This paper presents an automated system for leukemia detection from peripheral blood samples using machine learning techniques implemented primarily in MATLAB, with extensions in Python for advanced processing and a web-based interface for user interaction. Our approach involves image preprocessing, feature extraction using convolutional neural networks (CNNs), and classification via support vector machines (SVM) and deep learning models. The system achieves an accuracy of 98.5% on a dataset of 10,000 blood smear images, outperforming manual methods. Integration with a full-stack web application (HTML, CSS, JavaScript, PHP, MySQL) enables remote access, data storage, and real-time diagnostics. This work contributes to accessible hematology tools, potentially reducing diagnostic delays in resource-limited settings. This study focuses on the techniques used to segment and detect the type of leukemia by analyzing different features of the digital images of the white blood cells. Variations in these features are used as the classifier inputs which give information about different types of leukemia. To understand relative merits and demerits, comparisons of different techniques used for segmentation and classification are given.

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Tall buildings are particularly vulnerable to lateral movements and torsional deflections when subjected to earthquake forces. To maintain structural stability and mitigate these effects, increasing the building's lateral stiffness is essential. One of the most commonly used methods for achieving this is the incorporation of bracing systems within the frame structure. These systems function by transferring lateral loads into axial tension and compression forces in the members, similar to the behavior of a truss. This project presents a detailed review of existing literature on the behavior and analysis of various bracing systems. The studies examined cover several types of bracing configurations, such as K-bracing, V-bracing, inverted V-bracing, X-bracing, and single diagonal bracing. The general consensus across these studies emphasizes that implementing bracing significantly reduces lateral deflections and enhances the stability of tall structures under seismic loading. Building on these insights, the proposed research focuses on investigating a mixed bracing system, which combines elements from different conventional configurations. The goal is to achieve an optimized balance of lateral stiffness and structural stability, addressing the complex challenges posed by earthquake loads on tall buildings. Through thorough analytical modeling and experimental studies, the project aims to advance understanding of mixed bracing systems and their effectiveness. Ultimately, the research seeks to provide practical recommendations for improving the seismic performance and resilience of tall buildings, contributing valuable knowledge to the field of structural engineering.

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Conventional video editing interfaces primarily represent a video as a sequence of frames aligned on a timeline, which makes object-level editing both time-consuming and unintuitive. In this work, we propose an object-centric interaction framework for long-shot video manipulation by introducing operators on three semantic levels: background mosaics, object trajectories, and camera motions. The static background is estimated independently, while object movements are encoded as 3D space–time trajectories that serve as primary interaction primitives. Temporal object manipulations are achieved through direct and intuitive trajectory editing. Furthermore, camera operations are modeled as a scalable and movable aperture, enabling users to simulate pan, tilt, and zoom effects by constructing new camera paths. Experimental demonstrations highlight that the proposed representation and interaction operators enable efficient and user-friendly execution of complex, high-level video editing tasks.

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Industrial steel sheds are widely adopted for warehouses, workshops, and factories owing to their cost-effectiveness and ease of construction. A critical factor in the design of such structures is the choice of bay spacing, which governs structural stability, material consumption, and project economy. This study presents a comparative analysis of two shed configurations with bay spacings of 7.5 m and 12.0 m, each designed for identical overall dimensions (60 m × 49 m × 13 m). The analysis was conducted using STAAD Pro for structural steel design in accordance with IS 800:2007 (LSD), while reinforced concrete foundations were designed manually as per IS 456:2000. The loadings included dead, live, wind, seismic, and crane loads based on IS 875 and IS 1893. Results show that both shed configurations satisfy deflection criteria (Span/180, Height/200), but the 12.0 m bay exhibits nearly 2.3 times higher deflections compared to the 7.5 m bay. The total steel consumption increases by ~39% for the 12.0 m bay, primarily due to heavier gantry girders and secondary members. In contrast, civil works quantities (excavation, PCC, RCC, reinforcement) are reduced by 6–16% in the 12.0 m bay owing to fewer foundations. The overall cost analysis reveals that the 12.0 m bay is ~22% more expensive, with a unit cost of ₹7,721/m² compared to ₹6,024/m² for the 7.5 m bay. The study concludes that while 7.5 m bay spacing is more economical and stiffer, the 12.0 m bay spacing offers greater operational flexibility through wider column-free spans, making it suitable for heavy industries. The choice of bay spacing should therefore be based on project priorities—economy vs. functionality.

