Current Journal of Applied Science and Technology

Incidental Finding of Dirofilaria immitis in a Dog Presenting with Urethral Calculi

C. Mathivathani — 2026-03-20

A 6-year-old male Dachshund dog presented to Veterinary clinical campus with a history of good general appearance and normal appetite but had not voided faeces or urine for the past two days. Clinical examination revealed that there were no significant abnormalities observed except for signs of urinary retention. Upon routine radiographic imaging, the presence of urethral calculi in the pelvic urethra was observed, suggesting a potential obstruction. Additionally, a blood smear examination for parasite screening and complete blood count was conducted. It revealed presence of microfilariae indicating a concurrent infection with heartworm disease with elevated white blood cell count and low mean corpuscular volume. The other blood parameters were found normal except for the drop in mean corpuscular volume. The obstruction due to calculi was relieved using catheterization on the same day and consecutive day to empty the bladder. The animal was not presented again so the treatment for dirofilariasis couldn’t be attempted.

Pharmacological Actions and Adverse Effects of Anabolic–androgenic Steroids: A Review of Use in Sports and Social Abuse

Mriganka Kalita — 2026-03-30

Background: Anabolic-androgenic steroids (AAS) are artificial analogy of testosterone engineered to increase anabolic functions, such as muscle building and protein synthesis, and to enhance androgenic effects that cause masculine features. Although created with therapeutic intentions, their abuse in sports, bodybuilding, and body improvement has risen to become a principal concern worldwide due to the extensive health, psychological, and social impacts. The review demonstrates the pharmacological activities, treatment effectiveness and widespread side effects of the clinical and non-clinical use of AAS.

Materials and Methods: Peer-reviewed journals, scientific reports, and reliable databases, such as PubMed, Science Direct, and Google Scholar, among others, were used to conduct a comprehensive review of literature. The data gathered was directed towards the pharmacodynamics, pharmacokinetics, therapeutics of AAS, adverse effects, and socio-legal concerns of AAS. Also, new trends that were introduced, such as designer steroids, selective androgen receptor modulators (SARMs), and new methods of detection, were considered.

Findings: Sheath AAS have their physiological actions largely mediated by the induction of androgen receptors, with effects of activating muscle hypertrophy, retention of nitrogen, and erythropoiesis, as a result of the influence of gene transcription. They medically have serious effects against hypogonadism, cachexia, and anemia, but when they are abused, they cause multi-system toxicities, such as cardiovascular dysfunction, hepatic injury, reproductive suppression, as well as neuropsychiatric disorders. Body image and social influence are major factors in the increased misuse of AAS, particularly within athletes and non-athletes. Other developments in this area in the recent past have included analytical methods that have enhanced the detection aspects, such as liquid chromatography-mass spectrometry (LC-MS/MS) and hair analysis, and SARMs have shown a potential and appeared as possible alternatives to regulation.

Conclusion: The two-sided properties of AAS, as therapeutic resources and as drugs of abuse, require the integration of inter-disciplinary approaches to preserve non-harmful effects to the bare minimum. It necessitates cooperation among healthcare workers, educators, policymakers, and sports professionals in terms of effective prevention, regulation, and rehabilitation. The prospects of the mentioned study should involve long-term safety research, sexual/age-specific effects, and the building of less risky anabolic options. There should be educational programs and policy change solutions to curb abuse and enforce medical use responsibility.

Food Labels Awareness: Insights from India and the Global Context

Payal — 2026-03-31

Introduction: Food labels serve as an important public health tool that helps consumers make informed food choices. However, despite the presence of detailed labeling systems, many consumers struggle to interpret nutritional information effectively. This issue is particularly relevant in countries experiencing a rapid increase in processed food consumption and non-communicable diseases (NCDs).

Aim: The present review aims to analyze consumer awareness of food labels and evaluate their impact on health-related purchasing behaviour by synthesizing evidence from Indian and international studies.

