Values Of Parameters Research Articles

Estimation of Synthetic Unit Hydrographs’ Parameter Values: The Case of Wadi Al-Badan Sub-Catchment, West Bank, Palestine

Developing unit hydrographs is critical in gauged catchments. Due to the lack of local data, engineers should strive to estimate discharges based on empirical (synthetic) methods in ungauged catchments. Synthetic unit hydrograph methods, such as the Snyder and Soil Conservation Service are popular and play an essential role in hydrology. These methods are simple, requiring only catchment characteristics. Therefore, these methods are valuable tools for simulating runoff in ungauged catchments. This research paper focuses on the Al-Badan sub-catchment, which is located in the northeastern part of the West Bank in Palestine with an area of 83 km2. The study used a de-convolution matrix approach to develop an average one-hour unit hydrograph by selecting four significant rainfall events in the period (2005-2007), and the direct runoff hydrographs which are measured by Al-Badan flume located in the outlet of the sub-catchment. By computing the characteristics of the average one-hour UH with a peak discharge of 4.52 m3/sec and time to peak of 5 hours, Snyder and SCS UHs were developed to suit Al-Badan sub-catchment physical characteristics. The parameters were determined for the Snyder; peaking coefficient depends on the storage capacity of the catchment Cp = 0.88 and the non-dimensional regional coefficient representing catchment storage effects and slope Ct = 1.26 and for the SCS; Cp = 1.90 and the coefficient depending on the total runoff volume occur before peak discharge C = 2.92. These synthetic one-hour UHs were examined for two selected rainfall events that occurred in the period (2017–2019) by comparing observed and simulated direct runoff hydrographs. The performance was tested statistically. Results showed that the synthetic UHs are suitable for application and that the SCS method is more applicable.

The Risk of Coronary Artery Calcification according to Different Lipid Parameters and Average Lipid Parameters.

We compared the association between the baseline and average lipid parameters over time and the coronary artery calcification (CAC) risk. Participants who underwent annual (biannual) health examinations and coronary artery computed tomography to measure CAC at least twice between March 2010 and December 2019, with a baseline CAC of 0, were included. The levels of apolipoprotein B (ApoB), Apolipoprotein A-I (ApoA1), ApoB/ApoA1, non-high-density lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), TG/HDL-C, and TC/HDL-C were measured or calculated. The remnant cholesterol (RC) levels were calculated. The average lipid parameters before study entry were calculated using data from 2002 to 2010. The participants were divided into quartiles (Q) according to the parameter values. Cox proportional hazard modeling, adjusted for confounding factors, compared the CAC risk of the highest quartile to the lowest quartile. Among 29,278 participants (mean age, 39.19±5.21; men, 88.27%), 2,779 developed CAC ＞0. The highest quartile of ApoB showed a numerically strong association with CAC risk, compared with the lowest quartile of ApoB (Q1: reference; Q2: HR,1.41, 95% CI,1.25-1.59; Q3: HR,1.97, 95% CI,1.75-2.21; Q4: HR,2.72, 95% CI,2.41-3.07). RC showed a modest association with CAC risk (Q1: reference; Q2: HR,1.13, 95% CI,0.99-1.28; Q3: HR,1.3, 95% CI,1.15-1.47; Q4: HR,1.7, 95% CI,1.51-1.91). The strength of the association was comparable between the parameters at baseline and the average lipid parameters over time. A high ApoB level showed a strong association with CAC risk compared with the lowest ApoB quartile. The baseline lipid parameters can predict CAC development as effectively as the average of multiple measurements can.

Innovative Nanostructuring of Li–Zn ferrite/graphene composites with tunable properties

