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Background: Cesarean birth (CB) rates have been increasing rapidly globally, including in Bangladesh. This study aimed to assess national trends in CB rates and to investigate associated factors in Bangladesh. Methods: We analyzed data from the five most recent Bangladesh Demographic and Health Surveys (BDHS) between 2003 and 2018. A total of 27,328 ever‐married women aged 15–49 who had a live birth in the 2 years preceding the survey were included in this study. We estimated the prevalence of CB from 2003 to 2018, as well as changes in the prevalence. Logistic regression analysis was used to measure the association between dependent and independent variables. Results: The overall prevalence of CB among Bangladeshi mothers was 3.99% in 2003–04; this rate increased to 33.22% in 2017–18. The annual percentage change in CB rate was 16.34% from 2004 to 2017–18, which is alarming relative to the World Health Organization's cesarean birth recommended threshold. Several factors, such as maternal age, maternal and paternal education, working status of the mother, maternal BMI, age at first pregnancy, antenatal care (ANC) use, administrative division, and wealth status, had a significant influence on the rising rate of CB in Bangladesh. Conclusions: This study documents the alarming rate of CB increase in Bangladesh since 2003. It is critical that authorities implement more effective national monitoring measures to identify the causes of this dramatic increase and work to mitigate the rate of unnecessary CB in Bangladesh. [ABSTRACT FROM AUTHOR]

In recent days, supply chain and logistic industries have been going through a transformational wave of automation and digitization. Supply chain management (SCM) can involve machine learning (ML) abilities and prediction models to ensure that the demands are satisfied at a minimum cost. Intelligent models can be developed to determine whether adequate inventory is accessible to encounter the predictable rise in demands, and if not, the system spontaneously begins to adjust the orders with suppliers to source the raw materials for resolving the predicted future high demand. The conventional ways of SCM can be replaced by the design of recent artificial intelligence (AI) and deep learning (DL) techniques. By this motivation, this research presents a tree seed algorithm-based feature selection with optimum DL technique for supply chain management (TSA-ODLSCM). The proposed TSA-ODLSCM model involves the design of a feature subset selection approach using a tree search algorithm (TSA) algorithm. Besides, a new convolutional neural network with fuzzy cognitive maps (CNN-FCM) technique is designed for the classification process. Moreover, optimal parameter tuning of the CNN-FCM model was performed using the Henry gas solubility optimization (HGSO) technique. To exhibit the improved performance of the TSA-ODLSCM approach, a huge range of simulations were executed and outcomes were examined below several aspects. The experimental validation reported an enhanced 96.52% outcome of the TSA-ODLSCM approach over other methods. [ABSTRACT FROM AUTHOR]

Nanofluids represent a novel heat transfer liquid, making them an efficient medium for enhancing energy transmission. Nevertheless, significant knowledge gaps still exist concerning current strategies for improving heat transfer in nanofluids, underscoring the necessity for comprehensive research on these fluid systems. Therefore, this study considered theoretical analysis retrieves the influence of radiative two‐dimensional stagnation point flow of second‐grade micropolar fluid flow about an elongated channel surface implanted in porous media with magnetic effect, and modified heat and mass flux is under consideration. The major novel effect of the current study is to analyze the activation energy and thermal aspect of the system in the presence of nonlinear radiation effects that are considered in the revised mathematical framework by utilizing the boundary layer theory. The resulting set of coupled partial differential equations is further reduced and transformed into a dimensionless system of ordinary differential equations through appropriate scaling invariants. We initiate the RKF‐45 investigation scheme to numerically analyze the transformed dimensionless system, considering relevant parameters. The computational algorithm is implemented using MATLAB programming syntax. Plotted visuals are revealed for leading parameters against pertinent flow profiles graphically and with numerical data. Additionally, the convergence analysis of the numerical results for various flow profiles of the fluids were compared to establish the authenticity of the proposed flow problem. These research findings play a significant role in controlling heat transfer rates and fluid velocities in diverse manufacturing processes and industrial applications, ultimately aiding in achieving the desired product quality. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters. [ABSTRACT FROM AUTHOR]

