Applied Sciences

There are many issues affecting multi-hole parallel tunneling and long tunnel ventilation, such as insufficient fresh air supplied at the tunnel face, pollution by contaminated winds, and uncoordinated work among multiple fans. In view of these issues, this paper uses the numerical simulation software Fluent combined with the orthogonal analysis test method, and, based on the actual working conditions of Yuelongmen Tunnel on the Chenglan Line, simulates the parameters of the duct, the installation position of the jet fan at the cross channel, and the combined operation status of multiple fans. The main conclusions are as follows: (1) the primary factor affecting the CO discharge from the tunnel face is the distance between the exit of the duct and the tunnel face, and the secondary factor is the diameter of the duct, while the position of the duct is the least influential factor; (2) the jet fan at the intersection of the cross channel should be set close to the pollution source in such a way as to prevent the jet from hitting the windward side of the cross channel and thereby avoid causing the contaminated wind to contaminate the adjacent tunnel; (3) in tunnels in which blasting operations do not occur, the simultaneous use of high-power air supplies and jet fans will reduce the air pressure in the tunnel, generate an air pressure difference with the adjacent tunnel, trap polluted air, and reduce the ventilation effect. Full article

An identity management system (IDMS) manages and organizes identities and credentials information exchanged between users, identity providers (IDPs), and service providers (SPs) to ensure confidentiality and enhance privacy of users’ personal data. Traditional or centralized IDMS rely on a third party to store a user’s personal information, authenticate the user, and organize the entire process. This clearly constitutes threats to the privacy of the user, in addition to other issues, such as single point of failure (SPOF), user tracking, and data availability issues. Blockchain technology has many useful features that can contribute to solving traditional IDMS issues, such as decentralization, immutability, and anonymity. Blockchain represents an attractive solution for many issues related to traditional IDMS, including privacy, third-party control, data leakage, and SPOF, supported by Distributed Ledger Technology (DLT) security features and powerful smart contracts technology. The current study presents a systematic literature review and analysis for recently proposed solutions that adopt the traditional centralized approach, as well as solutions based on blockchain technology. The study also aims to provide a deep understanding of proposed IDMS solutions and best practices, and highlight the research gaps and open issues related to IDMSs and users’ privacy. In particular, the current research focuses on analyzing the blockchain-based solutions and illustrating their strengths and weaknesses, as well as highlighting the promising blockchain technology framework that can be utilized to enhance privacy and solve security issues in a centralized IDMS. Such a study is an important step towards developing efficient solutions that address the pressing needs in the field. Full article

This paper proposes a pseudo-haptic interface that depicts the virtual weights of dumbbells in a virtual gymnasium. When a user performs a dumbbell biceps curl, he/she fixes the elbow joint as a standard joint and lifts the dumbbell, with its movement trajectory represented as a circular arc. The trajectories and velocity of dumbbell bicep curls differ depending on human physiological characteristics. Therefore, the proposed system provides an adaptable exercise area and force visualization of virtual dumbbells using a velocity-based pseudo-haptic interface and computer vision-based tracking method. The system recognizes the position and rotation of joints related to a dumbbell biceps curl with the implementation of density-based spatial clustering of applications with noise (a clustering algorithm) and resizes the radius and angle of an integrated force circular gauge. Furthermore, when a user lifts a dumbbell, the system recognizes, using linear regression, the current position and lifting force of the virtual dumbbell and visualizes the current lifting force with a guided movement trajectory to match the lifting force. Experimental results show that the proposed pseudo-haptic interface increased weight perception and usability by up to 30% compared to conventional methods (p < 0.05). Full article

We previously proposed techniques to eliminate repeated three-dimensional (3D) images produced by the light field Mirage, which consists of circularly aligned multiple-slanted light field displays. However, we only constructed the lower half of the system to verify the proposed elimination techniques. In this study, we developed an alignment technique for a complete 360-degree display system. The alignment techniques for conventional 360-degree display systems, which use a large number of projectors, greatly depend on electronic calibration, which indispensably causes image quality degradation. We propose a systematic approach for the alignment for the light field Mirage, which causes less image quality degradation by taking advantage of the small number of display devices required for the light field Mirage. The calibration technique for light field displays, the image stitching technique, and the brightness matching technique are consecutively performed, and the generation of 360-degree 3D images is verified. Full article

This paper presents the results of preliminary tests carried out in an organization producing products for the automotive industry. From the many available systems used in this research, different values of results were obtained; these differences cause doubts among people deciding about the process approval and start of production. The main aim of the research presented in the article is to determine the influence of various factors on the measurement results, especially to compare the results of measurements obtained with the optical sensor, which is used during measurements with the use of a multisensor measuring machine. The results obtained with the use of the height gauge, which is used alternatively in the organization, raise further doubts. Experience has shown that the methodology and definition of the alignment during the measurement, which is different for each of the systems, have a great influence. Full article

