Appl. Sci., Volume 14, Issue 5 (March-1 2024) – 541 articles

The task of aspect-based sentiment analysis (ASBA) is to identify all the sentiment analyses expressed by specific aspect words in the text. How to identify specific objects (i.e., aspect words), describe the modifiers of the specific objects (i.e., opinion words), and judge the sentiment analysis expressed by opinion words (sentimental classification) in one step has become a focus of research in ASBA. ASTE (Aspect Sentiment Triplet Extraction) based on DREN (Deep Relationship Enhancement Networks) has been proposed in this paper. It aims to extract the aspect words and opinion words in the review text in one-step. They can judge the sentiment analysis expressed by the opinion words. Therefore, the study defines ten kinds of word relations; then, the study uses the parts of the speech feature, syntactic feature, relative position feature and tree distance relative feature to enhance the word representation relationship, which enriches the table of information in the relational matrix. Secondly, based on the word representation of BERT and GCN, the structural information of the texts are extracted; then, further extraction of higher-level word semantic information and word relationship information through SWDA (Sliding Window Dilated Attention) occurs, as SWDA can capture the multi-granularity relationship in words. Finally, the experimental results show that the proposed method is effective. Full article

Lateral overturns are the most frequent fatal accidents involving tractors. A tractor being able to travel safely on uneven or sloped terrain is still an open field of investigation. The design concept of a four-wheel-drive tractor that can traverse hilly and mountainous areas is described. The tractor’s locomotion system can actively adjust its roll angle by using the attitude adjustment mechanisms equipped on the rear wheels. With double quadrangle mechanisms, the front axle can cooperate with the rear axle to adjust this tractor’s attitude. This tractor can also level its body, steer, and transmit power. The principles and configurations of the two axles are presented. A mathematics/mechanical model of a tractor on lateral slopes was developed. This model considers the relationships of the ground supporting forces when the tractor adjusts its roll angle. Combining the model of specific attitude adjustment mechanisms and the above mechanics model, the lateral stability analysis associated with the active input of the attitude adjustment mechanism is conducted. The reliability of the proposed model is discussed based on a comparison of slope traversing experiments and numerical simulations. This designed tractor has potential application in the fields of hilly and mountainous terrains. The results show that posture/configuration adjustment is a positive way to enhance tractor lateral overturn stability. Full article

The introduction of a dynamic model in robot trajectory tracking control design can significantly improve its trajectory tracking accuracy, but there are many uncertainties in the robot dynamic model which can be dealt with through robust control and adaptive control. The prevailing robust control as well as adaptive control methods require real-time computation of robot dynamics, but the extreme complexity of the robot dynamics equations makes it difficult to apply these methods in real industrial systems. To this end, this article proposes a robust adaptive control method based on the desired trajectory, which uses the desired trajectory to compute most of the control terms offline, including the robot’s nominal dynamics and regression matrices, and substantially reduces the need for real-time computation of the feedback signals. The robust term modifies the perturbation of the inertial parameters of the links, the adaptive term learns the friction coefficients of the joints online, and an additional compensation term is designed to satisfy the Lyapunov stability condition of the system. Finally, taking a universal manipulator as the experimental platform, the control performances of different control methods are compared to show the feasibility of the controller and the effective reduction in real-time computational complexity. Full article

The task of 3D reconstruction of urban targets holds pivotal importance for various applications, including autonomous driving, digital twin technology, and urban planning and development. The intricate nature of urban landscapes presents substantial challenges in attaining 3D reconstructions with high precision. In this paper, we propose a semantically aware multi-view 3D reconstruction method for urban applications which incorporates semantic information into the technical 3D reconstruction. Our research primarily focuses on two major components: sparse reconstruction and dense reconstruction. For the sparse reconstruction process, we present a semantic consistency-based error filtering approach for feature matching. To address the challenge of errors introduced by the presence of numerous dynamic objects in an urban scene, which affects the Structure-from-Motion (SfM) process, we propose a computation strategy based on dynamic–static separation to effectively eliminate mismatches. For the dense reconstruction process, we present a semantic-based Semi-Global Matching (sSGM) method. This method leverages semantic consistency to assess depth continuity, thereby enhancing the cost function during depth estimation. The improved sSGM method not only significantly enhances the accuracy of reconstructing the edges of the targets but also yields a dense point cloud containing semantic information. Through validation using architectural datasets, the proposed method was found to increase the reconstruction accuracy by 32.79% compared to the original SGM, and by 63.06% compared to the PatchMatch method. Therefore, the proposed reconstruction method holds significant potential in urban applications. Full article

