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This paper investigates the impact of student-centered teaching strategies (SCTS) on the educational attainment of econometrics students, at a university based in the UK. Theoretical foundations underlying the usage of SCTS suggest that SCTS is better for students' long-term recall, comprehension, problem-solving abilities and interest in the subject. Yet, most existing studies have only examined short-run outcomes, and none in technical social-science fields. We contribute to the literature by empirically analyzing whether SCTS affects the long-term learning outcomes for a STEM-related social-science subject like econometrics, by comparing the impact of SCTS with traditional teacher-centered strategies using a repeated cross-sectional sample spanning over four academic years. Our results provide robust evidence that SCTS is positively associated with students' grades in the long run. Heterogeneity analyzes additionally indicate that female students and those in the upper quantiles of the grade-distribution disproportionately benefit more from SCTS.

This study documents the presence of an exotic loricariid catfish species, Pterygoplichthys disjunctivus (Weber, 1991), in the Wainganga River of Maharashtra, India. Originally native to South America, sailfin catfish are now commonly used as ornamental fish beyond their natural habitat. The species was observed near Gosekhurd Dam (20°52'15''N and 79°37'00''E) along the Wainganga River. Two individual specimens were collected during the study, with an average total length (320.31-338.63 mm) and weight (500.21-540.31 g). The rapid expansion of these species and its potential impact on local fish populations and inland fishing resources have raised concerns. Immediate preventive measures are necessary to control the establishment and subsequent uncontrolled spread of P. disjunctivus, may be imminent which would be pose a threat to the freshwater biodiversity of the River Wainganga. Effective management strategies should be implemented to preserve the natural habitat of these species. This study provides the first documented occurrence of P. disjunctivus, a member of the Loricariidae, near the Gosekhurd dam on the Wainganga River in Maharashtra, India.

Detecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due to complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed in recent research for lung disease classification, focusing on pneumonia analysis using chest X-ray images. We explore conventional machine learning methods, pretrained deep learning models, customized convolutional neural networks (CNNs), and ensemble methods. A comprehensive comparison of different classification approaches is presented, encompassing data acquisition, preprocessing, feature extraction, and classification using machine vision, machine and deep learning, and explainable-AI (XAI). Our analysis highlights the superior performance of transfer learning-based methods using CNNs and ensemble models/features for lung disease classification. In addition, our comprehensive review offers insights for researchers in other medical domains too who utilize radiological images. By providing a thorough overview of various techniques, our work enables the establishment of effective strategies and identification of suitable methods for a wide range of challenges. Currently, beyond traditional evaluation metrics, researchers emphasize the importance of XAI techniques in machine and deep learning models and their applications in classification tasks. This incorporation helps in gaining a deeper understanding of their decision-making processes, leading to improved trust, transparency, and overall clinical decision-making. Our comprehensive review serves as a valuable resource for researchers and practitioners seeking not only to advance the field of lung disease detection using machine learning and XAI but also from other diverse domains.

Abstract The agricultural sector faces challenges in managing water resources efficiently, particularly in arid regions dealing with water scarcity. To overcome water stress, treated wastewater (TWW) is increasingly utilized for irrigation purpose to conserve available freshwater resources. There are several critical aspects affecting the suitability of TWW for irrigation including salinity which can have detrimental effects on crop yield and soil health. Therefore, this study aimed to develop a novel approach for TWW salinity prediction using artificial intelligent (AI) ensembled machine learning approach. In this regard, several water quality parameters of the TWW samples were collected through field investigation from the irrigation zones in Al-Hassa, Saudi Arabia, which were later assessed in the lab. The assessment involved measuring Temperature (T), pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Salinity, through an Internet of Things (IoT) based system integrated with a real-time monitoring and a multiprobe device. Based on the descriptive statistics of the data and correlation obtained through the Pearson matrix, the models were formed for predicting salinity by using the Hammerstein-Wiener Model (HWM) and Support Vector Regression (SVR). The models’ performance was evaluated using several statistical indices including correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE). The results revealed that the HWM-M3 model with its superior predictive capabilities achieved the best performance, with R2 values of 82% and 77% in both training and testing stages. This study demonstrates the effectiveness of AI-ensembled machine learning approach for accurate TWW salinity prediction, promoting the safe and efficient utilization of TWW for irrigation in water-stressed regions. The findings contribute to a growing body of research exploring AI applications for sustainable water management.

The nanofluids provide various benefits over pure fluids in heat and mass transport applications; hence, their research is crucial. For instance, they can increase heat transfer rate by enhancing the fluid’s thermal conductivity and may enhance mass transfer rate by changing the surface characteristics. Furthermore, nanofluids are being demonstrated to effectively diminish pressure drops in exchangers for heat, which can lower energy consumption and operating expenses. In the existing literature, the majority of the theoretical studies considered self-similar flows. However, there are certain actual flow situations that do not allow for a self-similar solution. The current study considers such of those situations where the non-similarity of the transport phenomena is unavoidable. The non-similarity of the present problem is caused by the consideration of thermophoretic diffusion or the contribution of viscous dissipation when the wall temperature follows a power-law form. For a pure fluid, the same problem admits a self-similar solution in the absence of viscous dissipation effects. In this problem, the non-similarity is caused by the nature of the thermal transport process and not because of the momentum transport. Therefore, the consideration of viscous dissipation in the boundary layer of nanofluid is an interesting aspect to explore the behavior of thermal and mass transport phenomena. Moreover, the current analysis intends to investigate the transport enhancement in a non-similar flow of a nanofluid by utilizing the Buongiorno model. In the current nonsimilar modeling, possibilities for the existence of a self-similar solution are also highlighted. An implicit finite-difference numerical scheme, the Keller-Box method, is utilized. The problem involves several physical parameters of interest, such as the Eckert number, Lewis number, Brownian motion parameter, and thermophoresis parameter, whose potential impact on the non-similar nature of the problem and on thermal enhancement is analyzed and quantified.