The Li-ferrite and graphene oxide (GO) nanocomposites, denoted as (Li0.5-x/2ZnₓFe2.5-x/2O4: x = 0.25), were produced through ultrasonication. The wet method generated Li-ferrite, while the Hummers' method synthesized the GO. X-ray diffraction (XRD) was used to validate the development of Li-ferrite and GO in the nanocomposites. The dimensions of the crystal were determined between 41 and 52 nm, and the porosity of the composites increased with the higher quantity of GO. The scanning electron micrographs (SEM) of ferrites illustrated the formation of uniform and well-defined grains with consistent shapes, exhibiting properties well-suited for effective microwave absorption. The electrical resistance of spinel ferrite was measured at approximately 3.34 × 1011 Ω-cm and significantly lowered as the fraction of GO increased at ambient temperature. As per Maxwell Wagner's model, the electrical permittivity exhibited interfacial polarization with distinct real and imaginary components. The AC conductivity exhibited a positive correlation with the GO content due to the conductive properties of GO and the improved charge hopping mechanism. The frequency exponent parameter's value falls from 0.36 to 0.95 b y the Jonscher's Power law. The magnetic susceptibility of all the samples exhibited a progressive decrease as the temperature increased. The choice of using lithium-based ferrite as filler is made to strengthen the ferrite/GO composites and improve their magnetic and electrical properties. This is undertaken to harness these composites for high-frequency applications, such as their use as microwave absorbers in microwave devices.

Chemical Characterization of Moringa oleifera Lam. from Six Growth Locations in Central of Java: An Initiation of Standardization `

Moringa oleifera Lam. is in high demand as a raw material for pharmaceutical constituents and dietary supplements, particularly for producing stunting supplements. However, recommendations regarding M. oleifera quality standards have not supported this claim as scientific evidence of its safety and effectiveness. This research intends to expand knowledge regarding the quality standards of M. oleifera leaves, particularly their chemical properties. Height-variant samples of M. oleifera were collected from six locations in the Central Java, Indonesia. A hierarchical cluster analysis (HCA) was performed to group the most each parameter's values. The parameters included water-soluble extract, ethanol-soluble extract, total ash content, acid-insoluble ash, total flavonoid content was quantified as quercetin, and thin-layer chromatography (TLC) fingerprint. The suggestion of specific value for each parameter as a future reference for M. oleifera, including water- and ethanol-soluble extract content of at least 41% and 19%, and a total- and acid-insoluble ash content of a maximum of 11% and 4%, respectively. The minimal quercetin contains as the chemical marker compound of M. oleifera was a minimum of 1.285%. The chromatogram profile of TLC suggested 7 (254 nm) and 8 (366 nm) spots.

Predefined-Time Zeroing Neural Networks With Independent Prior Parameter for Solving Time-Varying Plural Lyapunov Tensor Equation.

As an extension of the Lyapunov equation, the time-varying plural Lyapunov tensor equation (TV-PLTE) can carry multidimensional data, which can be solved by zeroing neural network (ZNN) models effectively. However, existing ZNN models only focus on time-varying equations in field of real number. Besides, the upper bound of the settling time depends on the value of ZNN model parameters, which is a conservative estimation for existing ZNN models. Therefore, this article proposes a novel design formula for converting the upper bound of the settling time into an independent and directly modifiable prior parameter. On this basis, we design two new ZNN models called strong predefined-time convergence ZNN (SPTC-ZNN) and fast predefined (FP)-time convergence ZNN (FPTC-ZNN) models. The SPTC-ZNN model has a nonconservative upper bound of the settling time, and the FPTC-ZNN model has excellent convergence performance. The upper bound of the settling time and robustness of the SPTC-ZNN and FPTC-ZNN models are verified by theoretical analyses. Then, the effect of noise on the upper bound of settling time is discussed. The simulation results show that the SPTC-ZNN and FPTC-ZNN models have better comprehensive performance than existing ZNN models.

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction.

To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction. Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific constrained reconstruction problem defined for a fixed class of image functions, the optimal regularization parameter value is weakly subject-dependent and the dependence can be captured using few experimental data. The proposed method has four key steps: a) solution of a given constrained reconstruction problem for a few (say, 3) pre-selected regularization parameter values, b) extraction of multiple approximated quality metrics from the initial reconstructions, c) predicting the true quality metrics values from the approximated values using pre-trained neural networks, and d) determination of the optimal regularization parameter by fusing the predicted quality metrics. The effectiveness of the proposed method was demonstrated in two constrained reconstruction problems. Compared with L-curve-based method, the proposed method determined the regularization parameters much faster and produced substantially improved reconstructions. Our method also outperformed state-of-the-art learning-based methods when trained with limited experimental data. This paper demonstrates the feasibility and improved reconstruction quality by using machine learning to determine the regularization parameter in constrained reconstruction. The proposed method substantially reduces the computational burden of the traditional methods (e.g., L-curve) or relaxes the requirement of large training data by modern learning-based methods, thus enhancing the practical utility of constrained reconstruction.