Methodology: A comprehensive review of 34 studies published in peer-reviewed journals was conducted. The studies were analyzed to examine levels of consumer awareness, label-reading behavior, functional understanding of nutrition information, and FoPNL.

Results: The analysis underscores a persistent functional literacy gap in the effective utilization of food labels despite widespread general awareness. Although nearly 95% of urban consumers recognize the presence of food labels, only about 15% demonstrate awareness of front-of-pack labeling systems, with the majority primarily attending to manufacturing and expiry dates. Empirical evidence indicates that consistent engagement with nutrition labels is associated with improved dietary quality and healthier nutrient intake patterns. Furthermore, simplified interpretive labeling systems, such as Nutri-Score and warning labels, have been shown to enhance consumer comprehension, with approximately 93% of Indian consumers expressing a preference for warning-based front-of-pack labels.

Conclusion: The review highlights the need for simplified, interpretive labeling systems and improved nutrition literacy. Implementing mandatory front-of-pack labeling and strengthening consumer education can help bridge the gap between label awareness and healthier dietary choices.

Comparative Analysis of Machine Learning Models for Predicting Refractivity Gradients in Signal Propagation

K. C. Onawumi — 2026-03-23

Refractivity gradients play a critical role in atmospheric signal propagation, influencing communication systems and weather forecasting. Accurate prediction of these gradients remains challenging due to the complex interactions of atmospheric variables. This study presents a comparative analysis of machine learning models, including LightGBM, Random Forest, LSTM, and GRU, to predict refractivity gradients at 1000, 975, and 950 hPa levels. Using a meteorological dataset that was generated from the ERA5 reanalysis from (2002 - 2023) in ten different locations in West Africa, produced by the European Centre for Medium‑Range Weather Forecasts (ECMWF) through the Copernicus Climate Change Service (C3S). Models were trained on features including surface net solar radiation (SSR), potential evaporation (PEV), total precipitation (TP), and wind speed. Random Forest with all features outperformed others, achieving an of 0.9326, MSE of 45.39, and MAE of 4.02, followed by LightGBM (R² = 0.9138). Conversely, LSTM and GRU yielded negative R²values (e.g., -15.925 for LSTM), indicating poor generalization.

Feature importance analysis revealed PEV and month as the most critical predictors, underscoring their role in driving refractivity gradients. Predicted gradients exhibited strong correlations (0.94–0.95) with observed values, validated by scatter plots and error distributions. These results underscore the superiority of tree-based models over deep learning approaches for refractivity prediction, offering potential enhancements for signal propagation models in telecommunications and radar applications. Future work could optimize deep learning methods to improve their competitiveness.

Development and Preliminary Characterization of a Composite Material Produced from Recycled Plastic and Sand for Sustainable Construction Applications

Xavier Bazemo — 2026-03-24

The sustainable management of plastic waste is a critical environmental issue, particularly in developing countries where collection and recycling rates remain low. In this context, this study focuses on the development of an innovative composite material, made from recycled plastic and sand, to provide an alternative for the recovery of plastic waste in the construction materials sector. The composite material was manufactured by melting the recycled plastic at a temperature of approximately 255 °C, then gradually incorporating sand that had been previously sieved and analyzed for particle size distribution in accordance with standard NF EN 933-1. Three distinct formulations were produced by varying the proportions of recycled plastic relative to the sand. We used proportions of 15%, 20%, and 25% by mass of recycled plastic relative to the sand. The results of the particle size analyses revealed that the sand used falls within the 0/5 particle size class, with a uniform particle size distribution, conducive to obtaining a homogeneous material. Examination of the produced test specimens indicates a gradual improvement in compactness, surface appearance, and overall material quality as the percentage of recycled plastic increases. The composite containing 25% plastic exhibits a particularly dense structure, a smooth surface, and a significant reduction in intergranular voids, demonstrating improved cohesion between the matrix and the aggregates. The density decreases as the percentage of plastic relative to sand increases. Thus, we obtain densities of 1903.72 kg/m³ for the material containing 15% plastic, 1795.53 kg/m³ for the material containing 20% plastic, and 1693.58 kg/m³ for the material containing 25% plastic. The results obtained in this study highlight the potential of recycled plastic as an alternative binder to cement for producing innovative construction materials while helping to reduce the environmental footprint of plastic waste.

