Effect of banana tree leaves ash as cementitious material on the durability of concrete against sulphate and acid attacks.

The construction industry's rapid growth poses challenges tied to raw material depletion and increased greenhouse gas emissions. To address this, alternative materials like agricultural residues are gaining prominence due to their potential to reduce carbon emissions and waste generation. In this context this research optimizes the use of banana leaves ash as a partial cement substitution, focusing on durability, and identifying the ideal cement-to-ash ratio for sustainable concrete. For this purpose, concrete mixes were prepared with BLA replacing cement partially in different proportions i.e. (0 %, 5 %, 10 %, 15 %, & 20 %) and were analyzed for their physical, mechanical and Durability (Acid and Sulphate resistance) properties. Compressive strength, acid resistance and sulphate resistance testing continued for 90 days with the intervals of 7, 28 and 90 days. The results revealed that up to 10 % incorporation of BLA improved compressive strength by 10 %, while higher BLA proportions (up to 20 %) displayed superior performance in durability tests as compared to the conventional mix. The results reveal the potentials of banana leave ash to refine the concrete matrix by formation of addition C-S-H gel which leads towards a better performance specially in terms of durability aspect. Hence, banana leaf ash (BLA) is an efficient concrete ingredient, particularly up to 10 % of the mix. Beyond this threshold, it's still suitable for applications where extreme strength isn't the primary concern, because there may be a slight reduction in compressive strength.

The significance of chemical reaction, thermal buoyancy, and external heat source to optimization of heat transfer across the dynamics of Maxwell nanofluid via stretched surface.

Energy loss during the transportation of energy is the main concern of researchers and industrialists. The primary cause of heat exchange gadget inefficiency during transportation was applied to traditional fluids with weak heat transfer characteristics. Instead, thermal devices worked much better when the fluids were changed to nanofluids that had good thermal transfer properties. A diverse range of nanoparticles were implemented on account of their elevated thermal conductivity. This research addresses the significance of MHD Maxwell nanofluid for heat transfer flow. The flow model comprised continuity, momentum, energy transport, and concentration equations in the form of PDEs. The developed model was converted into ODEs by using workable similarities. Numerical simulations in the MATLAB environment were employed to find the outcomes of velocity, thermal transportation, and concentration profiles. The effects of many parameters, such as Hartman, Deborah, buoyancy, the intensity of an external heat source, chemical reactions, and many others, were also evaluated. The presence of nanoparticles enhances temperature conduction. Also, the findings are compared with previously published research. In addition, the Nusselt number and skin friction increase as the variables associated with the Hartman number and buoyancy parameter grow. The respective transfer rates of heat are 28.26 % and 38.19 % respectively. As a result, the rate of heat transmission increased by 14.23 % . The velocity profiles enhanced while temperature profiles declined for higher values of the Maxwell fluid parameter. As the external heat source increases, the temperature profile rises. Conversely, buoyancy parameters increase as it descends. This type of problem is applicable in many fields such as heat exchangers, cooling of electronic devices, and automotive cooling systems.

An efficient deepfake video detection using robust deep learning.

The creation and manipulation of synthetic images have evolved rapidly, causing serious concerns about their effects on society. Although there have been various attempts to identify deep fake videos, these approaches are not universal. Identifying these misleading deepfakes is the first step in preventing them from spreading on social media sites. We introduce a unique deep-learning technique to identify fraudulent clips. Most deepfake identifiers currently focus on identifying face exchange, lip synchronous, expression modification, puppeteers, and other factors. However, exploring a consistent basis for all forms of fake videos and images in real-time forensics is challenging. We propose a hybrid technique that takes input from videos of successive targeted frames, then feeds these frames to the ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network for training and classification. This proposed method helps identify artifacts in deepfake images that do not seem real. To assess the robustness of our proposed model, we used the open deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++). We achieved 96.23% accuracy when using the FF++ digital record. In contrast, we attained 78.33% accuracy using the aggregated records from FF++ and DFDC. We performed extensive experiments and believe that our proposed method provides more significant results than existing techniques.

