Appl. Sci., Volume 13, Issue 21 (November-1 2023) – 483 articles

As one of the most developed coastal cities, Shanghai experiences long-term ground surface settlement disasters during urban expansion periods, which has adverse effects on economic development. To date, many studies regarding Shanghai’s ground surface sedimentation have been conducted with microwave remote sensing technology. However, the systematic and timely analysis of the time series deformation results and risk evaluation is still absent. Therefore, we focused on the following aspects in this study: Firstly, revealing in detail the time series deformation characteristics during 2016–2022 with Sentinel-1A images and verifying the deformation results with different InSAR technologies and SAR data. Secondly, fully discussing the reasons for ground sedimentation from the aspects of subway construction, land use type, monthly rainfall, and human activities, and studying the correlation between surface deformation and rainfall with the singular spectrum analysis (SSA) method. Finally, conducting a risk evaluation and risk level division using the entropy method, combining the long time series deformation results and geoinformation data. Meanwhile, the following conclusions were reached: 1. There are six typical deformation areas, distributed in the Baoshan District, Minhang District, and Jinshan District of Pudong New District from 2016 to 2022. The maximum annual rate is −32.3 mm/a, and the maximum cumulative sedimentation reaches −188.6 mm. 2. Ground sedimentation is mainly due to engineering construction during city development and verifies the weak correlation between surface deformation and rainfall. 3. We obtained different levels of geological hazard risk areas, and Huangpu, Yangpu, Hongkou District, the northwest area of Pudong New Area, and the vicinity of Dishui Lake belong to higher-risk areas. The above time series deformation research results and systematic analysis of induced factors, and the higher-risk-area division, will provide valuable insights for urban risk management. Full article

Human-centred design as a research field is characterised by multidisciplinarity and a variety of many similar methods. Previous research attempted to classify existing methods into groups and categories, e.g., according to the degree of user involvement. The research question here is the following: How can human-centredness be measured and evaluated based on resulting product concepts? The goal of the paper is to present and apply a new metric—the Human-Centredness Metric (HCM)—for the early estimation of the quality of any human-centred activity based on the four goals of human-centred design. HCM was employed to evaluate 16 concepts, utilising a 4-point Likert scale, covering four different everyday products that were created by four students, which used three different human-centred design methods for this. The first concept was created without the application of any additional human-centred design method. The results illuminated trends regarding the impact of additional human-centred design methods on the HCM score. However, statistical significance remained elusive, potentially due to a series of limitations such as concept complexity, the small number of concepts, and the early developmental stage. The study’s limitations underscore the need for refined items and expanded samples to better gauge the impact of human-centred methods on product development. Full article

The generalized dynamic model of the rotor system, presented in this paper, is the first model that takes into account the interconnected oscillations of the “rotor–weakly conductive fluid–foundation” system under the action of parameters such as fluid and rotor motion, linear eccentricity, friction forces, foundation vibration and nonlinear characteristics of rolling bearings, as well as the action of a magnetic field on the fluid. Consistent equations of motion for the “rotor–weakly conductive fluid–foundation” system were derived and solved analytically. Forced and natural oscillations of the system were analyzed, and the distinctive features of the rotor system dynamics were revealed. The values of frequencies and amplitudes, which are one of the main factors determining the dynamic behavior of the system, were obtained and studied. Full article

The structurally diverse bioactive compounds found in marine organisms represent valuable resources for the food and pharmaceutical industries. The marine ecosystem encompasses over half of the world’s biota, providing an extensive range of bioactive compounds that can be extracted from various marine life forms, including marine microorganisms (such as bacteria, cyanobacteria, and actinobacteria), algae (both macroalgae and microalgae), invertebrates (including sponges, mollusks, echinoderms, and crustaceans), and, most importantly, fish. Many of these organisms thrive in extreme marine environments, leading to the production of complex molecules with unique biological functions. Consequently, marine biomolecules, such as lipids (especially polyunsaturated fatty acids), proteins/peptides, polysaccharides, carotenoids, phenolics, and saponins, exhibit a wide range of biological properties and can serve as valuable components in nutraceuticals and functional foods. Nevertheless, most of these biomolecules are susceptible to oxidation and degradation; encapsulation-based technologies tend to preserve them and increase their bioavailability and functions. These biological compounds demonstrate diverse activities, including antioxidant, anticancer, antithrombotic, anticoagulant, anti-inflammatory, antiproliferative, antidiabetic, antimicrobial, and cardioprotective effects, making them promising candidates for applications in the food industry. Despite their numerous health benefits, marine bioactive compounds have remained underutilized, not only in the food industry but also in the pharmaceutical and nutraceutical sectors. Therefore, this review aims to provide an overview of the various sources of marine bioactive compounds and their potential contributions to the food industry. Full article

