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Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda—Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (EMR) made it easier to separate the blood profile data and identify MBC patients. Monocytes revealed a noticeable mean difference between MBC patients as compared to healthy individuals. The accuracy of ML models was dramatically improved by removing outliers from the blood profile data. A Decision Tree (DT) classifier displayed an accuracy of 83% with an AUC of 0.87. Next, we deployed DT classifiers using Flask to create a web application for robust diagnosis of MBC patients. Taken together, we conclude that ML models based on blood profile data may assist physicians in selecting intensive-care MBC patients to enhance the overall survival outcome. [ABSTRACT FROM AUTHOR]

Big Data applications require extensive resources and environments to store, process and analyze this colossal collection of data in a distributed manner. Containerization with cloud computing provides a pertinent remedy to accommodate big data requirements, however requires a precise and appropriate load-balancing mechanism. The load on servers increases exponentially with increased resource usage thus making load balancing an essential requirement. Moreover, the adjustment of containers accurately and rapidly according to load as per services is one of the crucial aspects in big data applications. This study provides a review relating to containerized environments like Docker for big data applications with load balancing. A novel scheduling mechanism of containers for big data applications established on Docker Swarm and Microservice architecture is proposed. The concept of Docker Swarm is utilized to effectively handle big data applications' workload and service discovery. Results shows that increasing workloads with respect to big data applications can be effectively managed by utilizing microservices in containerized environments and load balancing is efficiently achieved using Docker Swarm. The implementation is done using a case study deployed on a single server and then scaled to four instances. Applications developed using containerized microservices reduces average deployment time and continuous integration. [ABSTRACT FROM AUTHOR]

This study presents the design and fabrication of an urban solar food cooking system with a phase change material (PCM) as a heat storage tank. The effort has been taken to test the system experimentally and explore its thermal performance under actual climatic conditions of Mumbai, India. The solar heat energy is stored in the tank using commercial-grade erythritol as PCM in current research work. A heat exchanger is well designed and fabricated to regulate the flow of solar heat energy from the storage tank to the cooking vessel, similar to the domestic liquefied petroleum gas (LPG) cooking system. This solar cooker is designed to cook food twice a day for four family members (equivalent to an energy of 5000 KJ). Cooking experiments were conducted on 19 April 2019 for the afternoon and evening slots with rice and potato as cooking loads, respectively. The time taken for cooking rice and potato are from 12:30 pm to 12:52 pm (22 minute) and from 05:30 pm to 05:59 pm (29 minutes), respectively. The heat transfer rate was also observed at different storage tanks and cooking unit points. The experiments show cooking is possible twice a day and considered as convenient as domestic LPG stoves. Furthermore, it was found that comparatively less time was required for cooking food than other existing solar cookers. [ABSTRACT FROM AUTHOR]

In this work, selenium nanoparticles (Se NPs) stabilized with cocamidopropyl betaine were synthesized for the first time. It was observed that Se NPs synthesized in excess of selenic acid had a negative charge with ζ-potential of −21.86 mV, and in excess of cocamidopropyl betaine—a positive charge with ξ = + 22.71 mV. The resulting Se NPs with positive and negative charges had a spherical shape with an average size of about 20–30 nm and 40–50 nm, respectively. According to the data of TEM, HAADF-TEM using EDS, IR spectroscopy and quantum chemical modeling, positively charged selenium nanoparticles have a cocamidopropylbetaine shell while the potential- forming layer of negatively charged selenium nanoparticles is formed by SeO32− ions. The influence of various ions on the sol stability of Se NPs showed that SO42− and PO43− ions had an effect on the positive Se NPs, and Ba2+ and Fe3+ ions had an effect on negative Se NPs, which corresponded with the Schulze-Hardy rule. The mechanism of coagulating action of various ions on positive and negative Se NPs was also presented. Also, influence of the active acidity of the medium on the stability of Se NPs solutions was investigated. Positive and negative sols of Se NPs had high levels of stability in the considered range of active acidity of the medium in the range of 1.21–11.98. Stability of synthesized Se NPs stability has been confirmed in real system (liquid soap). An experiment with the addition of Se NPs stabilized with cocamidopropyl betaine to liquid soap showed that the particles of dispersed phases retain their initial distributions, which revealed the stability of synthesized Se NPs. [ABSTRACT FROM AUTHOR]

Because of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture. [ABSTRACT FROM AUTHOR]

