Experimentation on Sargassum wightii as a flash powder igniter for attaining eco-friendly combustion on pyrotechnics.

Firecrackers are a vital element of cultural festivities that happen worldwide. However, the hazardous by-products they emit have a significant impact on environmental pollution, leading to the greenhouse effect and climate change. Aluminium powder serves as the fuel in the traditional flash powder mixture, nitrate of potassium serves as an oxidizing agent, and sulphur acts as the igniter at exact concentrations.

A comprehensive study on energy management, sensitivity analysis, and inertia compliance of feed-in tariff in IEEE bus systems with grid-connected renewable energy sources.

The need to incorporate renewable energy generators (REGs) into the electrical grid has become increasingly crucial due to the push for a more sustainable environment. This study advocates an innovative strategy for optimizing inertia-integrated generation and transmission expansion planning (GTEP) to implement feed-in tariffs (FiT). The application of the GAMS CPLEX solver to the model, which tested on an IEEE 6/IEEE 16 system, reveals that using FiT results in a 12.

An efficient brain tumor detection and classification using pre-trained convolutional neural network models.

In cases of brain tumors, some brain cells experience abnormal and rapid growth, leading to the development of tumors. Brain tumors represent a significant source of illness affecting the brain. Magnetic Resonance Imaging (MRI) stands as a well-established and coherent diagnostic method for brain cancer detection.

Primordial Molecular 1-Bit Magnitude Comparator in Consort with Excitation-Guided Mouldable Logic Systems: A Guided Design of Regular Interactions between Molecules.

In addition to designing certain excitation modulated logic systems, we have created the first-ever genuine molecular 1-bit magnitude comparator, using acid and base guided varied absorption responses at separate channels. Designed and manufactured Bifunctional Bis(indolyl)methane Derivative (1) demonstrates distinct optical responses (in UV-visible and fluorescence mode) to a range of chemical stimuli (acid, base, Hg, Cu, EDTA, GSH, etc.) in aqueous medium.

Two-dimensional Graphene/MoS2 vertical heterostructure for detection of hemoglobin concentration in blood samples.

This study demonstrates the use of computational methods to simulate the molecular dynamics involved in hemoglobin concentration sensing, utilizing Material Studio and the TCAD Silvaco device simulator. A non-invasive and flexible Graphene/MoS2 heterostructure has been proposed for sensing hemoglobin concentration in blood samples. The findings reveal a notable shift in the wavelength-dependent refractive index and extinction coefficient, as well as significant changes in the absorption coefficient and reflectivity of the Graphene/MoS2 heterostructure in response to different hemoglobin concentrations, specifically within an approximate range of 0.

Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification.

The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies.

Enhanced deep learning models for automatic fish species identification in underwater imagery.

Underwater cameras are crucial in marine ecology, but their data management needs automatic species identification. This study proposes a two-stage deep learning approach. First, the Unsharp Mask Filter (UMF) preprocesses images.

Experimental and simulation study of American saffron seed oil blended with diesel.

In a variety of industries, including transportation, agriculture, and manufacturing, diesel engines are often employed. Due of rising prices and environmental concerns, researchers examined whether biodiesels might replace diesel. The current study looks into American Saffron Oil's feasibility as a feedstock for biodiesel production.

Advancing breast ultrasound diagnostics through hybrid deep learning models.

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the "EfficientKNN" model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm.

Radiative flow of cross ternary hybrid nanofluid ( ) in a Darcy Forchheimer porous medium over a stretching cylinder with entropy minimization.

Stretching cylinders play a crucial role in the plastic and metal extrusion processes. This current investigation is to examine the Darcy Forchheimer flow of Carboxymethyl cellulose /water-based tri-hybrid nanofluid through a stretching cylinder. A composite of ternary hybrid nanofluid containing , and with CMC-water-based fluid has been added to the Crossflow model.

Optimization of two-wheeler Bike engine among three different designs using design of experiments.

