bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Section Coal Pillar Deformation Prediction Based on Fiber Optic Sensing Monitoring and Machine Learning Algorithms
Authors: Zhang Dingding , Wang Yu , Yang Jianfeng , Gao Dengyan , Chai Jing
Published: October 2024

The mining face under the close coal seam group is affected by the superposition of the concentrated stress of the overlying residual diagonally intersecting coal pillar and the mining stress, which can easily cause the instability and damage of the section coal pillars during the process of mining back to the downward face. Additionally, the traditional methods of monitoring such as numerical simulation, drilling peeping, and acoustic emission fail to realize the real-time and accurate deformation monitoring of the internal deformation of the section coal pillars. The introduction of the drill-hole-implanted fiber-optic grating monitoring method can realize real-time deformation monitoring for the whole area inside the coal pillar, which solves the short board problem of coal pillar deformation monitoring. However, fiber-optic monitoring is easily disturbed by the external environment, which is especially sensitive to the background noise of the complex underground mining environment. Therefore, taking the live chicken and rabbit well of Shaanxi Daliuta Coal Mine as the engineering background, the ensemble empirical modal decomposition (EEMD) is introduced for primary noise reduction and signal reconstruction by the threshold determination (DE) algorithm, and then the singular matrix decomposition (SVD) is introduced for secondary noise reduction. Finally, a machine learning algorithm is combined with the noise reduction algorithm for the prediction of the fiber grating strain signals of coal pillar in a zone, and DBO-LSTM-BP is constructed as the prediction model. The experimental results demonstrate that compared with the other two noise reduction prediction models, the SNR of the EEMD-DE-SVD-DBO-LSTM-BP model is improved by 0.8–2.3 dB on average, and the prediction accuracy is in the range of 88–99%, which realizes the over-advanced prediction of the deformation state of the coal column in the section.
A Study on the Impact of Blue Light and Its Brightness on Long-Term Memory
Authors: Kim Jin-Ho , Lee Kee-Sun , Shim Joung-Myo , Lee Chung-Won
Published: October 2024

The purpose of this study is to evaluate the effect of blue light and its brightness on long-term memory. Blue light was tested under four distinct illuminance conditions ranging from 400 to 1000 lux, and the results were compared with those of red light as a comparison group. A total of 25 individuals, including 16 men and 9 women with a mean age of 22.04 ± 1.74 years, participated in these tests based on a within-subject research design. Initially, each participant was exposed to individual light conditions for 30 min in the morning. Subsequently, they were asked to complete learning memory tasks, which involved memorizing specific words composed of randomly chosen English consonants. Exactly 20 min after these memory tasks, their long-term memory retention was measured through recall. It was found that blue light was superior to red light in terms of long-term memory enhancement; the difference was statistically significant, with a confidence level of 95%. Moreover, brighter light environments were found to affect long-term memory performance more significantly compared to darker light environments. That said, there was no significant difference in interaction effects between light color and brightness. These findings confirm the results of previous studies highlighting the effectiveness of blue light in enhancing memory retention while also demonstrating that the degree of enhancement may vary depending on light brightness.
Accuracy of the Mindlin Model in A0 Lamb Mode Scattering from Partial Through-Thickness Damage
Authors: Lints Martin , Ratassepp Madis
Published: October 2024

Guided wave tomography is an effective non-destructive method for mapping corrosion damage in thin-walled metal structures. Its efficiency and accuracy depend on the choice of a suitable forward model and inversion method. Current techniques mainly use acoustic forward models that, while computationally efficient, are approximate and fail to accurately represent wave propagation in physical experiments, making them less suitable for inversion. This study investigates the performance of Mindlin plate theory, which accounts for through-thickness shear deformations, enabling the modeling of flexural modes in a two-dimensional (2-D) plane. The scattering of A0 mode Lamb waves from defects of varying depth, width, and shape is analyzed using finite difference and pseudospectral simulations for 2-D and three-dimensional (3-D) defects, respectively. Results from the Mindlin model are compared to finite element model simulations. It is found that Mindlin plate theory accurately represents smooth defect scattering, but is less accurate for sharp-edged defects.
Optimization of the Process for Slow-Release Urea Fertilizer with Water Absorption Based on Response Surface Methodology
Authors: Li Yan , Ma Yu , Wang Yan , Chang Fan , Dai Jiakun
Published: October 2024

Fertilizers that release nutrients slowly can provide crops with consistent nutrients, while soils with good water-holding capacity can alleviate the impact of droughts on crops. Sodium alginate/carboxymethyl starch sodium/polydopamine/urea (SCPU) is a new kind of slow-release fertilizer with water absorption property. In this study, the Box–Behnken response surface methodology (RSM) was used to reveal the effects of concentrations of sodium alginate, carboxymethyl starch sodium, urea, calcium chloride and dopamine on the encapsulation efficiency and water absorption of SCPU. The results show that the optimum preparation conditions to obtain the highest level of encapsulation efficiency (89.27%) and water absorption (167.05%) are 2.2% sodium alginate, 5% carboxymethyl starch sodium, 30% urea, 1.9% calcium chloride and 0.52% dopamine.
Ultimate Compression: Joint Method of Quantization and Tensor Decomposition for Compact Models on the Edge
Authors: Alnemari Mohammed , Bagherzadeh Nader
Published: October 2024

