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A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establishment. The above approach performed surveillance of the surrounding area using cameras installed in UAVs and internet of things technologies, and the captured images seem useful for tracking the entire environment. However, innate images from unmanned aerial vehicles show an adaptable visual effect in an uncontrolled environment, making face-mask detection and recognition harder. The UAV picture first had to be converted to grayscale, then its contrast was amplified. Image contrast was improved using Optimum Wavelet-Based Masking and the Enhanced Cuckoo Methodology (ECM). According to the contrast-enhanced image, Gabor-Transform (GT) and Stroke Width Transform (SWT) methods are used to derive attributes that help categorise mask-wearers and non-mask-wearers. Using the retrieved attributes, a Weighted Naive Bayes Classification (WNBC) detected masks in the images. Additionally, a deep neural network-based, the faster Region-Based Convolutional Neural Networks (R-CNN) algorithm combined with Adaptive Galactic Swarm Optimization (AGSO) is being used to identify appropriate and incorrect face mask wear in images, as well as to monitor social distancing among individuals in crowded areas. When the system recognises unmasked individuals, it sends their information to the doctor and the nearby police station. One unmanned aerial vehicle’s automated system alert people via speakers, ensuring social spacing. The problem involves a large percentage of appropriate and incorrect face mask wear using data from GitHub and Kaggle, including a training repository of 16,000 images and a testing data set of 12,751 images. To enhance the performance of the model’s learning, the methodology of 10-fold cross-validation will be used. Precision, recall, F1-score, and speed are then measured to determine the efficacy of the suggested approach.

This work proposes the use of an unmanned aerial vehicle (UAV) with an autopilot to identify the defects present in municipal sewerage pipes. The framework also includes an effective autopilot control mechanism that can direct the flight path of a UAV within a sewer line. Both of these breakthroughs have been addressed throughout this work. The UAV's camera proved useful throughout a sewage inspection, providing important contextual data that helped analyze the sewerage line's internal condition. A plethora of information useful for understanding the sewerage line's inner functioning and extracting interior visual details can be obtained from camera‐recorded sewerage imagery if a defect is present. In the case of sewerage inspections, nevertheless, the impact of a false negative is significantly higher than that of a false positive. One of the trickiest parts of the procedure is identifying defective sewerage pipelines and false negatives. In order to get rid of the false negative outcome or false positive outcome, a guided image filter (GIF) is implemented in this proposed method during the pre‐processing stage. Afterwards, the algorithms Gabor transform (GT) and stroke width transform (SWT) were used to obtain the features of the UAV‐captured surveillance image. The UAV camera's sewerage image is then classified as “defective” or “not defective” using the obtained features by a Weighted Naive Bayes Classifier (WNBC). Next, images of the sewerage lines captured by the UAV are analyzed using speed‐up robust features (SURF) and deep learning to identify different types of defects. As a result, the proposed methodology achieved more favorable outcomes than prior existing approaches in terms of the following metrics: mean PSNR (71.854), mean MSE (0.0618), mean RMSE (0.2485), mean SSIM (98.71%), mean accuracy (98.372), mean specificity (97.837%), mean precision (93.296%), mean recall (94.255%), mean F1‐score (93.773%), and mean processing time (35.43 min). This work describes the construction of an image analysis‐based intelligent information analysis method that will identify issues within municipal sewerage pipes employing an autopilot‐controlled unmanned aerial vehicle (UAV).

