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The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package*
The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates on the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project. 2022. The Author(s). Published by the American Astronomical Society. -
A new optimal design and analysis method based on MADM for MEMS products development
Abstract This paper presents an optimum design method to support the total micro-electromechanical systems (MEMS) product/device optimization, and its evaluation at the conceptual stage itself using the multiple attribute decision making method. In the traditional MEMS product development cycle, simulation and design using software tools are very important due to the knowledge limitation and complexity in design, fabrication, and packaging processes. The available tools are time consuming and relay on trial and error to achieve an optimum solution. The proposed method simplifies the relationship between parameters of design, fabrication, materials, packaging, and the performance of the MEMS product. The methodology is explained with the help of design flow diagram and time chart. A MEMS-based radio frequency (RF) power sensor is designed and the methodology is demonstrated. The proposed sensitivity analysis method is more effective and less time consuming than traditional techniques. Sensitivity analysis is carried out by varying the thickness of the signal conductor. The results of RF power sensor with insertion loss 0.428 dB, reflection loss 25.956 and voltage standing wave ratio of 1.106 at 1.5 GHz are reported. Springer-Verlag London Limited 2012. -
Concurrent design, modeling and analysis of Microelectromechanical Systems products - Design for 'X' abilities
In this paper, we present the need for concurrent engineering in Microelectromechanical System (MEMS) device and product development. MEMS system is considered as six subsystems: micromachined element design subsystem, microelectronics circuit design subsystem, fabrication subsystem, packaging subsystem, materials subsystem and environment subsystem. Design for 'X' abilities is addressed by considering six subsystems/abilities. A concurrent model is developed using graph theory to show the interaction between subsystems. This work utilizes the advantages of the graph theoretic approach to consider all design aspects together in a single methodology with the help of a multinomial defined using matrix algebra. The design index developed using the proposed methodology shows the interaction among the subsystems and indicates whether the overall design is acceptable or not, by considering all the aspects related to micromachined element design, microelectronics circuit design, fabrication, packaging, materials, environment etc. A MEMS based RF power sensor is designed and the proposed methodology is explained. Simulated results of the RF MEMS power sensor are presented to validate the proposed methodology. A power sensor with VSWR of 1.08002 is reported. 2012 Bentham Science Publishers. -
Domain-Driven Summarization: Models for Diverse Content Realms
In todays information-rich landscape, automatic text summarization systems are pivotal in condensing extensive textual content into concise and informative summaries. The current study ventures into domain-agnostic summarization, delving into advanced models spanning various domains, such as business, entertainment, sports, politics, and technology. The study aims to uncover domain-specific enhancements, assess resource efficiency, and explore the boundaries of applicability. This study covers nine cutting-edge models, including Google Pegasus-Large, Facebook BART-Base, SSHLEIFER DistilBART-CNN-6-6, Facebook BART-Large, T5-Large, T5-Base, Facebook BART-Large-CNN, Facebook BART-Large-Xsum, and SSHLEIFER DistilBART-Xsum-12-1. Each model undergoes rigorous evaluation, revealing its efficacy within various domains. Google Pegasus-Large emerges as a standout choice for cross-domain summarization, while Facebook BART-Base demonstrates remarkable stability. Models like SSHLEIFER DistilBART-CNN-6-6, T5 variants, and others contribute to the evolving landscape of summarization. This study endeavors to establish a robust foundation for enhancing the efficiency and effectiveness of summarization techniques within various domains, thereby contributing valuable insights to the broader literature on text summarization. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Collaborative Ventures Between Public and Private Sectors in Technology and Sustainability
Governance models are needed in this age when climate change, resource depletion, and environmental degradation are now accelerating the pace of life. This chapter is a systematic review method using thematic-content analysis, in reality, critically analyzes public-private collaborative ventures as public-private partnership (PPP) matters, positioning them as the device to connect government policy frameworks with private sector technological expertise and investment capacity. With renewable energy and waste management, among other low-carbon technology applications, coupled with much public ground governance, one finds PPPs at the interface be-tween technology policy and a green governance agenda. The chapter delves into the dynamics of government factors, barriers, and replicable strategies. It applies them here using the case-study methodology of the Rewa Ultra Mega Solar Project and the Indore Smart City waste management initiative. 2026 by IGI Global Scientific Publishing. All rights reserved. -
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Modified Rice Husk Silica from Biowaste: An Efficient Catalyst for Transesterification of Diethyl Malonate and Benzyl Alcohol
