Journal of Basic and Applied Research International

Microplastics in Agroecosystems: Effects on Soil Microbiome, Nutrient Dynamics, and Fungal Interactions

Wed, 06 May 2026 00:00:00 +0000

Microplastics (MPs) have emerged as pervasive contaminants in agricultural soils, posing significant threats to soil health, microbial functionality, and sustainable crop production. This review comprehensively evaluates the sources, characteristics, detection approaches, and functional impacts of MPs on soil ecosystems, with a particular focus on microbiome dynamics, nutrient cycling, and fungal interactions. Major input pathways, including plastic mulching, organic amendments, irrigation, and atmospheric deposition, contribute to MP accumulation, reaching thousands of particles per kilogram of soil. Experimental evidence demonstrates substantial reductions in key enzymatic activities and alterations in carbon use efficiency, indicating impaired microbial metabolism due to mechanistic disruption of soil processes. MPs significantly disrupt nitrogen cycling, causing 15–40% declines in nitrogen fixation, 10–25% inhibition of nitrification, and shifts in functional gene abundance (nifH, amoA). Furthermore, MPs modify soil physicochemical properties and reshape rhizosphere interactions, adversely affecting plant growth and microbial colonization. Fungal systems, particularly basidiomycetes, exhibit altered biomass, reduced mycorrhizal associations, and limited polymer degradation potential, highlighting both ecological risks and bioremediation prospects. Additionally, MP exposure compromises biofertilizer efficiency, reducing microbial viability and root colonization. At the ecosystem level, these impacts may impair soil fertility and crop productivity. The potential transfer of MPs into the food chain raises critical concerns for food safety and human health. Despite growing evidence, major gaps remain in long-term field validation, mechanistic understanding, and methodological standardization, emphasizing the urgent need for mitigation strategies to ensure sustainable agroecosystem functioning.

Study on Optimization of Ash Gourd and Beetroot-based Ready-To-Serve (RTS) Beverage

Monika, Ekta — Tue, 28 Apr 2026 00:00:00 +0000

The intake of nutrient-rich beverages is becoming increasingly important for contemporary dietary health. This study investigates the creation and physico-chemical optimization of a Ready-To-Serve (RTS) drink made by blending ash gourd (Benincasa hispida) and beetroot (Beta vulgaris). Although these vegetables have been traditionally used in Indian cuisine for their aesthetic qualities and therapeutic effects—including managing conditions such as epilepsy, diabetes, peptic ulcers, and cardiovascular issues—such health beverages have recently declined in popularity. In this study, different RTS formulations were developed by varying the ratios of ash gourd and beetroot juice to improve consumer acceptance. The physicochemical assessments—measuring moisture, pH, Total Soluble Solids (TSS), and reducing sugars—show that the blends provide notable functional benefits. Beetroot was included to boost the beverage's health-promoting properties due to its high levels of bioactive compounds like betalains and to possibly prolong shelf life by reducing pH. Sensory evaluation of five RTS samples was conducted using a 9-point hedonic scale to identify the superior formulation. A panel assessed the samples based on appearance, flavor, taste, mouthfeel, and overall acceptability within a controlled environment. The highest-rated sample was selected for further study based on these sensory attributes. Recent research highlights the potential of utilizing local vegetables in food processing to satisfy increasing consumer demand for affordable and healthy options. While the produced RTS drink shows promising health benefits and appealing visuals, future studies should aim to enhance taste by adding local spices such as ginger or cardamom and test the product’s stability and retention of active compounds over time during storage.

Corrosion Inhibition of Dialium guineense Leaf Extract on Mild Steel in 1M HCl Using Electrochemical and Theorietical Studies

Loveth N. Emembolu, Ikenna Nwokedi — Tue, 05 May 2026 00:00:00 +0000

Mild steel is highly susceptible to corrosion in acidic media, leading to significant material degradation in industrial applications. Plant-based extracts like Dialium guineense offer an eco-friendly and cost-effective alternative for corrosion inhibition. The current work employed electrochemical tests to assess how well Dialium guineense leaf extract (DGLE) prevented mild steel (MS) from corroding in a 1M HCl solution. Surface analysis was done using atomic force microscopy (AFM) and scanning electronic microscopy (SEM-EDX). The chemical makeup of DGLE extract was examined using FTIR. Density functional theory (DFT) and molecular dynamics (MD) simulation were used to computationally investigate the inhibitory efficiency of DGLE. Using the MD modeling approach, the adsorption energies of the DGLE components on the Fe (110) plan were determined, and a relationship between the adsorption energy and the energy gaps was found. The electrochemical measurements showed that corrosion inhibition rose with increasing inhibitor concentration, reaching a moderate inhibition efficiency of 95.08% at 500 mg/L. DGLE compounds behaved as mixed-type inhibitors, according to polarization curves and the adsorption process followed Temkin isotherm. The adsorption of the chemicals in the extract was verified by SEM-EDX and AFM analysis of the corroded steel surface. Chemical quantum calculations were used to identify the primary components of the electronic properties of the natural extract, which also explained adsorption modalities and interactions between inhibitors and metal surfaces.