In the case of strong noise, when the damage occurs at different locations of the frame structure, the fault vibration signals generated are relatively close. It is difficult to accurately diagnose the specific location of the damage by using the traditional convolution neural network method. In order to solve this problem, this paper proposes a novel convolutional neural network. The method first uses wavelet decomposition and reconstruction to filter out the noise signal in the original vibration signal, then uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Analysis) to decompose the filtered signal to highlight the feature information in the filtered signal. Finally, a convolution neural network combined with WDCNN (First Layer Wide Convolution Kernel Deep Convolution Neural Network) and LSTM (Long Short-Term Memory Network) is used to achieve the accurate classification of the signal, so as to achieve the accurate diagnosis of the damage location of the frame structure. Taking the four-story steel structure frame of Columbia University as the research object, the fault diagnosis method proposed in this paper is used to carry out experimental research under strong noise conditions. The experimental results show that the accuracy of the fault diagnosis method proposed in this paper can reach 99.97% when the signal-to-noise ratio is −4 dB and the objective function value is reduced to 10⁻⁴. Therefore, the fault diagnosis method proposed in this paper has a high accuracy in the strong noise interference environment; it can realize a high precision diagnosis of the damage location of the frame structure under a strong noise environment. The contribution and innovation of this paper is to propose a novel fault diagnosis method based on the convolutional neural network, which solves the problem of accurate damage location diagnosis of frame structures under strong noise environment. Full article

In Situ monitoring of the calorific value of coal has the advantage of reducing the amount of unburned carbon by injecting an appropriate amount of combustion air immediately to induce complete combustion. High sulfur concentrations cause severe environmental problems such as acid rain. In order to estimate the calorific value and measure the sulfur concentration, a new powerful technique for mixed coals was studied. Laser-induced breakdown spectroscopy (LIBS) does not require sample preparation. Several blended coals were used for the experiment to replicate the actual coal-fired power plant conditions. Two well-known data processing methods in near-infrared spectroscopy have been adopted to enhance the weak sulfur emission lines. The performance of the partial least square regression model was established by the parameters such as coefficient of determination, R², relative error, and root mean square error (RMSE). The RMSE average was compared with the results of previous studies. As a result, the values from this study were smaller by 6.02% for the calibration line and by 4.5% for the validation line in near-infrared spectroscopy. The RMSE average values for calorific values were calculated to be less than 1%. Full article

Temporomandibular disorders (TMDs) are multi-factorial and polysymptomatic pathologies and their management must be customized for every patient. Numerous therapy techniques are available to treat temporomandibular disorders-related muscular discomfort and persistent orofacial pain. Botulinum toxin (BoNT) has emerged as a popular option for patients with myofascial TMD who do not completely recover from their condition after receiving conservative care and medication. A systematic search of the literature, from January 2000 until 1 April 2022, was performed in the MEDLINE (PubMed), Web of Science, and Lilacs databases. The following search terms combination: (temporomandibular disorders) OR (botulinum) OR (toxin) was employed. A total of 357 articles were initially found in the electronic search. After screening, 11 full-text articles satisfied the inclusion criteria. The Cochrane risk of bias tool (RoB 2) tool, which uses seven domains of bias to assess random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment of self-reported outcomes, blinding of outcome assessment of objective measures, incomplete outcome data, selective reporting, and other biases, was employed to analyze randomized controlled trials. The aim of this systematic review of randomized controlled trials is to provide an overview of the use of BoNT for TMDs by comparing the application of BoNT with other therapeutic approaches. BoNT-A could help patients that do not respond to conservative treatments. Low doses are recommended when BoNT-A is considered for persistent orofacial pain related to TMD. Future research should, however, conduct clinical trials with a stricter design. The results of BoNT-A could be confirmed by more randomized controlled trials with larger sample sizes, less bias, and longer follow-up times. Full article

Over the past few years, personalized recommendation systems aim to address the problem of information overload to help users achieve useful information and make quick decisions. Recently, due to the benefits of effective representation learning and no labeled data requirements, autoencoder-based models have commonly been used in recommendation systems. Nonetheless, auxiliary information that can effectively enlarge the feature space is always scarce. Moreover, most existing methods ignore the hidden relations between extended features, which significantly affects the recommendation accuracy. To handle these problems, we propose a Multi-Feature extension method via a Semi-AutoEncoder for personalized recommendation (MFSAE). First, we extract auxiliary information from DBpedia as feature extensions of items. Second, we leverage the LSI model to learn hidden relations on top of item features and embed them into low-dimensional feature vectors. Finally, the resulting feature vectors, combined with the original rating matrix and side information, are fed into a semi-autoencoder for recommendation prediction. We ran comprehensive experiments on the MovieLens datasets. The results demonstrate the effectiveness of MFSAE compared to state-of-the-art methods. Full article