This paper proposes the “ultimate compression” method as a solution to the expansive computation and high storage costs required by state-of-the-art neural network models in inference. Our approach uniquely combines tensor decomposition techniques with binary neural networks to create efficient deep neural network models optimized for edge inference. The process includes training floating-point models, applying tensor decomposition algorithms, binarizing the decomposed layers, and fine tuning the resulting models. We evaluated our approach in various state-of-the-art deep neural network architectures on multiple datasets, such as MNIST, CIFAR-10, CIFAR-100, and ImageNet. Our results demonstrate compression ratios of up to 169×, with only a small degradation in accuracy (1–2%) compared to binary models. We employed different optimizers for training and fine tuning, including Adam and AdamW, and used norm grad clipping to address the exploding gradient problem in decomposed binary models. A key contribution of this work is a novel layer sensitivity-based rank selection algorithm for tensor decomposition, which outperforms existing methods such as random selection and Variational Bayes Matrix Factorization (VBMF). We conducted comprehensive experiments using six different models and present a case study on crowd-counting applications, demonstrating the practical applicability of our method. The ultimate compression method outperforms binary neural networks and tensor decomposition when applied individually in terms of storage and computation costs. This positions it as one of the most effective options for deploying compact and efficient models in edge devices with limited computational resources and energy constraints.
Exponentially Graded Auxetic Structures: An Assessment of the Shear Correction Factor and Static Deflection
Authors: Loja Maria Amélia R. , Barbosa Joaquim I.
Published: October 2024

This work aims to study the influence of the material and geometric parameters that characterize re-entrant hexagonal honeycomb auxetic structures in the maximum transverse static deflection of beams. In addition, this study considers the composition of the material through the thickness results from the mixture of a metallic phase and one of four different selected ceramics, using the exponential volume fraction law. The first-order shear deformation theory within an equivalent single-layer approach is used to assess the material and geometric parameters’ influence on the structures’ deflection. Considering this theoretical approach, the impact of the material and geometric parameters on the shear correction factors, calculated for each specific case, is also analyzed. The results allow us to conclude how the shear correction factors and the structures’ maximum static deflection are affected by the re-entrant hexagonal honeycomb auxetic cells’ aspect ratios, by the angle associated with the direction of the inclined members of the hexagonal cells and the use of materials with differentiated Poisson’s ratios.
Analysis of the Impact of Reformulation of the Recipe Composition on the Quality of Instant Noodles
Authors: Marciniak-Lukasiak Katarzyna , Durajczyk Ewelina , Lukasiak Aleksandra , Zbikowska Katarzyna , Lukasiak Piotr , Zbikowska Anna
Published: October 2024

This study aimed to evaluate how adding whey protein and transglutaminase impacts the quality of fried instant noodles. This research focused on analyzing various quality characteristics of the noodles based on the type and quantity of additives used. In the study, the following samples were produced: a control sample without additives and samples with 1, 2, 3, 4 and 5% of whey protein added, and 1 and 2% of transglutaminase were applied to each sample with whey protein addition. The following features were determined: fat content, water content, hydration time, hardness, adhesiveness, firmness, colour, browning index and a sensory evaluation of the macarons. The addition of whey protein, either alone or in combination with transglutaminase, reduced the fat content and increased the water content. The lowest fat content was obtained for the sample containing 5% whey protein and 2% transglutaminase (15.13%). The water content was observed in the range 2.53–3.72%. The hydration time of the instant noodles obtained increased with the use of more additives, but did not exceed 5 min in any of the samples tested. The use of additives affected the colour parameters and improved the textural properties of the noodles.
Relationship between Upper Limb Muscle Power and Shooting Velocity in Elite Male Youth Rink Hockey Players
Authors: Arboix-Alió Jordi , Buscà Bernat , Trabal Guillem , Sarmento Hugo , Vaz Vasco , Moreno-Galcerán Dani
Published: October 2024

