bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiple Learning Strategies and a Modified Dynamic Multiswarm Particle Swarm Optimization Algorithm with a Master Slave Structure
Authors: Cheng Ligang , Cao Jie , Wang Wenxian , Cheng Linna
Published: August 2024

It is a challenge for the particle swarm optimization algorithm to effectively control population diversity and select and design efficient learning models. To aid in this process, in this paper, we propose multiple learning strategies and a modified dynamic multiswarm particle swarm optimization with a master slave structure (MLDMS-PSO). First, a dynamic multiswarm strategy with a master–slave structure and a swarm reduction strategy was introduced to dynamically update the subswarm so that the population could maintain better diversity and more exploration abilities in the early stage and achieve better exploitation abilities in the later stage of the evolution. Second, three different particle updating strategies including a modified comprehensive learning (MCL) strategy, a united learning (UL) strategy, and a local dimension learning (LDL) strategy were introduced. The different learning strategies captured different swarm information and the three learning strategies cooperated with each other to obtain more abundant population information to help the particles effectively evolve. Finally, a multiple learning model selection mechanism with reward and punishment factors was designed to manage the three learning strategies so that the particles could select more advantageous evolutionary strategies for different fitness landscapes and improve their evolutionary efficiency. In addition, the results of the comparison between MLDMS-PSO and the other nine excellent PSOs on the CEC2017 test suite showed that MLDMS-PSO achieved an excellent performance on different types of functions, contributing to a higher accuracy and a better performance.
Location and Capacity Optimization of Waste Recycling Centers: Mathematical Models and Numerical Experiments
Authors: Xie Shenming , Lim Ying Terk , Wang Huiwen , Yi Wen , Antwi-Afari Maxwell Fordjour
Published: August 2024

With rapid urbanization growth, considerable amounts of construction waste are generated on an annual basis, posing significant economic and environmental challenges worldwide. Re-cycling construction waste is a sustainable way for waste disposal, leading to the necessity of meticulous planning of recycling centers. A well-designed plan for constructing recycling centers can effectively improve the recycling rate of construction waste while minimizing investment. This paper formulates a two-stage stochastic model for planning recycling centers with the objective of maximizing the recycling rate under different scenarios. This study comprehensively considers various uncertain factors, such as the amount of construction waste generated and the demand for recycled materials. A case study of Guangzhou is used for validation, which demonstrates the effectiveness of the developed model in planning recycling center construction. The comparison between the proposed model and a conventional mean value model shows the importance of accounting for uncertainties. Specifically, the derived results indicate that 7% more construction waste is recycled with the same investment in constructing recycling centers. Additionally, via a sensitivity analysis, valuable managerial insights on investing resources in recycling center construction are provided to decision makers. Ultimately, the research findings are expected to enhance the recycling rate of construction waste, thereby contributing to sustainable industry development.
Contact Force and Friction of Generally Layered Laminates with Residual Hygrothermal Stresses under Mode II In-Plane-Shear Delamination
Authors: Polyzos Efstratios , Van Hemelrijck Danny , Pyl Lincy
Published: August 2024

Mode II (in-plane-shear) delamination tests are more complex than mode I (opening) due to the presence of a contact force between the two arms. This force is essential for the calculation of the energy release rate (ERR) and is closely linked to friction effects. A novel formulation is presented in this article to estimate the contact force analytically. Specifically, the contact force is derived within the context of the rigid, semi-rigid, and flexible joint models. The analytical solutions consider the case of a generally layered composite laminate with residual hygrothermal stresses and are used to evaluate the ERR. The new formulation is compared with numerical models created using the Virtual Crack Closure Technique (VCCT) and the Cohesive Zone Method (CZM) for a fiber–metal laminate. The results show that the new formulation provides nearly identical ERR predictions to those of the VCCT and CZM models. Additionally, it is demonstrated that the effect of friction on the ERR is less than 1%.
Application of High Hydrostatic Pressures and Refrigerated Storage on the Content of Resistant Starch in Selected Legume Seeds
Authors: Bojarczuk Adrianna , Le-Thanh-Blicharz Joanna , Michałowska Dorota , Kotyrba Danuta , Marszałek Krystian
Published: August 2024

