Federal University Lafia

The purpose of the article. The study analyses two prominent fiscal adjustment measures – Ways and Means financing, and austerity measures – to determine the most appropriate strategy for managing fiscalimbalances and economic crisis in Nigeria. Methodology. Documentary research design was used as the methodology. Data was derived from reports and publications of government agencies. Data analysis was done using mixed methods content analysis. Results of the research. Findings of the study highlighted that Ways and Means financing is justified since it provides the government with short-term funding to cover budgetary deficits. Nevertheless, if Ways and Means is applied without caution, it is likely to induce unchecked spending and money supply surges, resulting in inflation and currency devaluation. The study maintained that fiscal austerity, which advocates a policy of significant decrease in government deficits and stabilization of government debt through either tax hikes, spending cutbacks, or both, is a more effective measure in managing fiscal imbalances in Nigeria.

Heat stress reduces broiler performance by disrupting thermoregulation, redox balance, and nutrient handling. This study evaluated graded dietary L‑glutamic acid (0, 0.2, 0.4%) on thermoregulatory indices, serum electrolytes and pH, antioxidant status, and cytokine responses in broilers reared under tropical conditions. A total of 135 one‑day‑old Arbor Acre chicks were allocated to three treatments (3 replicates × 15 birds) in a Completely Randomized Design and fed experimental diets for 49 days. At day 49, birds receiving 0.4% L‑glutamic acid had significantly (P<0.05) lower body temperature and respiratory rate, a higher serum pH (P = 0.0323), and increased plasma potassium (P = 0.0453) compared with control. Malondialdehyde (MDA) increased numerically while glutathione (GSH) and cytokine indices showed non‑significant but favourable trends. Sodium, chloride, and calcium did not differ among treatments. The results indicate that 0.4% L‑glutamic acid improves thermotolerance and alters acid-base and potassium balance under heat challenge.

Distributed Denial of Service (DDoS) attacks remain a serious issue for network security, especially within cloud systems that rely on shared and scalable resources. In such settings, attackers can easily use large groups of compromised devices to flood targets with traffic, making genuine access almost impossible. Traditional intrusion detection systems (IDS) that rely on fixed signatures or manually defined rules often fail to keep up, since new and fast-changing attacks rarely fit known patterns. To improve detection, this research applied a machine learning–based approach that combines Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) models, supported by the Fisher Score Algorithm (FSA) for feature selection. The well-known CIC-DDoS2019 dataset from Kaggle was used because it includes both modern DDoS attacks and regular network traffic, giving a practical base for testing the models. Our workflow followed three stages: first, cleaning and preparing the data; second, selecting key features using FSA; and finally, training and evaluating the classifiers. We measured performance with accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC). XGBoost achieved the highest results of 97 % accuracy and an AUC of 0.99, while SVM‟s accuracy fell to 65 % after feature reduction, despite a small AUC gain to 0.65. The results of this study demonstrate that combining machine learning models of XGBoost with FSA significantly improves the detection of DDoS attacks in cloud environments. This highlights its potential for superior performance over traditional IDS methods.

BackgroundHIV-HBV coinfection accelerates liver disease, yet the immunological mechanisms underlying adverse outcomes remain incompletely characterized in African populations. We investigated relationships between HBV reactivation, cytokine dysregulation, T-cell dysfunction, and disease progression in a Nigerian cohort.MethodsWe screened 1,139 participants across four Nigerian states. Of 344 HIV-positive individuals, 53 (15.4%) had HBV coinfection. For detailed immunological and longitudinal analyses, 59 coinfected participants with complete datasets were included in the mechanistic cohort. Comprehensive assessments including HBV DNA quantification, S-gene sequencing, cytokine profiling, and HBV-specific T-cell responses were performed on 59 coinfected patients with longitudinal follow-up.ResultsPhylogenetic analysis indicated 71.4% (30/42) of cases with rising HBV DNA were consistent with reactivation. HBV genotype E predominated (94.3%). Coinfected patients demonstrated elevated IL-6 and TNF-α with reduced IFN-γ compared with HIV-monoinfected controls (all p < 0.001). The IL-6/IFN-γ ratio correlated with HBV viral load (r = 0.74), APRI score (r = 0.71), and CD4+ count (r = −0.64; all p < 0.001). HBV-specific polyfunctional CD8+ T-cells were markedly reduced (median 0.08% vs 3.8% in controls; p < 0.001). In multivariable Cox regression, IL-6/IFN-γ ratio >4.0 (HR 4.12, 95% CI 1.86–9.14), CD4+ <200 cells/µL (HR 3.24, 95% CI 1.58–6.64), and APRI >1.0 (HR 2.86, 95% CI 1.34–6.11) independently predicted progression, whilst preserved T-cell polyfunctionality was protective (HR 0.32, 95% CI 0.15–0.68).ConclusionsHIV-HBV coinfection was characterized by HBV reactivation, cytokine imbalance, and T-cell exhaustion, which were associated with disease progression and may inform risk stratification.

