Kano State University of Technology

Recently, the idea of reconfigurable antennas has made it feasible to design a single antenna that can support multiple wireless standards while maintaining the same performance as multiple antennas. The integration of a single antenna that can operate at multiple frequencies is required to enable multiple applications in a single device. This paper presents a compact frequency-reconfigurable microstrip antenna designed to support multiple wireless standards in modern communication devices. The proposed antenna, with dimensions of 30 × 15 × 1.52 mm³, is simulated using CST Studio Suite and achieves reconfigurability through the integration of PIN diodes as switches within strategically placed slots on the radiating structure. By controlling the bias states of the two PIN diodes (Sw1 and Sw2), the antenna can dynamically switch between four distinct operating modes: All switches ON: dual-band operation at 2.4 GHz (lower WLAN) and 5.6 GHz (higher WLAN), All switches OFF: single-band resonance at 4.2 GHz (radio altimeter applications), Sw1 ON and Sw2 OFF: single-band operation at 2.9 GHz (military and meteorological radars), Sw1 OFF and Sw2 ON: dual-band coverage at 3.5 GHz (5G sub-6 GHz) and 5.6 GHz (higher WLAN/5G). The design maintains consistent performance across these configurations while offering a low-profile, single-antenna solution that eliminates the need for multiple dedicated radiators. This makes it highly suitable for integration into space-constrained devices such as smartphones, laptops, tablets, IoT systems, and next-generation wireless networks. The proposed antenna demonstrates versatile multiband/frequency agility compatible with WLAN, 5G, radio altimeters, military radars, and weather radar applications, highlighting its potential to address the growing demand for efficient, multifunctional antennas in emerging wireless ecosystems.

This research employed a Markov model to assess the progression of the academic staff workforce in Nigerian colleges of education, using Sa'adatu Rimi College of Education, Kano as a case study. The objective was to underscore the importance of effective workforce planning in mitigating staff shortages or surpluses. By analysing transition probabilities derived from college planning department data, the study constructed a transition probability matrix depicting staff flow over time. The Markov model's findings projected an estimated 568 academic staff members for the college in the 2024/2025 session, distributed across academic ranks. Notably, the forecasted staff structure skewed towards higher ranks. Furthermore, the study offered insights into new staff members' anticipated years of service at different academic ranks. The research underscores the pivotal role of workforce planning in educational institutions, highlighting the Markov model's value in guiding staffing and workforce management decisions.

<title>Abstract</title> Network pharmacology is an emerging, cost-effective drug-development approach that uses systems biology and network theory to integrate data and reveal interactions between compounds and their biological targets. Diabetes mellitus is a widespread metabolic disease affecting millions worldwide, while traditional medicine represents knowledge of healing practices inherited across generations and indigenous communities. Allium cepa contains various nutraceutical compounds that give it notable antioxidant, antitumor, antidiabetic, and anti-inflammatory properties. Bioactive compounds of Allium cepa were identified using a phytochemical interactive database. Both the diabetic and bioactive compound target proteins were determined and screened for oral drug bioavailability potential with favorable pharmacokinetic properties. The target proteins underwent protein-protein interaction network analysis, and analyzed using molecular docking analysis. The interaction of kaempferol with PPARG, PPARA and GSK3B possesses − 8.5 kcal/mol, -8.1 kcal/mol, and − 8.0 kcal/mol, respectively. The results obtained showed that kaempferol identified with strong binding affinity with the selected diabetic target proteins and confirmed as the best bioactive compound with an overall interaction profile, antidiabetic property, and good oral drug likeness.

The global expansion of 5G and the approaching 6G era are pushing conventional single-mode fibers toward their fundamental capacity limits, necessitating a paradigm shift in optical network infrastructure. This study introduces a novel, AI-optimized tri-concentric-core fiber with an optimized grading profile (TCC-OGP) to overcome this capacity crunch through spatial-division multiplexing (SDM). The fiber design was realized through an integrated artificial intelligence framework, combining a neural network surrogate model with particle swarm optimization to efficiently navigate a complex multi-objective design space. The resultant TCC-OGP fiber supports six spatial-division-multiplexed LP modes, achieving a breakthrough in the traditional capacity–nonlinearity trade-off. A comprehensive numerical analysis demonstrates that the proposed structure achieves 92% of the theoretical Shannon capacity while simultaneously suppressing nonlinear impairments by 65% compared to the standard single-core fiber. Furthermore, the fiber exhibits low differential mode delay, a flattened dispersion of approximately 16 ps/(nm·km) at 1550 nm, strong bend tolerance (&lt;0.01 dB/m at a 30 mm radius), and excellent inter-modal crosstalk below −25 dB over 20 km. These performance metrics confirm the TCC-OGP fiber’s suitability for terabit-scale transmission in metro networks, dense 5G back-haul, and future 6G infrastructures, establishing a scalable and intelligent platform for next-generation optical networks.

Transformer models have significantly advanced abstractive summarization, achieving near-human performance. However, while effective for short texts, long-text summarization remains a challenge. This systematic review analyzes 56 studies on transformer-based long-text abstractive summarization published between 2017 and 2024, following predefined inclusion criteria. Findings indicate that 69.64% of studies adopt a hybrid approach while 30.36% focus on improving transformer attention mechanisms. News articles and scientific papers are the most studied domains, with widely used datasets including CNN/Daily Mail, PubMed, arXiv, GovReport, QMSum, and XSum. ROUGE is the dominant evaluation metric (61%), followed by BERTScore (20%), with others such as BARTScore, human evaluation, METEOR, and BLEU-4 also used. Despite progress, challenges persist, including contextual information loss, high computational costs, implementation complexity, lack of standardized evaluation metrics, and limited model generalization. These findings highlight the need for more robust hybrid approaches, efficient attention mechanisms, and standardized evaluation frameworks to enhance long-text abstractive summarization. This review provides a comprehensive analysis of existing methods, datasets, and evaluation techniques, identifying research gaps and offering insights for future advancements in transformer-based long-text abstractive summarization.

Streamflow modelling is regarded as a crucial part of managing and planning water resources. Water resources engineers face a variety of challenges when predicting streamflow. These difficulties are caused by complex natural processes that involve non-linearity, non-stationarity, and randomness. This research investigates the application of machine learning (ML)-based models for forecasting streamflow discharge (Q) using input variables, including temperature and rainfall. The study attains the essential stationarity required for precise modelling using Augmented Dickey-Fuller tests, data normalization, and transformation. In contrast, unit root tests identify initial-level non-stationarity and call for first-differencing. Correlation matrix analysis identifies relevant input combinations. The result findings are supported by statistical metrics including mean squared error (MSE), mean absolute error (MAE), and Pearson correlation coefficient (PCC). Notably, in terms of prediction accuracy, the Gaussian Process Regression (GPR) GPR-M3 model stands out as a notable performer, with a low MAE value of 0.034 in the calibration phase and 0.027 in the verification phase. The success of these techniques is further supported by first and second-order ensemble algorithms, with some models reaching a perfect PCC score during both the calibration and verification phases. The study emphasizes the significance of preprocessing, model selection, and ensemble procedures in improving the accuracy of streamflow prediction models. In addressing complex nonlinear interactions, artificial intelligence (AI)- based models are valuable tools for both technical applications and practical understanding.