Significance: The thermal analysis of nanofluid in a vertical cylinder (artery) in a magnetized environment holds significant implications in physiological and thermal regulation networks. This research is significant in biomedical engineering with applications like medical diagnostics and treatment strategies. Motive: This study investigates the thermal behavior of a magnetized blood-based ternary Carreau nanofluid flowing through a vertically bounded artery with quadratic convection. Velocity and heat transfer analysis is conducted within the artery, utilizing thermal radiation and quadratic convection in a magnetized setting. The base fluid consists of blood, augmented with three nanoparticles: CuO, Al2O3, and TiO2. The investigation centers on examining the properties of blood nanofluid, arterial geometry, and thermal dynamics. Methodology: The physical model generates a set of partial differential equations (PDEs) and similarities mechanism is utilized to fetch its non dimensional form in terms of ordinary differential equations (ODEs). Furthermore, numerical outcomes are obtained with Matlab function bvp4c and obtained data set is trained through robust scheme Levenberg-Marquardt neural network (LMNN) procedure to predict the solution. Findings: Velocity of blood is reduced with increased values of wiessenberg number, magnetic parameter and mixed convection parameter and velocity increases for Second-order convection parameter. Temperature profile decreases with curvature parameter, mixed convection parameter and permeability parameter.

Abstract Organic–inorganic hybrid thermoelectric (TE) materials have attracted tremendous interest for harvesting waste heat energy. Due to their mechanical flexibility, inorganic‐organic hybrid TE materials are considered to be promising candidates for flexible energy harvesting devices. In this work, enhanced TE properties of Tellurium (Te) nanowires (NWs)‐ poly (3‐hexylthiophene‐2, 5‐diyl) (P3HT) hybrid materials are reported by improving the charge transport at interfacial layer mediated via controlled oxidation. A power factor of ≈9.8 µW (mK2)−1 is obtained at room temperature for oxidized P3HT‐TeNWs hybrid materials, which increases to ≈64.8 µW (mK2)−1 upon control of TeNWs oxidation. This value is sevenfold higher compared to P3HT‐TeNWs‐based hybrid materials reported in the literature. MD simulation reveals that oxidation‐free TeNWs demonstrate better templating for P3HT polymer compared to oxidized TeNWs. The Kang–Snyder model is used to study the charge transport in these hybrid materials. A large σE0 value is obtained which is related to better templating of P3HT on oxygen‐free TeNWs. This work provides evidence that oxidation control of TeNWs is critical for better interface‐driven charge transport, which enhances the thermoelectric properties of TeNWs‐P3HT hybrid materials. This work provides a new avenue to improve the thermoelectric properties of a new class of hybrid thermoelectric materials.

Real-valued convolutional neural networks (RV-CNNs) in the spatial domain have outperformed classical approaches in many image restoration tasks such as image denoising and super-resolution. Fourier analysis of the results produced by these spatial domain models reveals the limitations of these models in properly processing the full frequency spectrum. This lack of complete spectral information can result in missing textural and structural elements. To address this limitation, we explore the potential of complex-valued convolutional neural networks (CV-CNNs) for image restoration tasks. CV-CNNs have shown remarkable performance in tasks such as image classification and segmentation. However, CV-CNNs for image restoration problems in the frequency domain have not been fully investigated to address the aforementioned issues. Here, we propose several novel CV-CNN-based models equipped with complex-valued attention gates for image denoising and super-resolution in the frequency domains. We also show that our CV-CNN-based models outperform their real-valued counterparts for denoising super-resolution structured illumination microscopy (SR-SIM) and conventional image datasets. Furthermore, the experimental results show that our proposed CV-CNN-based models preserve the frequency spectrum better than their real-valued counterparts in the denoising task. Based on these findings, we conclude that CV-CNN-based methods provide a plausible and beneficial deep learning approach for image restoration in the frequency domain.

In this study, biocrude was successfully produced by the hydrothermal liquefaction of municipal solid waste collected from the landfill site of Lahore, the capital of Punjab, Pakistan, boasting a population of 12 million and an annual waste collection of 10 million tons. The hydrothermal liquefaction process was performed at reaction parameters of 350 °C and 165 bars with 15 min of residence time. The solid waste was found to have 78 % dry matter, 22 % moisture contents, 22.2 % ash, 22.69 MJ/kg higher heating value, 52.062 % C, 8.007 % H, 0.764 % N, and 39.164 % O. Non-catalytic process only produced 10.57 % oil, however when using the catalytic process, the biocrude yield improved to 17.61 %, with 22.61 % energy recovery for biocrude and 12.14 % for solids, when using 2 g dose of K2CO3. The resultant biocrude has a 28.61 MJ/kg higher heating value, having 60.28 % C and 9.28 % H. In contrast, the aqueous phase generated had 4.43 pH, 71.5 g/L TOC, and 1.35 g/L Total Nitrogen. TGA indicated that biocrude contains approximately 80 % of volatile fractions of different fuels. The organic compounds having the six highest peak areas in GC-MS were Ethyl ether 25.74 %, 2-pentanone, 4-hydroxy-4-methyl 9.08 %, 2-propanone, 1,1-dimethoxy 5.62 %, Silane, dimethyl (docosyloxy) butoxy 5.08 %, 1-Hexanol, 2-ethyl 4.53 %, and. Phenol 4.07 %. This work makes the first-ever successful use of indigenous solid waste from a landfill dumping site in Lahore to successfully produce useful biocrude with aims of waste reduction and management, circular economy, and energy recovery.

Vehicles are frequently impacted objects during floods, susceptible to being washed away when the flow velocity and water depth surpass specific thresholds. Thus, understanding the characteristics of vehicle instability in floodwaters is crucial for establishing effective safety guidelines. This paper presents a comprehensive review, delving into previous studies on flooded vehicle stability during flood events and assessing progress in guideline formulation spanning from 2010 to 2023. According to the literature, 78 % of the previous investigations focused on the stability of static vehicles, while only 22 % considered vehicles in motion. Previous studies on vehicle stability during tsunamis have not been well-explored. Moreover, emergency vehicles, including police cars, fire trucks, and ambulances, received little attention and were only considered in two studies. On the other hand, extensive research has been conducted on defining the critical vehicle orientation with respect to incoming flow, which is reported to occur when the flow direction is perpendicular to the longitudinal vehicle axis. However, it is expected that this review will serve as a foundational resource, inspiring researchers to identify areas requiring attention and delve deeper into producing general safety guidelines. The ultimate goal is to prevent fatalities related to flooded vehicles and contribute to the broader field of flood risk mitigation.

