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The growing presence of diverse pollutants, including heavy metals, organic compounds, pharmaceuticals, and emerging contaminants, poses significant environmental and health risks. Traditional methods for pollutant removal often face limitations in efficiency, selectivity, and sustainability. This review provides a comprehensive analysis of recent advancements in innovative adsorbents designed to address these challenges. It explores a wide array of non-conventional adsorbent materials, such as nanocellulose, metal–organic frameworks (MOFs), graphene-based composites, and biochar, emphasizing their sources, structural characteristics, and unique adsorption mechanisms. The review discusses adsorption processes, including the basic principles, kinetics, isotherms, and the factors influencing adsorption efficiency. It highlights the superior performance of these materials in removing specific pollutants across various environmental settings. The practical applications of these adsorbents are further explored through case studies in industrial settings, pilot studies, and field trials, showcasing their real-world effectiveness. Additionally, the review critically examines the economic considerations, technical challenges, and environmental impacts associated with these adsorbents, offering a balanced perspective on their viability and sustainability. The conclusion emphasizes future research directions, focusing on the development of scalable production methods, enhanced material stability, and sustainable regeneration techniques. This comprehensive assessment underscores the transformative potential of innovative adsorbents in pollutant remediation and their critical role in advancing environmental protection.

Risk evaluation has always been of great interest for individuals wanting to invest in various businesses, especially in the marketing and product sale centres. A finely detailed evaluation of the risk factor can lead to better returns in terms of investment in a particular business. Considering this idea, this paper aims to evaluate the risk factor of investing in different nature of products in a supermarket for a better proportioning of investment based on the product’s sales. This is achieved using novel Picture fuzzy Hypersoft Graphs. Picture Fuzzy Hypersoft set (PFHSs) is employed in this technique, a hybrid structure of Picture Fuzzy set and Hypersoft Set. These structures work best for evaluating uncertainty using membership, non-membership, neutral, and multi-argument functions, making them ideal for Risk Evaluation studies. Also, the concept of the PFHS graph with the help of the PFHS set is introduced with some operations like the cartesian product, composition, union, direct product, and lexicographic product. This method presented in the paper provides new insight into product sale risk analysis with a pictorial representation of its associated factors.

Quality control is paramount in product manufacturing as it ensures consistent production to meet customer expectations, regulatory requirements and maintain a company’s reputation and profitability. Distance measures within fuzzy sets serve as powerful tools for quality control, allowing for data comparison and identification of potential defects or outliers within a system. This study aims to develop a hybrid concept by combining a Cubic Intuitionistic Fuzzy Set (CIFS) with Soft Set (SS) and extending it to Cubic Intuitionistic Fuzzy Hypersoft Set (CIFHSS). CIFHSS enables handling multiple distinct attributes at the sub-attribute level within a cubic set environment. The concept includes operations like internal, partial internal, external, complement, direct sum, and product. Additionally, six distance metrics are defined within CIFHSS and applied to establish a quality control management system for industrial applications. The versatility of CIFHSS in quality control management stems from its ability to capture and model uncertainty, vagueness, and imprecision in data. This makes it an effective tool for decision-making, risk analysis, and process optimization across a wide range of industrial applications.

Understanding bipolar information is crucial as it enables individuals to make informed decisions that consider both extremes of a spectrum, leading to more balanced and effective outcomes. Interval-valued bipolar fuzzy set (IVBFS) has already been introduced in the literature as a great decision-making tool that can capture interval-valued bipolar information to properly address uncertainty. In this article, we introduce a hybrid of Interval-valued bipolar fuzzy set (IVBFS) and bipolar hypersoft sets (BHSS) called interval-valued bipolar fuzzy hypersoft set , which merges the capabilities of IVBFS and BHSS. The rationale behind the design of the presented data structure is to manipulate and process information in decision-making scenarios when the data is bipolar, has multiple attributes that need to be addressed up to a sub-attributive level to get a proper representation of the data provided, and needs to be presented in the form of intervals. In , two hyper soft sets (HSSs) are used, one providing positive interval-valued membership information and the other providing negative interval-valued membership information. We outline the essential features and basic operations of in this paper, examining its commutative, associative, distributive, and De Morgan laws to ensure a comprehensive analysis. To demonstrate the significance of , we develop a preferential decision support algorithm for selecting the best alternative in e-learning, such as identifying the most suitable instructional method, which can effectively be formulated as a Multi-Attribute Decision-Making (MADM) problem. This approach allows for the systematic evaluation of various alternatives based on multiple parameters and sub-parameters, enabling a rational and well-informed decision. This algorithm helps select the best alternative from a given set of options, leveraging the versatile nature of . The presented study conducts both computation-based and structural comparisons to evaluate the adaptability and reliability of the proposed framework.