The present study examined the relationship between upper limb muscle power and shooting velocity in elite male youth rink hockey players. Seventeen participants (age: 18.2 ± 1.44 years) underwent assessments of upper limb power and shooting velocity. Upper limb power was evaluated through bench press exercises, including indirect 1RM, mean power, and peak power. Shooting velocity was measured using a shooting test, including static drive shot, static slap shot, dynamic drive shot, and dynamic slap shot techniques. Results showed significant positive correlations between upper limb power and shooting velocity in static slap shot (r = 0.62, p = 0.04) and dynamic slap shot (r = 0.86; p < 0.01). Dynamic slap shot also correlated significantly with peak power (r = 0.63; p = 0.03). Differences in shooting velocity were observed among the techniques (F(3,64) = 23.7; p < 0.01, ηp2 = 0.56), with dynamic slap shot displaying the highest velocity and static drive shot the lowest. These findings highlight the positive association between upper limb muscle power and shooting velocity in elite youth rink hockey players. Developing upper limb power can enhance shooting performance. The choice of shooting technique significantly affects shooting velocity, underscoring the importance of optimizing technique for maximizing performance. These findings provide practical insights for coaches and practitioners, informing the design of targeted training programmes aimed at improving shooting velocity in rink hockey players.
Plasma, Urinary, Erythrocyte, and Platelet Concentrations of Manganese and Molybdenum in Football Players: Differences between Sexes and during the Season
Authors: Toro-Román Victor , Grijota Fco Javier , Maynar-Mariño Marcos , Campos Amalia , Martínez-Sánchez Almudena , Robles-Gil María C.
Published: October 2024

Physical activity induces modifications in the concentrations of trace mineral elements. However, studies exploring sex-related differences in manganese (Mn) and molybdenum (Mo) levels among athletes are scarce. Mn and Mo are essentials metals required for a variety of metabolic functions, including those involved in normal human development, the activation of certain metalloenzymes, energy metabolism, and immune system function. They are important cofactors for a variety of enzymes, including those involved in neurotransmitter synthesis and metabolism. The presence of molybdenum (Mo) is essential for several enzymes, including xanthine oxidase (XO), aldehyde oxidase, sulfite oxidase (SO), and the mitochondrial amidoxime reductase component (mARC). This study aimed to: (a) analyse changes in plasma, urine, erythrocyte, and platelet Mn and Mo concentrations throughout a competitive season in men’s and women’s football players, and (b) investigate sex-based discrepancies. A total of 46 football players (22 men: age; 20.62 ± 2.66 years; height; 1.76 ± 0.061 m; weight; 71.50 ± 5.93 kg, and 24 women: age; 23.21 ± 4.11 years; height; 1.65 ± 0.06 m; weight; 59.58 ± 7.17 kg) participated in this study. Three assessments were conducted throughout the competitive season. Data were collected on anthropometry, body composition, nutritional intake, physical fitness, female hormones, haematology, and the determination of Mn and Mo in different biological compartments. Regarding Mn, significant sex differences were observed in plasma, urine, and erythrocyte concentrations (p < 0.05). Moreover, significant variations were observed throughout the season in all analysed biological compartments (p < 0.05). Regarding Mo, significant sex differences were reported in plasma concentrations (p < 0.05). Similarly, there were variations throughout the season in all analysed biological compartments (p < 0.05). Plasma, urine, erythrocyte, and platelet Mn and Mo concentrations could change during a competitive season in football players. On the other hand, sex differences could exist in plasma, urine, and erythrocyte Mn concentrations in football players.
Research on Abnormal Behavior Monitoring in University Laboratories Based on Video Analysis Technology
Authors: Ying Yangwei , Wang Haotian , Zhou Hong
Published: October 2024

The safety management of laboratories is of utmost importance in the construction and management of university laboratories. Abnormal behaviors such as smoking, incorrect wearing of personal protective equipment (PPE) like lab coats, hats, masks, and gloves pose significant safety hazards. In this paper, in order to improve the level of laboratory safety management and effectively provide an alert in the case of unsafe behaviors, video analysis technology is employed to achieve abnormal behavior recognition and monitoring through steps such as human key point detection, posture estimation, and behavior recognition. Firstly, the human pose estimation algorithm YOLO is used for human detection, followed by the extraction of human key points after segmentation. Finally, spatiotemporal graph convolution is used for feature detection and classification of abnormal behaviors. The experimental results show that the accuracy of abnormal behavior detection and recognition based on human key points reaches over 85%, which is of great significance for safety management and behavior warning in university laboratories, and thus, improves the efficiency and level of laboratory safety management.
Enhancement of Robot Position Control for Dual-User Operation of Remote Robot System with Force Feedback
Authors: Huang Pingguo , Ishibashi Yutaka
Published: October 2024

We focus on dual-user operation, where two users control a single remote robot equipped with a force sensor using haptic interface devices. We employ a cooperative work in which the two users control the remote robot to collaborate with remote robot systems with force feedback to carry an object. By measuring the force acting upon the object, we aim to better understand the underlying mechanisms by which the user with lower network latency can help the other user, as observed in our previous work. We notice that with increasing network delays, the force exerted on the object tends to intensify, indicating that it becomes more challenging for users to operate the remote robot effectively as network delays increase. We also measure the force applied to the object by changing the network delays between the remote robot and the two users to clarify why the user with the lower network delay can assist the other user. We find that when the total network delay is the same, the average force magnitude and the average maximum force magnitude remain nearly identical. This is because, despite the challenges faced by the user with the larger network delay, the user with the smaller delay can operate the remote robot more easily and assist the other user. In order to reduce the force acting upon the object, we propose an enhancement method for the robot position control, which determines the position of the remote robot arm while accounting for network delay, and investigate the effects by experiment. Experimental results demonstrate that our proposed method is effective and can reduce the applied force. This is because the proposed method adjusts the ratio between the user with the lower delay and the user with the higher delay. The user with the lower delay can operate the remote robot more easily and respond to it more quickly. Our findings and proposed method can be useful in improving work accuracy and operability when designing a remote robot system with force feedback for applications.
Flutter Mechanism of a Thin Plate Considering Attack Angles
Authors: Yan Chengjun , Wang Qi , Wu Bo , Huang Lin
Published: October 2024

