/mL for bacteria and between 1000 and 2000 µg/mL for yeasts. S. epidermidis CECT 4183 showed good potential for Cu(II) bioremediation and for the synthesis of CuO-NPs with biocidal capacity. S. epidermidis CECT 4183 showed good potential for use in Cu(II) biosorption, and its cell extract presented a high capacity for the green synthesis of CuO-NPs, which at the same time turned out to be good biocidal agents.
Cooperative Decay of an Ensemble of Atoms in a One-Dimensional Chain with a Single Excitation
Authors: Piovella Nicola
Published: August 2024

We propose a new expression of the cooperative decay rate of a one-dimensional chain of N two-level atoms in the single-excitation configuration. From it, the interference nature of superradiance and subradiance arises naturally, without the need to solve the eigenvalue problem of the atom–atom interaction Green function. The cooperative decay rate can be interpreted as the imaginary part of the expectation value of the effective non-Hermitian Hamiltonian of the system, evaluated over a generalized Dicke state of N atoms in the single-excitation manifold. Whereas the subradiant decay rate is zero for an infinite chain, it decreases as 1/N for a finite chain. A simple approximated expression for the cooperative decay rate is obtained as a function of the lattice constant d and the atomic number N. The results are obtained first for the scalar model and then extended to the vectorial light model, assuming all the dipoles aligned.
Stability and Convergence Analysis of the Discrete Dynamical System for Simulating a Moving Bed
Authors: Xie Chao-Fan , Zhang Hong , Hwang Rey-Chue
Published: August 2024

The efficiency of controlling the simulated moving bed (SMB) has long been a critical issue in the chemical engineering industry. Most existing research relies on finite element methods, which often result in lower control efficiency and are unable to achieve online control. To enhance control over the SMB process, this paper employs the Crank–Nicolson method to develop a discrete dynamical model. This approach allows for the investigation of system stability and convergence, fundamentally addressing the sources of error. During the discretization of partial differential equations (PDEs), two main types of errors arise: intrinsic errors from the method itself and truncation errors due to derivative approximations and the discretization process. Research indicates that for the former, the iterative process remains convergent as long as the time and spatial steps are sufficiently small. Regarding truncation errors, studies have demonstrated that they exhibit second-order behavior relative to time and spatial steps. The theoretical validation shows that the iteration works effectively, and simulations confirm that the finite difference method is stable and performs well with varying SMB system parameters and controller processes. This provides a solid theoretical foundation for practical, real-time online control.
Improving Lithium-Ion Battery Performance: Nano Al2O3 Coatings on High-Mass Loading LiFePO4 Cathodes via Atomic Layer Deposition
Authors: Salimi Pejman , Gottardi Gloria , Morais William G. , Bartali Ruben , Laidani Nadhira , Macchi Edoardo Gino
Published: August 2024

Lithium iron phosphate (LiFePO4 or LFP) is a promising cathode material for lithium-ion batteries (LIBs), but side reactions between the electrolyte and the LFP electrode can degrade battery performance. This study introduces an innovative coating strategy, using atomic layer deposition (ALD) to apply a thin (5 nm and 10 nm) Al2O3 layer onto high-mass loading LFP electrodes. Galvanostatic charge–discharge cycling and electrochemical impedance spectroscopy (EIS) were used to assess the electrochemical performance of coated and uncoated LFP electrodes. The results show that Al2O3 coatings enhance the cycling performance at room temperature (RT) and 40 °C by suppressing side reactions and stabilizing the cathode–electrolyte interface (CEI). The coated LFP retained 67% of its capacity after 100 cycles at 1C and RT, compared to 57% for the uncoated sample. Post-mortem analyses, including scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS), were conducted to investigate the mechanisms behind the improved performance. These analyses reveal that Al2O3 coatings are highly effective in reducing LFP electrode degradation during cycling, demonstrating the potential of ALD Al2O3 coatings to enhance the durability and performance of LFP electrodes in LIBs.
Predictive Methods for Thrombus Formation in the Treatment of Aortic Dissection and Cerebral Aneurysms: A Comprehensive Review
Authors: Komiya Kenji , Imada Shuta , Ujihara Yoshihiro , Sugita Shukei , Nakamura Masanori
Published: August 2024

