Atal Bihari Vajpayee Indian Institute of Information Technology and Management

In the recent years, microbial fuel cells (MFC) have been considered an emerging technology for bioenergy production as the MCFs have the potential to transform organic compounds directly into energy through electrochemical reactions catalyzed by microorganisms. MFC technology may be an attractive approach over traditional wastewater treatment in terms of low cost and environmental sustainability. This results in substantial energy savings, decreased sludge production, and improved energy conversion. This review provides a detailed overview of standard MFCs, elucidating their fundamental working principle and their major components including anode compartment, cathode compartment, membrane/salt bridge, substrate type, and microorganisms. Various parameters that enhance performance and scalability of the MFC are also discussed in this review, which include acidity, salinity, type of microbes, electrode materials, membrane type, geometric design of MFC, and operating parameters. Furthermore, we highlighted the practical applications of MFCs for wastewater treatment, biosensors, and secondary biofuel production. Overall, this review provides insight for a better understanding of all the mandatory parameters required for the practical implementation of MFC technology in the real-world sample.

The demand for explainable AI in misinformation detection is crucial for building user trust and understanding model behavior. Many recent methods try to explain how they spot fake news using text, images, or both (multimodal). However, these methods often rely on fixed-size explanations (text and images) generated through ranking-based systems, which fail to effectively differentiate between explainable and non-explainable components. This shortcoming results in vague explanations and limited model performance. To overcome these aforesaid issues, we come up with a multimodal EXplainable misinformation detection method based on ACute Thresholding mechanism (mEXACT) that identifies a variable-size bucket of check-worthy information, when removed, can flip the model’s prediction from fake to real. Identifying minimal set of significant information enables our model to distinguish between contributing and non-contributing misinformation components, thereby enhancing interpretability while improving classification performance. Extensive experiments on two real-world multimodal COVID-19 misinformation datasets, ReCOVery and MMCoVaR, demonstrate that mEXACT significantly outperforms state-of-the-art techniques, achieving $(6.8-8.7)\%$ and $(4.9-5.4)\%$ higher Accuracy-F1 scores, respectively.

This study aims to perform a comprehensive assessment of the influence of dopant profile variations such as junction features, variability, and uniformity in C-BAs substrate MOSFET devices with other semiconductor materials using Sentaurus TCAD. The principal objective of our inquiry is to establish the efficacy and reliability of C-BAs MOSFETs in the face of real-world challenges, thereby providing valuable insights into their robustness and reliability. To initiate our exploration, by conducting a detailed scrutiny of C-BAs MOSFET technology, with a particular focus on emerging issues related to persistence and side-channel attacks. Subsequently, a particular examination of the dopant profile will be carried out through a well-selected sample set, followed by the application of C–V methodology for dopant profile characterization. By following this methodology, potential opportunities for enhancing the security of C-BAs MOSFETs can be identified, thus laying the groundwork for future research endeavors in the field of semiconductor device design.

Technological advancements are reshaping traditional industrial processes, leading to the rapidly evolving landscape of intelligent manufacturing. In this context, machine learning and deep learning are articulated to revolutionize the complete lifecycle of products from design to production and delivery. Therefore, this study aims to provide a comprehensive overview of intelligent manufacturing practices by integrating machine learning and deep learning techniques. It employed robust bibliometric analysis over the 401 documents in the pertinent literature mined from the Scopus database. It delivers key insights on (i) pivotal journals, influential authors, and network mapping; (ii) delineation of theme-based clusters from keyword co-occurrences; and (iii) formulation of a futuristic research framework for scholars and practitioners. This domain demonstrates an increasing research trend from the articles published each year, with "IEEE Access" documenting the highest publications in this domain. The findings of this study illuminate the temporal trends and contemporary relevance within the domain of intelligent manufacturing by identifying the five clusters based on the keyword occurrence. Besides, the theoretical implications, managerial implications, and future research directions provide a roadmap for future scholars to explore and contribute to an enhanced understanding of machine learning and deep learning driven intelligent manufacturing practices.