Microplastics (MPs) have garnered global attention as emerging contaminants due to their adaptability, durability, and robustness in various ecosystems. Still, studies concerning their combination with heavy metals (HMs), their interactions with soil biota, and how they affect soil physiochemical properties and terrestrial plant systems are limited. Our study was set to investigate the combined effect of HMs (cadmium, arsenic, copper, zinc and lead) contaminated soil of Tongling and different sizes (T1 = 106 µm, T2 = 50 µm, and T3 = 13 µm) of polystyrene microplastics on the soil physiochemical attributes, both bacterial and fungal diversity, compositions, AMF (arbuscular mycorrhizal fungi), plant pathogens in the soil, and their effect on Lactuca sativa by conducting a greenhouse experiment. According to our results, the combination of HMs and polystyrene microplastic (PS-MPs), especially the smaller PS-MPs (T3), was more lethal for the lettuce growth, microbes and soil. The toxicity of combined contaminants directly reduced the physio-biochemical attributes of lettuce, altered the lettuce’s antioxidant activity and soil health. T3 at the final point led to a significant increase in bacterial and fungal diversity. In contrast, overall bacterial diversity was higher in the rhizosphere, and fungal diversity was higher in the bulk soil. Moreover, the decrease in MPs size played an important role in decreasing AMF and increasing both bacterial and fungal pathogens, especially in the rhizosphere soil. Functional prediction was found to be significantly different in the control treatment, with larger MPs compared to smaller PS-MPs. Environmental factors also played an important role in the alteration of the microbial community. This study also demonstrated that the varied distribution of microbial populations could be an ecological indicator for tracking the environmental health of soil. Overall, our work showed that the combination of HMs and smaller sizes of MPs was more lethal for the soil biota and lettuce and also raised many questions for further studying the ecological risk of PS-MPs and HMs.

Significance: The CuO, Al2O3, and TiO2 nanoparticles find extensive applications in advanced chemical reaction-based thermal transport and quadratic convective nanofluids due to their exceptional thermal properties and chemical stability. Increased thermal conductivity of these nanoparticles used to enhance heat transfer in various chemical reactors, resistance to chemical degradation and photocatalytic reactors and solar energy applications. Motive: This study brings the investigation about quadratic convection-based thermal transport to infinite shear rate for magnetized ternary radiative cross nanofluid with homogeneous-heterogeneous chemical reactions. Water is taken as base fluid and three nanoparticles are Copper oxide (CuO), aluminium oxide (Al2O3), and titanium dioxide (TiO2). Heat transport analysis is made through quadratic convection, magnetic field and thermal radiation. Concentration of nanofluid is scrutinized though homogeneous-heterogeneous chemical reactions. Method: ology: Physical problem with assumptions generates the system of partial differential equations (PDEs) and these PDEs are transformed into ordinary differential equations (ODEs) through similarity variables. Furthermore, a unique combination of Bvp4c and Levenberg Marquardt neural network (LM-NN) schemes is utilized to fetch the numerical solutions. Bvp4c is utilized to solve the governing equations, while LM-NN serves to enhance predictive capabilities and capture intricate nonlinear relationships. Findings: Magnetic environment, chemical process, radiations effects and volumetric fraction of nanoparticles make better heat transfer efficiency and control.

The BBX gene family encodes transcription factors that regulate several biological processes, such as plant growth, development, and stress response. They have been identified and thoroughly studied in numerous plant species. The functional role of the BBX gene family against UV-B stress in soybean has not been well documented. Herein, this study focused on a comprehensive investigation of the GmBBX genes in soybean and their expression patterns in response to UV-B exposure. A total of 184 BBX genes were identified and analyzed to determine the conserved domain. Single and double B-box and CCT domains were found across these proteins, indicating potential functional diversity. The protein physicochemical properties showcased diverse characteristics, highlighting molecular size, stability, and hydrophobicity. Phylogenetic analysis revealed five distinct clades, showcasing a non-uniform distribution of GmBBX genes. Chromosomal mapping showed a non-uniform distribution of GmBBX genes across the 20 chromosomes of soybean. The highest number of genes (26) were found on chromosome 13, whereas chromosomes 5 and 15 displayed the lowest number of genes (3 genes each). Gene structure analysis revealed variations in exon-intron patterns, while motif composition analysis unveiled conserved motifs among GmBBX proteins. Gene duplication indicated that the expansion of the GmBBX genes was linked with tandem and dispersed duplications. Gene ontology analysis revealed enrichment of terms associated with light stimulus, response to abiotic stress, and molecular functions such as zinc ion binding. The promoter region reveals cis-acting elements related to hormones, light, and stress responses. The GmBBX genes exhibited differential expression in response to UV-B stress, suggesting their possible role in plant defense against UV-B stress. This study will provide a solid foundation for further analysis of the molecular mechanisms of GmBBX genes in soybean against UV-B stress.

Abstract Background Peripheral insulin resistance and compromised insulin secretion from pancreatic β-cells are significant factors and pathogenic hallmarks of diabetes mellitus (DM). NF-κβ/TLR-4 and SERCA/Ca2+ pathways have been identified as potential pathways regulating insulin synthesis by preserving pancreatic β-cell functioning. The current study aimed to evaluate the therapeutic effect of aged garlic extract (AGE) against DM in a streptozotocin (STZ)-induced rat model with particular emphasis on pancreatic β-cell functioning. Methods AGE was characterized by gas chromatography-mass spectrometry (GC-MS), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) to evaluate its physio-chemical characteristics followed by in-vitro anti-diabetic and antioxidant potential. This was followed by the induction of DM in laboratory animals for investigating the therapeutic action of AGE by evaluating the role of NF-κβ/TLR-4 and the SERCA/Ca2+ pathway. The parameters assessed in the present experimental setup encompassed antioxidant parameters, metabolic indicators, insulin concentration, intracellular calcium levels, apoptotic markers (CCK-8 and Caspase Glo-8), and protein expression (P-62 and APACHE-II). Results AGE characterization by SEM, GC-MS, and X-ray diffraction (XRD) revealed the presence of phenylalanine, alliin, S-allylmercaptocysteine (SAMC), tryptophan, 1-methyl-1,2,3,4-tetrahydro-β-carboline-3-carboxylic acid as major bioactive constituents of AGE. Metabolic studies, including intraperitoneal glucose tolerance test (IPGTT), revealed significantly lower blood glucose levels in the AGE group compared to the disease control group. In contrast, the intraperitoneal insulin tolerance test (ITT) exhibited no significant difference in insulin sensitivity between the AGE supplementation group and the DM control group. Interestingly, AGE was found to have no significant effect on fasting glucose and serum insulin levels. In contrast, AGE supplementation was found to cause significant hypoglycaemia in postprandial blood glucose and insulin levels. Importantly, AGE causes restoration of intracellular Ca2+ levels by modulation of SERCA/Ca2 functioning and inhibition NF-κB/TLR-4 pathway. AGE was found to interact with and inhibit the DR-5/ caspase-8/3 apoptotic complex. Furthermore, microscopic studies revealed degeneration and apoptotic changes in pancreatic β-cells of the DM control group, while supplementation of AGE resulted in inhibition of apoptotic pathway and regeneration of pancreatic β-cells. Conclusion The current study suggests that AGE enhance glucose homeostasis by exerting their effects on pancreatic β-cells, without ameliorating peripheral sensitivity. Moreover, AGEs promote an increase in β-cell mass by mitigating the apoptosis of pancreatic β-cells. These findings suggest that AGE could aid in developing a viable alternative therapy for diabetes mellitus (DM).

