Search Results (1,352)

This study presents a soft-computing-based method for determining polymer pipelines’ creep function parameters (CFPs) and pressure wave speeds (PWSs) through transient flow analysis. To this end, first, a numerical model for transient flow in polymer pipes was developed in the time domain. Then, by considering a pipeline with a specific geometry, 2000 transient flow signals were generated for different CFPs and PWSs. The amplitudes obtained by transforming the time-domain pressure signals to the frequency domain using the fast Fourier transform algorithm are the inputs for an artificial neural network model. The results showed that the proposed approach accurately estimated the creep function of the polymer pipes. Full article

Evaluating the reliability of deep soft rock tunnels is a very important issue to be solved. In this study, we propose a Monte Carlo simulation reliability analysis method (MCS–RAM) integrating the adaptive momentum stochastic optimization algorithm (Adam), Bayesian inference theory and Gaussian process regression (GPR) with combined kernel function, and we developed it in Python. The proposed method used the Latin hypercube sampling method to generate a dataset sample of geo-mechanical parameters, constructed combined kernel functions of GPR and used GPR to establish a surrogate model of the nonlinear mapping relationship between displacements and mechanical parameters of the surrounding rock. Adam was used to optimize the hyperparameters of the surrogate model. The Bayesian inference algorithm was used to obtain the probability distribution of geotechnical parameters and the optimal surrounding rock mechanical parameters. Finally, the failure probability was computed using MCS–RAM based on the optimized surrogate model. Through the application of an engineering case, the results indicate that the proposed method has fewer prediction errors and stronger prediction ability than Kriging or XGBoost, and it can significantly save computational time compared with the traditional polynomial response surface method. The proposed method can be used in the reliability analysis of all shapes of tunnels. Full article

Monitoring tree growth helps operators better understand the growth mechanism of trees and the health status of trees and to formulate more effective management measures. Computer vision technology can quickly restore the three-dimensional geometric structure of trees from two-dimensional images of trees, playing a huge role in planning and managing tree growth. This study used binocular reconstruction technology to measure the height, canopy width, and ground diameter of Castanopsis hystrix and compared the growth differences under different nitrogen levels. In this research, we proposed a wavelet exponential decay thresholding method for image denoising. At the same time, based on the traditional semi-global matching (SGM) algorithm, a cost search direction is added, and a multi-line scanning semi-global matching (MLC-SGM) algorithm for stereo matching is proposed. The results show that the wavelet exponential attenuation threshold method can effectively remove random noise in red cone images, and the denoising effect is better than the traditional hard-threshold and soft-threshold denoising methods. The disparity images produced by the MLC-SGM algorithm have better disparity continuity and noise suppression than those produced by the SGM algorithm, with more minor measurement errors for C. hystrix growth factors. Medium nitrogen fertilization significantly promotes the height, canopy width, and ground diameter growth of C. hystrix. However, excessive fertilization can diminish this effect. Compared to tree height, excessive fertilization has a more pronounced impact on canopy width and ground diameter growth. Full article

Mobile Edge Computing (MEC) is crucial for reducing latency by bringing computational resources closer to the network edge, thereby enhancing the quality of services (QoS). However, the broad deployment of cloudlets poses challenges in efficient network slicing, particularly when traffic distribution is uneven. Therefore, these challenges include managing diverse resource requirements across widely distributed cloudlets, minimizing resource conflicts and delays, and maintaining service quality amid fluctuating request rates. Addressing this requires intelligent strategies to predict request types (common or urgent), assess resource needs, and allocate resources efficiently. Emerging technologies like edge computing and 5G with network slicing can handle delay-sensitive IoT requests rapidly, but a robust mechanism for real-time resource and utility optimization remains necessary. To address these challenges, we designed an end-to-end network slicing approach that predicts common and urgent user requests through T distribution. We formulated our problem as a multi-agent Markov decision process (MDP) and introduced a multi-agent soft actor–critic (MAgSAC) algorithm. This algorithm prevents the wastage of scarce resources by intelligently activating and deactivating virtual network function (VNF) instances, thereby balancing the allocation process. Our approach aims to optimize overall utility, balancing trade-offs between revenue, energy consumption costs, and latency. We evaluated our method, MAgSAC, through simulations, comparing it with the following six benchmark schemes: MAA3C, SACT, DDPG, S2Vec, Random, and Greedy. The results demonstrate that our approach, MAgSAC, optimizes utility by 30%, minimizes energy consumption costs by 12.4%, and reduces execution time by 21.7% compared to the closest related multi-agent approach named MAA3C. Full article