Bernstein Operational Matrix for Solving Variable Order Fractional Differential Equations with Generalized Caputo-Type

In this research, we present novel generalized derivative founded on the newly constructed (NGCFVODs) novel generalized Caputo Fractional variable order derivatives. Utilizing these operators, a numerical methodology has been formulated to resolve the Fractional Variable Order Differential Equations (FVODEs). We estimate the solution for (FVODEs) by employing bernstein polynomials as foundational vectors. We have further expanded the derivative operational matrix of bernstein Polynomials (bPs) to generalized derivative an operational matrix in sense of (NGCFVODs). The efficiency of developed numerical methodology is tested by a taking a various test examples. We also a compare results of our suggested approach with the methodologies existing in academic papers. In this study the Fractional variable order differential operator of new generalized Caputo was described by three categories: (i) various value in ρ and Fractional variable order a parameter, (ii) various value in a fractional parameters while Fractional variable order and ρ parameter are fixed, and (iii) various value in Fractional variable order parameter controlling fractional and ρ parameter.

Mixed convective viscous dissipative flow of Casson hybrid nanofluid with variable thermal conductivity at the stagnation zone of a rotating sphere

AbstractMixed convection flows across the revolving bodies have eminent applications in science and technology, such as fibre coating, polymer deposition, centrifugal blood pumps, rotatory machinery, and so forth. In the current work, magnetohydrodynamic (MHD) flow and heat transfer characteristics of Casson hybrid nanofluid (Ag/MgO as nanoparticles) over the rotating sphere at the stagnation zone are being studied. Moreover, an analysis of heat transmission is conducted by considering the influence of thermal radiation, temperature‐dependent thermal conductivity, magnetic field, and viscous dissipation. The relevant partial differential equations are reformed into ordinary differential equations by appropriate transformations, which are solved using the successive linearization method (SLM). The thermal field, velocity components in x and z directions, heat transfer rate, and skin friction coefficient are computed for various physical quantities like rotation parameter mixed convection parameter, radiation parameter, Eckert number, Casson parameter, magnetic parameter, variable thermal conductivity parameter, and so forth. The current findings align well with the literature in a limiting sense. Thermal enhancement in hybrid nanofluid is observed for the viscous dissipation parameter (Ec), thermal conductivity parameter (), and radiation parameter (Rd). The degree of heat transfer rises from 12.8% to 20% when the Casson parameter's value () increases. Also, a decrease of approximately 33% is depicted between the peak values of the velocity magnitude with an increase in rotation parameter.

The Effect of Foliar Application with Naphthalene Acetic Acid and Potassium Nitrate on the Growth, Sex Ratio, and Productivity of Cucumbers (Cucumis sativas L.) under High Temperatures in Semi-Arid Areas

High temperatures in late spring, summer, and autumn are one of the main elements limiting cucumber production. Heat stress in cucumber plants leads to significant changes in sex expression and negative effects at the physiological stage, including decreased absorption of water and nutrients, decreased photosynthetic functions, and increased respiration, which in turn affects growth and production. Therefore, it is very difficult for cucumber plants exposed to heat stress to improve their productivity, especially in arid and semi-arid regions. Cucumber plants bear a variety of flower forms, including staminate, pistillate, and hermaphrodites that occur in different arrangements. In addition, maleness is considered the main problem in cucumber production, as it significantly reduces fruit yield. Recently, the growth, flowering, and productivity of plants have been controlled with the help of growth-regulating substances, and one of these substances is naphthalene acetic acid (NAA), which is of great importance in modifying sex in cucumber and various cucurbit crops. Two experiments were conducted during 2022 and 2023 during the summer season to study the effect of foliar spraying of two levels of potassium nitrate, 500 mg/L and 1000 mg/L, and two levels of NAA, 50 mg/L and 100 mg/L, individually and together, and the comparison treatment in nine treatments on growth traits, sex ratio, fruit traits and yield of cucumber (Cucumis sativas L.) plants in three replicates in a completely randomized block design. The results indicated that applying the treatment containing 1000 mg/L KNO3 + 100 mg/L NAA led to obtaining the highest value in most vegetable growth, flowering, and production parameters, with a significant increase compared to the control and all other treatments. Followed by treatments consisting of potassium nitrate and NAA, which gave high values in the traits of sex ratio %, fruit setting %, fruit yield, and most other traits. This study showed the importance of spraying with both NAA and potassium nitrate, individually or in combination, and the high combined effect of both in the treatments that contained each of them in the vegetative growth traits and floral traits such as the number of female flowers, the sexual ratio, fruit set percentage, weight fruit, length fruit, and the fruit yield in cucumber plants grown in the summer season with high temperatures