Background: Breast cancer has a 14.8% incidence rate and an 8.5% fatality rate in Saudi Arabia. Mammography is useful for the early detection of breast cancer. Researchers have been developing artificial intelligence (AI) algorithms for early breast cancer diagnosis and reducing false-positive mammography results. The aim of this study was to examine the performance and accuracy of an AI system in breast cancer screening among Saudi women. Materials and Methods: This is a retrospective cross-sectional study that included 378 mammograms collected from 2017 to 2021 from government hospitals in Jeddah, Saudi Arabia. The patients’ demographic and clinical information were collected from files and electronic medical records. The radiologists’ assessments of the mammograms were based on Breast Imaging Reporting and Data System (BIRADS) scores. Follow-up or biopsy reports verified the radiologists’ findings. The MammoScreen system was the AI tool used in this study. Data were analyzed using SPSS Version 25. Results: The patients’ mean age was 50.31 years. Most patients had breast density B (42.3%) followed by A (27.2%) and C (25.9%). Most malignant cases were invasive ductal carcinomas (37.3%). Of the 181 cancer cases, 36.9% were BIRADS category V. The area under the curve for the AI detection (0.923; 95% confidence interval [CI], 0.893–0.954) was greater than that for the radiologists’ interpretation (0.838; 95% CI, 0.796–0.881). The AI detection agreed with the histopathological result in 167 positive (91.3%) and 182 negative cases (93.3%). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the AI system were 92.8%, 91.9%, 91.3%, 93.3%, and 92.3%, respectively. The radiologist’s interpretation agreed with the pathology report in 180 positive (73.8%) and 134 negative cases (100%). Its sensitivity, specificity, PPV, NPV, and accuracy were 100%, 67.7%, 73.8%, 100%, and 83.1%, respectively. Conclusions: The AI system tested in this study had better accuracy and diagnostic performance than the radiologists and thus could be used as a support diagnostic tool for breast cancer detection in clinical practice and to reduce false-positive recalls. Full article

Cutibacterium acnes (C. acnes) is a Gram-positive anaerobic facultative bacterium that is part of the human skin commensal microbiome. It colonizes various regions of the body, including the face, back, and chest. While typically a harmless commensal, under certain conditions, C. acnes can become pathogenic, leading to or promoting conditions such as acne vulgaris (AV), post-surgical infections, prostate cancer, and sarcoidosis. Current treatments for C. acnes infections often involve antibiotics, but the rise of antibiotic resistance has raised concerns. This review presents the virulence factors, clinical relevance, and current treatments of C. acnes, highlighting its association with AV, post-surgical infections, and other diseases. It also explores alternative innovative therapies such as phage therapy in development/research that are gaining prominence, with a growing focus on personalized medical approaches. To enhance C. acnes treatment while minimizing side effects and antibiotic prescription concerns, numerous clinical studies have been undertaken. These investigations span various pathological profiles and employ diverse strategies, such as utilizing bacterial extracts and compounds to restore healthy skin flora. The limitations and challenges of current and innovative treatments are also addressed, emphasizing the need for multidisciplinary strategies to combat C. acnes infections effectively. Full article