Comparative study of some non-Newtonian nanofluid models across stretching sheet: a case of linear radiation and activation energy effects.

The use of renewable energy sources is leading the charge to solve the world's energy problems, and non-Newtonian nanofluid dynamics play a significant role in applications such as expanding solar sheets, which are examined in this paper, along with the impacts of activation energy and solar radiation. We solve physical flow issues using partial differential equations and models like Casson, Williamson, and Prandtl. To get numerical solutions, we first apply a transformation to make these equations ordinary differential equations, and then we use the MATLAB-integrated bvp4c methodology. Through the examination of dimensionless velocity, concentration, and temperature functions under varied parameters, our work explores the physical properties of nanofluids. In addition to numerical and tabular studies of the skin friction coefficient, Sherwood number, and local Nusselt number, important components of the flow field are graphically shown and analyzed. Consistent with previous research, this work adds important new information to the continuing conversation in this area. Through the examination of dimensionless velocity, concentration, and temperature functions under varied parameters, our work explores the physical properties of nanofluids. Comparing the Casson nanofluid to the Williamson and Prandtl nanofluids, it is found that the former has a lower velocity. Compared to Casson and Williamson nanofluid, Prandtl nanofluid advanced in heat flux more quickly. The transfer of heat rates are 25.87 % , 33.61 % and 40.52 % at R d = 0.5 , R d = 1.0 , and R d = 1.5 , respectively. The heat transfer rate is increased by 6.91 % as the value of Rd rises from 1.0 to 1.5. This study is further strengthened by a comparative analysis with previous research, which is complemented by an extensive table of comparisons for a full evaluation.

Wall panel structure design optimization of a hexagonal satellite.

Considering that it satisfies high strength and stiffness at a low weight, the grid structure is the ideal option for meeting the requirements for developing the wall panel structure for the satellite. The most attractive grid structures for the satellite wall panel industry are isogrid and honeycomb structures. The first part of this work involves studying the mechanical and dynamic performance of five designs for the satellite wall panel made of 7075-T0 Al-alloy. These designs include two isogrid structures with different rib widths, two honeycomb structures with different cell wall thicknesses, and a solid structure for comparison. The performance of these designs was evaluated through compression, bending, and vibration testing using both finite element analysis (FEA) with the Ansys workbench and experimental testing. The FEA results are consistent with the experimental ones. The results show that the isogrid structure with a lower rib thickness of 2 mm is the best candidate for manufacturing the satellite wall panel, as this design reveals the best mechanical and dynamic performance. The second part of this work involves studying the influence of the length of the sides of the best isogrid structure in the range of 12 mm-24 mm on its mechanical and dynamic performance to achieve the lowest possible mass while maintaining the structure's integrity. Then, a modified design of skinned wall panels was introduced and dynamically tested using FEA. Finally, a CAD model of a hexagonal satellite prototype using the best-attained design of the wall panel, i.e., the isogrid structure with a 2 mm rib width and 24 mm-long sides, was built and dynamically tested to ensure its safe design against vibration. Then, the satellite prototype was manufactured, assembled, and successfully assessed.

Insight into the dynamics of heat and mass transfer in nanofluid flow with linear/nonlinear mixed convection, thermal radiation, and activation energy effects over the rotating disk.

In this paper, we study linear and nonlinear mixed convection, activation energy, and heat radiation effects caused by nanoparticles. This study aims to improve the understanding of how nanofluids behave in the presence of rotating disks and develop more efficient and effective cooling technologies. The flow problem consisted of partial differential equations (PDE). It is challenging to calculate these equations as a result of these nonlinear PDEs. Consequently, we use appropriate similarities to transform them into ordinary differential equations (ODEs). The bvp4c Matlab built-in technique is then used to resolve these ODEs. The velocities, temperature, and concentration outcomes with the various factors are examined graphically. Additionally, tables are employed to analyze the skin friction and Nusselt number values. It is analyzed that increasing the linear and linear mixed convection parameters enhances the velocity profiles of nanofluid. Enhancements in heat are analyzed by increasing nonlinear thermal radiation and enhancement in concentration is examined by increasing activation energy. Furthermore, as the variables for thermophoresis and Brownian motion are increased, the Nusselt number falls. The heat transfer rate is 27.16% for [Formula: see text] and 39.28% for [Formula: see text]. Thus, the heat transfer rate is enhanced 12.12%. This study's practical applications include improving the behavior of fluids and the transfer of heat in rotating frameworks, which may affect energy systems, heat exchangers, and cooling advances in technology.

Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector.

Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector. Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought. Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model. Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis.

Complex dynamics of induced vortex formation and thermal-fluid coupling in tri-hybrid nanofluid under localized magnetic field: a novel study.

Hybrid nanofluids offer higher stability, synergistic effects, and better heat transfer compared to simple nanofluids. Their higher thermal conductivity, lower viscosity, and interaction with magnetic fields make them ideal for various applications, including materials science, transportation, medical technology, energy, and fundamental physics. The governing partial differential equations are numerically solved by employing a finite volume approach, and the effects of various parameters on the nanofluid flow and thermal characteristics are systematically examined from the simulations based on a self-developed MATLAB code. The parameters included magnetic field strength, the Reynolds number, the nanoparticle volume fraction, and the number and position of the strips in which the magnetic field is localized. It has been noted that the magnetized field induces the spinning of the tri-hybrid nanoparticles, which generates the intricate structure of vortices in the flow. The local skin friction (CfRe) and the Nusselt number (Nu) increase significantly when the magnetic field is intensified. Moreover, adding more nanoparticles in the flow enhances both Nu and CfRe, but with different effects for different nanoparticles. Silver (Ag) shows the highest increase in both Nu (52%) and CfRe (110%), indicating strong thermal-fluid coupling. Alumina (Al2O3) and Titanium Dioxide (TiO2) show lower increases in both Nu (43% and 34%) and CfRe (14% and 10%), indicating weaker coupling in the flow. Finally, compared with the localized one, the uniform magnetic field has a minor effect on the flow and temperature distributions.

Propagation and reflection of thermoelastic wave in a rotating nonlocal fractional order porous medium under Hall current influence.

This investigation relates to the research on Hall current on propagation and reflection of elastic waves through non-local fractional-order thermoelastic rotating medium with voids. The system is split up into longitudinal and transverse components using the Helmholtz vector rule. It is observed that, through the frequency dispersion relation four coupled quasi-waves exist in the medium. The rotating solid modifies the nature of purely longitudinal and transverse waves toward the quasi-type waves. All the propagating waves are dispersive as they depend upon angular frequency. The quasi-longitudinal wave qP and quasi-transverse wave qSV faces cut-off frequencies. The nonlocal parameter affect all the waves except the quasi void wave. Analytically, the reflection coefficients of the wave are computed using suitable boundary conditions. MATLAB software is used to perform numerical computations for a chosen solid material. The amplitude ratios and the speed of propagation of the wave are plotted graphically for rotational frequency, nonlocal, fractional order, and Hall current parameter. The significant effect of the physical parameters on the computed results has been observed. The cut-off frequency of the waves is also presented graphically. The energy conservation law is proved in the form of energy ratios. The earlier findings in the literature are obtained as special case in the absence of rotation, Hall current parameter and porous voids.

SCDet: A Robust Approach for the Detection of Skin Lesions.

Red, blue, white, pink, or black spots with irregular borders and small lesions on the skin are known as skin cancer that is categorized into two types: benign and malignant. Skin cancer can lead to death in advanced stages, however, early detection can increase the chances of survival of skin cancer patients. There exist several approaches developed by researchers to identify skin cancer at an early stage, however, they may fail to detect the tiniest tumours. Therefore, we propose a robust method for the diagnosis of skin cancer, namely SCDet, based on a convolutional neural network (CNN) having 32 layers for the detection of skin lesions. The images, having a size of 227 × 227, are fed to the image input layer, and then pair of convolution layers is utilized to withdraw the hidden patterns of the skin lesions for training. After that, batch normalization and ReLU layers are used. The performance of our proposed SCDet is computed using the evaluation matrices: precision 99.2%; recall 100%; sensitivity 100%; specificity 99.20%; and accuracy 99.6%. Moreover, the proposed technique is compared with the pre-trained models, i.e., VGG16, AlexNet, and SqueezeNet and it is observed that SCDet provides higher accuracy than these pre-trained models and identifies the tiniest skin tumours with maximum precision. Furthermore, our proposed model is faster than the pre-trained model as the depth of its architecture is not too high as compared to pre-trained models such as ResNet50. Additionally, our proposed model consumes fewer resources during training; therefore, it is better in terms of computational cost than the pre-trained models for the detection of skin lesions.