Microbial plant biostimulants offer a promising, sustainable solution for enhancing plant growth and resilience, particularly under abiotic stress conditions such as drought, salinity, extreme temperatures, and heavy metal toxicity. These biostimulants, including plant growth-promoting rhizobacteria, mycorrhizal fungi, and nitrogen-fixing bacteria, enhance plant tolerance through mechanisms such as phytohormone production, nutrient solubilization, osmotic adjustment, and antioxidant enzyme activation. Advances in genomics, metagenomics, transcriptomics, and proteomics have significantly expanded our understanding of plant–microbe molecular communication in the rhizosphere, revealing mechanisms underlying these interactions that promote stress resilience. However, challenges such as inconsistent field performance, knowledge gaps in stress-related molecular signaling, and regulatory hurdles continue to limit broader biostimulant adoption. Despite these challenges, microbial biostimulants hold significant potential for advancing agricultural sustainability, particularly amid climate change-induced stresses. Future studies and innovation, including Clustered Regularly Interspaced Short Palindromic Repeats and other molecular editing tools, should optimize biostimulant formulations and their application for diverse agro-ecological systems. This review aims to underscore current advances, challenges, and future directions in the field, advocating for a multidisciplinary approach to fully harness the potential of biostimulants in modern agriculture.

Addressing uncertainty is paramount in all decision-making scenarios to ensure robust, well-informed outcomes that can adapt to unforeseen changes and risks. For this purpose, decision-support systems are the best course of action, as they enable decision-makers to deal with decision-making errors and compile results of human intuition in group decision-making processes. When designed with the concept of topology in mind, these decision-support systems lead to better results as they allow for addressing the decision-making variables in a more detailed manner. In this study, the concept of Interval-Valued Bipolar Fuzzy Hypersoft Topology ( ) is introduced as a novel extension of fuzzy set theory, aiming to enhance decision-making under uncertain conditions. The introduced structure integrates interval-valued bipolar fuzzy sets ( ) with hypersoft topological spaces ( ), allowing for a more refined representation of imprecise and conflicting information. Fundamental properties of such as closure, interior and exterior are explored in this paper. Also, a novel decision-making algorithm leveraging the presented structure for multi-criteria analysis and complex system modeling is designed. The algorithm is applied to select optimal renewable energy source based on their economic, environmental, and technical aspects. The effectiveness of our approach is demonstrated through comparative analyses and real-world applications, validating its superiority in handling uncertainty compared to existing fuzzy models. The versatile hybrid nature of the proposed structure allowed for efficient decision-making, showing great promise for applications involving human intuition. The findings and the method used in the analysis forms a strong foundation for future studies in topological fuzzy systems and intelligent decision-support frameworks.

Renewable energy sources (RES) are being used and integrated into the electrical grid as a result of the environment's effects and the ever-increasing demand for energy. Reliable and accurate forecasts are necessary to address environmental concerns and improve grid management due to the intermittent availability of renewable energy sources. This study focuses on improving ANN-based techniques for precise solar irradiance prediction as the prediction accuracy of an artificial neural network (ANN) is impacted by the random assignment of weights to its edges. As a result, we proposed hybrid solar irradiance forecasting models in which the cuckoo search algorithm (CSA) and adaptive moment estimation (ADAM) are used to optimize the weights assigned to the ANN's edges. Two models were presented in this study namely: ADAM-optimized ANN model and a novel two-stage optimization technique known as CSA-ADAM optimized ANN model for accurate and reliable forecasting of solar irradiance. Both models were tested using actual weather data, and standard error metrics like mean squared error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean square error (RMSE) to assess their accuracy. The outcomes demonstrate that ADAM-optimized ANN model produced MSE = 0.52, MAPE = 0.18%, MAE = 0.64, and RMSE = 0.72, and CSA-ADAM optimized ANN model obtained MSE = 0.25, MAPE = 0.17%, MAE = 0.43, and RMSE = 0.50. We evaluated the practicality of both models by comparing their average prediction times using the same test dataset. While the ADAM-optimized ANN model took an average of 0.1093 ± 0.0085 seconds to make predictions on the test data, the CSA-ADAM optimized ANN model took 0.1110 ± 0.0058 seconds. These findings demonstrate that using CSA to optimize the ANN weights increases the accuracy of solar irradiance predictions.

