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Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (

Unsaturated soil shear strength is a significant topic in geotechnical engineering. The measurement of unsaturated soil properties such as shear strength and matric suction could be costly, hard, time-consuming, and often impractical to obtain. The purpose of this research is to propose a new shear strength model for unsaturated soil and to predict the shear strength by using the degree of saturation directly because the water saturation is easy to obtain. More specifically, this study focused on Bishop’s shear strength theory and van Genuchten’s soil–water characteristic curve to describe the effect of saturation on shear strength. The new shear strength model was expressed as a function of saturation degree, residual degree of saturation, effective shear strength indices, net normal stress, and five parameters. The performance of the presented model in this paper was verified by fitting it to data obtained by laboratory tests on silty sand including the triaxial shear test and soil–water characteristic curve test. From these two laboratory tests, we obtained the variation in the matric suction and shear strength with the degree of saturation under various dry densities. This proposed model was also validated against the shear strength results of the clayed-silty sand and Ankara clay recorded in the literature. Comparing the result of the shear strength under the variation of one parameter, this model was much more sensitive regarding parameter m, which was related to the material characteristic. The comparison between the predicted values and experimental points was particularly fine and showed the ability of this model to be applied to a wide range of soils.

Categorization of space time block code (STBC) signals has become widely acknowledged as a crucial foundational mechanism for creating intelligent wireless transmissions in both the governmental and business sectors. The use of multiple antennas at a broadcaster complicates a signal categorization task because assumptions about the number and transmission matrix of the sent antennas must be made. STBC categorization has only been investigated in the context of non-relaying environments, and no methods for relaying transmissions have been reported. This work proposes a revolutionary strategy for categorizing STBC signals that can be implemented in amplify-and-forward (AF) relaying networks. Time-domain characteristics of the STBC waveforms provide the basis of the mathematical ingredients used in the offered categorization process. The employed STBC waveform is reflected in the spikes observed in the fast Fourier transform of the second-order lag product of the collected waveforms. This creates the foundation for an effective discriminating feature. Advantages of the described strategy include not requiring any prior awareness of the modulation type, channel conditions, signal-to-noise ratio (SNR), or the block timing synchronization of the STBC waveforms. The indicated strategy has been shown through simulation experiments to be capable of providing appropriate categorization accuracy despite the existence of transmission faults, even at relatively low SNR levels.

As the most widely used storage device today, hard disks are efficient and convenient, but the damage incurred in the event of a failure can be very significant. Therefore, early warnings before hard disk failure, allowing the stored content to be backed up and transferred in advance, can reduce many losses. In recent years, an endless stream of research on the prediction of hard disk failure prediction has emerged. The detection accuracy of various methods, from basic machine learning models, such as decision trees and random forests, to deep learning methods, such as BP neural networks and recurrent neural networks, has also been improving. In this paper, based on the idea of blending ensemble learning, a novel failure prediction method combining machine learning algorithms and neural networks is proposed on the publicly available BackBlaze hard disk datasets. The failure prediction experiment is conducted only with S.M.A.R.T., that is, the learned characteristics collected by self-monitoring analysis and reporting technology, which are internally counted during the operation of the hard disk. The experimental results show that this ensemble learning model is able to outperform other independent models in terms of evaluation criterion based on the Matthews correlation coefficient. Additionally, through the experimental results on multiple types of hard disks, an ensemble learning model with high performance on most types of hard disks is found, which solves the problem of the low robustness and generalization of traditional machine learning methods and proves the effectiveness and high universality of this method.

The gear test bench is important equipment for analyzing gear performance, detecting gear quality, and providing basic data for gear design and machining. In order to monitor the running state of the gear test bench, predict its running performance both timely and effectively, and guarantee its stable operation, a digital twin-based state monitoring method of the gear test bench is proposed in this paper. The state monitoring system of the gear test bench based on the digital twin model is constructed to simulate its normal running state in real time. On this basis, through the acquisition of physical information of the gear test bench, the developing of the state monitoring system, and the simulation of the digital twin model, the real-time state monitoring of the normal operation gear test bench is realized. The test results of the closed power flow gear test bench show that the digital twin simulation results of the gear test bench are basically consistent with it. The proposed state monitoring system can map the normal running state of the gear test bench, so as to realize the running state monitoring of the gear test bench.

Industrial defect detection methods based on deep learning can reduce the cost of traditional manual quality inspection, improve the accuracy and efficiency of detection, and are widely used in industrial fields. Traditional computer defect detection methods focus on manual features and require a large amount of defect data, which has some limitations. This paper proposes a texture surface defect detection method based on convolutional neural network and wavelet analysis: TSDNet. The approach combines wavelet analysis with patch extraction, which can detect and locate many defects in a complex texture background; a patch extraction method based on random windows is proposed, which can quickly and effectively extract defective patches; and a judgment strategy based on a sliding window is proposed to improve the robustness of CNN. Our method can achieve excellent detection accuracy on DAGM 2007, a micro-surface defect database and KolektorSDD dataset, and can find the defect location accurately. The results show that in the complex texture background, the method can obtain high defect detection accuracy with only a small amount of training data and can accurately locate the defect position.

Anomaly detection has been proven to be an efficient way to detect malicious behaviour and cyberattacks in industrial cyber–physical systems (ICPSs). However, most detection models are not entirely adapted to the real world as they require intensive computational resources and labelled data and lack interpretability. This study investigated the traffic behaviour of a real coal mine system and proposed improved features to describe its operation pattern. Based on these features, this work combined the basic deterministic finite automaton (DFA) and normal distribution (ND) models to build an unsupervised anomaly detection model, which uses a hierarchical structure to pursue interpretability. To demonstrate its capability, this model was evaluated on real traffic and seven simulated attack types and further compared with nine state-of-the-art works. The evaluation and comparison results show that the proposed method achieved a 99% F1-score and is efficient in detecting sophisticated attacks. Furthermore, it achieved an average 17% increase in precision and a 12% increase in F1-Score compared to previous works. These results confirm the advantages of the proposed method. The work further suggests that future works should investigate operation pattern features rather than pursuing complex algorithms.

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.