Resistant starch (RS) is a fraction of starch not digested and absorbed in the small intestine, and it is fermented by the intestinal microbiota in the colon, thereby influencing many health benefits. Legumes such as beans, lentils, and chickpeas are rich in fermentable dietary fiber, and RS can be included in this fiber group. These legumes are not considered a “typical” source of starch and have not been extensively studied as a source of RS. There are still insufficient data on modern non-thermal methods like high-pressure processing (HPP) and combining this method with refrigerated storage. The study aimed to investigate and compare the effects of HPP and HPP combined with refrigerated storage on the RS content of legumes, particularly white beans, green lentils, and chickpeas. Different pressure levels and processing times were used to evaluate changes in RS content and to assess the total fiber content and fiber fraction of the tested legumes. Our study showed that the increase in pressure and pressurization time affected changes in the RS content of the examined legumes. Furthermore, the cooling process of previously pressurized samples resulted in a significant increase in RS content.
Method of Planning and Scheduling the Production Process of Yellow Mealworm Larvae for a Small Enterprise
Authors: Kowalski Arkadiusz
Published: August 2024

In the context of the growing demand for alternative protein sources with the growth of the human population and increasing ecological awareness, the rearing of yellow mealworm larvae (Tenebrio molitor) is a promising option for the production of sustainable protein. The article presents a comprehensive approach to planning and scheduling the production of yellow mealworm larvae in a small enterprise, focusing on the organizational, technical, and economic aspects of the production process. The production installation, the method of rearing using an automated feeding system, and the monitoring of larvae development were described and an attempt was made to identify the key parameters of the process that affect its efficiency. Particular attention was paid to the calculation algorithm implemented in the spreadsheet, which allows the selection of the production batch size and the frequency of their launch, so as to maximize the available capacity of storage racks for cuvettes. In addition, the article analyses logistical challenges related to the production of larvae, including transport activities in order to meet, among others, the demand for feed. Finally, the estimation of revenues and economic indicators, such as profitability and return on investment, is presented, pointing to the need for further improvements in the production process and cost optimization to achieve favorable financial results. The results of the research emphasize the potential of rearing yellow mealworm larvae as a sustainable source of protein while simultaneously pointing to key areas that require further research and development.
Numerical Methods as an Aid in the Selection of Roof Bolting Systems for Access Excavations Located at Different Depths in the LGCB Mines
Authors: Pawelus Daniel , Butra Jan
Published: August 2024

The values of primary stresses are not allowed for as a criterion in the selection of roof bolting systems in mining excavations located at various depths in Polish copper ore mines. Therefore, in order to ensure enduring and safe operation of excavations, in particular, those driven in unfavourable geological and mining conditions, this problem has required solutions based on numerical methods. This article presents an example of applying numerical simulations to the evaluation of the stability of headings in Polish copper ore mines. The analyses included mining excavations located at various depths in the rock mass. This issue is of great importance, as safety regulations are prioritised in mining excavations which remain in operation even for several decades. The stability of the headings was evaluated with the use of the RS2 specialist numerical simulation software. This computer program uses the finite element method (FEM) for calculations. The rock parameters used in the numerical models have been determined on the basis of the Hoek–Brown classification. For that purpose, the RocLab 1.0 software was used. The parameters of the stress field were identified from the profile of the GG-1 shaft with the assumed hydrostatic state of stress. The numerical modelling was performed in a triaxial stress state and in a plane strain state. The numerical analyses were based on the Mohr–Coulomb failure criterion. The rock medium was described with the elastic-plastic model with softening (roof and walls) and with the elastic-plastic model (floor). The results of the numerical analyses served to provide an example of the application of a roof bolting system to protect headings located at the depths of 1000 m b.g.l. and 1300 m b.g.l.
Study on Multi-Span Tension Coupling Relationship of Gravure Printed Electronic Equipment
Authors: He Kui , He Pengbo , Li Shifa , Li Jian , Wei Xingmei
Published: August 2024

To improve the performance of the tension control system in gravure printed electronic equipment, it is necessary to study the tension system model of gravure-printing electronic equipment. This paper focuses on the coupling characteristics of multi-span tension systems. Firstly, based on the single-span tension model, the mathematical model of the printing tension subsystem is established and simplified into a general multi-span tension coupling model. Then, using the relationship between the inputs and outputs of the system, the coupling model is simulated using MATLAB/Simulink and verified through experiments on the gravure printed electronic platform. Finally, the coupling relationship and influence laws among multi-physical quantities of the system are analyzed. The research results show that changes in the input web tension of the multi-span system will affect the steady-state tension values of all subsequent spans. Moreover, the speed change in a roller in the multi-span system will not only affect the steady-state tension value of its own span, but also cause transient tension fluctuations in all subsequent spans. The findings of this paper provide an important theoretical basis for the research of the tension system in gravure printed electronic equipment, contributing to the enhancement of printed electronic product quality.
LogEDL: Log Anomaly Detection via Evidential Deep Learning
Authors: Duan Yunfeng , Xue Kaiwen , Sun Hao , Bao Haotong , Wei Yadong , You Zhangzheng , Zhang Yuantian , Jiang Xiwei , Yang Sangning , Chen Jiaxing , Duan Boya , Ou Zhonghong
Published: August 2024