Abstract De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors.

The aim of our work is to study two-dimensional fractured flow simulations for groundwater by using non-identical aperture of fractures via Analytic Element Method, and make a comparison between processing time of both techniques i.e. iterative and matrix method. Numerical solutions for the considered problems are totally based on series expansions, whereas the effects of every fracture are expressed as a series which obey Laplace's equation. In all examined cases, it is evident that the iterative method is not able to converge, while by introducing the matrix method, the expected results are obtained with minimum computational cost (processing time).

Diamer Basha Dam is an under-construction, 272-meter-high, roller compacted concrete (RCC) dam on the Indus River in Pakistan. Once constructed, it will be the world's highest RCC gravity dam with a 105-kilometer-long reservoir. Most of the reservoir lies in unstable moraine deposits with steep slopes. Events like saturation during reservoir filling, alternate wetting, drawdown during reservoir operation, or a seismic event could trigger a large mass movement of these slopes into the reservoir to disrupt the dam functionality. This work identified the 15 most vulnerable slide areas using digital slope maps, elevation maps, and satellite imagery. Deterministic slope stability analysis was carried out on the identified sections under various stages of reservoir operation for static and seismic loading, using pseudo-static and dynamic analysis approaches. Probabilistic analysis was then performed using Monte Carlo simulation. The findings showed that most moraine deposits would collapse under reservoir filling, rapid drawdown, or seismic activity. Following the assessments, landslide susceptibility maps were generated, and an assessment of potential impacts, including the generation of dynamic waves, reservoir blockage, increased sediment loads, and reduced reservoir storage capacity, was also performed.

Electrospinning provides a simple and versatile method for generating ultrafine fibers from a rich variety of materials that include polymers, composites, and ceramics. Find applications in various fields, including tissue engineering, filter media, reinforcement in nanocomposites, and micro/nano-electro-mechanical systems. In this study the polystyrene fibers in 10%, 20% and 30% (w/v) in THF: DMF solvents were characterized by ATR-FTIR, AFM and SEM. The obtained fibers exhibited a smooth surface with fine structures, emphasizing a ribbon-like morphology with bead-like features within the range of 3 µm to 5 µm in diameter. The aim of this study was to extract the parabens at optimized conditions such as pH, adsorbent dose, contact time and the elution of solvent. To examine the mechanism of adsorption kinetics, the two most commonly used kinetics models (pseudo-first order and pseudo-second order) were employed to test experimental data along with the Morris-Weber diffusion model. The obtained data showed that the maximum sorption efficiency obtained at pH 3.0, adsorbent dose optimized as 10 mg of fiber with time of 24 hours, the methanol was sufficient to elute paraben from fibers met. The experimental data of the adsorption followed by first-order reaction with a mass transfer-controlled diffusion mechanism

Preserving the unique ecosystems of specific coastal areas is crucial for environmental health and the welfare of local communities. This study explores the rich biodiversity of flora and fauna along the Kedah state coastline, a 440 km stretch along the Strait of Malacca. Through meticulous site surveys and comprehensive data collection across multiple locations, followed by laboratory analyses, this research reveals a vibrant tapestry of life. The Kedah coastal area boasts a remarkable diversity, with 60 fish species identified, predominantly from the Carangidae (12 %) and Mugilidae (10 %) families. Additionally, five marine mammal species were documented, though turtles and terrapins were notably absent. The condition of coral reefs varied, with ''fair'' conditions observed at stations St-1 and St-2 (31.67 % and 35.67 % live coral, respectively), and ''poor'' conditions at St-3 (10.67 % live coral). High densities of phytoplankton, zooplankton, macrobenthos, and seaweed were recorded, whereas seagrass vegetation was absent. The study on Kedah’s coastal flora and fauna revealed a diverse range of species, including fish, marine mammals, corals, phytoplankton, zooplankton, macrobenthos, and seaweed. Coral reefs were found to be in varying conditions, with some needing restoration. The study recommends long-term monitoring, seasonal surveys, and focused research on key species. Habitat protection, community awareness, and sustainable practices are also emphasized to preserve the ecological balance of these marine environments. Moreover, the findings of this study provide a critical understanding of the Kedah coastal ecosystem and offer valuable insights for local authorities to develop effective conservation strategies. This research underscores the importance of preserving biodiversity to maintain ecological balance and support sustainable development in coastal regions.

An insight has been provided into the eutectic formation and cutaneous wound healing potential of a binary allantoin/β-D-fructose system (AF). The system was blended with folic acid for light-aided wound therapy. Solid–liquid binary phase diagram showed a differential melting depression of 88°C from the ideal line and the existence of eutectic at X1 = X2 = 0.5. Simulations of molecular mechanics showed the involvement of hydantoin C=O and ureido –NH in hydrogen bonding with hydroxyl or hydroxymethyl groups of fructose. In the eutectic-folic acid blend (AFF), H-bonds were present between =N of pterin ring A and hydantoin –NH. Additionally, pi-sigma interactions involving pterin rings and pyranose moiety were revealed. The bulk density variations showed the disruption of the allantoin template after eutectic formation. An in-depth attenuated total reflectance infrared spectroscopic analysis endorsed the findings of other investigations and vibrational frequencies of interacting functionalities were shifted to different extents. The thermal stability of the eutectic mixture was markedly compromised after the addition of folic acid. Differential scanning calorimetry indicated the presence of co-crystal in addition to eutectic. Eutectic and its blend produced nearly the same healing effects in non-infected cutaneous wounds in rabbit models under normal and photodynamic wound therapy.