Thrombus formation plays a crucial role in the clinical treatment of certain diseases. In conditions such as aortic dissection and cerebral aneurysm, complete thrombus occlusion in the affected region is desired to reduce blood flow into the false lumen or aneurysm sac, leading to a decrease in the tension exerted on the vascular wall and making it less likely to rupture. However, desired thrombosis sometimes fails to occur. Predicting thrombus formation can provide valuable information in such cases. This article offers a comprehensive review of conventional methods for predicting thrombus formation. In reviews conducted from the year 2000 to the present, the number of published related papers every five years has increased more than tenfold. We also found that the predictive methods can be classified into two categories: those based on the hemodynamic evaluation parameters and those based on hemodynamic and mathematical models that simulate the transport and reaction of blood components. Through our discussions, we identified several challenges that need to be resolved, including predictions based on patient-specific condition, model validation, multi-scale problems, the mechanisms of thrombus formation, and ensuring cost effectiveness. This review aims to guide researchers interested in exploring thrombus formation prediction within clinical treatments.
Digital Dental Biometrics for Human Identification Based on Automated 3D Point Cloud Feature Extraction and Registration
Authors: Zhou Yu , Yuan Li , Li Yanfeng , Yu Jiannan
Published: August 2024

Background: Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification. Methods: We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification. In the data preprocessing stage, we use the curvature principle to extract distinguishable tooth crown contours from the original point clouds as the holistic feature identification samples. Based on these samples, we construct four types of local feature identification samples to evaluate identification performance with severe teeth loss. In the feature extraction stage, we conduct voxel downsampling, then extract the geometric and structural features of the point cloud. In the registration-based identification stage, we construct a coarse-to-fine registration scheme in order to realize the identification task. Results: Experimental results on a dataset of 160 individuals demonstrate that our method achieves a Rank-1 recognition rate of 100% using complete tooth crown contours samples. Utilizing the remaining four types of local feature samples yields a Rank-1 recognition rate exceeding 96.05%. Conclusions: The proposed framework proves effective for human identification, maintaining high identification performance even in extreme cases of partial tooth loss.
Planarian Mucus: A Novel Source of Pleiotropic Cytotoxic and Cytostatic Agents against Cancer Cells
Authors: Gambino Gaetana , Da Pozzo Eleonora , Salvetti Alessandra , Rossi Leonardo
Published: August 2024

Biological evolution has generated a vast array of natural compounds produced by organisms across all domains. Among these, secondary metabolites, selected to enhance an organism’s competitiveness in its natural environment, make them a reservoir for discovering new compounds with cytotoxic activity, potentially useful as novel anticancer agents. Slime secretions, the first barrier between epithelial surfaces and the surrounding environment, frequently contain cytotoxic molecules to limit the growth of parasitic organisms. Planarians, freshwater Triclads, continuously secrete a viscous mucus with multiple physiological functions. The chemical composition of planarian mucus has been only partially elucidated, and there are no studies reporting its cytotoxic or cytostatic effects. In this study, we developed a protocol for collecting mucus from Dugesia japonica specimens and we demonstrated that it inhibits the growth of cancer cells by activating cytostatic and ROS-dependent cytotoxic mechanisms inducing lipid droplet accumulation and mitochondrial membrane reorganization. Although further research is needed to identify the specific chemicals responsible for the anticancer activity of planarian mucus, this work opens up numerous research avenues aimed at better understanding the mechanisms of action of this product for potential therapeutic applications.
The Preventive and Therapeutic Effects of Acute and Severe Inflammatory Disorders with Heparin and Heparinoid
Authors: Song Ying , Wu Yuxiang , Ding Fangfang , Li Shuo , Shen Yaojia , Yang Bingyan , Tang Xinran , Ren Lige , Deng Lirong , Jin Xuewen , Yan Yishu
Published: August 2024

