Volume 13 | Issue 1 |

 Abstract

Amitav Ghosh's The Nutmeg's Curse examines the strong links between colonialism, capitalism, and environmental harm, critiquing how these elements have influenced today's ecological issues. By looking at the nutmeg trade in the 17th century and the harsh Dutch colonization of the Banda Islands, Ghosh connects the exploitation of natural resources in the past to current environmental damage. This paper will explore the link between The Nutmeg's Curse and the history of colonialism and its impact on today's environmental issues. Ghosh's story shows how the exploitation of nature and native populations during colonial times set the stage for the environmental damage and capitalist growth we see now. The book critiques historical wrongs while also highlighting how these past actions continue to cause inequality and ecological damage in the Anthropocene. Ghosh critiques the Anthropocene, suggesting its origins are in long-standing colonial exploitation and capitalist growth, which still negatively affect marginalized communities and ecosystems. By comparing this work to his other book, The Great Derangement, and postcolonial ecological writings by authors like Arundhati Roy and Vandana Shiva, Ghosh's ideas are placed in a wider literary framework. In the end, this paper argues that The Nutmeg's Curse urges a fundamental reevaluation of humanity's connection with nature and the global economy, providing valuable insights into current environmental and geopolitical discussions.

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 Keywords

Anthropocene, Ecology, Colonialism, Environmental destruction

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Human Resource (HR) management plays a pivotal role in steering talent management and succession planning within organizations. By aligning strategic initiatives with employee capabilities and aspirations, HR ensures the sustainability of organizational goals. This journal explores the integral functions of HR in talent acquisition, development, performance management, succession planning, and retention. It highlights key strategies and practices that foster a robust talent pipeline while addressing challenges in adapting to dynamic market demands.

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Talent Management, Succession Planning, HR Strategy, Talent Acquisition, Performance Management, Talent Retention

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Abstract Welded pipes are particularly susceptible to rapid degradation. Cracks can be detected during regular inspections, either mandated by the welding-filling process or by employing more intrusive methods like cutting and replacing sections. Such small discontinuities can evolve into fatigue cracks or stress corrosion cracks during service. Welding process parameters significantly influence fabricated joint quality. The characteristics of welding are crucial in the industrial sector, particularly in the manufacturing sector. One key process for ensuring the quality of welding is the post-welding non-destructive test (NDT), although it is both time-consuming and expensive. To minimize the occurrence of defects, online monitoring has been used to continuously measure certain welding parameters and forecast the quality of the weld. The objective of this study is to assess the dependability of non-destructive test (NDT) methods for examining pipeline welds used in the oil and gas industry. Techniques, includes ultrasonic methods manual and automatic, digital radiography, manual and automatic were utilized. Pipe discontinuities includes- lack of fusion, lack of penetration, porosity, slag, off-seam under cut, crack etc. The experiments were conducted on samples from pipelines that had been developed during the welding process. The findings indicate that automatic ultrasonic tests are more effective in detecting defects compared to manual ultrasonic and radiographic tests.

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 Keywords

Welded Pipes, NDT, Ultrasonic testing, Digital Radiography, Weld Defects

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 Abstract

Early detection and diagnosis of plant diseases are essential for maintaining crop health and ensuring food security. This paper presents a novel approach for identifying prevalent phytopathological conditions using Convolutional Neural Networks (CNN). CNNs, known for their strength in image recognition, are applied to analyze plant leaf images and detect diseases based on visual symptoms like spots, discoloration, and lesions. The proposed model is trained on a comprehensive dataset consisting of labeled leaf images representing various plant diseases, including bacterial blight, rust, and powdery mildew. Leveraging CNN's ability to automatically extract hierarchical features, the model achieves significant accuracy in disease classification. Techniques such as data augmentation and transfer learning are used to enhance performance and generalization across diverse plant types and environmental conditions. Evaluation metrics show that the CNN-based method surpasses traditional machine learning approaches in terms of accuracy, precision, and recall. The study also discusses the potential for deploying the system in mobile applications, allowing real-time disease detection for farmers. This research contributes to precision agriculture by providing a scalable, efficient, and accessible solution for early plant disease diagnosis, ultimately supporting sustainable farming practices and crop yield optimization.

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 Keywords

Convolutional Neural Networks (CNN), plant disease detection, agricultural pathology, phytopathology, image classification, deep learning, precision agriculture, plant disease diagnosis, sustainable farming, crop health monitoring.