Objective: To determine the effect of Cochlear Implants on electrophysiological and behavioural response in children with hearing impairment at frequencies of 500, 1000, 2000, and 4000 Hz. Study Design: Cross sectional study. Place and Duration of Study: Department of ENT, Combined Military Hospital Rawalpindi Pakistan, from Jul 2021 to Jul 2022. Methodology: Seventy children with hearing impairment aged 2-12 years of either gender, who had undergone cochlear implantation were part of the study. Language and hearing conditions were examined during the audiological assessment of candidates for cochlear implantation. At ER-tone 5A and TDH-39 using ISO 389-2 and 389-1 calibrations, warble tones given at different frequencies were used to derive behavioral air conduction thresholds. The Auditory Steady State Response test began with a carrier frequency of 500 Hz and advanced to frequencies of 1000, 2000, and 4000 Hz. Thresholds were set using a 10 dB down and 5 dB up procedure until no responses could be captured. Behavioral and Auditory Steady State Response responses were noted in all patients. Results: Mean age of the patients was 6.66±2.74 years. Behavioral threshold>110 dB HL were obtained in 8(11.4%) subjects, 10(14.3%) had behavioral thresholds from 100 to 110 dB HL, and 52(74.3%) had 110 dB HL (15.7%), 31(44%) achieved 100 to 110 dB HL and 28(40%) achieved

Pakistan has limited natural gas reserves, and most are found onshore. This article reports on the problems of an onshore gas gathering network (GGN) analysed through steady-state simulation modelling using PIPESIM software. The research methodology incorporates a comprehensive steady-state hydraulic analysis considering fluid flowing velocity limitations, liquid holdup and slugging along with other issues faced by gas gathering networks. The steady-state hydraulic analysis has led us to pinpoint specific GGN pipelines facing critically low gas velocities and consequent liquid holdup. Addressing these issues involved application of PIPESIM software for modelling, considering various operating schemes of gas-producing wells and their associated pipelines. To select an optimal operating scheme, the study utilized the Analytic Hierarchy Process (AHP) for operational optimization, to identify the most effective solution for reduced liquid holdup, improving production, and ensuring the safe operation among available alternatives. Findings from our hydraulic analysis highlight the importance of reducing GGN outlet pressure to mitigate challenges associated with liquid holdup which causes slugging and back pressure effect at source leading to low production and poor performance of the GGN. Study of three alternative cases reveals that decreasing outlet pressure lowers the liquid holdup, improve gas flowing velocities, and enhanced overall production. These findings validate our hypothesis that reducing GGN outlet pressure is a viable strategy to lower the liquid holdup in pipelines. This research offers significant value by providing a comprehensive solution to GGN liquid holdup, low flowing velocities, back pressure and low production challenges. The integration of steady-state hydraulic analysis, simulation modelling with PIPESIM, and the application of AHP for optimization contributes novel insights into the optimization of operation of gas gathering networks. Emphasizing the reduction of liquid holdup and enhancing production through outlet pressure adjustments offers a practical framework for optimizing the functionality of gas gathering networks.

In this paper, a comprehensive review of the impacts and imminent design challenges concerning such EV charging stations that are based on solar photovoltaic infrastructures is presented, which is based on state-of-the-art frameworks for PV-powered charging stations and the latest case studies. The main factors that are targeted in this review are the management of an EV charging system that is a composite of PV and public grid, as well as a charging system business model that can affect consumer behavior by charging at different rates and the best design infrastructure for a PV electric vehicle charging services. The economic, environmental, and social impacts caused by the installation of PV-driven charging infrastructures are also compared. Moreover, the framework for recently emerging vehicle-to-grid and vehicle-to-home services, as well as the integration of these frameworks, is discussed. Finally, survey results for future EV demand and its public acceptance are presented. Appertaining to these issues, the case studies highlight that EV transportation between parking lots and charging stations is necessary to make the best use of the available charging stations. Statistically, in this study, it was inferred that there are no limitations on the amount of EV battery capacity that can be stored, and users of EVs can charge in both slow and fast modes. Furthermore, V2G systems are not suitable for widespread industrial use. Moreover, finding solutions and overcoming numerous obstacles remain unaddressed issues, and the size of the EV industry must reach a certain level to make profitable sales of V2B, V2H, V2G, and other V2X systems. The collected statistics indicate that although respondents generally have a favorable opinion of the mobility applications powered by photovoltaic cells, it seems unlikely that they will use them anytime soon.

Elevated sodium level (Na+) poses significant threat to crop plant physio-biochemical processes, leading to impaired growth followed by decline in productivity. Addressing this challenge, requires an eco-friendly and cost-effective strategy that enhances plant salt stress tolerance capacity. In this context, the exogenous source of plant growth regulators (PGRs) proved to be an efficient approach. Of various PGRs, salicylic acid (SA) is an emerging signaling molecule that boosts plant stress endurance mechanism. This study investigates SA-mediated salt stress tolerance in maize (Zea mays L.) seedlings, by examining morpho-physiological and biochemical traits. Maize seedlings were subjected to varying levels of salt stress (0, 25, 50, 75, 100, and 150 mM NaCl) for a period of 10-days. The results revealed that, a substantial decline in germination percentage, shoot and root length, plant biomass, vigour index, and various other physiological parameters under salt stress causing concentrations. Conversely, salt stress increased oxidative stress indicators, including hydrogen peroxide (H2O2) and malondialdehyde (MDA), osmolytes and elemental concentrations as well as antioxident enzymes (SOD, CAT, POX, APX, GR, AsA). However, the exogenous supplementation of SA at 0.1 mM significantly restored most morpho-physiological attributes in maize under salt stress conditions. This suggests that SA actively triggers the ascorbate-glutathione (AsA-GSH) pathway and other key enzymes, leading to sodium extrusion and improving antioxidant defense in maize seedlings. This finding provides valuable insights for maize farmers that employing SA could lead to improved maize production in saline soils.