Lifestyle predictors of colorectal cancer in European populations: a systematic review

BackgroundColorectal cancer (CRC) is the second most prevalent cancer in Europe, with one-fifth of cases attributable to unhealthy lifestyles. Risk prediction models for quantifying CRC risk and identifying high-risk groups...

Explainable Reverse Verification of Goodness of Classification of MRI Images by Clinical Experts.

Radiology offers a presumptive diagnosis. The etiology of radiological errors are prevalent, recurrent, and multi-factorial. The pseudo-diagnostic conclusions can arise from varying factors such as, poor technique, failures of visual perception, lack of knowledge, and misjudgments. This retrospective and interpretive errors can influence and alter the Ground Truth (GT) of Magnetic Resonance (MR) imaging which in turn result in faulty class labeling. Wrong class labels can lead to erroneous training and illogical classification outcomes for Computer Aided Diagnosis (CAD) systems. This work aims at verifying and authenticating the accuracy and exactness of the GT of biomedical datasets which are extensively used in binary classification frameworks. Generally such datasets are labeled by only one radiologist. Our article adheres a hypothetical approach to generate few faulty iterations. An iteration here considers simulation of faulty radiologist's perspective in MR image labeling. To achieve this, we try to simulate radiologists who are subjected to human error while taking decision regarding the class labels. In this context, we swap the class labels randomly and force them to be faulty. The experiments are carried out on some iterations (with varying number of brain images) randomly created from the brain MR datasets. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate our work, average classification parameter values of faulty iterations are compared with that of original dataset. It is presumed that, the presented approach provides a potential solution to verify the genuineness and reliability of the GT of the MR datasets. This approach can be utilized as a standard technique to validate the correctness of any biomedical dataset.

Synthesis of Optimal Control of Spacecraft Angular Momentum for Spatial Turn Taking into Account Energy Costs Using Quaternions

Background: In this paper, we propose solving the specific original problem of control synthesis of spacecraft attitude. Optimization of the control program is made with the use of a new criterion of quality that combines energy costs and duration of reorientation under restrictions on control (the presence of a time factor limits the duration of slew maneuver). Methods: The construction of optimal control for angular momentum change is based on the quaternion method and L.S. Pontryagin maximum principle. An analytical solution to the problem was obtained on the base of a differential equation relating the orientation quaternion and angular momentum of a spacecraft. Results: Key properties of the optimal solution are formulated in analytical form; the features of optimal motion are studied in detail. The control law is formulated in the form of explicit dependence between control and phase variables. In a case when the controlling torque is limited by the given restriction (at the beginning and end of a turn), analytical formulas have been written for the duration of braking and acceleration. Main relations which determine optimal values of parameters of the algorithm for control of angular momentum are given. Examples and results of mathematical modeling of spacecraft motion formed by optimal control were given. This data in addition to the theoretical descriptions illustrates the process of reorientation in evident form and demonstrates the practical feasibility of a designed method for control of angular momentum during spatial turn. Conclusion: The designed optimal algorithm of control of spacecraft motion improves the efficiency of spacecraft attitude system, and originates more economical performance of spacecraft during flight on orbit.