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect networks from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises the training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. The experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.
Load Effect Analysis Method of Cable-Stayed Bridge for Long-Span Track Based on Adaptive Filtering Method
Authors: Ding Peng , Li Xiaogang , Chen Sheng , Huang Xiangsheng , Chen Xiaohu , Qi Yong
Published: August 2024

Aiming at the problems of large capacity, narrow transverse width, large excitation, high safety level, and difficulty in accurately grasping the working state of the cable-stayed bridge for the long-span track, this research obtains the structural response data in real time by establishing a health monitoring system. The adaptive filtering method was employed to separate the train load response and the temperature load response. Then, a train load effect analysis method based on the influence line and a temperature load effect analysis method based on the correlation were proposed to assess the operational status of the bridge in real time and objectively. The Chongqing Nanjimen Railway Track Bridge (hereinafter Chongqing Nanjimen track bridge) project was utilized as a case study to demonstrate the application of these methods. The results show that the adaptive filtering method can effectively separate the response of train and temperature loads. The normalized cross-correlation (NCC) results of the measured train load response and the influence line’s finite element calculation show a high degree of fit between the measured values and the theory, proving that no significant anomalies are found in the bridge. There is a strong correlation between the ambient temperature difference and the Pearson correlation coefficient of structural response, which indicates that the Chongqing Nanjimen track bridge is currently in normal working condition.
A Closer Look at Few-Shot Classification with Many Novel Classes
Authors: Lin Zhipeng , Yang Wenjing , Wang Haotian , Chi Haoang , Lan Long
Published: August 2024

Few-shot learning (FSL) is designed to equip models with the capability to quickly adapt to new, unseen domains in open-world scenarios. However, there is a notable discrepancy between the multitude of new concepts encountered in the open world and the limited scale of existing FSL studies, which focus predominantly on a small number of novel classes. This limitation hinders the practical implementation of FSL in real-world situations. To address this issue, we introduce a novel problem called Few-Shot Learning with Many Novel Classes (FSL-MNC), which expands the number of novel classes more than 500 times compared to traditional FSL settings. This new challenge presents two main difficulties: increased computational load during meta-training and reduced classification accuracy due to the larger number of classes during meta-testing. To tackle these problems, we introduce the Simple Hierarchy Pipeline (SHA-Pipeline). In response to the inefficiency of traditional Episode Meta-Learning (EML) protocols, we redesign a more efficient meta-training strategy to manage the increased number of novel classes. Moreover, to distinguish distinct semantic features across a broad array of novel classes, we effectively reconstruct and utilize class hierarchy information during meta-testing. Our experiments demonstrate that the SHA-Pipeline substantially outperforms both the ProtoNet baseline and current leading alternatives across various numbers of novel classes.
Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
Authors: Ultsch Johannes , Pfeiffer Andreas , Ruggaber Julian , Kamp Tobias , Brembeck Jonathan , Tobolář Jakub
Published: August 2024

In vertical vehicle dynamics control, semi-active dampers are used to enhance ride comfort and road-holding with only minor additional energy expenses. However, a complex control problem arises from the combined effects of (1) the constrained semi-active damper characteristic, (2) the opposing control objectives of improving ride comfort and road-holding, and (3) the additionally coupled vertical dynamic system. This work presents the application of Reinforcement Learning to the vertical dynamics control problem of a real street vehicle to address these issues. We discuss the entire Reinforcement Learning-based controller design process, which started with deriving a sufficiently accurate training model representing the vehicle behavior. The obtained model was then used to train a Reinforcement Learning agent, which offered improved vehicle ride qualities. After that, we verified the trained agent in a full-vehicle simulation setup before the agent was deployed in the real vehicle. Quantitative and qualitative real-world tests highlight the increased performance of the trained agent in comparison to a benchmark controller. Tests on a real-world four-post test rig showed that the trained RL-based controller was able to outperform an offline-optimized benchmark controller on road-like excitations, improving the comfort criterion by about 2.5% and the road-holding criterion by about 2.0% on average.
A Vibrational Energy Harvesting Sensor Based on Linear and Rotational Electromechanical Effects
Authors: Lehman Sean K. , Fisher Karl A.
Published: August 2024

In this investigation, a magnetically coupled double-spring design is presented for harvesting low-level non-stationary random vibrational energy. The sensor relies on multimodal coupling between the translation and rotation of a two-spring magnet and coil system to widen the harvesting bandwidth. Energy methods are used to develop a model to characterize the electromechanical response of the system, the solution of which is obtained using stochastic techniques based on a particle swarm algorithm. This approach provides an efficient method to estimate system parameters that otherwise are difficult or impossible to determine with independent measurements. The experimental results demonstrate agreement with the theoretical predictions over a limited bandwidth. The sensor can effectively harvest non-stationary vibration energy down to 10−4 g within a limited bandwidth of 130–150 Hz. The sensor prototype has an operational volume of 2.6 cm3 with a calculated power density of 0.2 W/cm3. The sensor’s small size results in a coupling efficiency of approximately 6% across the tested bandwidth.
Wheat Powdery Mildew Detection with YOLOv8 Object Detection Model
Authors: Önler Eray , Köycü Nagehan Desen
Published: August 2024