The inertial Navigation Systems/global navigation satellite system (SINS/GNSS) has become a research hotspot in the field of train positioning. However, during a uniform straight-line motion period, the heading misalignment angle of the SINS/GNSS is unobservable, resulting in the divergence of the heading misalignment angle and ultimately causing a divergence in the train’s speed and position estimation. To address this issue, this paper proposes an estimation and compensation method for the heading misalignment angle for train SINS/GNSS integrated navigation system based on an observability analysis. When the train enters a straight-line segment, the alignment of the train’s sideslip angle and the satellite velocity heading angle allows the achievement of velocity heading observation values that resolve the issue. In a curved segment, the heading angle becomes observable, allowing for an accurate estimation of the SINS’s heading misalignment angle using GNSS observations. The results showed that, whether the train is on a straight or curved track, the position estimation accuracy meets the simulation design criteria of 0.1 m, and the heading accuracy is better than 0.25°. In comparison to the results of pure GNSS position and velocity-assisted navigation, where heading divergence occurs during constant velocity straight-line segments, the method proposed in this paper not only converges but also achieves an accuracy comparable to the GNSS velocity-based heading alignment. The simulation results demonstrate that the proposed strategy significantly improves the accuracy of the heading misalignment angle estimation, thereby enhancing the accuracy of speed and position estimation under a GNSS-denied environment. Full article

Code cloning is a common practice in software development, where developers reuse existing code to accelerate programming speed and enhance work efficiency. Existing clone-detection methods mainly focus on code clones within a single programming language. To address the challenge of code clone instances in cross-platform development, we propose a novel method called TCCCD, which stands for Triplet-Based Cross-Language Code Clone Detection. Our approach is based on machine learning and can accurately detect code clone instances between different programming languages. We used the pre-trained model UniXcoder to map programs written in different languages into the same vector space and learn their code representations. Then, we fine-tuned TCCCD using triplet learning to improve its effectiveness in cross-language clone detection. To assess the effectiveness of our proposed approach, we conducted thorough comparative experiments using the dataset provided by the paper titled CLCDSA (Cross Language Code Clone Detection using Syntactical Features and API Documentation). The experimental results demonstrated a significant improvement of our approach over the state-of-the-art baselines, with precision, recall, and F1-measure scores of 0.96, 0.91, and 0.93, respectively. In summary, we propose a novel cross-language code-clone-detection method called TCCCD. TCCCD leverages the pre-trained model UniXcode for source code representation and fine-tunes the model using triplet learning. In the experimental results, TCCCD outperformed the state-of-the-art baselines in terms of the precision, recall, and F1-measure. Full article

Laser absorption spectroscopy tomography is an effective combustion diagnostic method for obtaining simultaneous two-dimensional distribution measurements of temperature and gas molar concentrations. For the reconstruction process of complex combustion flames, a new algorithm named ‘multi-iterative adaptive optimization regularization’ (MIARO) is proposed. This algorithm is a further development of another algorithm known as the ‘modified adaptive algebraic reconstruction technique’ (MAART) with the improvement of the initial value and adaptive regularization parameter selections. In MIARO, the problem of the MAART’s initial value sensitivity is compensated for, and in addition, reconstruction parameters are also introduced into the regularization so that both the quality of reconstruction and the convergence of regularization are guaranteed. In butane burner experiments, an average relative error of 1.82% was achieved with MIARO, compared to 2.44% with MAART, which is a significant reduction of 25.1%. The simulation and experimental results clearly demonstrate that the MIARO algorithm can be used to reconstruct dynamic combustion fields and eliminate boundary artifacts with improved measurement accuracy and robustness. Full article

Convolutional neural networks (CNN) are widely used for structural damage identification. However, the presence of environmental disturbances introduces noise into the acquired acceleration response data, impairing the performance of CNN models. In this study, we apply empirical mode decomposition (EMD) and variational mode decomposition (VMD) to denoise the data from a steel truss bridge. By comparing the smoothness and convergence of the obtained modal functions (IMFs) using EMD and VMD, we confirm the effectiveness of VMD in smoothing and denoising the bridge structure signals. Additionally, we propose a convolutional self-attention neural network (CSANN) model to extract features and identify damage in the denoised data using VMD. Comparative analysis of the CNN, LSTM, and GRU models reveals that the VMD-CSANN model outperforms the others in terms of damage localization and identification accuracy. It also exhibits excellent performance when handling noise-contaminated data with a noise level of 10%. These findings demonstrate the efficacy of the proposed method for identifying internal damage in steel truss structures, while maintaining smoothness and robustness during processing. Full article