It is well known that the flutter performance of a section is sensitive to the changing wind angles of attack. Exploring the thin plate’s flutter mechanism under different angles of attack is excellent, which helps understand inner flutter characteristics and ensures structural safety. This study investigated flutter derivatives of a thin plate with an aspect ratio of 40 under different wind angles of attack via the forced vibration technique. The otherness of aerodynamic damping and phase lag under different wind angles, which helps in understanding the flutter mechanism, are analyzed using the bimodal-coupled flutter method. It is shown that coupled vertical–torsional flutter dominated the flutter modality under 0° and 3° wind angles of attack. The critical flutter velocity dramatically decreased with increasing wind angles of attack which is attributed to the increasing negative aerodynamic damping induced by coupled self-excited forces and the decreasing positive aerodynamic damping induced by uncoupled self-excited forces. Moreover, the vertical motion lags behind the torsional motion under the 7° angle of attack, which was totally contrary to other angles of attack. Major works in this study reveal the aerodynamic mechanism of the weakened flutter performance of thin plates under large wind angles of attack and provide a reference for the flutter analysis of thin plates.
Proactive Measures for Preventing Highway Embankment Failures on Expansive Soil: Developing an Early Warning Protocol
Authors: Nobahar Masoud , Khan Sadik
Published: October 2024

Efficient data use for early warnings is a critical component of failure management, which encompasses activities such as vulnerable zone mapping, prediction, warning elements, prevention, planning, and action. This study proposes an early warning protocol (EWP) against highway embankment (HWE) failures constructed on expansive soil, implementing in filed situ/lab testing, instrumentation, geophysical testing, 2D/3D finite element method (FEM) analysis, development of machine learning-based predictive models, and analysis of the slope vulnerability index (SVI) factor. Six referenced HWEs were investigated, instrumented, monitored, and considered to measure their evaluative parameters (soil in situ, soil index properties, and factor of safety). The field-recorded data were validated using 2D electrical resistivity imaging. The 2D/3D FEM numerical models were developed based on the field-recorded rainfall volume to analyze the flow and coupled flow deformation of the HWEs’ slopes. Predictive models were implemented to analyze the SVI, and the EWP was developed. The action plan was found to be the main component of the proposed EWP. Observing, planning, deciding, and acting are the proposed EWP’s key predecessors. The developed EWP for embankment slope failure mitigation could benefit various public and private transportation agencies.
Quantum Noise: Particles, Waves and Mixture
Authors: López-Hernández Leonardo , Lara-Bustillos Diego Alberto , Rojas-Prado Guillermo de Jesús , Cantera Josué , Hernández Edna M. , López-Aparicio Jehú , Vargas Carlos Ernesto , Velázquez Víctor
Published: October 2024

We carry out a theoretical investigation about quantum noise in coherent mixtures of states with different statistics. On the one hand, we investigate the dependence between the behavior wave-particle and associated noise. On the other hand, we are interested in studying the persistence of chaotic behavior in mixtures of states with quantum and classical behavior. For this, we perform mixtures of single photons with coherent states of different averages. We find that the noise associated with quantum chaos depends on the proportion of the quantum–classical mixture and that quantum wave states are more efficient than classical wave states in terms of their randomness properties.
Gut Microbiota as an Endocrine Organ: Unveiling Its Role in Human Physiology and Health
Authors: Pires Lara , Gonzalez-Paramás Ana Maria , Heleno Sandrina A. , Calhelha Ricardo C.
Published: October 2024

The gut microbiota, recognised for its vital functions in host health, operates as an endocrine organ, exerting systemic effects beyond the gastrointestinal tract. This “virtual organ” produces hormones that influence distal organs, including the brain. With its diverse microbial composition, the gut microbiota surpasses the biochemical complexity of traditional endocrine organs, generating neurotransmitters like GABA, dopamine, and serotonin. Despite challenges in culturing gut bacteria, advances in research methodologies have elucidated their role in behaviour, metabolism, appetite, and insulin resistance. As microbial endocrinology continues to evolve, further exploration of the intricate connections between hormones and the microbiome are anticipated, highlighting hormones’ pivotal role in the dynamic host–microbiota relationship.
Efficient Design of Reversible Adder and Multiplier Using Peres Gates
Authors: Kadbe Premanand K. , Markande Shriram D.
Published: October 2024