The present study is aimed at designing bilayer-floating tablets to improve the drug concentration in the stomach for enhanced therapeutic efficacy. The tablets are comprised of an upper layer of levofloxacin (466.5 mg) and a lower layer of famotidine (133.5 mg). Five formulations (F1-F5) were developed by using hydroxypropyl methylcellulose grades (K4M, K15M, and K100M) along with Carbopol 934. In the case of the effervescent system (F1-F3), sodium bicarbonate was added to impart buoyancy to the tablets; while in the case of noneffervescent formulations (F4 & F5), guar gum and xanthan gum were incorporated to induce flotation and swelling and retard the release of a drug. The precompression characteristics of tablets depict the suitability of all formulation powder for direct compression. The ATR-FTIR analyses have shown that the components of both effervescent and noneffervescent tablets are compatible with each other. The total weight of each tablet was 600 mg, with a weight variation of about ≤10 mg. Both the layers were smooth and flat with a thickness ranging from 3.16 ± 0.04 to 3.54 ± 0.01 mm. The diameters of prepared floating tablets were about 15 mm, optimum for oral administration. After adjusting the tablet's hardness to 6-7 kg/cm2, its friability was found to be <0.35 percent. The mean drug content of the formulations was above 90%. The floating lag time of all formulations (F2-F5) was below 25 seconds, except F1 which took almost 50 seconds to start floating on the surface of gastric content due to its higher density. The total floating time of effervescent (F1-F3) and noneffervescent formulations was in the range of 15-25 hours, thereby providing sufficient time to complete drug release and absorption in the gastric area. The total floating time of noneffervescent formulations was higher (p ≤ 0.05) than effervescent formulations due to efficient wettability and swelling characteristics. The release of drugs from both layers of noneffervescent tablets was significantly controlled when compared to the effervescent system, and an anomalous non-Fickian diffusion was found for the drug release. The stability study of the optimized formulation proved the integrity and stability of the developed formulation. Thus, developed formulations are deemed suitable for controlled codelivery of active pharmaceutical ingredients for the effective treatment of H. pylori. [ABSTRACT FROM AUTHOR]

Introduction Alopecia areata (AA) is a non-scarring, inflammatory type of hair loss, ranging in severity from patchy hair loss to alopecia totalis and universalis. Various treatment modalities are available for alopecia areata, including topical, intralesional, and systemic steroids, minoxidil, and systemic immunosuppressive. Multiple studies demonstrated the variable efficacy of vitamin D analogs in alopecia areata, but none have compared them with topical tacrolimus. Methods It was a randomized control trial conducted at Tehsil Headquarters Hospital TakhtBhai, Mardan. A total of 80 patients were enrolled in the study, 40 in each group. Using the technique of a consecutive non-probability sampling, patients were separated into groups A and B. Group A received topical calcipotriol (0.005%), while group B patients were treated with tacrolimus (0.1%). Both drugs were used twice a day for 6 months. Lesions are examined every month, and at the end of 6 months, a final SALT score is calculated. Results In our study, group A had 52% males and 48% females, while group B had 49% males and 51% females. The mean age was 32.6±6.7 and 31.5±7.4 in Group A and B respectively. The mean baseline SALT score in group A was 14.3±5.2 while in group B was13±5.1. On the other hand, the mean SALT score after 6 months was 6.2±2.3 in Group A and 8.5±5.6 in Group B. The efficacy in Group A came out to be 67%, while in group B 33% efficacy was noted (p-value=0.0132). Conclusion This study found that topical calcipotriol is superior to topical tacrolimus for hair growth in patients with alopecia areata. The difference was statistically significant. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient optical fiber communication scheme for an optical metro access network (OMAN) based on an optical multicarrier (OMC) generator. The central office (CO)'s foundation is a novel, cost‐effective OMC‐generating technique. The OMC is designed from a pulsed laser source and three cascaded electro‐optic modulators (EOMs) driven by an amplified RF source of 20 GHz signal that has generated more than 60 frequencies with excellent tone‐to‐noise ratio (TNR) ~ 40 dB and amplitude excursions from zero to 3 dB. The conventional CO is replaced with a cost‐effective OMC‐based source with an effective switching mechanism, which is necessary and equally important to be handled reliably. The downstream data of 20 Gbps per carrier is transmitted using a duo‐binary coding scheme. Six remote nodes (RNs) supported by the suggested OMC source, at each RN 10 optical network units (ONUs), can be deployed having distance laps of 5 km between the RNs, the distance between the RNs and the ONUs is kept at 2 km, the ONUs were tested w.r.t the bit error rate (BER) and the power penalties for the proposed transmissions. The BER for different fiber lengths from 5 to 25 km at various RNs and ONUs ranges were calculated and demonstrated from 1e−3 to 1e−13, showing the feasibility of the proposed OMAN. [ABSTRACT FROM AUTHOR]

