The growing demand for sustainable and self-powered technologies in automotive applications has led to increased interest in energy harvesting from vehicle suspensions. Recovering mechanical energy from road-induced vibrations offers a viable solution for powering wireless sensors and autonomous electronic systems, reducing dependence on external power sources. This study presents the design, modeling, and experimental validation of a hybrid energy-harvesting system that integrates piezoelectric and magnetoelectric effects to efficiently convert mechanical vibrations into electrical energy. A model-based systems engineering (MBSE) approach was used to optimize the system architecture, ensuring high energy conversion efficiency, durability, and seamless integration into suspension systems. The theoretical modeling of both piezoelectric and magnetoelectric energy harvesting mechanisms was developed, providing analytical expressions for the harvested power as a function of system parameters. The designed system was then fabricated and tested under controlled mechanical excitations to validate the theoretical models. Experimental results demonstrate that the hybrid system achieves a maximum power output of 16 µW/cm² from the piezoelectric effect and 3.5 µW/cm² from the magnetoelectric effect. The strong correlation between theoretical predictions and experimental measurements confirms the feasibility of this hybrid approach for self-powered automotive applications.

A better understanding of the urban heat island (UHI) effect has heightened interest in measuring, assessing, and modeling the outdoor thermal environment in ur

In the context of autonomous environmental monitoring, this study investigates a surface acoustic wave (SAW) sensor designed for selective carbon dioxide (CO₂) detection. The sensor is based on a LiTaO₃ piezoelectric substrate with copper interdigital transducers and a polyetherimide (PEI) layer, chosen for its high electromechanical coupling and strong CO₂ affinity. Finite element simulations were conducted to analyze the resonance frequency response under varying gas concentrations, film thicknesses, pressures, and temperatures. Results demonstrate a linear and sensitive frequency shift, with detection capability starting from 10 ppm. The sensor’s autonomy is ensured by a piezoelectric energy harvester composed of a cantilever beam structure with an attached seismic mass, where mechanical vibrations induce stress in a piezoelectric layer (PZT-5H or PVDF), generating electrical energy via the direct piezoelectric effect. Analytical and numerical analyses were performed to evaluate the influence of excitation frequency, material properties, and optimal load on power output. This integrated configuration offers a compact and energy-independent solution for real-time CO₂ monitoring in low-power or inaccessible environments.

This study introduces an automated test bench designed to optimize flocculation-thickening processes in the wastewater treatment industry. Addressing current challenges in operational efficiency and cost reduction, the test bench employs Model-Based Systems Engineering (MBSE) principles, leveraging SysML modeling within the CESAM framework. By integrating advanced technologies, including PLC programming and a closed-loop control system, this bench provides precise and efficient testing under varying operational conditions. Economic implications are explored, demonstrating the cost-effectiveness of optimized flocculation processes, which reduce chemical use and operational expenditures while enhancing water clarity and sludge management. The system’s 3D modeling enables detailed simulations, aiding in both research and pedagogical applications. This platform highlights the potential of MBSE in creating scalable, robust solutions that contribute to sustainable water management.

International building sustainability assessment tools (BSATs) offer a comprehensive framework for assessing environmental, economic, and social sustainability. However, these tools cannot fill the gap between their standards and the regional needs of developing countries such as Morocco. This paper presents a new framework to assess the sustainability of buildings in Morocco. The methodology proposed is the Fuzzy Delphi method to minimize the list of indicators with the help of 14 local experts and give an appropriate weight to the indicators and sub-indicators. The two-round analysis found a balanced weighting for the environmental, economic, and social dimensions, with the social pillar ranked highest in importance. A hierarchical framework of six consensus-based categories and 63 sub-indicators was developed. Consensus was measured using the dispersion threshold approach ≤ 0.2. The results show that waste and pollution (0.80), adaptability and resilience (0.78), and resources (0.75) are prioritized over the innovation category. Notably, sewage management, water reuse, and public infrastructure emerged as critical sub-indicators. A comparative evaluation against local BSATs from the region—Ethiopia, Sub-Saharan Africa, Saudi Arabia, and Oman—revealed convergence in core indicators like energy and water, yet divergence in economic and resilience criteria, reflecting regional specificities. This work contributes to the literature by presenting a validated, expert-driven assessment tool that aligns with local needs, offering a practical basis for national green certification and sustainable housing policy in Morocco and similar contexts.

This study presents a comprehensive assessment of lighting conditions in the Intensive Care Units (ICUs) of two major hospitals in the Democratic Republic of Congo (DRC): Hospital du Cinquantenaire in Kinshasa and Jason Sendwe Hospital in Lubumbashi. A mixed-methods approach was employed, integrating continuous illuminance monitoring with structured staff surveys to evaluate visual comfort in accordance with the EN 12464-1 standard for indoor workplaces. Objective measurements revealed that more than 52.2% of the evaluated ICU workspaces failed to meet the recommended minimum illuminance level of 300 lux. Subjective responses from healthcare professionals indicated that poor lighting significantly reduced job satisfaction by 40%, lowered self-rated task performance by 30%, decreased visual comfort scores from 4.1 to 2.6 (on a 1–5 scale), and increased the prevalence of well-being symptoms (eye fatigue, headaches) by 25–35%. Frequent complaints included eye strain, glare, and discomfort with posture, with these issues often exacerbated during the rainy season due to reduced natural daylight. The study highlights critical deficiencies in current lighting infrastructure and emphasizes the need for urgent improvements in clinical environments. Moreover, inconsistent energy supply to these healthcare settings also impacts the assurance of visual comfort. To address these shortcomings, the study recommends transitioning to energy-efficient LED lighting, enhancing access to natural light, incorporating circadian rhythm-based lighting systems, enabling individual lighting control at workstations, and ensuring a consistent power supply via the integration of solar inverters to the grid supply. These interventions are essential not only for improving healthcare staff performance and safety but also for supporting better patient outcomes. The findings offer actionable insights for hospital administrators and policymakers in the DRC and similar low-resource settings seeking to enhance environmental quality in critical care facilities.