bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Fault Diagnosis Method of Rotating Machinery Based on Improved Variational Modal Decomposition and Probabilistic Neural Network Algorithm
Authors: Li Zhangjie , Zou Chao , Chen Zhimin , Lu Hong , Xie Shiwen , Zhang Wei , He Jiaqi
Published: August 2024

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery.
A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features
Authors: Xi Ji , Yu Hang , Xu Zhe , Zhao Li , Tao Huawei
Published: August 2024

When people tell lies, they often exhibit tension and emotional fluctuations, reflecting a complex psychological state. However, the scarcity of labeled data in datasets and the complexity of deception information pose significant challenges in extracting effective lie features, which severely restrict the accuracy of lie detection systems. To address this, this paper proposes a semi-supervised lie detection algorithm based on integrating multiple speech emotional features. Firstly, Long Short-Term Memory (LSTM) and Auto Encoder (AE) network process log Mel spectrogram features and acoustic statistical features, respectively, to capture the contextual links between similar features. Secondly, the joint attention model is used to learn the complementary relationship among different features to obtain feature representations with richer details. Lastly, the model combines the unsupervised loss Local Maximum Mean Discrepancy (LMMD) and supervised loss Jefferys multi-loss optimization to enhance the classification performance. Experimental results show that the algorithm proposed in this paper achieves better performance.
Development of Methodologies and Software for Design, Simulation and Optimization of Oil Hydraulic Cylinders of Large Dimensions and Power
Authors: Cvejić Stefan , Petrović Radovan , Andjelković Maja , Ilić Ivana , Mutavči Vjačeslav , Radosavljević Mihajlović Ana , Vuruna Mladen
Published: August 2024

As part of the research carried out in the field of processing systems and the production process of oil-hydraulic cylinders of large dimensions and power, the specifics of fluid power transmission, in the functioning of hydropower facilities, were analyzed. The research also includes the optimization of the physical–mathematical model of non-stationary processes, which take place inside the chamber of a large hydrocylinder. In parallel with the definition of the optimization model, the work parameters that affect the process of fluid flow and piston movement were determined. The operating and technological construction parameters of the hydraulic cylinder, which most significantly affect the operation of the hydraulic cylinder, were defined, and the observed parameters were optimized, based on which a prototype with improved characteristics compared to existing solutions was realized.
Single EX-CCCII-Based First-Order Versatile Active Filter
Authors: Kumngern Montree , Khateb Fabian , Kulej Tomasz
Published: August 2024

This paper presents a new current-mode first-order versatile active filter employing one extra-x second-generation current controlled current conveyor (EX-CCCII) and one grounded capacitor. The proposed filter can realize first-order filtering functions of a low-pass filter (LPF), high-pass filter (HPF), and all-pass filter (APF) within the same topology with low-input and high-output impedances required for current-mode circuits. This multiple-output EX-CCCII-based filter can provide six transfer functions as both non-inverting and inverting filtering functions of the LPF, HPF, and APF are obtained. The filter also offers electronic control of the pole frequency of all filtering. The proposed current-mode filter can be applied to work as a mixed-mode active filter, namely in the transadmittance-mode (TAM), transimpedance-mode (TIM), and voltage-mode (VM). Each operation mode can provide six transfer functions. The proposed filter was simulated and designed using SPICE and 0.18 µm CMOS technology. Experimental results using the commercially available integrated circuit AD844 were used to confirm the functionality of the new circuits.
From Evaluation to Prediction: Analysis of Diabetic Autonomic Neuropathy Using Sudoscan and Artificial Intelligence
Authors: Toderean Roxana , Cobuz Maricela , Dimian Mihai , Cobuz Claudiu
Published: August 2024

A dangerous side effect of diabetes that can significantly lower quality of life and raise the death rate of diabetic individuals is diabetic autonomic neuropathy. It is essential to identify and anticipate this disease early on for prompt intervention and care. This study aims to predict this diabetic complication using Sudoscan and artificial intelligence. In this study, 172 individuals with type 1 or type 2 diabetes mellitus provided clinical and demographic information. Sudoscan was used to evaluate the subjects’ sudomotor dysfunction. Statistical methods were used to link various electrochemical skin conductance values with risk factors for neuropathy such as age, BMI, age of diabetes, or biochemical values such as cholesterol and triglycerides. Different machine-learning algorithms were used to predict the risk of diabetic autonomic neuropathy based on the collected data. The accuracy achieved with Logistic Regression is 92.6%, and with the Random Forest model is 96.3%. Lazzy Classifiers also show that six classifiers have a high performance of 97%. Thus, the use of machine learning algorithms in this field of metabolic diseases offers new perceptions for diagnosis, treatment, and prevention, and improves the quality of life of diabetic patients by reducing the incidence of complications related to this disease.
Stability Analysis of Surrounding Rock and Initial Support of Tunnel Undercrossing Multi-Situational Goafs: A Reference of Construction Guidance
Authors: Zhao Meng , Guo Jiaqi , Li Shaohua , Liang Shifan , Sun Fengwei
Published: August 2024