This paper details the approach to the efficient design and optimization of a reversible adder and multiplier utilizing Peres gates, which is a three-input, three-output gate. Peres gates are recognized for their universality and energy-efficient properties and present an intriguing option for constructing reversible circuits. Reversible logic is characterized by its ability to uniquely determine input states from output states. The design methodology involves a cascading arrangement of Peres gates, each performing a reversible XOR operation on corresponding bits of the input numbers and the carry-out from the preceding stage. This paper presents a detailed schematic representation, simulation results, and analysis of the proposed designs of different adders and multipliers, showcasing their potential for reversible applications. The integration of Peres gates in the adder and multiplier design signifies a step forward in the exploration of reversible logic circuits and their applications in contemporary computing paradigms. The overall hardware reduction is the main achievement of this research in terms of quantum cost.
Effect of Food Additive E171 and Titanium Dioxide Nanoparticles (TiO2-NPs) on Caco-2 Colon Cancer Cells
Authors: Baranowska-Wójcik Ewa , Rymuszka Anna , Sierosławska Anna , Szwajgier Dominik
Published: October 2024

The food coloring agent E171 raises many questions concerning its negative impact on human health because of the fact that it contains nanoparticle fractions (NPs, diameter < 100 nm). Numerous studies showed its influence on organisms, including the ability to disrupt the intestinal barrier. In the present study, we verified the potential toxicity and pro-inflammatory activity of three different E171 samples (containing NPs fractions) and one TiO2 NPs sample (60–600 µg mL−1) towards Caco-2 colon cancer cells. The experiments revealed no significant changes in terms of the vitality of Caco-2 cells after 24 h of exposure (XTT test). However, after 72 h, a decrease in the proliferation of Caco-2 cells caused by three TiO2 substances was observed. Moreover, deterioration of the metabolic activity of Caco-2 cells (ATP test) by all analyzed substances at 300 and 600 µg mL−1 was seen. While a 24-h exposure to each tested substance resulted in a negligible release of LDH, a prolonged exposure (72 h) indicated an elevated release of LDH, suggesting potential toxicity. All TiO2 samples induced the elevated release of two primary proinflammatory cytokines, i.e., IL-1β and TNF-α, in a dose-independent manner. The discrepancies in the results come from the differences in the share of individual sizes in four TiO2 products.
Modeling the Performance of a Multi-Hop LoRaWAN Linear Sensor Network for Energy-Efficient Pipeline Monitoring Systems
Authors: Alhomyani Haneen , Fadel Mai , Dimitriou Nikos , Bakhsh Helen , Aldabbagh Ghadah
Published: October 2024

In recent years, LoRa technology has emerged as a solution for wide-area coverage IoT applications. Deploying a LoRa single-hop network on applications may be challenging in cases of network deployments that require the installation of linear sensor network topologies covering very large distances over unpopulated areas with limited access to cellular networks and energy grids. In such cases, multi-hop communication may provide better alternative solutions to support these challenges. This research aims to study the deployment of multi-hop linear sensor networks that are energy efficient. The focus will be on assessing the coverage, throughput, and energy consumption benefits that can be achieved and the related tradeoffs that have to be considered when using multi-hop solutions. Since monitoring systems in long-distance infrastructures may benefit from solutions based on multi-hop communication, we consider oil pipeline infrastructures in the Saudi Arabian desert as a case study. An analytical model is considered for estimating the above-stated parameters and evaluating the performance of the multi-hop LoRa WSN (MHWSN) against the single-hop LoRa WSN (SHWSN). In addition, the model is used to study the tradeoffs between throughput and energy consumption in different settings of MHWSNs. Scenarios of oil pipeline monitoring systems in Saudi Arabia are specified for studying the proposed multi-hop system’s performance. The obtained results show that when we have a large-scale network, such as an oil pipeline with medium traffic load requirements, multi-hop topologies may be an efficient deployment solution.
Sustainable Building Tool by Energy Baseline: Case Study
Authors: Castrillón-Mendoza Rosaura , Rey-Hernández Javier M. , Castrillón-Mendoza Larry , Rey-Martínez Francisco J.
Published: October 2024

This study explores innovative methodologies for estimating the energy baseline (EnBL) of a university classroom building, emphasizing the critical roles of data quality and model selection in achieving accurate energy efficiency assessments. We compare time series models that are suitable for buildings with limited consumption data with univariate and multivariate regression models that incorporate additional variables, such as weather and occupancy. Furthermore, we investigate the advantages of dynamic simulation using the EnergyPlus engine (V5, USDOE United States) and Design Builder software v7, enabling scenario analysis for various operational conditions. Through a comprehensive case study at the UAO University Campus, we validate our models using daily monitoring data and statistical analysis in RStudio. Our findings reveal that model choice significantly influences energy consumption forecasts, leading to potential overestimations or underestimations of savings. By rigorously assessing statistical validation and error analysis results, we highlight the implications for decarbonization strategies in building design and operation. This research provides a valuable framework for selecting appropriate methodologies for energy baseline estimation, enhancing transparency and reliability in energy performance assessments. These contributions are particularly relevant for optimizing energy use and aligning with regulatory requirements in the pursuit of sustainable building practices.
Developing an Automatic Asbestos Detection Method Based on a Convolutional Neural Network and Support Vector Machine
Authors: Matsuo Tomohito , Takimoto Mitsuteru , Tanaka Suzuyo , Futamura Ayami , Shimadera Hikari , Kondo Akira
Published: October 2024