Analysing Students’ Performance in Linear Programming: A Modelling-based Error Analysis of the Simplex Method

Wed, 13 May 2026 00:00:00 +0000

Aims: This study aims to analyse students’ performance in mathematical modelling and identify error patterns when solving a linear programming problem using the simplex method.

Study Design: A descriptive–exploratory study was conducted.

Place and Duration of Study: The study was conducted in a mathematics education program at a public university in Indonesia during a semester in which students studied linear programming.

Methodology: Twenty-four undergraduate students completed a contextual optimisation task requiring model construction and solution using the simplex method. Students’ responses were analysed using a mathematical modelling framework and Newman’s error analysis. Descriptive and cross-case analyses were conducted to identify performance trends and dominant error types.

Results: Students demonstrated strong performance in early modelling stages, including understanding the problem (M = 2.88), simplifying/structuring (M = 2.88), and mathematising (M = 2.75). However, performance declined substantially in the working mathematically stage (M = 1.29) and remained low in interpreting (M = 0.38) and validating (M = 0.33). The most frequent errors were process skill errors, particularly those related to elementary row operations during simplex iterations (P4), which appeared in the majority of student responses. Cross-pattern analysis revealed that low performance in algorithmic stages was consistently associated with procedural errors, while difficulties in interpreting and validating were linked to failure in recognising optimality conditions.

Conclusion: The findings indicate a gap between students’ modelling competence and procedural fluency in executing the simplex method. Strengthening algorithmic reasoning alongside modelling instruction is essential to support students’ complete engagement in optimisation tasks.

Species diversity of Geometer Moths (Lepidoptera: Geometroidea) from Gangau Wildlife Sanctuary, Madhya Pradesh, India

Sanjay Paunikar, Akhil Nair — Sat, 23 May 2026 00:00:00 +0000

Geometer moths (family Geometridae) are ecologically important insects that serve as herbivores, pollinators, and sensitive bioindicators of environmental change in forest ecosystems. However, the diversity and distribution of these moths remain poorly documented in many protected areas of Central India, including Gangau Wildlife Sanctuary, highlighting the need for baseline faunal surveys and conservation studies. The current study is the first to provide information on the Geometridae moth species of Gangau Wildlife Sanctuary of Madhya Pradesh. Thus, our goal was to studies on this family are crucial due to their high species diversity, sensitivity to climate change and widespread, often threatened habitat. The survey was conducted in order to explore the species diversity of Geometer moths during three surveys in 2022 to 2023. Moths were collected by light traps, light sheets, and insect nets in different forest ranges of the sanctuary. A total of 29 species, belonging to 21 genera across 3 subfamilies of Geometridae moths, were recorded. The subfamily Ennominae (13 and 19) had the highest number of genera and species, followed by Geometrinae (5 and 5), Sterrhinae (3 and 5), and had the lowest. As such we bring new knowledge about Geometer moth reported from the study area. This observation provides a confirmed record of the species diversity across the different forest ranges of Gangau Wildlife Sanctuary, contributing to regional faunal documentation of the wildlife sanctuaries and other conservation areas of Madhya Pradesh.

A Study on Explicit Particular Solutions for First and Second Order Generalized Leonardo-Type Recurrences with Polynomial-Exponential Forcing

Yüksel Soykan — Sat, 30 May 2026 00:00:00 +0000

Recurrence relations offer a versatile framework for analyzing numerical sequences, with applications across both classical and modern branches of mathematics. In earlier work, explicit iterative formulas were established for polynomial–exponential type particular solutions of generalized Leonardo-type sequences. The present article builds on that foundation by presenting concrete examples for orders

m = 1,2

where the input function takes the form

c(n) = p(n)dⁿ,

with p(n) = \(\sum_{i=0}^s c_i n^i\) a polynomial in n. For such sequences, we construct particular solutions of the form

\[W_n^{(C)}\ = nr \left(\sum_{i=0}^sA_i n^i\right) d^n,\]

and illustrate the computation of the coefficients A_iusing the established iterative scheme. These examples show how the multiplicity r of the root d in the characteristic equation shapes the structure of the particular solution, and they highlight resonance phenomena in non-homogeneous cases. By working through explicit instances, the paper provides a clear and
accessible demonstration of the general theory, strengthening the link between abstract recurrence relations and concrete symbolic computation. We present two representative examples that demonstrate how resonance and root multiplicities influence the construction of particular solutions in polynomial–exponential-driven recurrence relations. In the case of the generalized Fibonacci sequence, the input polynomial-exponential is non-resonant, allowing the particular solution to be obtained directly without the need for correction. By contrast, the generalized Mersenne sequence highlights the resonant situation, where the root 2 of the characteristic equation necessitates a multiplicity-aware adjustment in the solution process.