Demographic change confronts us with an ever-increasing number of elderly people who face isolation and socialization issues. Background: The main challenge of this study is to inject emotional and aesthetic aspects into the design process of a virtual reality (VR) social space for the elderly. In this context, we asked architects and artists to improve the perception elderly people have of their way of communicating with others. Artists, in collaboration with computer engineers, designed experiences that evoke positive cognitive and emotional feelings and memories by following design trends and aesthetic values likely to be appreciated by older people, which were integrated in VR. Methods: We approached our goal by implementing an innovative art-driven methodology, using a plethora of technologies and methods, such as VR, artificial intelligence algorithms, visual analysis, and 3D mapping, in order to make design decisions based on a detailed understanding of the users’ preferences and collective behavior. Results: A so-called virtual village “Cap de Ballon” was co-created, having a public space inspired by the villages of Santorini and Meteora and a private space inspired by the 3D scanning of an elderly person’s apartment. Conclusions: The overall concept of the VR village‘s utility, design, and interior design were appreciated by the end users and the concept was evaluated as original and stimulating for creativity. Full article

The present paper describes a comparison of the efficiency of different types of rotors used in the refining of aluminium melt at a foundry degassing unit (FDU). Physical modelling was used to obtain data for six different rotor types under defined experimental conditions. In order to evaluate the data from the physical model, an evaluation method based on the interpolation of degassing curves was developed in such a way that the resulting characteristic can be expressed by a single parameter. Using the new methodology, the datasets were replaced by a single dimensionless parameter, a, which characterizes the rotor performance at a given gas flow rate. Based on the comparison of these performance parameters, it was possible to mutually compare the rotor efficiency depending on the selected conditions. The comparison is also demonstrated on the expected degassing time to a certain required concentration. Based on the physical model results, the study found that rotor D had the highest degassing efficiency, followed by rotors F and A. Rotors B and E had similar efficiency at a flow rate of 17 Nl·min⁻¹. However, rotor B showed better efficiency at higher inert gas flow rates (19 and 21 Nl·min⁻¹), while rotor E showed better efficiency at lower flow rates (13 and 15 Nl·min⁻¹). Full article

As important load-bearing structures, suspension cables have been widely used in suspension bridges, engineering ropeways, cable suspension systems and other special equipment. Their dynamic problems have always been a research hotspot. Especially for complex cable systems such as engineering ropeways and cable lifting equipment, there will be moving loads acting on multi-span continuous friction-slip cable structures, resulting in nonlinear coupled vibration. Therefore, few scholars have studied how to calculate the nonlinear coupling vibration effect between such moving loads and multi-span continuous cables considering friction slip. Therefore, this paper proposes the use of the combination of the direct stiffness method and the Newmark-β integration method to solve the nonlinear system of equations of motion, which can be derived from the coupled vibration response between the moving load and the main cable. The corresponding calculation program is prepared. Combined with the dynamic load test and simulation results of engineering cases, the correctness and reasonableness of the coupled vibration equations and the program can be verified through comparative analysis. The results show that the calculation results of the self-programmed program are in good agreement with the dynamic load test results, in which the maximum error of the vertical displacement in the span is −4.40% and 0.86%, and the error of the static calculation reaches −13.90%. The impact effect is more obvious when hoisting the weight out of the pulling cable, in which the impact coefficient of the main cable can be up to 2.0. The impact coefficient of the deviation of the cable tower is 4.0. During the traveling process of the moving load, the vertical downward deflection of the main cable at the action point is the largest, and the upward deflection is in the region of 0.2~0.8L from the action point. Full article

