Plants in the Tephrosia genus are members of the Fabaceae family. It is a flowering plant that belongs to the angiosperm family and has over 350 different species. It is widely distributed throughout the world's tropical and subtropical climates. Since the herbal remedy is popular because it has few side effects, the genus Tephrosia is frequently used in traditional medicine to treat a variety of ailments. Among their many pharmacological effects are hepatoprotective, anti-diabetic, antioxidant, anti-hyperlipidemic, larvicidal, anti-inflammatory, wound healing, anti-cancer, and in some species, anti-feedant functions. They also have larvicidal, larvicidal, anti-inflammatory, and anti-ulcer properties. In order to support the continued use of these plants and lay the groundwork for future research, it is crucial to build a foundation by compiling a variety of pertinent data into a single document. As a result, the present study reviewed the key studies done on the Tephrosia genus.

This research paper delves into the revolutionary influence of automation technology on conventional manual workflows spanning diverse industries. As organizations fervently pursue efficiency, precision, and adaptability in their operations, the integration of automation tools and methodologies has surged in prominence. By scrutinizing fundamental principles, real-world case studies, and deployment tactics, this paper illuminates the ways in which automation technology optimizes workflows, amplifies productivity, and fosters groundbreaking innovation. By embracing automation, organizations stand to unlock a plethora of new opportunities for fostering growth, sharpening competitiveness, and bolstering sustainability in the contemporary digital landscape.

In the realm of network management and optimization, Artificial Intelligence (AI) has emerged as a transformative force, offering unprecedented capabilities to enhance infrastructure performance. This paper explores the integration of AI technologies within network systems, a practice increasingly referred to as "Intelligent Networks." By leveraging AI algorithms and machine learning techniques, Intelligent Networks can automatically analyze traffic data, predict network loads, and optimize resource allocation, thus significantly enhancing the efficiency and reliability of network infrastructure. The study begins by outlining the fundamental components of AI-driven network systems, including data collection methodologies, machine learning models, and AI algorithms specifically designed for network analysis and optimization. It delves into various case studies where AI has been successfully implemented to manage complex network tasks—such as dynamic routing, load balancing, and anomaly detection—highlighting the substantial improvements in performance and user satisfaction. Furthermore, the paper discusses the challenges and considerations inherent in adopting AI solutions, such as the need for significant training data, the implications of model bias, and the management of privacy concerns. It also explores potential future developments in Intelligent Networks, including the integration of more advanced neural network models and the expansion of AI applications in network security and IoT infrastructures.

The research examined ICT phobia and electronic resources usage by undergraduates Bamidele Olumilua University of Education, Science and Technology, Ikere-Ekiti. The research design used in the study was a descriptive survey. The entire 200L and 300L student body of the College of Technology made up the study's population, and the complete enumeration sampling approach was applied. The instrument for gathering data was a questionnaire. Frequency counts and simple percentages were used to examine the data. Online Public Access Catalogue (OPAC), e-books, e-journals, and e-databases are the electronic resources that undergraduate students utilize the most frequently in BOUESTI, according to the research. Furthermore, the survey discovered that students use electronic resources for term papers, test preparation, homework, and class assignments because they believe them to be relevant to their academic work. The study revealed that the consumption of e-resources was sporadic, suggesting that more lobbying and awareness-raising efforts are required to encourage continuous utilization.

This study presents an integrated computational framework for predicting early-stage building energy performance under conditions of uncertainty and incomplete design information. By combining BIM-based geometric abstraction with probabilistic scenario generation, reduced-order thermal simulation, and sensitivity-informed variance analysis, the framework demonstrates that conceptual design decisions can yield structured, interpretable performance insights. The methodology identifies dominant parameter influences, interaction effects, and performance gradients, revealing that geometric determinants such as orientation, glazing ratio, and shading depth exert disproportionate control over early energy outcomes. Findings show that conceptual performance is statistically patterned rather than random, and that prediction reliability increases when uncertainty-aware modeling is applied. The study contributes a methodological advancement by demonstrating how BIM-integrated workflows, uncertainty quantification, and sensitivity analysis can operate collaboratively to support informed conceptual decision-making. These results establish a foundation for predictive reasoning in early-stage design and provide new opportunities for performance-driven architectural practice.

