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The tyrosinase enzyme has a vital role in the browning of vegetables and fruits and the biosynthesis of melanin. In this work, we synthesized a diverse library of coumarin–triazole hybrids, and these compounds were characterized by using suitable analytical techniques. Our research work extends beyond the synthetic effort to explore the therapeutic potential of these compounds. We put the synthesized compounds through meticulous in vitro screening against the tyrosinase enzyme, and these coumarin derivatives evinced good IC50 values in the range of 0.339 ± 0.25 µM to 14.06 ± 0.92 µM. In the library of synthesized compounds, six compounds were found to be more potent than standard ascorbic acid (IC50 = 11.5 ± 1.00), and among them, 17e and 17f, being the most active, exhibited remarkable anti-tyrosinase potential, with IC50 values of 0.339 ± 0.25 μM and 3.148 ± 0.23 μM, respectively. Furthermore, an in silico modeling study was carried out to determine the key interactions of these compounds with the tyrosinase protein (PDB ID: 2Y9X) and thus to authenticate our experimental findings. The quantitative SAR studies exhibited a good correlation between the synthesized derivatives of coumarin and their anti-tyrosinase activity. The docking studies verified the experimental results, and ligand 17e showed good interaction with the core residues of tyrosinase. This study not only expands the field of coumarin–triazole hybrid synthesis but also provides valuable insights for the development of novel tyrosinase inhibitors.

This article introduces a novel paradigm for enhancing the administration of decisions regarding sustainable energy planning. This is achieved by deploying novel spherical fuzzy aggregation operators that have been meticulously tailored to address the inherent complexities of uncertainty and imprecision prevalent in energy planning datasets. These operators vastly increase the precision and efficacy of decision-making processes, thereby transforming the entire sustainable energy landscape. This study focuses predominantly on the complex domain of multi-attribute decision-making (MADM), in which the interplay of parameters is characterized by a discernible hierarchy of importance. This method generates aggregation operators based on the assignment of non-negative real values to clearly defined priority echelons, a framework known as priority degrees. This effort results in the development of two notable prioritized operators: the “spherical fuzzy prioritized averaging operator with priority degrees” and the “spherical fuzzy prioritized geometric operator with priority degrees”. The efficacy of these conceptual frameworks is vividly demonstrated through the application of extensive case studies, in which observable results clearly demonstrate their superiority over conventional methodologies. The empirical findings unequivocally demonstrate the superiority of the proposed operators, resonating with substantial performance and efficiency improvements. This study not only adds a seminal dimension to the field of sustainable energy management but also reveals a revolutionary application of spherical fuzzy aggregation operators at the forefront of effective decision-making paradigms. The seamless fusion of theoretical innovation and practical utility outlines a path forward, with transformative prospects and far-reaching implications for the sustainable energy landscape.

Due to insufficient healthcare facilities for the fight against cancer, a large percentage of individuals die. Utilizing computational tools inside the health and medical system helps to minimize fatalities. Timely cancer detection enhances the likelihood of effective therapy. Cancer risk assessment is important for legal and regulatory reasons, for cancer prevention, and to avoid the risks. The approach for assessing cancer risk based on the q-rung orthopair fuzzy set (q-ROFS) is described. The technique is predicated on a multifactor evaluation of the likelihood of a cancerous. q-ROFS is a robust approach for modeling uncertainties in multicriteria decision making (MCDM). The combinative distance-based assessment (CODAS) technique integrates two separate approaches, namely the “simple additive weighting” (SAW) method and the “weighted product method (WPM)”. In this study, the CODAS approach is extended to the q-rung orthopair fuzzy framework with application to cancer risk assessment. Additionally, the symmetry of the optimal decision in cancer risk assessment is carried out by a comparison analysis of the suggested model with some existing models.

Nowadays, there is an ever-increasing diversity of materials available, each with its own set of features, capabilities, benefits, and drawbacks. There is no single definitive criteria for selecting the perfect biomedical material; designers and engineers must consider a vast array of distinct biomedical material selection qualities. The goal of this study is to establish fairly operational rules and aggregation operators (AOs) in a linear Diophantine fuzzy context. To achieve this goal, we devised innovative operational principles that make use of the notion of proportional distribution to provide an equitable or fair aggregate for linear Diophantine fuzzy numbers (LDFNs). Furthermore, a multi-criteria decision-making (MCDM) approach is built by combining recommended fairly AOs with evaluations from multiple decision-makers (DMs) and partial weight information under the linear Diophantine fuzzy paradigm. The weights of the criterion are determined using incomplete data with the help of a linear programming model. The enhanced technique might be used in the selection of compounds in a variety of applications, including biomedical programmes where the chemicals used in prostheses must have qualities similar to those of human tissues. The approach presented for the femoral component of the hip joint prosthesis may be used by orthopaedists and practitioners who will choose bio-materials. This is due to the fact that biomedical materials are employed in many sections of the human body for various functions.

