Magnetic resonance imaging (MRI) is an information-rich research tool used in diagnostics using image processing applications (IPAs), and the results are utilized in machine learning. Therefore, testing of IPAs for credible results is vital. A deficient IPA would cause the related taxonomies of the machine learning to be defective as well and diagnosis will not be perfect. Accurate disease detection by IPA, without surgical intervention, leads to improved quality of treatment. Current challenges for testing of IPA include an absence of a test oracle. One way to alleviate the test oracle problem is metamorphic testing which identifies the specific properties called metamorphic relations of the system under test. Previously metamorphic testing approaches have been applied and evaluated on IPAs, but there is no previous work on evaluation of metamorphic testing on MRI images. In this work, we have evaluated effectiveness of metamorphic testing on edge detection of MRI images. The aim of this study is to determine which metamorphic relations are more effective for metamorphic testing of edge detection in MRI images such as T1, T2 and flair images. Our results show that the fault detection rate of MR₄ is highest and MR₂ is the lowest among all type of MRI images at the threshold of 0.95. Full article

Based on the Brazilian tests of continental shale with different layer orientations, combined with AE monitoring, the influence of layer orientation on the anisotropy of mechanical properties, fracture mode, and fracture mechanism of continental shale was analyzed. The results show that the tensile strength and deformation at the peak stress decrease with the increase of layer orientation at a constant deformation loading rate of 0.06mm/min, and the splitting modulus decreases first and then increases. The tensile strength was 90° > 60° > 45° > 30° > 0°, and the maximum and minimum tensile strengths were 5.154 MPa and 0.669 MPa, respectively. Under the action of splitting load, the samples with 30°, 45°, and 60° layer orientations mainly undergo shear failure along the layer orientation, while the samples with 0° and 90° layer orientations undergo tensile failure. In addition, the crack propagation in the 0° and 30° samples penetrated the bedding. These characteristics have important reference significance for the study of the mechanism of hydraulic fracture communication, propagation, and activation of structural planes. Full article

Isometric and isotonic exercises are important modes of resistance training for enhancement of athletic performance. However, less is known about their effects on fitness and health in recreationally physically active individuals. This study evaluates the effect of isometric and isotonic training protocols on health-related fitness components in young university students. A group of one hundred males (18–24 years) underwent a 12-week isotonic and isometric training program (two days per week, 45 min per session). They were randomized into two groups, group A (n = 50) and group B (n = 50). While group A underwent the isotonic resistance training, the group B completed isometric resistance training. Prior to and after training programs, body mass index (BMI) was measured and the bench press 1RM test, sit-ups test, sit and reach test, and 12 min run/walk test were performed. Results showed significant improvements in BMI, bench press 1RM test, sit-ups test, sit and reach test, and 12 min run/walk test after both isotonic and isometric training protocols. The isotonic training group increased in BMI by 2.70%, bench press 1RM by 34.45%, number of sit-ups by 24.13%, sit and reach distance by 29.12%, and 12 min run/walk distance by 19.82%. Isometric training group increased in BMI by 1.96%, bench press 1RM by 14.23%, number of sit-ups by 7.80%, sit and reach distance by 6.92%, and 12 min run/walk distance by 6.99%. A comparison of these training protocols revealed that the isotonic group improved significantly more than the isometric group in the bench press 1RM (20.22%), number of sit-ups (16.33%), and sit and reach distance (22.2%) but not in the 12 min run/walk distance (12.83%) and BMI (0.74%). These findings indicate that both isotonic and isometric resistance training protocols improve health-related fitness components in young adults; however isotonic training is more efficient than isometric training in increasing their muscle strength, muscular endurance, and flexibility. Full article

The extension of aft-body fuselage has become an important trend in the design of Hybrid Wing Body (HWB) civil aircraft due to its improved effect on flight control authority of aircraft. However, it is still unclear how efficient the extension of aft-body will be in improving flight control authority and how it affects other aspects of the flight performance of HWB. To address these problems, this paper evaluated and compared the flight performances of four HWB configurations with different aft-body lengths. A physics-based Multidiscipline Design Optimization (MDO) platform was firstly constructed, and four optimal design works were developed based on this platform, with different static margin constraints. The configurations to be studied came from the results of the four optimization design works, which presented different aft-body lengths under the influence of correspondence between static stability margins and aft-body layout. By investigating these four HWB configurations with the weight, basic takeoff and landing performance, and flight control authority, we can determine the influence of the aft-body extension design on the performances of the HWB civil aircraft and provide advice for the layout design of the HWB aircraft. Full article