The present study focused on the synthesis of zinc oxide nanoparticles (ZnO NPs) utilizing an aqueous extract (leaves) of Tradescantia spathacea and assessed their antibacterial and anticancer activities. The characterization of NPs was performed using XRD, UV-Vis spectroscopy, Fourier-transform infrared, scanning electron microscopy-energy-dispersive X-ray analysis, and HRTEM with selected area electron diffraction. The antibacterial activity of ZnO NPs was evaluated using the disc diffusion method and the trypan blue dye exclusion method. The anticancer effects in HeLa cells were assessed using the MTT assay, while cellular uptake was assessed through Rhodamine B isothiocyanate-labeled ZnO NPs. The cytotoxic properties of NPs were assessed by estimating the mitochondrial membrane potential (MMP) and apoptosis by Hoechst, propidium iodide, Annexin V-FITC staining, and cell cycle distribution. The synthesized NPs exhibited antibacterial ability with the highest inhibition zone measuring 13.2 mm at a concentration of 1 mg·mL⁻¹. The MTT assay on HeLa cells showed dose-dependent viability ranging from 88% to 24%, with an IC50 value of 84.26 μg·mL⁻¹. JC-1 and Hoechst staining assays confirmed the impairment of MMP and apoptosis, with significant cell cycle arrest observed in the Sub G0/G1, S, and G2/M phases, indicating a disruption in the regular cell cycle. In conclusion, green-synthesized ZnO NPs displayed significant antibacterial and anticancer properties, emphasizing their potential for use in biomedicine and healthcare applications.

This study explores disruptive innovation (DI) within the context of organisational capabilities (OCs) and sustainable customer‐centric innovation performance (SCCIP). It proposes a comprehensive framework to examine (a) OC's impact on DI, (b) the relationship between DI and SCCIP and (c) DI's moderating role between OC and SCCIP. Data were collected from 829 respondents across 128 innovative firms over 10 months. Statistical methodologies were employed to validate the research hypotheses, including descriptive statistics, exploratory and confirmatory factor analyses and structural equation modelling. The results indicate that OC significantly influences DI, which in turn enhances SCCIP. Furthermore, DI moderates the relationship between OC and SCCIP. These findings provide critical insights for theoretical advancements and offer practical guidance for organisations that leverage DI for improved SCCIP. The study's implications extend to managerial strategies and decision‐making processes, highlighting OC, DI and SCCIP dynamics.

Underwater acoustic sensor network (UASN) is a key technology for the development of Internet of Underwater Things (IoUT) applications. It comprises of sensor nodes to sense and measure oceanographic information. IoUT is the enabling technology that helps to connect ocean environment to real world applications with the use of UASN. However, the lack of GPS, low bandwidth, and the hostile environment of UASN make it challenging to realize node localization. Multiple anchor nodes enhance location precision; nonetheless, their failure reduces network lifetime. Earlier research has tackled this issue utilizing multiple anchor nodes but failed to handle fault tolerance while being energy effective. Thus, a fault-resilient single anchor-based iterative localization (FSAIL) scheme is proposed to increase localization accuracy and network lifetime with reduced complexity. The proposed method continuously assess the anchor node's energy against the threshold value to select a new anchor node. Further, localization accuracy is enhanced using a fast convergent self-adaptive naked mole-rat algorithm (SAMRA). Simulation results show that the FSAIL saves energy by 48.43% and improves accuracy by 38.46% with a convergence time of 6.3 s.