Wheat powdery mildew is a fungal disease that significantly impacts wheat yield and quality. Controlling this disease requires the use of resistant varieties, fungicides, crop rotation, and proper sanitation. Precision agriculture focuses on the strategic use of agricultural inputs to maximize benefits while minimizing environmental and human health effects. Object detection using computer vision enables selective spraying of pesticides, allowing for targeted application. Traditional detection methods rely on manually crafted features, while deep learning-based methods use deep neural networks to learn features autonomously from the data. You Look Only Once (YOLO) and other one-stage detectors are advantageous due to their speed and competition. This research aimed to design a model to detect powdery mildew in wheat using digital images. Multiple YOLOv8 models were trained with a custom dataset of images collected from trial areas at Tekirdag Namik Kemal University. The YOLOv8m model demonstrated the highest precision, recall, F1, and average precision values of 0.79, 0.74, 0.770, 0.76, and 0.35, respectively.
The Functional Assessment of the Shoulder in Water Polo Players with Surface Electromyography and Kinematic Analysis: A Pilot Study
Authors: Sgrò Francesco , Demeco Andrea , Marotta Nicola , Merati Giampiero , Lipoma Mario , Ammendolia Antonio , Costantino Cosimo , Iona Teresa
Published: August 2024

Scapular dyskinesia, glenohumeral internal rotation deficit, upper posterior labral anterior tears, and rotator cuff injuries are common in athletes who play “overhead” sports due to their repetitive excessive movements. The aims of this study are to propose a new protocol with kinematic analysis coupled with sEMG and to objectively analyze the effect of a specific prevention exercise protocol. Thirty-two subjects (age: 22 ± 4 years, height: 183 ± 3.2 cm, BMI: 23 ± 0.96 kg/m2), including sixteen healthy subjects (Group A) and sixteen male water polo athletes (Group B), underwent a three-dimensional motion analysis based on optoelectronic and sEMG systems. A functional evaluation was performed on Group A and Group B to assess the reliability of the operator-dependent tasks and collect a series of normative data, before starting the prevention protocol (T0) and after 8 weeks (T1). The athletes performed a specific exercise protocol to prevent shoulder injuries. In Group B, the movements of abduction (T0: 111° ± 24°; T1: 140° ± 13°) and extension (T0: 72°± 10°; T1: 84° ± 2.8°) of the glenohumeral joint and the scapulothoracic joint (T0: 33° ± 8.36°; T1: 40.5° ± 10.6°) significantly improved. A significant reduction (in %) in the maximum voluntary contraction (MCV) at T1 of the upper trapezius, teres minor, and pectoralis major was observed. This protocol provides objective data in a simple and reliable way for the functional assessment of the shoulder in water polo players during the sport season.
Research on Drought Stress Monitoring of Winter Wheat during Critical Growth Stages Based on Improved DenseNet-121
Authors: Yao Jianbin , Wu Yushu , Liu Jianhua , Wang Hansheng
Published: August 2024

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth’s key periods is feasible and effective.
Monocular-Vision-Based Method for Locating the Center of Anchor Holes on Steel Belts in Coal Mine Roadways
Authors: Lei Mengyu , Zhang Xuhui , Chen Xin
Published: August 2024

The precise positioning of anchoring-hole centers on the steel belts used for anchor support in coal mines is essential for improving the automation and efficiency of roadway support. To address the issues of poor positioning accuracy and the low support efficiency caused by the manual determination of anchoring-hole-center positions, this paper proposes a monocular-vision-based method for locating anchoring-hole centers. Firstly, a laser pointer and an industrial camera are used to build an anchoring-hole positioning device, and its visual positioning model is constructed to achieve the automatic and precise localization of the anchoring-hole center. Secondly, to overcome the difficulty of obtaining high-precision spot centers using edge and grayscale information-based spot extraction methods, a spot center extraction method based on two-dimensional arctangent function fitting is proposed, achieving high precision and the stable acquisition of spot pixel coordinates. The experimental results show that the average measurement errors of the anchoring-hole centers in the camera’s coordinate system along the X-axis, Y-axis, and Z-axis are 3.36 mm, 3.30 mm, and 5.75 mm, respectively, with maximum errors of 4.23 mm, 4.39 mm, and 6.63 mm. The average measurement errors of the steel belt’s pitch, yaw, and roll angles in the camera’s coordinate system are 0.16°, 0.16°, and 0.08°, respectively, with maximum errors of 0.21°, 0.27°, and 0.13°. The proposed method can achieve the precise localization of anchoring holes, improve the efficiency of roadway support, and provide new insights for the automation and intelligentization of roadway anchor support.
Fuzzy Logic Theory and Spatiotemporal Modeling of the Fungus Phakopsora pachyrhizi Based on Differential Equations
Authors: Longo Sartor Zagui Nayara , Krindges Andre , Minussi Carlos Roberto , Cecconello Moiseis dos Santos
Published: August 2024

