Nanofluids have captured the attention of scientists due to their superior thermophysical properties compared to ordinary liquids. Consequently, nanofluids serve as suitable cooling agents applicable to systems requiring swift response to thermal changes, such as vehicle engines. Therefore, the current article scrutinizes the characteristics of heat transfer on the pressure-driven flow of magnetized nanofluid between two curved corrugated surfaces in the presence of heat generation/absorption. Copper oxide nanoparticles (Cu) have been combined with pure water to form a nanofluid called Cu/H2O. The geometry of the channel is represented mathematically in an orthogonal curvilinear coordinate system. The corrugation grooves are described by sinusoidal functions with phase differences between the corrugated curved walls. The boundary perturbation method is used to find the analytical solution for the velocity field, temperature field, and volumetric flow rate taking the corrugation amplitude as the perturbation parameter. The impact of dissimilar parameters such as the curvature parameter , wave number , magnetic parameter , and wave amplitude on the flow fields are analyzed through graphs and discussed in detail. The results show that the peak of the velocity increases with the radius of curvature and the width of the channel for a constant pressure gradient. If we increase the magnetic parameter from 1 to 4, the velocity profile at the specified point decreases by 30 %. If we increase the heat source/sink parameter from 2 to 5, the temperature profile at the specified point increases by approximately 17 %. The flow rate is increased by the corrugations for any phase difference between the corrugated curved walls depending on the corrugation wavenumber and the channel radius of curvature.
Investigating Drug-Target Interactions (DTI) is crucial for drug repositioning and discovery tasks. However, discovering DTIs through experimental approaches is time-consuming and requires substantial financial resources. To address these challenges, machine learning-based methodologies have been adopted to reduce costs and save time. Unfortunately, the effectiveness of these methods has been limited due to the binary classification approach and the lack of empirically validated negative samples. The availability of abundant DTI datasets and protein structure data has enabled the development of new approaches, such as redefining the DTI problem as a regression task. Given this context, we propose an innovative deep-learning approach to predict binding affinities between drugs and targets. Our model, named the Convolution Self-Attention Network with Attention-based Bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), integrates convolutional neural network (CNN) blocks with self-attention mechanisms to create an attention-based bidirectional long short-term memory (BiLSTM) model, followed by fully connected layers. Due to the model's complexity, proper hyperparameter tuning is essential. To optimize this, we employ the Differential Evolution (DE) technique to select the most suitable hyperparameters. Experimental results demonstrate that the DE-based CSAN-BiLSTM-Att model outperforms previous approaches. Specifically, the model achieved a concordance index of 0.898 and a mean square error of 0.228 on the DAVIS dataset, and a concordance value of 0.971 with a mean square error of 0.014 on the KIBA dataset.
Cyberattack classification involves applying deep learning (DL) and machine learning (ML) models to categorize digital threats based on their features and behaviors. These models examine system logs, network traffic, or other associated data patterns to discriminate between standard activities and malicious actions. Efficient cyberattack classification is vital for on-time threat detection and response, permitting cybersecurity specialists to categorize and reduce potential risks to a system. Handling class-imbalanced data in cyberattack classification using DL is critical for achieving exact and robust models. In cybersecurity databases, instances of normal behavior frequently significantly outnumber instances of cyberattacks, foremost due to biased methods that may complete poorly on minority classes. To address this issue approaches such as oversampling the lesser class, undersampling the popular class, or using more advanced systems can be used. These plans defend that the DL technique is more complex when determining cyberattacks, so it increases complete performance and adapts the effect of the imbalance class on the classification results. This study presents a novel Hybrid Salp Swarm Algorithm with a DL Approach for Cyberattack Classification (HSSADL-CAC) technique. The HSSADL-CAC method intends to resolve class imbalance data handling with an optimum DL model for the recognition of cyberattacks. At first, the HSSADL-CAC method experiences data normalization as a pre-processing stage. The HSSADL-CAC technique uses the ADASYN approach to handle class imbalance problems. In addition, the HSSADL-CAC technique applies an HSSA-based feature selection approach. The HSSADL-CAC technique detects cyberattacks using a deep extreme learning machine (DELM) model. Finally, the hyperparameter tuning of the ELM model takes place by utilizing the beluga whale optimization (BWO) model. The performance analysis of the HSSADL-CAC technique employs a benchmark database. The comprehensive comparison research indicates the superior performance of the HSSADL-CAC technique in the cyberattack detection procedure.