Background: Square faces, which are influenced by genetic factors and structural features, are considered undesirable among the Asian population. Surgical interventions, such as mandibular angle reduction, aim to alter these characteristics, though complications may arise. We aimed to investigate the morphology of the mandibular angle and masseter muscle thickness using computed tomography (CT) and to analyze hard and soft tissue correlations to enhance surgical outcomes for patients with square faces. Methods: This retrospective clinical study included 100 Taiwanese patients aged 18–50 years. CT was used to analyze key clinical parameters, including bilateral mandibular width, mandibular divergence angle, ramus height, distance from the mandibular angle to the inferior alveolar nerve (IAN), and the thickness of the masseter muscle. Results: Significant correlations were noted between the patients’ physical height and weight, mandibular width, ramus height, masseter thickness, and distance from the angle to the IAN. Males exhibited a significantly longer and thicker ramus height (66.48 ± 4.28 mm), greater masseter thickness (15.46 ± 2.35 mm), and greater safety range for mandibular angle reduction surgery (18.35 ± 3.19 mm) (p < 0.00008). Significant correlations were observed among all parameters, except between mandibular width and gonial angle and the distance from the angle to the IAN and between mandibular divergence and masseter muscle thickness (p > 0.1). Conclusions: Our study highlighted the complex interplay among factors that contribute to square facial morphology. Careful preoperative assessments and customized surgical planning are essential for addressing this multifaceted clinical challenge. Full article

Odontogenic cysts are frequently encountered in clinical practice. However, residual cysts, a specific type of inflammatory odontogenic cyst, are relatively rare. These cysts may slowly expand over time, damaging surrounding soft tissues and bone, typically without posing a threat to life. We report the case of a 67-year-old man with liver failure and a cystic tumor in his right maxilla that had invaded his oral cavity and cheek, causing nasal obstruction and severe bleeding following an incisional biopsy. A computed tomography (CT) scan of the mass was nonspecific, and an initial histopathological analysis of the tissues was inconclusive due to chronic inflammation and hemorrhagic alterations, complicating the diagnostic pathway. The suspicion of a potentially aggressive malignant neoplasm and the need for immediate intervention due to bleeding necessitated a tracheotomy, tumor removal, percutaneous endoscopic gastrostomy, and transfusions of red blood cells. A subsequent histopathological examination revealed features indicative of a residual cyst. The entire lesion was excised through functional endoscopic sinus surgery. The surgical treatment was performed safely and effectively. Follow-up CT confirmed complete removal of the lesion. This case highlights a rare yet possible complication of odontogenic cysts and underscores the necessity of early diagnosis and comprehensive prophylaxis to prevent severe complications. Full article

Adult-acquired flatfoot has been considered to arise from tibialis posterior tendon deficiency. Recent evidence shows that arch stability is mainly maintained by structures such as plantar fascia and spring ligament. The dysfunction of these ’passive’ stabilizers results in loss of arch integrity that causes forefoot pronation and reactive tendon overload, especially in the tibialis posterior tendon and peroneus longus tendon. The peroneus longus tendon (PLT) spans several midfoot joints and overloads with arch lengthening. The biomechanical stress/changes that occurs in this tendon are not well recognized. This study evaluates the biomechanical consequences that fusions have on peroneus longus tendon stresses in soft-tissue deficiencies associated with flatfoot deformity. A complete computational human foot model was used to simulate different scenarios related to the flatfoot deformity and associated common midfoot/hindfoot fusions, to quantify the biomechanical changes in the peroneus longus tendon. The results showed that the stress of the peroneus longus tendon is especially affected by the fusion of hindfoot joints and depends on the soft tissue types that fail, causal in generating the flatfoot. These results could be useful to surgeons when evaluating the causes of flatfoot and the secondary effects of surgical treatments on tissues such as the peroneus longus tendon. Full article