The present work shows the biochemical and structural fundamentals for the stress induced anxiety and stress adjustment response of the CA3 hippocampus area. Adult male Wistar rats were repeatedly exposed to a 3 h day restraint stress, for either 3 or 6 days. The concentration of corticosterone and testosterone in the CA3 hippocampus area was divergent, while oxidative stress was progressively increased during the stress exposure. The mitochondrial lysis in the CA3 neurons confirmed the oxidative stress events. Immunohistochemical findings showed that oligodendrocytes (OCs) proliferation and neuroglobin (Ngb) expression were stimulated, whereas MeCP2 expression was decreased as a balance reaction in stress exposure under corticosterone signaling. Remarkably, ultrastructural changes such as mitochondrial lysis, endoplasmic reticulum swelling, and perivascular lysis with platelets adherence to endothelium in the CA3 area were seen in the 6th day of restraining. The anxiety-like behavior was noticed 6 days later after stress exposure. These results suggest that the duration of the exposure, but not the intensity of the stress, is the key factor in the stress-buffering function by the CA3 hippocampus area via up-regulation of the Ngb-OCs bionome. The imbalance of the Ngb-OCs communication may be involved in the development of CA3-dependent anxious behavior. Full article

Breast cancer is the second most dominant kind of cancer among women. Breast Ultrasound images (BUI) are commonly employed for the detection and classification of abnormalities that exist in the breast. The ultrasound images are necessary to develop artificial intelligence (AI) enabled diagnostic support technologies. For improving the detection performance, Computer Aided Diagnosis (CAD) models are useful for breast cancer detection and classification. The current advancement of the deep learning (DL) model enables the detection and classification of breast cancer with the use of biomedical images. With this motivation, this article presents an Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection (AOBNN-BDNN) model on BUI. The presented AOBNN-BDNN model follows a series of processes to detect and classify breast cancer on BUI. To accomplish this, the AOBNN-BDNN model initially employs Wiener filtering (WF) related noise removal and U-Net segmentation as a pre-processing step. Besides, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. Next, the BNN algorithm will be utilized to allocate appropriate class labels to the input images. Finally, the AO technique was exploited to fine-tune the parameters related to the BNN method so that the classification performance is improved. To validate the enhanced performance of the AOBNN-BDNN method, a wide experimental study is executed on benchmark datasets. A wide-ranging experimental analysis specified the enhancements of the AOBNN-BDNN method in recent techniques. Full article

Predicting the stress levels of working professionals is one of the most time-consuming and difficult research topics of current day. As a result, estimating working professionals’ stress levels is critical in order to assist them in growing and developing professionally. Numerous machine learning and deep learning algorithms have been developed for this purpose in previous papers. They do, however, have some disadvantages, including increased design complexity, a high rate of misclassification, a high rate of errors, and decreased efficiency. To address these concerns, the purpose of this research is to forecast the stress levels of working professionals using a sophisticated deep learning model called the Deep Recurrent Neural Network (DRNN). The model proposed here comprises dataset preparation, feature extraction, optimal feature selection, and classification using DRNNs. Preprocessing the original dataset removes duplicate attributes and fills in missing values. Full article

The jobs-housing imbalance and long commuting distances for residents in many megacities in China are promoting the increase in mode share with rail transit. The emergence of free-floating bike sharing (FFBS) provides an attractive and cost-effective multi-modal solution to the first/last mile problem. This study identifies the mobility patterns of free-floating bikes as a feeder mode to 277 rail transit stations in Beijing using detailed GPS data, and the relationships between these patterns, culture and spatial layout of the city are examined. The results show that the distribution of free-floating bikes, as a feeder mode to rail transit, exhibits an aggregating feature in the spatial-temporal pattern on weekdays. According to the results of the Clusters method and ANOVA analysis, the operation characteristics of free-floating bikes are related to the location of the transit station and the job-to-housing ratio around that area, and imbalanced usage of shared bikes across the city may result from the extreme values of job-to-housing ratios. Based on the fitted distance decay curve, accessing distance is greatly influenced by urban morphology and location. Based on these findings, recommendations for planning, management, and rebalancing of the FFBS system as a feeder mode to rail transit are proposed to promote the integration of FFBS and the rail transit system. Full article