This paper presents a novel closed-loop control strategy that can be applied to quadrotor trajectory tracking to improve the control accuracy and stability. Inspired by intelligent creatures, the composite control strategy combines the open-loop mode and closed-loop control to overcome some disadvantages of oscillation and overshoot under feedback control mechanisms. The open-loop mode was realized by the quadrotor maneuverability model, which is proposed to build the bridge between the flight control commands and the desired acceleration. Then, a real-time control and acquisition platform with high-precision positioning, low-delay communication, and asynchronous distribution was built to collect the UAV’s real-time flight trajectory data for training the maneuverability model and to control the quadrotors asynchronously. Based on the platform and the trained model, the composite control mode on the closed-loop of the Proportional–Integral–Derivative (PID) control and the open-loop of the Long Short-Term Memory (LSTM) maneuverability model was implemented and verified. The experimental results show that the open-loop control strategy has a better advantage in the unity of time and space over the existing techniques for improving the UAV dynamic obstacle avoidance capability.

Acoustic emission (AE), recorded during uniaxial compressive loading with constantly increasing stress and stepped stress increments until the fracture of prismatic marble specimens, were analyzed in terms of non-extensive statistical mechanics (NESM). Initially introduced by Tsallis, NESM has proven to be an autonomous robust theoretical framework for studying fracture mechanisms and damage evolution processes during fracture experiments in specimens made of brittle materials. In the current work, the time intervals of the recorded AE data are analyzed in terms of NESM. For each examined specimen, the corresponding q entropic indices and the βq parameters were calculated, and their variability in terms of the stress applied were studied. Furthermore, a possible linear relationship between the entropic index q and the parameter βq was examined, and it was investigated whether the observed deviation from monotonicity between q and βq may signal increased accumulation of damage, eventually leading to the final fracture of the specimens. Through this work, the emergence of an additional pre-failure indicator (i.e., the deviation from monotonicity between q and βq) alongside well-established ones can provide further insight regarding the underlying crack development mechanisms and damage accumulation processes during the fracture of rock materials.

In many academic fields, predicting student academic success using data mining techniques has long been a major research issue. Monitoring students in higher education institutions (HEIs) and having the ability to predict student performance is important to improve academic quality. The objective of the study is to (1) identify features that form clusters that have holistic characteristics and (2) develop and validate a prediction model for each of the clusters to predict student performance holistically. For this study, both classification and clustering methods will be used using Support Vector Machine (SVM) and K-means clustering. Three clusters were identified using K-means clustering. Based on the learning program outcome feature, there are primarily three types of students: low, average, and high performance. The prediction model with the new labels obtained from the clusters also gained higher accuracy when compared to the student dataset with labels using their semester grade.

Computer recognition of human activity is an important area of research in computer vision. Human activity recognition (HAR) involves identifying human activities in real-life contexts and plays an important role in interpersonal interaction. Artificial intelligence usually identifies activities by analyzing data collected using different sources. These can be wearable sensors, MEMS devices embedded in smartphones, cameras, or CCTV systems. As part of HAR, computer vision technology can be applied to the recognition of the emotional state through facial expressions using facial positions such as the nose, eyes, and lips. Human facial expressions change with different health states. Our application is oriented toward the detection of the emotional health of subjects using a self-normalizing neural network (SNN) in cascade with an ensemble layer. We identify the subjects’ emotional states through which the medical staff can derive useful indications of the patient’s state of health.

Theoretically, the topological charge l in the vortex can be any integer or fraction, thus the vortex carrying different topological charges can form an infinitely orthogonal orbital angular momentum state space, which can provide new dimensional resources for optical communication. However, high-capacity optical communication requires low delay, thus real-time detection of the OAM is significant for communication. Metasurfaces have the characteristics of low loss, ultra-thin, easy integration, and flexible phase controls, which provide a meaningful way to realize integrated OAM generation and detection. Here, an optimized streamlined metasurface (OSM) is presented, which can detect high-order vortex beams in a single, simple, and rapid manner by photon momentum transformation (PMT). Since different vortices are converted into focusing modes with distinct azimuthal coordinates on a transverse plane through PMT, a single measurement can determine OAMs in an ample mode space. In addition, the OSM can detect more and higher order OAMs compared with a discrete metasurface (DM) at the same size, due to its better wavefront sampling capabilities. With the merits of an ultra-compact device size, simple optical structure, and outstanding vortex recognition ability, our approach may underpin the development of integrated optics and quantum systems.

Automated retina vessel segmentation of the human eye plays a vital role as it can significantly assist ophthalmologists in identifying many eye diseases, such as diabetes, stroke, arteriosclerosis, cardiovascular disease, and many other human illnesses. The fast, automatic and accurate retina vessel segmentation of the eyes is very desirable. This paper introduces a novel fully convolutional autoencoder for the retina vessel segmentation task. The proposed model consists of eight layers, each consisting of convolutional2D layers, MaxPooling layers, Batch Normalisation layers and more. Our model has been trained and evaluated on DRIVE and STARE datasets with 35 min of training time. The performance of the autoencoder model we introduce is assessed on two public datasets, the DRIVE and the STARE and achieved quite competitive results compared to the state-of-the-art methods in the literature. In particular, our model reached an accuracy of 95.73, an AUC_ROC of 97.49 on the DRIVE dataset, and an accuracy of 96.92 and an AUC ROC of 97.57 on the STARE dataset. Furthermore, our model has demonstrated the highest specificity among the methods in the literature, reporting a specificity of 98.57 on the DRIVE and 98.7 on the STARE dataset, respectively. The above statement can be noticed in the final blood vessel segmentation images produced by our convolutional autoencoder method since the segmentations are more accurate, sharp and noiseless than the result images of other proposed methods.

This paper investigates the effect of three vanadium complexes with Schiff base-type tetradentate ligands of general formula N,N′-1,2-cyclohexylenebis(3,5-dichlorosalicylideneiminate) (V1); LVCl2 (L = N,N′-1,2-cyclohexylenebis(5-chlorosalicylideneiminate) (V2); and N,N′-1,3propylenebis (salicylideneiminate) (V3) on the fluidity of liposome membranes obtained by the sonication of natural lecithin (EYL) and synthetic lecithin (DPPC). The study was carried out with TEMPO and 16DOXYL spin probes using the EPR technique. The results show that the effect of the complexes on the fluidity of liposomes whose membranes are in the liquid crystalline phase is much stronger as compared to the liposome membranes in the gel phase.