Brazil has been one of the largest soybean producers in recent years. The soybean is a legume commonly found in family meals. Among the diseases affecting the grains, Asian soybean rust is one of the most concerning. The fungus causing the disease is spread by the wind, making it difficult to control. Although it has been researched since its first records, not much data are available regarding the macro propagation behavior of spores. Therefore, this research aimed to model its dispersion based on a partial differential equation, the diffusion–advection equation, used by researchers to model the behavior of any pollutant. The terms of this equation were developed from real data, processed by fuzzy logic, and the simulation results were compared with disease records throughout a harvest. By using this approach to model the spatiotemporal dynamics of this fungus, it was possible to simulate its spread satisfactorily. Additionally, its results were used as input variables for a fuzzy system that estimates the susceptibility of a given location to disease development.
Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
Authors: Dong Zifa , Zheng Shuangjie , Jiao Lizhe , Xu Xiaoqing , Yao Yao , Gao Zhuoru , Li Haifeng
Published: August 2024

To enhance the uplift capacity and facilitate the installation of multi-row perfobond connectors at shallow burial depths, this study puts forward a novel notched T-perfobond connector. The design incorporates an integrated flange at the bottom of the connector and a notch at the edge of the hole. Through pull-out model tests on four notched T-perfobond connectors, this research investigates their failure mechanisms and pull-out capacities. Utilizing the explicit dynamics method in ABAQUS, a finite-element model of the pull-out resistance test for notched T-perfobond connectors is established and verified against experimental data. Furthermore, a detailed parametric analysis involving 54 models is conducted, examining crucial parameters such as rib dimensions, hole geometry, flange size, notch width, bar diameter, and material properties. Based on the combined experimental and numerical results, this paper assesses the suitability of current formulas for calculating the pull-out capacity of perfobond connectors and proposes a refined calculation method specifically for notched T-perfobond connectors. All the findings reported in this paper can serve as a reference in the design and construction of composite structures.
IV-SSIM—The Structural Similarity Metric for Immersive Video
Authors: Dziembowski Adrian , Nowak Weronika , Stankowski Jakub
Published: August 2024

In this paper, we present a new objective quality metric designed for immersive video applications—IV-SSIM. The proposed IV-SSIM metric is an evolution of our previous work—IV-PSNR (immersive video peak signal-to-noise ratio)—which became a commonly used metric in research and ISO/IEC MPEG standardization activities on immersive video. IV-SSIM combines the advantages of IV-PSNR and metrics based on the structural similarity of images, being able to properly mimic the subjective quality perception of immersive video with its characteristic distortions induced by the reprojection of pixels between multiple views. The effectiveness of IV-SSIM was compared with 16 state-of-the-art quality metrics (including other metrics designed for immersive video). Tested metrics were evaluated in an immersive video coding scenario and against a commonly used image quality database—TID2013—showing their performance in both immersive and typical, non-immersive use cases. As presented, the proposed IV-SSIM metric clearly outperforms other metrics in immersive video applications, while also being highly competitive for 2D image quality assessment. The authors of this paper have provided a publicly accessible, efficient implementation of the proposed IV-SSIM metric, which is used by ISO/IEC MPEG video coding experts in the development of the forthcoming second edition of the MPEG immersive video (MIV) coding standard.
GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
Authors: Yang Yifan , Wang Siqin , Li Daoyang , Sun Shuju , Wu Qingyang
Published: August 2024

