Volume 21 Issue 1 2026

Food borne illnesses may result from the consumption of food contaminated by microbial pathogens, toxic chemicals or radioactive materials. So far most studies have considered pathogenic and chemical aspect of food contamination. The study assesses the amount of radiation exposed to road side cooked meats within Makurdi metropolis. Different samples of the meat where collected in strategic locations and the Radiation Alert Inspector EXP. was used to detect the level of exposure in the meat samples. Results showed very low amount of radiation in the samples ranging between 1.9 × 10⁻⁷ ± 0.002 Gy h⁻¹ – 2.7 × 10⁻⁷ ± 0.003 Gy h⁻¹.

The backbone of the modern world is electronic communication. Data is transferred from one place to another in almost no time using the electronic medium. But it also exposes the confidential data to the intruder. RSA is the most common and efficient cryptography technique that is used for the purpose of encrypting the content and then sending it over the channel, then than at receivers end the content is decrypted and converted in to original form. Although there are many security mechanisms are available. But there is a continuous need to improve the existing methods. Cryptography is a security mechanism which caters the security services of world in perfect manner.

Image Inpainting is an art of modifying the digital image in such a way that the modifications are undetectable to an observer who has no idea about the original image. The essential thought behind the system is to consequently fill in lost or missing parts of a picture utilizing data from the encompassing region. It is utilized for rebuilding of old movies and protest expulsion in computerized photos. Different calculations have introduced in the past to accomplish the undertaking of picture inpainting. In this paper we give a survey of various systems utilized for picture Inpainting. We talk about various inpainting systems like Wavelet Transform inpainting, Exemplar based picture inpainting, PDE based picture inpainting, surface combination based picture inpainting.

Significant regard for MapReduce framework has been trapped by a wide range of areas. It is presently a practical model for data-focused applications because of its basic interface of programming, high elasticity, and capacity to withstand the subjection to defects. Additionally, it is fit for preparing a high extent of data in Distributed Computing environments (DCE). MapReduce, on various events, has turned out to be material to a wide scope of areas. MapReduce is a parallel programming model and a related usage presented by Google. In the programming model, a client determines the calculation by two capacities, Map and Reduce. The basic MapReduce library consequently parallelizes the calculation and handles muddled issues like data dispersion, load adjusting, and adaptation to non-critical failure. Huge data spread crosswise over numerous machines, need to parallelize. Moves the data, and gives booking, adaptation to non-critical failure. A writing survey on the MapReduce programming in different areas has completed in this paper. An examination course has been distinguished by utilizing a writing audit.

Predictions from species distribution models (SDMs) often fail when transferred to new geographic regions or time periods, limiting their utility for biodiversity forecasting under environmental change. This lack of transferability stems from functional responses in habitat selection, where animals respond to the same habitat differently depending on the availability of alternative habitats. The Generalized Functional Response (GFR) framework explicitly models selection coefficients as functions of habitat availability, but existing polynomial implementations face a fundamental tradeoff: low-order polynomials are too rigid to capture complex responses, while high-order polynomials overfit and transfer poorly. We developed flexible extensions of the GFR framework that replace global polynomial functions with local radial basis functions (RBF-GFR) and combined both approaches with modern machine learning methods: classification and regression trees (CART), random forests (RF) and extreme gradient boosting (XGBoost). We systematically compared the out-of-sample predictive performance of 12 modeling approaches using block cross-validation across four contrasting datasets—two individual-based simulations with known ecological mechanisms, wolf telemetry data and sparrow colony surveys. Ensemble methods combining functional response frameworks with RF or XGBoost consistently ranked in the top three performers across all datasets. Out-of-sample R² scores improved substantially over traditional GLMs, with increases from 0.25 to 0.85 in individual cases and typical gains of 0.20–0.50. The RBF-GFR-RF model showed the most consistent transferability. Critically, ensemble averaging provided similar protection against overfitting as explicit regularization while achieving superior out-of-sample accuracy. Spatial predictions revealed that standard GLMs systematically under-predicted abundance hotspots, while unregularized flexible GFR models exaggerated extremes. Combining functional response theory with ensemble learning, particularly random forests, offers a practical path toward robust, transferable SDMs. Our comparative framework demonstrates that local basis functions paired with ensemble methods consistently outperform traditional approaches across diverse ecological systems and data types. The methods are computationally feasible for realworld applications and provide substantial improvements in predicting species distributions under novel environmental conditions, addressing a critical limitation in current SDM practice.