This study investigated the crucial factors for measuring the success of the information system used in the e-learning process, considering the transformations in the work environment. This study was motivated by the changes caused by COVID-19 witnessed after the shift to fully online learning environments supported by e-learning systems, i.e., learning emphasized with information systems. Empirical research was conducted on a sample comprising teaching staff from two European universities: the University of Novi Sad, Faculty of Technical Sciences in Serbia and the Polytechnic Institute of Castelo Branco in Portugal. By synthesizing knowledge from review of the prior literature, supported by the findings of this study, the authors propose an Extended Information System Success Measurement Model—EISSMM. EISSMM underlines the importance of workforce agility, which includes the factors of proactivity, adaptability, and resistance to change, in the information system performance measurement model. The results of our research provide more extensive evidence and findings for scholars and practitioners that could support measuring information system success primarily in e-learning and other various contextual settings, highlighting the importance of people’s responses to work environment changes.

Industry 5.0 and the associated transformation into Society 5.0 require a complete realignment of the skills required of the engineers of tomorrow. Thereby, the pre-dominant and policy-driven drivers of digitalization, and sustainability in particular, demand a complex variety of enhanced special competencies (e.g., lean thinking, data science skills) and transversal competencies from engineers working in manufacturing companies which must be systematically developed and continuously expanded. Since methodical approaches to systematic competence development in the environment of Industry 5.0-related engineering education are scarce, this article develops a competence profile for industrial logistics engineering education as an example of Industry 5.0-related vocational education and training initiatives. After elaborating on the relevant theoretical aspects of systematic competence development in adult education, an exemplary competence profile for industrial logistics engineering education is developed. Moreover, this paper presents a preliminary investigation of the impact of competencies on job performance and job satisfaction. The research results serve as a basis for the development of new teaching and learning concepts as well as for the investigation of causal relationships between competence-orientation and individual performance for industrial engineers in manufacturing enterprises.

The Falling Number (FN) test is not suitable for the determination of α-amylase activity in malt flour because the amylolytic activity is too high to be determined by the FN instrument. The aim of this study was to modify the FN method to allow quantification of high α-amylase activity in malt flour. The modifications were performed in two ways: using different amounts of malt flour (0.05, 0.1, 0.2, and 0.3 g) or by using malt extracts (0.5, 1, 2, 3, 4, 5, and 6 mL). In both cases, 6 g of standard wheat starch was used as substrate. The results of the modified methods were compared with the α-amylase activity determined by the standard Ceralpha method. Linear and nonlinear exponential regression was used to calculate the predicted amylase activity, and Bland-Altman analysis was used to measure the agreement between standard and modified methods. The modifications of the FN method using 0.1 g of malt flour showed that the modified method was able to accurately measure high levels of α-amylase activity in malt flour, and the results obtained by the modified method were in agreement with the results obtained by the standard Ceralpha method.

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approaches to identify DDoS attacks in SDN networks between 2018 and the beginning of November 2022. To search the contemporary literature, we have extensively utilized a number of digital libraries (including IEEE, ACM, Springer, and other digital libraries) and one academic search engine (Google Scholar). We have analyzed the relevant studies and categorized the results of the SLR into five areas: (i) The different types of DDoS attack detection in ML/DL approaches; (ii) the methodologies, strengths, and weaknesses of existing ML/DL approaches for DDoS attacks detection; (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature; (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature; and (v) current research gaps and promising future directions.

Hydro generation is the simplest and oldest method of electricity generation, with a century of successful operation. Using a pump as a turbine (PAT) is an optimal solution for minimizing the cost, particularly in low-head and small-scale hydro plants. Commercially available centrifugal pumps have become a popular solution for small-scale hydro and pumped-hydro facilities owing to their simple geometry, ease of operation, maintenance, and abundant availability in local markets. Variations in the water flow in hydro facilities, such as pumped-hydro and small-scale hydro facilities, are common; however, a PAT is unable to respond to variable flows because it is a fixed-speed device. To overcome this problem, different techniques have been suggested by researchers: (a) a system of parallel PATs; (b) geometrical modifications in the impeller of the PAT; and (c) power electronics-based variable frequency drives. All the aforementioned techniques have limitations, such as low output, high cost, complexity, transportation, and operation and maintenance. In this study, a simple and economical technique is proposed to smooth the output of a PAT on variable/decreasing water flow profiles. In the proposed technique, water columns connected in parallel (PWCs) are used to produce a pressurized water flow, as they have a convergent nozzle at the outlet. The PWC creates more space for water, and this additional mass of water boosts the water flow at the outlet. In this manner, the PWC technique maintains the flow at the inlet of the turbine. A serial integration of five PWCs with the same dimensions was conducted to inject the additional flow into the existing PAT penstock, governing a 37-kW generator. The design flow was maintained at 192.1 L/s without any additional power usage at the inlet of the PAT, and the output was smoothed even at the minimum water flow/head. Pump design and computational fluid dynamics simulations were performed using ANSYS software, whereas generator simulations were performed using MATLAB/Simulink software.

In order to minimize damage in the event of a fire, the ignition point must be detected and dealt with before the fire spreads. However, the method of detecting fire by heat or fire is more damaging because it can be detected after the fire has spread. Therefore, this study proposes a Swin Transformer-based object detection model using explainable meta-learning mining. The proposed method merges the Swin Transformer and YOLOv3 model and applies meta-learning so as to build an explainable object detection model. In order for efficient learning with small data in the course of learning, it applies Few-Shot Learning. To find the causes of the object detection results, Grad-CAM as an explainable visualization method is used. It detects small objects of smoke in the fire image data and classifies them according to the color of the smoke generated when a fire breaks out. Accordingly, it is possible to predict and classify the risk of fire occurrence to minimize damage caused by fire. In this study, with the use of Mean Average Precision (mAP), performance evaluation is carried out in two ways. First, the performance of the proposed object detection model is evaluated. Secondly, the performance of the proposed method is compared with a conventional object detection method’s performance. In addition, the accuracy comparison using the confusion matrix and the suitability of real-time object detection using FPS are judged. Given the results of the evaluation, the proposed method supports accurate and real-time monitoring and analysis.

