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The sound source identification of elastic network regularized generalized inverse beamforming based on iterative shrinkage thresholding
No full textIn this paper, aiming at the problem of noise source location of underwater targets, considering the spatial sparsity of sound sources, the elastic network regularization generalized inverse beamforming with iterative shrinkage threshold is employed to realize the localization of the noise source. Firstly, the L1 norm is introduced according to the sparsity of the sound source, and the objective function combining the L1 norm with the generalized inverse beamforming is obtained. The iterative shrinkage threshold algorithm is proposed to solve the function and get the position information of the sound source. Secondly, sound source identification is easily affected by noise when there is only an L1 norm, which reduces its robustness. Therefore, this paper proposes employing the L2 norm to obtain the objective function jointly constrained by the L1 norm and the L2 norm, the elastic net regularized generalized inverse beamforming. The combination of the L1 norm and the L2 norm can ensure that the convergence result is more robust. Then the iterative shrinkage threshold algorithm is used to solve the elastic network regularized generalized inverse beamforming and obtain the position information of the sound source. Finally, the performance of the proposed method is compared with other noise source localization methods through simulation and experimental data processing. The proposed method has the highest noise source localization accuracy and resolution
Waste Management Strategies for Sustainable Development: Exploring Alternatives to Waste Incineration to Minimize Ecological Footprints
No full textMunicipal solid waste management is a major challenge for modern environmental policies. Although incineration effectively reduces waste volume, it generates greenhouse gases and heavy metal residues that threaten ecosystems. Furthermore, residues from incineration processes introduce heavy metals into landfill soils, thereby endangering surrounding ecosystems. In this study, soil samples collected from an uncontrolled landfill subjected to waste incineration were found to contain elevated concentrations of heavy metals. Genotoxic assessments using Vicia faba indicated reduced mitotic indices and increased micronucleus formation, suggesting genetic damage from contaminants. Enzymatic analyses showed increased activities of peroxidase, catalase, and superoxide dismutase in plants experiencing toxic stressors attributable to heavy metal contamination and oxidative damage. These findings underscore significant ecological hazards associated with current incineration practices. By contrast, recycling emerges as a more sustainable alternative capable of mitigating emissions while conserving resources and decreasing reliance on both landfilling and incineration methods. This study compares the environmental impacts of incineration and recycling, emphasizing the need for a transition towards recycling
Pixel Intensity in Mammography: A Factor of Error in Breast Cancer Detection
No full textDoctors may find it challenging to identify breast cancer through mammography, but image processing can assist. Tumors and macrocalcifications are typically detectable using pixel intensity analysis. The issue with this approach is that its high false positive rate causes diagnostic errors and needless biopsies.
In this work, we investigate the dependability of mammography analysis techniques based on pixel intensity. Otsu thresholding, K-means clustering, and “contrast-limited adaptive histogram equalization (CLAHE)” are examples of segmentation and classification techniques that frequently have flaws, according to research in the literature. These errors are caused by variations in breast tissue, noise sensitivity, and imaging artifacts.
Although hybrid methods (like CNNs, SVMs, and CANs) can reduce false positives by up to 30%, they are challenging to apply for small lesions. Based on previous research, we discovered that tumors cannot be reliably classified using only pixel intensity. Combining morphological, textual, and contextual parameters is crucial for improving breast cancer detection ans reducing false positives
Aval du futur : Orano associe sa supply chain dès la phase initiale
No full textAvec son programme Aval du futur, Orano se projette dans le XXIIe siècle. Pour mener à bien ce projet industriel colossal, l’entreprise française et ses partenaires construisent une approche plus synergique pour relever des défis, eux, bien actuels
Field study on scorpions and scorpionism in the southeast of El Jadida, Morocco
No full textMorocco has a rich and diverse fauna of scorpions. However, certain regions such as Boulaouane remain underdocumented despite their importance for public health. Against this backdrop, our research aims to describe the local fauna of scorpions and evaluate the potential risks of envenomation. Systematic surveys were conducted in four distinct habitats, combining nocturnal UV detection with diurnal manual collection. The collected specimens were taxonomically identified, and their distribution analyzed in relation to environmental factors.
