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Breaking barrier of binding buffer in colorimetric aptasensing of tetracycline in food fish using peroxidase mimic gold NanoZyme

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Methods 2025年 240卷 145-153页

作者： Sarkar, Dhruba Jyoti Raja, Ramij Santhana Kumar, V. Bhattacharyya, Soumyadeb Pal, Souvik Mukherjee, Subhankar Das, Basanta Kumar Aquatic Environmental Biotechnology Division ICAR-Central Inland Fisheries Research Institute Barrackpore West Bengal Kolkata 700120 India Centre for Development of Advanced Computing-Kolkata West Bengal Kolkata 700091 India

Tetracycline is extensively used in aquaculture as a therapeutic agent that needs to be monitored due to food safety concerns. Aptasensing has been revealed as a suitable diagnostic platform for tetracycline sensing in food matrix due to its quick, low cost and robust nature. But, the colorimetric aptasensing of tetracycline employing the peroxidase activity of gold nanoparticles (AuNPs) to 3,3,5,5-tetramethylbenzidine (TMB) was unsuitable until now owing to the aptamer-specific alkaline binding buffer. The present study developed a method with an optimized reaction protocol diminishing the inhibitory effect of binding buffer on the sensor probe (AuNPs-aptamer + TMB + H₂O₂). The overall peroxidase activity of the sensor probe was only inhibited by tetracycline through selective adsorption on the AuNPs-aptamer complex. The peroxidase inhibition percentage in the test range of 0.01 to 0.5 mg L^-1 tetracycline gave a logarithmic response (R², 0.99) with a detection limit of 0.017 mg L^-1 which is less than the prescribed limit (0.1 mg L^-1) set by EU and FSSAI. The developed sensing system in fish muscle showed high recovery (111–115 %) with great potential for rapid detection of tetracycline in fish muscle. © 2025 Elsevier Inc.

关键词： Aptamer Fish Gold nanoparticle NanoZyme Tetracycline

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development of Fedora Linux Distribution for RISC-V (RV64G) Architecture 24

Development of Fedora Linux Distribution for RISC-V (RV64G) ...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Surendra Billa Arif Badar Rushikesh Jadhav Yogeshwar Sonawane Sanjay Wandhekar HPC Technologies Group Centre for Development of Advanced Computing Pune India

ISBN: (纸本)9798350355543

The rapid evolution of the RISC-V architecture presents both opportunities and challenges, particularly for systems lacking support for compressed instructions (RV64G). This paper explores the development of a Fedora Linux distribution tailored specifically for the RV64G architecture, providing a comprehensive narrative of the process from inception to implementation. Key milestones include establishing a robust filesystem hierarchy, creating a cross-compiler, preparing and bootstrapping target image, integrating a native GCC compiler, and leveraging the Koji build system to streamline package rebuilding. Additionally, we introduce a custom Python application to automate the Koji builds, enhancing efficiency and consistency. Our innovative approach not only addresses the immediate needs of RV64G systems but also lays the groundwork for future advancements in High-Performance computing (HPC) on the RISC-V platform. This work aims to bridge the gap in the current ecosystem, offering a scalable and maintainable solution that promotes the broader adoption of RISC-V technology [1].

关键词： C-DAC Vega Microprocessor

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IoT enabled Battery Monitoring System (BMS) for Industrial, Commercial and Home Applications

IoT enabled Battery Monitoring System (BMS) for Industrial, ...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Shubham Madane Priyusha K R Ashutosh Singh T Praveen Kumar Vishal J. Rathod Kaushik Nanda Hari Babu Pasupuleti S D Sudarsan Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka IN