The characteristics of elderberries and their flowers as well as selected properties of chlorogenic acid related to phenolic compounds are described. The aim of this study was to optimize the content of chlorogenic acid in ethanol extracts of elderberry flowers using the response surface methodology (RSM). The experiment was carried out with four research variants: fresh raw material treated with warm ethanol (50 °C) or cold ethanol (25 °C) and the same procedure for raw material that was frozen for a month and then dried. For each variant, nine methods of obtaining extracts were prepared using three different ethanol concentrations by volume (40, 68 or 95%) and three different extraction times (7, 20 or 30 days). Higher contents of chlorogenic acid were found in variants treated with warm ethanol (106.3 to 384.8 µg/cm3) in comparison to the samples treated with cold ethanol (60.77 to 298.3 µg/cm3). Optimization models of the response surface showed that with small losses of efficiency, the extraction process can be carried out for up to 20 days (instead of 30) with the use of ethanol with a concentration of approx. 68% (instead of 95%). The optimization of the efficiency of technological processes in the processing of herbs can be supported by the use of the response surface methodology.

In this study, the multi-criteria decision-making (MCDM) technique is used in collaboration with K-medoids clustering to establish a novel algorithm for extending the lifetime of wireless sensor networks (WSNs) in the presence of uncertainty. One of the most important problems in WSNs is the energy consumption. Furthermore, extending the network lifetime in WSNs is highly dependent on selecting the appropriate cluster heads (CHs), and this can be a challenging task for the decision makers. In addition, parameters associated with WSNs are completely unexpected due to uncertainty. Therefore, after proposing K-medoids clustering and a MCDM technique, we have developed a novel algorithm for extending the lifetime of WSNs. As criteria, we have taken into account four important aspects of the proposed WSN: the distance from sink, average distance of cluster nodes, reliability of cluster and residual energy. To represent uncertain parameters in this work, we have considered triangular fuzzy numbers (TFNs). Finally, an experiment involving a WSN under uncertainty was investigated, and the findings have been graphically displayed. In this research, it has been observed that the proposed strategy with the novel algorithm exhibits 42% greater network lifetime as compared with a hybrid energy efficient distributed (HEED) algorithm and 11% and 18% with respect to optimal clustering artificial bee colony (OCABC) and particle swarm optimization (PSO) applied to a clustering optimization problem. We have also conducted statistical hypotheses for the purpose of confirming the presented outcomes.

Recently, various metaheuristic (MH) optimization algorithms have been presented and applied to solve complex engineering and optimization problems. One main category of MH algorithms is the naturally inspired swarm intelligence (SI) algorithms. SI methods have shown great performance on different problems. However, individual MH and SI methods face some shortcomings, such as trapping at local optima. To solve this issue, hybrid SI methods can perform better than individual ones. In this study, we developed a boosted version of the reptile search algorithm (RSA) to be employed for different complex problems, such as intrusion detection systems (IDSs) in cloud–IoT environments, as well as different optimization and engineering problems. This modification was performed by employing the operators of the red fox algorithm (RFO) and triangular mutation operator (TMO). The aim of using the RFO was to boost the exploration of the RSA, whereas the TMO was used for enhancing the exploitation stage of the RSA. To assess the developed approach, called RSRFT, a set of six constrained engineering benchmarks was used. The experimental results illustrated the ability of RSRFT to find the solution to those tested engineering problems. In addition, it outperformed the other well-known optimization techniques that have been used to handle these problems.

Traditional eddy current technology identifies metal information with information of single frequency of limited frequency spectrum. To solve existing problems, this paper proposes a discrete wavelet transform-based metal material analysis model by using a constant phase angle pulse eddy current (CPA-PEC) sensor which collects and depicts metal feature information from multiple dimensions; then, the quantification calculation model of metal material by CPA-PEC feature is presented; finally, an experimental platform is built to collect the CPA-PEC features of various metal samples and verify recognition accuracy of the proposed metal material analysis model. In the investigation, 1000 eddy current signals from four standard metals (Cu, Fe, Al, St) and three types of metallic irons (Fe-K162, Fe-K163, Fe-K240) are measured and the features are identified by discrete wavelet transform. The feature correlation and significance are determined by regression analysis. Finally, the calculation model of feature evaluation index is present. The experimental analysis indicates that the stability of the quantitative evaluation index of eddy current features reaches 97.1%, the comprehensive accuracy error is less than 0.32% and the average measurement speed is about 50 ms for 1000 random sampling tests on standard metals.

A traffic survey using an unmanned aerial vehicle on the Haixia road in Chongqing, China found a significant correlation between the velocity of the vehicle and the distance of the vehicle when moving forward and laterally. To mitigate driving disruptions owing to vehicle intrusion at a desired distance, personal space (PS) is introduced to analyze the car-following behavior of continuous mixed traffic flow formed by human-driven (HD) vehicles and cooperative adaptive cruise control (CACC) vehicles. PS is a virtual boundary that refers to the space where psychological tension is caused by the invasion of others. Further, an intelligent driver model (IDM) was used to establish a mixed traffic car-following PS-IDM. The stability of the disturbance transfer function analytical model in homogeneous and heterogeneous traffic flows was used to calculate the traffic flow stability region under different CACC vehicle permutations. The results show that the PS-IDM-based car-following model effectively improves the stability fitting effect of mixed traffic flow, and the driving comfort is increased by up to 20.7% when compared with that of the single car-following model. In addition, there is a negative correlation between the PS and the unstable velocity range of the traffic flow. Compared with the homogeneous HD traffic flow, the average intrusion rate of homogeneous CACC traffic flow is reduced by 4.6%, and the driving comfort of vehicles is improved by approximately 65%.

At present, polarization imaging detection experiments of underwater objects mainly focus on the degree of polarization but lack of Stokes vector imaging effect of each element. Based on the principle that the polarization characteristics of different materials are different, the experiment of underwater target detection by laser pulse polarization is carried out in this paper, and the influence of different depths of an underwater object and material factors on polarization imaging detection is studied. The results show that in air, the average degree of polarization of iron sheet is 0.56, that of ceramic tile is 1.00, and that of plastic is 0.48. In water, the average degree of polarization of iron sheet is 0.3625 at 7.5 m, the average degree of polarization of ceramic tile at 7.5 m is 0.359, and that of plastic at 7.5 m is 0.3805. The medium will change the degree of polarization of the measured object. The targets made of different materials have different polarization characteristics, and the polarization information is of great value in improving the detection performance of man-made targets. Polarization detection can obtain better information about underwater objects than traditional radiation intensity detection and can effectively suppress the absorption and scattering of light by water.