Due to the fact that there is no symmetry in the division of cancer cells, it is important to consider this asymmetrical behavior. Because of this heterogeneity during any therapy, not every cancer cell that is killed only is abolished, which is sensitive to the particular treatment chosen. Mathematical models that describe these pathways are critical for predicting cancer cell proliferation behavior. The literature on the mathematical modeling of cancer onset, growth, and metastasis is extensive. Both deterministic and stochastic factors were used to develop mathematical models to mimic the development rate of cancer cells. We focus on the cell’s heterogeneity in our model so that the cells generally responsible for spreading cancer, which are called stem cells, can be killed. Aggregation operators (AOs) play an important role in decision making, especially when there are several competing factors. A key issue in the case of uncertain data is to develop appropriate solutions for the aggregation process. We presented two novel Einstein AOs: q-rung picture fuzzy dynamic Einstein weighted averaging (q-RPFDEWA) operator and q-rung picture fuzzy dynamic Einstein weighted geometric (q-RPFDEWG) operator. Several enticing aspects of these AOs are thoroughly discussed. Furthermore, we provide a method for dealing with multi-period decision-making (MPDM) issues by applying optimal solutions. A numerical example is presented to explain how the recommended technique can be used in cancer therapy assessment. Authenticity analysis is also presented to demonstrate the efficacy of the proposed technique. The suggested AOs and decision-making methodologies are generally applicable in real-world multi-stage and dynamic decision analysis.

The concept of m-polar spherical fuzzy sets (mPSFS) is a combination of m-polar fuzzy sets (mPFS) and spherical fuzzy sets (SFS). An mPSFS is an optimal strategy for addressing multipolarity and fuzziness in terms of ordered triples of positive membership grades (PMGs), negative membership grades (NMGs), and neutral grades (NGs). In this study, the innovative concept of m-polar spherical fuzzy topology (mPSF-topology) is proposed for data analysis and information aggregation. We look into the characteristics and results of mPSF-topology with the help of several examples. Topological structures on mPSFSs help with both the development of new artificial intelligence (AI) tools for different domain strategies and the study of different kinds of uncertainty in everyday life problems. These strategies make it possible to recognise and look into a situation early on, which helps professionals to reduce certain risks. In order to address various group decision-making issues in the m-polar spherical fuzzy domain, one suggestion has been to apply an extended linear assignment model (LAM) along with the SIR method known as superiority and inferiority ranking methodology in order to analyze road accident issues and dispute resolution. In addition, we examine the symmetry of optimal decision and perform a comparative study between the research carried out using the suggested methodology and several existing methods.

The current research was carried out to characterize the phytosociology of the forests of one of Pakistan’s most valuable tree species (Deodar) across its native range. In this context, our main hypothesis was that, along the altitudinal gradient, we would find different plant communities that would be driven by different environmental variables (climatic, edaphic, and physiographic). Therefore, to assess the vegetation structure of the pure Deodar forests of the unexplored Manoor Valley (Northwestern Himalaya), Pakistan, frequent field visits were carried out during different seasons of 2015–2018. Ecological methods: Line transects sampling (23 stands) and phytosociological attributes were evaluated in relation to geographical and environmental variables. Various statistical software applications (i.e., PCORD, RStudio 4.0, and R 3.6.1) were used to examine all of the gathered data of plant species and environmental variables. A total of three different plant communities (Cedrus–Isodon–Cynodon, Cedrus–Cynodon–Dryopteris, and Sambucus–Cedrus–Desmodium) were identified by grouping 162 species and 23 stands in pure Deodar forests under the influence of geographic, slope, edaphic, and climatic variables, ranging from 1580.8 to 2373.8 m. The altitude (1936–2373 m), slope angle (25–85°), sandy (29–48%) and loamy soil texture, wind speed (1.45 ms−1), and temperature (25.8 °C) all had a strong influence on the Sambucus–Cedrus–Desmodium community. In contrast with this, the Cedrus–Cynodon–Dryopteris community showed a positively significant relationship with the northeastern slope, silty (32–58%) and sandy (15.8–55%) loamy soil texture, and barometric pressure (814.3 pa). Nonetheless, the Cedrus–Isodon–Cynodon community revealed a significant positive association with the northeastern to southwestern slope, pH (6.3), wet bulb (19.7), and dew point (17.7). We found significant differences (p < 0.001) among the three communities found in the pure Deodar forests in the four diversity indexes. The Sambucus–Cedrus–Desmodium community has the maximum number of plants (129 species), Shannon’s diversity (H’ = 3.7), and Simpson’s dominance (0.98) values among the recorded communities. The Pielou’s evenness index value was led by the Cedrus–Isodon–Cynodon community (0.97). Beta diversity showed a dissimilarity lower than 50% among the three communities. Simple term effects in the canonical correspondence analysis model revealed significant (p < 0.05) differences in altitude, slope angle, slope (southeastern), and wind speed variables. The present investigation sheds light on vegetation pattern and species contribution as a function of environmental gradients and provides a baseline for future studies.