446494 research outputs found
Sort by
High resolution topography analysis of Sidi Moussa cliff (Salé, Morocco) to assess coastal flooding susceptibility
No full textMoroccan coastal areas are becoming more and more exposed to hazards related to storms and sea level rise. The Sidi Moussa cliff, which is situated south of Salé, is a sensitive coastal sector regularly hit by marine flooding during extreme phenomena. This study will create a high-resolution Digital Terrain Model (DTM) of the area to better determine its morphological characteristics and its susceptibility to marine inundation. The topographic data were gathered by a differential Global Positioning System Real Time Kinematic (GPS-RTK) method. Indeed, the collected data have been treated in a geographic information system environment to generate the DTM and to conduct elevation analyses in detail. The obtained model will be a fundamental dataset for the future numerical simulation of coastal flooding and for the mapping of potentially flooded zones. This study will also offer, for the first time, a clear and detailed morphological expression of the Sidi Moussa cliff, for which no topographic data was previously available. A first look at the results shows a topography that appears irregular, where micro-reliefs and active erosion predominate, increasing the vulnerability of some sectors to wave overtopping during storms. The latter approach points out the contribution of high-resolution topographic data to the enhancement of coastal assessment and management
Comparative Phytochemical Profiling and Evaluation of the Antibacterial and Antifungal Efficacy of
No full textThis study evaluates and compares the phytochemical profiles and antimicrobial efficacy of Marrubium vulgare L. and Stevia rebaudiana Bertoni hydroethanolic extracts. Using HPLC-ESI-MS/MS, we identified including marrubiin, verbascoside, and steviol glycosides. We tested these extracts against four bacterial strains and Candida albicans. Gram-positive strains exhibited significant susceptibility to M. vulgare (MIC 0.78-1.56 mg/mL). In contrast, S. rebaudiana showed superior antifungal activity against C. albicans (MIC 1.56 mg/mL). These distinct profiles highlight the specific potential of M. vulgare as an antibacterial and S. rebaudiana as an antifungal agent in natural medicine
The Rosemary of the Oriental Region: Toward Sustainable Governance Between Local Participation and Institutional Steering
No full textThis article offers an analysis of the dynamics surrounding the management and valorization of rosemary stands in Morocco's Oriental region, based on a documentary review of institutional reports, expert missions, and field studies. The aim is to understand to what extent the governance framework—built on partnership contracts between the forestry administration and local cooperatives—effectively contributes to the construction of a shared territorial project that reconciles ecological sustainability, social resilience, and economic valorization. The analysis highlights the progress achieved as well as the limitations of a model still largely driven from the top down, through three lines of inquiry: the nature of the relationship between forest cooperatives and the administration, the scope of the PGI (Protected Geographical Indication) in a territorial development perspective centered on a specific resource, and the actual pursuit of socio-ecological sustainability of the rosemary stands within this governance framework
A Comprehensive Strategy for Activated Sludge Wastewater Treatment Facilities for Nutrient Removal Treatment
No full textTraditional wastewater processing systems employ techniques that require a very lot of energy, so we need to get creative about doing that better and more sustainable. Activated sludge, which is found in many places around the world as a method of cleaning wastewater, is essential for removing nitrogen and phosphate. Proponents of these approaches will argue that traditional nutrient recovery methods (such as those based on physical, chemical and biological processes) are not necessarily "renewable" nor energy-efficient long-term solutions because they rely on destabilizing bio-nutrients rather than harvesting them, take a lot more energy to work through a thicker and different feed stock photo list and possess a variety of influent characteristics. In the situation, to simply optimize the biology process or operation will not satisfy new stringent discharge standards. The present study offers a complete and integrated approach for nutrient removal in activated sludge WWTPs. The developed framework includes the advanced monitoring and control strategy design along with system configuration selection and critical operating parameter optimization to improve biological N&P removal performances
IoT-Based Landslide Monitoring and Prediction Using Machine Learning
No full textLandslides are one of the most devastating natural disasters that result in massive human and infrastructural losses and economic inconveniences. To minimize these effects, it is essential to monitor and make early predictions. In this paper, the author introduces an IoT-based landslide monitoring and forecasting system that uses geotechnical and environmental sensors combined with machine learning algorithms. This system records the real-time data on the main parameters, which are the soil cohesion, intensity of rainfall, the angle of internal friction, the angle of slope, the slope height, and the factor of safety (FOS). These readings are sent through the IoT communication protocols to a cloud storage, pre-processed, and processed by an analytical processing platform. This paper has tested three machine learning algorithms, which include Multilinear Regression, Random Forest, and Decision Tree, to identify and forecast landslide occurrences. It also describes the system architecture, data collection process, feature engineering, and the model performance, giving a comparative analysis of the prediction accuracy of each algorithm. The proposed system integrates the IoT-based sensing with the solutions that are based on data to improve the early warning, enable informed decisions of hazard-management, and safeguard human life, infrastructure, and environment in zones of landslides
Hybrid sentiment analysis on Twitter data using VADER and RoBERTa models