Cyber security is becoming a challenging issue, because of the growth of the Internet of Things (IoT), in which an immense quantity of tiny smart gadgets push trillions of bytes of data over the Internet. Such gadgets have several security flaws, due to a lack of hardware security support and defense mechanisms, thus, making them prone to cyber-attacks. Moreover, IoT gateways present limited security features for identifying such threats, particularly the absence of intrusion detection techniques powered by deep learning (DL). Certainly, DL methods need higher computational power that exceeds the capability of such gateways. This article focuses on the development of Planet Optimization with a deep convolutional neural network for lightweight intrusion detection (PODCNN-LWID) in a resource-constrained IoT environment. The presented PODCNN-LWID technique primarily aims to identify and categorize intrusions. In the presented PODCNN-LWID model, two major processes are involved, namely, classification and parameter tuning. At the primary stage, the PODCNN-LWID technique applies a DCNN model for the intrusion identification process. Next, in the second stage, the PODCNN-LWID model utilizes the PO algorithm as a hyperparameter tuning process. The experimental validation of the PODCNN-LWID model is carried out on a benchmark dataset, and the results are assessed using varying measures. The comparison study reports the enhancements of the PODCNN-LWID model over other approaches. Full article

Salinity is one of the most important abiotic stresses affecting crop yield. It is important to exploit pomegranates’ potential against salts because they are considered beneficial plants for human health due to their antioxidants and they are often exposed to severe salinity stress in the field. Three pomegranate cvs. were chosen as model plants for assessing the impact of different salt stress in the cultivation. The aim of this study was to evaluate the physiological and biochemical response of three pomegranate varieties (Punica granatum L.) (Wonderful, Ermioni, and Grenada) under different saline conditions. The plants were grown in a sand/perlite substrate in a 1:1 ratio and, throughout the experiment, were irrigated with a Hoagland nutrient solution, modified to contain four concentrations (0, 25, 50, and 75 mM) of NaCl, KCl, and K₂SO₄. At the end of the experiment, we measured the (a) concentrations of carotenoids and porphyrin of leaves; (b) phenols and flavonoids contents, and antioxidant capacity of leaves; (c) lipid peroxidation level; (d) leaf water potential; and (e) proline concentration. Ermioni contained the maximum concentration of proline phenols and flavonoids and antioxidant capacity in all salts. Furthermore, reductions in chlorophyll and carotenoid concentration were recorded in all cultivars. Grenada possessed the lowest porphyrin concentration. In conclusion, our results showed that Grenada was the most salt-susceptible cultivar. Salinity treatment triggered the enhancement in lipid peroxidation in the sensitive cultivar, while no change in lipid peroxidation level was observed in the tolerant cultivars. These data provide further support to the hypothesis that a mechanism exists that excludes salinity from the roots of tolerant cultivars, as well as an internal mechanism of tolerance that minimizes the accumulation of lipid peroxides through a higher proline content related to osmoregulation and membrane stabilization. Full article

Driver fatigue and distracted driving are the two most common causes of major accidents. Thus, the on-board monitoring of driving behaviors is key in the development of intelligent vehicles. In this paper, we propose an approach which detects driver fatigue and distracted driving behaviors using vision-based techniques. For driver fatigue detection, a single shot scale-invariant face detector (S3FD) is first used to detect the face in the image and then the face alignment network (FAN) is utilized to extract facial features. After that, the facial features are used to determine the driver’s yawns, head posture, and the opening or closing of their eyes. Finally, the random forest technique is used to analyze the driving conditions. For distracted driving detection, a convolutional neural network (CNN) is used to classify various distracted driving behaviors. Also, Adam optimizer is used to reinforce optimization performance. Compared with existing methods, our approach is more accurate and efficient. Moreover, distracted driving can be detected in real-time running on the embedded hardware. Full article

This study presents multi-response optimization and prediction tribological behaviors polytetrafluoroethylene (PTFE) matrix composites. For multi-response optimization, the Taguchi model was hybridized with grey relational analysis to produce grey relational grades (GRG). A support vector regression (SVR) model was combined with novel Harris Hawks’ optimization (HHO) and swarm particle optimization (PSO) models to form hybrid SVR–HHO and SVR–PSO models to predict the GRG. The prediction ability of the models was appraised using the coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), root mean square (RMSE), and mean absolute percentage error (MAPE). The results of the multi-response optimization revealed that the optimal combination of parametric values of GRG for minimum tribological rate was 9 N-1000 mesh-0.14 ms⁻¹-55 m (L3G1SD3SS3). An analysis of variance of the GRG showed that a grit size of 94.56% was the most significant parameter influencing the tribological behavior of PTFE matrix composites. The validation results revealed that an improvement of 52% in GRG was achieved. The prediction results of all models showed that the SVR–PSO and SVR–HHO models were superior to the SVR model. Furthermore, the SVR–HHO model produced superior prediction error and the best goodness of fit over the SVR–PSO model. These findings concluded that hybrids models are promising tools in the multi-response optimization and prediction of tribological behaviors of PTFE matrix composites. They can serve as a guide in the design and development of tribological materials. Full article