Abstract COVID-19 and the associated epidemics have rapidly spread globally. Due to the contagiousness of the disease, the effort to stop the spread proves abortive. As a result, physical appointments with healthcare practitioners become a challenge. To address this challenge, Telemedicine (TM) is widely employed to provide healthcare services to infected individuals. Thus, this study aimed to provide a comprehensive literature review of TM applications based on machine learning (ML) for managing individuals infected with COVID-19 and related epidemics. The SLR methodology of this research considered published papers between 2015 and 2022. Out of 733 papers that were initially retrieved from six bibliographic databases, 24 primary studies were selected after passing the election criteria and quality assessment test. Analysis was carried out on the selected papers by answering six research questions related to the research topic. However, the findings from the SLR reveals that remote monitoring of patients during the pandemic outbreak is made easy through the application of TM and ML technique. The study also reveals different types of pandemic outbreaks, health data used by telemedicine programs, different types of ML, performance measures, and functionality of TM. Open research challenges and future research directions are also provided in this research domain. Therefore, this SLR can help researchers in future research and medical practitioners with ML-based TM applications in subsequence epidemic outbreaks.

Topological descriptors are key tools for analyzing the intrinsic properties of chemical graphs and nanostructures, capturing molecular symmetry through graph invariants such as vertex degree, distance, and spectrum. This study investigates vertex-distance-based indices, namely: the Wiener and Hosoya indices, for selected anticancer drugs. Using linear regression models, the indices were correlated with the drugs’ physicochemical and biological properties through a Quantitative Structure–Property Relationship (QSPR) analysis, providing insight into their linear relationships and potential applications in drug design.

This paper demonstrates the unpredictable nature of wireless propagation of Logistic Regression and a dynamic N4SID control, where a single system is built in a form that analyses physiological signals measured from users wearing a wireless Electrodermal activity device interacting with a user-assigned task. The purpose was to synchronise the events wirelessly through the proposed model’s integration. The results show that by employing integration-based control mechanisms, Cognitive Wireless Networks (CWNs) demonstrate superior stability and convergence speed compared to purely reactive systems. This is crucial for applications demanding ultra-low latency and high reliability, such as autonomous vehicles.

This paper argues that the perceived divide between anthropocentric and ecocentric perspectives on human-nature relations can be bridged by Chris Okezie Ijiomah’s philosophical framework of Harmonious Monism. Anthropocentrism, which prioritises human interests, and ecocentrism, which assigns intrinsic value to nature, have long been treated as opposing worldviews. Moving beyond this dichotomy, Harmonious Monism offers an integrative framework that recognises the complementarity of humans and nature. Through its principles of balance, tolerance and interconnectedness, I propose a reconciling perspective in which ecological value and human needs are seen not as mutually exclusive, but as interdependent. This harmonising worldview thus has the potential to transcend the anthropocentric/ecocentric divide and promote a more holistic and ethically grounded human-nature relationship. Rather than treating human and ecological interests as opposites, I employ harmonious monism to present them as interdependent realities within the environment. The emphasis on balance, tolerance and interconnectedness reflects that all beings - human and non-human - coexist and contribute to a greater whole. This avoids privileging one side over the other; instead, it calls for ethical practices that support both human flourishing and ecological integrity as mutually reinforcing goals.