This research presents the results of a fin heat transfer study of three different models of the motorcycle engine. The objective of this study is to minimize the external body temperature because the optimum design will be found when the heat transfer rate is maximized. The study obtains the procurement of a balance between the outer boundary temperature due to convective heat transfer and air-cooled fin design.

Designing Unconventional Molecular Ternary INHIBIT Logic Gate and Crafting Multifunctional Molecular Logic Systems.

The paper describes an improved method for building flexible interswitchable logic gates such as rare-type molecular ternary INHIBIT and combinational logic circuits using a specially designed pyridine-end oligo--phenylenevinylene compound featuring alkyl substituents (-CH) in a THF medium. The probe molecule showed distinct opto-chemical signals upon interaction with Cu(II) and Hg(II) in THF medium. It is interesting to note that the presence of certain anions (S, I, and CN) could specifically mask the interaction of either of these metal ions or both.

Improved solar still productivity using PCM and nano- PCM composites integerated energy storage.

The study investigates the impact of Phase Change Material (PCM) and nano Phase Change Materials (NPCM) on solar still performance. PCM and a blend of NPCM are placed within 12 copper tubes submerged in 1 mm of water to enhance productivity. Thermal performance is assessed across four major scenarios with a fixed water level of 1 mm in the basin.

Bio-inspired deep learning-personalized ensemble Alzheimer's diagnosis model for mental well-being.

Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for individual input samples, leading to the problem of easily overlooking personalized differences between samples. This research introduces a customized dynamically ensemble convolution neural network (PDECNN), which is able to build a specific integration strategy based on the distinctiveness of the sample. In this paper, we propose a personalized dynamic ensemble alzheimer's Diagnosis classification model.

A novel deep learning framework for accurate melanoma diagnosis integrating imaging and genomic data for improved patient outcomes.

Background: Melanoma is one of the most malignant forms of skin cancer, with a high mortality rate in the advanced stages. Therefore, early and accurate detection of melanoma plays an important role in improving patients' prognosis. Biopsy is the traditional method for melanoma diagnosis, but this method lacks reliability.

Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approach.

In this study, shell and heat exchangers are optimized using an integrated optimization framework. In this research, A structured Design of Experiments (DOE) comprising 16 trials was first conducted to systematically determine the essential parameters, including mass flow rates (mh, mc), temperatures (T1, t1, T2, t2), and heat transfer coefficients (€, TR, U). By identifying the first four principal components, PCA was able to determine 87.

Entropy optimization of lid-driven micropolar hybrid nanofluid flow in a partially porous hexagonal-shaped cavity with relevance to energy efficient storage processes.

Hydromagnetically associated heat convection can greatly enhance the performance of high-efficiency thermal appliances and renewable energy sources through an optimized design. This investigation examines the production of thermodynamic irreversibility and heat convection for a double lid-driven flow within a partially porous stratified hexagonal enclosure. The top and bottom-wall are moving in the opposite direction with an equal velocity U.

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis.

Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts.

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques.

The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.

Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers.

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response.

Design, control, aerodynamic performances, and structural integrity investigations of compact ducted drone with co-axial propeller for high altitude surveillance.

Compact multi-rotor unmanned aerial vehicles (UAVs) can be operated in many challenging environmental conditions. In case the UAV requires certain considerations in designing like lightweight, efficient propulsion system and others depending upon the application, the hybrid UAV comes into play when the usual UAV types cannot be sufficient to meet the requirements. The propulsion system for the UAV was selected to be coaxial rotors because it has a high thrust-to-weight ratio and to increase the efficiency of the propulsion system, a unique propeller was proposed to achieve higher thrust.

A novel flexible CO gas sensor based on polyvinyl alcohol/yttrium oxide nanocomposite films.

Polyvinyl alcohol/yttrium oxide (PVA/YO) nanocomposite films with five different weight ratios of PVA and YO nanoparticles (NPs) were prepared using a simple solution casting method. The prepared polymer nanocomposite (PNC) films were examined using Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). FTIR spectra exhibited a strong interaction between the PVA matrix and YO NPs.