To ensure the sustainable development of artificial intelligence (AI) application in urban and geospatial science, it is important to protect the geographic privacy, or geo-privacy, which refers to an individual’s geographic location details. As a crucial aspect of personal security, geo-privacy plays a key role not only in individual protection but also in maintaining ethical standards in geoscientific practices. Despite its importance, geo-privacy is often not sufficiently addressed in daily activities. With the increasing use of large multimodal models (LMMs) such as GPT-4 for open-source intelligence (OSINT), the risks related to geo-privacy breaches have significantly escalated. This study introduces a novel GPT-4-based model, GeoLocator, integrated with location capabilities, and conducts four experiments to evaluate its ability to accurately infer location information from images and social media content. The results demonstrate that GeoLocator can generate specific geographic details with high precision, thereby increasing the potential for inadvertent exposure of sensitive geospatial information. This highlights the dual challenges posed by online data-sharing and information-gathering technologies in the context of geo-privacy. We conclude with a discussion on the broader impacts of GeoLocator and our findings on individuals and communities, emphasizing the urgent need for increased awareness and protective measures against geo-privacy breaches in the era of advancing AI and widespread social media usage. This contribution thus advocates for sustainable and responsible geoscientific practices.
Clinical and Functional Outcomes in Faller and Non-Faller Older Adults Clustered by Self-Organizing Maps: A Machine-Learning Approach
Authors: Almeida Milena L. S. , Cavalcanti Aline O. , Sarai Rebeca , Silva Mateus A. , Melo Paulo R. V. , Silva Amanda A. M. , Caldas Rafael R. , Buarque Fernando , Trombini-Souza Francis
Published: August 2024

A wide range of outcomes makes identifying clinical and functional features distinguishing older persons who fall from non-fallers challenging, especially for professionals with less clinical experience. Thus, this study aimed to map a high-dimensional and complex clinical and functional dataset and determine which outcomes better discriminate older adults with and without self-reported falls. For this, clinical, functional, and cognitive outcomes of 60 community-dwelling older adults classified as fallers and non-fallers were selected based on self-report of a single fall in the last 12 months. An unsupervised intelligent algorithm (Self-Organizing Maps—SOM) was used to cluster and topographically represent the data studied. The SOM model mapped and identified two different groups (topographic error: 0.00; sensitivity: 0.77; precision: 0.42; accuracy: 0.53; F1-score: 0.55) based on self-report of a single fall. We concluded that although two distinct groups were mapped and clustered by the SOM, participants were not necessarily fallers or non-fallers. The increased cost of cognitive demands regarding a motor task (Timed Up and Go Test) and the effect of the Trail Making Test (TMT) Part B regarding TMT Part A could discriminate the functional and cognitive patterns in community-dwelling older adults. Therefore, in clinical practice, identifying patterns involving the interaction between cognition and motor skills, even in once-only faller older adults, can be an efficient approach to assessment and, consequently, to compound intervention programs to prevent falls in this population.
The Use of Computational Algorithms to Display the Aircraft Instruments That Work with Gyroscopic and Magnetic Physics (Theory for Programming an Elementary Flight Simulator)
Authors: Ramirez-Lopez Adan
Published: August 2024

The present study shows the development of computational algorithms to represent aircraft instruments such as the attitude indicator or the turn-and-slip indicator; moreover, the algorithms represent a magnetic compass and other instruments that function according to other physical theories. These instruments work by using the gyroscope and magnetic principles and help the pilot in navigation. These are considered to be the basic instruments required to provide location-related and positional information about the actual aircraft attitude. The algorithms developed in this study are capable of working in concordance with other instruments and the physical conditions established. The programming language used was C++ and the algorithms were compiled in independent files and subroutines for computational efficiency, eliminating unnecessary code. The display options were successfully tested. Additionally, an analysis that evaluated the error and approached flight simulation as a function of step time (Δt) is also described.
Transcranial Direct Current Stimulation in the Treatment of Chronic Knee Pain: A Scoping Review
Authors: Tedeschi Roberto , Benedetti Maria Grazia , Berti Lisa , Donati Danilo , Platano Daniela
Published: August 2024

Background: Chronic knee pain in older adults is a prevalent condition that significantly impacts quality of life. Transcranial Direct Current Stimulation (tDCS) has emerged as a potential non-invasive treatment option. This scoping review aims to evaluate the efficacy of tDCS in treating chronic knee pain among older adults. Methods: A comprehensive search of peer-reviewed articles was conducted, focusing on randomized controlled trials and pilot studies. Studies were included if they met specific Population, Concept, and Context (PCC) criteria. The primary outcomes assessed were pain reduction and functional improvement. Results: Eleven studies met the inclusion criteria, with a total of 779 participants. However, the results varied across studies, with some showing minimal differences between active tDCS and sham treatments. Advanced neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS), provided insights into the neuromodulatory effects of tDCS, revealing changes in brain activity related to pain perception. Conclusions: Transcranial Direct Current Stimulation (tDCS) presents a promising avenue for treating chronic knee pain in elderly individuals. However, the current body of research offers mixed results, emphasizing the need for more extensive and standardized studies. Future research should focus on understanding the underlying mechanisms, optimizing treatment protocols, and exploring the long-term effects and safety of tDCS.
A Human Body Simulation Using Semantic Segmentation and Image-Based Reconstruction Techniques for Personalized Healthcare
Authors: So Junyong , Youm Sekyoung , Kim Sojung
Published: August 2024