Systematic inflammatory response syndrome (SIRS) and the accompanying sepsis pose a huge threat to human health worldwide. Heparin is a part of the standard supportive care for the disease. However, the molecular mechanism is not fully understood yet, and the potential signaling pathways that play key roles have not yet been elucidated. In this paper, the main findings regarding the molecular mechanisms associated with the beneficial effects of heparin, including inhibiting HMGB-1-driven inflammation reactions, histone-induced toxicity, thrombo-inflammatory response control and the new emerging mechanisms are concluded. To set up the link between the preclinical research and the clinical effects, the outcomes of the clinical trials are summarized. Then, the structure and function relationship of heparin is discussed. By providing an updated analysis of the above results, the paper highlights the feasibility of heparin as a possible alternative for sepsis prophylaxis and therapy.
Advancements in Green Nanoparticle Technology: Focusing on the Treatment of Clinical Phytopathogens
Authors: Mukherjee Sunny , Verma Anamika , Kong Lingxue , Rengan Aravind Kumar , Cahill David Miles
Published: August 2024

Opportunistic pathogenic microbial infections pose a significant danger to human health, which forces people to use riskier, more expensive, and less effective drugs compared to traditional treatments. These may be attributed to several factors, such as overusing antibiotics in medicine and lack of sanitization in hospital settings. In this context, researchers are looking for new options to combat this worrying condition and find a solution. Nanoparticles are currently being utilized in the pharmaceutical sector; however, there is a persistent worry regarding their potential danger to human health due to the usage of toxic chemicals, which makes the utilization of nanoparticles highly hazardous to eukaryotic cells. Multiple nanoparticle-based techniques are now being developed, offering essential understanding regarding the synthesis of components that play a crucial role in producing anti-microbial nanotherapeutic pharmaceuticals. In this regard, green nanoparticles are considered less hazardous than other forms, providing potential options for avoiding the extensive harm to the human microbiome that is prevalent with existing procedures. This review article aims to comprehensively assess the current state of knowledge on green nanoparticles related to antibiotic activity as well as their potential to assist antibiotics in treating opportunistic clinical phytopathogenic illnesses.
Impact of Coping Strategies on Health-Related Quality of Life in Young Adults with Multiple Sclerosis
Authors: Lo Buono Viviana , Corallo Francesco , Sessa Edoardo , D’Aleo Giangaetano , Rifici Carmela , Quartarone Angelo , Bonanno Lilla
Published: August 2024

Multiple sclerosis (MS) is a chronic and progressive neurological disease that affects the central nervous system, resulting in a wide spectrum of cognitive, emotional, and physical deficits. The progressive course of MS poses significant challenges to patients and has a profound impact on health-related quality of life (HRQoL). The style of coping adopted plays a critical role in determining how individuals with MS adapt to and face the challenges of the disease and their overall well-being. This paper aims to examine the impact of coping strategies on HRQoL in young adults un-/minimally impaired (<5 years, EDSS ≤ 2.5) by MS (age 18–35 years). This retrospective cross-sectional cohort study included 98 young adults (33 males and 65 females) with relapsing–remitting MS who underwent neurological assessment using the Expanded Disability Status Scale. Participants completed the Italian version of the Multiple Sclerosis QoL-54 (MSQoL-54), which provides a physical and mental health score, and the Coping Orientation to Problems Experienced Inventory (Brief-COPE). The results showed a significant relationship between COPE scores and physical and mental health. Subjects affected by MS who tend to use more frequent coping strategies such as active planning, personal growth, and acceptance showed a better overall well-being and quality of life. These findings are relevant to clinical practice given the need to understand the coping variable to improve HRQoL. Understanding these relationships is crucial for developing effective interventions to enhance the well-being of MS subjects.
Prediction of Ultra-High-Performance Concrete (UHPC) Properties Using Gene Expression Programming (GEP)
Authors: Qian Yunfeng , Yang Jianyu , Yang Weijun , Alateah Ali H. , Alsubeai Ali , Alfares Abdulgafor M. , Sufian Muhammad
Published: August 2024