Despite the critical role of balanced nutrition in crop productivity, the use of potash (K) and zinc (Zn) is not much practiced by Pakistani farmers. The reduced nutrient uptake and crop productivity together increase the costs associated with fertilization and revisit farmers’ confidence in the efficacy and profitability of fertilizers. To address this problem, a field study was conducted in the research area of the MNS-University of Agriculture, Multan, in collaboration with Engro Fertilizers Limited. The research plan consisted of five treatments, including T1 = control (without N, P, K, and Zn fertilizers), T2 = NP in practice (NP at 32–23–0 kg acre-1), T3 = recommended NP (NP at 48–34.5 kg acre-1), T4 = balanced NPK (NP+K at 48–34.5–30 kg acre-1), and T5 = balanced NPK + Zn (NPK+Zn at 48–34.5–30 + 7.5 kg acre-1). Wheat was used as a test crop, and its growth, yield, and physiological and nutritional parameters were studied. The results indicated that NPK+Zn balanced nutrition increased plant height, spike length, photosynthetic rate, water use efficiency, transpiration rate, stomatal conductance, and grain yield by 13%, 15%, 44%, 60%, 63%, 39%, and 78%, respectively, compared with the control. It was found that the combined application of NP, K, and Zn improved the recovery efficiency of applied nutrients, i.e., nitrogen recovery efficiency (NRE) by 230%, phosphorus recovery efficiency (PRE) by 136%, potassium recovery efficiency (KRE) by 135%, and zinc recovery efficiency (ZnRE) by 136% compared to NP-alone application. Agronomic use efficiency of applied fertilizers, such as potassium agronomic use efficiency (KAUE) by 71%, phosphorus agronomic use efficiency (PAUE) by 72%, nitrogen agronomic use efficiency (NAUE) by 70%, and zinc agronomic use efficiency (ZnAUE) by 72%, was observed compared to NP-alone application. The results showed that NPUE, PPUE, NPUE, and ZnPUE were reduced by 5%, 3%, 3%, and 5%, respectively, compared to NP-alone application. Our findings suggest that K and Zn should be made an essential part of wheat nutrition management for higher yield and better quality of produce.

In the present study, the frequency-dependent dielectric relaxation and electrical conduction mechanisms in sol-gel-derived Zn0.5Cd0.5Fe2O4 (ZCFO) spinel ferrite were studied in the temperature range of 343–438 K. The formation of the ZCFO spinel ferrite phase with space group Fd3m was confirmed by X-ray diffraction analysis. The dielectric relaxation and electrical conduction mechanisms were studied using complex impedance spectroscopy (CIS). In the Nyquist plots, depressed semicircles were fitted with an equivalent circuit model with configuration (RGBQGB) (RGQG), signifying the contributions from grain boundaries and grains to the charge transport mechanism in the sample. The frequency-dependent AC conductivity was found to follow Jonscher's power law, and the frequency exponent term depicted the overlapping large polaron hopping (OLPH) model as the dominant transport mechanism. The activation energies for conductivity, electric modulus and impedance were calculated to identify the nature of the charge carriers governing the relaxation and conduction mechanisms in the prepared sample. Complex modulus studies confirmed the non-Debye type of dielectric relaxation, whereas tangent loss and dielectric constant analyses confirmed the thermally activated hopping mechanism of charge carriers in Zn0.5Cd0.5Fe2O4 spinel ferrite.

Background: Abnormal CSF accumulation within ventricles of brain leading to raised intracranial pressure is a potentially life-threatening state that is commonly seen in neurosurgical patients. Aim of this study is to assess the efficacy of endoscopic third ventriculostomy (ETV) in treatment of pediatric hydrocephalus in pediatrics. Materials and Methods: A total of sixty-two patients were included in this prospective study. Patients were generally discharged from the hospital on the 2nd or 3rd postoperative day unless some complication arose. All the information was recorded in a pre-structured proforma, and data were analyzed by SPSS. Statistical significance was defined as p-value less than or equal to 0.05. Results: At follow up, clinical improvement and radiological findings did not necessarily correlate with each other, as ventricular size was reduced in only 42 (67.7%) out of 62 patients. Remaining 20 (32.3%) patients had no change in the size of ventricles. The difference was not statistically significant. However, as observed by cine PC MRI, CSF flow was observed in all the patients. Conclusion: In non-communicating hydrocephalus, ETV is a simple technique and safe and effective treatment if done by an expert surgeon correctly. It is a reasonable alternate of VPS due to its simplicity and effectiveness.

For sustainable agriculture, irrigation is one of the most important management practices in a semi-arid climate. Determining the site-specific soil type-based irrigation water requirements requires the development of a soil water characteristics curve (SWCC). This study focuses on exploring the suitability of analytical SWCC models based on the observed data of SWCC developed using the pressure membrane apparatus. A novel SelectKbest gene expression programming (GEP) model is used to create the generalized machine learning SWCC model for Atlantic Canadian soils. The composite soil samples with representative soil textures of loamy sand, loam, and sandy loam are collected from the selected locations in Prince Edward Island and New Brunswick provinces of Canada. The suitability of models at three regions shows that at all sites, including PEI-1 (loam soil) 1, PEI-2 (loamy sand), and NB-3 (sandy loam) if SWCC data has four measured suction potential levels, then McBee & Bomb and Tani exponential models may be selected. Van Genuchten, double exponential, Ruso, Kosuki, and Gardner's four parameters models should be a good choice for five-section potential. Similarly, for six measured suction potential values, the Fredlund & Xing, Omuto, and Van Genuchten models having parameters should be selected for determining the optimum soil hydraulic parameters. The HydroMe program with Brooks and Corey, four, and five parameters Van Genuchten models performed well. The HydroMe program is calibrated and validated with standard RETC for selected regions of Atlantic Canada, which suggests that the HydroMe program is flexible in developing efficient agricultural water management algorithms. Various performance metrics like R2 and RMSE are employed to assess the robustness of the model. A generalized SWCC model (R2=0.976 and RMSE=0.04) is developed using a novel SelectKbest-based GEP modeling approach for Atlantic Canadian soils. These novel approaches can significantly improve site-specific agricultural water management and decision support systems.

