The genetic property of subclinical eating behaviour (SEB) and the link between SEB and polycystic ovary syndrome (PCOS) has been studied before but the role of leptin within this connection has never been investigated. The objective of this study was 1). to study the genetic property of SEB. 2). To find a link between leptin, SEB and PCOS. One hundred and fifty four (77 pairs) female-female Iranian twins including 96 MZ individuals (48 pairs) and 58 DZ individuals (29 pairs) participated in the study. Clinical, biochemical and ultrasound tools were used to diagnose polycystic ovary syndrome. BITE questionnaire was filled out for subjects. Eight percent of subjects were diagnosed for subclinical eating disorder. No significant difference was found between intraclass correlation of MZ and DZ (z = 0.57, P = 0.569). Serum leptin level correlated significantly with bulimia score (P < 0.007). The mean (+/-SD) value for bulimia score was found to be higher among PCOS(positive) subjects (3.27 +/- 5.51) in comparison with PCOS(negative) subjects (2.06 +/- 4.48) (P < 0.001). The genetic property of subclinical eating disorder was not confirmed as shared environment might have played a major role in likeliness of DZ twins as well as MZ. Leptin is linked with both subclinical eating disorder and PCOS.
Shear stress comprises basic information for predicting the average depth velocity and discharge in channels. With knowledge of the percentage of shear force carried by walls (%SFw) it is possible to more accurately estimate shear stress values. The %SFw, non-dimension wall shear stress ([Formula: see text]) and non-dimension bed shear stress ([Formula: see text]) in smooth rectangular channels were predicted by a three methods, the Bayesian Regularized Neural Network (BRNN), the Radial Basis Function (RBF), and the Modified Structure-Radial Basis Function (MS-RBF). For this aim, eight data series of research experimental results in smooth rectangular channels were used. The results of the new method of MS-RBF were compared with those of a simple RBF and BRNN methods and the best model was selected for modeling each predicted parameters. The MS-RBF model with RMSE of 3.073, 0.0366 and 0.0354 for %SFw, [Formula: see text] and [Formula: see text] respectively, demonstrated better performance than those of the RBF and BRNN models. The results of MS-RBF model were compared with three other proposed equations by researchers for trapezoidal channels and rectangular ducts. The results showed that the MS-RBF model performance in estimating %SFw, [Formula: see text] and [Formula: see text] is superior than those of presented equations by researchers.
With advancements in the automated industry, electromagnetic inferences (EMI) have been increasing over time, causing major distress among the end-users and affecting electronic appliances. The issue is not new and major work has been done, but unfortunately, the issue has not been fully eliminated. Therefore, this review intends to evaluate the previous carried-out studies on electromagnetic shielding materials with the combination of Graphene@Iron, Graphene@Polymer, Iron@Polymer and Graphene@Iron@Polymer composites in X-band frequency range and above to deal with EMI. VOSviewer was also used to perform the keyword analysis which shows how the studies are interconnected. Based on the carried-out review it was observed that the most preferable materials to deal with EMI are polymer-based composites which showed remarkable results. It is because the polymers are flexible and provide better bonding with other materials. Polydimethylsiloxane (PDMS), polyaniline (PANI), polymethyl methacrylate (PMMA) and polyvinylidene fluoride (PVDF) are effective in the X-band frequency range, and PDMS, epoxy, PVDF and PANI provide good shielding effectiveness above the X-band frequency range. However, still, many new combinations need to be examined as mostly the shielding effectiveness was achieved within the X-band frequency range where much work is required in the higher frequency range.
In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.
In this study, a new approach to basis of intelligent systems and machine learning algorithms is introduced for solving singular multi-pantograph differential equations (SMDEs). For the first time, a type-2 fuzzy logic based approach is formulated to find an approximated solution. The rules of the suggested type-2 fuzzy logic system (T2-FLS) are optimized by the square root cubature Kalman filter (SCKF) such that the proposed fineness function to be minimized. Furthermore, the stability and boundedness of the estimation error is proved by novel approach on basis of Lyapunov theorem. The accuracy and robustness of the suggested algorithm is verified by several statistical examinations. It is shown that the suggested method results in an accurate solution with rapid convergence and a lower computational cost.
The polymer solar cells also known as organic solar cells (OSCs) have drawn attention due to their cynosure in industrial manufacturing because of their promising properties such as low weight, highly flexible, and low-cost production. However, low η restricts the utilization of OSCs for potential applications such as low-cost energy harvesting devices. In this paper, OSCs structure based on a triple-junction tandem scheme is reported with three different absorber materials to enhance the absorption of photons which in turn improves the η, as well as its correlating performance parameters. The investigated structure gives the higher value of η = 14.33% with Jsc = 16.87 (mA/m2), Voc = 1.0 (V), and FF = 84.97% by utilizing a stack of three different absorber layers with different band energies. The proposed structure was tested under 1.5 (AM) with 1 sun (W/m2). The impact of the top, middle, and bottom subcells' thickness on η was analyzed with a terse to find the optimum thickness for three subcells to extract high η. The optimized structure was then tested with different electrode combinations, and the highest η was recorded with FTO/Ag. Moreover, the effect of upsurge temperature was also demonstrated on the investigated schematic, and it was observed that the upsurge temperature affects the photovoltaic (PV) parameters of the optimized cell and η decreases from 14.33% to 11.40% when the temperature of the device rises from 300 to 400 K.