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Deep neural networks (DNNs) are powerful artificial intelligence devices that replicate the hierarchical learning structure of the human brain. This work explores the usage of DNNs to classify grayscale garments images by applying the Fashion MNIST data set. This research is interested in the training stages of networks, vanishing/ exploding gradients, transfer learning, and optimizers. Implementation is compared among different optimizer like Adam, RMSprop, Adagrad, and SGD with and without momentum, and regularization techniques like L1, L2, and L1_L2 to prevent over fitting. The Adam optimizer performs optimum with a test accuracy of around 93.39%. The project shows the practical efficiency of DNNs for image classification. provide more detailed information.

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The global plastic waste crisis has intensified the demand for sustainable and efficient waste management strategies, particularly those that recover energy and valuable materials from municipal solid waste (MSW) and plastic residues. This review synthesizes findings from recent studies on waste-to-energy (WTE) technologies and advanced recycling techniques, highlighting their technical feasibility, environmental impacts, and economic viability. Incineration and gasification have been widely explored for energy recovery from MSW and plastic waste, with studies indicating that technologies such as high-temperature steam gasification and fluidized bed co-gasification can yield hydrogen-rich syngas with high calorific values and reduced pollutant emissions. However, economic barriers, including high capital costs and unfavorable energy tariffs, often hinder large-scale adoption. Pyrolysis—particularly catalytic pyrolysis—has shown promise in converting plastic waste into high-quality fuels and monomers, though challenges remain in catalyst cost, deactivation, and regulatory classification. Dissolution–reprecipitation methods offer a non-destructive alternative for polymer recovery, yielding virgin-like plastics with minimal degradation, especially when bio-based solvents such as dimethyl isosorbide are employed. Solvent-based processes also enable selective separation of mixed plastic streams and effective additive removal, thereby enhancing recyclate quality. While each technology has unique strengths and limitations, an integrated approach—combining mechanical, chemical, and thermal methods—emerges as essential for achieving a circular plastic economy. Policy support, infrastructure development, and continued innovation in reactor design and solvent systems will be crucial for scaling these technologies sustainably. Recent advancements in chemical and photochemical recycling technologies offer promising solutions to the growing challenge of plastic waste management, particularly by enabling hydrogen production and recovery of valuable chemicals within a circular economy framework.

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High penetration of photovoltaic (PV) generation in medium-voltage distribution feeders causes midday over-voltage, rapid voltage fluctuations, and frequent On-Load Tap Changer (OLTC) operations. Conventional OLTC logic—typically a fixed deadband with short dwell times—responds poorly to fast PV variability, accelerating mechanical wear and exacerbating transformer hot-spot temperatures that reduce insulation life. This paper develops a comprehensive framework that (i) models the electrical and thermal behavior of a distribution transformer, feeder, PV inverters, and switched capacitors; (ii) introduces a supervisory Volt/VAR control that prioritizes inverter VAR droop → capacitor steps → OLTC; and (iii) embeds a thermal guard into OLTC decision-making to protect transformer life. The system is implemented in MATLAB/Simulink with multi-rate solvers. Case studies on a 33/11 kV, 10 MVA transformer feeding a 5 km radial feeder with 4 MW distributed PV show: (1) voltage compliance within ±5% under diverse conditions; (2) a ~55% reduction in tap operations compared with PV-rich conventional control; (3) peak hot-spot capped at ≈115 °C with ~35% lower daily loss-of-life; and (4) PV hosting capacity improved from ~30% to ~50% of feeder rating. The results demonstrate that asset-aware, hierarchical Volt/VAR control provides both power quality and asset longevity.

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