Land use land cover change due to urbanization is the prime driving forces to environmental problem and land surface temperature. The gap of the study is the lack of awareness of stakeholders regarding the protection of native forests, fruit trees, and BEBEKA coffee plantations. Deforestation for urban functions, including timber production, construction materials, and firewood, adversely affects the environment. The aim of this study was to analyze the effect of urbanization on Land Use Land Cover Change (LULCC) at Mizan Aman city, southwest Ethiopia from 1992 to 2022 using geographic information systemand remote sensing technique. The study employed systematic sampling household surveys and high-resolution remote sensing techniques to identify the impact of urbanization on land use land cover change and land surface temperature change. Sample household survey was focused on family size, education level, parcel, year of construction of the house, type of employment and monthly household income. The LULC classification were based on eight land cover class (settlement, dense forest, moderate forest, sparse forest, closed grassland, open grassland, open shrub land, annual crop land). Preprocessing, classification of the images and accuracy assessment were tested separately using the kappa coefficient. The analysis incorporates factor graph optimization for ambiguity resolution. The results indicated that cumulative accuracy were 81.52%, 82.96%, 85.41% and 84.46% and kappa coefficient 82.41%, 84.86%, 89.45% and 88.76%% for the year 1992, 2002, 2012 and 2022 respectively. This research showed that dense forest, moderate forest, sparse forest and open shrub land were significantly decreased by 68.96%, 24.60%, 31.36% and 8.28% respectively in the last 30 years. Urban settlement were increased at alarming rate due to land demand for housing, infrastructure and manufacturing. Therefore, urban planners must prioritize sustainable environmental management, integrated land use zoning, and active community involvement in order to protect against unsustainable changes in land use and land cover. For future research, incorporating methodologies such as multi-source remote sensing and high-resolution imaging will help differentiate land cover more effectively. Mizan Aman City experiences a nine-month rainy season with a hot climate, and cloud cover can affect image quality, making it challenging to map land covers clearly. Utilizing SENTINEL high-resolution data can enhance ambiguity resolution and improve spatio-temporal monitoring frameworks. Furthermore, integrating CO 2 estimation techniques could offer deeper insights into the environmental changes associated with urbanization.

In this work, Density Functional Theory (DFT) on Gaussian 09 W software was utilized to investigate the phenylephrine (PE) molecule (C9H13NO2). Firstly, the optimized structure of the PE molecule was obtained using B3LYP/6-311 + G (d, p) and CAM-B3LYP/6-311 + G (d, p) basis sets. The electron charge density is shown in Mulliken atomic charge as a bar chart and also as a color-filled map in Molecular Electrostatic Potential (MEP). Using these properties, the possibility of different charge transfers occurring within the molecule was evaluated. The calculated values of the energy gap from HOMO-LUMO mapping, illustrated in Frontier Molecular Orbitals (FMO) and Density of State (DOS), were found to be similar for both the neutral and anion states in the gaseous and water solvent phases. Both the global and local reactivity were studied to understand the reactivity of the PE molecule. Using the thermodynamic parameters, the thermochemical property of the title molecule was understood. Non-covalent interaction was studied to understand the Van der Waals interactions, hydrogen bonds, and steric repulsion in the title molecule. Natural Bond Orbital (NBO) Analysis was performed to understand the strongest stabilization interaction. In the vibrational analysis, Total Electron Density (TED) assignments were done in the intense region where the frequency of the title molecule was shifted distinctly. For vibrational spectroscopy, FT-IR and Raman spectra in the neutral and anion states were plotted and compared. Using the TD-DFT technique, the UV-Vis spectra along with Tauc’s plot were studied. Finally, topological analysis, electron localized function (ELF), and localized orbital locator (LOL) were performed in the PE molecule.

Integrating noble metal nanostructures, specifically silver nanoparticles, into sensor designs has proven to enhance sensor performance across key metrics, including response time, stability, and sensitivity. However, a critical gap remains in understanding the unique contributions of various synthesis parameters on these enhancements. This study addresses this gap by examining how factors such as temperature, growth time, and choice of capping agents influence nanostructure shape and size, optimizing sensor performance for diverse conditions. Using silver nitrate and sodium borohydride, silver seed particles were created, followed by controlled growth in a solution containing additional silver ions. The size and morphology of the resulting nanostructures were regulated to achieve optimal properties for biochemical sensing in wireless sensor networks. Results demonstrated that embedding these nanostructures in Polyvinyl Alcohol (PVA) matrices led to superior stability, maintaining 93% effectiveness over 30 days compared to 70% in Polyethylene Glycol (PEG). Performance metrics revealed significant improvements: reduced response times (1.2 ms vs. 1.5 ms at zero analyte concentration) and faster responses at higher analyte levels (0.2 ms). These outcomes confirm that higher synthesis temperatures and precise shape control contribute to larger, more stable nanostructures.The enhanced stability and responsiveness underscore the potential of noble metal nanostructures for scalable and durable sensor applications, offering a significant advancement over current methods.