With recent technological advancements, the commercialization of autonomous vehicles (AVs) is expected to be realized soon. However, it is anticipated that a mixed traffic of AVs and human-driven vehicles (HVs) will persist for a considerable period until the Market Penetration Rate reaches 100%. During this phase, AVs and HVs will interact and coexist on the roads. Such an environment can cause unpredictable and dynamic traffic conditions due to HVs, which results in traffic problems including traffic congestion. Therefore, the routes of AVs must be controlled in a mixed traffic environment. This study proposes a multi-objective vehicle routing control method using a deep Q-network to control the driving direction at intersections in a mixed traffic environment. The objective is to distribute the traffic flow and control the routes safely and efficiently to their destination. Simulation results showed that the proposed method outperformed existing methods in terms of the driving distance, time, and waiting time of AVs, particularly in more dynamic traffic environments. Consequently, the traffic became smooth as it moved along optimal routes. Full article

Aiming at solving the problems of local halo blurring, insufficient edge detail preservation, and serious noise in traditional image enhancement algorithms, an improved Retinex algorithm for low-light mine image enhancement is proposed. Firstly, in HSV color space, the hue component remains unmodified, and the improved multi-scale guided filtering and Retinex algorithm are combined to estimate the illumination and reflection components from the brightness component. Secondly, the illumination component is equalized using the Weber–Fechner law, and the contrast limited adaptive histogram equalization (CLAHE) is fused with the improved guided filtering for the brightness enhancement and denoising of reflection component. Then, the saturation component is adaptively stretched. Finally, it is converted back to RGB space to obtain the enhanced image. By comparing with single-scale Retinex (SSR) algorithm and multi-scale Retinex (MSR) algorithm, the mean, standard deviation, information entropy, average gradient, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) are improved by an average of 50.55%, 19.32%, 3.08%, 28.34%, 29.10%, and 22.97%. The experimental dates demonstrate that the algorithm improves image brightness, prevents halo artifacts while retaining edge details, reduces the effect of noise, and provides some theoretical references for low-light image enhancement. Full article

Building a new tunnel adjacent to an existing tunnel has become a common means of transformation in engineering. Existing tunnels are prone to some deterioration, such as cavities and cracks under long-term traffic load. This kind of deterioration tunnel is prone to collapsing under the action of blasting. Therefore, the vibration caused by blasting should be strictly controlled. Based on the reconstruction project of the Bo Jiling Tunnel, this paper puts forward the method of mechanical cutting in a central position combined with an ordinary detonator to reduce blasting vibrations. ANSYS/LS-DYNA version 19.2, was used to simulate two conditions of full-section blasting and central mechanical cutting blasting. By comparing the stress and velocity of the existing tunnel, the damping effect of mechanical cutting blasting is analyzed. Via field experiments, the superiority of the mechanical cutting method in reducing blasting vibration is further discussed. At the same time, the relationship between the main vibration frequency and the peak velocity of the existing deterioration tunnel is obtained by wavelet packet analysis of the field experimental data. The frequency band energy distribution in each direction of vibration velocity is also obtained. The results show that the central mechanical cutting increases the blasting free surface, and the mechanical cutting method reduces the vibration velocity by 36.3%. The third frequency band (31.25~46.875 Hz) is the most concentrated, which is the dominant frequency band of the signal. The novelty of this paper is to propose mechanical cutting of the central hole instead of traditional blasting for existing deterioration tunnels. The feasibility of this method is verified by numerical simulation and field tests. The relationship between peak vibration velocity, band energy, and tunnel frequency is clarified, which can better control blasting vibration and ensure the safety of existing deterioration tunnels. Full article