Partial-Power Conversion for Increased Energy Storage Capability of Li-Ion Battery Energy Storage System

Full-power converters are used in battery energy storage systems (BESS) because of their simple structure, high efficiency and relatively low cost. However, cell-to-cell variation, including capacity, state of charge (SOC), and internal resistance, will decrease the available capacity of serially connected battery packs, thereby negatively affecting the energy utilization rate (EUTR) of BESS. This article proposes a novel BESS scheme that combines a modular converter with partial power conversion architecture to make a modular partial power converter (MPPC) that addresses the issue. The MPPC consists of input-serial and output-paralleled (ISOP) isolated phase-shift full-bridge (PSFB) sub-modules. It processes only the serial compensation power, about 1/3 of the full power. Consequently, the MPPC shrinks the converter capacity, which can reduce the cost and power loss. Furthermore, this article develops a BESS model considering cell-to-cell variations to analyze the energy storage capability of the MPPC-BESS compared with the existing full-power BESS. To test the model, we run a simulation using parameter values from 100 real retired batteries. Our simulation results show that the MPPC can significantly alleviate the reduction of EUTR as the voltage level increase. Finally, we construct a 36V/720W MPPC-BESS prototype with two battery packs and PSFB sub-modules to verify the bidirectional operating stability and energy storage capability.

Search for radio halos in starburst galaxies

Context. Starburst galaxies are undergoing intense episodes of star formation. In these galaxies, gas is ejected into the surrounding environment through winds created by the effect of hot stars and supernova explosions. When interacting with the intergalactic medium, these winds can produce strong shocks capable of accelerating cosmic rays. The radiation from these cosmic rays mainly occurs in radio and gamma rays. The radio halo can be characterized using the scale height, which is an important parameter for understanding cosmic ray acceleration and transport. Aims. We searched for the presence of radio halos in a sample of edge-on starburst galaxies gathered from the MeerKAT 1.28 GHz Atlas of Southern Sources in the IRAS Revised Bright Galaxy Sample. The investigation of how the radio halos relate to the global properties of the galaxies can shed light on the understanding of the halo origin and the underlying cosmic ray population. Methods. We selected a sample of 25 galaxies with inclinations i > 80° from the original sample and modeled their disk and halo contributions. We determined the scale heights and the radio luminosity of the halos when detected. Results. We have detected and characterized 11 radio halos from a sample of 25 edge-on galaxies. Seven of them are reported here for the first time. The average radio scale height is ∼1 kpc. We found that the halo scale heights increase linearly with the radio diameters and this relation does not depend on the star formation rate. All galaxies in our sample follow the radio-infrared relation with a q parameter value of 2.5 ± 0.1. The halo luminosity linearly increases with the infrared luminosity and star formation rate. Conclusions. The dependence of the halo luminosity on the star formation rate and the infrared luminosity supports the hypothesis that the radio halos are the result of synchrotron radiation produced by relativistic electrons and points toward the fact that the star formation activity plays a crucial role in halo creation. The average scale height of 1 kpc implies a dynamical range of 4 Myr, several orders of magnitude greater than the synchrotron losses for electrons of 10 TeV. This suggests that some process must exist to reaccelerate cosmic rays in the halo if gamma-ray emission of a leptonic origin is detected from the halo. According to the relation between the radio and gamma-ray luminosities, we found that NGC 4666 is a potential gamma-ray source for future observations.

A hybrid stacking classifier with feature selection for handling imbalanced data

Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a ‘Multi classifier ShapRFECV based EOC’ (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%.

The Block-Correlated Pseudo Marginal Sampler for State Space Models

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches. The improvement is achieved by the following innovations. First, a novel block version of PMMH that works with multiple particle filters is proposed. Second, the trimmed mean of the unbiased likelihood estimates of the multiple particle filters is used. Third, the article develops an efficient auxiliary disturbance particle filter, which is necessary when the bootstrap disturbance filter is inefficient, but the state transition density cannot be expressed in closed form. Fourth, a novel sorting algorithm, which is as effective as previous approaches but significantly faster than them, is developed to preserve the correlation between the logs of the likelihood estimates at the current and proposed parameter values. The performance of the sampler is investigated empirically by applying it to nonlinear Dynamic Stochastic General Equilibrium models with relatively high state dimensions and with intractable state transition densities and to multivariate GARCH diffusion-driven volatility in the mean models. Although we only apply the method to state space models, the approach will be useful in a wide range of applications such as large panel data models and stochastic differential equation models with mixed effects.