Background: Distinguishing between Charcot Neuroarthropathy (CN), osteomyelitis (OM), and CN complicated with superimposed OM in diabetic patients is crucial for the treatment choice. Given that current diagnostic methods lack specificity, advanced techniques, e.g., magnetic resonance imaging (MRI) and 99mTc-HMPAO–WBC Single Photon Emission Computed Tomography (SPECT/CT), are needed. This study addresses the challenges in distinguishing OM and CN. Methods: We included diabetic patients with CN and soft tissue ulceration. MRI and 99mTc-HMPAO–WBC SPECT/CT were used for the diagnosis. The patients were classified into three probability levels for OM (i.e., Definite, Probable, and Unlikely) according to the Consensus Criteria for Diabetic Foot Osteomyelitis (CC-DFO). Results: Eight patients met the eligibility criteria. MRI, supported by SPECT-CT and CC-DFO, showed consistency with the OM diagnosis in three cases. The key diagnostic features included the location of signal abnormalities and secondary features such as skin ulcers, sinus tracts, and abscesses. Notably, cases with inconclusive MRI were clarified by SPECT/CT, emphasizing its efficacy in challenging scenarios. Conclusions: The primary objective of this study was to compare the results of MRI and 99mTc-HMPAO–WBC SPECT/CT with the CC-DFO score in the diabetic foot with CN and suspected OM. Advanced imaging offers a complementary approach to distinguish between CN and OM. This can help delineate the limits of the disease for presurgical planning. While MRI is valuable, 99mTc-HMPAO–WBC SPECT/CT provides additional clarity, especially in challenging cases or when metallic implants affect MRI accuracy. Full article

The present work demonstrates the manufacturing process of a pneumatic bellow actuator with an embedded sensor, utilizing a novel manufacturing approach through the complete use of additive manufacturing techniques, such as direct ink writing (DIW) and traditional fused deposition modeling (FDM) methods. This study is innovative in its integration of a dielectric electroactive polymer (DEAP) structure with sensing electrodes made of conductive carbon grease (CCG), showcasing a unique application of a 3D-printed DEAP with CCG electrodes for combined DEAP sensing and pneumatic actuation. Initial experiments, supported by computational simulations, evaluated the distinct functionality of the DEAP sensor by itself under various pressure conditions. The findings revealed a significant change in capacitance with applied pressure, validating the sensor’s performance. After sensor validation, an additive manufacturing process for embedding the DEAP structure into a soft pneumatic actuator was created, exhibiting the system’s capability for dual sensing and actuation, as the embedded sensor effectively responded to applied actuation pressure. This dual functionality represents an advancement in soft actuators, especially in applications that require integrated and responsive actuation and sensing capabilities. This work also represents a preliminary step in the development of a 3D-printed dual-modality actuator (pneumatic and electrically activated DEAP) with embedded sensing. Full article

In recent years, material extrusion-based additive manufacturing, particularly fused filament fabrication (FFF), has gained significant attention due to its versatility and cost-effectiveness in producing complex geometries. This paper presents the characterization of seven novel materials for FFF and twenty-two commercially available filaments in terms of X-ray computed tomography (CT) numbers, as tissue mimicking materials for the realization of 3D printed radiological phantoms. Two technical approaches, by 3D printing of cube samples and by producing cylinders of melted materials, are used for achieving this goal. Results showed that the CT numbers, given in Hounsfield unit (HU), of all the samples depended on the beam kilovoltage (kV). The CT numbers ranged from +411 HU to +3071 HU (at 80 kV), from −422 HU to +3071 HU (at 100 kV), and from −442 HU to +3070 HU (at 120 kV). Several commercial and custom-made filaments demonstrated suitability for substituting soft and hard human tissues, for realization of 3D printed phantoms with FFF in CT imaging. For breast imaging, an anthropomorphic phantom with two filaments could be fabricated using ABS-C (conductive acrylonitrile butadiene styrene) as a substitute for breast adipose tissue, and ASA-A (acrylic styrene acrylonitrile) for glandular breast tissue. Full article