This article is to analyze the bottleneck problems of NoC in many more applications like multi-processor communication, computer architectures, and network interface processors. This paper aims to research the advantages and disadvantages of low congestion protocols on highway environments like multiple master multiple slave interconnections. A long-term evolution and effective on-chip connectivity solution for secured, congestion aware and low power architecture is emerged for Network-on-Chip (NoC) for MCSoC. Applications running simultaneously on a different chip are often exchanged dynamically on the chip network. Of-course, in general on chip communication, resources mean that applications may interact with shared resources to influence each other's time characteristics. [ABSTRACT FROM AUTHOR]

The rapid development of railway transportation towards high speed, high density and heavy load has led to even higher requirements for the safety of railway signal equipment. The safety of railway signal equipment is an important part of ensuring railway traffic safety, thus, it is very necessary to study a system that can diagnose the fault of railway signal equipment according to the actual situation. This article utilizes the deep learning algorithm of artificial intelligence for investigating the interlocking faults in the railway transportation. This paper uses ADASYN data synthesis method to synthesize few category samples, uses TF-IDF to extract features and transform vectors, and proposes a deep learning integration method based on combined weight. The results show that BiGRU has better overall classification performance when evaluated on the index of primary and secondary fault classification accuracy. The classification accuracy improvement of 5% is achieved for primary fault classification and the comprehensive evaluation index of secondary fault classification is improved by about 9%. It was revealed that when compared with ADASYN + BiLSTM neural network, the comprehensive evaluation index of primary fault classification accuracy is improved by about 6%, and the comprehensive evaluation index of secondary fault classification is improved by about 10%. It is demonstrated that deep learning integration is an effective method to improve the classification performance of turnout fault diagnosis model. [ABSTRACT FROM AUTHOR]

In today's modern era, e-commerce is making headway through the process of bringing goods within everyone's grasp. Consumers are not even required to step out of the comfort of their homes for buying things, which makes it very convenient for them. Moreover, there is a wide variety of brands to choose from. Since more customers depend on online shopping platforms these days, the value of ratings is also growing. To buy these products, people rely solely on the reviews that are being provided about the products. To analyze these reviews, sentiment analysis needs to be performed, which can prove useful for both the buyers and the manufacturer. This paper describes the process of sentiment analysis and its requirements. In this paper, Amazon Review dataset 2018 has been used for carrying out our research and Long Short-Term Memory (LSTM) has been combined with word2vec representation, resulting in improving the overall performance. A gating mechanism was used by LSTM during the training process. The proposed LSTM model was evaluated on four performance measures: accuracy, precision, recall, and F1 score, and achieved overall higher results when compared with other baseline models. [ABSTRACT FROM AUTHOR]

There is a growing demand for information and computational technology for surgeons help with surgical planning as well as prosthetics design. The two-dimensional images are registered to the three-dimensional (3D) model for high efficiency. To reconstruct the 3D model of knee joint including bone structure and main soft tissue structure, the evaluation and analysis of sports injury and rehabilitation treatment are detailed in this study. Mimics 10.0 was used to reconstruct the bone structure, ligament, and meniscus according to the pulse diffusion-weighted imaging sequence (PDWI) and stir sequences of magnetic resonance imaging (MRI). Excluding congenital malformations and diseases of the skeletal muscle system, MRI scanning was performed on bilateral knee joints. Proton weighted sequence (PDWI sequence) and stir pulse sequence were selected for MRI. The models were imported into Geomagic Studio 11 software for refinement and modification, and 3D registration of bone structure and main soft tissue structure was performed to construct a digital model of knee joint bone structure and accessory cartilage and ligament structure. The 3D knee joint model including bone, meniscus, and collateral ligament was established. Reconstruction and image registration based on mimics and Geomagic Studio can build a 3D model of knee joint with satisfactory morphology, which can meet the requirements of teaching, motion simulation, and biomechanical analysis. [ABSTRACT FROM AUTHOR]

