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Modelling Transient Hydraulic Hammer Behavior in Pumped Systems Using WHAMO Software
No full textThis paper presents a mathematical model of the hydraulic hammer phenomenon. The model incorporates the momentum and continuity equations, the construction of the system layout, and the junctions within the pipeline network. Potential scenarios during system operation are analyzed using the Water Hammer and Mass Oscillation (WHAMO) software to simulate transient hydraulic behavior. In closed hydraulic systems, a hydraulic hammer occurs when the system transitions from a stable to an unstable state, causing the kinetic energy of the fluid to be rapidly converted into pressure energy. This results in a powerful pressure surge accompanied by a reverse flow wave. Such pressure fluctuations can lead to extremely low pressures, increasing the risk of contaminant intrusion through cracks or pipe damage. This phenomenon often accompanied by a hammer-like sound poses a significant challenge in drinking water treatment systems. Because the governing equations are nonlinear and hyperbolic, analytical solutions are not feasible; therefore, numerical modeling is required. The main goal of this study is to analyze pump behavior during transient conditions associated with the water hammer phenomenon
Intelligent Sludge Management in Urban Water Treatment Facilities: A Sustainable Decision-Making Framework Informed by Industrial Ecology Principles
No full textWithin the context of urban infrastructure, this study presents a sustainable decision-making framework designed for the efficient management of sludge in water treatment facilities. The framework is comprised of various integral components, incorporating statistical computations for precise sludge production assessment and an exhaustive evaluation of water treatment plant (WTP) efficiency, rooted in a historical analysis. A comprehensive database was first established, drawing on pertinent literature that classifies construction materials applicable to WTP sludge, while prioritizing primary criteria from economic and environmental standpoints. Further, a collection of secondary indicators, encompassing factors such as operational ease, durability, product quality, and safety indices, were aggregated from diverse construction products. To streamline the decision-making process, a blend of three multi-criteria decision-making (MCDM) techniques was deployed, including the Analytic Hierarchy Process (AHP), the Order of Preference by Similarity to Ideal Solution (TOPSIS), and Ordered Weighted Averaging (OWA). Additionally, this study integrates a rigorous appraisal based on Porter\u27s Five Forces (PFF) methodology, employing a combination of questionnaires and descriptive statistical analysis to construct a comprehensive conceptual model. The findings underscore the adaptability of the proposed framework, showcasing its efficacy in facilitating intelligent scheduling and adept management of water treatment sludge. Notably, the framework is designed to encompass the principles of industrial ecology and account for eco-environmental considerations, thereby presenting a holistic approach to sustainable sludge management within the realm of urban water treatment
Machine Learning-Driven Cross-Layer IDS Architecture for Next-Generation IoT Networks
Get PDFThe proliferation of Internet of Things (IoT) devices across critical infrastructures introduces significant security risks due to their heterogeneous and resource-constrained nature. This study extends cross-layer Intrusion Detection System (IDS) research by systematically comparing three machine learning models—Support Vector Machine (SVM), Random Forest (RF), and a hybrid CNN-LSTM—using benchmark datasets (NSL-KDD, BoT-IoT, and CICIDS2017). Unlike prior works that focus on single-layer IDS or isolated model evaluation, our approach aggregates features from multiple OSI layers (network, transport, and application), providing a holistic view of IoT traffic. The findings demonstrate that CNN-LSTM achieves the highest detection accuracy (97.4%) but requires substantial computational resources, whereas RF offers a near-optimal trade-off between accuracy (96.8%) and efficiency, making it suitable for deployment on resource-constrained IoT devices. Scalability analysis confirms stable detection performance up to 200 IoT nodes with only minor accuracy degradation. This work highlights both the strengths and limitations of cross-layer ML-based IDS and provides insights for future enhancements through lightweight deep learning, federated learning, and explainable AI (XAI) for 6G-IoT environments
Fusion of Blockchain, IoT, Artificial Intelligence, and Robotics for Efficient Waste Management in Smart Cities