To ensure the construction and operational safety of tunnel undercrossing multi-situational goafs, the Huaying Mountain High-Speed Rail Tunnel, a critical section of the Xi’an-Chongqing High-Speed Railway, was taken as a case study. Based on a three-dimensional finite difference numerical simulation platform, twelve situations were established to analyze the effects of three factors: distance, scale, and angle. The stability analysis was conducted by examining the displacement and deformation characteristics of the surrounding rock, stress changes, and axial forces of the initial support for each situation. The results show that in tunnel undercrossing multi-situational goafs, the vertical deformation, horizontal convergence of the surrounding rock, and the maximum axial force of initial support are all affected. Within a certain range, changes in distance significantly impact subsidence and settlement deformation of the surrounding rock. However, as the distance increases, the horizontal and vertical displacements of the tunnel and the axial force of the initial support tend to decrease. Conversely, the scale and angle of the goaf have an opposite effect on the surrounding rock: as the scale and angle increase, the stability of the surrounding rock deteriorates. In this case study, when the distance exceeds 1.13 times the tunnel span, the influence of the goaf on the stability of the surrounding rock gradually decreases. When the angle exceeds 45°, vertical displacement decreases, and the increasing trend of horizontal displacement gradually diminishes. The conclusions of this paper can provide guidance for designing reinforcement schemes for tunnels crossing through multi-situational goafs. The findings provide valuable insights and guidance for similar engineering projects.
Biochar Amendment in Green Roof Substrate: A Comprehensive Review of the Benefits, Performance, and Challenges
Authors: Nguyen Cuong Ngoc , Chau Hing-Wah , Kumar Apurv , Chakraborty Ayon , Muttil Nitin
Published: August 2024

Green roofs (GRs) are a well-established green infrastructure (GI) strategy that have been extensively studied for decades to address a growing array of social and environmental challenges. Research efforts have been continuously made to contribute to the awareness of benefits of GRs and towards their widespread application. The substrate, which is one of the crucial layers of a GR system, plays a major role in the serviceability of GRs. Thus, several studies have been undertaken to alter the substrate characteristics by applying innovative substrate additives. Biochar, a carbon-rich material with a highly porous structure and large specific surface area, has been found advantageous in several areas such as agriculture, water filtration, environmental remediation, construction, and so on. However, the application of biochar in GRs has been insufficiently studied, partially because biochar amendment in GRs is a relatively recent innovation. Furthermore, a comprehensive review of the performance of biochar-amended GR substrates is lacking. This review paper aims to summarize the past performance of GRs enhanced with biochar by considering the various benefits that biochar offers. The results indicate that most of the reviewed studies observed increased retention of runoff and nutrients when utilizing biochar. Additionally, the capabilities of biochar in improving thermal insulation, plant performance, and microbial diversity, as well as its effectiveness in sequestrating carbon and controlling soil erosion, were mostly agreed upon. Notwithstanding, a definitive conclusion cannot yet be confidently made due to the limited research information from biochar–GR systems and the uneven research focus observed in the studies reviewed. The influence of biochar-related variables (including amendment rates, application methods, processed forms, and particle size) on the effectiveness of biochar was also discussed. Opportunities for future research were suggested to fill the research gaps and address challenges restricting the application of biochar in GRs. Detailed information from past research findings could serve as a foundation for further investigations into the large-scale implementation of biochar in GRs.
Anatomical-Based Customized Cervical Orthosis Design in Automation
Authors: Hsiao Yu-Ching , Chen Chung-De , Wu Yi-Hsiu , Fang Jing-Jing
Published: August 2024

Cervical orthoses, vital for neck immobilization in medical care and sports, often struggle to provide adequate support due to individual neck shape and size variations. This study addresses this issue by developing a specific computer-aided orthosis design software tailored for creating customized 3D-printed cervical orthoses. The self-developed software embedded anatomical and rehabilitation knowledge into the orthosis design process, ensuring consistency and reducing manual modification. Finite element analysis of cervical orthoses determined that a minimum thickness of 5 mm PLA (polylactic acid) material is necessary to meet safety requirements. This study highlights the automation potential of customized computer-aided orthosis design and underscores the potential to revolutionize orthopedic care. We also applied easy-to-access 3D printing technology to fabricate well-fitting and immobilized cervical orthoses. These customized cervical orthoses offer a promising future with the advantages of being cost-effective, lightweight, immobility, comfortable, easy to wear, and minimal accessories to meet clinical needs, enhancing patient comfort and compliance and providing reassurance about the economic benefits of the technology.
Exploring Digital Technologies for Addressing Risk Factors of Solitary Death in South Korea
Authors: Jun Jiwon , Kim Jieun , Lee SangYup , Lee Heangwoo , Park Eunsoo
Published: August 2024

