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The interacting effects of N, P, and S limitation were investigated by applying four different organic components, i.e., cysteine, chitosan, glucose-6-phosphate, and glucose, to a soil at two different clearly defined N, P, and S levels in a 5-fold range, one with sufficient and one with limited nutrient supply. Initially, MB-CN, MB-CP, and MB-CS ratios were lower after organic substrate amendments with the higher concentration of N, P, and S. The close relationship between the nutrient supply and elemental MB ratios was strongly modified within the next 14 days for the MB-CN ratio, probably due to a strong shift in the microbial community composition towards fungi, determined by the ergosterol content of soil. This shift was promoted by high N and low P and S availability, contrasting the view that S is important for the formation of fungal biomass. However, the negative interactions between P limitation and MB-CS ratio suggest that the microbial S metabolism has specific importance under P-limiting conditions. Low substrate CN ratio increased carbon use efficiency (CUE) by 20% in comparison with high substrate CN ratio, calculated at day 5, solely due to an increased formation of microbial residues, as the formation of MBC was not affected by differences in substrate CN ratio. In contrast, high substrate CP and CS ratios reduced MBC formation but did not affect CUE values.

Fuzzy sets play a central role in decision-making theory, modeling uncertainty by means of membership grade (MG) and non-membership grade (NMG). Non-linear Diophantine fuzzy sets (N-LDFSs), which are the natural extensions of linear Diophantine fuzzy sets and q-linear Diophantine fuzzy sets, are quite successful in modeling data thanks to their larger domains. However, in an N-LDFS, the MG, NMG and reference parameters (RPs) of an element to a set are given just by a pair of certain numbers from the closed interval [0, 1] that causes a strict modelling. Various types of interval-valued fuzzy sets, multi-fuzzy sets, or circular fuzzy sets change this strict modeling with a sensitive one. In this paper, we propose a theoretical framework for Continuous Non-Linear Diophantine Fuzzy Sets, which introduces continuous functions (CFs) over closed intervals to reduce uncertainty, which represent the MG and NMG functions supported by RPs, which enhance the sensitivity and applicability of decision-making tools. We develop continuous non-linear Diophantine fuzzy algebraic aggregation operators and apply them to a multi-attribute decision-making problem in renewable energy source selection. A case study demonstrates the effectiveness of the proposed CN-LDFS framework using a weighted geometric operator. Comparative analysis with existing methods highlights the superiority and success of our approach in improving decision-making accuracy and reliability.

The main purpose of water filtration techniques is to eliminate poisonous chemicals and microbes from sources of water in order to provide clear and safe water. Water purification is necessary for supplying the vital need for clean water to consume in a variety of areas, which involves the biological, medication, and health care industries. Despite the demands of manufacturing, its importance affects a country’s stability and success. Experts throughout the globe are thus investigating a number of promising methods to expand and preserve water supply. Finding the optimal water filtration technique for optimizing the health of humans requires the implementation of multi-criteria decision-making (MCDM) techniques. Therefore, the current manuscript addresses the task of identifying the best water filtration technique by introducing a novel method called the \"LFF-WASPAS technique,\" relying on the implementation of linguistic fractional fuzzy set (LFFS). An LFFS serves as a generalization of all linguistic fuzzy sets. For this reason, at first, we address the linguistic fractional fuzzy sets along with their weighted averaging and weighted geometric aggregation operators (AoPs), in addition to various basic properties of all of these AoPs. Finding the weight data used in decision-making situations becomes more challenging whenever the experts’ weights are missing. To address this, we present an entropy measure and an Analytic Hieratical Process (AHP). Additionally, we successfully use the freshly described operators and the suggested strategy to choose the most efficient approach for water filtration on a commercial scale. Finally, we investigate the sensitive hypothesis over the suggested method in relation to water filtration techniques. Additionally, by contrasting the suggested decision-making method with those that already are available, we assess their effectiveness and reliability.