The shading of panels in an array has a considerable impact on solar array power production. Partial shading occurs when a portion of a solar array is shaded, resulting in uneven irradiance distribution throughout the array’s panels. This causes the shaded PV panels to become reverse biased and operate as a load, resulting in the Hot Spot phenomenon. To prevent this, bypass diodes linked in shunt to the PV panels provide an alternate channel for the flow of current under shade situations. Although damage to shaded photovoltaic modules is avoided, a mismatch between row currents is still caused, leading to many peaks in the power-voltage and current-voltage characteristic curves caused by bypass diodes. To address this issue, this study proposes a novel reconfiguration approach called the Fibonacci Number Generator, which reshuffles the panels in an array to spread the shadow better. This newly proposed technique is compared to a standard TCT interconnection and other reconfigurations like Skyscraper, Ken-Ken, and Chaotic Baker map to test its efficacy based on Global Maximum Power Point and Mismatch Loss on both software and hardware platforms.

Accurate segmentation of retinal blood vessels is essential for the early diagnosis and effective treatment of various ophthalmic diseases. However, the complex structure of blood vessels, variations in vessel width, and the presence of low-contrast regions in retinal images pose significant challenges. To address these challenges, this paper presents a collaborative transformer attention U-Net (CTAUNet) framework for blood vessel segmentation. A U-Net architecture built with convolutional neural network (CNN) is employed by CTAUNet to achieve accurate blood vessel localization, while a swin transformer module is integrated to capture both fine details and broad image context within the retinal image. Furthermore, a collaborative feature fusion mechanism effectively combines multi-scale features from both CNN and transformer branches, improving the segmentation of blood vessels across different scales. Extensive experiments conducted on multiple benchmark datasets (STARE, CHASE DB1, and DRIVE) show improvement over existing methods, achieving F1-scores of 0.872, 0.888, and 0.879 and accuracy of 0.965, 0.989, and 0.981 on DRIVE, STARE, and CHASE DB1 datasets, respectively.

Road accidents pose a significant concern, leading to numerous fatalities and societal disruptions. The Internet of Vehicles (IoVs), when integrated with communication technology, offers a promising solution to mitigate these accidents. However, current IoV systems face challenges such as data integrity, user privacy, centralized storage, and secure authentication. Blockchain technology emerges as a viable solution, providing tamper‐proof data storage and a trustless authentication independent of central authorities. However, in blockchain‐based IoV systems, the inefficiency of traditional consensus mechanisms suffers from high computational costs, network delays, and limited scalability. To address these challenges, we introduce HybridChain, an IoT‐enabled blockchain framework that uses the Reputation‐Based Practical Byzantine Fault Tolerance (RB‐PBFT) consensus mechanism, which enhances transaction throughput, reduces consensus delay, and mitigates block congestion by incorporating a reputation‐based trust model. RB‐PBFT ensures that only trusted entities participate in block validation. Furthermore, HybridChain integrates a sidechain‐based storage mechanism to manage the large volume of data, ensuring that only essential metadata is recorded on the main blockchain, thereby enhancing scalability and reducing network congestion. The experiment results show that the amount of data transmitted is 4.3 times more in PoW than RB‐PBFT with varying vehicles, while in the case of varying block size, it is 3.1 times more in PoW than RB‐PBFT.

Nonorthogonal multiple access (NOMA) is a key element of sixth‐generation (6G) wireless networks, designed to improve spectrum efficiency. The introduction of deep learning (DL) in wireless networks has advanced wireless system performance compared to previous generations. In a NOMA system, power is distributed to different users in a cluster or cell. This paper focuses on distributing power to different service demands of a single user in a cell with minimal error rate. We introduce the service‐based downlink autoencoder (AE)‐NOMA scheme for multiservice NOMA transmission for a single user. The power is distributed to different services through an equidistant power allocation scheme. Integrating AE, NOMA, and the power allocation scheme is developed to enable end‐to‐end (E2E) signal transmission with an enhanced signal‐to‐noise (SNR) ratio. It aims to improve the wireless network's block error rate (BLER) performance. The proposed scheme surpasses traditional standard state‐of‐the‐art (SOTA) techniques, the classical successive interference cancellation (SIC)‐NOMA scheme, and the AE‐based NOMA system. The simulation results highlight the effectiveness and adaptability of the proposed scheme across various scenarios.

