The goal of this paper is to derive the Lie algebra of Killing vector fields for the locally rotationally symmetric Bianchi type-I spacetime within the framework of f(Q) gravity, where f(Q) gravity is a modified gravitational theory that extends General Relativity by introducing a function of the non-metricity tensor Q to explore alternative models of gravity. To achieve this, various algebraic methods and direct integration techniques are employed. Different metric functions are analyzed, and the associated Killing vectors are determined for each case. It is observed that the spacetime under investigation can support either4, 6, or 10 Killing vector fields.

Cassava garri is the most common food consumed in West Africa sub-region with more than 70% of the product harvested in Nigeria. This study was aimed to analyze the chemical and biological properties of processed garri marketing in Kebbi State for food safety perspective. Nutritional and anti-nutritional factors of the garri samples were analyzed using standards methods. Heavy metals were analyzed using atomic absorption spectroscopy. Bacteria and fungi were also isolated and identified. DNA was extracted from E. coli and A. flavus and was successfully used for PCR. The results revealed that the proximate shows moisture (12.6±0.94 %), crude fiber (5.56±0.08 %), Ash (2.96±0.12 %) and crude protein (2.09±0.16 %) had the highest percentage whereas crude fat (0.77±0.02 %) had the least percentage. The anti-nutritional factors shows that cyanides had the highest content of (111.8±2.28 mg/100g) compare to alkaloid (10.7±3.66 mg/100g) and phytates (2.11±0.34 mg/100g), whereas the oxalates had the least contents of (0.18±0.03 mg/100g). The heavy metals shows that cadmium (Cd) 0.73±0.09 ppm, lead (Pb) 0.61±0.35 ppm and asenic (As) 0.25±0.02 ppm had the highest concentration, whereas mercury (Hg) had the least concentration 0.19±0.02 ppm. A 338 cultured (100%) of bacteria were identified: the highest frequency was recorded from Escherichia coli 136(40.2 %), Micrococcus luteus 129(38.2 %) and Streptococcus pneumonia 39(11.5%), while the least was recorded from Staphylococcus aureus 34(10.1 %). A 26 cultured (100%) of fungus were identified: with Aspergillus flavus 10(38.5 %), Aspergillus niger 8(30.8 %), Aspergillus fumigatus 3(11.5 %) and Aspergillus clavatus 3(11.5 %), had the highest frequency of isolation, whereas Rhizopus sp 2(7.7 %), had the least frequency of isolation. The amplified products obtained with primers specific for both Escherichia coli and Aspergillus flavus were 900 bp and 700 bp. The study suggests that people should avoid regular intake of garri without a proper thermal treatment so as to minimize disease/intoxication before consumption.

In this study we have studied "Killing Motion of Static Cylindrically Symmetric Spacetimes in f(R) Gravity" by using algebraic and direct integration techniques. This study investigates the Killing motions of static cylindrically symmetric spacetimes with in framework of f(R) gravity, a generalization of Einstein’s General Relativity. We explore the existence of Killing vector fields to understand the symmetries and conserved quantities in such spacetimes. By analysing the modified field equations, we determine the constraints imposed by f(R) gravity on the geometry and dynamics of cylindrically symmetric spacetimes. These contribute to understanding the interplay between symmetry properties and gravitational theories beyond General Relativity. The results have implications for astrophysical and cosmological models influenced by alternative gravity theories. We discussed four cases and found that the dimension of Killing vector fields is either three, four or ten.

This research examines Artificial Intelligence (AI) and Machine Learning (ML) applications in financial risk assessment and investment strategies for mergers and acquisitions (M&A). A comprehensive analysis of over 20,000 global deals from 2013-2023 shows that integrating Ai to predict M&As could reduce transaction failure rates and increase valuation accuracy. After testing multiple algorithms, the XGBoost classifier performed best for merger success with a recall of 60% and precision of 62% on historical data from the Institute of Mergers, Acquisitions and Alliances. The main contribution of this work is a new integrated framework that combines AI-driven quantitative analysis with human expert judgment, addressing the fundamental limitations of both purely algorithmic and solely human-driven approaches to M&A evaluation. This outperforms existing benchmarks in the literature and is a big step forward in predictability. The results also suggest explainable AI architectures, human-machine collaboration protocols and multidisciplinary training for professionals to ensure accountability, transparency and operational efficiency. The research concludes that AI in M&A is not just about the technology but about how it’s integrated with human judgment, ethical governance and context specific communication frameworks