Building Information Modeling (BIM) technology has been widely used in the construction industry, especially in the field of civil construction. BIM standards, basic software and management platforms are relatively mature. The urban rail transit projects are linear projects, they not only span long lines, multiple regions, involve multiple disciplines, and are difficult to coordinate, but also have complex surrounding environments and high safety requirements. Therefore, their needs for integrated construction and operation applications are more concentrated. In order to solve the problems of data isolation, single display form, abnormal situation notification and delayed processing in urban rail transit construction monitoring, combined with GIS+BIM technology, a complete set of construction monitoring information management process and data organization plan is proposed, and the development is oriented. The construction monitoring system of project construction management focuses on solving the problems of the integration, display, early warning and secondary early warning of construction monitoring data.The system realizes the functions of input, storage, processing, three-dimensional display and early warning of measuring point information and daily measurement information. It is integrated with the GIS+BIM management and control platform, and the project is carried out in the construction project of Qingdao Rail Transit Line 8. Application, interact with functions such as model browsing, schedule control, engineering quantity management, video monitoring, etc., to improve the management efficiency and safety quality level of on-site construction.The mainstream GIS and BIM data based research on construction monitoring data standards promote the in-depth integration of construction monitoring data and improve the data entry and association efficiency. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-024-59111-6.Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-59111-6, published online 16 April 2024The original version of this Article contained an error in the spelling of the author Benjula Anbu Malar M.B which was incorrectly given as Benjula Anbu M.B.The original Article has been corrected.By Suresh Kumar Natarajan; Jayanthi S; Sandeep Kumar Mathivanan; Hariharan Rajadurai; Benjula Anbu Malar M.B and Mohd Asif ShahReported by Author; Author; Author; Author; Author; Author [Extracted from the article]

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-023-29064-3. Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-023-29064-3, published online 01 February 2023 The original version of this Article omitted an affiliation for Mohd Asif Shah. [Extracted from the article]

Cancer has arisen from both genetic mutations and epigenetic changes, making epigenetics a crucial area of research for innovative cancer prevention and treatment strategies. This dual perspective has propelled epigenetics into the forefront of cancer research. This review highlights the important roles of DNA methylation, histone modifications and non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long non-coding RNAs, which are key regulators of cancer-related gene expression. It explores the potential of epigenetic-based therapies to revolutionize patient outcomes by selectively modulating specific epigenetic markers involved in tumorigenesis. The review examines promising epigenetic biomarkers for early cancer detection and prognosis. It also highlights recent progress in oligonucleotide-based therapies, including antisense oligonucleotides (ASOs) and antimiRs, to precisely modulate epigenetic processes. Furthermore, the concept of epigenetic editing is discussed, providing insight into the future role of precision medicine for cancer patients. The integration of nanomedicine into cancer therapy has been explored and offers innovative approaches to improve therapeutic efficacy. This comprehensive review of recent advances in epigenetic-based cancer therapy seeks to advance the field of precision oncology, ultimately culminating in improved patient outcomes in the fight against cancer. [ABSTRACT FROM AUTHOR]

Wound infections resulting from pathogen infiltration pose a significant challenge in healthcare settings and everyday life. When the skin barrier is compromised due to injuries, surgeries, or chronic conditions, pathogens such as bacteria, fungi, and viruses can enter the body, leading to infections. These infections can range from mild to severe, causing discomfort, delayed healing, and, in some cases, life-threatening complications. Zinc oxide (ZnO) nanoparticles (NPs) have been widely recognized for their antimicrobial and wound healing properties, while cinnamic acid is known for its antioxidant and anti-inflammatory activities. Based on these properties, the combination of ZnO NPs with cinnamic acid (CA) was hypothesized to have enhanced efficacy in addressing wound infections and promoting healing. This study aimed to synthesize and evaluate the potential of ZnO-CN NPs as a multifunctional agent for wound treatment. ZnO-CN NPs were synthesized and characterized using key techniques to confirm their structure and composition. The antioxidant and anti-inflammatory potential of ZnO-CN NPs was evaluated through standard in vitro assays, demonstrating strong free radical scavenging and inhibition of protein denaturation. The antimicrobial activity of the nanoparticles was tested against common wound pathogens, revealing effective inhibition at a minimal concentration. A zebrafish wound healing model was employed to assess both the safety and therapeutic efficacy of the nanoparticles, showing no toxicity at tested concentrations and facilitating faster wound closure. Additionally, pro-inflammatory cytokine gene expression was analyzed to understand the role of ZnO-CN NPs in wound healing mechanisms. In conclusion, ZnO-CN NPs demonstrate potent antioxidant, anti-inflammatory, and antimicrobial properties, making them promising candidates for wound treatment. Given their multifunctional properties and non-toxicity at tested concentrations, ZnO-CN NPs hold significant potential as a therapeutic agent for clinical wound management, warranting further investigation in human models. [ABSTRACT FROM AUTHOR]