Due to their advantages, artificial ground freezing methods are widely used in deep shaft construction and repair with the continuous exploitation of coal and other mineral resources. The boundary convection due to ventilation conditions will affect the formation and development of this frozen soil wall, which needs to be studied systematically. Thus, in this study, a numerical calculation model of a freezing temperature field was established based on the actual conditions of the east ventilation shaft in the Chengjiao coal mine during repair by the freezing method, and the temperature and thickness laws of the frozen soil wall and the shaft wall were studied by changing the influencing parameters. The results indicated that the thickness of the outside position gradually exceeded that of the inside position of the frozen soil wall due to the ventilation effect, and the difference between these two parameters was approximately 0.2~0.3 m, while the temperature difference was no more than 1 °C. The frozen soil wall did not complete a cross-loop within 180 d under ventilation conditions when the freezing tube pitch exceeded a certain threshold, which was about 2.3~2.5 m for this ventilation shaft. The soil moisture content played an important role in the initial freezing under ventilation conditions in the full combination calculation. This paper provides theoretical support for studying the application of the artificial ground freezing method for shaft construction and repair under ventilation conditions.

The SM9 algorithm is an Identity-Based Encryption (IBE) algorithm independently made by China. The existing SM9 searchable encryption scheme cannot be effective against insider keyword guessing attacks and violates users’ data privacy. This article utilizes the SM9 encryption method to propose a Registered Public Keyword Searchable Encryption based on SM9 (RKSE-SM9), which uses the SM9 user keys in the registration keyword algorithm. For RKSE-SM9 to generate the keyword ciphertext or trapdoor, a secure server must first register the keyword, which effectively and reasonably protects users’ data and resists honest and curious cloud servers. From there, we also utilize Beaver’s triple to construct an improved registered keyword generation algorithm, defining and proving that the improved algorithm satisfies the concept of indistinguishability against registration keywords, achieving a higher level of privacy. In addition, compared with existing SM9 searchable encryption, our scheme proved to guarantee better security while reducing the computational efficiency by only 1%; compared with the existing registered keyword searchable encryption scheme, the overall operational efficiency increases by 63%.

The transportation sector is sharply shifting towards electric vehicles (EVs) to reduce environmental issues and the energy crisis. To enhance the driving range and performance of EVs, the integral parts of EVs are being developed with higher energy densities and compact structures. Traction inverters are one of the important parts of EVs which are continuously updating to higher power densities with smaller sizes. This has led to issues of high heat generation, which causes the performance degradation and failure of traction inverters. Therefore, an efficient cooling strategy needs to be proposed for the effective thermal management of traction inverters in EVs. In the present work, the magnetohydrodynamics (MHD) pump-based cooling system is developed for the thermal management of traction inverter for EVs. The cooling performance of traction inverters is investigated using a MHD pump-based cooling system with water and ferrofluid as coolants. The outlet velocity, inverter maximum temperature, and Nusselt number are numerically simulated as the cooling performance characteristics for various operating conditions of inlet velocity, magnetic field intensity, voltage, and volume fraction of ferrofluid. The coupled numerical model is developed using COMSOL Multiphysics commercial software to simulate the cooling performance of a traction inverter with an MHD pump-based cooling system under various conditions. The MHD pump improves the cooling performance of a traction inverter for ferrofluid cooling over water cooling. The cooling performance of the traction inverter improves with an increase in inlet velocity for both water and ferrofluid cooling. However, with an increase in voltage, magnetic field intensity, and volume fraction, the cooling performance of the traction inverter improves only for ferrofluid cooling. The outlet velocity, inverter maximum temperature and Nusselt number in the case of water cooling are 4.03 mm/s and 7.02 mm/s, 49.65 °C, respectively, whereas that in the case of ferrofluid cooling are 40.96 °C, 15.35, and 18.49, respectively. Further, the cooling performance improves for ferrofluid cooling at a magnetic field intensity of 0.4 T and volume fraction of 10% with outlet velocity, inverter maximum temperature, and Nusselt number approach to 12.08 mm/s, 32 °C and 21.43, respectively.

In a world where the energy crisis is becoming overwhelming, demand for integrating renewable energy sources is increasing and forming microgrids is becoming an essential solution. The new microgrid systems, which depend mainly on renewable energy sources instead of conventional synchronous generators, come with a low inertia concern. This paper proposes a virtual inertia controller based on a high-pass filter (HPF) to support the frequency of the AC microgrid while maintaining the DC voltage of the DC microgrid within the nominal ranges in cases of contingencies. The proposed system encounters an AC-DC microgrid with a renewable energy source on the DC microgrid alongside constant power load and resistive loads, while on the AC microgrid side, a synchronous generator is used to present the low inertia of the grid with dynamic loads and static loads. The state-space linearized model of the system is developed and verified using Matlab Simulink. The dynamic response of the proposed controller is compared to the low-pas filter (LPF)-based controller. Moreover, the effect of changing the system’s parameters on eigenvalues is investigated.

With its benefits of efficiency and speed, the orthogonal experimental design method is currently often employed in many domains for multi-factor experimental design. In this study, the wind–wave–flow multifunctional experimental flume was used to replicate surge circumstances using the orthogonal technique in order to investigate the contributing factors and their interactions on the sea surface IR characteristics of underwater vehicles’ wakes. According to the correlation analysis, the height of the swell and the surface temperature difference of the wake had only a weak negative correlation, while the dive depth of the underwater vehicle and the surface temperature difference of the wake had a significant negative correlation (p = −0.833). It was tentatively concluded that the surface temperature differential of the wake was found to be more sensitive to dive depth than to swell height. An investigation of the impact of trailing IR features can use this finding as a pertinent reference.