Global neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, but it has remained challenging to bridge knowledge between empirically tractable scales. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states with microscopic changes at the level of cells. In particular, adaptive exponential integrate-and-fire (AdEx) mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains red heavy and vast areas of the parameter space remain unexplored. In this work, we report that our HPC framework, a modular set of methods used here to implement the TVB-AdEx model for the graphics processing unit (GPU) and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating a finer-resolution exploration of vast parameter spaces as well as longer simulations that were previously near impossible to perform. Comparing our GPU implementations of the TVB-AdEx framework with previous implementations using central processing units (CPUs), we first show correspondence of the resulting simulated time-series data from GPU and CPU instantiations. Next, the similarity of parameter combinations, giving rise to patterns of functional connectivity, between brain regions is demonstrated. By varying global coupling together with spike-frequency adaptation, we next replicate previous results indicating inter-dependence of these parameters in inducing transitions between dynamics associated with conscious and unconscious brain states. Upon further exploring parameter space, we report a nonlinear interplay between the spike-frequency adaptation and subthreshold adaptation, as well as previously unappreciated interactions between the global coupling, adaptation, and propagation velocity of action potentials along the human connectome. Given that simulation and analysis toolkits are made public as open-source packages, this framework serves as a template onto which other models can be easily scripted. Further, personalized data-sets can be used for for the creation of red virtual brain twins toward facilitating more precise approaches to the study of epilepsy, sleep, anesthesia, and disorders of consciousness. These results thus represent potentially impactful, publicly available methods for simulating and analyzing human brain states. Full article

Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person’s life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient’s survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called “SETL_BMRI”, which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model’s effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy. Full article

In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities. Full article

Pathogenic microorganisms surviving in dry products have regularly led to recalls and foodborne disease outbreaks. Therefore, the microbiological quality of 61 dried mushrooms samples purchased online and in supermarkets were analyzed. Counts of aerobic mesophiles (AMCs), Enterobacteriaceae (EB), yeasts and molds, presumptive Bacillus cereus (pBC), the presence of Salmonella spp., and L. monocytogenes were investigated. Isolates of pBC were screened for their partial panC gene sequences and their toxin genes’ profiles. The microbiological quality of the dried mushrooms investigated in this study was generally found to be acceptable. Average AMCs, EB, yeasts, and molds were 3.9 log, 1.1 log, 1.6 log, and 1.5 log cfu/g, respectively. All mushroom samples tested negative for Salmonella spp. and L. monocytogenes. Presumptive BC were detected in 59.0% of the samples, but the contamination level was low (1.0 to 3.4 log cfu/g). None of the isolates were positive for the ces gene. Incomplete labeling was found in 45.9% of the samples, mainly in the form of missing heating instructions (31.1%) and/or country of origin (16.3%). Contamination by pathogens can occur in dried mushrooms. Adequate information on home cooking practices is essential to reduce the risk of foodborne illness to the consumer and to provide a safe food product. Full article

A control method for a cable-driven robot in a teleoperation system is proposed using the hardware-in-the-loop (HIL) simulation technique. The main components of the teleoperated robotic system are a haptic device, also called a delta robot, and a cable-driven hyper-redundant (CDHR) robot. The CDHR manipulator has higher flexibility and multiple degrees of freedom (DOF), and, therefore, its inverse kinematics are complex. For this reason, the Jacobian method is used in place of the conventional method to calculate the inverse kinematics. Moreover, the two robots constituting the telerobotic system are different in terms of their mechanical structures and workspaces. Therefore, the position mapping method is applied to ensure that the two workspaces are utilized together. However, a singularity area appears when the mapping parameter is adjusted to expand the workspace. Therefore, a haptic algorithm is proposed to prevent the robot from moving into the singularity region and generate force feedback at the end-effector of the haptic device to warn the operator. Because experimental verification of this control strategy is difficult, the HIL technique is used for demonstration in this study to ensure stability and safety before implementation of the method at the experiment scale. The CDHR robot is designed using SolidWorks 2021. Then, the Simscape model is used to simulate the telerobotic system. In addition, the protocol between the haptic device and the laptop is programmed using C/C++ language to facilitate communication with the CDHR robot in MATLAB Simulink 2022a. A few trials are conducted to evaluate and demonstrate the effectiveness of the proposed method. Full article