South Korea has experienced rapid aging, and it is predicted that people aged 65 years or older will account for 20% of the total population by 2047. The proportion of one-person households is also increasing rapidly at the same time, and is expected to account for 37% of the total number of households by 2047. Such a demographic shift has led to more isolated households and social isolation, and consequently, to higher risks of solitary deaths. Central and local governments have implemented various measures, such as care services for the elderly living alone and regular checks on their status; however, there are limitations due to workforce and budget constraints. Therefore, it is necessary to explore innovative solutions that utilize digital technologies such as the internet of things (IoT) and artificial intelligence (AI) as new low-cost, high-efficiency alternatives. Innovations in digital technology have the potential to suggest new solutions to the issue of solitary death. Advanced technologies can play an important role in maximizing the effectiveness of solitary death prevention, management, and the prevention of social isolation. Although there is a growing body of research on the development of digital technology-based care services for the elderly, these studies have primarily focused on the applicability and early implementation stages of specific technologies. These studies have limitations in fully understanding the market trends of technologies and competition patterns within the industry. Therefore, it is essential to investigate trends in the latest technologies and R&D directions in a particular technology field through patents filed to protect new technologies. This study is intended to contribute to an in-depth understanding of the current status and development direction of digital technologies for the prevention of solitary deaths, and to provide a basis for future technology development. This study also contributes to establishing government policies and R&D strategies.
Life Cycle Carbon Assessment of Mortars with Carbonated and Non-Carbonated Recycled Aggregates
Authors: Brazão Farinha Catarina , Maia Pederneiras Cinthia , Infante Gomes Ricardo , Bastos David , Veiga Rosário
Published: August 2024

Global warming is one of the most important issues that the world is currently facing. The cement industry accounts for around 7% of total global CO2 emissions. According to the 13th United Nations Sustainable Development Goals, cement plants must become carbon neutral by 2050. This neutrality may be achieved by a reduction in CO2 emissions complemented with carbon capture, utilization and storage (CCUS) technologies. In accordance with these sustainable goals, several approaches have been studied. This paper investigates life cycle carbon of mortars produced with carbonated recycled aggregates. In previous works, the carbon dioxide capture capacity of construction and demolition waste (CDW) was analysed, and mortars with CDW recycled aggregates submitted to high levels of CO2 were evaluated in terms of their mechanical performance. This paper focus on the life cycle carbon impact assessment (LCCA) of industrial mortar formulations in a cradle-to-gate boundary. This assessment is carried out through a global warming potential environment impact assessment, since it represents the amount of CO2 equivalent that is sent to the atmosphere and contributes to the “greenhouse effect”. This LCCA includes the impacts associated with the treatment and additional transportation routes of the recycled aggregates. With this work, it was found that mortars with carbonated recycled aggregates have a considerably lower global warming potential impact than mortars without recycled aggregates. The mortars with recycled aggregates presented lower CO2 emissions of up to 6.31% for 100% incorporation of non-carbonated recycled aggregates. These values were incremented with the carbonation of the recycled aggregates, achieving a reduction of CO2 emissions of up to 36.75% for 100% of incorporation.
Analytical Solution for Lined Circular Water Conveyance Tunnels under the Action of Internal and External Hydraulic Pressure
Authors: Xu Yunqian , Bao Tengfei , Yuan Mingdao , Liu Yijie , Zhang Shu
Published: August 2024

The interaction between the surrounding rock and the support structure in a circular water conveyance tunnel with lining comprises two main aspects: internal and external hydraulic pressures, and the contact load between the post-excavation lining and the surrounding rock. There is currently no reasonable calculation method to consider both factors simultaneously. Therefore, by utilizing the assumption of smooth contact between the surrounding rock and the lining, an analytical model for a circular water conveyance tunnel with lining is developed through the complex function method. Smooth contact indicates continuity of radial contact stress, coordination of radial displacement, and the absence of shear stress transmission. Considering the inner and outer boundary stress conditions of the lining, two sets of undetermined analytical functions are established, corresponding to internal and external water pressure, as well as the contact stress between the surrounding rock and the lining. Ultimately, the stress and displacement components at any point within the surrounding rock and lining can be derived under the conditions outlined in this study. The analytical model elucidates the mechanism of load transfer within the circular water conveyance tunnel with lining, considering the combined effects of internal and external water pressure and excavation loads. Of particular note, it quantifies the restrictive impact of external water pressure on lining hydrofracturing when subjected to high internal water pressure. Additionally, the model offers a theoretical foundation for designing and assessing support structures for use in long-distance water conveyance projects.
Advances in the Neural Network Quantization: A Comprehensive Review
Authors: Wei Lu , Ma Zhong , Yang Chaojie , Yao Qin
Published: August 2024