Most patients diagnosed with chronic obstructive pulmonary disease (COPD) present with hallmark features of airway mucus hypersecretion, including cough and expectoration. Airway mucus function as a native immune system of the lung that severs to trap particulate matter and pathogens and allows them to clear from the lung via cough and ciliary transport. Chronic mucus hypersecretion (CMH) is the main factor contributing to the increased risk of morbidity and mortality in specific subsets of COPD patients. It is, therefore, primarily important to develop medications that suppress mucus hypersecretions in these patients. Although there have been some advances in COPD treatment, more work remains to be done to better understand the mechanism underlying airway mucus hypersecretion and seek more effective treatments. This review article discusses the structure and significance of mucus in the lungs focusing on gel-forming mucins and the impacts of CMH in the lungs. Furthermore, we summarize the article with pharmacological and nonpharmacological treatments as well as novel and interventional procedures to control CMH in COPD patients.

Wastewater treatment is considered one of the key components of the environmental management system necessary to reduce the impact of rapid industrialization on the environment. Wastewater contains many recalcitrant, pathogenic, and toxic compounds, including several emerging contaminants, which are impractical to remove during conventional treatment. However, invasive plants pose serious challenges to biodiversity worldwide due to their rapid propagation in native ecosystems. Furthermore, the biomaterial synthesized using invasive alien plant species (IPAS) can be used during wastewater treatment to remove contaminants from the liquid matrix to attain ecological sustainability. Prepared NPs have been reported to possess many medicinal, antibacterial, antifungal, and antioxidant properties. In addition, their catalytic and adsorptive properties in different wastewater treatment processes have been studied. Meanwhile, the sorption study has been conducted on biomaterials prepared from different invasive plant derivatives with further modification. In this study, a comprehensive review was carried out to evaluate the suitability of green-mediated NPs and biosorbents derived from invasive plant sources in catalytic oxidation and/or sorption processes. The mechanism, reaction conditions, and efficacy of the oxidation process were discussed in detail. Moreover, special attention has been paid to assessing the suitability, scalability, and sustainability of NPs preparation and pollutant treatment.

This work used the 6-311++G(d, p) basis set in the DFT/B3LYP and DFT/CAM-B3LYP technique to build the molecular structures of the nicotine and caffeine molecules. The minimum energy gives stability to these molecules with their corresponding dipole moment. The optimized structure to compute Raman spectroscopy and UV-Vis in CCl4 and DMSO solvent, employing the basis set 6-311++G(d, p), the DFT/B3LYP and CAM-B3LYP hybrid function, with the C-PCM model. The re-optimized molecule is used to study NLOs property which also give the dipole moment, polarizability and hyperpolarizability of titled molecules. We used AIM to investigate these molecules’ intramolecular interactions, bond critical points, and interbasin paths. Multiwfn software 3.8 produces the NCI-RGD diagram, which we use to determine weak interaction, electron density, Van der Waals interaction, steric effect, and hydrogen bond. Similarly, we analyze the covalent bond with the molecular surface using ELF and LOL techniques.

Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical features and deep learning techniques. The methodology involves preprocessing with the Shearlet Transform, segmentation using Improved Otsu thresholding and Canny edge detection, followed by feature extraction through Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and 1st-order statistical descriptors. These features are input into a 2D BiLSTM-CNN model designed to learn spatial and sequential patterns in mammogram images. Evaluated on the MIAS dataset, the proposed method achieved 97.14% accuracy, outperforming several benchmark models. The results indicate that this hybrid strategy offers improvements in classification performance and may assist radiologists in more effective breast cancer screening.

Human Activity Recognition (HAR) is an important research topic that aims to monitor and analyse human movements using sensor or visual data. Despite tremendous advances, HAR continues to encounter challenges in obtaining high accuracy and computing efficiency, especially in cross-domain settings. This work introduces a new Hybrid Multi-Layer Perceptron (MLP) model with a selective stacked ensemble classifier that combines Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Logistic Regression. Smartphone accelerometer and gyroscope data are fused, processed via the MLP for discriminative feature extraction, and then sent to RF and XGBoost, whose outputs are concatenated and fed into LR for final classification. On the source HAR dataset, the proposed approach achieves up to 99% accuracy, outperforming conventional models such as K-Nearest Neighbours (KNN), Decision Trees (DT), standalone MLP, and Convolutional Neural Networks (CNN). To assess cross-dataset generalisation, the model trained on the HAR dataset was evaluated on the PAMAP2 dataset without retraining, and it outperformed the CNN baseline by 2% in both accuracy and F1-score. Furthermore, it achieved competitive performance to the CrossHAR method—within 0.8% F1-score—despite avoiding computationally intensive hierarchical self-supervised pretraining. These results demonstrate that the proposed method delivers high accuracy, strong cross-domain adaptability, and efficient inference, making it suitable for real-world IoT-based activity recognition systems.