In passive seismic exploration, the number and location of underground sources are very random, and there may be few passive sources or an uneven spatial distribution. The random distribution of seismic sources can cause the virtual shot recordings to produce artifacts and coherent noise. These artifacts and coherent noise interfere with the valid information in the virtual shot record, making the virtual shot record a poorer presentation of subsurface information. In this paper, we utilize the powerful learning and data processing abilities of convolutional neural networks to process virtual shot recordings of sources in undesirable situations. We add an adaptive attention mechanism to the network so that it can automatically lock the positions that need special attention and processing in the virtual shot records. After testing, the trained network can eliminate coherent noise and artifacts and restore real reflected waves. Protecting valid signals means restoring valid signals with waveform anomalies to a reasonable shape. Full article

After nearly a decade of rapid development, woody biomass has been widely used in Japan for power generation and heating. However, it has faced bottlenecks in recent years, leading to a decline in its popularity. This study aimed to elucidate the current status of woody biomass utilization in Japan by reviewing relevant research papers on upstream resource supply and downstream case studies in the supply chain. The supply potential of woody biomass estimated by reviewed articles ranges from 1.2 to 5.5 m³/year/ha, yet a significant portion of this potential cannot be exploited. The utilization of government subsidies, mechanization, and aggregated forests can substantially enhance the availability. The utilization of woody biomass has garnered widespread attention from the Japanese government and private enterprises, presenting an economic impact ranging from 66 to 249 million JPY/t, along with a GHG emission reduction spanning from −17.29 to 202.44 kg-CO_2eq/GJ. However, balancing cost and scale remains the primary challenge facing woody biomass utilization in Japan. Full article

Ensuring noise immunity is one of the main tasks of radio engineering and telecommunication. The main task of signal receiving comes down to the best recovery of useful information from a signal that is destructed during propagation and received together with interference. Currently, the interference and noise control comes to the fore. Modern elements and methods of processing, related to intelligent systems, strengthen the role of the verification and recognition of targets. This makes noise control particularly relevant. The most-important quantitative indicator that characterizes the quality of the useful signal is the signal-to-noise ratio. Therefore, determining the noise parameters is very important. In the present paper, a signal model is used in the form of an additive mixture of useful signals and Gaussian noise. It is an ordinary model of a received signal in radio engineering and communications. It is shown that the phase portrait of this signal has the shape of an ellipse at the low noise level. For the first time, an expression of the width of the ellipse line is obtained, which is determined by the noise dispersion. Currently, in electroencephalography, diagnosis is based on the Fourier transform. But, many brain diseases are not detected by this method. Therefore, the search and use of other methods of signal processing is relevant. Full article

The aim of this study was to morphologically evaluate the differences in the cutting surfaces of bone segments obtained by reciprocating saw (RS) and two piezosurgical devices (Piezosurgery Medical—PM—and Piezosurgery Plus—PP) in ex vivo human fibulae. The ultimate goal was to identify the presence of debris, scratches, and microcracks on the cutting surface that might affect bone healing, a key aspect in oral and maxillofacial surgery. Ten patients who underwent a microsurgical reconstruction of the mandible with a free fibula flap were enrolled. The fibula segments usually discarded after surgery were cut using RS, PM, and PP, obtaining transverse sections to analyze under an environmental scanning electron microscope to perform a histomorphological qualitative evaluation. Bone surfaces cut with the RS presented several scratches, and haversian canals were frequently filled with bone debris/chips. On the contrary, PM and PP devices produced smoother and sharper cutting surfaces, with lower production of bone debris/chips, preventing vascular spaces’ closure. Microcracks were found in both PM and PP cut specimens, and they could be associated with the triggering of bone remodeling, thus improving the formation of new bone, while their presence was rarely observable in RS cut samples. The use of piezosurgical devices showed superior performance, providing cleaner and smoother cutting surfaces that favor vascularization and bone remodeling; altogether, these processes could lead to accelerated bone healing, a fundamental goal in all surgical procedures that involve bone cutting. Full article