The global healthcare market is expanding, with a particular focus on personalized care for individuals who are unable to leave their homes due to the COVID-19 pandemic. However, the implementation of personalized care is challenging due to the need for additional devices, such as smartwatches and wearable trackers. This study aims to develop a human body simulation that predicts and visualizes an individual’s 3D body changes based on 2D images taken by a portable device. The simulation proposed in this study uses semantic segmentation and image-based reconstruction techniques to preprocess 2D images and construct 3D body models. It also considers the user’s exercise plan to enable the visualization of 3D body changes. The proposed simulation was developed based on human-in-the-loop experimental results and literature data. The experiment shows that there is no statistical difference between the simulated body and actual anthropometric measurement with a p-value of 0.3483 in the paired t-test. The proposed simulation provides an accurate and efficient estimation of the human body in a 3D environment, without the need for expensive equipment such as a 3D scanner or scanning uniform, unlike the existing anthropometry approach. This can promote preventive treatment for individuals who lack access to healthcare.
Assessment of Exoskeletons for Work Activities: The Dilemma behind the Product
Authors: Lefint Jérémy , Moniz António B.
Published: August 2024

The introduction of exoskeletons by many companies has often resulted in potential users not wanting to wear them. Evaluations of the exoskeleton reveal benefits of use, including ergonomic advantages and a reduction of work-related musculoskeletal disorders. How can this contradiction be explained? By searching the available literature, we can identify the methods used to develop these devices and the methods employed to evaluate their acceptance and benefit. By looking at the product from different viewpoints and involving different disciplines, we will get to the root of this discrepancy. Our findings indicate that the product definition, development focus, and evaluation methods do not sufficiently and adequately address the primary goal of exoskeletons. The development of such devices should be prioritized not only for the delivery of support but also for the motivation of the user. Finally, we put forward the proposal to discuss the establishment of a novel development method for the prospective elaboration of exoskeletons.
Field Test and Numerical Simulation Study on Water Pressure Distribution and Lining Deformation Law in Water-Rich Tunnel Crossing Fault Zones
Authors: Feng Zhongju , Li De , Wang Fuchun , Zhang Liang , Wang Siqi
Published: August 2024

This study investigates the water pressure distribution and deformation patterns in tunnel linings within water-rich tunnels traversing fault zones, focusing on the Gudou Mountain Tunnel. The study utilized field tests and numerical simulations to assess the water pressure distribution around test sections. Following the confirmation of consistent water pressure distribution patterns from field tests and simulations, we analyzed the deformation patterns of tunnel linings at various water levels. The results showed that water pressure is highest at the tunnel’s inverted arch and arch foot, moderately high at the vault and spandrel, and lowest at the arch waist. The sections RK51 + 590 and LK51 + 640, located on opposite sides of a fault crush zone, experience high fragmentation of surrounding rock. This allows rainfall and reservoir water to seep through fractures, causing increased water pressure and significant deformation at the inverted arch of these sections. With rising groundwater levels, deformation intensifies at the inverted arch, arch foot, and vault. The appearance of macro-cracks in these critical areas leads to groundwater seepage through the cracks, severely impacting tunnel operations. Consequently, reinforcing the inverted arch, arch foot, and vault is crucial to reduce the risk of water leakage in the tunnel.
Modeling of Coupled Structural Electromagnetic Statistical Concept for Examining Performance Sensitivity of Antenna Array to Distortion at Millimeter-Wave
Authors: Famoriji Oluwole John , Shongwe Thokozani
Published: August 2024

Millimeter-wave (mmWave) antenna arrays are pivotal components in modern wireless communication systems, offering high data rates and improved spectrum efficiency. However, the performance of mmWave antenna arrays can be significantly affected by structural distortions, such as mechanical deformations and environmental conditions, which may lead to deviations in beamforming characteristics and radiation patterns. In this paper, we present a comprehensive sensitivity study of mmWave antenna arrays to structural distortion, employing a coupled structural–electromagnetic statistical concept. The proposed model integrates structural analysis techniques with electromagnetic simulations to assess the impact of structural distortions on the performance of mmWave antenna arrays. In addition, the model incorporates random element positioning, making it easy to analyze radiation pattern sensitivity to structural deformation. Demonstrating the applicability of the model, a 10 × 10 microstrip patch antenna array is designed to assess the performance of the model with a random position error and saddle shape distortion. The results of the model are then compared against the acceptable results from the HFSS software (version 13.0), where a good agreement is observed between the two results. The results show the gain variation and sidelobe level under various degrees of distortion and random errors, respectively. These results provide a guide for design, deployment, and optimization of mmWave communication networks in real-world environments. In addition, the model provides valuable insights into the trade-offs between antenna performance, structural integrity, and system reliability, paving the way for more efficient and dependable mmWave communication systems in the era of 5G and beyond.
A Review of Sustainable Pavement Aggregates
Authors: Ramírez-Vargas Jaime R. , Zamora-Castro Sergio A. , Herrera-May Agustín L. , Sandoval-Herazo Luis C. , Salgado-Estrada Rolando , Diaz-Vega María E.
Published: August 2024

