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Abstract This study examines the multi-decadal variability and trends of surface air temperature and precipitation in the Indian Cardamom Hills (ICH), a degraded tropical rainforest area unique for cardamom cultivation. Utilizing observed long-term climatic data (1958–2017), statistical methods such as the Mann–Kendall test (MKT), Sen’s Slope Estimator (SSE), and Incremental Trend Analysis (ITA) were applied to assess the impact of surface air temperature, rainfall, and the number of rainy days on cardamom yield. The analysis revealed a significant decline in annual rainfall by approximately 13.62 mm per year, with pronounced seasonal declines 0.87 mm for winter, 12.33 mm for pre-monsoon, 24.93 mm for southwest monsoon, and 18.10 mm for post-monsoon. Simultaneously, the number of rainy days dropped by nearly 19.75 days over the 40-year period. A noticeable increase in decadal minimum and average temperatures was observed, highlighting potential adverse effects on cardamom yield and irrigation water resources. The findings suggest that excessive rainfall during the southwest monsoon negatively correlates with cardamom yield, while slightly warmer temperatures show a weak positive correlation. The study also emphasizes the need for adaptive agricultural practices and climate-resilient policies to mitigate the effects of changing climatic conditions on cardamom production. This research contributes valuable insights for farmers and other stakeholders as well as policymakers aiming to ensure sustainable cardamom cultivation amidst climate change.

Background: The current research aims to improve the oral bioavailability of ibrutinib (IBR), a class II drug with low solubility, through the formulation of nanosponges (NSPs) that incorporate IBR, utilizing Hydroxypropyl β-cyclodextrin (HPβCD) and 1,1′-carbonyldiimidazole (CDI) as cross-linking agent. Methods: IBR-loaded HPβCD-NSPs were formulated by optimizing the molar proportion of HPβCD to CDI, as well as stirring rate and duration using a design-based methodology. The synthesized nanoparticles (NSPs) were examined for size, potential, and entrapment of drug. Characterization was performed by X-ray diffraction analysis, Fourier Transform Infrared Spectroscopy (FT-IR), and Differential Scanning Calorimetry (DSC), to assess compatibility. Permeability studies were conducted, followed by in vitro and in vivo assessments. Results: The optimized IBR-loaded HPβCD NSPs demonstrated a mean particle size of 145.6 ± 6.8 nm, a PDI of 0.170 ± 0.036, and an EE of 71.04 ± 2.40%. Further validation through zeta sizing, microscopic and spectral analysis, release studies, and pharmacokinetic assessments confirmed the optimization. The HPβCD NSPs demonstrated 14.96 times higher AUC0-t (area under the curve) with a Cmax increase of 6.45 times compared to the free drug, indicating a substantial improvement in bioavailability. Conclusion: IBR-loaded HPβCD NSPs offer a promising strategy for improved drug release and bioavailability, which could significantly benefit melanoma treatment.

The current article deals with the SM of a NFs on a 3D moving surface containing bioconvection, microorganisms, and convective conditions over anisotropic slip. This work reports describes the characteristics of bioconvection aspects of nanofluid containing microorganisms with anisotropic slip condition. The study may be important from the application point of view in microfluidic devices, antibiotics, enhanced oil recovery and many other areas. Applying the similarity variables, the basic gov. Eqs are translated to ordinary ones, and such Eqs are calculate by R-K-F fourth order programming with shooting scheme by MATLAB software. The physical parameters on the moving surface are explained in detail via graphs. We found that the temperature increases with a higher revolution and larger numerical numbers (convection parameter), while the HTR declined for various values of the SFP. The HTR enhances with surface convection, and therefore, the convection condition is helpful in reducing the viscous drag on the moving SS.

Abstract Group decision-making (GDM) is crucial in various components of graph theory, management science, and operations research. In particular, in an intuitionistic fuzzy group decision-making problem, the experts communicate their preferences using intuitionistic fuzzy preference relations (IFPRs). This approach is a way that decision-makers rank or select the most desirable alternatives by gathering criteria-based information to estimate the best alternatives using a wider range of knowledge and experience. This article proposes a new statistical measure in a fuzzy environment when the data is ambiguous or unreliable to solve a decision-making problem. This study uses the variation coefficient measure combined with intuitionistic fuzzy graphs (IFG) and Laplacian energy (LE) to solve a GDM problem that utilizes intuitionistic fuzzy preference relations (IFPRs) to select a reliable alliance partner. Initially, the Laplacian energy determines the weight of individual standards, and the obtained weight average further estimates the overall criterion weight vector. We establish the authority criteria weights using the variation coefficient measure and then ultimately rank the alternatives for each criterion using the same measure. We examine four distinct companies Alpha, Beta, Delta, and Zeta to conduct a realistic GDM to choose which alliance partner would be ideal. We successfully implemented the suggested technique, determining that Alpha satisfies company standards and is ranked first among other companies. Moreover, this technique is useful for all kinds of Intuitionistic fuzzy group decision-making problems to select optimal ones.