Cancer affects millions of lives worldwide each year, and finding the most effective treatment is a difficult and important decision for healthcare professionals. This research aims to develop a new method for selecting the most appropriate and affordable cancer treatments. For this purpose, fractional Diophantine fuzzy sets (FDFSs) and the Muirhead mean operator are introduced to deal with uncertainty in medical decision-making. A difficult problem in decision-making is to detect the hidden weight information of criteria and decision-making experts. To solve this problem, the analytical hierarchy process (AHP) is used to calculate unknown weights for both criteria and decision-making experts. Furthermore, a fractional Diophantine fuzzy-TOPSIS (FDF-TOPSIS) technique is proposed to evaluate and select the best treatment process. Next, the proposed FDF-TOPSIS technique is applied to a real-life numerical example of finding the optimal cancer treatment process. Finally, we compare our proposed method with other existing methods and demonstrate the reliability, accuracy, and feasibility of the proposed technique, highlighting its potential to improve decision-making during cancer treatment.

The primary objective of this study is to empirically evaluate the role of various levels of financial friction in explaining stock returns through different asset pricing models. This study enhances asset pricing model estimates by incorporating diverse levels of financial friction by introducing a novel least minus more frictional asset pricing factor specifically constructed for emerging economies. The empirical analysis is conducted using data from a sample including five countries: China, India, Pakistan, Bangladesh, and Sri Lanka. Monthly data from 735 listed manufacturing firms is used to estimate stock returns from 2009 to 2024. These models are rigorously tested for optimal estimation using panel data models. The findings indicated that different levels of financial friction collectively exert inverse effects on stock returns. Macroeconomic and microeconomics frictions are found to be more pronounced in Pakistan compared to other countries, while financial market frictions are more acute in India, and firm-level frictions are most significant in China. The results further reveal that stock returns are overestimated in conventional asset pricing models. Incorporating different levels of financial frictions into these models substantially reduced the abnormal returns. This study has profound implications at macroeconomic, microeconomics, financial market, emerging the economies that are. Managers can leverage these insights to formulate superior strategies aimed at enhancing profitability, fostering robust business-to-business relationships, and minimizing costs across various levels. The findings enable firms to preemptively optimize their operations within the context of prevailing financial frictions.

Insulin resistance has been intricately linked to impaired Akt signaling due to the hyperactivation of protein phosphatase 2 A (PP2A). Specifically, the regulatory subunit PPP2R5B plays a crucial role in this dysregulation, making it a promising therapeutic target. This study aimed to identify novel curcumin-derived phytochemicals capable of inhibiting PPP2R5B and improving insulin sensitivity. Initially, approximately 85 curcumin-related compounds were retrieved from the PubChem database and subjected to extensive virtual screening via molecular docking. Among these, curcumin-bicyclopentadione emerged as the lead candidate, exhibiting the strongest binding affinity (− 9.2 kcal mol⁻¹) due to its extensive interactions with key residues ARG64, GLN439, and ARG385. Further MD simulations confirmed their robust binding stability, highlighting sustained hydrogen bonds and minimal structural fluctuations. Pharmacokinetic analyses using DeepPK profiling predicted favorable ADMET properties, including minimal toxicity, no significant cytochrome P450 inhibition, and negligible cardiotoxicity risks. These computational predictions suggest that curcumin-bicyclopentadione and closely related derivatives could effectively inhibit PPP2R5B activity, thereby restoring Akt phosphorylation and insulin-mediated glucose uptake. While promising, these findings necessitate subsequent validation through rigorous experimental assays. The integration of computational and experimental methodologies may ultimately facilitate the development of novel curcumin-based interventions for insulin resistance and associated metabolic disorders, expanding the therapeutic utility of phytochemicals in metabolic disease management.

