This study evaluates sustainable water management strategies using TOPSIS and Fuzzy TOPSIS (FTOPSIS) to address global water scarcity by comparing rainwater harvesting, water recycling, and desalination across five criteria: water efficiency, cost-effectiveness, environmental impact, social equity, and technological feasibility. The results show Rainwater Harvesting as the most balanced option with a relative closeness value of \({C}_{i}^{+}\)=0.640, excelling in social equity and environmental sustainability. Water Recycling ranks closely behind (\({C}_{i}^{+}\)=0.608), highlighting its adaptability and technological feasibility, while Desalination, though highly efficient, is hindered by lower cost-effectiveness (\({C}_{i}^{+}\)=0.578). By integrating TOPSIS and FTOPSIS, the study addresses uncertainties and subjective criteria, providing a robust multi-dimensional assessment framework for resource management. This methodology aids decision-makers in identifying strategies that align with sustainable development goals and adapt to regional priorities. Future work can expand this framework to include stakeholder engagement and policy factors, enhancing water management strategies for resilient, long-term solutions.

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

Transitioning to renewable energy sources (RES) is essential for achieving sustainable energy systems and reducing environmental impacts. Integrating RES into smart grids presents a complex decision‐making challenge, as it involves evaluating multiple, often conflicting, criteria such as cost, energy efficiency, environmental impact, reliability, and scalability. This study applies the Fuzzy TOPSIS (FTOPSIS) method to assess and rank four common RES: solar, wind, biomass, and hydropower. The evaluation was based on expert opinions from 25 industry professionals, taking into account various factors that impact smart grid integration. The final ranking, determined by the FTOPSIS method, showed that wind energy ranked highest with a closeness coefficient ( C _i ) of 0.72, followed by solar ( C _i = 0.65), hydropower ( C _i = 0.59), and biomass ( C _i = 0.48). These findings highlight the strengths and trade‐offs of each energy source, providing valuable insights for energy planners to optimize smart grid integration. This study demonstrates the effectiveness of FTOPSIS in managing uncertainty and providing a structured decision‐making framework for renewable energy selection, while offering new insights into smart grid integration by emphasizing criteria such as scalability, adaptability, and real‐world expert input. Further research could explore the inclusion of additional criteria and real‐time data to refine the decision‐making process.

Marine pollution in coastal areas poses increasing threats to aquatic ecosystems, necessitating the deployment of efficient, real-time monitoring systems. This study addresses the critical challenge of selecting the most effective sensor network architecture for such monitoring by applying the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). Three alternative configurations, namely fixed buoy systems, hybrid mobile gliders with satellite relays, and IoT-enabled underwater sensor grids, are evaluated using five criteria: detection accuracy, coverage area, operational cost, energy consumption, and system resilience. Expert opinions from 30 professionals across multiple domains were collected via linguistic surveys, converted into triangular fuzzy numbers, and aggregated into a fuzzy decision matrix. Results indicate that the hybrid glider-satellite architecture achieved the highest closeness coefficient (0.68), balancing accuracy, adaptability, and sustainability. The findings underscore the value of integrating expert input and fuzzy logic into multi-criteria decision frameworks for designing intelligent marine pollution monitoring systems.

In this paper, an all‐optical analog‐to‐digital converter based on nonlinear with silicon materials is designed and simulated. The proposed structure consists of three nonlinear nanocavity that control the optical signal power intensity. The nonlinear material used is aluminum gallium arsenide (AlGaAs). Aluminum gallium arsenide (AlGaAs) with a linear refractive index of n₀ = 1.4 and a nonlinear refractive index of n₂ = 1.5 × 10⁻¹⁷. Due to the small path length of the waveguides, the optical signals move a short distance and as a result, the power optical losses along the path are reduced and on the other hand, the speed of the structure is increased. The transmission percentage is between 90% and 100%. The overall dimensions of the structure are 324 μm². The plane wave expansion (PWE) method is used to calculate the band structure. The two‐dimensional finite difference time domain (2D‐FDTD) method is used to calculate the transmission power spectrum and the simulation results.

Urban underground space (UUS) provides innovative solutions to urban challenges such as overpopulation, resource scarcity, and environmental issues. However, UUS's lack of comprehensive evaluation standards hinders its sustainable development. This study systematically introduces a novel framework for evaluating green ecological UUS, integrating methodologies such as factor analysis, the analytic hierarchy process (AHP), and the Delphi method. The framework assesses UUS projects across their lifecycle, focusing on energy efficiency, resource conservation, and environmental protection. Validation through real-world projects, including Chengdu's Tianfu Square and East Railway Station, demonstrated its practical utility, classifying both projects as two-star ecological grades. Real-world projects, such as Chengdu's Tianfu Square and East Railway Station, validated the framework, classifying them into two-star ecological grades based on the proposed standards. This research provides a valuable theoretical and practical tool for establishing a robust evaluation standard for sustainable UUS development, contributing to global urban sustainability efforts.