In cloud computing, dynamic storage of data generated by users, applications, tasks, workflows, etc. requires frequent access operations, so traditional encryption cannot be applied in this case. Considering the vulnerability of dynamic data, its protection needs to consider an efficient and dynamic security protection scheme. In data-oriented access control, the traditional approach is generally static policy matching, which cannot deal with emergencies and has the problem of privileged users. To solve this problem, this paper proposes a data-oriented risk-based access control model, which adds risk assessment to the traditional attribute-based access control and aims at the source of risk from three aspects: subject attribute, resource attribute, and environment attribute. A set of risk assessment indexes is proposed, and the calculation method of risk assessment is quantitatively analyzed by combining fuzzy consistency AHP analysis method, and finally, the realization of XACML is given. The validity of the proposed model is analyzed, and the carried out experimental analysis verifies its effectiveness. The proposed model benefits cloud data storage applications that require dynamic data storage, for example, medical/patient data storage.

This paper presents a relatively simple numerical approach to predict the cutting force during PDC (polycrystalline diamond contact) cutting of rock. The rock failure model is based on a damage-viscoplasticity model, with the Drucker–Prager yield surface and the modified Rankine surface as the tensile cut-off. The damage part of the model has separate scalar damage variables for tension and compression. The PDC cutter is idealized to a rigid surface and its interaction with the rock is modelled by contact mechanics, while solving the global equations of motion explicitly in time. A damage-based erosion criterion is applied, to remove the contact nodes surrounded by heavily damaged elements. The eroded elements are left in the mesh as ghost elements that do not contribute to the load transfer but preserve the mass conservation. Numerical simulations on granite, demonstrate that the method reliably predicts the cutting force of a single PDC cutter at different cutting depths and rake angles. publishedVersion

With the rapid growth of web networks, the security and privacy of online users are becoming more compromised. Especially, the use of third-party services to track users’ activities and improve website performance. Therefore, it is unavoidable that using personal information to create unique profiles may violate individuals’ privacy. Recently, several tools have been developed such as anonymity, anti-tracking, and browser plugins to ensure the protection of users from third-party tracking methods by blocking JavaScript programs and other website components. However, the current state lacks an efficient approach that provides a comprehensive solution. In this paper, we conducted a systematic analysis of the most common privacy protection tools based on their accuracy and performance by evaluating their effectiveness in correctly classifying tracking and functional JavaScript programs, then evaluating the estimated time the browser takes to render the pages for each tool. To achieve this, we automatically browsed the most 50 websites determined in 2022 and categorized them according to different fields to get the in-page (as part of HTML script tags), and all external JavaScript programs. Then we collected data and datasets of 1578 JavaScript elements and obtained six diverse Firefox profiles when the tools were enabled. The results found that Ghostery has the highest percentage of allowing most functioning scripts with the lowest average error rate (AER). While at the same time NoScript achieved the highest percentage of blocking most tracking scripts since it is the highest blocker of third-party services. After that, we examined the speed of the browser finding that, Ghostery improved the load time by 36.2% faster than the baseline, while Privacy Badger only reduced the load time by 7.1%. We believe that our findings can help users decide on a privacy tool that meets their needs. Moreover, researchers and developers can use our findings to improve the privacy of internet users by designing more effective privacy protection techniques.

Automated machining feature recognition is an essential component linking computer-aided design (CAD) and computer-aided process planning (CAPP). Deep learning (DL) has recently emerged as a promising method to improve machining feature recognition. However, training DL-based recognition models typically require annotating large amounts of data, which is time-consuming and labor-intensive for researchers. Additionally, DL models struggle to achieve satisfactory results when presented with small labeled datasets. Furthermore, existing DL-based approaches require significant memory and processing time, thus hindering their real-world application. To address these challenges, this paper presents a semi-supervised learning framework that leverages both labeled and unlabeled data to learn meaningful visual representations. Specifically, self-supervised learning is utilized to extract prior knowledge from a large dataset without annotations, which is then transferred to improve downstream feature recognition tasks. Furthermore, we apply lightweight network techniques to two established feature recognizers, FeatureNet and MsvNet, to develop reduced-memory, computationally efficient models termed FeatureNetLite and MsvNetLite, respectively. To validate the effectiveness of the proposed approaches, we conducted comparative studies on the FeatureNet dataset. With only one training sample per class, MsvNetLite outperformed MsvNet by about 19%, whereas FeatureNetLite outperformed FeatureNet by approximately 20% in machining feature classification. On a common X86 CPU, MsvNetLite gained 6.68× improvement in speed over MsvNet, and FeatureNetLite was 2.49× faster than FeatureNet. The proposed semi-supervised learning framework shows a significant improvement in machining feature recognition on small labeled data while achieving the optimal balance between recognition accuracy and inference speed compared to other DL-based approaches.

Removal of agricultural pipes used in crop support and greenhouse agriculture is manpower-intensive. However, most agricultural workers are elderly. Therefore, auxiliary devices should be used to allow pipe removal with as little force as possible. In this study, the design parameters of an extraction device were optimized within constraints to minimize the force required to remove agricultural pipes. The optimization parameters are the length of each link and the initial link angle of the device. The driving force, according to the design parameters, was calculated by applying the theory of kinematics. An optimal design plan was derived using an optimization algorithm to minimize the force driving the device within the desired constraint. As a result of the optimization, it was confirmed that the force required to remove the pipe was reduced by 87.1% compared with the initial design value and was designed to suit the user’s convenience.

The application of deep neural networks to address automatic modulation recognition (AMR) challenges has gained increasing popularity. Despite the outstanding capability of deep learning in automatic feature extraction, predictions based on low-data regimes with imbalanced classes of modulation signals generally result in low accuracy due to an insufficient number of training examples, which hinders the wide adoption of deep learning methods in practical applications of AMR. The identification of the minority class of samples can be crucial, as they tend to be of higher value. However, in AMR tasks, there is a lack of attention and effective solutions to the problem of Imbalanced-class in a low-data regime. In this work, we present a practical automatic data augmentation method for radio signals, called SigAugment, which incorporates eight individual transformations and effectively improves the performance of AMR tasks without additional searches. It surpasses existing data augmentation methods and mainstream methods for solving low-data and imbalanced-class problems on multiple datasets. By simply embedding SigAugment into the training pipeline of an existing model, it can achieve state-of-the-art performance on benchmark datasets and dramatically improve the classification accuracy of minority classes in the low-data imbalanced-class regime. SigAugment can be trained for uniform use on different types of models and datasets and works right out of the box.