When buildings containing asbestos are demolished, fine asbestos fibers are released, which can result in serious adverse health effects. Therefore, leakage is monitored to prevent the dispersion of asbestos fibers. Airborne asbestos fibers are monitored via microscopic observation, which requires significant manual labor. In this study, we developed a machine-learning model to automatically detect asbestos fibers in phase-contrast microscopy images. The model was based on a pre-trained convolutional neural network as its foundation, with fully connected layers and a support vector machine (SVM) serving as classifiers. The effects of fine-tuning, class weighting, and hyperparameters were assessed to improve model performance. Consequently, the SVM was chosen as a classifier to improve overall model performance. In addition, fine-tuning improved the performance of the models. The optimized detection model exhibited high classification performance with an F1 score of 0.83. The findings of this study provide valuable insights into effectively detecting asbestos fibers.
Exploring Non-Thermal Mechanisms of Biological Reactions to Extremely Low-Frequency Magnetic Field Exposure
Authors: Radil Roman , Carnecka Lucia , Judakova Zuzana , Pobocikova Ivana , Bajtos Marek , Janousek Ladislav
Published: October 2024

The increasing evidence regarding biological effects of exposure to an extremely low frequency magnetic field is of utmost interest not only to the scientific community, but also to legislative bodies and the public. However, the research in this field is full of controversial and inconsistent results, originated from a lack of widely acceptable physical mechanisms that could sufficiently describe the principle of such a field’s action. This experimental study addresses and points to possible sources of ambiguities via investigation of the ion parametric resonance mechanism at 50 Hz frequency. The chosen methodology incorporates exposure of the Saccharomyces cerevisiae yeast strain based on an established exposure protocol with special attention to the measurement of an applied time-varying magnetic field corresponding to the ion parametric resonance requirements. Subsequently, the differences in cell growth as a reaction to changes in magnetic flux density are evaluated and statistically analyzed. It is found that fluctuations in the magnetic field within the exposure setup need to be addressed properly, since this could have an impact on replication of the experiments and reliability of the results. Furthermore, comparison of two independently performed sets of 10 experiments showed statistically significant effects even in conditions that did not fulfill the requirements of the resonance theory, putting the validity and practical application of the ion parametric resonance model into question.
An ELECTRA-Based Model for Power Safety Named Entity Recognition
Authors: Liu Peng , Sun Zhenfu , Zhou Biao
Published: October 2024

Power safety named entity recognition (NER) is essential for determining the cause of faults, assessing potential risks, and planning maintenance schedules, contributing to the comprehension and analysis of power safety documentation content and structure. Such analysis is crucial for the development of a knowledge graph within the power safety domain and the augmentation of the associated dataset. This paper introduces a power safety NER model using efficiently learning an encoder that classifies token replacements accurately (ELECTRA) model. This model employs root mean square layer normalization (RMSNorm) and the switched gated linear unit (SwiGLU) activation function, which substitutes the conventional layer normalization (LayerNorm) and the Gaussian error linear units (GeLU). This model also integrates bidirectional long short-term memory (BiLSTM) with conditional random fields (CRF) to bolster performance in NER tasks. Experimental results show that the improved ELECTRA model achieved an F1 value of 93% on the constructed power safety NER dataset. It outperforms the BERT-BiLSTM-CRF model, achieving a 3.3% performance improvement.
Perspectives of Hydrogen Generation in Cavitation–Jet Hydrodynamic Reactor
Authors: Mamytbekov G. K. , Danko I. V. , Beksultanov Zh. I. , Nurtazin Y. R. , Rakhimbayev A.
Published: October 2024

The article investigates the potential for producing hydrogen by combining the methods of water splitting under cavitation and the chemical activation of aluminum in a high-speed cavitation–jet flow generated by a specialized hydrodynamic reactor. The process of cavitation and water spraying causes the liquid heating itself until it reaches saturated vapor pressure, resulting in the creation of vapor–gaseous products from the splitting of water molecules. The producing of vapor–gaseous products can be explained through the theory of non-equilibrium low-temperature plasma formation within a high-speed cavitation–jet flow of fluid. Special focus is also given to the interactions occurring at the interface boundary phase of aluminum and liquid under cavitation condition. The primary solid products formed on aluminum surfaces are bayerite, copper oxides (I and II), iron carbide, and a compound of magnesium oxides and aluminum hydroxide. A high hydrogen yield of 60% was achieved when using a 0.1% sodium hydroxide solution as a working liquid compared to demineralized water. Moreover, hydrogen methane was also detected in the volume of the vapor–gas mixture, which could be utilized to address the challenges of decarbonization and the recycling of aluminum-containing solid industrial and domestic waste. This work provides a contribution to the study of the mechanism of hydrogen generation by cavitation–jet processing of water and aqueous alkali solutions, in which conditions are created for double cavitation in the cavitation–jet chamber of the hydrodynamic reactor.
Analysis, Generation, and Validation of New Boards for the Game Micro Robots
Authors: Silva Kevin , Besoain Felipe , Barriga Nicolas A.
Published: October 2024