Adaptive structural systems have emerged as a promising approach for addressing the growing demand for energy-efficient buildings while maintaining high levels of structural performance. Traditionally, structural systems have been designed as static entities, with limited consideration of their potential influence on building energy performance. This study investigates the impact of adaptive structural systems on building energy performance through an integrated structural and energy-based analytical framework. A comparative quantitative approach is adopted in which an adaptive structural system is evaluated against a conventional static system under identical environmental, loading, and operational conditions. Structural response characteristics and operational energy demand indicators are analyzed simultaneously to capture the interaction between adaptability and energy efficiency. The results demonstrate that adaptive structural systems lead to consistent reductions in operational energy consumption and peak energy demand while simultaneously improving key structural performance metrics such as displacement control and load redistribution. The findings reveal that moderate, well-calibrated adaptability yields the most significant energy benefits, whereas excessive adaptability results in diminishing returns. Furthermore, the study confirms that adaptability introduces a synergistic effect, enhancing both structural and energy performance without compromising reliability. By positioning structural adaptability as an active contributor to energy regulation, this research advances current understanding of energy-efficient building design and highlights the expanded role of structural engineering in achieving high-performance and sustainable buildings.

This study investigates whether the seismic sloshing protection of large liquid storage tanks can be achieved solely through geometric redesign, without recourse to a single internal baffle, base isolator, or supplementary damping device. Motivated by the persistent vulnerability of conventional circular cylindrical tanks to earthquake-induced sloshing damage—documented in failures ranging from elephant-foot buckling to floating roof collapse—the research challenges the century-old design canon that treats the circular cross-section as the default and immutable choice for liquid containment. Drawing inspiration from the commonplace observation that a square cup dampens liquid oscillations more rapidly than a round one, the investigation scales this intuition to industrial dimensions through a dual-methodology architecture that tightly couples small-scale shaking table experiments with high-fidelity computational fluid dynamics simulations. The experimental program tested a full factorial matrix of 400-millimeter scale models encompassing four geometric variables: cross-sectional polygonality (square, hexagonal, octagonal), wall inclination (vertical versus 7.5- and 15-degree inward taper), floor topography (flat versus domed with a central rise of one-tenth the liquid depth), and internal surface texture (smooth versus longitudinally grooved). These models were subjected to a curated suite of seven recorded earthquake ground motions scaled to a uniform peak ground acceleration of 0.5g. The computational model, developed in OpenFOAM using the volume-of-fluid method with k-omega SST turbulence closure, was validated against the experimental data to within 1.8 percent for fundamental frequency and 6.2 percent for peak wave height. The validated solver was then extrapolated, using Froude similarity, to a full-scale 100,000-barrel prototype. A Taguchi design-of-experiments framework efficiently surveyed the four-dimensional geometric parameter space through 18 full-scale simulations. The results demonstrate that the synergistic combination of a square cross-section, a 15-degree inward wall taper, a domed floor rising to 10 percent of the liquid depth, and heavy longitudinal surface striations achieves an 80 percent reduction in normalized peak convective wave height (from 0.187 to 0.038 of the liquid depth), a near-tripling of the convective damping ratio (from 0.42 to 1.24 percent), and a 99 percent increase in the fundamental sloshing frequency (from 0.126 to 0.251 Hz) relative to an unbaffled circular baseline. When compared directly to a conventionally baffled circular tank designed according to API 650 recommendations, the optimized geometric tank exhibited a 7 percent lower peak wave height, a 5 percent higher damping ratio, and an 86 percent higher fundamental frequency, at the expense of an 18 percent increase in peak wall stress that remains within code-allowable limits. The four geometric mechanisms operate synergistically: the square cross-section provides preventive frequency detuning, the inward taper constrains convective mass participation kinematically, the domed floor scatters the standing wave pattern through topographic refraction, and the textured surface contributes distributed skin-friction damping. All five research hypotheses were confirmed, and the central proposition—that pure geometric redesign can match or exceed the performance of internal baffles—is supported by the numerical evidence. The findings establish a new paradigm of intrinsic, passive, shape-driven seismic protection that eliminates the lifecycle costs, inspection burdens, and corrosion vulnerabilities associated with internal baffles, offering a resilient and maintenance-free alternative for critical liquid storage infrastructure.