Design and Performance Evaluation of a Fixed-Bed Torrefaction Reactor and Machine Learning Prediction of Higher Heating Value of Coconut Shell Biomass

Tue, 02 Jun 2026 00:00:00 +0000

Rising global energy demand alongside environmental constraints has intensified interest in biomass as a sustainable alternative energy source, although its direct utilisation is limited by challenges in thermochemical conversion and fuel quality variability. Torrefaction has therefore emerged as a promising pre-treatment technique for enhancing the energy density and combustion characteristics of biomass, necessitating reliable and cost-effective methods for accurately estimating its heating value for industrial applications. The project aims to design a torrefaction reactor and evaluate the energy content of torrefied coconut shells using artificial neural network (ANNs), random forest and linear regression. The best method for predicting the HHV of coconut shell was determined. The designed reactor enables optimal torrefaction, facilitating the production of high-quality torrefied coconut shell. Machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest, and Linear Regression, are employed to predict the Higher Heating Value (HHV) of the torrefied coconut shell. The evaluation reveals strong correlations between the predicted HHV values and the actual HHV values extracted from literature sources. The ANN model had the highest level of accuracy followed by the linear regression model and then the random forest model. The ANN achieved a Mean Absolute Error (MAE) of 1.399 and Mean Squared Error (MSE) of 4.083 for proximate datasets and a Mean Absolute Error (MAE) of 1.046 and Mean Squared Error (MSE) of 2.565 for ultimate datasets. Torrefied biomass feature importance analysis highlights the significant influence of fixed carbon, ash, and volatile matter on HHV prediction. The findings contribute to understanding the torrefaction process, optimising reactor design, and advancing machine learning techniques for predicting torrefied coconut shell's energy content. The ANN model demonstrated the best predictive performance among the evaluated models, achieving the lowest MAE and MSE values for both proximate and ultimate datasets. The empirical correlations also showed strong agreement with literature HHV values, with ultimate analysis producing slightly better predictive accuracy than proximate analysis. The findings demonstrate the suitability of integrating torrefaction reactor design with machine learning techniques for biomass energy characterization and sustainable waste-to-energy applications.

Comparative Seismic Vulnerability Assessment of Residential Buildings in Chattogram Using Multiple RVS Methods with Post-seismic Vulnerability Parameters

Maruful Hasan Mazumder, Md. Ridwan Alam Adnan, Md. Naim Sarker — Sat, 06 Jun 2026 00:00:00 +0000

Bangladesh is located on several geological fault lines, and its second biggest city, Chattogram, has been expanding at a rapid rate, often without adequate planning and engineering supervision. Consequently, many residential structures are now in critical condition when faced with even moderate earthquakes. To evaluate the seismic vulnerability of typical residential buildings, an investigation of 100 residential buildings was conducted in Jalalabad Ward of Chattogram City Corporation. Three internationally recognised rapid visual screening procedures (FEMA 154, FEMA 310, and ASCE 41-23) were applied, and post‑seismic safety factors as per the country’s building code, such as road width, availability of rescue facilities, and distance from gas and electric lines, were also considered. Over one‑third of the buildings evaluated as not meeting the safety cut‑off level when using FEMA 154 required detailed engineering evaluation. Even worse was the situation with the more thorough checklists, with almost half of the buildings non‑compliant with FEMA 310 requirements and 60% non‑compliant with the more stringent ASCE 41-23 Life Safety structural checklist. A statistically significant moderate negative relationship was found between lower FEMA 154 scores and a higher count of non‑compliant parameters on the detailed checklists. Less than 3 meters of front road width was found in 59% of buildings, and almost three‑quarters of buildings lacked proper rescue facilities. Narrow roads were significantly associated with increased building vulnerability (r= 0.54, odds ratio= 3.2), making such buildings more than three times as likely to be highly vulnerable. Thus, it is concluded that a few parameters of building weakness which are important locally are not yet being identified by standard screening methods. The study suggests that post‑seismic vulnerability parameters should be incorporated into the rapid assessment protocol for adaptation to Bangladesh. Chattogram city authorities should focus on the most vulnerable areas, including Baizid Bostami, for detailed assessment.