It is difficult to select a suitable robot for a specific purpose and production environment among the many different models available on the market. For a specific purpose in industry, a Pakistani production company needs to select the most suitable robot. In this article, we introduce a novel Triangular fuzzy neural network with Yager aggregation operator. Furthermore, the Triangular fuzzy neural network applied to the decision making model for the selection of the most suitable robot for a Pakistani production company. In this decision model, we first collect four expert information matrices in the form of Triangular fuzzy numbers about the robot for a specific purpose and production environment. After that, we calculate the criteria weights of inputs signals by using the distance measure technique. Moreover, we use the Yager aggregation operator to calculate the hidden layer information of the Triangular fuzzy neural network. Follow that, we calculate the criteria weights of hidden layer information by using the distance measure technique. Furthermore, we use the Yager aggregation operator to calculate the output layer information, and also calculate the score value of the output layer information of Triangular fuzzy neural network. Finally, we use two activation functions to calculate the output results of the Triangular fuzzy neural network and rank the output results to select the best robot for a specific purpose and production environment.

This study investigates the role of financial frictions on firm value within the framework of earnings management, including the impact of productivity growth. In contrast to prior studies, the present study employed an autoregressive model to examine the temporal dynamics of the variables to determine their short-term and long-term connection patterns. The results of the study indicate a negative association between financial frictions and firm value. Accrual earnings management, a practice employed by organizations to enhance their profit margins, serves as a mediator between financial frictions and firm value. This mediation of earnings management alleviates the adverse impact of financial frictions. The enhancement of productivity growth amplifies the conditional, indirect influence of earnings management. Moreover, this study reveals that financial frictions have a significant influence in the short-term, leading to overestimation of factor loadings. However, this impact stabilizes over time in the long run. Financial market frictions have the most prominent impact on firm value compared to the other two forms of frictions, namely, macroeconomic frictions and microeconomic frictions. Larger firms are more inclined to attain higher firm value than smaller enterprises. Managers can enhance firm value by exerting control over the influence of financial frictions in the economy through earnings management. The effectiveness of this strategy is contingent upon the level of productivity growth.

Cellular senescence is a biological aging hallmark that plays a key role in the development of neurodegenerative diseases. Clinical trials are currently underway to evaluate the effectiveness of senotherapies for these diseases. However, the impact of senescence on brain aging and cognitive decline in the absence of neurodegeneration remains uncertain. Moreover, patient populations like cancer survivors, traumatic brain injury survivors, obese individuals, obstructive sleep apnea patients, and chronic kidney disease patients can suffer age-related brain changes like cognitive decline prematurely, suggesting that they may suffer accelerated senescence in the brain. Understanding the role of senescence in neurocognitive deficits linked to these conditions is crucial, especially considering the rapidly evolving field of senotherapeutics. Such treatments could help alleviate early brain aging in these patients, significantly reducing patient morbidity and healthcare costs. This review provides a translational perspective on how cellular senescence plays a role in brain aging and age-related cognitive decline. We also discuss important caveats surrounding mainstream senotherapies like senolytics and senomorphics, and present emerging evidence of hyperbaric oxygen therapy and immune-directed therapies as viable modalities for reducing senescent cell burden.

Fuzzy integral equations play an important role in addressing uncertain mathematical problems. There are various techniques present in the literature to solve fuzzy linear integral equations. Different methodologies provide numerical solutions for fuzzy nonlinear integral equations. However, there are few recognized methods for finding an exact solution. The fuzzy set has limitations because it lacks a non-membership degree for investigating uncertainty. To address this limitation, we use an intuitionistic fuzzy set that considers both membership and non-membership degrees together. Using the parametric forms of an intuitionistic fuzzy number, the nonlinear Fredholm integral equation is decomposed into a set of four equations. This set of four equations is then named the intuitionistic fuzzy nonlinear Fredholm integral equation. For an exact solution to the intuitionistic fuzzy nonlinear Fredholm integral equation, we use the Direct Computational Method. We solve two different examples in detail to demonstrate the reliability, effectiveness, and applicability of the proposed methodology. Graphs made using MATLAB represent visual judgments on how uncertainty impacts solutions. The results obtained for both examples are carefully examined and discussed in detail. The proposed method is compared to different decomposition and deep learning methods to ensure its accuracy. It is concluded that the proposed method is valid and reliable to get an exact solution for an intuitionistic fuzzy nonlinear Fredholm integral equation.