Population expansion and the development of technology have led to an increase in construction activities. In many cases, foundation grounds do not have a high enough bearing capacity and are not capable of ensuring the safe exploitation of the construction. A soil with poor mechanical characteristics must be improved using various methods, such as adding hydraulic binders (lime and cement), natural fibres, or more recently, plastic waste materials. This work aims to study the behaviour of plastic waste materials made from polyethylene terephthalate (PET) in soil improvement. Thus, the mechanical characteristics of a clay improved with shredded PET were studied. PET was added in relation to the dry mass of the clay, in percentages of 2%, 4% and 6%. The studied clay was collected from a construction site around Cluj-Napoca, Romania, from a depth of 1 ÷ 10 m. PET was provided by a local plastic waste repository. It comes from recycled water, beer and soda bottles and was cleaned using specific methods for cleaning and recycling plastic waste. PET was shredded into irregular shapes with sizes ranging from 3 mm to 12 mm and was randomly distributed in the test specimens. Compression and direct shear tests were carried out to study the compressibility and shear parameters of the improved soil (internal friction angle and cohesion). The experimental results showed an improvement in the mechanical characteristics of the clay even at a low PET addition of 2% and 4%. This method can contribute to solving two current problems of the modern world: reducing pollution by recycling plastic waste materials and using them to improve the mechanical characteristics of soil. Full article

Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a mature deformation-monitoring technology, InSAR has been widely used in landslide detection, but the steep terrain and dense vegetation in the Qinling-Daba Mountains make detection challenging. Hence, it is important to choose suitable data sources and methods for landslide detection via InSAR in this area. This study was the first to collect ALOS/PALSAR−2 and Sentinel−1A images to detect landslides in the Qinling-Daba Mountains, applying a method combining IPTA and SBAS. In total, 88 landslides were detected and validated. The results show that the deformation-detection error rate of Sentinel−1A is 2% higher than that of ALOS/PALSAR−2 and that its landslide-recognition rate is 47.7% lower than that of ALOS/PALSAR−2. Upon comparing and analyzing the visibility, coherence, closed−loop residuals, and typical time series of landslide deformation from the two kinds of data, it was found that the extremely low quality of available Sentinel−1 A summer data is a major factor influencing that system’s performance. ALOS/PALSAR−2 is more likely to detect landslides in areas with high vegetation coverage, meeting more than 90% of the monitoring needs. It is thus highly suitable for landslide detection in the Qinling–Daba Mountains, where seasonality is significant. In this paper, for the first time, multiple data sources are compared in detail with regard to their utility in landslide detection in the Qinling–Daba Mountains. A large number of accuracy metrics are applied, and the results are analyzed. The study provides important scientific support for the selection of data sources for future landslide monitoring in the Qinling–Daba Mountain area and similar areas and for the selection of methods to evaluate the accuracy of InSAR monitoring. Full article

The road traffic state is usually analyzed from a temporal and macroscopic perspective; however, traffic flow parameters, such as density and spacing, can explain the evolution of traffic states from the microscopic perspective and the spatial distribution of vehicles in lanes. In this paper, we attempt to take both temporal and spatial characteristics into consideration simultaneously, and a parameter is defined as the traffic spatiotemporal state of urban road sections to represent the operational status of road traffic, using advanced prediction techniques to estimate its short-term trends. An estimation method is constructed for the traffic spatiotemporal state considering travel times, speeds, and queuing situations from temporal and spatial perspectives. Then, based on Takens’ theorem and the single variable phase space, the phase space of multiple traffic parameters is reconstructed and the chaotic characteristics are analyzed. Next, an LSTM prediction model is constructed based on the phase space reconstruction of multiple variables, and the traffic parameters are predicted by empirical analysis. The results show the proposed estimation method has a significantly improved accuracy. Finally, combined with RFID data, the traffic spatiotemporal state of the case section is calculated, which provides a theoretical basis and practical reference for road traffic state evaluations. Full article