Abstract: Molybdenum and lanthanum oxide modified silica-based catalysts were prepared from the agricultural waste rice husk. These synthesized catalysts were characterized by various spectroscopic and non-spectroscopic techniques. The catalytic performance was investigated by transesterification reaction between diethyl malonate and benzyl alcohol in the liquid phase using modified silica as a heterogeneous catalyst. Molybdenum modified silica-based catalyst showed the highest conversion efficiency of 95.6% and selectivity of 96.8% for dibenzyl malonate. The reaction conditions were optimized to give maximum efficiency with the highest selectivity in a solvent-free green method. Graphic Abstract: [Figure not available: see fulltext.]. 2019, Springer Nature B.V. -
Role of mesoporous silica supported mixed oxides of ceria and samaria for the synthesis of ?-caprolactone at room temperature
Mesoporous silica was prepared from rice husk by pyrolysis method. Mixed oxides of ceria and samaria (50/50) were disp?ersed on silica by rotavapor assisted wet impregnation method. Catalysts were further modified by doping with MoO3, La2O3 and mixed MoO3La2O3. The prepared systems were characterized by various physicochemical techniques such as BET surface area analysis, scanning electron microscopy, elemental detection analysis, transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, Thermogravimetric analysis, n-butylamine titration and X-ray photoelectron spectroscopy analysis. The catalytic activity of all the systems were studied in the oxidation of cyclohexanone to ?-caprolactone. Various parameters such as time, molar ratio of cyclohexanoneH2O2, temperature, solvent and the amount of catalyst were studied thoroughly to optimize the favorable conditions for the oxidation reaction. Higher ?-caprolactone selectivity of 88.9% was observed in the presence of hydrogen peroxide in acetonitrile medium. The recyclability tests were performed up to six cycles without any appreciable loss in activity, which confirmed the stability of the prepared systems. Good yield with high selectivity was achieved at room temperature, which makes the protocol greener. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
Intelligent Research Summarization: Enhancing Academic Productivity through AI for a More Sustainable Future
The efficiency of researchers is often hindered by the enormous volume of academic research, which hinders the extraction of useful insights in a timely manner. This paper proposes an extension for summarizing research papers using artificial intelligence to ease the process through automated concise summarization. Our system leverages cutting-edge natural language processing tools, including PyPDF2 for text extraction, LangChains text chunking method, FAISS for similarity-based information retrieval, and Googles Gemini AI for high-quality summarization. We evaluated the performance of the model using a variety of metrics, including Rouge scores in conjunction with human judgments. Experimental results show that the suggested approach significantly improves research efficiency, reducing reading time by 60% without compromising high accuracy rates. 2025 IEEE. -
Effects of Performance and Target Pressure on the Psychological Well-Being of Corporate Employees
The main objective of this study is to analyze the work stress on the performance of the employees effectively and to analyze the effect of work stress on the well-being of the employees. The influence of psychological well-being and the capability to tackle workload in the company environment. The main objectives are better job performance which helps in achieving higher productivity. Individual objectives and the organizational objectives of the company assists in the overall performance and gross achievement of the organization. The significant well-being of the overall performance of the employee, their influence, and the management of stress of the performance of the employees. This analysis showcases the impact of work stress on the growth of the employees, which effects performance and significant growth to employee development. 2023, Journal for ReAttach Therapy and Developmental Diversities. All Rights Reserved. -
Discovering Consumer Behavior Towards Back-of-Pack Nutrition Labels: A Systematic Literature Review
This systematic literature review aims to examine the impact of back-of-pack (BOP) labels on food manufacturers' practices in the field of consumer behaviour research. The review comprehensively analyses a wide range of articles spanning over two decades to provide an up-to-date and comprehensive analysis of the subject matter. It focuses specifically on how BOP labels affect consumers, food manufacturers' behaviors and practices. The findings highlight that BOP labels conveying intuitive information effectively prompt product reformulation, particularly in reducing unhealthy nutrients such as sodium, sugar, and calories. Voluntary BOP labeling has limited uptake and is often applied to already healthier products. Consumers and food producers' response varies based on label design and enforcement type, suggesting strategic labeling of healthier choices. The review provides valuable insights for future public health research and policymaking efforts, emphasizing the importance of mandatory policies and specific guidance in BOP labels. This research brings novelty by comprehensively examining the impact of back-of-pack (BOP) labeling on consumers and food manufacturers' practices. The findings contribute to the literature by highlighting the differential effects of mandatory and voluntary BOP labeling approaches and offering insights into label design and enforcement types. As per the researcher knowledge there is no available systematic literature review (SLR) specifically focusing on BOP labeling in recent years. Future research should explore the long-term impacts of mandatory versus voluntary BOP labeling on consumer dietary habits and food manufacturers' product reformulation strategies. 2024 The Author(s). -