Artificial intelligence technologies based on deep convolutional neural networks and large language models have made significant breakthroughs in many tasks, such as image recognition, target detection, semantic segmentation, and natural language processing, but also face a conflict between the high computational capacity of the algorithms and limited deployment resources. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is an important and essential technique for efficient deployment and cost reduction in edge computing. This paper analyzes various existing quantization methods, showcases the deployment accuracy of advanced techniques, and discusses the future challenges and trends in this domain.
A Proposal of Behavior-Based Consumption Profiles for Green Software Design
Authors: Larracoechea Jorge Andrés , Ilarri Sergio , Roose Philippe
Published: August 2024

Despite the increase in the efficiency of energy consumption in information and communication technology, software execution and its constraints are responsible for how energy is consumed in hardware hosts. Consequently, researchers have promoted the development of sustainable software with new development methods and tools to lessen its hardware demands. However, the approaches developed so far lack cohesiveness along the stages of the software development life cycle (SDLC) and exist outside of a holistic method for green software development (GSD). In addition, there is a severe lack of approaches that target the analysis and design stages of the SDLC, leaving software architects and designers unsupported. In this article, we introduce our behavior-based consumption profile (BBCP) external Domain-Specific Language (DSL), aimed at assisting software architects and designers in modeling the behavior of software. The models generated with our external DSL contain multiple sets of properties that characterize features of the software’s behavior. In contrast to other modeling languages, our BBCP emphasizes how time and probability are involved in software execution and its evolution over time, helping its users to gather an expectation of software usage and hardware consumption from the initial stages of software development. To illustrate the feasibility and benefits of our proposal, we conclude with an analysis of the model of a software service created using the BBCP, which is simulated using Insight Maker to obtain an estimation of hardware consumption and later translated to energy consumption.
Innovative Orthopedic Solutions for AI-Optimized Piezoelectric Implants for Superior Patient Care
Authors: Oladapo Bankole I. , Olawumi Mattew A. , Olugbade Temitope Olumide
Published: August 2024

This research aims to optimize piezoelectric implants for orthopedic applications, enhancing energy harvesting efficiency and mechanical integrity. Our objectives include comparing piezoelectric materials (PZT, PVDF, and BaTiO3) and employing advanced theoretical modeling, finite element analysis (FEA), and validation to identify optimal configurations. Methodologically, this study integrates machine learning and AI-driven techniques to refine design parameters and predict performance outcomes. Significant findings have revealed that PZT demonstrated the highest sensitivity (2 V/mm), achieving a maximum power output of 4.10 Watts, surpassing traditional solutions by over 100%. The optimization process ensured uniform stress distribution, reducing mechanical failure risk, with predictive models showing high accuracy (R-squared value of 97.77%). Error analysis indicated minimal discrepancies, with an average error margin of less than 2%. The conclusions highlight the significant potential of optimized piezoelectric implants in developing durable, efficient, and patient-friendly orthopedic solutions, setting a new standard in intelligent medical device innovation and contributing to enhanced patient care and improved clinical outcomes.
A Novel Data-Driven Approach with a Long Short-Term Memory Autoencoder Model with a Multihead Self-Attention Deep Learning Model for Wind Turbine Converter Fault Detection
Authors: Torres-Cabrera Joel , Maldonado-Correa Jorge , Valdiviezo-Condolo Marcelo , Artigao Estefanía , Martín-Martínez Sergio , Gómez-Lázaro Emilio
Published: August 2024

The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address this challenge, we analyzed the Supervisory Control and Data Acquisition (SCADA) data to identify significant differences between the relationship of variables based on data reconstruction errors between actual and predicted values. This study proposes a hybrid short- and long-term memory autoencoder model with multihead self-attention (LSTM-MA-AE) for WT converter fault detection. The proposed model identifies anomalies in the data by comparing the reconstruction errors of the variables involved. However, more is needed. To address this model limitation, we developed a fault prediction system that employs an adaptive threshold with an Exponentially Weighted Moving Average (EWMA) and a fixed threshold. This system analyzes the anomalies of several variables and generates fault warnings in advance time. Thus, we propose an outlier detection method through data preprocessing and unsupervised learning, using SCADA data collected from a wind farm located in complex terrain, including real faults in the converter. The LSTM-MA-AE is shown to be able to predict the converter failure 3.3 months in advance, and with an F1 greater than 90% in the tests performed. The results provide evidence of the potential of the proposed model to improve converter fault diagnosis with SCADA data in complex environments, highlighting its ability to increase the reliability and efficiency of WTs.
YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
Authors: Zhang Tian , Zhang Jie , Pan Pengfei , Zhang Xiaochen
Published: August 2024