Background: Two of the most popular resilient attachment systems for mandibular implant-supported overdenture (MISOD) are locator and ball attachments. The purpose of the present retrospective cohort study was to assess the long-term prosthetic aftercare and oral hygiene status in edentulous patients rehabilitated with MISOD. Materials and Methods: Forty-five consecutive patients were included (22, group A- ball vs. 23, group B- locator attachments). Attachment incorporation into the MISOD was conducted in a direct (chair-side) intraoral technique at the time of denture insertion. Routine follow-up included yearly visits. The number of visits requiring prosthetic aftercare, either during the follow-up or during the additional visit, was recorded. Outcome parameters included—prosthetic aftercare—the number of aftercare (primary outcome parameter) visits, and dental treatment received (pressure sores relief, liner changes due to loss of retention, loss of retention due to debris accumulation, denture repair—secondary outcome parameters); oral hygiene—gingival index (primary outcome parameter). Results: The mean follow-up of the entire study population was 84 ± 21 months, range 39–120 months. Statistical analysis revealed a lower need for prosthetic aftercare interventions in group A (p < 0.001). The mean number of visits dedicated to pressure sores relief (6.09 ± 1.04 vs. 3.03 ± 0.77, p < 0.001) and liner exchange due to loss of retention (5.6 ± 1.03 vs. 2.09 ± 1.04, p < 0.001), were significantly lower in group A. Debris (food/calculus) accumulation inside the attachment was noted only for the locator’s group (p < 0.001). No statistically significant differences between the groups were noted for denture repair (p = 0.318). Oral hygiene also exhibited statistically significant differences in favor of group A (2.3 ± 0.3 vs. 1.03 ± 0.2, p < 0.001). Conclusions: It can be concluded that using ball attachments for MISOD yields less need for aftercare treatments and improved oral hygiene status over the years. Full article

In this study, we report on the effectiveness of electrochemical and biological wastewater treatment for artificially prepared industrial wastewater, comprising small amounts of technologically important dyes, namely Acid Mixture composed of Acid Violet 90 (AV90) and Acid Red 357 (AR357) in Na₂SO₄ (ESS—electrolyte supporting solution), as well as their impact on the environment, using Lemna minor as a bioindicator. Our study revealed that among the tested dyes, the raw ones (AM in ESS+OM) and those subjected to electrooxidation with the use of an iron anode and a copper cathode [AM_Fe/Cuox in ESS+OM (OECD medium is a medium recommended by the Organization for Economic Co-operation and Development for Lemna sp. Growth Inhibition Test)] were the most phytotoxic for L. minor. No phytotoxicity was detected for the tested plants in solution after electrooxidation with graphite anode and cathode (AM_Cox in ESS+OM). Quantitative identification of acid mixture removal was carried out by supplementary UPLC/MS-MS (Ultra-Performance Liquid Chromatography/tandem Mass Spectrometry) and UV-VIS (UltraViolet-Visible spectroscopy) instrumental analysis. The final removal after electrochemical and biological treatment of AV90 and AR357 dye components was 98 and over 99%, respectively. The results suggest that it may be a suitable replacement/addition for the generally used wastewater treatment methods. Full article

Vision-based localization techniques and detection technologies are key algorithms for the localization and navigation of unmanned vehicles. Especially in scenarios where GPS signals are missing, Simultaneous Localization and Mapping (SLAM) techniques that rely on vision, inertial navigation system (INS) and other sensors have important applications. Among them, vision combined with the IMU SLAM system has the advantage of realistic scale, which is lacking in monocular vision and computational power compared to multi-visual vision, so it is suitable for application in an unmanned vehicle system. In this paper, we propose a fusion localization algorithm that combines a visual-inertial SLAM system and map road information, processing road information in a map under structured roads, and detecting lane lines and locating its local position by a monocular camera, applying a strategy of position prediction and update for map-SLAM fusion localization. It solves the problem of accumulating errors in a pure SLAM system without loopback and provides accurate global-local positioning results for unmanned vehicle positioning and navigation. Full article