Using soft materials in robotic mechanisms has become a common solution to overcome many challenges associated with the rigid bodies frequently used in robotics. Compliant mechanisms allow the robot to adapt to objects and perform a broader range of tasks, unlike rigid bodies that are generally designed for specific applications. However, soft robotics presents its own set of challenges in both design and implementation, particularly in sensing and control. These challenges are abundant when dealing with the force control problem of a compliant gripping mechanism. The ability to effectively regulate the applied force of a gripper is a critical task in many control operations, as it allows the precise manipulation of objects, which drives the need for enhanced force control strategies for soft or flexible grippers. Standard sensing techniques, such as motor current monitoring and strain-based sensors, add complexities and uncertainties when establishing mathematical models of soft grippers to the required gripping forces. In addition, the soft gripper creates a complex non-linear system, compounded by adding an adhesive-type sensor. This work develops a unique visual force sensor trained on synthetic data generated using finite element analysis (FEA) and implemented by integrating a non-linear model reference adaptive controller (MRAC) to control gripping force on a fixed 6-DOF robot. The robot can be placed on a mobile platform to perform various tasks. The virtual FEA sensor and controller, combined, are termed virtual reference adaptive control (VRAC). The VRAC was compared to other methods and achieved comparable control sensing and control performance while reducing the complexity of the sensor requirements and its integration. The VRAC strategy effectively controlled the gripping force by driving the dynamics to match the desired performance after a limited amount of training cycles. The controller proposed in this work was designed to be generally applicable to most objects that the gripper will interact with and easily adaptable to a wide variety of soft grippers. Full article

Background: Temporomandibular disorders are common conditions characterized by discomfort within the temporomandibular joints, acoustic changes, and restricted mandibular movement. Accurate diagnosis and subsequent treatment rely heavily on clinical examination, but it is often necessary to add radiological examinations to the diagnostics. Magnetic Resonance Imaging (MRI) is the gold standard for visualizing the disc, while cone-beam computed tomography (CBCT) is primarily used for evaluating condylar morphology. Ultrasound (US) serves as a real-time imaging modality for soft tissues. The objective of the present study was to explore the association between clinical manifestations observed in patients with temporomandibular joint disorders and corresponding radiographic findings. Methods: A total of 63 adult patients (51 female and 12 male) with temporomandibular joint disorders were included in this cross-sectional study. Each patient underwent a clinical examination, followed by appropriate radiological examinations (MRI, CBCT, or US). The level of statistical significance was set at an alpha of 0.05. The Shapiro–Wilk test assessed the normality of numerical variables. The Wilcoxon rank sum test compared two independent groups with non-normally distributed data. Relationships between categorical variables were evaluated using the Pearson chi-square test or Fisher’s exact test. The Kendall tau (τ) method analyzed the correlation between two binary variables. Results: The analysis included 63 patients with TMD, predominantly females (80.95%). Ages ranged from 18 to 74 years with a median of 39 years. In the CBCT study, we observed rarefied changes in the left bone structures in patients with bruxism (p = 0.010). MRI and ultrasound imaging revealed changes in patients with limited jaw opening: erosions in the right mandibular head on ultrasound (p = 0.008) and abnormal right bone structures on MRI (p = 0.009). In CBCT, asymmetry in the left joint space was correlated with a high incidence of right side muscle tension (p = 0.004). Additionally, both CBCT and ultrasound showed a correlation between muscle tension and erosion (p = 0.040 in ultrasound, p = 0.020 in CBCT). Acoustic changes, when compared with radiological imaging, were evident in all three studies, like temporomandibular joint pain or palpation. Conclusions: Our study compared three radiographic imaging methods with clinical examinations to assess their correlation with clinical symptoms. Each imaging technique provided unique insights depending on the specific symptoms presented. The observed correlations varied, highlighting the unique contributions of each modality to the diagnostic process. This underscores the importance of employing multiple diagnostic approaches for a thorough assessment of the temporomandibular joint. However, a limitation of our study is the small sample size and the uneven distribution of participants among the groups. Additionally, not all patients underwent every imaging modality. Full article