This document presents a study of the board game Micro Robots and the development of a pipeline for the generation and validation of new boards of variable sizes. The implementation was carried out in the C# language, using graph theory and a depth-first search algorithm to explore various board configurations and their possible solutions. The main objective is the creation of boards that are different from the original ones, offering greater variety in the gaming experience. The impact of the modifications on the game’s dynamics and complexity was evaluated by comparing the generated boards with those from the commercial game. The results show that no major differences were found between the new boards’ structure and the original ones, maintaining an average complexity across all configurations. This work not only contributes to the study of board game design, but also opens new opportunities for innovation in this field.
CL-YOLOv8: Crack Detection Algorithm for Fair-Faced Walls Based on Deep Learning
Authors: Li Qinjun , Zhang Guoyu , Yang Ping
Published: October 2024

Cracks pose a critical challenge in the preservation of historical buildings worldwide, particularly in fair-faced walls, where timely and accurate detection is essential to prevent further degradation. Traditional image processing methods have proven inadequate for effectively detecting building cracks. Despite global advancements in deep learning, crack detection under diverse environmental and lighting conditions remains a significant technical hurdle, as highlighted by recent international studies. To address this challenge, we propose an enhanced crack detection algorithm, CL-YOLOv8 (ConvNeXt V2-LSKA-YOLOv8). By integrating the well-established ConvNeXt V2 model as the backbone network into YOLOv8, the algorithm benefits from advanced feature extraction techniques, leading to a superior detection accuracy. This choice leverages ConvNeXt V2’s recognized strengths, providing a robust foundation for improving the overall model performance. Additionally, by introducing the LSKA (Large Separable Kernel Attention) mechanism into the SPPF structure, the feature receptive field is enlarged and feature correlations are strengthened, further enhancing crack detection accuracy in diverse environments. This study also contributes to the field by significantly expanding the dataset for fair-faced wall crack detection, increasing its size sevenfold through data augmentation and the inclusion of additional data. Our experimental results demonstrate that CL-YOLOv8 outperforms mainstream algorithms such as Faster R-CNN, YOLOv5s, YOLOv7-tiny, SSD, and various YOLOv8n/s/m/l/x models. CL-YOLOv8 achieves an accuracy of 85.3%, a recall rate of 83.2%, and a mean average precision (mAP) of 83.7%. Compared to the YOLOv8n base model, CL-YOLOv8 shows improvements of 0.9%, 2.3%, and 3.9% in accuracy, recall rate, and mAP, respectively. These results underscore the effectiveness and superiority of CL-YOLOv8 in crack detection, positioning it as a valuable tool in the global effort to preserve architectural heritage.
Exploring the Adsorption Behavior of Organic UV Filter on Carbon-Based Materials as Potential Carriers of Organic Contaminants in the Aquatic Environment
Authors: Beljin Jelena , Kragulj Isakovski Marijana , Simetić Tajana , Đukanović Nina , Molnar Jazić Jelena , Maletić Snežana , Vujić Maja
Published: October 2024

Environmental pollution poses significant risks to human health and ecosystems, necessitating costly and time-consuming remediation efforts. Consequently, there’s a growing interest among researchers in developing and utilizing next-generation materials. Carbon-based materials have emerged as promising candidates due to their environmentally friendly nature, although their application presents both positive and negative aspects, as evidenced by existing literature. A diverse range of low-cost carbonaceous sorbents, like biochars, have been investigated for their suitability in water treatment. Given the substantial volume of agricultural waste biomass generated globally, the cost-effective production of these materials from residual biomass holds promise for addressing additional environmental challenges, such as biomass waste management. Various biochars derived from corn, hemp, and straw were studied to evaluate the adsorption potential for removing a commonly used organic UV filter 3-(4′-methylbenzylidene)-camphor (4-MBC). The adsorption isotherms obtained were well-described by the Freundlich model, with nonlinearity values below 0.9. Generally, all investigated adsorbents exhibited a higher affinity for 4-MBC, underscoring the importance of such research in identifying safe adsorbents for water remediation purposes. Moreover, this paper also tackles the interactions between 4-MBC and microplastics as polymer carbon-based materials, indicating the highest adsorption capacity of polyethylene terephthalate.
Research on High-Strength Economic Support Technology for Soft Rock Roadway with Roof Drenching under Thin Bedrock Irregular Surface
Authors: Wang Junfeng , Tai Lianhai , Li Chong , Qu Qundi , Yu Xiaoxiao , Liu Yitao , Yao Wei
Published: October 2024