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Uninterruptible Power Supplies (UPS) supply power in the event of a failure, which is critical for stationary applications. Engineers, on the other hand, face challenges in sustaining peak efficiency batteries because maintenance must be done on a frequent basis. The most common solution is Battery Monitoring Systems (BMS), which are primarily designed for industrial applications and may make it difficult to efficiently interpret battery data patterns without regular monitoring. Furthermore, battery technology will be evaluated, accelerating the evolution of the BMS from a simple monitoring unit to a multi-functional integrated one. To address these challenges, the goal of this paper is to design a robust Battery Monitoring System that uses the Internet of Things (IoT) to make it smart and provide continuous service. This work contains IoT-enabled hardware that monitors current, voltage, temperature, state of charge, internal resistance, and remaining runtime of the battery. The performance of the proposed solution is rigorously verified in a real-time testbed installed at the office. In addition, this work includes a web application that allows customers to easily configure parameters and assures efficient battery monitoring. The results demonstrate precise measurement and functional state of the battery in a timely manner. The proposed method can be readily integrated into any existing industrial or commercial system to improve operational efficiency.

关键词： Temperature sensors Temperature measurement Resistance Runtime Real-time systems Batteries Internet of Things State of charge Monitoring Uninterruptible power systems

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Literature Survey: Decentralized Health Record Management System

Literature Survey: Decentralized Health Record Management Sy...

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AIML-Applications for Engineering & Technology (ICAET), International Conference on

作者： Anshika Pandita Suchitra Morwadkar Padmaja Joshi Janvi Girase Nikita Gosavi Isha More Cummins College of Engineering for Women Pune India Centre for Development of Advanced Computing Mumbai India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

In today's fast-paced world of technology, we rely on digital interactions more than ever. But with this convenience comes a growing need for security and trust. As we navigate our daily lives online, issues such as data breaches, unauthorized access, and fraud are real concerns that can affect us all. It is vital to prioritize the safety of our digital experiences to build the trust we need to thrive in this digital age. These challenges are particularly acute in the management of personal health records (PHRs), where existing centralized systems do not offer reliable, easily traceable, and securely managed control over patient's medical data. This paper explores the revolutionary capabilities of blockchain technology in addressing the critical shortcomings of traditional PHR management systems. The unique characteristics of the blockchain are decentralization, security, immutability, transparency, and anonymity, which presents a groundbreaking solution to the issues plaguing current systems. By eliminating the risks associated with centralized data storage, blockchain provides a secure and transparent platform for managing health records, ensuring that they remain tamper-proof. The literature survey conducted in this paper examines various research studies that have proposed and explored Blockchain-based solutions for health record management. These studies highlight the significant advantages of blockchain, including enhanced data integrity, privacy, and patient control over health information. Comparative analysis underscores the need for a paradigm shift from centralized to decentralized systems that enhance both the security and efficiency of managing health records.

关键词： Surveys Data privacy Navigation Scalability Smart contracts Memory Medical services Blockchains Safety Reliability

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Performance Analysis of Machine Learning Classifiers for Metastatic Breast Cancer Diagnosis Within 90 Days

Performance Analysis of Machine Learning Classifiers for Met...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Vaishnavi Chopade Pritam Tambe Sayali Satokar Anuradha D. Thakare Manisha Mantri Department of Computer Science & Engineering (AI&ML) Pimpri Chinchwad College of Engineering Pune Pune India Artificial Intelligence and Quantum Group at C-DAC Centre for Development of Advanced Computing Pune India HPC-Medical and Bioinformatics Group at C-DAC Centre for Development of Advanced Computing Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Metastatic breast cancer, commonly referred to as stage IV breast cancer or advanced breast cancer, is a severe form of the disease, occurs when breast cancer cells migrate to different parts of the body such as the lungs, bones, liver and brain. This study focuses on predicting the of metastatic breast cancer diagnosis within a 90-day window. The dataset was meticulously processed, addressing missing values and encoding categorical variables to ensure a more structured and interpretable dataset. Visualizations were utilized to explore insights and relationships within the data. The results of different machine learning algorithms and deep learning model were evaluated, including Naive Bayes, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and TabNet. Our findings reveal that GBM and TabNet outperformed other algorithms with respect to performance, achieving accuracies of 81.68% and 81.64%, respectively. Furthermore, each algorithm's performance was evaluated using the AUC-ROC curve.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Prediction algorithms Boosting Breast cancer Classification algorithms Decision trees Random forests

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Evaluating the Performance of IEEE 802.15.4g for Smart City Applications