Recent advancements in nanofluids (NFs) nanomaterials have led to diverse applications across multiple disciplines, enhancing heat transfer (HT) performance in clinical systems, engineering, cooling technologies, engine generators (EG), and more. These materials play a critical role in diagnosing underlying issues within human organs that rely on peristaltic pumping for fluid transfer, such as the stomach, intestines, and ureters. They are also integral to devices like flow meters, magnetohydrodynamic (MHD) generators and pumps, nuclear reactors using liquid metals, geothermal energy systems, and solar power absorbers. This research focuses on the influence of a magnetic field (MF) on peristaltic flow within a porous channel containing Williamson fluid (WF), driven by both injection and vertical pressure gradients. The objective is to analyze how peristaltic motion affects heat transfer efficiency in such systems. The fluid dynamics are modeled under the assumptions of long wavelengths and small Reynolds numbers. The study aims to evaluate key factors affecting pressure and frictional forces in the porous channel, including Hartmann number (HN), suction and injection parameters, and the rheological properties of Williamson fluid. Nonlinear differential equations governing the flow are solved analytically using perturbation techniques. The findings indicate that increasing the suction and injection parameters enhances volumetric flow rates, while the relationship between pressure rise and time-averaged volumetric flow rate is also explored. Results show that pressure rise decreases as the Hartmann number increases, consistent with the findings of Shapiro et al. The study concludes that the interaction between magnetic fields and peristaltic motion significantly influences fluid behavior, with potential applications in both biological and industrial systems.

Abstract The present research theoretically analyzes the impact of magnetohydrodynamics (MHD) and viscosity variation on couple stress squeeze film properties of a cylinder and a rough planar surface. The generalized stochastic Reynolds flow equation is derived by utilizing Stokes flow theory and Christensen's stochastic flow model. The solutions for pressure, load-carrying capability, and squeeze film time for longitudinal and transverse roughness patterns are derived and illustrated graphically to examine the impacts of MHD, couple stress, and viscosity variation factors. The results reveal that, in comparison with the smooth case, the one-dimensional longitudinal (transverse) roughness pattern decreases (enhances) the pressure, squeeze film time, and load-carrying capability. The numerical comparative results between Sreekala et al. (Lect Notes Electr Eng 928:765–774, 2023) analysis and the current analysis are performed and found excellent agreement.

Q-rung orthopair fuzzy sets (q-ROFS), which are better than the intuitionistic and Pythagorean fuzzy sets, are a significant tool for expressing ambiguous information. Their key feature is that their ability to represent a larger space of uncertain information is based on the fact that the product of the qth power of the membership degree and the qth power of the degrees of non-membership is equal to or less than 1. Under these circumstances, we train group decision-making problems in the study using q-rung orthopair fuzzy inclination associations. Through the calculation of the standard deviation of one separable q-rung orthopair inclination association to the others and the unclear evidence of q-rung orthopair fuzzy inclination connections, we propose a novel approach to estimate the qualified reputation weights of authority. The “internal” and “impartial” evidence of authority is taken into consideration by this new mindset. Subsequently, we included the weights of authorities into the q-rung orthopair fuzzy inclination relations and used a relative similarity approach to determine the relevance of replacements and the best substitutes. The planned techniques' usefulness and realism are demonstrated by the contrast analysis with additional methods through mathematical demonstrations, both of which show the fuzzy set’s membership degree and non-membership degree, respectively.

Business intelligence can play a significant role in improving the healthcare industry. Healthcare organizations generate massive amounts of data, including patient information, financial data, and operational data. The tools can help healthcare organizations to analyze and utilize this data to improve their operations, reduce costs, and provide better patient care. Business intelligence technologies can be used to analyze this data and improve operational and clinical efficiency. However, healthcare organizations need to assess their readiness and develop an overall the strategy to effectively use the data. This research proposes a framework for developing a domain specific maturity model, that meets specific healthcare industry. The study demonstrates the effectiveness of this framework by applying it to the development of maturity model in healthcare. The results indicate framework is able to guide the development of a BI maturity model that is acceptable to expert practitioners field. Our research is important because it addresses the need for a domain specific BI maturity model in healthcare. While there are generic models available, they may not be suitable for the specific needs of the healthcare industry. The proposed framework can be used by healthcare organizations to assess their BI readiness and develop a BI strategy that is tailored to their specific needs. Overall, this research has practical implications for healthcare organizations looking to leverage their data through Business intelligence technologies. By using the proposed framework to develop a domain specific Business intelligence maturity model, organizations can improve their operational and clinical efficiency and ultimately provide better patient care.