Printed circuit boards present several challenges to the detection of defects, including targets of insufficient size and distribution, a high level of background noise, and a variety of complex types. These factors contribute to the difficulties encountered by PCB defect detection networks in accurately identifying defects. This paper proposes a less-parametric model, YOLO-RRL, based on the improved YOLOv8 architecture. The YOLO-RRL model incorporates four key improvement modules: The following modules have been incorporated into the proposed model: Robust Feature Downsampling (RFD), Reparameterised Generalised FPN (RepGFPN), Dynamic Upsampler (DySample), and Lightweight Asymmetric Detection Head (LADH-Head). The results of multiple performance metrics evaluation demonstrate that YOLO-RRL enhances the mean accuracy (mAP) by 2.2 percentage points to 95.2%, increases the frame rate (FPS) by 12%, and significantly reduces the number of parameters and the computational complexity, thereby achieving a balance between performance and efficiency. Two datasets, NEU-DET and APSPC, were employed to evaluate the performance of YOLO-RRL. The results indicate that YOLO-RRL exhibits good adaptability. In comparison to existing mainstream inspection models, YOLO-RRL is also more advanced. The YOLO-RRL model is capable of significantly improving production quality and reducing production costs in practical applications while also extending the scope of the inspection system to a wide range of industrial applications.
Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
Authors: Piras Giuseppe , Muzi Francesco , Tiburcio Virginia Adele
Published: August 2024

Digital Twin is becoming an increasingly powerful resource in the field of building production, replacing traditional processes in the Architecture, Engineering, Construction and Operations sector. This study is concerned with the development of a DT, enabled by Building Information Modeling, artificial intelligence, machine learning, and the Internet of Things to implement space management strategies. It proposes an application case for the Lazio Region headquarters, which has partly adopted smart working typology post-COVID-19. The aim is to create an accurate digital replica of the building based on BIM, integrated with real-time data. This will help to improve the use of space, the management of resources, and the quality of services provided to the community. It also improves energy efficiency, reducing consumption by 530.40 MWh per year and reducing greenhouse gas emissions by 641.32 tons of CO2 per year. The research proposes a holistic framework for the implementation of innovative solutions in the context of public infrastructure space management through the use of digital technology, facilitating the promotion of efficiency and sustainability in decision-making and operational processes through the application of a digital methodology.
Application of Technological Procedure Automated Guided Vehicles in the Production Hall of a Company Due to Increasing the Automation—Case Study
Authors: Gnap Jozef , Kubasáková Iveta , Kubáňová Jaroslava , Pauer Denis
Published: August 2024

This paper discusses the possibility of deploying automated guided vehicle (AGV) technology in a company’s production hall. Chronometric measurements of material handling and people handling were performed. The handling processes were divided into five sub-processes, which were analysed in terms of production line operation, distance, and carrying capacity of the automated guided vehicles. Based on the measurements, we found that it was necessary to use four vehicles for handling in the foam production hall. The company is applying this automation project to foams, and after successful testing, plans to continue with automation for leather upholstery. To ensure the correct chronometric results, we measured the distances between each workstation on the production floor at the company. Based on an analysis of material handling on the production lines, we evaluated that there is a possibility of applying AGVs on the entire production floor. We analysed the volumes of material transfer and the possibilities of interconnection within the existing material handling in the enterprise. Complications arise when deploying AGVs in the plant because the handling to the production lines is not only provided by the parent company, but also by the component suppliers themselves. Based on this observation, we have identified potential collision points within the operation between automated guided vehicles and other material handling equipment or people. For this reason, we deployed one more AGV on the handling routes to prevent unwanted collisions. This means that a human would be able to perform even more processes than a machine and can circulate 80 times, where a machine under the same conditions would only perform the entire circulation 57 times. There is room here to use humans for other processes and to automate the handling of goods, although it introduces delays that can be eliminated by deploying additional automated guided vehicles. This opens room for further research questions on how these vehicles can be deployed, even in a small space, and how existing material handling can be automated.
Adaptive Attention-Enhanced Yolo for Wall Crack Detection
Authors: Chen Ying , Wu Wangyu , Li Junxia
Published: August 2024

With the advancement of social life, the aging of building walls has become an unavoidable phenomenon. Due to the limited efficiency of manually detecting cracks, it is especially necessary to explore intelligent detection techniques. Currently, deep learning has garnered growing attention in crack detection, leading to the development of numerous feature learning methods. Although the technology in this area has been progressing, it still faces problems such as insufficient feature extraction and instability of prediction results. To address the shortcomings in the current research, this paper proposes a new Adaptive Attention-Enhanced Yolo. The method employs a Swin Transformer-based Cross-Stage Partial Bottleneck with a three-convolution structure, introduces an adaptive sensory field module in the neck network, and processes the features through a multi-head attention structure during the prediction process. The introduction of these modules greatly improves the performance of the model, thus effectively improving the precision of crack detection.
Enhancing Diabetes Prediction and Prevention through Mahalanobis Distance and Machine Learning Integration
Authors: Dashdondov Khongorzul , Lee Suehyun , Erdenebat Munkh-Uchral
Published: August 2024