Get PDFRapid urbanization and population growth are accelerating waste generation in cities worldwide, posing serious environmental and socio-economic challenges. Traditional waste management systems, often centralized and infrastructure-deficient, struggle with inefficiencies, unscheduled collection, and a lack of real-time data. These limitations hinder progress toward smart and sustainable urban environments. Blockchain, the Internet of Things (IoT), Artificial Intelligence (AI), and Robotics are reshaping waste collection, sorting, and recycling. This review examines how these technologies integrate to create secure, efficient, and sustainable waste management in smart cities. An analysis of 184 studies published between January 2022 and July 2025 reveals key shortcomings in conventional waste management systems and showcases the benefits of smart waste management solutions. The results showed that cities are already using IoT-enabled smart bins, AI-driven route optimization, Blockchain for waste tracking, and robotic sorting. However, challenges such as data privacy concerns, limited Blockchain scalability, system interoperability gaps, sensor reliability issues, and high computational demands limit broader adoption. The review outlines future research priorities, including AI-powered waste forecasting, swarm robotics, real-time edge computing, and enhanced cybersecurity. By providing a roadmap for technological innovation and integration, this study supports policymakers, urban planners, and industry leaders in developing intelligent, cost-effective, and environmentally resilient waste management systems
Machine Learning Analysis of Social Media Usage Patterns and Mental Health Indicators
No full textThe rapid growth of social media has created new opportunities for connection but has also raised concerns about its impact on mental health. This study investigates how demographic factors, digital behaviour, and self-reported psychological indicators jointly relate to users’ mental states. Using an open dataset of 5000 social media users, we analyse numerical and categorical variables including age, gender, daily screen time, social media time, counts of positive and negative interactions, sleep duration, physical activity, anxiety, stress, mood, and a three-level mental-state label (Healthy, At_Risk, Stressed). Descriptive statistics and correlation analysis show that longer daily screen and social media time are strongly associated with higher stress and anxiety and lower mood, while sleep and physical activity display the opposite pattern. K-means clustering applied to combined behavioural and psychological features reveals three coherent user profiles that align with the Healthy, At_Risk, and Stressed categories, highlighting a clear gradient from balanced to high-risk digital lifestyles. A decision-tree classifier trained only on behavioural features (excluding anxiety, stress, and mood to avoid target leakage) achieves an overall accuracy of about 97% on a held-out test set and provides interpretable if then rules linking specific usage patterns to mental states. The results emphasise that intensive, unbalanced social media use especially when coupled with reduced sleep and low physical activity is strongly linked to adverse mental-health outcomes, and they illustrate how simple machine-learning models can support early risk detection based on non-intrusive behavioural data
Life Cycle and Environmental Impact Assessment of Sustainable Energy Systems in Building Construction: Comparative Analysis of Fossil Fuels and Solar Energy in Mashhad
No full textRapid urbanization and the growing demand for sustainable development have emphasized the need to transition from fossil fuels to renewable energy sources in the construction sector. This study presents a comprehensive Life Cycle Assessment (LCA) and Environmental Impact Assessment (EIA) to compare the carbon footprints of fossil fuel-based and solar energy systems in residential buildings in Mashhad, Iran. Results from Revit simulations and MATLAB modeling based on Leopold matrix highlight the significant advantages of solar energy, with life cycle CO₂ emissions peaking at only 2.5 kg in the most emission-intensive months, compared to 120 kg for fossil fuels during electricity generation in July. Furthermore, the annual cumulative emissions of fossil fuels reached nearly 1800 kg CO₂, whereas solar energy remained under 100 kg CO₂. These findings show the critical role of solar energy in achieving sustainability. The research offers actionable insights for reducing greenhouse gas emissions and advancing green engineering practices by addressing the seasonal and lifecycle phases of energy systems
Real-Time Volume Control Using OpenCV
