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Abstract A new formulation of geometrically exact planar Euler-Bernoulli beam in multi-body dynamics is proposed. For many applications, the use of the Euler-Bernoulli model is sufficient and has the advantage of being a nodal displacement-only formulation avoiding the integration of rotational degrees of freedom. In this paper, an energy momentum method is proposed for the nonlinear in-plane dynamics of flexible multi-body systems, including the effects of revolute joints with or without torsional springs. Large rotational angles of the joints are accurately calculated. Several numerical examples demonstrate the accuracy and the capabilities of the new formulation.

The present paper is concerned with the elastic design optimisation of continuous composite beams. This optimisation is based on the analysis of the beam in the inelastic range including the concrete creep and shrinkage, the tension stiffening and temperature difference effects as well as the possible local buckling instability. The finite element program PONTMIXTE (adapted to study continuous beams at real scale with short time computation) is first presented with its different sections: Pre-design (in accordance with Eurocode specifications), Non linear finite element calculation and Post-processing. In order to validate the proposed model, the numerical calculations are compared against experimental results from tests on a two-span beam in reduced scale (7.5 m length for each span) without taking into account the local buckling phenomenon avoided in the experimental test by using web-stiffeners. Next, special attention is paid to study the influence of the local buckling instability on the internal moment redistribution coefficient between hogging and sagging zones. The application concerns different 3-span beams of bridge at real scale with medium span lengths (40–60–40 m). The post-buckling behaviour represented by moment-rotation curves (M-θ) is deduced from a 3D finite element model of the cross-section developed using Castem finite element code. The M-θ curves describing the local buckling phenomenon are approximated using hyperbolic functions and implemented in PONTMIXTE using a specific rotational spring finite element. The influence of this instability on the moment redistribution coefficients calls the Standart predictions into question.

Introduction: Non-compressive myelopathy is a neurological disorder due to pathological processes affecting the spinal cord in the absence of clinical and radiological evidence of spinal cord compression. Two commonly used diagnostic tools for non-compressive myelopathy are somatosensory evoked potentials (SSEPs) and magnetic resonance imaging (MRI). SSEPs are a neurophysiological tool used to assess the functional integrity of the spinal cord. MRI, on the other hand, is the mainstay imaging modality used for identifying compressive lesions and other structural abnormalities in the spinal cord. The aim of this study was to test the diagnostic accuracy of SSEPs versus spine MRI in the diagnosis and assessment of the severity of non-compressive myelopathy using the Modified Japanese Orthopaedic Association (mJOA) clinical severity score., Methods: Our study included 63 subjects. Whole spine MRI and SSEPs (median and tibial SSEP bilaterally) were done for all subjects; their results were compared according to their relation to the mJOA score and classified into mild, moderate, and severe. The control group was examined to establish normative data for SSEP results and compared with cases. Blood investigations such as complete blood count, thyroid function test, A1C, HIV tests, venereal disease research laboratory test, erythrocyte sedimentation rate, C-reactive protein, and antinuclear antibody tests were done. Blood tests for vitamin B12 levels were done for patients who were suspected of sub-acute combined degeneration of the spinal cord; cerebrospinal fluid (CSF) analysis was done for patients suspected of multiple sclerosis (MS), acute transverse myelitis (ATM), or other inflammatory/infectious diseases. CSF was analyzed for cell count, cytology, protein, and oligoclonal bands (if indicated)., Results: No mild categories were registered in this study; 30% of patients were moderate and 70% were severe. Causes for non-compressive myelopathy in this study were hereditary degenerative ataxias in 12 (38.71%), ATM in 8 (25.81%), and MS in 5 (16.13%); other causes included vitamin B12 deficiency in 2 (6.45%), ischemia in 2 (6.45%), and an unknown cause in 2 (6.45%). SSEPs showed abnormal results in all patients (31; 100%) whereas MRI showed abnormality in only seven patients (22.6%). SSEP sensitivity for detecting severe cases was around 63.6% while that for MRI was 27.3%., Conclusion: The study concluded that SSEPs were more reliable for the detection of non-compressive myelopathies rather than MRI and correlated better with clinical severity. Performing SSEPs is recommended for all patients with non-compressive myelopathy, especially those with negative imaging., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Mohammed et al.)

The emergence of the Industry 4.0 revolution to upgrade the Internet of Things (IoT) standards provides the prominence outcomes for the future wireless communication systems called 5G. The development of 5G green communication systems suffers from the various challenges to fulfill the requirement of higher user capacity, network speed, minimum cost, and reduced resource consumption. The use of 5G standards for Industry 4.0 applications will increase data rate performance and connected device's reliability. Since the arrival of novel Covid-19 disease, there is a higher demand for smart healthcare systems worldwide. However, designing the 5G communication systems has the research challenges like optimum resource utilization, mobility management, cost-efficiency, interference management, spectral efficiency, etc. The rapid development of Artificial Intelligence (AI) across the different formats brings performance enhancement compared to conventional techniques. Therefore, introducing the AI into 5G standards will optimize the performances further considering the various end-user applications. We first present the survey of the terms like 5G standard, Industry 4.0, and some recent works for future wireless communications. The purpose is to explore the current research problems using the 5G technology. We further propose the novel architecture for smart healthcare systems using the 5G and Industry 4.0 standards. We design and implement that proposed model using the Network Simulator (NS2) to investigate the current 5G methods. The simulation results show that current 5G methods for resource management and interference management suffer from the challenges like performance trade-offs., Competing Interests: Conflict of interestThere are no conflicts of interest., (© King Abdulaziz City for Science and Technology 2021.)