Various abnormal scenarios might occur during the shield tunneling process, which have an impact on construction efficiency and safety. Existing research on shield tunneling construction anomaly detection typically designs models based on the characteristics of a specific anomaly, so the scenarios of anomalies that can be detected are limited. Therefore, the research objective of this article is to establish an accurate anomaly detection model with generalization and identification capabilities on multiple types of abnormal scenarios. Inspired by energy dissipation theory, this paper innovatively detects various anomalies in the shield tunneling process from the perspective of energy consumption and designs the AD_SI model (Anomaly Detection and Scenario Identification model of shield tunneling) based on machine learning. The AD_SI model first monitors the shield machine’s energy consumption status based on the VAE-LSTM (Variational Autoencoder–Long Short-Term Memory) algorithm with a dynamic threshold, thereby detecting abnormal sections. Secondly, the AD_SI model uses the correlation of construction parameters to represent different known scenarios and further clarifies scenarios of the abnormal sections, thus achieving anomaly identification. The application of the AD_SI model in a shield tunneling construction project demonstrates its capability to accurately detect and identify different anomalies, with a recall value exceeding 0.9 and F1 exceeding 0.8, thereby providing guidance for accurately detecting multiple types anomaly scenarios in practical applications. Full article

This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) campus in Taiwan. Firstly, the proposed wireless heterogeneous multi-sensor module aggregates BME680, SCD30, PMS7003, and BH1750 sensors with a TTGO ESP32 Wi-Fi device based on the I²C and UART interface standards of series communication. Through the DYU-802.1X Wi-Fi network with the WPA2 Enterprise security directly, the wireless multi-sensor monitoring module further forwards the observation data of environmental conditions on campus via the DYU-802.1X Wi-Fi network to the public ThingSpeak IoT platform, which is a cloud service platform to aggregate, visualize, and analyze live sensing data of air quality index (AQI), concentrations of PM_1.0/2.5 and CO₂, brightness, ambient temperature, and relative humidity (RH). The results illustrate the proposed DYU Air Box for monitoring the indoor environmental conditions on campus and validate them with sufficient accuracy and confidence with commercialized measurement instruments. In this work, the wireless smart environment-monitoring IoT system features monitoring and automatic alarm functions for monitoring AQI, CO₂, and PM concentrations, as well as ambient illumination, temperature, and RH parameters and collaboration and interoperability through the Enterprise Intranet. All the organizational stakeholders interested in the environmental conditions of the DYU campus can openly access the information according to their interests. In the upcoming future, the information of the environmental conditions in the DYU campus will be developed to be simultaneously accessed by all the stakeholders through both the public ThingSpeak IoT platform and the private EMIoT system. Full article

This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit’s electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (SSC), Acidity and pulp percentage (PP) in passion fruit. The investigation entailed measuring the relative dielectric constant (ε′) and dielectric loss factor (ε″) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both ε′ and ε″ showed a declining trend. Moreover, there was a discernible correlation between the fruit’s physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the SSC, Acidity and PP in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the SSC (R² = 0.9240, RMSE = 0.2595) and the PP (R² = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting Acidity (R² = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05–6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit. Full article