A total of 56 specimens belonging to the families Scorpionidae and Buthidae were collected. Scorpio maurus (36%) was the most commonly reported species in the region, with limited medical significance, while Buthus occitanus (16%) and Androctonus mauritanicus (12%) were unevenly distributed but were known to be responsible for the most serious cases of envenomation, particularly in children. These results provide essential reference data for assessing localized risks, developing antivenom production strategies, and guiding public health management in Morocco
Solar-Powered Electrochemical Processes for Sustainable Wastewater Treatment and Green Hydrogen Production: A Review
No full textThe development of cities as well as industries starts generating more and more domestic and industrial effluents which are rich in organic matter, total dissolved solids (TDS) and heavy metals. Effluents of such kinds effect aquatic ecosystems adversely and present both technical and economic challenges to conventional treatment technologies. In this respect combination processes of electrochemistry with renewable energies, and more particularly photovoltaic solar energy, seems a promising means of developing efficient and sustainable wastewater treatment technologies. Electrocoagulation uses sacrificial aluminum electrodes to generate coagulants in situ. This helps in reducing COD, TDS, and turbidity. On the other hand, electrooxidation using DSA boron-doped diamond electrodes treats the waste by oxidizing resistant organic pollutants. Furthermore, it identifies the usability and utility benefits of such technologies, such as for designing energy-autonomous wastewater treatment facilities, minimizing dependence on the electrical grid, and reducing carbon prints. Furthermore, the systems have the potential to provide operational resilience and environmental sustainability as an added advantage of pollutant removal and energy recovery. In spite of these encouraging results, a number of technical and economic limitations still exist, including effluent variability, complexity of systems, and high capital investment. These limitations are reviewed in this chapter and opportunities for continued research are outlined, such as process optimization, increased hydrogen production, modular reactor design, and deployment of decentralized treatment systems. In brief, synergy between solar power and electrochemical processes is a strategic path towards having sustainable, energy-scarce, and environmentally friendly wastewater treatment systems
Recovery of Vanadium as Iron Vanadate (FeVO
No full textUsed sulfuric acid catalysts are a hazardous industrial waste and also a secondary resource enriched with valuable transition metals such as iron (Fe) and vanadium (V), which can be recycled. Improper disposal poses a severe impact on environmental pollution, while the material can easily be recycled for producing next-generation materials. This study deals with valorization and selective recovery of iron vanadate (FeVO4) from a used sulfuric acid catalyst. This study proposed a hydrometallurgical method to recycle the vanadium component in used sulfuric acid catalysts, which involves the treatment with a 0.5 mol. L−1 oxalic acid solution under optimised conditions, such as a solid-liquid (S/L) ratio of 1:25 (g/mL), a temperature of 80°C, and stirring speed of 300 rpm. This procedure helps in the creation of a crystalline FeVO4 material, which has been detected through X-ray diffraction (XRD) analysis, with a resultant recovery yield of approximately 20% vanadium. This synthesized material possesses a similar material structure to that which can be obtained from laboratory pure materials. This study, therefore, not only finds applications in producing FeVO4 materials, which will have potential uses in the production of steel, batteries, and photocatalysis, etc., but also helps in recycling industrial waste material
Electric Vehicle integration in Economic Load Dispatch with Renewable Energy Sources: A Systematic Literature Review
No full textIntegration of Electric vehicles (EVs) and renewable energy sources (RES) into the power systems makes the economic load dispatch (ELD) problem becomes more complex. This research shows that EVs are evolving into manageable, bidirectional energy nodes equipped to both supplying and consuming power through Vehicle-to-Grid (V2G) systems. Under operating conditions, grid stability and EV charging are affected by the uncertainty of wind and solar energy sources. This review discusses efficient grid operation and smooth power dispatch using advanced optimization techniques, such as evolutionary algorithms, hybrid metaheuristic, and artificial neural networks. Key constraints include coordinated EV-RES arrangements, smart charging systems, and multi-objective formulations. Case studies in MATLAB indicate that battery aging, infrastructure limitations, and policy support remain significant challenges. The paper concludes by emphasizing the need for collaboration among researchers, industry, and policymakers to develop integrated, flexible ELD models for effective, economical, and ecological energy managemen
A Hybrid Signal Processing and Deep Learning Framework for Accurate Transformer Fault Identification
No full textAccurate discrimination among magnetizing inrush currents and internal fault currents is still a major problem in power transformer protection. Conventional time domain analysis gives little or no insight into transient characteristics and stimulates advanced signal processing techniques. This paper presents a new hybrid approach by combining the Wavelet Transform and the Curvelet Transform to exploit feature extraction. The Wavelet Transform is effective in picking up localized time frequency information while the Curvelet Transform provides optimal sparse representation of directional discontinuities. Classification is further enhanced for accuracy by submitting the features to advanced deep learning methods, i.e. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The resultant Wavelet-Curvelet-DL method is found to be more robust, computationally efficient, and of higher accuracy in distinguishing magnetizing inrush from internal fault currents. Simulation results have also proved that the methodology allows for fast and reliable fault detection and this is a step forward in the protection schemes required in modern smart grids
Anomaly detection using hyperspectral and remote sensing image
No full textAnomaly detection in hyperspectral imaging is still a challenge owing to the complexity of the data with extremely high dimensionality and subtle changes in spectral reflectance between the background and target pixels. In this study, we address this well-known challenge by developing an advanced anomaly detection framework that combines both a sparse representation and low-rank decomposition to distinguish anomalous behavior from the background. The flexibility of the overall framework is to improve the detection of small, weak anomalies and decrease false alarms due to clutter in the background, where the proposed method develops two independent dictionaries: a background dictionary that describes the primary spectral behavior and an anomaly dictionary that identifies rare or outlier behaviors. Each pixel is assessed based on its residual coding against both dictionaries to determine the likelihood of anomalous behavior. The innovation to this approach is the integration of dual-dictionary learning with joint low-rank and sparse representation, providing excellent separation of background and anomalies in hyperspectral images with changing conditions