In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus of current research work is to improve knowledge regarding application of one of the new ML techniques, i.e., gene expression programming (GEP), to anticipate the ultra-high-performance concrete (UHPC) properties, such as flowability, flexural strength (FS), compressive strength (CS), and porosity. In addition, the process of training a model that predicts the intended outcome values when the associated inputs are provided generates the graphical user interface (GUI). Moreover, the reported ML models that have been created for the aforementioned UHPC characteristics are simple and have limited input parameters. Therefore, the purpose of this study is to predict the UHPC characteristics while taking into account a wide range of input factors (i.e., 21) and use a GUI to assess how these parameters affect the UHPC properties. This input parameters includes the diameter of steel and polystyrene fibers (µm and mm), the length of the fibers (mm), the maximum size of the aggregate particles (mm), the type of cement, its strength class, and its compressive strength (MPa) type, the contents of steel and polystyrene fibers (%), and the amount of water (kg/m3). In addition, it includes fly ash, silica fume, slag, nano-silica, quartz powder, limestone powder, sand, coarse aggregates, and super-plasticizers, with all measurements in kg/m3. The outcomes of the current research reveal that the GEP technique is successful in accurately predicting UHPC characteristics. The obtained R2, i.e., determination coefficients, from the GEP model are 0.94, 0.95, 0.93, and 0.94 for UHPC flowability, CS, FS, and porosity, respectively. Thus, this research utilizes GEP and GUI to accurately forecast the characteristics of UHPC and to comprehend the influence of its input factors, simplifying the procedure and offering valuable instruments for the practical application of the model’s capabilities within the domain of civil engineering.
Numerical Stability Analysis of Sloped Geosynthetic Encased Stone Column Composite Foundation under Embankment Based on Equivalent Method
Authors: Peng Bo-Cheng , Zhang Ling , Xu Ze-Yu , Cui Peng-Lu , Liu Yang-Yang
Published: August 2024

As an effective technology for the rapid treatment of soft-soil foundations, geosynthetic encased stone column (GESC) composite foundations are commonly used in various embankment engineerings, including those situated on sloped soft foundations. Nevertheless, there is still a scarcity of stability studies for sloped GESC composite foundations. Several 3D numerical models for sloped GESC composite foundations were established using an equivalent method. The influences of the area replacement ratio and the tensile strength of geosynthetic encasement on the stability were investigated. The results showed that the stability increased nonlinearly with the area replacement ratio, and there existed an optimal area replacement ratio (e.g., 24.56% in this study) to balance the safety and economic requirements. The stability increased linearly with the tensile strength of geosynthetic encasement at low tensile strength levels (lower than 105 kN/m in this study), and the impact was relatively limited compared with that of the area replacement ratio. In addition, the stability generally decreased nonlinearly as the foundation slope decreased, and high-angle (foundation slope close to 30°) sloped GESC composite foundations are recommended to be treated with multiple reinforcement techniques. The relationship between the minimum area replacement ratio and the foundation slope was further quantified by an exponential function, allowing for the determination of the area replacement ratio of various sloped GESC composite foundations and providing theoretical guidance for engineering practice.
Evaluation of Machine Learning and Traditional Methods for Estimating Compressive Strength of UHPC
Authors: Li Tianlong , Jiang Pengxiao , Qian Yunfeng , Yang Jianyu , AlAteah Ali H. , Alsubeai Ali , Alfares Abdulgafor M. , Sufian Muhammad
Published: August 2024