Diabetes mellitus (DM) is a global health challenge that requires advanced strategies for its early detection and prevention. This study evaluates the South Korean population using the Korea National Health and Nutrition Examination Survey (KNHANES) dataset from 2015 to 2021, provided by the Korea Disease Control and Prevention Agency (KDCA), focusing on improving diabetes prediction models. Outlier removal was implemented using Mahalanobis distance (MAH), and feature selection was based on multicollinearity (MC) and reliability analysis (RA). The proposed Extreme Gradient Boosting (XGBoost) model demonstrated exceptional performance, achieving an accuracy of 98.04% (95% CI: 97.89~98.59), an F1-score of 98.24%, and an Area Under the Curve (AUC) of 98.71%, outperforming other state-of-the-art models. The study highlights the significance of rigorous outlier detection and feature selection in enhancing the predictive power of diabetes risk models. Notably, a significant increase in diabetes cases was observed during the COVID-19 pandemic, particularly linked to male sex, older age, rural location, hypertension, and obesity, underscoring the need for enhanced public health strategies for early intervention and targeted prevention.
Channel Characteristics of Hybrid Power Line Communication and Visible Light Communication Based on Distinct Optical Beam Configurations for 6G IoT Network
Authors: Ding Jupeng , I Chih-Lin , Wang Jintao , Song Jian
Published: August 2024

In the envisioned 6G internet of things (IoT), visible light communication (VLC) has emerged as one promising candidate to mitigate the frequency spectrum crisis. However, when working as the access point, VLC has to be connected with the backbone network via other wire communication solutions. Typically, power line communication (PLC) is viewed as an excellent match to VLC, which is capable of providing both a power supply and backbone network connection. Generally, the integration of PLC and VLC is taken into consideration for the above hybrid system for channel characteristics analysis. However, almost all current works focus on hybrid PLC and VLC, based on a conventional Lambertian optical beam configuration, and fail to address the applications of hybrid PLC and VLC based on distinct optical beam configurations. To address this issue, in this paper, the channel characteristics of hybrid PLC and VLC, based on distinct optical beam configurations, are explored and illustrated. Numerical results show that, for a central position of the receiver, compared with an achievable rate of about 194 Mbps for hybrid PLC and VLC with a baseline Lambertian optical beam configuration, the counterparts of a hybrid channel based on Rebel and NSPW optical beams are about 173.4 Mbps and 222.4 Mbps. Moreover, the effect of azimuth rotation is constructed and estimated for hybrid PLC and VLC, adopting a typical rotating asymmetric beam configuration.
Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
Authors: Padmanathan Panneerselvam , Aswin Seenu , Satheesh Anbalagan , Kanna Parthasarathy Rajesh , Palani Kuppusamy , Devi Neelamegam Rajan , Sobota Tomasz , Taler Dawid , Taler Jan , Węglowski Bohdan
Published: August 2024

This paper deals with the topic of reducing drag force acting on aircraft wings by incorporating novel winglet designs, such as multi-tip, bird-type, and twisted. The high-speed NASA common research model (CRM) was selected as the baseline model, and winglet designs were retrofitted while keeping the projected wingspan constant. Computational analysis was performed using RANS coupled with the Spalart–Allmaras turbulence model to determine aerodynamic coefficients, such as CL and CD. It was observed that the multi-tip and bird-type designs performed exceptionally well at a low angle of attack (0°). A parametric study was conducted on multi-tip winglets by tweaking the parameters such as sweep angle (Λ), tip twist (Є), taper ratio (λ), and cant angle (Φ). The best combination of parameters for optimal aerodynamic performance while maintaining the wing root bending moment was determined using both the Taguchi method and Taguchi-based grey relational analysis (T-GRA) coupled with principal component analysis (PCA). Also, the percentage contribution of each parameter was determined by using the analysis of variance (ANOVA) method. At the design point, the optimized winglet design outperformed the baseline design by 18.29% in the Taguchi method and by 20.77% in the T-GRA coupled with the PCA method based on aerodynamic efficiency and wing root bending moment.
An Assessment of the Impact of Locally Recycled Cementitious Replacement Materials on the Strength of the Ultra-High-Performance Concrete
Authors: Ngo Thuc V. , Tran Viet Ba , Le Bao Hoai , Dang Huyen T. , Matos José , Tran Minh Q. , Dang Son N.
Published: August 2024

