Volume 13 | Issue 8 |

 Abstract

This study explores the multifaceted concept of teacher professional identity through thematic analysis of reflective narratives provided by teachers. It analysed open-ended responses to identify how teachers perceive their roles, values, and the challenges inherent in maintaining a coherent professional identity. The findings suggest that identity is fluid and contextually embedded, shaped by personal values, institutional environments, professional relationships, and evolving pedagogical commitments. Implications are discussed in light of teacher education, policy reform, and sustainable professional development.

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 Keywords

teacher identity, thematic analysis, reflective practice, qualitative research, professional development

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 Abstract

This review paper examines the performance of outrigger systems in supertall structures subjected to combined seismic and wind hazards. Through comprehensive analysis of 45 contemporary studies (2020-2025). The authors, evaluated how outriggers transform structural behavior by engaging perimeter columns to enhance lateral stability beyond conventional core-only systems. Experimental wind tunnel tests and nonlinear dynamic simulations demonstrate that strategically designed outriggers achieve 18 to 35% reductions in top displacement, inter story drift, and floor acceleration-critical metrics for occupant safety and serviceability. Multi-tiered configurations optimized through machine learning algorithms show efficacy, with height ratios of 0.58 to 0.65 yielding balanced wind-seismic performance, while aerodynamic modifications at outrigger levels suppress vortex-induced vibrations by up to 24%. Hybrid innovations integrating buckling-restrained braces and tuned mass dampers further enhance resilience during seismic sequences and typhoon events, limiting residual drifts to under 0.45% and reducing collapse probability by 35 to 40%. However, these systems introduce nuanced trade-offs: stiffer outriggers increase foundation demands in soft soils, while conventional welded connections risk fatigue failure after 50,000 wind load cycles. Life-cycle assessments confirm that initial cost premiums of 7 to 14% deliver net economic benefits through 17 to 22% long-term savings in non-structural repairs, though viability depends on regional hazard profiles. Persistent challenges include soil-structure interaction effects in clay soils, torsional vulnerabilities in asymmetric plans, and fire-induced connection degradation. Crucially, research has evolved from single-hazard analysis to address synergistic typhoon-earthquake scenarios in Pacific Rim regions. This synthesis establishes evidence-based design principles for optimizing outrigger placement, stiffness, and detailing-providing engineers with actionable strategies to balance structural efficiency, economic feasibility, and occupant safety in next-generation supertall construction.

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 Keywords

Outrigger systems,Supertall buildings, Core-perimeter engagement, Wind-seismic interaction

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Brain cancer is a critical health condition that poses a great threat to human life due to its rapid progression and high mortality rate. Accurate and timely detection plays a pivotal role in enabling effective treatments and improving patient's survival outcomes. Recently deep learning has raised as a great tool for medical imaging analysis, particularly in the domain of automated cancer diagnosis. This study presents an efficient brain cancer classification framework based on the LeNet-5 CNN architecture, optimized for analysis of magnetic resonance imaging (MRI) scans. The proposed model leverages the inherent capability of CNNs to automatically extract hierarchical features from medical images, thereby reducing reliance on manual feature engineering. The research employs a publicly available brain MRI dataset encompassing multiple tumor types as well as non-tumorous cases. Data preprocessing steps like normalizing, resize, and augmentation were applied to enhance model generalization and mitigate over fitting. The modified LeNet-5 architecture was trained and validated using stratified value splits to ensure class representation. Model's performance evaluated using standard classification metrics, such as Accuracy, Precision, Recall, and F1-Score. Results demonstrated that the proposed algorithm achieves competitive accuracy compared to contemporary deep learning models, while maintaining lightweight architecture suitable real-time clinical applications. The findings suggest that an optimized LeNet-5 model can work as a cost effective and computationally efficient solution for brain cancer-detection, potentially assisting radiologists in early diagnosis and treatment planning. This work contributes to the growing field of AI-driven healthcare by illustrating how classical CNN architectures, when appropriately adapted, can deliver reliable diagnostic performance in medical imaging tasks.

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 Keywords

Brain Cancer Classification, LeNet-5, CNN, MRI, Deep Learning, Image Processing, Medical Image Analysis, Data Augmentation, Precision, Recall, F1-Score.

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This work introduces a unique design for a collapsible instant tea cup that integrates an ingredient chamber, a detachable filtration mesh, and a liquid-permeable brewing mechanism for quick tea preparation. The cup eliminates the need for an external teapot or separate equipment. It is composed of three primary parts: the upper drinking section, a removable perforated net, and a sealed bottom compartment that stores pre-measured tea-making ingredients such as tea leaves, ginger, sugar, and spices. Once hot water is poured, it flows through the filtration net into the base chamber, extracting flavors before returning upward for consumption. The foldable design enhances portability, while the detachable net allows easy cleaning and refilling. This solution provides a convenient, eco-friendly, and resource-efficient method of preparing fresh tea, particularly beneficial for travelers, office workers, and outdoor users.

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collapsible design, integrated ingredient chamber, instant beverage preparation

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A complex polysaccharide generated from plants, pectin is essential for food, medicine, and pharmaceutical applications because of its gelling, stabilizing, and health-promoting qualities. Conventional pectin extraction, which has historically been done from apple pomace and citrus peels using hot acid solutions, has several drawbacks, including high energy consumption, lengthy processing periods, and environmental issues related to strong mineral acids. In response, cutting-edge "green" methods that provide increased yields, shorter extraction times, and better functional quality--ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and enzyme-assisted extraction (EAE)--have surfaced. In order to improve solvent penetration and mass transfer while reducing heat degradation, UAE uses acoustic cavitation to break down plant cell walls. MAE speeds up pectin solubilization, cell rupture, and diffusion by using fast, selective heating via dielectric interactions. By using particular hydrolytic enzymes to dissolve structural barriers, EAE provides customized control over the molecular weight and esterification level. In addition to analyzing extraction mechanisms, important parameters, benefits, drawbacks, and new research advancements, this review compares standard and non-traditional approaches. Process optimization, environmental sustainability, and the possibility of combining these methods to produce pectin in an effective, environmentally responsible, and superior manner are highlighted.