Ankle and foot orthotics are suggested for gait rehabilitation therapy after a stroke. Clinical practice necessitates adjusting the ankle foot orthosis torque to accommodate each patient's unique stride and body function. The objective of this paper is to develop a customized, cost-effective footplate orthosis from carbon fibers, manufactured using 3D printing, for individuals with partial foot amputations. The footplate is designed for use in developing nations and aimed to be made available with a reduced lead time. The experiment work carried out in this research is used to design, analyze, and validate a prosthetic footplate. A foot of size UK 7 with a 2 mm thick model of the human foot was modeled using Unigraphics-NX. The footplate's design is being tested both conceptually and empirically. The footplate manufactured by applying load at toe and heel shows that the carbon fiber successfully restores leg length and reduces pressure on the feet's delicate distal end. [ABSTRACT FROM AUTHOR]

Problem: Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process. Aim: This study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity. Methods: We introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India. Results: The proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls. Conclusion: Our findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes. [ABSTRACT FROM AUTHOR]

Microalgae presents an inducing potential as a primary raw material in crafting plant‐based seafood alternatives, revolutionizing the landscape of sustainable food production. These microscopic organisms display a rich nutritional profile, presenting an array of nutrients such as essential amino acids, polyunsaturated fatty acids, vitamins, and minerals comparable to those found in seafood. Their versatile nature allows for the replication of seafood flavors and textures, addressing the sensory aspects crucial to consumer acceptance of substitutes. Furthermore, microalgae cultivation requires minimal land and resources, making it an environmentally friendly and scalable option for meeting the increasing demand for sustainable protein sources. The biochemical diversity within microalgae species provides a wide spectrum of options for developing various seafood substitutes. Moreover, advancements in biotechnology and processing techniques continue to enhance the feasibility and palatability of these alternatives. Modern technologies, such as 3D printing, provide convenient and efficient technological options to reproduce the identical texture properties of seafood. As society gravitates toward eco‐conscious food choices, the exploration of microalgae as a core ingredient in plant‐based seafood alternatives aligns with the quest for ethical, environmentally sustainable, and nutritious food sources. This expanding field holds immense potential for reshaping the future of food by offering appealing, cruelty‐free alternatives while reducing dependence on traditional, unsustainable modes of seafood production. [ABSTRACT FROM AUTHOR]

The goal of the current study is to analyze several nonlinear two-dimensional time-fractional Rosenau–Hyman equations. The two-dimensional fractional Rosenau–Hyman equation has extensive use in engineering and applied sciences. The fractional view analysis of two-dimensional time-fractional Rosenau–Hyman equations is discussed using the homotopy perturbation approach, Adomian decomposition method, and Yang transformation. Some examples involving two-dimensional time-fractional Rosenau–Hyman equations are provided to better understand the suggested approaches. The solutions appear as infinite series. We offer a comparison between the accurate solutions and those that are generated employing the proposed approaches to demonstrate the effectiveness and applicability of the proposed techniques. The results are graphically illustrated using two-dimensional and three-dimensional graphs. It has been noted that the obtained results and the targeted problems real solutions are quite similar. Calculated solutions at various fractional levels describe some of the problems useful dynamics. A comparison between the numerical solutions of the models under study and the exact solutions in cases when a solution is known serves as a clear demonstration of the viability and dependability of the suggested approaches. Other fractional problems that arise in other fields of science and engineering can be solved using a modified version of the current techniques. [ABSTRACT FROM AUTHOR]

The identification of relevant biomarkers from high-dimensional cancer data remains a significant challenge due to the complexity and heterogeneity inherent in various cancer types. Conventional feature selection methods often struggle to effectively navigate the vast solution space while maintaining high predictive accuracy. In response to these challenges, we introduce a novel feature selection approach that integrates Random Drift Optimization (RDO) with XGBoost, specifically designed to enhance the performance of cancer classification tasks. Our proposed framework not only improves classification accuracy but also offers valuable insights into the underlying biological mechanisms driving cancer progression. Through comprehensive experiments conducted on real-world cancer datasets, including Central Nervous System (CNS), Leukemia, Breast, and Ovarian cancers, we demonstrate the efficacy of our method in identifying a smaller subset of unique and relevant genes. This selection results in significantly improved classification efficiency and accuracy. When compared with popular classifiers such as Support Vector Machine, K-Nearest Neighbor, and Naive Bayes, our approach consistently outperforms these models in terms of both accuracy and F-measure metrics. For instance, our framework achieved an accuracy of 97.24% in the CNS dataset, 99.14% in Leukemia, 95.21% in Ovarian, and 87.62% in Breast cancer, showcasing its robustness and effectiveness across different types of cancer data. These results underline the potential of our RDO-XGBoost framework as a promising solution for feature selection in cancer data analysis, offering enhanced predictive performance and valuable biological insights. [ABSTRACT FROM AUTHOR]