Withstanding extreme events is increasingly a significant challenge for the construction industry. Where civil infrastructures remain using traditional concrete, which has low tensile strength, poor durability, and weak crack resistance, in this regard, ultra-high-performance concrete (UHPC), with its outstanding mechanical properties and high strength, offers the prospect of wide application. This advanced technology allows for the fabrication of thin and light-dimensional structures to accelerate construction while increasing corrosion resistance to minimize maintenance intervention and extend the service life of the infrastructures. Despite this, UHPC is less eco-friendly due to consuming more cement than the usual material, which requires replacement materials, such as silica fume (SF) and rice husk ash (RHA), which are readily available from other local material production. This study proposes an experimental approach to assess the influence of SF and RHA content on the properties of UHPC. Different SF and RHA compositions will be adjusted to analyze their effects on slump flow, compressive strength, flexural strength, tensile strength, and the stress–strain relationship in UHPC tension testing. Based on the results, the most effective ratio is RHA replacing 50% of the SF in the UHPC mixture. Specialized tensile experiments reveal enhanced tensile strength with judicious RHA incorporation at 5-day and 28-day stages, particularly in initial crack and damage conditions. Stress–strain curves for 5% to 15% RHA samples show increased ductility, indicating that optimal RHA-SF ratios enhance UHPC cracking characteristics. Based on the results, a discussion on the appropriate proportions for utilizing most local materials will be derived, especially for regions of Vietnam. It is evaluated as a feasible and promising solution to reduce greenhouse gas emissions threatening global climate change.
Yolo Based Defects Detection Algorithm for EL in PV Modules with Focal and Efficient IoU Loss
Authors: Ding Shen , Jing Wanting , Chen Hao , Chen Congyan
Published: August 2024

Considering the defect detection issues in electroluminescence (EL) of photovoltaic (PV) cell systems, lots of factors result in performance degradation, including defect diversity, data imbalance, scale difference, etc. Focal-EIoU loss, an effective defect detection solution for EL, is proposed based on the improved YOLOv5. Firstly, by analyzing the detection background and scale characteristics of EL defects, a binary classification is carried out in the system. Subsequently, a cascade detection network based on YOLOv5 is designed to further extract features from the binary-classified defects. The defect localization and classification are achieved in this way. To address the problem of imbalanced defect samples, a loss function is designed based on EIoU and Focal-F1 Loss. Experimental results are illustrated to show the effectiveness. Compared with the existing CNN-based deep learning approaches, the proposed focal loss calculation-based method can effectively improve the performance of handling sample imbalance. Moreover, in the detection of 12 types of defects, the Yolov5 algorithms can always obtain higher MAP (mean average precision) even with different parameter levels (Yolov5m: 0.791 vs. 0.857, Yolov5l: 0.798 vs. 0.862, Yolov5x: 0.802 vs. 0.867, Yolov5s: 0.793 vs. 0.865).
Enhanced Coconut Yield Prediction Using Internet of Things and Deep Learning: A Bi-Directional Long Short-Term Memory Lévy Flight and Seagull Optimization Algorithm Approach
Authors: Alkhawaji Rami N. , Serbaya Suhail H. , Zahran Siraj , Vita Vasiliki , Pappas Stylianos , Rizwan Ali , Fotis Georgios
Published: August 2024

In coastal areas, coconuts are a common crop. Everyone from farmers to lawmakers and businesses would benefit from an accurate forecast of coconut production. Internet of Things (IoT) sensors are strategically positioned to continuously monitor the environment and gather production statistics to obtain accurate agricultural output predictions. To effectively estimate coconut prediction, this study presents an enhanced deep learning classifier called Bi-directional Long Short-Term Memory (BILSTM) with the integrated Lévy Flight and Seagull Optimization Algorithm (LFSOA). LASSO feature selection is applied to eliminate the superfluous characteristics in the yield estimation. To further enhance the coconut yield estimate, the optimal set of hyperparameters for BILSTM is tuned by the LFSOA, which helps to avoid the overfitting issue. For the results, the BILSTM is compared against different classifiers such as Recurrent Neural Network (RNN), Random Forest Classifier (RFC), and LSTM. Similarly, LFSOA-based hyperparameter tuning is contrasted with different optimization algorithms. The outputs show that LFSOA-based hyperparameter tuning in BILSTM achieved accuracy, precision, recall, and f1-score of 98.963%, 99.026%, 99.155%, and 95.758%, respectively, which are higher when compared to existing methods. Similarly, the BILSTM-LFSOA accomplished better results in statistical measures, including the Root Mean Square Error (RMSE) of 0.105, Mean Squared Error (MSE) of 0.011, Mean Absolute Error (MAE) of 0.094, and coefficient of determination (R2) of 0.954, respectively. From the overall analysis, the proposed BILSTM-LFSOA improves coconut yield prediction by achieving better results in all the performance measures when compared with existing models. The results of this study are important to many stakeholders, including but not limited to policymakers, farmers, banks, and insurance companies. As coconuts are an important crop in developing countries, accurate coconut yield forecasting will lead to greater financial and food security in these regions.
Characterizations of Interfacial Solar Water Evaporation
Authors: Yi Huan , Wang Xiaoshuai , Huang Yijun , He Shucheng , Cui Jiao , Wu Liwei , Feng Ribao , Huang Shaolong
Published: August 2024

