Projectile motion is a fundamental topic in physics, typically taught under idealized conditions such as the absence of air resistance. However, real-world scenarios—such as basketball shooting—show significant deviations from these idealized trajectories. In this study, we investigate the effects of air drag on basketball trajectories, focusing on how it alters the projectile’s path, the effective drag coefficient, and the optimal launch angle for maximum range. We assume standard conditions: gravitational acceleration at 9.8 m/s², air density at 1.225 kg/m³, and negligible Magnus effect. Despite limitations in our model (discussed later), it successfully reveals the differences between ideal and real trajectories, estimates the average drag coefficient (~1.15), and identifies a realistic optimal launch angle (lower than the ideal 45°). This research encourages further refinement of the model and highlights the complexity of real-world physics beyond textbook assumptions.

Expansive soils cause significant damage to infrastructure due to their swelling and shrinkage behavior with changing moisture content. This study evaluated the potential of Costus chartaceus mass ash (CCMA), an agricultural waste, in stabilizing expansive lateritic and clay soils from Nigeria for use as pavement subgrade material. Specimens of the problematic soils were mixed with varying proportions (2.5-10%) of CCMA and a fixed 5% cement content. The geotechnical properties of maximum dry density, optimum moisture content, consistency limits, California bearing ratio (CBR), unconfined compressive strength (UCS), and free swell index were determined via standardized tests. Results showed the maximum dry density generally decreased with higher CCMA contents due to ash particles occupying soil voids. However, inclusion of up to 2.5% CCMA increased density through beneficial pozzolanic reactions. Optimum moisture content rose with greater ash amounts owing to its hydrophilic nature. Consistency limits like liquid limit and plasticity index decreased significantly, while plastic limit increased, demonstrating modifications to clay mineralogy restricting water absorption. Both unsoaked and soaked CBR ratios augmented noticeably with rising CCMA up to 7.5%, reflecting strengthened inter-particle bonds. UCS strengths also steadily climbed to a peak at 7.5% CCMA, signifying physicochemical improvements. A judicious combination of 7.5% CCMA and 5% cement achieved optimal performance for controlling volume change and imparting bearing capacities to the problematic soils. This reflects balancing of pozzolanic gain versus dilution effects. The study validates use of CCMA-cement composite for rectifying consistency issues and upgrading strength characteristics of expansive soils. CCMA shows potential as an economical, eco-friendly stabilizer leveraging its high silica and pozzolanic properties compared to other agricultural wastes. Further work could explore CCMA blending with additional activators to maximize stabilization efficacy.

This study investigates the effectiveness of Combretum Combretaceae exudates as a natural coating aimed at enhancing both the strength and chloride corrosion resistance of steel pipes. Corrosion, particularly chloride-induced corrosion, presents significant challenges across various industries, including oil and gas, water supply, and infrastructure, leading to substantial economic losses estimated at over $300 billion annually in the U.S. alone. It poses severe safety risks, necessitating the urgent need for effective and environmentally responsible corrosion management strategies. Traditional corrosion protection methods, such as synthetic coatings and galvanization, often rely on harmful chemicals that can have detrimental environmental impacts. This scenario underscores the importance of exploring eco-friendly alternatives that do not compromise protective efficacy. The research presented herein demonstrates that steel pipes coated with Combretum exudates exhibit markedly improved corrosion resistance, evidenced by a reduction in corrosion rates of up to 98.6% compared to uncoated samples. This substantial decrease in corrosion rates highlights the potential of natural coatings in mitigating the adverse effects of corrosive environments. Mechanical testing further reveals that these coatings effectively preserve key mechanical properties, including tensile and yield strength, mitigating degradation over extended exposure periods of up to 210 days. Specifically, coated samples retained 91% of their original tensile strength and 89% of their yield strength, demonstrating the coatings’ ability to provide robust protection against corrosion while maintaining the structural integrity of the steel. The study establishes a clear relationship between the thickness of the Combretum coatings and their protective performance, indicating that thicker coatings correspond to enhanced resistance to corrosion and mechanical degradation. This relationship emphasizes the potential of natural coatings as sustainable solutions for corrosion prevention, paving the way for their application in various industrial settings where both environmental and structural considerations are paramount.