Mediterranean olive cultivation faces challenges in the global environmental change context. Pests and diseases caused by arthropods such as Bactrocera oleae, Prays oleae, and certain vectors of Xylella fastidiosa are expected to increase and spread in part due to this global scenario. The control of these arthropods has relied on synthetic pesticides, the misuse of which has led to pest population resistance and concerns about their negative impacts on biodiversity and global health. Integrated pest management (IPM) methods have emerged through the careful consideration of all available control techniques and the subsequent integration of appropriate measures that discourage the development of pest populations. This paper reviews the IPM guidelines for olive cultivation, prioritizing the use of biological control methods, and the integration of genetics and biotechnology, which bring precision, efficacy, and safety. It evidences the importance of genetic analysis in pest populations, pesticide resistance and in the contributions of predators to pest control. Advances in formulations and delivery systems for pesticides such as Bacillus thuringiensis, plant-incorporated protectants, improved SIT techniques, and the specific efficacy of biologicals pesticides are covered. Finally, this paper explores promising tools such as RNAi and gene drive while recognizing the ethical, environmental, and regulatory challenges associated with their use. Shortly, these innovations have the potential to reduce the environmental impacts of pests while ensuring the long-term viability of the olive industry. Full article

To address the contradiction between the convergence error and convergence rate in the LMS algorithm, this study proposes a variable-step-size adaptive filter algorithm with a momentum term based on the logistic function. First, the normalization LMS algorithm is obtained by seeking the extremum under the Lagrange function constraint. Second, to reduce the convergence error of the algorithm, the logistic model is used as a function model of step size variation with error, resulting in a variable-step normalization LMS algorithm. Our experimental results demonstrate that this algorithm achieves smaller convergence errors compared to those of the traditional LMS algorithm. Finally, to further improve the convergence rate of the algorithm, a momentum term is introduced into the weight coefficient update process of the LMS algorithm. This leads to the development of a variable-step adaptive filter algorithm with a momentum term based on the logistic function. The impact of different parameters on the algorithm performance is also investigated. In order to verify the rationality of the proposed algorithm, a dynamic system mathematical model was identified using the proposed algorithm. The results showed that the proposed algorithm had an identification accuracy of over 97% for the mathematical model parameters and a suppression of over 99% for noise. In order to verify the engineering application value of the proposed algorithm, real-time vibration data fitting experiments were conducted in the Aeroelasticity Laboratory of the China Aerodynamics Research and Development Center, and their results were compared with three algorithms: ARMAX, N4SID, and LMS. The results showed that the proposed algorithm had a higher fitting accuracy than the three others. Through simulations and experiments, it is demonstrated that this study has value both theoretically and in engineering applications, promoting engineering applications of adaptive filtering algorithms and providing strong support for the research of adaptive control. Full article

Aiming at the problem that online parameter identification, based on the Model Reference Adaptive System (MRAS), is easily affected by the high-frequency noise of the sensor, an improved MRAS, based on variable bandwidth linear Active Disturbance Rejection Control (ADRC) adaptive law, is proposed. The proposed ADRC adaptive law, including an extended state observer, exhibits good anti-disturbance ability while effectively tracking the error signal through adjusting the bandwidth of the controller according to the observation error. When the observation error is large, the bandwidth of the controller is increased to enhance the tracking accuracy; in the opposite situation, the bandwidth is reduced to improve the anti-disturbance performance. To illustrate feasibility, a stability analysis of the proposed ADRC adaptive law is carried out and some comparison and validation experiments are designed to identify the inductance and flux linkage of the permanent magnet synchronous motor (PMSM). The experimental results show that the proposed method has better anti-disturbance performance than the MRAS based on traditional Proportional Integral (PI) adaptive law or switched PI adaptive law. Full article

A quantum simulation experiment pairing Hamiltonians of nearest-neighbor interacting superconducting qubits was performed with a complete set of algorithms on an IBM Quantum Computer-IBMq Lima. The experiment revealed that the fidelity is a function of iteration using Suzuki–Trotter decomposition for four different types of nearest-neighbor Heisenberg, XY, transverse, and longitudinal Ising superconducting qubit couplings of Hamiltonians. The experiment displayed the models of how the experimental density matrices shift from the theoretical density matrices and how their behavior changes with different numbers of iterations. It also demonstrated the reconstruction of quantum states and how the states change as a function of iteration with the IBM Quantum Computer-IBMq Lima. The time evolutions of the states for different models were also shown to predict the dominance of each state. Full article

