This research looks at different ways computers can automatically spot skin cancer from images of skin problems. They tested two main approaches. The first one pulls out important details from images using basic tools and then feeds those details into various machine learning programs to make a diagnosis. The second approach uses more advanced deep learning methods like convolutional neural networks that can both find the important image details and make the diagnosis all in one go. The researchers tried out several traditional machine learning methods including Logistic Regression, k-Nearest Neighbors, Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine. They also tested four different CNN models: VGG16, ResNet60, InceptionV3, and Inception-ResNetV2. For the first approach, they used four different ways to pull out image features and combined them together to get the best possible feature set, which should lead to better accuracy in spotting cancer. To test how well these methods work, they used a collection of skin lesion images from the ISIC database that includes both harmless and cancerous skin conditions. They measured how effective each algorithm was using standard performance metrics and also kept track of how long each one took to train, giving them insight into both accuracy and computational demands.

Mining plays a vital role in global economic development, yet it remains one of the most environmentally disruptive activities. This study compares the environmental impacts of surface mining and underground mining, focusing on land degradation, air quality, water contamination, noise pollution, and biodiversity loss. The aim was to assess which mining method poses greater ecological risks and to provide insights for sustainable resource management. A mixed-method approach was employed, combining field data from three mining regions in Nigeria with secondary data from published studies. Indicators measured included suspended particulate matter (SPM, µg/m³), heavy metal concentration in water (mg/L), noise intensity (dB), and percentage of land cover loss (%). Results showed that surface mining exhibited higher land degradation (35% vegetation cover loss) and air pollution (SPM: 180 µg/m³), while underground mining had greater water contamination (Pb: 0.19 mg/L, exceeding WHO standard of 0.01 mg/L) and higher occupational risks. Noise levels were comparable, with underground operations averaging 92 dB against 89 dB for surface mining. These findings highlight the need for tailored mitigation strategies—land reclamation for surface mines and groundwater protection for underground mines. The study contributes to environmental impact assessment frameworks and provides baseline data for sustainable mining policy and regulatory decision-making.

With the high demand for affordable housing in developing nations, locally sourced materials have become a significant focus for cost-effective and sustainable building solutions. This study investigates the durability and water absorption characteristics of sandcrete blocks made from locally sourced materials, including sharp sand, laterite, and ordinary Portland cement (OPC). Blocks were produced with a mix ratio of 1:6 (cement:sand), 1:4:2 (cement:sharp sand:laterite), and 1:3:3, respectively, maintaining a water-cement ratio of 0.5. The samples were cured for 7, 14, and 28 days and tested for water absorption, compressive strength, and visual durability under wet-dry cycles. Compressive strength at 28 days reached 4.57 N/mm² for laterite-enhanced samples compared to 3.88 N/mm² for standard sandcrete. Water absorption was 9.8% for standard mix and 7.3% for laterite mix. Blocks with laterite exhibited better durability under cyclic wetting and drying conditions. Locally sourced sandcrete blocks with laterite are suitable for low-cost housing in rural areas with moderate environmental exposure.

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.