Machine Learning Enabled Financial Statements in Assessing a Business's Performance
Machine Learning Enabled Financial Statements (MLEFS) revolutionize corporate performance analysis. This study examines MLEFS's dramatic effects using data gathering, model creation, interpretability, deployment, and ethics. We found that MLEFS accurately predicts crucial financial measures, helping investors, lenders, and financial analysts make better judgments. The study emphasizes the importance of financial measures like Return on Assets (ROA) in supporting financial theories and models. The research also stresses interpretability and ethics, promoting responsible machine learning in finance. Future trends include enhanced interpretability, strong ethical frameworks, real-time analysis, big data integration, regulatory adaption, and industrial acceptance. This study opens the door to data-driven financial analysis and decision-making, improving strategic planning, risk reduction, and investor trust. 2024 IEEE. -
EXPLORING THE NEXUS: DESIGN CHARACTERISTICS OF URBAN LOCAL PUBLIC SPACES AND CHILDRENS PLAY BEHAVIOR IN BANGALORE, INDIA
Research in the field of childrens play highlights its diverse benefits on developmental requisites. Specifically, parks and playgrounds emerge as key public spaces in an urban environment, which facilitates a range of play experiences conducive to developmental processes. The main aim of the study is to examine the design characteristics of formal public spaces that influence play behavior of children and the supervision modalities in the Indian context. To achieve this objective the study investigated a park and playground in a rapidly developing neighborhood in Bangalore. Systematic observations were conducted to observe childrens play opportunities with respect to the physical environments including adult supervision modalities. The outcomes reveal that childrens play in the urban context is a supervised activity. The study demonstrates a strong correlation between the age demographics and utilization patterns of play spaces. Though affordances for functional play and rule based games were exhibited in these public spaces, the research found minimal occurrences of Constructive, Imaginative and Exploratory play. Implications for planning and design includes adopting an age-responsive approach to accommodate diverse developmental needs and preferences of children while integrating natural and manipulable materials to enhance play value of play spaces. 2024, Jomard Publishing. All rights reserved. -
GEMS: Gas-Enhanced Marine Search for Optimizing Fusion Mamba-Attention Networks for Fake Review Classification
The rise of fake reviews has become a major problem for trust in e-commerce sites. As for traditional machine learning solutions, they fail to capture the nuanced language that separates real reviews from fake reviews. In this work, we introduce a new hybrid metaheuristic algorithm that optimizes the Fusion Mamba-Attention Network (FMA-Net) for fake review detection, called GEMS (Gas-Enhanced Marine Search). GEMS is a unique combination of the exploration capabilities of the Enhanced Marine Predators Algorithm and the exploitation process of Henry Gas Solubility Optimization, offering a dual-phase optimization design for high-dimensional, asymmetric, metaheuristic-configured GEMS-optimized FMA-Net. Geometric enhancement of GEMS optimization provides GEMS-optimized FMA-Net with an accuracy of 96.8%, F1-score of 95.4%, and AUC-ROC of 97.2%, marking 37% improvement over the current best models for fake review detection on the Yelp, Amazon, and Google Reviews datasets. We lower the average time of hyperparameter optimization using GEMS with FMA-Net to achieve 68% reduction in overall time spent in grid search and 42% lower for complexity in comparison to genetic algorithms. The contributions of this work are the first hybrid metaheuristic for transformers, a mathematically formulated GEMS algorithm, and an extensive empirical study for proving multi-dimensional metric plausibility. 2026 by the authors. -
Blockchain-Based Financial Transparency Models for Leather Export Industries in India
The leather export sector in India is confronted with repeated issues of providing financial transparency, traceability, and trust of the stakeholders because of a fragmented payment system and manual records. To resolve these, a blockchain-based financial transparency model is elaborated based on a distributed ledger, which operates under smart contracts, ensuring immutable records of transactions and automated verification. The model uses a secure financial exchange by using hash-based encryption and a consensusbased validation in order to synchronize export payments between decentralized nodes. The Ethereum-based Hyperledger Fabric was simulated to check the accuracy, latency, and scalability of the model. Experimental results indicate that there is a 31.7 % increase in financial traceability, 24.5 % decrease in processing delay, 18.9 % increase in cost efficiency, and 27.6 % high trust score among existing methods. The proposed framework will provide real-time, non-tampered, and verifiable financial transactions, which will provide a long-term solution to the Indian leather export industry with a pathway to transparent and responsible export management. 2026 IEEE. -
Designing Remote-Sensed Intelligent Visual Analytics Algorithms for Environmental Monitoring Systems