Modern aviation security systems are largely tied to the work of screening operators. Due to physical characteristics, they are prone to problems such as fatigue, loss of attention, etc. There are methods for recognizing such objects, but they face such difficulties as the specific structure of luggage X-ray images. Furthermore, such systems require significant computational resources when increasing the size of models. Overcoming the first and second disadvantage can largely lie in the hardware plane. It needs new introscopes and registration techniques, as well as more powerful computing devices. However, for processing, it is more preferable to improve quality without increasing the computational power requirements of the recognition system. This can be achieved on traditional neural network architectures, but with the more complex training process. A new training approach is proposed in this study. New ways of baggage X-ray image augmentation and advanced approaches to training convolutional neural networks and vision transformer networks are proposed. It is shown that the use of ArcFace loss function for the task of the items binary classification into forbidden and allowed classes provides a gain of about 3–5% for different architectures. At the same time, the use of softmax activation function with temperature allows one to obtain more flexible estimates of the probability of belonging, which, when the threshold is set, allows one to significantly increase the accuracy of recognition of forbidden items, and when it is reduced, provides high recall of recognition. The developed augmentations based on doubly stochastic image models allow one to increase the recall of recognizing dangerous items by 1–2%. On the basis of the developed classifier, the YOLO detector was modified and the mAP gain of 0.72% was obtained. Thus, the research results are matched to the goal of increasing efficiency in X-ray baggage image processing. Full article

Post-mortem (PM) imaging has potential for identifying individuals by comparing ante-mortem (AM) and PM images. Radiographic images of bones contain significant information for personal identification. However, PM images are affected by soft tissue decomposition; therefore, it is desirable to extract only images of bones that change little over time. This study evaluated the effectiveness of U-Net for bone image extraction from two-dimensional (2D) X-ray images. Two types of pseudo 2D X-ray images were created from the PM computed tomography (CT) volumetric data using ray-summation processing for training U-Net. One was a projection of all body tissues, and the other was a projection of only bones. The performance of the U-Net for bone extraction was evaluated using Intersection over Union, Dice coefficient, and the area under the receiver operating characteristic curve. Additionally, AM chest radiographs were used to evaluate its performance with real 2D images. Our results indicated that bones could be extracted visually and accurately from both AM and PM images using U-Net. The extracted bone images could provide useful information for personal identification in forensic pathology. Full article

Foreign objects in coal flow easily cause damage to conveyor belts, and most foreign objects are often occluded, making them difficult to detect. Aiming at solving the problems of low accuracy and efficiency in the detection of occluded targets in a low-illumination and dust fog environment, an image detection method for foreign objects is proposed. Firstly, YOLOv5s back-end processing is optimized by soft non-maximum suppression to reduce the influence of dense objects. Secondly, SimOTA label allocation is used to reduce the influence of ambiguous samples under dense occlusion. Then, Slide Loss is used to excavate difficult samples, and Inner–SIoU is used to optimize the bounding box regression loss. Finally, Group–Taylor pruning is used to compress the model. The experimental results show that the proposed method has only 4.20 × 10⁵ parameters, a computational amount of 1.00 × 10⁹, a model size of 1.20 MB, and an mAP_0.5 of up to 91.30% on the self-built dataset. The detection speed on the different computing devices is as high as 66.31, 41.90, and 33.03 FPS. This proves that the proposed method achieves fast and high-accuracy detection of multi-layer occluded coal flow foreign objects. Full article