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A novel macrocyclic copper(II) complex, [Cu(L)Cl?], was successfully synthesized using the template method. The complex was thoroughly characterized through a combination of advanced analytical techniques, including elemental analysis, conductometric titration, mass spectrometry, nuclear magnetic resonance (NMR), UV-Visible spectroscopy, and Fourier-transform infrared (FT-IR) spectroscopy. These studies provided detailed insights into the structural and electronic properties of the synthesized complex, confirming its successful formation and stability

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Macrocyclic, Template, UV-Visible, Fourier

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Nanotechnology, the art of manipulating and controlling matter at the nanoscale, has emerged as a revolutionary scientific discipline with profound implications across numerous domains. Nano Wonders delves into the fascinating realm of nanoscience and nanotechnology, uncovering the immense potential and versatile applications of nanoscale materials and devices. The journey begins with an exploration of the core principles underpinning nanotechnology, shedding light on the unique behaviours of materials at the nanoscale. Phenomena such as quantum effects, surface interactions, and the interplay of forces are analysed to understand the driving factors behind advancements in this transformative field. A significant focus is placed on nanomaterials, encompassing nanoparticles, nanocomposites, nanotubes, and nanowires. Their synthesis, properties, and applications are examined, emphasizing their critical role in revolutionizing fields like medicine, electronics, and energy. In healthcare, nanotechnology is pivotal, enabling breakthroughs in diagnostics, drug delivery, and imaging. The convergence of nanotechnology with electronics and photonics emerges as a key theme, showcasing innovations that pave the way for faster, smaller, and more efficient devices. From quantum dots to nanoscale transistors, these advancements highlight nanotechnology's transformative impact on information technology and telecommunications. Nanotechnology's role in addressing energy challenges is also explored, spotlighting nanoscale materials designed for efficient energy harvesting and storage. From advanced batteries to nanogenerators, these innovations underscore the potential to reshape sustainable energy solutions and drive a greener future.

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Nano Wonders: Modern Approaches, Relevance, Challenges and Constraints

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 Abstract

Use of porous inserts in a forced convection system is an effective technique for improving heat transfer and so it has a wide range of engineering applications like heat exchangers, nuclear reactors and fuel cells. The effects of porosity, thermal conductivity and Reynolds number on pressure drop and heat transfer rate when water is passed through a pipe fitted with porous inserts and subjected to constant heat flux are studied and compared with the clear flow case without porous material. The permeability, form coefficient and thermal dispersion coefficients are found out numerically and are validated.

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Porous Media, Thermal Dispersion, Permeability

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 Abstract

Include in paper

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nyay, rajnitik mulya, pratha, riti-riwaj

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Facial expressions serve as a powerful medium of non-verbal communication, offering insights into human emotions. This paper presents a real-time facial emotion detection system leveraging Convolutional Neural Networks (CNNs) for accurate emotion classification and Haar Cascade Classifiers for efficient face detection. The system is trained on the FER-2013 dataset, which includes seven emotion categories: angry, disgust, fear, happy, neutral, sad, and surprise. By preprocessing facial images into grayscale and resizing them to 48x48 pixels, the model ensures consistent input quality. The CNN architecture extracts intricate features from facial images, enabling robust emotion recognition. The system integrates real-time webcam input, allowing continuous emotion detection and overlaying the predicted emotion on the video feed. Experimental results demonstrate the system's ability to achieve high accuracy on both static and dynamic datasets, with minimal latency during real-time processing. Applications of this work span diverse fields, including mental health monitoring, human-computer interaction, and adaptive learning systems. Future enhancements include expanding the dataset, optimizing for edge devices, and incorporating multimodal data for improved accuracy and adaptability.

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Facial Emotion Detection, Convolutional Neural Networks (CNNs), Haar Cascade Classifier, FER-2013 Dataset, Real-Time Emotion Recognition, Webcam Input, Image Preprocessing, Human-Computer Interaction, Mental Health Monitoring, Adaptive Learning Systems.

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With the use of computer-aided design software, 3D printing technology allows for rapid prototyping and can create solid things with a variety of geometric shapes by sequentially depositing many layers. The customization of medications with individually adjusted doses, on- demand tailored manufacturing, unparalleled flexibility in the design, manufacturing of complex and sophisticated solid dosage forms, and financial benefits are the main advantages of three- dimensional printing (3DP) technology over traditional pharmaceutical manufacturing. A lot of researchers have been working recently to utilize 3DP technology to the production of pharmaceutical products and various drug delivery methods. Several 3DP methods, like as stereolithography, semi-solid extrusion, fused deposition modeling, and selective laser sintering, can be implemented in a variety of customizable and programmable medications. 3DP technology has previously produced sublingual, orodispersible, and fast-dissolving drug delivery formulations. Recently, 3DP was used to produce drug-loaded tablets with modified-release characteristics, doughnut-shaped multilayered tablets with linear release kinetics, and controlled release formulation with different properties.Still, only a small number of 3DP techniques result in relatively porous structures and dose forms with irregular geometries. Current obstacles that may prevent the widespread use of 3DP technology for pharmaceutical products include the cost of transition, facility adaption, obtaining regulatory approval, etc. Intense research is being done to modify the 3DP methodologies in order to get over the shortcomings and present constraints of this technology.

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 Keywords

3D printing technology , Micrometer, FDA

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Creative Commons Attribution 4.0 and The Open Definition