In this era of learning, e-learning plays a vital role, without which learning is incomplete. Handling the uncertainty and impreciseness in this domain is very crucial. As we know, aggregation operators play a pivotal role in real-life decision-making. The study of various aggregation operators on the class of trapezoidal-valued intuitionistic fuzzy numbers (TrVIFNs) is essential in recent days due to its flexibility in modelling real-life decision-making problems since it is a real generalization of real-valued intuitionistic fuzzy numbers, interval-valued intuitionistic fuzzy numbers, and triangular-valued intuitionistic fuzzy numbers. In this work, we first establish some new basic operations on the environment of TrVIFNs based on the Aczel-Alsina (AA) operator, such as AA sum, AA product, and AA scalar multiplication. Secondly, we introduce a new trapezoidal-valued intuitionistic fuzzy Aczel-Alsina weighted geometric aggregation operator, along with trapezoidal-valued intuitionistic fuzzy Aczel-Alsina ordered weighted geometric aggregation operator, trapezoidal-valued intuitionistic fuzzy Aczel-Alsina hybrid geometric aggregation operator and study their mathematical properties by establishing various theorems. Thirdly, we introduce a modified trapezoidal-valued intuitionistic fuzzy TOPSIS (TrVIF-TOPSIS) method by incorporating the proposed aggregation operators. Fourthly, we show the numerical illustration of our proposed algorithm by solving a model real-life problem in selecting the best e-learning platform. Then, we discuss the sensitivity analysis of our proposed method by examining the different weights of the criteria. Finally, we show the efficacy of our proposed method by comparing our proposed method with various existing methods in the literature.

About 300,000 leukemia cases are diagnosed every year, with the total number of active cases rising to 2.3 million in 2015. Although the number of adults diagnosed with leukemia is pretty high, this is the most common type of cancer found in children in developed countries. Its ability to recur and expensive diagnostic process make patients unable to undergo the diagnosis on a timely basis and consequently can prove fatal for many. The proposed novel model PyraNet aims to tackle the requirement of high-precision machinery and human expertise, as there might not be enough resources for the latter. Proportionate fine-tuning and construction make the model to accurately and precisely detect the presence of leukemic blast cells and classify them into their respective class types. Also, this novel architecture proposed here is a step towards solving the problem of unbalanced classes that often arises when the quantitative distribution of data within different classes is highly biased. As an initiative to tackle it, we have used multi-model-layer training. The analysis of experimental results shows that the proposed model is capable of correctly predicting a higher number of classes with better accuracy.

This paper focuses on the utilization of hesitant and intuitionistic fuzzy sets (HFS & IFS) in a computational intelligent approach, mainly for decision modelling under complex vague surroundings. Indeed, classical fuzzy sets can be powerful in some applications, but they may not describe the broad range of epistemic imprecision and non-stationary uncertainty involved in making decisions at any given time. HFS & IFS overcome it by providing the ability to quantify degrees of hesitation and membership uncertainty. HFS allows a decision maker to express the multiple reference degrees associated with an option and also show uncertainty about how to assign that precise level of degree or real value. On the other hand, IFS allows for more possibilities of representing ambiguity. In this research, we incorporate the proposed model into a computational intelligent approach for improving decision-making, particularly in the context of multi-criteria decision analysis (MCDA), resource allocation and task completion parameters. The results show that the proposed HFS-IFS method presents improved performance in terms of accuracy and uncertainty treatment across a series of case studies.