Prior research has demonstrated that incorporating solid waste from various sources, such as plastics, civil engineering waste, and industrial and mining waste, into pavement and civil works mixes has notable environmental benefits. This includes reducing the over-exploitation of aggregate banks and preventing waste materials from being deposited in open-pit landfills for extended periods. This review focuses on sustainable aggregates produced from solid waste with rheological or mechanical properties suitable for replacing conventional aggregates in asphalt or hydraulic concrete mixtures. The aim is to determine the optimal replacement percentage in the mixture to directly impact pavement performance. This review also delves into the impact on fatigue resistance and permanent deformation based on the type of waste material used in construction. Additionally, using sustainable aggregates presents added benefits for pavement binders, such as inhibiting reflection cracks, reducing traffic noise, and prolonging the service life of the pavement. However, it is crucial to study the percentage of replacement of sustainable aggregates in conjunction with other materials using mathematical models and simulations to ensure a substantial contribution to the sustainability of the construction industry.
Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review
Authors: Giannuzzi Valeria , Fatiguso Fabio
Published: August 2024

Recent advancements in digital technologies and automated analysis techniques applied to Historic Built Environment (HBE) demonstrate significant advantages in efficiently collecting and interpreting data for building conservation activities. Integrating digital image processing through Artificial Intelligence approaches further streamlines data analysis for diagnostic assessments. In this context, this paper presents a scoping review based on Scopus and Web of Science databases, following the PRISMA protocol, focusing on applying Deep Learning (DL) architectures for image-based classification of decay phenomena in the HBE, aiming to explore potential implementations in decision support system. From the literature screening process, 29 selected articles were analyzed according to methods for identifying buildings’ surface deterioration, cracks, and post-disaster damage at a district scale, with a particular focus on the innovative DL architectures developed, the accuracy of results obtained, and the classification methods adopted to understand limitations and strengths. The results highlight current research trends and the potential of DL approaches for diagnostic purposes in the built heritage conservation field, evaluating methods and tools for data acquisition and real-time monitoring, and emphasizing the advantages of implementing the adopted techniques in interoperable environments for information sharing among stakeholders. Future challenges involve implementing DL models in mobile apps, using sensors and IoT systems for on-site defect detection and long-term monitoring, integrating multimodal data from non-destructive inspection techniques, and establishing direct connections between data, intervention strategies, timing, and costs, thereby improving heritage diagnosis and management practices.
Advanced Methods for Conformable Time-Fractional Differential Equations: Logarithmic Non-Polynomial Splines
Authors: Yousif Majeed A. , Agarwal Ravi P. , Mohammed Pshtiwan Othman , Lupas Alina Alb , Jan Rashid , Chorfi Nejmeddine
Published: August 2024

In this study, we present a numerical method named the logarithmic non-polynomial spline method. This method combines conformable derivative, finite difference, and non-polynomial spline techniques to solve the nonlinear inhomogeneous time-fractional Burgers–Huxley equation. The developed numerical scheme is characterized by a sixth-order convergence and conditional stability. The accuracy of the method is demonstrated with 3D mesh plots, while the effects of time and fractional order are shown in 2D plots. Comparative evaluations with the cubic B-spline collocation method are provided. To illustrate the suitability and effectiveness of the proposed method, two examples are tested, with the results are evaluated using L2 and L∞ norms.
Revealing a Correlation between Physical Parameters and Differential Voltage Analysis of a Commercial Li-Ion Battery Based on Fiber Optic Sensors
Authors: Matuck Lucca , Ferreira Marta S. , Nascimento Micael
Published: August 2024

This work describes a specialized optical fiber hybrid sensing configuration conceived to monitor internal physical parameters (temperature and pressure) within Li-ion batteries (LiBs) and correlate them with electrochemical performance in operando. The batteries underwent thorough cycling tests under C/3 and C/5 operating rate conditions. Throughout the cycling tests, the optical fiber sensors revealed a compelling correlation between internal and external temperature behavior. Additionally, the application of differential voltage analysis derivative curves during battery operation unveiled insights into the relationship between pressure and temperature changes and the batteries’ electrochemical performance. This optical sensing approach contributes to an understanding of internal LiB dynamics, offering implications for optimizing their performance and safety across diverse applications.