Abstract A mathematical analysis is communicated to the thermal radiation and heat absorption effects on 3D MHD Williamson nanoliquid (NFs) motion via stretching sheet. The convective heat and mass boundary conditions are taken in sheet when liquid is motion. As a novelty, the effects of thermal radiation, heat absorption and heat and mass convection are incorporated. The aim is to develop heat transfer. Williamson NFs are most important source of heat absorption, it having many significant applications in “energy generation, HT, aircraft, missiles, electronic cooling systems, gas turbines” etc. The suitable similarity transformations have been utilized for reduce basic governing P.D. E’s into coupled nonlinear system of O.D. E’s. Obtained O.D. Es are calculated by help of R–K–F (“Runge–Kutta–Fehlberg”)4th order procedure with shooting technique in MATLAB programming. We noticed that, the skin friction coefficient is more effective in Williamson liquid motion when compared with NFs motion with higher numerical values of stretching ratio parameter, Williamson liquid motion is high when compared to NFs motion for large values of magnetic field. We compared with present results into previous results for various conditions. Finally, in the present result is good invention of previous results.

This research proposes a new similarity measure on hesitancy fuzzy graphs. The similarity measures are crucial concepts to explore the closeness between fuzzy graphs. The available imprecise and inconsistent data were more effectively handled by fuzzy similarity measures and intuitionistic fuzzy similarity measures. With time in decision-making theory, a complex frame of the background that occurs cannot be specified entirely by these fuzzy graphs but generalized fuzzy graphs like the hesitancy fuzzy graph can handle such a situation efficiently. The applicability of Hesitancy Fuzzy Graph (HFG) attracted the researchers to generalize ranking order based on the working procedures I, II of Xu’s approach and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach. For this purpose, we first define hesitancy fuzzy preference relations, Laplacian energy, and similarity measures in these HFG. The decision-making technique is then proposed in which a weighting technique is developed by building an optimal model based on the proposed Laplacian energy and similarity measure under a hesitancy fuzzy environment. This research studies the decision-making problem in which the preference information given by the experts takes the form of Hesitancy Fuzzy Preference Relations (HFPRs) and the information about experts’ weights are completely unknown. This research utilizes the hesitancy fuzzy weighted averaging operator to aggregate all individual HFPRs into a collective HFPR. Then, based on the degree of similarity between the individual HFPRs and the collective ones, we develop an approach to determine the experts’ weights. Moreover, based on HFPRs, in which the similarity measures between the collective preference relation and hesitancy fuzzy ideal solution are used to rank the given alternatives. We provide an illustrative numerical example to illustrate the mentioned approach and also we compare the aggregate results of the two techniques.

The present study aims to provide a mathematical model of the Williamson fluid flow via a permeable stretching/shrinking sheet in the MHD boundary layer in the presence of a heat source, chemical reaction, and suction. This study is novel because it investigates the physical effects of thermal and solutal stratification on convective heat and mass transport using thermal radiation. The flow’s PDEs are numerically solved using the BVP4c approach and the pertinent similarity variables until a stable solution is found. Through visual analysis, the effects of dimensionless factors on temperature, velocity, and concentration profiles are examined. This encompasses the mass transfer rate, the heat transfer rate, and the coefficient of friction. The results of the present analysis are found to be consistent with those of previously published studies. The findings demonstrate that enhanced temperature and concentration profiles cause the Williamson, magnetic, and permeability parameters to rise in conjunction with a drop in the dimensionless velocity. In relation to temperature, the thermal stratification parameter exhibits the opposite tendency. Regarding the solutal stratification parameter, concentration profiles are seen to show the opposite trend. Lastly, the current work will have important implications for the removal of dust and viruses from viscoelastic fluid in bioengineering, the medical sciences, and medical equipment.

In the current investigation, it is examined numerically entropy generation (EG) on inherent irreversibility motion of couple stress cold liquid with porous medium via Horizontal channel in presence of viscous dissipation. Present work was studied heat generation on couple stress liquid with porous channel. This work considerable importance in many industrial applications like “Control Mechanism in Material Manufacturing”, “Manufactures of Electronic Chips”, “Crystal Formation”, “Scientific Treatment Problems of Irrigation”, “Soil Erosion and tile drainage” are the present focus of development of channel motion. The formulated physical liquid equations are subsequently calculated by shooting technique with R-K-F (“Runge-Kutta Fehlberg”) scheme. The velocity, temperature as predicted via graphically. we found the velocity dwindle, temperature high production by an escalating statistical value of K (“Couple Stress Parameter”), Ec (“Eckert number”) and Q (“Heat Generation parameter”) respectively. Gradient constraint in addition to improve by an enhancement into Bejan number for various values of “pressure gradient parameter”.

In the present work, a mathematical model is developed and analyzed to study the influence of nanoparticle concentration through Brownian motion and thermophoresis diffusion. The governing system of PDEs is transformed into a coupled non-linear ODEs by using suitable variables. The converted equations are then solved by using robust shooting method with the help of MATLAB (bvp4c). The impacts of dynamic parameters on the flow, energy and concentration are discussed graphically. It is noticed that the mass transfer rate in case of regular fluid is lower than that of nanofluid and the axial velocity converges to the boundary very fast in case of temperature dependent viscosity case than the regular viscous case.