Colostrum, the first secretion of mammalian breasts after giving birth, contains a wealth of components believed to have a beneficial effect on human skin, including lactoferrin, immunoglobulin (Ig)A, beta-carotene, fat-soluble vitamins, and zinc. The present study examines the effect of a cosmetic preparation containing 15% lyophilisate of sheep colostrum on acne skin. A group of 27 volunteers with mild or moderate acne applied the cream twice a day for eight weeks. Before and after using the cream, the level of skin hydration, sebum level, and TEWL were measured using a standardized Courage–Khazaka Multi Probe Adapter. The participants also completed a survey rating the effects of using the cosmetic preparation. Regular application of the cream with sheep colostrum resulted in an objective improvement in hydration and TEWL and a reduction in sebum; this is extremely desirable for acne-prone skin. Three-quarters of the participants reported that the tested cream reduced acne lesions (blackheads, papules, pustules, and erythema) by around 40%. Moreover, the cosmetic preparation improved hydration by 40% and reduced seborrhea by 29% in 82% of subjects and kept the skin in good condition in 90%. As the cream improves the hydrobarrier and overall condition of the skin, it could serve as an addition to local acne treatment, e.g., with retinoids. Full article

This work applies an adaptive PD controller based on MRAS (Model Reference Adaptive System) using Lyapunov theory with sliding mode theory to a Fixed-wing MAV (Mini Aerial Vehicle). The objective is to design different adjustment mechanisms to obtain a robust adaptive control law in the presence of unknown perturbation due to wind gusts. Four adjustment mechanisms applied to an adaptive PD controller are compared. The adjustment mechanisms are Lyapunov theory, Lyapunov theory with first-order sliding mode, Lyapunov theory with second-order sliding mode, and Lyapunov theory with high-order sliding mode. Finally, after several simulations, a significant reduction and almost elimination of the unknown perturbations are presented with the addition of the sliding mode theory in the design of the adjustment mechanism for the adaptive PD controller. Full article

Convolutional Neural Networks (CNNs) have become essential in deep learning applications, especially in computer vision, yet their complex internal mechanisms pose significant challenges to interpretability, crucial for ethical applications. Addressing this, our paper explores CNNs by examining their topological changes throughout the learning process, specifically employing persistent homology, a core method within Topological Data Analysis (TDA), to observe the dynamic evolution of their structure. This approach allows us to identify consistent patterns in the topological features of CNN kernels, particularly through shifts in Betti curves, which is a key concept in TDA. Our analysis of these Betti curves, initially focusing on the zeroth and first Betti numbers (respectively referred to as Betti-0 and Betti-1, which denote the number of connected components and loops), reveals insights into the learning dynamics of CNNs and potentially indicates the effectiveness of the learning process. We also discover notable differences in topological structures when CNNs are trained on grayscale versus color datasets, indicating the need for more extensive parameter space adjustments in color image processing. This study not only enhances the understanding of the intricate workings of CNNs but also contributes to bridging the gap between their complex operations and practical, interpretable applications. Full article

This study analyzes the ambient vibrations induced while running the Mount Changlong pumped-storage power station (PSPS). The ground vibration data of the power station during its operation were acquired with vibration sensors. Different units were selected and compared under working conditions, and the conclusions were as follows: (1) Ambient vibrations induced by the running of units constituted the primary source of vibration, and they attenuated as the distance increased. (2) The vibration acceleration under pumping conditions was larger than that under power generation conditions, and the ground vibration acceleration increased with an augmentation of the power. (3) The running of adjacent units generated mutual interference, and the types of units were different, which led to complex variations in the spectrum maps. (4) The vibration acceleration of the lower flat tunnel was prone to surpassing the standard when the number of units running together exceeded three. Full article