The Reliability Gap in Modern Lakehouse Architectures: A Failure Fingerprinting Framework for Operational Intelligence

Mogana Kumaran Sivaraman — 2026-03-26

Modern Lakehouse platforms provide extensive operational observability, including queryable telemetry, execution histories, and runtime diagnostics. Yet data engineering teams repeatedly diagnose and resolve the same failure patterns with no platform-level memory of prior resolutions. This paper formalizes this disconnect as the Observability–Reliability Gap: a structural property of platforms that can reconstruct any individual failure in detail but cannot recognize a current failure as an instance of a previously resolved class. I introduce Failure Fingerprinting, a technique that encodes each failure episode as a normalized feature vector derived from operational signals at failure time, maps it to a stable SHA-256-based identifier, and stores it in a queryable registry. I propose a four-layer reference architecture spanning signal collection, feature engineering, fingerprint generation, and a failure intelligence layer supporting historical matching, fleet-wide pattern detection, and pre-execution predictive warnings. I describe a five-phase incremental adoption methodology delivering standalone operational value at each phase. I further consider the framework in the context of AI-native workloads—embedding pipelines, vector index builds, and large language model inference batches—where static threshold-based alerting is inadequate. This paper presents a prospective framework contribution; all quantitative projections are grounded in published benchmarks from analogous systems, and a complete empirical evaluation design is specified for future validation on production Lakehouse failure data.

Finite Element Modeling of the Mechanical Behavior of Diagonally Stressed Masonry Reinforced with Welded Mesh

A. Parraguirre Gancedo — 2026-03-27

The aim of this study was to evaluate the mechanical behavior of reinforced masonry constructed with concrete blocks from two regions of Mexico (Veracruz and Querétaro) and to develop a calibrated numerical model capable of reproducing its structural response under shear loading. Experimental testing included compressive strength of individual units and prisms, as well as diagonal tension tests on masonry walls with and without steel reinforcement. Experimental data were used to develop a computational model based on the Finite Element Method (FEM), complemented with the damage–plasticity microplane theory. Model calibration incorporated critical parameters such as elastic modulus, uniaxial, biaxial and triaxial compressive strengths, and Poisson’s ratio. Comparison between experimental and numerical results demonstrated satisfactory agreement, validating the predictive capacity of the proposed model. Findings highlight that the inclusion of steel reinforcement in masonry walls significantly improves shear resistance and ductility, thereby enhancing structural resilience. These outcomes contribute valuable insights for strengthening local construction standards and provide practical guidance for building and rehabilitation projects in seismic-prone areas. Furthermore, the study emphasizes the importance of integrating experimental testing with advanced modeling tools to achieve more reliable structural assessments. Overall, the results demonstrate that properly characterized and modeled reinforced masonry constitutes a viable alternative for the development of safe and sustainable housing solutions. The knowledge generated serves as a reference framework for engineers and designers to support decision-making processes guided by safety, cost-efficiency, and durability criteria.