Low soil fertility and high fertilizer costs are constraints to wheat production, which may be resolved with integrating fertilizer phosphorus (P) and farm-yard manure (FYM). Study objectives were to evaluate P source impacts on soil, P efficiency, and wheat growth in a calcareous soil. Treatments included P fertilizer (0, 17, 26, or 39 kg P ha-1) and/or FYM (0 or 10 T ha-1) in a: 1) incubation experiment and 2) wheat (Triticum aestivum spp.) field experiment. Soil organic matter increased (30-72%) linearly for both fertilizer and FYM, whereas pH decreased (0.1-0.3 units) with fertilizer only. Addition of fertilizer and FYM increased plant available P (AB-DTPA extractable soil P) an average of 0.5 mg P kg-1 soil week-1 with incubation. The initial increase was 1-9 mg P kg-1, with further increase after 84 d of ~3-17 mg P kg-1. There was also a significant increase of available P in the soil supporting plants in the field study, although the magnitude of the increase was only 2 mg kg-1 at most for the highest fertilizer rate + FYM. Grain (66 to 119%) and straw (25-65%) yield increased significantly, peaking at 26 kg P ha-1 + FYM. The P Absorption Efficiency (PAE), P Balance (PB), and P Uptake (PU) increased linearly with P rate, with the highest levels at the highest P rate. The P Use Efficiency (PUE) was highest at the lowest rates of P, with general decreases with increasing P, although not consistently. Principal component analysis revealed that 94.34 % of the total variance was accounted for with PC1 (84.04 %) and PC2 (10.33 %), with grain straw yield significantly correlated to SOM, PU, and PAE. Regression analysis showed highly significant correlation of PB with P-input (R2= 0.99), plant available P (R2= 0.85), and PU (R2= 0.80). The combination of FYM at the rate of 10 T ha-1 and fertilizer P at 26 kg P ha-1 was found as the optimum dose that significantly increased yield. It is concluded that FYM concoction with fertilizer-P not only improved SOM and residual soil P, but also enhanced wheat yields with reasonable P efficiency.

The economic development of a country is profoundly influenced by how it protects and utilizes its natural resources, one of which is wastewater, with emphasis on environmental sustainability. For this multi-factorial sustainability analysis, this study introduces a novel Fuzzy Parameterized Fuzzy Hypersoft Set (FPFHSS) structure to develop a comprehensive decision-making and performance evaluation system for wastewater treatment facilities. The practicality of the designed system is explored by highlighting its ability to evaluate urban projects and work as a decision-making system for environmental policy design. The study introduces an algorithm based on the hypersoft structure, allowing the division of attributes into sub-attributes for a more concise analysis. The sub-parametric values are first parameterized into fuzzy numbers and then evaluated based on their relative importance. With proper fuzzy parameterization of each sub-attribute, novel specialized FPFHSS based Technique of Ordered Preference Similar to Ideal Solution (TOPSIS) and Multi-Objective Optimization on the basis of a Ratio Analysis (MULTIMOORA) approaches are developed that provide a versatile analysis in addition to the newly developed algorithm. With these 3 specialized systems, 4 case studies were developed based on 19 pseudo-realistic environmental, social, technical, and economic factors each simulating a different scenario faced by nations highlighting sustainability, technical performance and the environment allowing for informed decision-making while addressing uncertainty making them highly suitable as a computational AI solution for real-data analysis. This fuzzy analysis offers a reference for making informed decisions in the context of environmental remediation and complex scenario simulations.

The extraordinary optoelectronic properties of platinum diselenide (PtSe2), whose structure is similar to graphene and phosphorene, has attracted great attention in new rapidly developed two-dimensional (2D) materials beyond the other 2D material family members. We have investigated the surface plasmon resonance (SPR) sensors through PtSe2 with the transfer matrix method. The simulation results show that the anticipated PtSe2 biochemical sensors have the ability to detect analytic. It is evident that only the sensitivities of Ag or Au film biochemical sensors were observed at 118°/RIU (refractive index unit) and 130°/RIU, whereas the sensitivities of the PtSe2-based biochemical sensors reached as high as 162°/RIU (Ag film) and 165°/RIU (Au film). The diverse biosensor sensitivities with PtSe2 suggest that this kind of 2D material can adapt SPR sensor properties.