This work investigates a new class of statistical models and presents a specific example from this class. We created a new family of distributions using trigonometric functions, known as the cosine pie-power odd-G family. The paper details the fundamental properties of this proposed family of distributions. By using the Weibull distribution as the underlying model, we present a specific distribution within this family that exhibits various hazard function shapes, including bathtub, reverse-j, increasing, and j-shaped curves. The statistical characteristics of this new distribution are thoroughly analyzed. Parameters of the suggested distribution are determined using the maximum likelihood estimation (MLE) method. To verify the precision of this estimation process, Monte Carlo simulations are conducted, which show a decrease in biases and mean square errors as sample sizes increase, even when samples are small. To demonstrate the practical utility of the proposed distribution, two real-world datasets are analyzed. The performance of the proposed distribution model is assessed through various criteria of model selection and fitness results. Results from these assessments indicate that the recommended model execute better than seven other existing models.
The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks.
Accurate and timely flow prediction is the most significant element for intelligent traffic management systems. However, developing a robust and potential prediction method is a challenge because of the nonlinear characteristics and inherent randomness of the traffic flow in smart cities. Deep learning can analyze historical traffic data and predict future traffic patterns in traffic flow prediction. This can be done by training deep neural networks on large datasets, such as traffic speed and volume data, to learn the underlying relationships between various factors influencing traffic flow. The resulting models can then be used to predict future traffic conditions, helping optimize traffic management, reduce congestion, and improve safety. This study introduces a Hunter Prey Optimization with Hybrid Deep Learning-Driven Traffic Flow in smart cities Prediction (HPOHDL-TFPM). The HPOHDL-TFPM approach's primary goal is to accurately and rapidly forecast traffic flow. The HPOHDL-TFPM technique uses Z-score normalization to normalize the traffic data to achieve this. In addition, the CBLSTM-AE model, which combines convolutional bidirectional long short-term memory and autoencoder, is utilized in the prediction of traffic flow in smart cities. Moreover, the HPO technique is applied as a hyperparameter optimizer to select the hyperparameter values properly. The experimental validation of the HPOHDL-TFPM approach is tested in several contexts. Numerous comparative studies demonstrated the improved performance of the HPOHDL-TFPM approach over other existing methods.
With the integration of dance art and computer technology, automatic dance score generation has become a new research direction in computer vision and machine learning, but generating the corresponding Laban symbols by capturing the skeletal key points of dance movements is a challenging task. In this study, we propose an automatic dance score generation model that utilizes local spatio-temporal features to address the inefficiency and creativity limitations of traditional choreography methods. Specifically, we propose the Multiscale Spatio-Temporal Convolution (MSConv) module to capture local spatio-temporal features in human skeletal motion sequences. In addition, the Compressed Pyramid Attention (CPA) mechanism is used to achieve effective fusion of global and local features. This mechanism facilitates the interaction between global and local spatio-temporal information and automatically generates dance sequences by analyzing motion data from dance videos to extract key features. We validate the proposed method on Laban 16 and Laban 48 dance score datasets, and the generated Laban sequences preserve the original style of the dance sequences with a combined accuracy of 94.2% and 93.7%, respectively.
The motivation of this recent article is to study dusty Walter’s B fluid flow due to its wide range of applications in biology and polymer industry. The fluid is traveling peristaltically through an asymmetric channel with wall slip. A discussion is also presented to examine heat transfer effects with thermal radiation and slip.
The regular perturbation technique is employed to evaluate the mathematical model of the problem, which is first simplified by using stream functions. Mathematical results are simulated to illustrate flow characteristics of fluid and solid particles in salient quantities. Also, graphs of temperature distribution of fluid and dust particles have been discussed to study the impacts of various parameters.
Walter’s B fluid parameter reduces speed of both fluid and dust particles. By increasing thermal slip parameter, temperature transference becomes slower through the fluid, while Brinkman number significantly raises the temperature profile of both fluid and particles. This article presents a theoretical analysis of the problem. Moreover, the characteristics of liquids involved in the plastic industry and medical science can also be understood using the current analysis.
Walter’s B fluid with dust particle suspension has not been investigated for slip and thermal radiation effects.
In response to the escalating issue of water scarcity, the United Nations has allocated Sustainable Development Goal 6 of ‘Clean Water and Sanitation’ to address the issue by providing clean water and improved sanitation. Solar stills are an attractive solution to water scarcity as they are simple, cost-effective, and convenient for communities with limited resources. However, they have shortcomings, such as limited production and nocturnal ineffectiveness. The present study proposes several alternatives to address these issues using the conceptual design technique. The customer requirements were met using the Quality Function Deployment (QFD) method targeted during the design stage. An integrated AHP-TOPSIS was used to evaluate the design of alternatives considering seven criteria. This proposed method includes many factors, including system efficiency, cost, and ease of operation and maintenance. The three alternatives combine solar stills with adsorption desalination units. Two weighting methods were used, consistency-based ranking index for decision making (CRITIC) and Entropy, to evaluate the results' reliability. The findings showed that the most favorable alternative with CRITIC value of 0.975 and entropy of 0.988, combines a pyramid solar still and an evacuated tube solar collector. The purpose of this investigation is to build on the body of knowledge of solar desalination and support decision-makers in the evaluation process of selecting an appropriate solar still system.
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