The Internet of Things (IoT) system has been a hot topic in recent years. Its operation is a system that stores data in data storage and is completed by the exchange of network information about things. Therefore, the security of information between network transmissions is very important. In recent years, the most likely cause of information security problems has been a distributed denial of service (DDoS) attack. In this paper, we proposed an autonomous defense system that combines edge computing with a two-dimensional convolutional neural network (CNN) to recognize whether the data server in IoT suffers from DDoS attacks and identify the attack mode. The accuracy of trained two-dimensional CNN is up to 99.5% and 99.8% for packet traffic and packet features training, respectively. A field experiment’s results show that the data server in the proposed system can effectively distinguish the difference between the DDoS attacks and the normal transmission to reduce the impact of DDoS attacks on the IoT data storage while it is under attack. Full article

Articular cartilage injuries are a common source of joint pain and dysfunction. As articular cartilage is avascular, it exhibits a poor intrinsic healing capacity for self-repair. Clinically, osteochondral grafts are used to surgically restore the articular surface following injury. A significant challenge remains with the repair properties at the graft-host tissue interface as proper integration is critical toward restoring normal load distribution across the joint. A key to addressing poor tissue integration may involve optimizing mobilization of fibroblast-like synoviocytes (FLS) that exhibit chondrogenic potential and are derived from the adjacent synovium, the specialized connective tissue membrane that envelops the diarthrodial joint. Synovium-derived cells have been directly implicated in the native repair response of articular cartilage. Electrotherapeutics hold potential as low-cost, low-risk, non-invasive adjunctive therapies for promoting cartilage healing via cell-mediated repair. Pulsed electromagnetic fields (PEMFs) and applied direct current (DC) electric fields (EFs) via galvanotaxis are two potential therapeutic strategies to promote cartilage repair by stimulating the migration of FLS within a wound or defect site. PEMF chambers were calibrated to recapitulate clinical standards (1.5 ± 0.2 mT, 75 Hz, 1.3 ms duration). PEMF stimulation promoted bovine FLS migration using a 2D in vitro scratch assay to assess the rate of wound closure following cruciform injury. Galvanotaxis DC EF stimulation assisted FLS migration within a collagen hydrogel matrix in order to promote cartilage repair. A novel tissue-scale bioreactor capable of applying DC EFs in sterile culture conditions to 3D constructs was designed in order to track the increased recruitment of synovial repair cells via galvanotaxis from intact bovine synovium explants to the site of a cartilage wound injury. PEMF stimulation further modulated FLS migration into the bovine cartilage defect region. Biochemical composition, histological analysis, and gene expression revealed elevated GAG and collagen levels following PEMF treatment, indicative of its pro-anabolic effect. Together, PEMF and galvanotaxis DC EF modulation are electrotherapeutic strategies with complementary repair properties. Both procedures may enable direct migration or selective homing of target cells to defect sites, thus augmenting natural repair processes for improving cartilage repair and healing. Full article

Fueled by the development of deep neural networks, breakthroughs have been achieved in plenty of computer vision problems, such as image classification, segmentation, and object detection. These models usually have handers and millions of parameters, which makes them both computational and memory expensive. Motivated by this, this paper proposes a post-training quantization method based on the clipping operation for neural network compression. By quantizing parameters of a model to 8-bit using our proposed methods, its memory consumption is reduced, its computational speed is increased, and its performance is maintained. This method exploits the clipping operation during training so that it saves a large computational cost during quantization. After training, this method quantizes the parameters to 8-bit based on the clipping value. In addition, a fully connected layer compression is conducted using singular value decomposition (SVD), and a novel loss function term is leveraged to further diminish the performance drop caused by quantization. The proposed method is validated on two widely used models, Yolo V3 and Faster R-CNN, for object detection on the PASCAL VOC, COCO, and ImageNet datasets. Performances show it effectively reduces the storage consumption at 18.84% and accelerates the model at 381%, meanwhile avoiding the performance drop (drop < 0.02% in VOC). Full article

Abstract: In this paper, the load-transfer mechanism and settlement behaviors of the pile-supported reinforced embankment are reviewed by laboratory model tests, and a series of finite element method (FEM) modellings are conducted to analyze the soil-arching geometry and embankment deformation patterns of the pile-supported reinforced embankment [...] Full article