A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features.

Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth-Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM's accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method.

The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient's abnormal activities; based upon this information, the patient's psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model's performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.

Psychophysiological responses to manual lifting of unknown loads.

BACKGROUND: The handling of unknown weights, which is common in daily routines either at work or during leisure time, is suspected to be highly associated with the incidence of low back pain (LBP). OBJECTIVES: To investigate the effects of knowledge and magnitude of a load (to be lifted) on brain responses, autonomic nervous activity, and trapezius and erector spinae muscle activity. METHODS: A randomized, within-subjects experiment involving manual lifting was conducted, wherein 10 participants lifted three different weights (1.1, 5, and 15 kg) under two conditions: either having or not having prior knowledge of the weight to be lifted. RESULTS: The results revealed that the lifting of unknown weights caused increased average heart rate and percentage of maximum voluntary contraction (%MVC) but decreased average inter-beat interval, very-low-frequency power, low-frequency power, and low-frequency/high-frequency ratio. Regardless of the weight magnitude, lifting of unknown weights was associated with smaller theta activities in the power spectrum density (PSD) of the central region, smaller alpha activities in the PSD of the frontal region, and smaller beta activities in the PSDs of both the frontal and central regions. Moreover, smaller alpha and beta activities in the PSD of the parietal region were associated only with lifting of unknown lightweights. CONCLUSIONS: Uncertainty regarding the weight to be lifted could be considered as a stress-adding variable that may increase the required physical demand to be sustained during manual lifting tasks. The findings of this study stress the importance of eliminating uncertainty associated with handling unknown weights, such as in the cases of handling patients and dispatching luggage. This can be achieved through preliminary self-sensing of the load to be lifted, or the cautious disclosure of the actual weight of manually lifted objects, for example, through clear labeling and/or a coding system.

High Aspect Ratio Thin-Walled Structures in D2 Steel through Wire Electric Discharge Machining (EDM).

Thin structures are often required for several engineering applications. Although thick sections are relatively easy to produce, the cutting of thin sections poses greater challenges, particularly in the case of thermal machining processes. The level of difficulty is increased if the thin sections are of larger lengths and heights. In this study, high-aspect-ratio thin structures of micrometer thickness (117-500 µm) were fabricated from D2 steel through wire electrical discharge machining. Machining conditions were kept constant, whereas the structure (fins) sizes were varied in terms of fin thickness (FT), fin height (FH), and fin length (FL). The effects of variation in FT, FH, and FL were assessed over the machining errors (FT and FL errors) and structure formation and its quality. Experiments were conducted in a phased manner (four phases) to determine the minimum possible FT and maximum possible FL that could be achieved without compromising the shape of the structure (straight and uniform cross-section). Thin structures of smaller lengths (1-2 mm long) can be fabricated easily, but, as the length exceeds 2 mm, the structure formation loses its shape integrity and the structure becomes broken, deflected, or deflected and merged at the apex point of the fins.

Using Electroencephalography (EEG) Power Responses to Investigate the Effects of Ambient Oxygen Content, Safety Shoe Type, and Lifting Frequency on the Worker's Activities.