When a steel box girder is constructed using the jacking method, the contact area between the jack and the bottom of the girder is subjected to complex forces, and it is very critical to ensure the local stability of the girder. When the phenomenon of unsynchronized jacking occurs, it will lead to changes in the contact area and affect the structural safety. In order to solve the above problems, this paper takes the background of the incremental launching construction of the main bridge across the Yellow River on Jiao Ping Expressway, adopts the Midas FEA NX 2021 finite element software to establish a finite element hybrid unit model under the maximum cantilever condition for the first time, and analyzes the local stresses in this state. The results show that the local maximum equivalent stress of the steel box girder is 198.301 MPa, which meets the requirements. The effect of jacking asynchrony on the structural forces is analyzed by simulating jacking asynchrony in the local model. The results show that both vertical jacking asynchrony and lateral deflection will lead to an increase in local stresses in the steel box girder and even steel yielding. On the basis of the above single-parameter study, a two-parameter correlation analysis is carried out to obtain the two-parameter control equation of jacking, the control threshold of the vertical jacking height difference is formulated to be 15 mm, and the dynamic control of lateral deflection is realized according to the control equation. Through comparison, it is found that the two-parameter control threshold of jacking synchronization is reduced, which can supplement the unfavorable state missed during single-parameter control and is a safer and more effective means of control. Full article

Vibration is challenging and significant in solving engineering problems. The issue of vibration in loaded objects by utilizing a three-dimensional model and experiments. Typically, an object is subjected to a random frequency, which changes the notch shape depending on the frequency model. The investigations determined the performance difference by conducting modal analysis with the finite element method and examining the various forms of each mode. We simulated metal plates with V notch and multiple notch locations on both sides and one side of the notch. The test kits included an accelerometer and a force sensor for correcting the national frequency via Simulink Matlab^® and verifying the result from the finite element methods. The V-shaped vibration testing provided significant insights into its accuracy and potential for predicting damage and fracture through experimentation and the finite element method. The tested specimen analyzed the behavior of two models and found that the two V-shaped exhibited varying natural frequency values. Specifically, the double-sided V-shaped increased natural frequency, whereas the single-sided notched V-shaped cutting showed a significant decrease in natural frequency. Accordingly, this investigative approach, the result of the experiment, and the finite element shows that correlation disposition can be utilized to forecast various random frequencies for vibration analysis. Full article

Fine dust can have serious effects on human health and crop growth. Fodder crops can reduce airborne dust by coagulating soil particles and reducing wind speed on the surface and have the effect of reducing fine dust by adsorbing it on the crop surface. In this study, the dust reduction coefficient of crops was derived through a self-manufactured wind tunnel experiment to quantitatively assess the dust reduction effect of crops by type and planting density. Additionally, a dust reduction formula considering crop growth and weather conditions during the cultivation period was derived. The dust reduction coefficient was measured by the gravimetric method and the real-time size distribution of dust concentration before and after the crop. The PM reduction coefficient showed triticale at PM-2.5 82.2 mg/m³, PM-10 120 mg/m³, and barley at PM-2.5 14.5 mg/m³, PM-10 26.9 mg/m³ under moderate planting density. During the general planting density cultivation period of triticale and barley, PM-10 was reduced by 37.8 kg/ha and 8.5 kg/ha, respectively, and PM-2.5 was reduced by 25.9 kg/ha and 4.6 kg/ha. The dust reduction effect during the cultivation period was up to 126.1 kg/ha in terms of PM-10 when triticale was cultivated with densely sowing planting density. Full article

Eye-tracking technology has numerous applications in both commercial and research contexts. The recent introduction of affordable wearable sensors has significantly broadened the scope of potential uses, spanning fields such as computer gaming, education, entertainment, health, neuromarketing, and psychology, among others. Another development is the use of downloadable software, which permits participants to record their responses to presented images with the use of calibrated webcams without leaving their homes, allowing for easier recruitment of larger numbers of study volunteers online. This paper reviews findings from seven Building Studies conducted by the Human Architecture + Planning Institute with iMotions-Online eye-tracking software, highlighting the significance of the new technology and its ability to assess the human experience of the built environment, as well as its limitations. Overall, images with a certain type of organized complexity, such as that found in nature and pre-modern architecture, attracted the gaze faster and kept it for significantly longer, as compared to images lacking these features. These results add to our existing knowledge about environmental complexity and give us insights into non-conscious and early conscious actions such as first gaze attraction, helping us understand the role of specific morphological features in the architectural/environmental experience. Full article