This study extended an existing semi-analytical technique, the Homotopy Perturbation Method, to the Block Homotopy Modified Perturbation Method by solving two n × n crisp triangular intuitionistic fuzzy (TIF) systems of linear equations. In the original system, the coefficient matrix is considered as real crisp, while the unknown variable vector and right hand side vector are regarded as triangular intuitionistic fuzzy numbers. The Block Homotopy Modified Perturbation Method is found to be efficient and practical to solve n × n TIF linear systems as it only requires the non-singularity of the n × n TIF linear system’s coefficient matrix, whereas the point Homotopy Perturbation Method and other classical numerical iterative methods typically require non-zero diagonal entries in the coefficient matrix. A set of theorems relevant to this study are presented and demonstrated. We solve an engineering application, i.e. a current flow circuit problem that is represented in terms of a triangular intuitionistic fuzzy environment, using the suggested method. The unknown current is then obtained as a triangle intuitionistic fuzzy number. The proposed semi-analytic method is used to solve some numerical test problems in order to validate their performance and efficiency in comparison to other existing techniques. The numerical results of the example are displayed on graphs with different degrees of uncertainty. The efficiency and accuracy of the proposed method are further demonstrated by comparisons to block Jacobi, Adomain Decomposition method, Successive Over-Relaxation method and the classical Gauss-Seidel numerical method. [ABSTRACT FROM AUTHOR]

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s. [ABSTRACT FROM AUTHOR]

In this work, we use two unique methodologies, the homotopy perturbation transform method and Yang transform decomposition method, to solve the fractional nonlinear seventh-order Kaup–Kupershmidt (KK) problem. The physical phenomena that arise in chemistry, physics, and engineering are mathematically explained in this equation, in particular, nonlinear optics, quantum mechanics, plasma physics, fluid dynamics, and so on. The provided methods are used to solve the fractional nonlinear seventh-order KK problem along with the Yang transform and fractional Caputo derivative. The results are significant and necessary for exploring a range of physical processes. This paper uses modern approaches and the fractional operator to develop satisfactory approximations to the offered problem. To solve the fractional KK equation, we first use the Yang transform and fractional Caputo derivative. He's and Adomian polynomials are useful to manage nonlinear terms. It is shown that the suggested approximate solution converges to the exact one. In these approaches, the results are calculated as convergent series. The key advantage of the recommended approaches is that they provide highly precise results with little computational work. The suggested approach results are compared to the precise solution. By comparing the outcomes with the precise solution using graphs and tables we can verify the efficacy of the offered strategies. Also, the outcomes of the suggested methods at various fractional orders are examined, demonstrating that the findings get more accurate as the value moves from fractional order to integer order. Moreover, the offered methods are innovative, simple, and quite accurate, demonstrating that they are effective for resolving differential equations. [ABSTRACT FROM AUTHOR]

The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.23%. The model's capacity to identify spatial and temporal relationships in health data is improved by novel techniques such as the use of CNN for spatial analysis and feature extraction and LSTM for temporal sequence modeling. Early intervention is made easier by this synergistic approach, which enhances trend identification and anomaly detection in vital signs. A variety of datasets are used to validate the model's robustness, highlighting its efficacy in remote patient care. This study shows how using ensemble models' advantages might improve health monitoring's precision and promptness, which would eventually benefit patients and ease the burden on healthcare systems. [ABSTRACT FROM AUTHOR]

Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation. [ABSTRACT FROM AUTHOR]

Small-scale Darrieus wind turbines have a wide scope in areas that are isolated from the power grid for such small-scale household applications. Applications of wind turbines on house roofs are one potential way to generate electricity from wind energy harvesting in low-wind urban locations. This work studies the aerodynamic behavior of a vertical axis wind turbine based on a MATLAB programming mathematical model. The NACA0021 airfoil profile blade was used in this present research investigation. The turbine was fabricated with dimensions such as chord length, c = 95 mm, blade height, h = 600 mm, and turbine diameter, D = 600 mm. The experimental results of the turbine for air velocity from 1 to 12 ms−1 were used in this paper and compared with analytical results. It has been observed that the fixed-pitch turbine does not start by itself at a low air velocity of 1 to 5 m/s due to a minimum and negative torque. [ABSTRACT FROM AUTHOR]