In the present study, we aimed to determine whether the shelf life of effervescent tablets may be assessed during storage using total directional hemispherical reflectance (THR). We also analyzed selected pharmacopeial parameters of the tablets and used X-ray microtomography to assess the internal structure of the tablets. Two types of effervescent tablets of one commercial product containing magnesium and vitamin B6 (expired and unexpired) were analyzed. In addition, randomly selected unexpired and expired tablets were dried in a vacuum oven for 24 h at 50 °C. The expired effervescent tablets disintegrated in a significantly shorter time than the unexpired tablets (68 s vs. 83 s, p = 0.016). The remaining pharmacopeial parameters did not differ between the two types of tablets. We observed that expired effervescent tablets showed lower mean values of THR in five spectral ranges, which indicates that a greater amount of radiation beam was transmitted into their inner structure than for unexpired tablets. The density of the inner structure assessed on the microtomographic scans differed significantly between all the analyzed tablets (p < 0.001). The highest mean density was observed in the case of the unexpired tablets dried in a vacuum oven (1.273 g/cm3), while the lowest density was observed in the case of the expired tablets (1.261 g/cm3). The expired tablets had a larger pore diameter compared to the unexpired tablets (0.095 mm vs. 0.074 mm, respectively; p = 0.007). Additionally, the percentage of porosity was higher in the expired tablets compared to the unexpired tablets (p < 0.001).

Coastal zones are extremely vulnerable and, at the same time, anthropogenically pressed. Excessive enrichment of marine waters with nutrients and organic matter can lead to “red tides”, oxygen deficits, decreasing assimilation capacity, etc. The purpose of this work is to study atmospheric precipitations as a source of nutrients directly affecting waters of the coastal areas of Crimea and, ultimately, strengthening eutrophication consequences. In 2004–2008, and from 2015 to present, samples of atmospheric precipitations have been collected at the Marine Hydrometeorological Station in Sevastopol. They have been analyzed for the content of inorganic nitrogen, phosphorus, and silica. For 2009–2014, direct measurements are unavailable and a previously retrieved multiple nonlinear regression equation has been used to estimate the concentration of inorganic nitrogen in atmospheric precipitations depending on meteorological conditions, including the number of precipitations, number of days without precipitations, relative air humidity, wind direction vectors, and air temperature. Data obtained in this study have revealed that atmospheric precipitations are one of important inputs of nutrients for local areas. Their relative contribution increases on the time scale of days, while the role of rivers remains the most important on the annual scale. The contribution of atmospheric precipitations to the inventory of nutrients becomes more significant in the summer, when seasonal stratification in the water column prevents vertical mixing of waters, and the ambient concentration of nutrients in the upper layer of water is minimal.

The residual magnetic field in a magnetic shielding device with a multilayer high permeability material (permalloy) structure can be obtained at the nanotesla (nT) level or even lower. At present, in the process of designing a magnetic shielding device, most of the external environmental magnetic field settings are set at the size of the Earth’s environmental magnetic field, but the instruments inside the magnetic shielding device need to be powered, the active compensation coil needs to be powered, and the degaussing coil of passive shielding layer needs to be powered, so substations need to be used around magnetic shielding devices. The magnetic field generated by the substation will affect the magnetic shielding device, so this paper analyzes and measures the magnetic field generated by the substation. Firstly, the finite element model of a substation is established, and the influence of different substations on the environmental magnetic field is analyzed by changing the power. Secondly, the test method of a substation environment magnetic field is determined. Finally, the site test was carried out to measure the influence of different power substations and different distances on the magnetic field, and its influence on the magnetic shielding device was analyzed, which provided an important basis for the construction of the magnetic shielding device.

Waterline usually plays as an important visual cue for the autonomous navigation of marine unmanned surface vehicles (USVs) in specific waters. However, the visual complexity of the inland waterline presents a significant challenge for the development of highly efficient computer vision algorithms tailored for waterline detection in a complicated inland water environment that marine USVs face. This paper attempts to find a solution to guarantee the effectiveness of waterline detection for the USVs with a general digital camera patrolling variable inland waters. To this end, a general deep-learning-based paradigm for inland marine USVs, named DeepWL, is proposed, which consists of two cooperative deep models (termed WLdetectNet and WLgenerateNet, respectively). They afford a continuous waterline image-map estimation from a single video stream captured on board. Experimental results demonstrate the effectiveness and superiority of the proposed approach via qualitative and quantitative assessment on the concerned performances. Moreover, due to its own generality, the proposed approach has the potential to be applied to the waterline detection tasks of other water areas such as coastal waters.

In this work we present the development, testing and comparison of three different physics-informed deep learning paradigms, namely the ConvLSTM, CNN-LSTM and a novel Fourier Neural Operator (FNO), for solving the partial differential equations of the RANS turbulence model. The 2D lid-driven cavity flow was chosen as our system of interest, and a dataset was generated using OpenFOAM. For this task, the models underwent hyperparameter optimization, prior to testing the effects of embedding physical information on performance. We used the mass conservation of the model solution, embedded as a term in our loss penalty, as our physical information. This approach has been shown to give physical coherence to the model results. Based on the performance, the ConvLSTM and FNO models were assessed in forecasting the flow for various combinations of input and output timestep sizes. The FNO model trained to forecast one timestep from one input timestep performed the best, with an RMSE for the overall x and y velocity components of 0.0060743 m·s−1.

Rock image classification is a fundamental and crucial task in the creation of geological surveys. Traditional rock image classification methods mainly rely on manual operation, resulting in high costs and unstable accuracy. While existing methods based on deep learning models have overcome the limitations of traditional methods and achieved intelligent image classification, they still suffer from low accuracy due to suboptimal network structures. In this study, a rock image classification model based on EfficientNet and a triplet attention mechanism is proposed to achieve accurate end-to-end classification. The model was built on EfficientNet, which boasts an efficient network structure thanks to NAS technology and a compound model scaling method, thus achieving high accuracy for rock image classification. Additionally, the triplet attention mechanism was introduced to address the shortcoming of EfficientNet in feature expression and enable the model to fully capture the channel and spatial attention information of rock images, further improving accuracy. During network training, transfer learning was employed by loading pre-trained model parameters into the classification model, which accelerated convergence and reduced training time. The results show that the classification model with transfer learning achieved 92.6% accuracy in the training set and 93.2% Top-1 accuracy in the test set, outperforming other mainstream models and demonstrating strong robustness and generalization ability.