This research provides a comparative analysis of the optimization of ultra-high-performance concrete (UHPC) using artificial neural network (ANN) and response surface methodology (RSM). By using ANN and RSM, the yield of UHPC was modeled and optimized as a function of 22 independent variables, including cement content, cement compressive strength, cement type, cement strength class, fly-ash, slag, silica-fume, nano-silica, limestone powder, sand, coarse aggregates, maximum aggregate size, quartz powder, water, super-plasticizers, polystyrene fiber, polystyrene fiber diameter, polystyrene fiber length, steel fiber content, steel fiber diameter, steel fiber length, and curing time. Two statistical parameters were examined based on their modeling, i.e., determination coefficient (R2) and mean square error (MSE). ANN and RSM were evaluated for their predictive and generalization capabilities using a different dataset from previously published research. Results show that RSM is computationally efficient and easy to interpret, whereas ANN is more accurate at predicting UHPC characteristics due to its nonlinear interactions. Results show that the ANN model (R = 0.95 and R2 = 0.91) and RSM model (R = 0.94, and R2 = 0.90) can predict UHPC compressive strength. The prediction error for optimal yield using an ANN and RSM was 3.5% and 7%, respectively. According to the ANN model’s sensitivity analysis, cement and water have a significant impact on compressive strength.
Elastic Local Buckling Analysis of a Sandwich Corrugated Steel Plate Pipe-Arch in Underground Space
Authors: Che Chengwen , Sun Zhanying , Xu Pengsen , Shi Feng , Liu Junxiu , Li Kai
Published: August 2024

In underground spaces, corrugated steel plate (CSP) pipe-arches may experience local buckling instability, which can subsequently lead to the failure of the entire structure. Recently, sandwich CSP pipe-arches have been used to enhance the stability of embedded engineering outcomes, and their buckling behaviors require in-depth research. In this paper, we establish a theoretical model by simplifying soil support and using Hoff sandwich plate theory to focus on the local buckling stability of the straight segment in embedded sandwich CSP pipe-arches using the Rayleigh–Ritz method. Through stability analysis, the instability criteria for embedded sandwich CSP pipe-arches are analytically determined. Numerical calculations reveal that the critical buckling load of a sandwich CSP pipe-arch is affected by several factors, including the elastic modulus, thickness, Poisson’s ratio, rotational constraint stiffness, and the length of the straight segment. Specifically, increasing the thickness of the sandwich CSP pipe-arch can substantially enhance the critical buckling load. Meanwhile, the wavenumber is affected by the elastic modulus and the length of the straight segment. The analytical results are in agreement with those obtained from finite element analysis. These findings provide a theoretical basis and guidance for the application of sandwich CSP pipe-arches in fields such as subway stations, tunnel construction, underground passages, and underground parking facilities.
Assessing the Conceptualizations of Coping and Resilience in LGBTQ2S+ People with Cancer: Working towards Greater Awareness in Cancer Care
Authors: Singh Sarthak , Spiropoulos Athina , Deleemans Julie , Carlson Linda E.
Published: August 2024

People with cancer may suffer negative psychosocial outcomes due to the challenges of cancer. LGBTQ2S+ people routinely experience negative psychosocial outcomes in health care settings, but have showcased resilience in the face of discrimination; however, this has never been studied in a cancer context. Thus, this study aims to assess coping and resilience in LGBTQ2S+-identifying people diagnosed with cancer using a strengths-based approach. A qualitative exploratory design was used. Ten self-identified LGBTQ2S+ people who have completed their cancer treatment were recruited. Participants completed clinical, health, and demographic questionnaires and, subsequently, semi-structured qualitative interviews. Conceptualizations of coping and resilience in the semi-structured interviews were analyzed using interpretative phenomenological analysis (IPA). Participants were members of various gender identities and sexual orientations. In addition to identifying needed LGBTQ2S+-specific resources, four narratives emerged: support networks, regaining control in life, conflicting identities, and traditional coping methods. Most participants’ cancer journeys were characterized by a ‘Second Coming-Out’ phenomenon, where LGBTQ2S+ people with cancer use coping strategies, similar to those used when coming out, to produce resilience throughout their cancer journey. This work provides exploratory insight into LGBTQ2S+ people with cancer, but more research is required with a larger sample.