Migraine is a periodic disorder in which a patient experiences changes in the morphological and functional brain, leading to the abnormal processing of repeated external stimuli in the inter-ictal phase, known as the habituation deficit. This is a significant feature clinically of migraine in both two types with aura or without aura and plays an essential role in studying pathophysiological differences between these two groups. Several studies indicated that the reason for migraine aura is cortical spreading depression (CSD) but did not clarify its impact on migraine without aura and lack of habituation. In this study, 22 migraine patients (MWA, N = 13), (MWoA, N = 9), and healthy controls (HC, N = 19) were the participants. Participants were exposed to the steady state of visual evoked potentials also known as (SSVEP), which are the signals for a natural response to the visual motivation at four Hz or six Hz for 2 s followed by the inter-stimulus interval that varies between 1 and 1.5 s. The order of the temporal frequencies was randomized, and each temporal frequency was shown 100 times. We recorded from 128 customized electrode locations using high-density electroencephalography (HD-EEG) and measured amplitude and habituation for the N1-P1 and P1-N2 from the first to the sixth blocks of 100 sweep features in patients and healthy controls. Using the entropy, a decrease in amplitude and SSVEP N1-P1 habituation between the first and the sixth block appeared in both MWA and MWoA ( p = 0.0001, Slope = -0.4643), ( p = 0.065, Slope = 0.1483), respectively, compared to HC. For SSVEP P1-N2 between the first and sixth block, it is varied in both MWA ( p = 0.0029, Slope = -0.3597) and MWoA ( p = 0.027, Slope = 0.2010) compared to HC. Therefore, migraine patients appear amplitude decrease and habituation deficit but with different rates between MWA, and MWoA compared to HCs. Our findings suggest this disparity between MWoA and MWA in the lack of habituation and amplitude decrease in the inter-ictal phase has a close relationship with CSD. In light of the fact that CSD manifests during the inter-ictal phase of migraine with aura, which is when migraine seizures are most likely to occur, multiple researchers have lately reached this conclusion. This investigation led us to the conclusion that CSD during the inter-ictal phase and migraine without aura are associated. In other words, even if previous research has not demonstrated it, CSD is the main contributor to both types of migraine (those with and without aura).

Skin cancer is one of the major types of cancer with an increasing incidence in recent decades. The source of skin cancer arises in various dermatologic disorders. Skin cancer is classified into various types based on texture, color, morphological features, and structure. The conventional approach for skin cancer identification needs time and money for the predicted results. Currently, medical science is utilizing various tools based on digital technology for the classification of skin cancer. The machine learning-based classification approach is the robust and dominant approach for automatic methods of classifying skin cancer. The various existing and proposed methods of deep neural network, support vector machine (SVM), neural network (NN), random forest (RF), and K-nearest neighbor are used for malignant and benign skin cancer identification. In this study, a method was proposed based on the stacking of classifiers with three folds towards the classification of melanoma and benign skin cancers. The system was trained with 1000 skin images with the categories of melanoma and benign. The training and testing were performed using 70 and 30 percent of the overall data set, respectively. The primary feature extraction was conducted using the Resnet50, Xception, and VGG16 methods. The accuracy, F1 scores, AUC, and sensitivity metrics were used for the overall performance evaluation. In the proposed Stacked CV method, the system was trained in three levels by deep learning, SVM, RF, NN, KNN, and logistic regression methods. The proposed method for Xception techniques of feature extraction achieved 90.9% accuracy and was stronger compared to ResNet50 and VGG 16 methods. The improvement and optimization of the proposed method with a large training dataset could provide a reliable and robust skin cancer classification system.

