Volume 5

ABSTRACT: Elimination of dyes and pollutants from aquatic systems is crucial due to their poisonous and detrimental characteristics. The objective of the current work was to create agricultural-based adsorbents with improved adsorption characteristics derived from agricultural residues, specifically banana Peel (BP) and wheat Straw (WS). The impact of adsorbent dosage and pH on the adsorption of Orange G (OG) dye from water was assessed. The BP and SW were acquired from the Shaheed Benazirabad district in Sindh, Pakistan. Agri-waste underwent thermal transformation in an anoxic atmosphere at 400°C to provide Banana Peel Char (BPC) and Wheat Straw Char (WSC). Furthermore, the mixture of both components, char and potassium hydroxide (KOH), is referred to as Modified Banana Peel Char (KMBPC), and Modified Wheat Straw Char (KMWSC) correspondingly. The kinetic analysis demonstrated that the experimental data for the sorption process is most consistent with the kinetic models. The equilibrium data was optimally fitted to Langmuir isotherm model (R²>0.98), signifying a chemisorption process. The findings revealed that the maximum sorption capacities of OG on BPC and KMBPC were (27.89 and 37.33 mgg^-1), correspondingly, at pH 4.5, at dye conc: of 80 mgL^-1, an adsorbent dose of 2 gL^-1. Similarly, for WSC and KMWSC, the capacities were 31.81 mgg^-1and 41.4 mgg^-1, correspondingly. Sorption percentages of dye on BPC, WSC, KMBPC, and KMWSC were determined to be 66.01, 75.67, 96.10, and 98.01%, respectively. The alteration process for WSC, BPC, and KOH-modified materials was examined utilizing Uv-Vis spectra and FTIR methods. The findings demonstrated the presence of highly porous crystalline structures inside the amorphous matrix, hence enhancing the adsorbent’s surface area for the removal of toxic OG dye. This findings suggest that our innovative material may provide a more effective alternative for the removal of harmful dyes from wastewater. Moreover, this will also promote further research on pollution remediation via the utilization of modified agricultural waste. products

ABSTRACT: This research examines the structural and electrical characteristics of Cd-doped Zr₃C₂ MXenes by density functional theory (DFT). Results from structural optimization show that Cd doping changes the lattice properties, which is a sign of local atomic distortions. Calculations of cohesive energy and formation enthalpy show that both pure and Cd-doped structures are thermodynamically stable. Electronic band structure and density of states (DOS) investigations show that adding Cd to Zr₃C₂ adds more electronic states close to the Fermi level while keeping the metallic properties of Zr₃C₂. Partial DOS shows that the d-orbitals of Cd and Zr, as well as the p-orbitals of C, make considerable contributions. These results indicate that the addition of Cd improves the electronic properties of Zr₃C₂ MXenes, positioning them as viable candidates for practical applications in nanoelectronic (e.g., transistors and interconnects), chemical sensors, and energy storage devices, including batteries and supercapacitors.

ABSTRACT: Alzheimer’s disease (AD) is a continuous increasing neurological disorder that is an important reason of dementia which is mostly in older adults. An early and accurate diagnosis of AD is necessary for timely detection and treatment. This survey review the techniques used in deep learning (DL) and machine learning (ML) for AD classification and detection. Mainly  focused on  brain network analysis to identify alterations in local and global connectivity and spectral and coherence investigations of EEG data to identify disturbances associated with AD, are two research strategies that have highlighted  the mechanisms behind AD. Two examples of robust deep learning models that have achieved notable precision in identifying AD using neuroimaging datasets like MRI and EEG are conventional neural networks (CNNs) and ensemble learning. Using sleep EEG to detect mild cognitive impairment (MCI),  research has demonstrated the utility of functional connectivity metrics. Furthermore, hybrid models that merge CNNs and ensemble learning have shown promise in both feature extraction and classification. Latest inventions, like gated graph convolutional networks(GGCN) for working with non-Euclidean data and transformer-based speech recognition models, have taken the explainable and multimodal diagnostic frameworks to the next level. This study review and gives summarize knowledge about techniques and methods used to detect Alzheimer, comparison of techniques used to detect and classify the unlabeled data, furthermore limitations of different  work are discussed  and  give future search guide for new researchers.

ABSTRACT: In order to protect animals and address issues that arise when they encounter humans, we need less costly methods to learn about how animals behave in the wild. Although still and video camera monitoring is helpful, it often produces a lot of data that makes it expensive and difficult to look for specific species. This paper discusses a novel and cutting-edge method for identifying and differentiating animal-specific behaviors utilizing computer technology. We can do this by seeing pictures or videos. Our first objective was to differentiate between animals that are very similar, including tigers, leopards, and hyenas. Considering how similar tigers and leopards seem, this multiclassification task was very difficult. Using a dataset of enormous pictures of wild life animals in the forests and zoo. We created a deep learning model capable of accurately recognizing these species. With multiclassification accuracy rates of 98.05%, our deep learning models for automatic picture identification produced impressive results, demonstrating the efficacy of our methodology. We developed a new technique for locating creatures of interest in shifting habitats by including video footage into our approach, going beyond still photos. We believe that this is the first instance of this system being exploited in this manner.

ABSTRACT: Various research has confirmed that lead causes serious health risks on human. Cyclone separators are highly effective industrial devices used for separating material particles from air. The study investigates the influence of particle size and inlet velocity on the separation efficiency of lead (Pb) particles in a cyclone separator using Computational Fluid Dynamics (CFD). The cyclone separator is designed of a height of 0.11m with an outlet height of 0.0625m, inlet height of 0.15m with depth of 0.1m using ANSYS Fluid Flow. The net particles used is ranged from 1 µm to 9 µm with an inlet velocity of 3 m/s to 8 m/s When operating at an inlet velocity of 3 m/s, the efficiency is lowest at 2.2%, for particles measuring 1 µm, while for 9 µm particles, it reaches a peak efficiency of 95.4%. When operating at an inlet velocity of 8 m/s, the efficiency is lowest at 9.9% for particles measuring 1 µm, while for 9 µm particles, it reaches a peak efficiency of 100%. The Simulation results indicate that the performance of a cyclone separator is influenced by both particle size and inlet velocity and observed as a highly effective tool for the removal of lead (Pb) particles present in air to reduce the breathing problems. By capturing particles before they can disperse into the atmosphere, the device can substantially reduce risks of respiratory problems and related health issues in humans. Cyclone separator plays a vital role in safeguarding human health and balancing the ecosystem.