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Pectin extractionnon, Conventionalextraction methods, Ultrasound-assisted extraction (UAE), Microwave-assisted extraction (MAE), Enzyme-assisted extraction (EAE), Green extraction technologies, Sustainable extraction.

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Creative Commons Attribution 4.0 and The Open Definition

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Since online sales are now the most popular and can be accessed from any location at any time given that they posses internet connectivity, any customer in digital era will choose them. Because they can pick up their products without having to travel to the company location, customers enjoy the ease of online shopping. The majority of MSMEs marketing procedures really still depend on traditional transaction recording techniques and manual transaction processing systems for direct purchases. One study target aimed at resolving the insufficiency of MSMEs in adapting to information technology is the implementation of an e-commerce information system for MSMEs, which would facilitate online transactions for both MSMEs and customers and enable MSMEs to adjust to the digital era. The analysis results shows the electronic commerce system's features are tailored to the analysis of user needs. For example, the system has features for products, ordering, transaction details, and reports that make it easier for customers to make purchases through the e-commerce system. This means that the system has superior features to enhance the quality of MSME services and keep customers loyal so that online transactions can be conducted. In order to boost MSMEs' expansion in the digital age, this study suggests maximizing analytics fueled digital media tactics along with collaborations regional platforms In order aimed to understand how social media and e-commerce are transforming marketing techniques among Indian MSMEs, this article offers a multi-theoretical framework.

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Micro, Small, and Medium Enterprises , Development strategies, Social Media, E Commerce platforms.

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Aluminium (Al), the third most abundant element in the Earth's crust, is extensively used in industrial processes, consumer products, and certain pharmaceuticals. Despite its non-essential role in human physiology, increasing evidence associates chronic aluminium exposure with neurotoxic effects, including oxidative stress, neuroinflammation, mitochondrial dysfunction, and cognitive impairment. Aluminium can cross the blood-brain barrier through transferrin-mediated and citrate-complex transport, accumulating preferentially in brain regions responsible for memory and learning. Experimental studies in rodents demonstrate dose- and time-dependent behavioural deficits, neuropathological alterations, and cholinergic system disruption. Factors influencing aluminium toxicity include dose, chemical form, exposure route, age, and renal function. Public health concerns persist regarding occupational, environmental, and dietary exposure, yet causal relationships with human neurodegenerative diseases remain debated. This review consolidates current evidence on aluminium sources, exposure pathways, mechanisms of neurotoxicity, behavioural outcomes, and research gaps, aiming to guide future studies and inform regulatory policies.

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Aluminium, Neurotoxicity, Oxidative stress, Blood-brain barrier, Neuroinflammation, Cognitive impairment, Environmental exposure, Mitochondrial dysfunction.

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This study examines the relationship between corporate financial performance and the implementation of Sustainable Development Goals (SDGs), with a focus on prosperity-related SDGs. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), the research analyzes a sample of 26 companies to explore the interaction between SDGs and market capitalization, particularly emphasizing SDG 8 (Decent Work and Economic Growth) and SDG 11 (Sustainable Cities and Communities). The findings indicate that companies aligning their strategies with these specific SDGs tend to be associated with higher market capitalization, demonstrating the financial advantages of incorporating sustainability into business models. Conversely, the absence of certain SDGs, such as SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure), is associated with lower market value, suggesting that prioritizing specific sustainability initiatives is vital for financial success. Notably, the absence of SDG 10 (Reduced Inequalities) was found to have no significant impact on this research. These results contribute to the growing body of literature on sustainable business practices and provide valuable insights for companies aiming to integrate global sustainability goals while improving long-term profitability. Future research should investigate industry-specific variations and the long-term effects of SDG integration on financial performance.

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SDGs, Corporate Financial Performance, ESG, Capitalization, SDG , Disclosure

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Abstract--Multimodal learning empowers large language models (LLMs) to process text, images, and videos, enabling advanced capabilities in tasks like visual question answering (VQA), image captioning, and video understanding. This paper introduces the Multimodal Fusion Transformer (MFT), a novel architecture that employs a hybrid attention mechanism to dynamically align and fuse features from heterogeneous modalities. The MFT achieves 10-15% performance improvements over state-of-the-art models on benchmarks such as VQA v2.0, COCO, and MSRVTT, while reducing computational overhead. A comprehensive literature review identifies gaps in existing approaches, and a compact flow diagram illustrates the MFT architecture. Extensive experiments, including ablation studies and robustness analysis, validate its efficiency, scalability, and adaptability for real-world multimodal tasks. Index Terms--Multimodal Learning, Large Language Models, Text-Image-Video Integration, Hybrid Attention, Transformer Architecture

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 Keywords

A.I , Machine Learning, Transformer, large language model, Multimodel Fusion Transformer

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Khiladiyo Ke Mashpeshi Tantra Ke Karyo Per Yog Ev Preskshadhyan Ka Prabhav

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Creative Commons Attribution 4.0 and The Open Definition

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Khiladiyo Ke Mashpeshi Tantra Ke Karyo Per Yog Ev Preskshadhyan Ka Prabhav

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Creative Commons Attribution 4.0 and The Open Definition