This review article examines the integration of Artificial Intelligence (AI) in Sequential Batch Reactors (SBRs) to enhance operational efficiency in wastewater treatment. SBRs facilitate the treatment process through distinct phases—fill, react, settle, and decant—within a single tank. The transitions between these phases are pivotal for optimizing treatment performance. Traditional control strategies often rely on manual interventions and fixed schedules, which may lead to inefficiencies and suboptimal outcomes. AI technologies, including machine learning and predictive analytics, present a transformative opportunity to improve operational management by enabling real-time monitoring, adaptive control, and predictive maintenance. This article discusses various AI applications in SBR systems, highlighting their potential to enhance treatment efficiency, reduce energy use, and improve effluent quality. Furthermore, it addresses the challenges and future directions of implementing AI within wastewater treatment frameworks, emphasizing the need for continued innovation to meet evolving environmental standards. Through a comprehensive analysis, this review underscores the role of AI in revolutionizing wastewater treatment processes, paving the way for more sustainable and efficient practices in the management of water resources.

Abstract: Abattoir waste can be detrimental to the environment if improperly managed. In Nigeria, such wastes are dump in the open or nearby stream/river course, causing high oxygen demand, becoming heavy breeders of pathogens and micro-organism as well as increasing the risks of zoonotic disease outbreak threatening the quality of life and public health. This study explored the use of vermifiltration; a low-cost, odorless and sustainable wastewater treatment method that uses earthworms and microorganisms to break down abattoir waste effluent. A bio-reactor (presence of earthworms) and a control reactor system (absence of earthworms) each consisting of a collection tank, vermifiltration bed and a fabricated treated water collection tank used to treat the abattoir wastewater by varying the flow rate and concentration of the effluent that entered the vermifiltration system. This was across four levels and three replicates with Hydraulic Retention Time of 42,011.49 sec (116.69hrs) and Hydraulic Loading Rate of 8.7x10-6 m/s. Physico-chemical properties of discharge from bio-reactor recorded 11.010±1.755NTU for turbidity, 24.228±6.319mg/L for dissolved oxygen, 7.562±3.402mg/L for total suspended solid, 51.27±14.00mg/L for total dissolved solid, 6.3562±0.2584 for pH, 250.0±101.1CFU/mL for total coli count and 1296±200CFU/mL for total bacteria count respectively as against results from the control reactor which had mean values of 36.351±3.993NTU for turbidity, 14.277±2.241mg/L for dissolved oxygen, 19.569±1.996mg/L for total suspended solid, 137.21±37.40mg/L for total dissolved solid, 5.0958±0.5596 for pH, 2500.0±522.7CFU/mL for total coli count and 6413±1725CFU/mL for total bacteria count. The vermifiltration process was thus able to greatly reduce and filter out the pathogens found in the influent through the combined action of the earthworms and the filter layer of fine sand and gravel to a high degree. Thus, confirming that vermifiltration system could achieve good performance in the treatment of abattoir waste water.

Abstract: The purpose of this study is to correctly assess the current status of railway development and forecast the development trend, assess the advantages, limitations and make recommendations to perfect the railway development strategy to 2045. To have arguments to assess the current status and make accurate forecasts correctly, this study studies the State and Ho Chi Minh City documents on the current status of railway construction and development in the past and the railway development plan to 2045. The results show that, in the past, urban railways have received great attention and investment, but the results have not been high, only 01 railway line has been put into operation. The implementation of other railway lines still faces many difficulties. Based on the assessment of the current situation and the railway development strategy of the government and Ho Chi Minh City, this study identifies the limitations and causes of these limitations, thereby proposing solutions to improve the railway development strategy in the future.

Indeed, AI on the rise in health has given a way forward to cause revolutionary changes in diagnostic capability. With this, critical issues relating to bias and fairness arise, particularly regarding the under-representative in any given dataset. It attempts to explore the relevance of probably the leading challenge that biases in diagnostic artificial intelligence impose on health and the contribution of representative, various train datasets toward reducing such biases for guaranteeing health equity. This study is a quantitative research in which data through purposive sampling was collected with the use of survey methodology from 160 stakeholders, including clinicians, patients, and AI developers. The structured questionnaires captured perceptions related to AI bias, fairness, and the effectiveness of mitigation strategies. Results findings show great concerns over biases in AI systems, with 48.75% agreeing that bias within the training dataset is a major hindrance in clinical decision-making. A majority of 53.33% said it is the diversity in the datasets that helps in promoting fairness, while 76.88% support frequent algorithmic audits. Other key strategies recommended were collaboration among stakeholders and transparency. The current study, therefore, infers that these biases in diagnostic Artificial Intelligence tools need to be treated along the dimensions of data diversity, auditing algorithms, and collaboration with various stakeholders, to whom the transparency of processing should be allowed. Hence, there is a need for improvement in robust frameworks assurance for the equity of AI so that it could guarantee fairness, reliability, and inclusion among various populations for diagnoses.