Interfacial solar water evaporation has emerged as an effective technique for converting solar energy to thermal energy, which can then be applied to clean water production and sewage treatment. This review discusses the primary characterization techniques used to evaluate the key aspects of interfacial solar water evaporators: the intrinsic and performance evaluation. Each technique is discussed with a brief explanation of its foundations, followed by carefully selected examples. This review is aimed at assisting materials chemists and physicists, particularly students, in choosing the most appropriate techniques for characterizing photothermal materials.
Mathematical Modelling and Optimization of Seed Metering Unit Performance in Precision Peanut Seeding
Authors: Da Costa Eudulino , Yazgi Arzu
Published: August 2024

The objective of this study was to determine, develop mathematical models, and optimize the in-row seed distribution uniformity performance of a vacuum-type seed metering unit in peanut seeding under different operating conditions. The greased belt stand trials based on the Central Composite Design (CCD) were. Quality of feed index, multiple index, miss index, precision, and coefficient of precision were examined as dependent variables, while the peripheral speed of the seed plate, hole diameter of the seed plate, and vacuum pressure were selected as independent variables. Based on the analysis of experimental data from five different levels of each independent variable, performed using the CCD, a meaningful mathematical model was developed in cubic form for the quality of feed index for peanut seeding, and the model was optimized. The ideal conditions were determined from the model as 0.079 ms−1, 6.94 mm, and 7.07 kPa for peripheral speed of the seed plate, hole diameter, and vacuum pressure, respectively. Based on the findings, quality in seeding by selecting the parameters that affect the performance of precision peanut seeding can be enhanced, resulting in valuable information that can be used as a useful guide to farmers, machine manufacturers, and researchers.
Cardiovascular Risk in HIV Patients: Ageing Analysis of the Involved Genes
Authors: Boccuto Fabiola , De Rosa Salvatore , Veltri Pierangelo , Torella Daniele , Guzzi Pietro Hiram
Published: August 2024

Acquired immunodeficiency syndrome (AIDS) has transitioned from a progressive, fatal disease to a chronic, manageable disease thanks to better defining of antiretroviral therapy, contributing to increased life expectancy. In parallel, a growing number of subjects without clinical signs of disease but living with chronic HIV infection (also indicated as PLWHs, i.e., People Living With HIV) are experiencing early cardiovascular disease, and the risk increases with age. However, a progressive increase in the prevalence of multiple comorbidity diseases has been reported as these patients age, including cardiovascular disease (CVD). Cardiovascular mortality can be related to viral infection, a progressive reduction in response to antiretroviral therapy, chronic inflammation, and lifestyle. Cardiovascular ageing represents a relevant issue in the management of HIV-infected patients. Although the exact pathophysiological mechanism that leads PLWHs to develop cardiovascular disease is not entirely understood, there is substantial evidence that they accumulate age-related conditions earlier than the general population. Furthermore, since the proportion of PLWHs growing older than 50 years has progressively increased, this results in a complex interaction between disease-related pathophysiology and the exposition of a growing burden of cardiovascular risk factors. We performed a study to relate the effect ageing gas on genes associated with HIV and cardiovascular diseases. We performed a systematic review of the genes most frequently associated with ageing in HIV-infected subjects, followed by a bioinformatic analysis to explore the biological impact of the ageing-related genes.
Non-Thermal and Thermal Physical Procedures—Optimistic Solutions in the Winemaking Industry
Authors: Luchian Camelia Elena , Scutarașu Elena Cristina , Colibaba Lucia Cintia , Motrescu Iuliana , Cotea Valeriu V.
Published: August 2024

Consumer demand for food and drink products with special nutritional properties is constantly increasing. To respond to new consumption trends, research in winemaking focuses on optimizing the technological process to increase quality while preserving the traditional character and typicality of the product. Lately, winemakers are implementing a range of physical non-thermal (ultrasound technology and cold plasma technology) and thermal (microwave treatment) processes to streamline and optimize winemaking technologies, reduce costs, speed up production, and improve sustainability. This study examines the existing literature regarding the effects of various physical approaches on the quality of wines. Scientific articles are concentrating on enhancing the extraction of phenolic compounds and other active compounds—especially those that contribute positively to wine quality. The reviewed literature only offers a limited amount of information on this subject; further investigation is required to determine the significance, applicability, and efficacy of thermal and non-thermal procedures in the wine industry.