The control of soft surrounding rock stability has always been a hot academic issue. Soft rock has poor stability and low strength, and the deformation of a soft rock tunnel becomes more serious after it is affected by water for a long time. In this paper, the Jintong Coal Mine is taken as the research object, and nondestructive immersion experiments are used to study the change in mechanical properties of rock after being affected by water. The FLAC numerical model is used to analyze the stress evolution characteristics of the surrounding rock after being affected by water, and the results of the study show that the water absorption of siltstone is always higher than that of coarse-grained sandstone, and the uniaxial compressive strength of siltstone and coarse-grained sandstone decreases by 54.59% and 67.99%, respectively, under a state of saturated water compared with that under a state of dryness. Influenced by a T-shaped surface, the maximum principal stress concentration area occurs in the rock layer below the T-shaped surface and outside the joint. Concentrations of maximum shear stress occur within the “T” channel. Vertical stress concentration zones occur at the higher ground level and the bottom of the slope. The maximum shear stress of the roof fluctuates before the face reaches the surface of the “1” section, and continues to increase with and continues to increase with the distance of the face. After entering below the surface of the “1” section, the maximum shear stress of the roof increases rapidly, and the influence range is about 24 m. The maximum shear stress distribution plays a dominant role in the stability of the surrounding rocks of the two roadways. We analyze the principle of high-strength economic support, propose a “four-in-one” surrounding rock control technology based on “controlled hydrophobicity, structural adjustment, district management, and gradient control”, and propose a surrounding rock control scheme of district management. The measured data on site show that the roadway surrounding the rock is reasonably controlled. This provides a reference for the stable control of the surrounding rock of the roadway under similar conditions.
Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
Authors: Shao Mengmeng , Xie Xiao , Li Kaiyuan , Li Changgui , Zhou Xiran
Published: October 2024

Coastal land use represents the combination of various land cover forms in a coastal area, which helps us understand the historical events, current conditions, and future progress of a coastal area. Currently, the emergence of high-resolution optical satellite images significantly extends the scope of coastal land cover recognition, and deep learning models provide a significant possibility of extracting high-level abstract features from an optical satellite image to characterize complicated coastal land covers. However, recognition systems for labeling are always defined differently for specific departments, organizations, and institutes. Moreover, considering the complexity of coastal land uses, it is impossible to create a benchmark dataset that fully covers all types of coastal land uses. To improve the transferability of high-level features generated by deep learning to reduce the burden of creating a massive amount of labeled data, this paper proposes an integrated framework to support semantically enriched coastal land use recognition, including foundation model-powered multi-label coastal land cover classification and conversion from coastal land cover mapping into coastal land use semantics with a vector space model (VSM). The experimental results prove that the proposed method outperformed the state-of-the-art deep learning approaches in complex coastal land use recognition.
Dynamic Characteristics Analysis of Cylindrical Roller Bearing with Dimensional Deviations in Cage Pocket
Authors: Xin Jiajia , Wang Zhi , Hao Xu , Qi Xiaoye , Wang Yongjie , Wen Baogang
Published: October 2024

Dimensional deviations in the cage pocket of a roller bearing can significantly affect the bearing’s dynamic performance, directly determining the positional stability of the roller. These deviations can result in roller misalignment, increasing friction and wear. Deviations arise from machining errors and deformation during motion, etc. A dynamic model of a cylindrical roller bearing that accounts for cage flexibility was developed to explore the impact of deviations. The flexible cage provides a more realistic representation compared to the rigid cage. The effects of deviations in the length and width of the cage pocket on the bearing’s dynamic behavior were analyzed, and the results show that deviations in cage pocket dimensions lead to notable changes in bearing dynamics. Specifically, when the length deviation is negative and increasing, the amplitude of cage motion decreases, while both transitional and rotational speeds rise. It also causes greater fluctuations in the rotational speeds of the inner ring and rollers. Conversely, the cage’s equivalent stress and the contact load decrease and the amplitude of cage motion increases with increases in width deviation.
Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
Authors: Han Dae Cheol
Published: October 2024

Accurate traffic volume data are crucial for effective traffic management, infrastructure development, and demand forecasting. This study addresses the challenges associated with traffic volume data collection, including, notably, equipment malfunctions that often result in missing data and inadequate anomaly detection. We have developed a deep-learning-based model to improve the reliability of predictions for annual average daily traffic volume. Utilizing a decade of traffic survey data (2010–2020) from the Korea Institute of Civil Engineering and Building Technology, we constructed a univariate time series prediction model across three consecutive sections. This model incorporates both raw and adjusted traffic volume data from 2017 to 2019, employing long short-term memory (LSTM) techniques to manage data discontinuities. A power function was integrated to simulate various error correction scenarios, thus enhancing the model’s resilience to prediction inaccuracies. The performance of the model was evaluated using certain metrics, such as the mean absolute error, the root mean squared error, and the coefficient of determination, thus validating the effectiveness of the deep learning approach in refining traffic volume estimations.