Developing efficient electrode materials is a key towards high power electrochemical energy storage devices. Two-dimensional (2D) MXene shows excellent conductivity and electrochemical performance among other materials. However, the restacking of MXene layers may degrade their specific capacity and cycling performance. Considering this challenge, here we have designed a composite made of 2D MXene nanosheets and lanthanum oxide (La2O3) nanoparticles to overcome the limitations. The bifunctionality of La2O3 nanoparticles prevents the restacking of MXene layers and enhances the electrochemical properties of the electrode due to its good Faradic characteristics. The specific capacitance of the La2O3/MXene composite electrode is 366 F/g at 1 A/g, which is 4.5 and 3 times higher than those of the individual La2O3 and MXene. The composite electrode displays a capacitance retention of 96% after 1,000 cycles, which is due to synergistic effects between the two components and indicates the potential of La2O3/MXene composite for supercapacitors. In this work, we designed a novel composite made of 2D MXene nanosheets and lanthanum oxide (La2O3) nanoparticles to enhance the supercapacitor performance. The excellent electrochemical performance stems from the synergistic effect between La2O3 nanoparticles and 2D MXene nanosheets of the composite electrode, where the La2O3 nanoparticles prevent MXene nanosheets from restacking, and MXene nanosheets offer fast charge transportation. [ABSTRACT FROM AUTHOR]

Molybdenum disulfide (MoS2), an emerging two-dimensional semiconductor material, has been keenly studied for field-effect transistors (FETs). In this work, we explored the optical and electrical properties of FETs fabricated by MoS2 flakes grown by chemical vapor deposition (CVD) and transferred to the electrodes through propylene carbonate film. Large-area, high-quality and highly crystalline MoS2 monolayers up to 58 µm are obtained through CVD. Flakes are characterized by optical microscopy, atomic force microscopy, Raman spectroscopy, and photoluminescence analysis. The back-gated measurements are performed in ambient conditions without any encapsulation of the device. The fabricated device reveals n-type behavior with high field-effect mobility of 32 cm2/V s and high current ON/OFF ratio of 106. Good ohmic contact is achieved while using indium as source/drain electrodes. The large sized, highly crystalline flakes of MoS2 and the fabricated device showing high field-effect mobility and ON/OFF ratio make them potential candidates for high-performance nanoelectronics and optoelectronics devices. [ABSTRACT FROM AUTHOR]

IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The diseases in the human body can be detected with the help of Transfer Learning (TL), an advanced Convolutional Neural Network (CNN) approach. The baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells (HEP) type II cells and the Sub Class Discriminant (SDA) analysis technique used to detect autoimmune diseases. The technique yielded an accuracy of up to 90.03%, which was not reliable for detecting autoimmune disease in the mitotic cells of the body. In the current research, the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of TL. Data augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL models. These models are named Inception V3, Dens Net 121, VGG-16, and Mobile Net, and their performance can be calculated through parameters of the confusion matrix (accuracy, precision, recall, and F1 measures). The results show that the accuracy value of VGG-16 is 78.00%, Inception V3 is 92.00%, Dense Net 121 is 95.00%, and Mobile Net shows 88.00% accuracy, respectively. Therefore, DenseNet-121 shows the highest performance with suitable analysis of autoimmune diseases. The overall performance highlighted that TL is a suitable and enhanced technique compared to its counterparts. Also, the proposed technique is used to detect autoimmune diseases with a minimal margin of errors and flaws. [ABSTRACT FROM AUTHOR]