No full textTwitter and other social media platforms generate large volumes of user-generated text that reflect public opinion in real time. However, sentiment analysis of Twitter data remains challenging due to informal language, abbreviations, contextual dependencies, and the frequent presence of implicit sentiment. Lexicon-based approaches such as VADER provide computational efficiency but often fail to capture contextual meaning, while transformer-based models such as RoBERTa achieve higher accuracy at the cost of increased computational requirements. This paper presents a hybrid sentiment analysis framework that integrates VADER and RoBERTa to balance accuracy and efficiency. The proposed system is implemented as a Flask-based web application with MySQL database support, enabling user authentication, keyword-based tweet filtering, and result visualization. GPU acceleration and batch processing techniques are employed to optimize performance. Experimental evaluation conducted on a dataset of 1.6 million tweets demonstrates that the hybrid approach achieves an accuracy of 89.1%, outperforming standalone lexicon-based methods. Statistical analysis confirms that the observed improvements are significant (p < 0.001). The results indicate that the proposed framework is suitable for large-scale sentiment analysis applications, including brand monitoring, political opinion analysis, and market trend assessment
Prospective Competence of Magnesium-ion over Lithium-ion Batteries in Electric Propulsion
No full textThe rising demand for sustainable electric propulsion led to extensive research on alternative battery chemistries beyond lithium-ion systems. Even though, lithium-ion batteries (LIBs) currently serve as the primary energy storage technology in electric vehicles (EVs), concerns related to resource criticality, dendrite induced safety hazards, and environmental impact prompted interest in magnesium-ion batteries (MIBs). As a potential replacement, MIBs offer several inherent advantages, including the use of Earth abundant and non-toxic magnesium, absence of dendrite formation, high theoretical volumetric capacity, and improved thermal stability. The present review deals with comprehensive comparison of LIBs and MIBs based on electrochemical performance, safety parameters, ion transport characteristics, cycle life, and environmental considerations. Special focus is given to recent developments in MIB component materials, such as bismuth-based and titanium-based anodes, transition metal oxide and chalcogenide cathodes, and hybrid or solid-state electrolytes that support reversible Mg²⁺ transport. EVs enabled with MIBs can reduce the net CO2 footprint nearly to 24,800 kg when compared to 26,500 kg and 37,500 kg associated respectively with LIB powered EVs and ICEVs for a span of 10 years. As electrification becoming central to all powertrain architectures, the analysis highlights that although MIBs remain at a lower technology readiness level, their favorable energy densities, extended cycling stability, and reduced ecological footprint position them as strong candidate for future EV battery applications
Comprehensive Evaluation of Electric Vehicle Powertrain Dynamics Under Realistic Drive Cycles
No full textThe shift towards electric mobility is critical in addressing global environmental challenges, including reducing greenhouse gas emissions and urban air pollution. However, Electric Vehicles (EVs) still face issues related to range, efficiency, and performance under real-world driving conditions. This paper presents a comprehensive simulation-based analysis of EV performance in MATLAB/Simulink, considering the effects of diverse international and regional drive cycles. A detailed EV model incorporating realistic powertrain dynamics, driver behaviour, using a Permanent Magnet Direct Current Motor (PMDC) formulated to evaluate energy consumption and efficiency. Furthermore, by comparing drive cycles with distinct speed, acceleration, and vehicle specifications, the study reveals significant variations in EV behaviour and energy consumption, emphasising the need for localised and application-specific cycle analysis. The paper also investigates the dynamic effects of different driving patterns, Indian Drive Cycle (IDC), Modified Indian Drive Cycle (MIDC) and Japanese 10 mode, and the effect of regenerative braking in the SOC of the battery. Through comparative performance analysis, this work aids the enhancement of EV design, deployment, and overall acceptance
Optimized VLSI floor planning using genetic algorithm
No full textVLSI floorplanning is a fundamental step in physical design automation that directly impacts chip performance, power consumption, and area utilization. Classic optimization methods include SA, DMT, and PSO; their convergence speeds are rather poor, especially for large-scale designs. This paper addresses both 2D and 3D floorplanning using a Genetic Algorithm (GA). GA represents the floorplans as chromosomes and applies selection, crossover, and mutation to improve block placement iteratively. Experimental comparisons, presented in a two-row tabular format, show that the GA achieves higher efficiency in block placement, wirelength reduction, and computation time compared to SA, DMT, and PSO. The results confirm that GA is indeed one of the effective solutions for large-scale floor planning problems
The Significance of Economic and Environmental Sustainability Management in the Operations of Russian Manufacturing Enterprises
No full textIn contemporary circumstances, it is evident that the activities of industrial enterprises, particularly in the Russian economic system, have a substantial and detrimental impact on the environment. Consequently, it is imperative to transform the management systems of Russian manufacturing companies by integrating sustainability management mechanisms into their structures, encompassing both environmental and economic dimensions, has become increasingly pressing. This article analyzes the distinctive features and characteristics of sustainable development tools as a potential direction for transforming the management mechanisms of contemporary Russian manufacturing companies. The primary objective of this article is to analyse and synthesize the concepts related to the development and implementation of these mechanisms within the management systems of Russian manufacturing companies. The central section presents an analysis of the fundamental characteristics involved in constructing modern Russian production from the perspective of addressing the challenges of sustainable development. Definitions and concepts formulated by Russian and foreign researchers regarding the establishment of mechanisms for the sustainable development of manufacturing companies are scrutinized. The fundamental principles of sustainable development and their potential application within the management systems of Russian manufacturing companies are succinctly analysed. In conclusion, the authors present their interpretations of how management functions (planning, organization, motivation and control) can be transformed in the context of establishing economically and environmentally sustainable production