A method of local anode effect prediction is proposed for the problem that it is difficult to detect the local anode effect in large aluminum reduction cell in real time. Firstly, a fuzzy classification of local anode effect prediction in terms of fuzziness level is proposed considering various working conditions of anode current in the region. Secondly, a current volatility detection method based on time-sliding window density is designed from the problem of uneven current distribution in the region, and the anode currents in the region are classified and tracked for prediction according to the different current volatility. Thirdly, an improved Gated Recurrent Unit (GRU) neural network structure is proposed to improve the prediction accuracy of fluctuating currents. Finally, simulation experiments are conducted based on actual data, and compared with Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), the proposed method has certain advantages in both prediction time, training time, and the mean absolute error (MAE) and mean square error (MSE), which verifies the effectiveness of the proposed method. Full article

We deal with the problem of class imbalance in data mining and machine learning classification algorithms. This is the case where some of the class labels are represented by a small number of examples in the training dataset compared to the rest of the class labels. Usually, those minority class labels are the most important ones, implying that classifiers should primarily perform well on predicting those labels. This is a well-studied problem and various strategies that use sampling methods are used to balance the representation of the labels in the training dataset and improve classifier performance. We explore whether expert knowledge in the field of Meteorology can enhance the quality of the training dataset when treated by pre-processing sampling strategies. We propose four new sampling strategies based on our expertise on the data domain and we compare their effectiveness against the established sampling strategies used in the literature. It turns out that our sampling strategies, which take advantage of expert knowledge from the data domain, achieve class balancing that improves the performance of most classifiers. Full article

The stages of pre-project preparation and design development are the fundamentals to the further implementation of an investment and construction project. The success of construction and commissioning of facilities depends on the smooth completion of these stages. The duration of stages depends on the competence and coordination of interaction between participants of an investment and construction project, rational decision making by the project manager and a number of other factors. Identifying these factors and finding rational planning options in terms of the stages of pre-project preparation and design development is a highly relevant task. With this in mind, the authors have developed an organizational and management model of a rational procedure to be implemented by the project manager at the stages of pre-project preparation and design development for a facility to be constructed. The model takes into account the influence of negative factors. The authors have also developed a method for selecting rational solutions at the stages in question. The analysis of the research literature and regulatory documents was performed for this purpose. The method of expert evaluations, elements of numerical analysis, mathematical processing of practical results and methods of mathematical modeling were applied. As a result, the authors have developed an organizational and management model for a rational combination of procedures at the stages of pre-project preparation and design development for a facility to be constructed and derived a formula determining the duration of implementation of each stage and a technique designated for selecting rational solutions at the stages in question. The findings of this study can simplify project planning and process management at the stages of pre-project preparation and design development in terms of information modeling. Full article

The enlarged head pressure-dispersed anchor cable is a new type of anchor cable, and the enlarged head of the anchor solid is a multisegment body with variable cross section. Compared with the traditional tension type anchor cable structure, the stress mode of the anchor solid is changed from tensile stress to compressive stress, which is more reasonable. The development process of the plastic zone of the soil around the enlarged head anchor solid is verified by a simulation method. The spherical plastic zone space formed at the enlarged head anchorage end is conducive to the load transfer between the anchor solid and the surrounding soil and reduces the displacement of the anchor solid in the soil. In this pursuit, the present study examines the force mechanism of the enlarged head pressure-dispersed anchor cable. Subsequently, taking an actual project as an example, the anchoring capacity of the enlarged head pressure-dispersed anchor cable and enlarged head tension-type anchor cable was analyzed and compared using FLAC3D software. The results indicate that the support effect of the enlarged head pressure-dispersed anchor cable was better than that of the enlarged head tension anchor cable. Full article

This paper proposes a novel spatiotemporal chaotic system with two-dimensional dynamic pseudo-random coupled map lattices (2D-DPRCML) based on partitioned elementary cellular automata (PECA). In the system iteration, coupling lattices are chosen based on the chaotic PECA, and the iterative results of PECA are also employed as the perturbation for the system. We investigate the system’s chaotic properties, including bifurcation diagrams, Kolmogorov-Sinai entropy density and universality. In addition, the output sequences were analyzed for uniformity and randomness. The correlations between the system lattices are also explored. The simulation results and theoretical analysis demonstrate that the 2D-DPRCML system possesses excellent chaotic performance, and the output sequences show good uniformity and randomness, indicating that the 2D-DPRCML system is capable of resisting the return maps attack. It is evident from all these advantages that the proposed system is ideal for use in cryptography. Full article