Convolution neural network hyperparameter optimization using modified particle swarm optimization

Based on the literature review, a convolutional neural network (CNN) is one of the deep learning techniques most often used for classification problems, especially image classification. Various approaches have been proposed to improve accuracy performance. In CNN architecture, parameter determination is very influential on accuracy performance. Particle swarm optimization (PSO) is a type of metaheuristic algorithm widely used for hyperparameter optimization. PSO convergence is faster than genetic algorithm (GA) and attracts many researchers for further studies such as genetic algorithms and ant colony. In PSO, determining the value of the weight parameter is very influential on accuracy. Therefore, this paper proposes CNN hyperparameter optimization using modified PSO with linearly decreasing randomized weight. The experiments use the modified National Institute of Standards and Technology (MNIST) dataset. The accuracy of the proposed method is superior, and the execution time is slower to random search. In epoch 1, epoch 3, and epoch 5, the proposed method is superior to baseline CNN, linearly decreasing weight PSO (LDWPSO), and RL-based optimization algorithm (ROA). Meanwhile, the accuracy performance of the proposed method is superior to previous studies, namely LeNet-1, LeNet-2, LeNet-3, PCANet-2, RANDNet-2, CAE1, CAE-2, and bee colony. Otherwise, lost to PSO-CNN, distributed PSO (DPSO), recurrent CNN, and CNN-PSO. However, the four methods have a complex architecture and wasteful execution time.

A new hybrid parallel genetic algorithm for multi-destination path planning problem

This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles.

Ultrasonic evaluation MAFLD in screening severety matabolic disorders

Metabolic dysfunction associated fatty liver disease (MAFLD) is a new proposed term for non-alcoholic fatty liver disease (NAFLD). a liver steatosis associated with Type 2 diabetes mellitus or increased physical weight/thickness, or with two or more metabolic disorders. This indicates metabolic abnormalities that result in fat accumulation in hepatocytes without the presence of alcoholic liver disease or other causes that would lead to hepatocyte abnormalities. Our goal was determine the correlation between certain stages of fatty liver disease determined by ultrasonography with increased body weight/obesity, diabetes mellitus and other metabolic disorders and with the values of biochemical parameters of liver enzymes. A prospective study was conducted at the public health institution Zivinice Health Center from August 2022 until January 2024 on a random sample of 101 patients. Observing the entire examined group, a difference in the gender distribution of hepatic steatosis is noticed, and graphically it is shown that men have hepatic steatosis more often than women. In fatty liver disease (MAFLD), stage G2 was most prevalent in 32% of patients. Ultrasonography, along with clinical and biochemical indicators, should be an integral part of screening and monitoring of metabolic disorders as it is a non-invasive and easily accessible imaging method and allows timely therapeutic action and prevention of hepatic and extrahepatic complications.

Combinative distance-based assessment method using linguistic T-spherical fuzzy aggregation operators and its application to multi-attribute group decision-making

Clean energy has become a hot topic worldwide because of the continuous rise in the climate's temperature. Reduced air pollution is only one of the many economic and environmental advantages of clean energy. An effective contractor with experience and knowledge in clean energy projects is always required to handle the complicated process of planning, implementing, and maintaining the new clean energy infrastructure. Contractor selection for renewable energy projects (REPs) is a multi-attribute group decision-making (MAGDM) problem. Linguistic T-spherical fuzzy set (LT-SFS) can be compatible with fuzzier information, and the contractor selection problem also has a lot of fuzzy information, which makes LT-SFS very suitable for contractor selection for REPs. Hence, the present work proposes a MAGDM methodology utilizing a combinative distance-based assessment (CODAS) method within the LT-SF context. For that, the Einstein and Frank t-norm and t-conorm has been used to define new and generalized operational laws for linguistic T-spherical fuzzy numbers. Then, the linguistic T-spherical fuzzy Einstein and linguistic T-spherical fuzzy Frank aggregation operators are proposed to integrate the information data provided by experts. Moreover, the proposed aggregation operators based-CODAS approach is designed to evaluate and rank the available alternatives. A real-life decision problem of selecting the best suitable contractor for a renewable energy project is solved to verify our suggested technique. Moreover, the sensitivity analysis is also carried out by changing the parameter's value to check the consistency of the rank order. Finally, the new model is compared with existing approaches to demonstrate its strength. The comparative analysis shows that the results of the suggested technique are more feasible and beneficial than those of existing approaches.