Increasing climate variability and the rapid degradation of natural ecosystems have necessitated the development of intelligent systems that can track and assess environmental changes in real-time. By combining multi-modal remote sensing data with advanced machine learning and visual analytics techniques, this paper introduces a novel framework for Remote-Sensed Intelligent Visual Analytics (RS-IVA), which aims to improve environmental monitoring systems. To offer a comprehensive, scalable, and adaptable monitoring system, the proposed framework utilizes ground sensor inputs, UAV-based aerial photography, and high-resolution satellite imaging. To identify anomalies such as deforestation, urbanization, water pollution, and changes in air quality, a hybrid deep learning-based algorithm is employed. Explainable AI (XAI) elements make sure that the decision-making process is transparent and accessible. To assist stakeholders, investigate spatiotemporal patterns, forecast environmental hazards, and enhance evidence-based policy decisions, an interactive visual analytics dashboard is being developed. Experiments using benchmark datasets demonstrate that the system is highly accurate in identifying significant environmental changes and exhibits greater adaptability across a wide range of climatic and geographic regions. Intelligent analytics and remote sensing technologies collaborate to improve situational awareness and provide early warnings for sustainable resource planning and disaster management. This research advances the development of next-generation innovative environmental monitoring systems by integrating human-in-the-loop visualization, AI-driven analytics, and remote sensing for informed ecological governance. 2025, Interdisciplinary Publishing Academia. All rights reserved. -
Bollywood, biopics and biographies: Understanding the transmutation of narratives /
A biopic or a biographical motion picture, that charts the lifetime of a person featured in that film, is not a trend or fad that came about in recent years. One could start with arguing upon the stance of the biopics of actually being a genre of itself, despite having been the part of earliest days of silent cinema. The paper studies three biopics in Bollywood along with the biographies/autobiographies that have been used as the background source for the film‘s narration. -
Dynamic Offloading Technique for Latency Sensitive Iot Applications Using Fog Computing
The Internet of Things (IoT) has evolved as one of the most popular technological newlineinnovations that offers processing power to different types of entities connected to it. IoT has made traditional applications smarter and easier to use. IoT offers reliable service to different sectors such as healthcare, industrial control, agriculture, autonomous vehicles, traffic management etc. IoT nodes are generally energy-constrained and hence depend on cloud platforms for storage and analytics of generated data. The cloud provides required services for the newlineconnected applications based on pay per use policy. But cloud datacenter being at remote location fails to accommodate the time requirements of delay-sensitive IoT newlineapplications. Edge/fog computing was designed to address the demands of timesensitive IoT applications. The IoT-Fog-Cloud architecture reduces the delay and response time incurred by the IoT-Cloud model. The fog layer in the three-tier architecture is distributed in nature. Hence the latency depends on how well the underlying offloading algorithms distribute the tasks among available fog nodes. Different offloading policies are mentioned in the literature to address this issue. This work initially tries to solve the offloading problem using one of the novel newlineoffloading approaches Flamingo Search Algorithm (FSA). Later, the results obtained from FSA are fine-tuned using another metaheuristic algorithm, the Honey Badger Algorithm (HBA). Finally, both FSA and HBA are hybridized to generate the HB-FS algorithm which effectively solves the task offloading problem. The performance evaluation of the proposed approach is done with different existing metaheuristic algorithms and the evaluations show that the newlineproposed work outperforms the existing algorithms in terms of latency, average newlineresponse time and execution time. The methodology also offers a lesser degree of newlineimbalance and standard deviation than the compared approaches. -
I am less biased than others: themediating effect of career exploration on decision styleandbiasblind spot
Purpose: This study primarily investigated the tendency of management students to exhibit bias blind spots on three biases related to career decision-making. Second, it also explores how different decision styles, namely rational and intuitive, relate to bias blind spots, considering career exploration as a key factor that might influence this relationship. Design/methodology/approach: To gather data from 277 second-year MBA students specializing in management and business administration from two southern states of India. SPSS software was used to measure the bias blind spot score, and partial least squares structural equation modeling (PLS-SEM) was used to test the hypotheses. Findings: The research findings highlight that students exhibit a bias blind spot tendency during self and environment exploration, and the decision styles indirectly affect bias blind spots through the mediating effects of career exploration. Originality/value: These findings have implications for future research in career psychology, career guidance, and social psychology and for developing interventions to enhance career exploration behavior and decision-making styles to mitigate cognitive biases in career decision-making. 2025, Emerald Publishing Limited.