OBJECTIVE: The study assesses the changes in electroencephalography (EEG) power spectral density of individuals in hypoxia when wearing a different type of safety shoes under different lifting frequencies. It also assesses the EEG response behavior induced via the process of lifting loads related to these variables. METHODS: The study was conducted in two consecutive phases: training and acclimatization phase and experimental lifting phase. Ten male college students participated in this study. A four-way repeated measures design was used in this research with independent variables: ambient oxygen content ("15%, 18%, and 20%"), safety shoes type ("light-duty, medium-duty, and heavy-duty"), lifting frequency ("1 and 4 lifts/min"), and replication ("first and second"). And the dependent variables were alpha, theta, beta, gamma, θ/α, θ/ß, α/ß, ß/α, (θ + α)/ß, and (θ + α)/(α + ß). The participant was allowed to determine his maximum acceptable weight of lift (MAWL) in fifteen minutes of lifting using psychophysically technique. Then, he continued lifting the MAWL for another five minutes, where all the data were collected. RESULTS: Results showed that the EEG responses at lower levels of the independent variables were significantly high than at higher levels; except for oxygen content, the EEG responses at lower levels were considerably lower than at a higher level. It also showed that an upsurge in the physical demand increased lifting frequency and replication and caused decreasing in alpha power, theta/beta, alpha/beta, (theta + alpha)/beta, (theta + alpha)/(alpha + beta) and increasing in the theta power and the gamma power. Furthermore, several interactions among independent variables had significant effects on the EEG responses. CONCLUSION: The EEG implementation for the investigation of neural responses to physical demands allows for the possibility of newer nontraditional and faster methods of human performance monitoring. These methods provide effective and reliable results as compared to other traditional methods. This study will safeguard the physical capabilities and possible health risks of industrial workers. And the applications of these tasks can occur in almost all working environments (factories, warehouses, airports, building sites, farms, hospitals, offices, etc.) that are at high altitudes. It can include lifting boxes at a packaging line, handling construction materials, handling patients in hospitals, and cleaning.

Effect of Cycling on a Stationary Bike While Performing Assembly Tasks on Human Physiology and Performance Parameters.

Objective: This study evaluated participants' ability to assemble a computer keyboard while at a cycling workstation. Depending on task completion time, error percentage, and workload based on subjective workload ratings, subjective body discomfort, electroencephalography (EEG) and electrocardiographic (ECG) signals, human performances were compared at four different cycling conditions: no cycling, low level cycling (15 km/h), preferred level cycling, and high level cycling (25 km/h). Method: The experiment consisted of 16 participants. Each participant performed the test four times (each cycling condition) on different days. Results: The repeated measure test showed that the alpha and beta EEG signals were high during session times (post) when compared with session times (pre). Moreover, the mean interbeat (R-R) interval decreased after the participants performed the assembly while pedaling, possibly due to the physical effort of cycling. Conclusions: Pedaling had no significant effect on body discomfort ratings, task errors, or completion time.

Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques.

Single-point incremental forming (SPIF) is a technology that allows incremental manufacturing of complex parts from a flat sheet using simple tools; further, this technology is flexible and economical. Measuring the forming force using this technology helps in preventing failures, determining the optimal processes, and implementing on-line control. In this paper, an experimental study using SPIF is described. This study focuses on the influence of four different process parameters, namely, step size, tool diameter, sheet thickness, and feed rate, on the maximum forming force. For an efficient force predictive model based on an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a regressions model were applied. The predicted forces exhibited relatively good agreement with the experimental results. The results indicate that the performance of the ANFIS model realizes the full potential of the ANN model.

A new method to produce T-shaped tubular fittings with experimental and simulation results.

A tube is an important structural element for fluid manipulation in piped networks in many industries. Tube branching is achieved using tube fittings of various shapes, including T, Y, X, and L shapes. This study proposes a new innovative technique to produce T-shaped tubular fittings. The technique uses a specially designed die setup where a tube is placed inside a T-shaped die cavity and a metallic insert is used to deform the tube into the cavity, creating the T-fitting shape. Experimental and numerical methods are used to evaluate the process. The main outcome of this research is the successful creation of T-shaped copper tube fittings using a technique similar to tube hydroforming without the need for internal pressure. This technique could be modified to assist the production of T-fittings with thicknesses outside the hydroforming limits.