The study of airspace sector demarcation can help controllers to deal with complex air situations, provide reference for air traffic control services, reduce the workload of controllers, and ensure safe and efficient airspace operation. Based on complex network theory, this paper proposes a sector division method based on a mean shift clustering algorithm and a Voronoi diagram. Firstly, a flight conflict network is constructed based on the structural characteristics of the airborne flight situation, combined with the three-dimensional velocity barrier method and the aircraft protected area model. Secondly, six network topology indexes, such as the total node degree, average point strength and network density, are selected to construct the flight-situation-assessment index system, and the busy and idle time segments of the airspace are divided according to the comprehensive network indexes; finally, based on the historical flight data, the airborne aircraft in the busy and idle time segments are clustered using the mean shift clustering algorithm and the Voronoi diagram method, respectively, so as to obtain a sector division scheme. The simulation results show that this method can equalize the network topology indexes and provide a reference for the scientific allocation of controllers’ energy. Full article

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation. Full article

Artificial Intelligence is an indispensable element of the modern world, constantly evolving and contributing to the emergence of new technologies. We meet it in everyday applications, primarily using intelligent systems that aim to improve our lives. Artificial Intelligence techniques must inspire users’ trust because they significantly impact virtually every industry and person. For this reason, systems using Artificial Intelligence are subject to many requirements to verify their trustworthiness in various aspects. This review focused on users’ physical and environmental security, considering the safety and robustness dimensions of Trustworthy Artificial Intelligence. We examined these Trustworthy Artificial Intelligence solutions and dimensions because security is one of the most-critical aspects of human life and can be considered in many different contexts. We examined the trustworthiness of Artificial Intelligence techniques in systems supporting road safety and securing computer network users. Also, we analyzed the challenges and requirements of the newly designed solutions using Trustworthy Artificial Intelligence methods. Verifying Trustworthy Artificial Intelligence solutions and their practical use will increase users’ physical and environmental security. Full article

Most small SUVs in the automotive market are equipped with torsion beam suspension for the rear wheels. Torsion beam suspension consists of a cross-member and a trailing arm. The cross-member plays a crucial role in preventing the vehicle from twisting; therefore, a shape that can withstand loads is essential. In this study, various shapes of cross-member reinforcements were added to the existing torsion beam suspension to analyze its structural strength when subjected to arbitrary forces. Analysis results were obtained for stiffness and driving stability factors such as smooth road shake, impact hardness, and memory shake. Based on these results, we identified the optimal cross-member shape with low torsional stiffness and a small side view swing arm angle by examining the changes in driving stability. Full article

As the most common geological disaster problem in mines, slope geological disasters have become a focus of research, along with the difficulty of mine safety and ecological environment protection together with the ecological restoration of open-pit mines. At present, a large number of slope-retaining wall structures lack research on safety monitoring, real-time acquisition, and intelligent early warning. Therefore, this paper combines cement-modified loess with gravity retaining wall structures and puts forward a new type of greening gravity retaining wall structure. From the perspective of “the Internet of Things + construction”, a video monitoring system is established to monitor the retaining wall structure in real time. Finally, based on video image processing technology, the deformation of the retaining wall surface is identified and the inclination angle of the wall surface is calculated, so as to improve the real-time and intelligent monitoring of the new greening gravity retaining wall. The results show that the new greening gravity retaining wall based on video monitoring proposed in this paper has the characteristics of a gravity retaining wall and ecological retaining wall, which are conducive to improving the real-time and intelligent monitoring of the new greening gravity retaining wall. Cement-modified loess is used as the planting matrix, and the cement mixing ratio should not exceed 10%. Considering the requirements of economy and shear strength, the cement mixing ratio should be selected from 5% to 12%. Full article