Coronavirus disease 2019 (COVID-19) has attracted significant attention of researchers from various disciplines since the end of 2019. Although the global epidemic situation is stabilizing due to vaccination, new COVID-19 cases are constantly being discovered around the world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, has been used to ameliorate diagnosis. It helps reveal missed diagnoses due to the ambiguity of nucleic acid polymerase chain reaction. Therefore, this study investigated how quickly and accurately hybrid deep learning (DL) methods can identify infected individuals with COVID-19 on the basis of their lung CT images. In addition, this study proposed a developed system to create a reliable COVID-19 prediction network using various layers starting with the segmentation of the lung CT scan image and ending with disease prediction. The initial step of the system starts with a proposed technique for lung segmentation that relies on a no-threshold histogram-based image segmentation method. Afterward, the GrabCut method was used as a post-segmentation method to enhance segmentation outcomes and avoid over-and under-segmentation problems. Then, three pre-trained models of standard DL methods, including Visual Geometry Group Network, convolutional deep belief network, and high-resolution network, were utilized to extract the most affective features from the segmented images that can help to identify COVID-19. These three described pre-trained models were combined as a new mechanism to increase the system's overall prediction capabilities. A publicly available dataset, namely, COVID-19 CT, was used to test the performance of the proposed model, which obtained a 95% accuracy rate. On the basis of comparison, the proposed model outperformed several state-of-the-art studies. Because of its effectiveness in accurately screening COVID-19 CT images, the developed model will potentially be valuable as an additional diagnostic tool for leading clinical professionals., Competing Interests: The authors declare no conflict of interest., (© 2022 John Wiley & Sons Ltd.)

Introduction: Microvascular decompression with transposition of the involved vessels provides good surgical outcomes in cases of complex and recurrent neurovascular compression syndromes. We conducted a literature review to illustrate the variations in the surgical techniques used for transposition and to provide a practical decision-making scheme for transposition of the involved vessel., Methods: A PubMed Medline database record search was conducted using the following algorithm ("Microvascular Decompression Surgery"[Mesh]) OR (((Microvascular) OR (Macrovascular)) AND decompression AND surgery) AND (transposition). Only articles that detailed the intraoperative techniques were included., Results: A total of 48 articles were included. The adjacent anatomical walls to which the compressing vessel can be anchored were divided into 4 groups; A: roof (tentorium cerebelli), B: anterior wall (posterior surface of petrous bone and clivus), C: posterior wall (petrosal surface of the cerebellum), and D: "no wall" required. A new decision-making scheme based on the following 2 questions was designed: 1) is the conflicting vessel amenable to transposition to a nearby wall in the cerebello-brainstem space? 2) what is the closest wall to secure the transposed vessel?, Conclusions: Transposition of the involved vessel is a valuable procedure for microvascular decompression of the posterior fossa cranial nerves. Anchoring the vessel to the adjacent anatomical wall ensures secure transposition. The proposed algorithm provides a systemic scheme to identify the optimal anatomical wall, and to determine the technique and material that can be used to anchor involved vessel. This scheme is an efficient method to inform the intraoperative decision-making process., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The bean leaves can be affected by several diseases, such as angular leaf spots and bean rust, which can cause big damage to bean crops and decrease their productivity. Thus, treating these diseases in their early stages can improve the quality and quantity of the product. Recently, several robotic frameworks based on image processing and artificial intelligence have been used to treat these diseases in an automated way. However, incorrect diagnosis of the infected leaf can lead to the use of chemical treatments for normal leaf thereby the issue will not be solved, and the process may be costly and harmful. To overcome these issues, a modern deep learning framework in robot vision for the early detection of bean leaves diseases is proposed. The proposed framework is composed of two primary stages, which detect the bean leaves in the input images and diagnosing the diseases within the detected leaves. The U-Net architecture based on a pre-trained ResNet34 encoder is employed for detecting the bean leaves in the input images captured in uncontrolled environmental conditions. In the classification stage, the performance of five diverse deep learning models (e.g., Densenet121, ResNet34, ResNet50, VGG-16, and VGG-19) is assessed accurately to identify the healthiness of bean leaves. The performance of the proposed framework is evaluated using a challenging and extensive dataset composed of 1295 images of three different classes (e.g., Healthy, Angular Leaf Spot, and Bean Rust). In the binary classification task, the best performance is achieved using the Densenet121 model with a CAR of 98.31%, Sensitivity of 99.03%, Specificity of 96.82%, Precision of 98.45%, F1-Score of 98.74%, and AUC of 100%. The higher CAR of 91.01% is obtained using the same model in the multi-classification task, with less than 2 s per image to produce the final decision., (© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021.)

Polymeric macrocycles are important as complexation agents in chemistry. The multivalent binding of heteroatoms inside the macrocycle to metals and organic pollutants has gained much interest. A protecting group strategy was applied to synthesise functionalized crown ether based on glucose. The 4,6-position was protected by benzylidene, and arms were introduced on the remaining hydroxyls in 2- and 3-positions. Cyclization was achieved through sodium sulfide in ethanol. The selective cleavage of the benzylidene protecting group left the free hydroxyl group on C-6 position. The propargyl group was introduced and attached the macrocycles to polyvinyl azide by click reaction. The compounds were identified by nuclear magnetic resonance, infrared spectroscopy and mass spectrometry, and the morphology and thermal stability of the polymer were characterized via field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray powder diffraction and differential scanning calorimetry. The removal of metal ions Co, Cd and Pb was performed by atomic absorption spectrometry in different experimental conditions. The results indicated that synthesised crown ether can effectively removal such metals and has highly sufficient recyclability., (Copyright © 2020 Elsevier Ltd. All rights reserved.)