This study aimed to improve the quality of Maralfalfa silage to address the seasonal forage deficit in Niger. The effect of storage duration (30, 45, 60 days), the addition of 4% salt, and enrichment with locally available by-products (wheat bran, rice bran, cottonseed meal) on microbiological and organoleptic parameters was evaluated. The results indicate that 60-day storage improves stability by significantly reducing the butyric acid bacteria population. The addition of salt proved crucial, significantly lowering the pH (from 4.85 to 4.08) and eliminating undesirable odors and a slimy texture. The best performance was achieved through the synergy between salt and an energy by-product. The Maralfalfa+wheat bran+salt and Maralfalfa+rice bran+salt treatments consistently produced silage with a low pH, increased stability, 100% good odors, and a firm texture. For a simple and economical approach, adding 4% salt (Maralfalfa+salt treatment) is recommended. For optimal quality and enhanced nutritional value, treatments combining salt and bran (Maralfalfa+wheat bran+salt or Maralfala+rice bran+salt) are preferable. These accessible techniques allow for the sustainable use of Maralfalfa and the creation of high-quality forage reserves for the dry season.

This study examines how child malnutrition shapes lower secondary completion rates across 35 Sub-Saharan African countries between 1990 and 2024, paying particular attention to the nonlinear of this relationship. Anchored in human capital theory, the educational production function, and insights from cognitive neuroscience, the analysis highlights the multiple channels through which nutritional deprivation undermines learning, from impaired cognitive development to reduced school participation and weakened educational systems. Applying Hansen’s (1999) Panel Threshold Regression, the study uncovers a critical malnutrition threshold (𝛾̂= 4.37%), beyond which the negative effect on educational attainment intensifies almost sixfold. Empirically, lower secondary completion rates (LSCR) drop from an average of 50.7% in the moderate-malnutrition regime to just 31.8% once the threshold is crossed (R² = 0.67). Robustness checks across specifications and subperiods confirm the stability of this nonlinearity. The findings demonstrate that even moderate malnutrition severely undermines learning performance, further amplifying existing educational disparities. Policy simulations further indicate that reducing malnutrition specifically above the identified threshold produces educational gains nearly six times greater than uniform national interventions. Taken together, these findings underscore the urgent need for coordinated action across health and education sectors. They also highlight the importance of geographically targeted nutrition strategies to accelerate progress toward SDG 2 (Zero Hunger) and SDG 4 (Quality Education).

Artificial Intelligence (AI) is fundamentally transforming the cognitive, ethical, and strategic dimensions of managerial work. Managers are no longer mere decision-makers but have evolved into interpreters, integrators, and collaborators within hybrid human–machine systems. The advent of AI necessitates a new managerial mindset—one characterized by cognitive adaptability, ethical foresight, attention orchestration, and socio-technical fluency. Drawing upon attention-based theories, inductive reasoning models, and critical perspectives on accounting and control, this paper explores how AI redefines leadership by merging analytical precision with emotional intelligence. The discussion integrates evidence from multiple empirical studies, demonstrating that effective leadership in the AI age depends not on replacing human cognition, but augmenting it through intelligent collaboration. Ultimately, the study offers a holistic framework for managerial evolution—reconceptualizing managers as ethical architects, strategic sensemakers, and adaptive learners in an era of algorithmic decision-making.

Methanotrophs or methane-oxidizing bacteria, play a pivotal role in mitigating methane emissions, a potent greenhouse gas by converting methane into biomass and carbon dioxide. This review consolidates recent advancements in methanotroph research, highlighting their environmental relevance, physiological adaptations, and potential biotechnological applications. Prominence is placed on their ecological spreading, metabolic pathways, response to environmental factors such as pH, and novel and innovative applications in biotechnology.

The significance of animals for human society, ecosystems, and biodiversity makes understanding their behaviour and emotional states incredibly necessary in scientific and societal contexts. Nonetheless, the classic assessment of animal behaviour primarily rests on long-term, manual observation and interpretation, which is often slow, subject to biases, hard to scale across any environment. On the other hand, the ongoing cameans of vision, sensing technologies, and artificial intelligence directions an even more objective and automated way of analyzing animal behaviour. This study deals with the recognition of behaviour-based and emotional states in domestic animals utilizing deep learning methodology with a general perspective on dogs and cats using the data set of images and videos. The proposed system implements influential features from Convolutional Neural Network (CNN) and transfer learning based on VGG16 and VGG19 Structures, which automatically extract some key discriminating visual features of facial expressions, postures, and moving activities. To cater to various problems, such as variations of illuminance, general background clutter, and occlusion issues, pre processing plays a crucial functional role in this system. Public datasets as well as instances from custom-data collection imagery taken in unconstrained conditions were employed for the study. Evaluation was carried out on several performance matrices, accuracy, precision, recall, and F1 score in particular. The experimental results were highly impressive in demonstrating that the presented models have been reciprocated an impressive classification accuracy rate of 92.6% with a custom convolutional neural network (CNN), 95.96% with VGG16, and 95.21% with VGG19 surpassing traditional machine-learning methods. This research, in a wider context, highlights its potential applications in animal welfare monitoring, veterinary diagnostics, and intelligent behavioural assessment.