High voltage transmission lines are crucial infrastructure that are demanded to supply an increasing request of electric energy. In the design and operations stages, sag represents a key parameter which must respect specific constraints. Therefore, sag continuous monitoring is becoming essential to guarantee the correct functioning of the line and to optimize the current flow. Different solutions have been proposed in literature, but they are still lacking efficiency and reliability to be used during operations. In this work, a simple and efficient method, based on conductor parabolic approximation, is developed and used to compute the sag through the measurement of the conductor slope in proximity of the span extremities. The angular measurements are obtained using wireless sensors equipped with MEMS accelerometers developed by authors and employed for HVTL conductor vibration monitoring. The proposed method and its implementation in the monitoring system was tested in a laboratory environment on a real conductor. The values of sag at different tensile loads have been obtained and compared to the measured ones, with satisfactory results according to the accelerometer resolution. The solution developed therefore represents a complete and innovative tool to be adopted in the field to monitor, in real time, both the sag and the level of vibration due to the wind action, allowing to increase the performance reliability of HVTL.

Automatic target recognition technology is an important research direction in the field of machine vision. Artificial ground targets, such as bridges, airports and houses, are mostly composed of straight lines. The ratio of geometric primitive lines to the triangle area formed by their combination is used as the feature quantity to describe the group of lines, so as to characterize the artificial ground target. In view of the shortcomings of traditional edge detection methods, such as background suppression, non-prominent targets, missing positions, etc., this paper proposed an image edge detection method based on depth learning. By combining the traditional edge detection algorithm with the edge detection algorithm based on an improved HED network, the real-time target image edge detection was completed. An automatic target recognition method based on template matching was proposed. This method solved the problem of both homologous template matching and heterogeneous template matching, which has important theoretical value. First, the lines were combined to form the geometric primitives of the line group, and then the relationship of the lines in the group was determined by using the characteristic quantity of the line group. The best line group matching the target template was found in the image edge, and the homonymous points in the real-time image and the target template were calculated. The affine transformation matrix between the two images was obtained according to the homonymous points, and then the accurate position of the target in the real-time image was found.

Existing persona-based dialogue generation models focus on the semantic consistency between personas and responses. However, various influential factors can cause persona inconsistency, such as the speaking style in the context. Existing models perform inflexibly in speaking styles on various-persona-distribution datasets, resulting in persona style inconsistency. In this work, we propose a dialogue generation model with persona selection classifier to solve the complex inconsistency problem. The model generates responses in two steps: original response generation and rewriting responses. For training, we employ two auxiliary tasks: (1) a persona selection task to fuse the adapted persona into the original responses; (2) consistency inference to remove inconsistent persona information in the final responses. In our model, the adapted personas are predicted by an NLI-based classifier. We evaluate our model on the persona dialogue dataset with different persona distributions, i.e., the persona-dense PersonaChat dataset and the persona-spare PersonalDialog dataset. The experimental results show that our model outperforms strong models in response quality, persona consistency, and persona distribution consistency.

Vertical displacement acceleration and the pitch angle record produce the phenomenon of beat vibration when testing a 200 m electro-dynamic suspension (EDS) magnetic levitation (maglev) test vehicle with high-temperature superconducting (HTS) at the CRRC Changchun Railway Vehicles Co., Ltd., where the vehicle is clamped and in planar motion. First, to examine this phenomenon, this paper establishes dynamic equations of the vehicle with three degrees of freedom (DOF), and the levitation force on each superconducting magnet (SCM) is calculated by dynamic circuit theory. Second, the theory vertical equilibrium point is obtained from the average of the levitation force for a different velocity and the magneto-motive force (MMF) of the SCM. Third, this paper decouples SCM levitation forces from each other using MATLAB/SIMULINK, and a multi-body dynamic model with six DOF is developed in SIMPACK. All vertical displacements and acceleration responses, as well as the pitch angle and acceleration response from the simulation, appear to show the phenomenon of beat vibration since there are two closing natural frequencies of approximately 2 Hz and 2.4 Hz. Finally, based on the traversing method considering the influence of the velocity, initial vertical displacement, and the MMF of the SCM, the multi-body dynamic model is frequently utilized to study the response of the mean and amplitude of vertical displacement and that of the pitch angle. The results show that increasing the MMF or velocity could decrease the vertical displacement and pitch angle; the mean vertical displacement is a little larger than the theory equilibrium point; and the amplitude of vertical displacement is small when the initial vertical displacement is near the theory equilibrium point. Both the numerical and experimental results verify the validity of the dynamic circuit model and mechanical model in this paper.

The accumulation of biowastes is one of the main concerns of modern society. One of the most environmentally friendly solutions to convert biowaste into a product is composting. Biowastes may contain unknown substances that are persistent in the final compost, thus contributing to soil contamination and salinization. The effectiveness of the composting process depends on the microbial communities involved, which is the number of investigations’ targets. The present work studied the bacterial diversity of mesophilic and thermophilic phases of composting developed in two different sites. The study was conducted through next-generation Illumina HiSeq sequencing and phylogenetic communities, revealing the dynamics and changes in specific mesophilic and thermophilic habitats of composting piles. The results showed a higher number of bacterial species in the mesophilic phase than in the thermophilic one, proved by the Shannon and Chao indices. In addition, the diversity of bacterial species expressed by the operational taxonomic units was much higher at the site of Harmanli than at the Yasno pole. Higher abundance was found of the genera Sphingobacterium, Sphingomonas, Paracoccus, Pseudomonas, and Halomonas in both studied sites. In the compost of Harmanli genera Streptomyces, Truepera, and Flavobacterium were found to be much more abundant compared to the compost of the Yasno pole. Finally, we conclude that the two plots show relatively significant differences in the diversity of bacteria during biowaste composting. Substantial differences were also observed between the mesophilic and thermophilic phases, with the first showing a significantly higher degree of species richness.