Background: Nonsurgical periodontal therapy is the first step of periodontal diseases treatment. It could be performed with various instruments and in recent years lasers have been tested too. The aim of the present study is to evaluate the long-term clinical effectiveness of Er:YAG laser monotherapy in the treatment of moderate chronic periodontitis. Methods: 451 teeth (1099 periodontal pockets) from 30 patients with moderate chronic periodontitis are allocated for subgingival scaling and root planing into two groups—hand instrumentation (control) and Er:YAG laser instrumentation with 100 mJ/15 Hz (test). Patients are examined for probing pocket depth, gingival recession, clinical attachment level, bleeding on probing, plaque presence at baseline, 1, 3, 6 and 12 months after instrumentation. Results: One year after therapy significant reduction of all clinical parameters examined is observed. The pocket depth decreases with 1.55 mm for the control group and 1.70 mm in the test group. The attachment level gain reveals 1.09 mm for hand instrumentation and 0.59 for laser instrumentation. The bleeding on probing reduces more significantly in the test group, where one year after treatment the index is 19.1%, whereas in the control group, it is 33.8%. Conclusions: The Er:YAG laser demonstrates similar clinical effectiveness to hand instrumentation in the non-surgical periodontal treatment. Better long-term stability is observed in the test group. Full article

It is an economical way to use the pile-supported embankment for the construction of the embankment over soft soil. The combined use of piles and reinforcement effectively reduces the differential settlement of the embankment surface. However, the previous analysis of embankment stress and settlement did not take into account the anisotropy in the embankment filler. In this paper, the UMAT subroutine is developed by using the material subroutine interface in ABAQUS 2016 finite element software. The anisotropy of soil cohesion and friction angle has been incorporated into the Mohr–Coulomb yield criterion so that it can consider the anisotropy of soil. The accuracy of the anisotropic yield criterion in this paper is verified by an ABAQUS source program and related engineering examples. It is found that the anisotropy value of soil cohesion is inversely proportional to the stress ratio on the pile–soil interface while being directly proportional to the tensile stress applied to the geogrid. The results show that the anisotropy of the friction angle decreases with the soil arching effect but increases by 23.1% with the tensile stress on the geogrid. The position of the settlement plane remains relatively constant at 2.3 m as the friction angle anisotropy coefficient increases. These research results provide valuable theoretical guidance for on-site construction design. Full article

Ocean reverberations, a significant interference source in active sonar, arise as a response generated by random scattering at the receiving end, a consequence of randomly distributed clutter or irregular interfaces. Statistical analysis of reverberation data has revealed a predominant adherence to the Rayleigh distribution, signifying its departure from specific distribution forms like the Gaussian distribution. This study introduces the Gaussian mixture model, capable of simulating random variables conforming to a wide array of distributions through the integration of an adequate number of components. Leveraging the unique statistical attributes of reverberation, we initiate the Gaussian mixture model’s parameters via the frequency histogram of the reverberation data. Subsequently, model parameters are estimated using the expectation–maximization (EM) algorithm and the most suitable statistical model is selected based on robust model selection criteria. Through a comprehensive evaluation that encompasses both simulated and observed data, our results underscore the Gaussian mixture model’s effectiveness in accurately characterizing the distribution of reverberation data, yielding a mean squared error of less than 4‰. Full article

Animation visualization is one of the primary methods for analyzing unsteady flow fields. In this paper, we addressed the issue of data visualization for large-scale unsteady flow fields using animation. Loading and rendering individual time steps sequentially can result in substantial frame delay, whereas loading and rendering all time steps simultaneously can result in excessive memory usage. To address these issues, the proposed method analyzes the variable description information in the data files to bypass redundant variables and read the flow field data as required. Second, a hash table is constructed to derive the two-dimensional surface mesh of the flow field and complex mesh cells are simplified into simple linear cells to reduce the mesh’s complexity. This paper presents a method for reducing the memory usage of complex data sets by more than 90%, compared with the ParaView data reading method. The proposed method is tested on four sets of unstructured unsteady flow field data with different data structures. The animation visualization method based on simplified data can achieve an average frame rate of less than 100ms and supports real-time user interaction on personal computers. It extends the ability of personal computers to analyze large-scale unstructured unsteady flow fields. Full article