Impact of Emergency Preparedness on Emergency Response of Healthcare Workers in a Public Hospital in Rivers State, Nigeria

Deborah I. Omelogu — 2026-03-31

This research was conducted to examine the impact of emergency preparedness on the emergency response of a public hospital in Rivers State. Emergency preparedness was defined by factors such as emergency training, planning, logistics, the triage system, and surge capacity, while emergency response was assessed through promptness and responsiveness. The study employed cross-sectional and inferential research designs. The inferential statistics is found useful in comparative analysis using Analysis of Variance (ANOVA) for indication of level of significance between the contribution of the various constructs and impact on emergency response. Purposive sampling was used to select government-owned hospitals in the State, specifically the University of Port-Harcourt Teaching Hospital, Rivers State. Data collection involved questionnaires and checklists. Analyses included descriptive statistics, Pearson’s correlation, and Multiple linear regression. Descriptive results showed a moderate level of emergency preparedness in the sampled hospital across emergency training, planning, logistics availability, triage system, and surge capacity, with mean scores of 3.29, 3.85, 3.30, 3.20, and 3.44, respectively. Conversely, emergency response was high among healthcare workers regarding promptness and responsiveness, with mean scores of 3.91 and 4.10. Regression analysis revealed that emergency planning (β = 0.866, p < 0.0001) and the emergency triage system (β = 0.625, p = 0.015) significantly and positively influenced promptness. However, emergency training (β = -0.461, p < 0.0001) had a significant negative effect on promptness, while logistics (β = 0.217, p = 0.346) and surge capacity (β = -0.146, p = 0.447) did not significantly affect promptness. Additionally, emergency training (β = 0.373, p < 0.0001), planning (β = 0.403, p = 0.001), logistics (β = -0.835, p < 0.0001), triage system (β = 0.413, p = 0.043), and surge capacity (β = 0.315, p = 0.042) significantly impacted responsiveness. In conclusion, the hospital demonstrated a good level of emergency preparedness and response, although logistics and surge capacity did not significantly influence promptness. It is recommended that hospital management focus on enhancing surge capacity and logistics, as these are the only emergency preparedness factors that did not significantly impact promptness.

Integrated Assessment of Algal Bioaccumulation, Microbial Indicators, and Metal Pollution in a Human-impacted Kuwano River System, Basti (U.P.), India

Anuradha Tripathi — 2026-03-31

Aims: This study assessed chemical and microbial contamination in the Kuwano River by (i) quantifying concentrations and spatial distribution of selected heavy metals (Cd, Pb, Zn, Cu, Cr, and trace metals), (ii) evaluating microbial pollution via total coliforms, faecal coliforms, and Escherichia coli, and (iii) determining the Bioaccumulation Factor (BAF).

Study Design and Location: An observational field–laboratory investigation was conducted across five stretches (Shivaghat, Atara, Amhat, Mahson, and Lalganj) of the Kuwano River, Basti, Uttar Pradesh, India, during 2025.

Methodology: Surface water samples were collected in pre-cleaned HDPE and sterile glass bottles. Heavy metals in water and algal biomass were quantified using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS). Microbial contamination was assessed via membrane filtration to determine CFU/100 mL of total coliforms, faecal coliforms, and E. coli. BAF was calculated to evaluate metal accumulation in algae. Pearson correlation and Canonical Correspondence Analysis were applied to examine relationships between variables.

Results: Heavy metal concentrations were higher in algal biomass than in water, indicating significant bioaccumulation. Potassium dominated in water, while Cr, Mn, Fe, and Ni showed spatial variability. High BAF values for Ni, Mn, and Cr confirmed strong accumulation capacity of algae. Cd, Pb, and Cr at some sites approached or exceeded guideline limits. The correlation matrix shows two clusters: microbial indicators and heavy metals. Strong positive correlations among E. coli, faecal coliforms, and total coliforms suggest a common faecal contamination source. Microbial analysis showed elevated total coliforms (585–702 CFU/100 mL), faecal coliforms (292–308 CFU/100 mL), and E. coli (293–398 CFU/100 mL), indicating substantial faecal contamination. Multivariate analysis linked metal distribution to geochemical and anthropogenic factors, and microbial pollution primarily to sewage discharge.

Conclusion: The Kuwano River exhibits combined heavy metal and microbial contamination, posing ecological and public health risks. Algae serve as effective biomonitors, while persistent faecal indicators highlight ongoing sewage pollution, necessitating integrated monitoring and improved wastewater management.