Objective: to find a link between a single nucleotide polymorphism (SNP) called rs20417 in the cyclooxygenase-2 gene and Aspirin resistance in Pakistani patients with heart disease. Study Design: Cross-sectional study. Place and Duration of Study: Pharmacology Department, National University of Medical Sciences, in collaboration with the National Institute of Heart Diseases and the Institute of Biomedical and Genetic Engineering in Islamabad Pakistan, from Oct 2018 to Dec 2021. Methodology: The study was carried out on 384 patients (272 males and 112 females) with ischemic heart disease. Patients who had been on Aspirin for at least seven days were selected using non-probability convenience sampling. Platelet aggregation was performed using a Transmission Aggregometer, with arachidonic acid as an agonist. DNA extraction was done using the kit method (Invitrogen, Thermofisher). Then, Polymerase chain Reaction, Restriction Fragment Length Polymorphism, and gel electrophoresis were used to identify SNP rs20417. Results: In this study, 14.0% (n=54) of ischemic heart disease patients were found to be Aspirin resistant, while 86.0% (n=330) were Aspirin responders. The genotyping of COX2 SNP rs20417 showed that 55.46% (n=213) of patients were homozygous with the GG genotype, 34.11%(n=131) had CG (heterozygous), and 10.41% (n=40) of patients were carriers of homogeneous (CC). Statistical analysis did not demonstrate a significant association of any of the alleles of the evaluated SNP with Aspirin resistance (p>0.05). Conclusion: In the Pakistani population, Aspirin resistance is not associated with any specific polymorphic form of Cyclooxygenase-2 at SNP rs20417.

The tolerance to salinity stress is an intricate phenomenon at cellular and whole plant level that requires the knowledge of contributing physiological and biochemical processes and the genetic control of participating traits. In this context, present study was conducted with objective to evaluate the physiological, biochemical, and genetic responses of different wheat genotypes including bread wheat (BW) and synthetic hexaploids (SHs) under saline and control environment. The experiment was conducted in two factorial arrangement in randomized complete block design (RCBD), with genotypes as one factor and treatments as another factor. A significant decline in physiological traits (chlorophyll, photosynthesis, stomatal conductance, transpiration, and cell membrane stability) was observed in all genotypes due to salt stress; however, this decline was higher in BW genotypes as compared to four SH genotypes. In addition, the biochemical traits including enzymes [superoxide dismutase, catalase, and peroxidase (POD)] activity, proline, and glycine betaine (GB) illustrated significant increase along with increase in the expression of corresponding genes (TaCAT1, TaSOD, TaPRX2A, TaP5CS, and TaBADH-A1) due to salt stress in SHs as compared to BW. Correspondingly, highly overexpressed genes, TaHKT1;4, TaNHX1, and TaAKT1 caused a significant decline in Na+/K+ in SH as compared to BW genotypes under salt stress. Moreover, correlation analysis, principal component analysis (PCA), and heatmap analysis have further confirmed that the association and expression of physiological and biochemical traits varied significantly with salinity stress and type of genotype. Overall, the physiological, biochemical, and genetic evaluation proved SHs as the most useful stock for transferring salinity tolerance to other superior BW cultivars via the right breeding program.

Nanotechnology has emerged as a prominent field in recent times. The fabrication of biocompatible materials has taken on highlighted significance owing to their requisite application in diverse sectors including medicine, water treatment and purification, health, and other related fields. There has been a lot of research done recently on the green synthesis of various nanoparticles (NPs). Copper a high-performance metal used in agriculture to combat pathogenic attacks, has received less attention. The current work demonstrates the successful preparation of green synthesized copper oxide nanoparticles (CuO.NPs) from Mangifera indica (M. indica) leaf extract. The spectral and morphological characterization biosynthesized were observed using, FTIR, XRD, and TEM analysis. The FTIR analysis revealed the functional groups present in plant extracts. XRD was carried out to demonstrate the crystalline nature and size of nanoparticles using the Scherrer formula. UV was performed to observe the optical properties of NPs. Further, Transmission electron spectroscopy (TEM) was carried out to confirm the physical shape of CuO.NPs with 50 nm. The M. indica mediated NPs were evaluated against gram-negative and positive bacteria Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) at different concentrations. The in vivo fungicidal activity was performed against Rhizophus oryzae (R. oryzae) on Diospyros kaki (persimmon fruit). The detach fruit method was applied to evaluate the potential of NPs. Higher exposure of 100 µg·mL−1 CuO.NPs showed mycelia inhibition followed by 30, 60, and control treatments. Furthermore, Green CuO.NPs showed prominent antioxidant activities as compared to plant sources. The findings obtained suggest that the green-formulated CuO.NPs could be further investigated for the treatment of many phytopathogenic diseases.

In the brain-computer interface, the SSVEP (steady-state visual evoked potential) method serves to foster collaboration between humans and robots. SSVEP-based detection methods require complex multichannel data acquisition, making them difficult to deploy due to discomfort during extended use and the complexity of the algorithms involved. On the other hand, single-channel setup offers simplicity and ease of use. However, in a single channel, achieving encouraging performance in the SD (subject-dependent) scenario is challenging, and accuracy drops further in the SI (subject-independent) scenario. This requires the development of a generalized approach to improve performance in both scenarios. This study proposes (VMD-DNN) to detect SSVEP in single-channel setups for SD and SI scenarios. The novelty of the proposed method lies in utilizing VMD (Variational Mode Decomposition) as a preprocessor, leveraging harmonic information and Kurtosis of the cross-correlation function to select harmonics from VMD decomposed signal. The preprocessed reconstructed signal uses complex spectrum features as input to the DNN for classification. The results show an average accuracy of 93%, 95.3% in SD and 79%, 92.33% in SI scenarios tested on two publicly available datasets, respectively. The ITR (Information transfer rate) was 67.50 bit/min, 92.31 bit/min for SD, and 46.13 bit/min, 85.94 bit/min for SI for both datasets, respectively. In SD, accuracy is improved by 3.34% and 5%, and ITR by 8.87% and 12.91% over baseline methods for both datasets respectively. The proposed VMD-DNN model is effective, with improved performance and lower computational complexity. The robust single-channel approach makes it user-friendly for human-robot collaboration.