Evaluating the Performance of IEEE 802.15.4g for Smart City ...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Pavithra Arcot Dhivya G Kartheek Inti Nalla Kranthi Kumar Konisetti Murali V Krishna Karuturi Gopal Babu Dennis Joshua D Poonguzhali P IoE & Embedded Systems Group Centre for Development of Advanced Computing Chennai India Department of ECE Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The demand for Low Power Wide Area Network (LPWAN) solutions stems from the need for low data rate, long-range, and energy-efficient connectivity. LPWAN technologies are ideally suited for Smart City applications, where small amounts of data are transmitted infrequently over extended distances. Proprietary LPWAN technologies like LoRa, Sigfox, and Ingenu often require dependence on a specific company's infrastructure and ecosystem. In contrast, open LPWAN standards offer significant benefits, including enhanced interoperability, cost savings, flexibility, and innovation. They foster market competition, reduce reliance on individual vendors, and support the development of a diverse, compatible ecosystem of IoT devices and services. IEEE 802.15.4g is an open LPWAN standard that provides several advantages over proprietary LPWAN technologies. This paper provides a performance analysis of IEEE 802.15.4g in outdoor environments. The study assesses the performance by measuring the packet error rate (PER) in various outdoor conditions. The results of the PER measurements are used to establish the maximum communication range between the transmitter and receiver.

关键词： IEEE 802.15 Standard Performance evaluation Technological innovation Smart cities Error analysis Transmitters Ecosystems Receivers Standards Low-power wide area networks

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Investigating Quantum-Inspired Evolutionary Algorithm with Restarts for Solving IEEE CEC 2022 Benchmark Problems 5th

Investigating Quantum-Inspired Evolutionary Algorithm with R...

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5th International Conference on Recent developments in Control, Automation and Power Engineering, RDCAPE 2023

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity School of Engineering and Technology Amity University Noida India Amity Innovation and Design Centre Amity University Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9789819600465

This paper investigates the effectiveness of two restart methods, local and global restarts, in enhancing the performance of the quantum-inspired evolutionary algorithm (QiEA) to solve optimization problems. The study compares the result of these modifications by testing their performance against the widely recognized and challenging IEEE CEC 2022 benchmark functions. The objective is to analyze the impact of the restart methods on the convergence and optimization outcomes of the QiEA. Through experimental evaluation, it was sought to determine whether the inclusion of restart methods in the QiEA framework would lead to improved convergence toward the optimal solution. Also, a novel Greedy Local Restart algorithm is proposed. The results demonstrate that the QiEA variants consistently outperform the basic QiEA, achieving superior optimization results in terms of convergence and accuracy. Restart approaches are thus observed as an indispensable asset in handling complex optimization problems, greatly enhancing the effectiveness and applicability of evolutionary algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Evolutionary algorithms

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Exploring 2D representation and transfer learning techniques for indoor localization

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Multimedia Tools and Applications 2025年 1-13页

作者： Kerdjidj, O. Himeur, Y. Atalla, S. Copiaco, A. Sohail, S.S. Amira, A. Fadli, F. Mansoor, W. Gawanmeh, A. Centre of Development of Advanced Techniques Algiers Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom School of Computing Science and Engineering VIT Bhopal University Kothri Kalan India Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

Indoor localization (IL) plays a critical role in various applications, including asset tracking, emergency response, and indoor navigation. Traditional methods relying on one-dimensional (1D) representations of received signal strength indicator (RSSI) data often suffer from limitations such as low accuracy, environmental sensitivity, and susceptibility to interference. To overcome these challenges, this paper proposes Scalogram-Based Transfer Learning for Indoor Localization (STL-IL), a novel framework that significantly enhances localization performance. The proposed approach transforms 1D RSSI signals into two-dimensional (2D) representations using scalogram-based time-frequency analysis, coupled with advanced transfer learning (TL) techniques. By leveraging the scalogram method to generate image representations of the data and employing diverse wavelet types. Furthermore, pre-trained deep learning models, including GoogleNet and SqueezeNet, are utilized to achieve state-of-the-art performance. Experimental results demonstrate that the STL-IL system achieves remarkable accuracy levels of 95% and 96% for SqueezeNet and GoogleNet, respectively. The proposed framework not only improves localization accuracy but also enhances robustness in dynamic and challenging environments, making it a promising solution for real-world indoor localization applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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Peroxidase mimic feroxyhyte (FeOOH) nanoparticles enabled highly specific colorimetric detection of arsenate in water and fish