There is a very thick water-bearing key strata above the coal seam in the Binchang mining area. When the mining scale is large, it easily breaks and leads to rockburst with a surge of water gushing in the panel. Adopting the layout pattern of a small panel and a large coal pillar can improve the stability of the main key strata, but at present, the research on the layout pattern of a small panel and a large coal pillar under extra-thick water-bearing key strata is still not perfect. Therefore, taking the second and third panels of a mine in Binchang as the engineering background, the width of the coal pillar and the mining scale of the panel are optimized by means of theoretical analysis, field measurement, and numerical simulation to prevent rockburst and control water inflow. The results show: (1) through theoretical calculation, it is deduced that the critical width of instability of the isolated coal pillar in the current mining scale is 257 m, and the critical mining scale of breaking and instability of the main key strata in the third panel is 537 m; (2) considering the bearing capacity of the isolated coal pillar and the recovery rate of coal resources, the reasonable width of the isolated coal pillar is 210~270 m, and when the width is 200 m and 250 m, the reasonable mining scale of the third panel is 490~550 m and 640~700 m, respectively; (3) the field practice shows that the actual width of the coal pillar between the second and third panels is less than the reasonable width, and the stress concentration in the isolated coal pillar area is relatively high, so the roof deep hole blasting and large-diameter drilling in coal seam are adopted to relieve pressure. After taking pressure relief measures, the stress concentration in the isolated coal pillar area is effectively reduced, and the pressure relief effect is remarkable. Full article

Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent processing. It is challenging for traditional methods to realize efficient and accurate microseismic velocity inversion due to the low signal-to-noise ratio of field data. Deep learning can efficiently invert the velocity model by constructing a mapping relationship from the waveform data domain to the velocity model domain. The predicted and reference values are fitted with mean square error as the loss function. To reduce the feature mismatch between the synthetic and real microseismic data, data augmentation is also performed using correlation and convolution operations. Moreover, a hybrid training strategy is proposed by combining synthetic and augmented data. By testing real microseismic data, the results show that the Unet is capable of high-resolution and robust velocity prediction. The data augmentation method complements more high-frequency components, while the hybrid training strategy fully combines the low-frequency and high-frequency components in the data to improve the inversion accuracy. Full article

The aim of this study was to analyse the performance of technical–tactical actions in two different types of tournaments and the influence of biological age on the performance of young basketball players. Thirty-seven under-13 male basketball players (age = 12.91 ± 0.57 years) were selected from four southeast Spanish teams to participate in two different tournaments on two consecutive days. The following technical–tactical variables were analysed: (a) Ball Obtained; (b) Ball Handler Player Actions; (c) Ball Handler Player Finished Actions; and (d) Ball Handler Shooting Performance. The results showed that reduced basket height and a closer three-point line promoted a higher number of balls obtained, 1 vs. 1 situations, finished ball player actions, shots, and the efficacy of offence phases. There was a significant increase in the number of balls obtained, 1 vs. 1 situations played, the number of plays finished with a lay-up or shot, number of received personal fouls, number of plays finished in 1 vs. 2, and those finished in equality and inferiority with a high defence opposition. The modified version presented a higher number of technical–tactical actions in Late Maturity players. The authors of this study believe that it is necessary to conduct more experimental studies and use bio-banding strategies in young basketball competitions. Full article

Pyrolysis is a promising way to reuse of waste tires. However, the carbon black generated in the process is often contaminated with various pyrolysis products. This study aims to recycle low-quality recycled carbon black (rCB) from waste tire pyrolysis, addressing the challenges posed by organic residues (up to 5 wt% bituminous substances, 112.2 mg/kg PAH). This causes the agglomeration of particles and decreases the active specific surface area. Cavitational vortex milling (both wet and dry) emerges as a promising method to valorize contaminated rCB, allowing for a significant reduction in the concentration of contaminants. This novel method allows for the generation of hydrophilic and hydrophobic black pigments. In parallel experiments, low-quality rCB is incorporated into solid biofuel to enhance its calorific value. The addition of 10 wt% rCB) to peat residues significantly elevates the calorific value from 14.5 MJ/kg to 21.0 MJ/kg. However, this improvement is accompanied by notable increases in CO₂ and SO₂ emissions. This dual effect underscores the necessity of considering environmental consequences when utilizing recycled carbon black as a supplement to solid biofuels. The findings provide valuable insights into the potential of cavitational vortex milling for carbon black valorization and highlight the trade-offs associated with enhancing biofuel properties through the addition of rCB. Full article