Traffic signs have great importance regarding smooth traffic flow and safe driving. However, due to many distractions and capricious factors, spotting and perceiving them may become hazardous. Traffic sign detection and recognition have gained popularity to put an end or to lessen the issue, and massive efforts have been realized in this regard. Despite considerable endeavors put together for traffic sign detection and recognition, there is a lack of attention in this area where these traffic signs contain text in them. A handful of studies may be found in state-of-the-art (SOTA) methods for text-based traffic sign detection, and particularly lesser for text recognition of detected text. The proposed method focuses on developing a robust semi-pipeline intelligent system to detect and understand text from traffic road signs boards in various weather conditions. For this purpose, a customized YOLOv5s is deployed for initial panel detection. Subsequently, MSER with preprocessing techniques is used for localization of text. Finally, OCR with NLP is utilized to recognize the text. The proposed method employed the ASAYAR dataset for training and different datasets for testing. The proposed approach produced satisfactory outcomes on them in contrast with SOTA approaches.

This article presents an extensive examination of antennas rooted in nature and biology, showcasing their remarkable performance across a wide spectrum of frequencies—from microwave to terahertz. The limitations of traditional antenna design have become increasingly evident in the face of burgeoning demands for novel communication technologies. Conventional analytical-equation-based approaches struggle to deliver the combined performance characteristics– encompassing bandwidth, gain, radiation pattern, and miniaturization– that emerging technologies necessitate. This has fueled an interest in bio-inspired antenna designs, a paradigm shift drawing inspiration from the ingenious structural solutions found in the living and non-living world, from plant leaves to bird feathers. These bio-inspired designs offer distinct advantages such as broader bandwidth and reduced sizes, making them highly appealing alternatives to the limitations of conventional antenna designs. This review explores a diverse range of bio-inspired designs. Among them are fractal geometries, inspired by self-repeating patterns in nature, which achieve optimal performance. Numerous designs in this category draw inspiration from nature, incorporating patterns observed in snowflakes, tree branches, clouds, and butterflies. Furthermore, nano-antennas have attracted significant attention for their vast potential applications in microwave and optical frequencies, playing a pivotal role in high-resolution spectroscopy, biomedical diagnosis and sensing, quantum photonics, and solar cell applications. By examining design methodologies and potential benefits, this article highlights the transformative potential of nature-inspired antennas. The compelling advantages of bio-inspired approaches necessitate a thorough exploration of their potential, paving the way for the development of next-generation communication systems with unprecedented capabilities.

The current work is categorized into the following formatBackground: Heat transfer controlling through therapy and blood flow has clinical importance in different levels of the human body. Biological faculties utilize blood in which iron oxide nanoparticles are incorporated for contrast agents and in the biomedical fields as they possess key and intrinsic characteristics, like low toxicity, capability of blood carrying nanoparticles, surface engineering and colloidal stability. This paper has a unique approach for analysis of blood flow containing nanoparticles with mathematical model of Carreau nanofluid. Furthermore, heat transport analysis has been provided by using the melting heat conditions and free convection.Carreau nanofluid for blood flow gives a set of partial differential equations (PDEs), which are handled by using the Keller Box scheme to obtain the numerical performances of the results. The obtained results are compared with Bvp4c, which is MATLAB built in program and found smooth agreement. The graphical plots and statistical tables have been provided for various dimensionless parameters to get the numerical simulations of the problem against velocity, temperature, and heat transfer Nusselt number. From the obtained results, the velocity and temperature of the fluid diminishes by the virtue of an amplification in unsteady parameter A. A positive variation in melting parameter M in the case of Prandtl number Pr and unsteady parameter A magnifies the heat transfer Nusselt number. This article shows the novelty in the sense that the effect of Tiwari and Das model accompanied with melting heat transfer phenomenon in the case of unsteady Carreau fluid has not investigated before in the available literature related to the Carreau fluid.

The ongoing expansion of the Industrial Internet of Things (IIoT) is enabling the possibility of effective Industry 4.0, where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols. This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face, providing ad-hoc functions for scheduling and guaranteeing the network operations. Recently, the large development of Software-Defined Networking (SDN) and Artificial Intelligence (AI) technologies have made feasible the design and control of scalable and secure IIoT networks. This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks. After surveying the state-of-the-art research efforts in the subject, the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network (AI-SDIN) that divides the traditional industrial networks into three functional layers. And with this aim in mind, key technologies (Blockchain-based Data Sharing, Intelligent Wireless Data Sensing, Edge Intelligence, Time-Sensitive Networks, Integrating SDN&TSN, Distributed AI) and improve applications based on AI-SDIN are also discussed. Further, the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

Groundwater salinization is a crucial socio-economic and environmental issue that is significant for a variety of reasons, including water quality and availability, agricultural productivity, health implications, socio-political stability and environmental sustainability. Salinization degrades the quality of water, rendering it unfit for human consumption and increasing the demand for costly desalination treatments. Consequently, there is a need to find simple, sustainable, green and cost-effective methods that can be used in understanding and minimizing groundwater salinization. Therefore, this work employed the implementation of cost-effective neurocomputing approaches for modeling groundwater salinization. Before starting the modeling approach, correlation and sensitivity analyses of the independent and dependent variables were conducted. Hence, three different modeling schema groups (G1–G3) were subsequently developed based on the sensitivity analysis results. The obtained quantitative results illustrate that the G2 input grouping depicts a substantial performance compared to G1 and G3. Overall, the evidential neural network (EVNN), as a novel neurocomputing technique, demonstrates the highest performance accuracy, and has the capability of boosting the performance as against the classical robust linear regression (RLR) up to 46% and 46.4% in the calibration and validation stages, respectively. Both EVNN-G1 and EVNN-G2 present excellent performance metrics (RMSE ≈ 0, MAPE = 0, PCC = 1, R2 = 1), indicating a perfect prediction accuracy, while EVNN-G3 demonstrates a slightly lower performance than EVNN-G1 and EVNN-G2, but is still highly accurate (RMSE = 10.5351, MAPE = 0.1129, PCC = 0.9999, R2 = 0.9999). Lastly, various state-of-the-art visualizations, including a contour plot embedded with a response plot, a bump plot and a Taylor diagram, were used in illustrating the performance results of the models.