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Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy 2025年 339卷 126299页

作者： Sarkar, Dhruba Jyoti Raja, Ramij Das, Basanta Kumar Bhattacharyya, Soumyadeb Pal, Souvik Mukherjee, Subhankar Aquatic Environmental Biotechnology Division ICAR-Central Inland Fisheries Research Institute Barrackpore West Bengal Kolkata700120 India Centre for Development of Advanced Computing-Kolkata West Bengal Kolkata700091 India

Arsenic poses a serious health risk to humans. Hence, a simple and robust analytical approach for monitoring arsenic levels in water and food matrixes is required. We present a simple and rapid approach for quantifying arsenate in water and fish using feroxyhyte (FeOOH) nanoparticles as a sensor probe. The FeOOH nanoparticles showed peroxidase mimetic activity oxidizing 3,5,3′5′-tetramethylbenzidine (TMB) to a blue product (oxTMB, λmax 650 nm) in the presence of H2O2. However, arsenate's presence inhibits the peroxidase activity of FeOOH nanoparticles through binding on the catalytic active sites. Based on this principle, the presently developed method obtained a good linear response (R2, 0.99) over the range of 0.005 to 5.000 mg L−1 arsenate with 0.006 mg L−1 as the detection limit which is less than the prescribed limit (0.010 mg L−1) by WHO for drinking water. The average recoveries at different fortification levels ranged from 89.51 to 115.61 % in water and 101.11 to 106.99 % in fish muscle. The present analytical technique showed good selectivity due to pronounced peroxidase inhibition alibility (60.53–103.78 %) by arsenate than other non-target ions like PO43−, NO3−, Cr2O72−, etc. The FeOOH nanoparticles showed a promising application prospect for colorimetric detection of arsenate with a wide detection range in water and fish samples. © 2025 Elsevier B.V.

关键词： Nanoparticles

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Image analysis and fine-tuned ResNet50 model for effective malware detection

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Procedia Computer Science 2025年 260卷 1098-1106页

作者： Sanjeev Kumar Yudhishthira Sapru Jitendra Kumar Sugandha Sapru Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India Indian Computer Emergency Response Team CERT-In Delhi India Department of Mathematics Bioinformatics and Computer Applications MANIT Bhopal India Department of Mathematics PGGC Panjab University Chandigarh India

With the rapid growth of connected assets to the Internet, malware attacks have increased exponentially, often causing substantial damage to these connected assets. Early detection of evolving malware attacks can avoid such damage to the Internet ecosystem. However, it requires an efficient and robust approach with little reliance on existing signature-based approaches, as these are no longer effective in detecting obfuscated and complex malware attacks. This study presents a new malware detection and classification approach using image analysis and a fine-tuned ResNet50 convolution neural network (CNN) model through transfer learning. The raw binary executable programs are transformed into grayscale images and resized to 224 × 224 × 1 image size to be fed to the deep CNN model for feature extraction. The pre-trained ResNet50 model is customized to extract the textural features by retaining the learned weight and bias from the source ImageNet data and making the last fully connected layer non-trainable. After that, a fully connected dense layer is added for dimension reduction of the feature map into N × 128, with N as the number of malware files. Two public datasets are used in experiments - MalImg and Microsoft BIG. This study uses comprehensive performance metrics to evaluate the model and obtained an accuracy of 98.50% for the MalImg dataset and 94.85% for the Microsoft BIG data set. The experiment results show that the proposed methodology can handle malware obfuscated by standard polymorphic techniques and is better than similar methods in the literature.

关键词： Cybersecurity Convolution neural network ResNet Malware classification Transfer learning

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