No full textThis paper introduces a novel method for controlling computer volume through hand gestures using OpenCV, a computer vision library. Instead of traditional input methods like buttons or a mouse, users can adjust volume levels by waving their hands in front of a webcam. OpenCV tracks these hand movements and interprets them in real-time, providing accurate and responsive volume control. Our experiments demonstrate the effectiveness of this approach, offering an intuitive solution that enhances user interaction, particularly for individuals with mobility impairments. Furthermore, OpenCV\u27s compatibility with various platforms and programming languages increases the system\u27s versatility. This research advances gesture-based interaction techniques and underscores the potential of computer vision technology to improve user experience across different applications, making computing more interactive and accessible
A Scan Line Survey for Early Detection of Landslide Potential in Hard Rock Slopes
No full textDiscontinuity surveys involve collecting rock data through fieldwork and are an important characteristic of evaluating the quality of rock masses in rock engineering. The characteristics of a rock mass, such as strength, deformability, and permeability, are considerably influenced by its discontinuities. Landslides and slope collapse in hard rocks demonstrate distinct qualities in comparison with those occurring in soft geological formations. The primary purpose of the investigation is to employ a scan line survey technique to assess and estimate the frequency of landslides in the Warcha Sandstone outcrop located in Karuli Piran village, Chakwal district, Pakistan. Scan line approach and physical classification of rock types are frequently utilized to identify controlling factors. We carried out a systematic investigation of the stability of the Warcha Sandstone cliff to recognize potential failure modes. The outcomes highlight a potential risk of vertical cliff instability through toppling, with the expected failure direction identified from northeast to southwest. A comprehensive physical inspection estimate underscores the gravity of the situation, indicating that a probable landslide could lead to substantial damage and road blockage. It is recommended to promptly implement precautionary measures, such as controlled blasting to remove the high-risk toppling region or to enhance resistance to stabilize the slope
Customer Decision-Making Factors for Taxi Booking Apps: A Comparative Analysis of Uber, Careem, and Bolt in Qassim City, Saudi Arabia
No full textThis study aims to identify the factors influencing consumers\u27 choice of three taxi booking mobile applications in Qassim: Uber, Careem, and Bolt. The suggested independent factors are marketing mix, mobile applications, and customer behavior. Moreover, the research is based on the survey methodology to study the relationship between these variables and customer choice of applications. An online questionnaire is used to collect the data. The sample study consisted of 419 users of three taxi applications (Uber, Careem, and Bolt). Additionally, frequency was used to describe the study samples and apply the appropriate statistical tests to the research hypotheses. The results of the study show that there is a statistically significant relationship between offered service, cash payment methods, car cleanliness, car model, customer service presence, customer income level, and influential social networks and customer choice for one of the available applications for booking a taxi. As well, it indicates a high rate of Bolt application users, which is considered a local application compared to its competitors in the market (Uber and Careem). Accordingly, the recommendations were written about the importance of focusing on the factors related to consumers\u27 choice of applications
Transitioning Towards a Sustainable Energy System: A Case Study of Baden-Württemberg
No full textThe development and use of renewable energy sources are important to combat current climate change. The paper examines possible pathways to a climate-neutral Baden-Württemberg by 2045, with a focus on a significant reduction in CO2 emissions in the different sectors. In this paper, a reference energy system of the region was modelled using the EnergyPLAN model based on data from 2020. Regarding the expansion targets for renewable energies in Baden-Württemberg by 2040, four scenarios were developed. These focus on the main sectors: transport, heat, industry and electricity. This includes a complete substitution of fossil fuels with renewable energies, and in the industrial sectors, conventional energy is replaced by green hydrogen. In all scenarios, significant CO2 emission reductions of up to 8.68 Mt can be achieved, which underlines the feasibility of climate neutrality in Baden-Württemberg through the expansion of renewable energies and technological change. This work provides some of the key insights needed to further support policymakers and researchers in their work to improve energy systems. This can therefore help to develop better strategies to effectively reduce emissions and thus advance Baden-Württemberg\u27s goals of climate- neutral economy. As this paper was a first step, further research in this direction is needed to successfully achieve these goals