Autoencoder-based deep generative models leverage symmetrical encoding–decoding operations for visual anomaly detection and perform effectively across various scenarios. However, their ability to handle diverse static and dynamic irregularities is limited, often leading to the undesired generalization of abnormal visuals as normal. To address these shortcomings, we propose an asymmetrical encoder-decoder architecture featuring hybrid encoding, deformable decoding, and cascade attention for unsupervised anomaly detection. Our method emphasizes compressing comprehensive features and selectively reconstructing normal patterns. The encoder employs hybrid convolution operations to enhance spatial feature extraction, while the decoder integrates deformable operations to capture finer spatial details during reconstruction. The cascade attention mechanism refines encoding and decoding by focusing on the most relevant regions, ensuring more accurate anomaly detection. A multi-objective loss function constrains the reconstruction ability, mitigating overgeneralization and enhancing the model’s ability to differentiate between normal and abnormal visuals. This strategy enables high-fidelity reconstruction of normal visuals while disrupting abnormal pattern reconstruction. Extensive evaluations of benchmark datasets demonstrate the robustness and effectiveness of the proposed model. Our approach achieves competitive performance, with scores of 81% on Shanghai Tech, 99.1% on UCSD Ped2, and 90.1% on the CUHK Avenue dataset, highlighting its capability to detect visual irregularities across diverse environments.

This study investigates the factors influencing organizational performance within the healthcare industry, emphasizing sustainable practices such as green product development, green innovation, and sustainable strategies. The primary objectives are to explore the key factors contributing to organizational performance and examine their interrelationships. By employing the Interpretive Structural Modelling (ISM) approach, the research identifies eleven essential factors, which are categorized into five levels. The ISM model highlights both the driving and dependent forces within the system, providing a comprehensive understanding of the relationships among these factors. Key findings indicate that "Green supply chain" and "Green culture" are the most significant driving forces for enhancing organizational performance. Other factors, such as "Green Distribution" and "Sustainable Practices," are dependent on these core drivers and are positioned higher in the hierarchy with a driving power of 8,7 and a dependence power of 11,11, respectively. The ISM model allows for a clearer interpretation of the direct and indirect relationships among these factors, offering healthcare organizations a practical framework for decision-making and policy formulation. The study's findings have practical implications for healthcare organizations aiming to balance performance optimization with environmental responsibility. Additionally, the research contributes to the academic literature by offering a more concrete theoretical framework for understanding organizational performance in healthcare.

This study aims to examine the strategic decisions and uncertainties encountered by Indian Railways (IR) and illustrate the relationship between these elements to enhance competitive business performance. The research employs a comprehensive review of current literature, a structured questionnaire, and interviews to gather qualitative data from seasoned individuals in the railway sector with significant decision-making authority. The analysis utilizes the Fuzzy DEMATEL approach. The components of railway policy, initiatives for sustainability and expansion, service differentiation, cost reduction, and resource limitations have been found to exert considerable causal effects on various performance metrics. Emerging railway technologies and fluctuations in service pricing are recognized as relevant concerns. The findings furnish essential information for decision-makers and policymakers to prioritize and tackle significant performance issues inside the Indian railway system. It should focus on critical areas determined by D-R values, such as railway policies (0.4505), service differentiation (0.4305), and technological advancement, to enhance its efficiency and effectiveness in complex and uncertain environments. The study is interesting as it offers a distinctive examination of the strategic and business environment factors within the framework of Indian Railways, emphasizing identification, priority, and interconnection.

High-dimensional time series data present challenges in terms of computational costs, storage demands, and noise sensitivity, limiting the efficiency of data mining techniques. Addressing the pervasive challenge of high dimensionality in time series data, a common issue in sensor data analysis, this study introduces a novel solution—the golden-section symbolic approximation (GSSX) representation. Overcoming limitations associated with existing symbolic approaches, particularly the need for predefined user parameters, GSSX leverages the foundations of symbolic aggregate approximation in a two-stage process. Firstly, an adaptive linear space approximation automatically segments time series elements for dimensionality reduction. Additionally, a golden-section-based supervised approach facilitates the discretization process by determining cutpoints based on the actual time series distribution. Experimental validation of GSSX’s applicability includes classification and clustering tasks, demonstrating its ability to retain essential information even with high compression ratios. The study illustrates that GSSX representation competes favorably with state-of-the-art methods. Furthermore, the research investigates GSSX’s robustness through noise and missing data experiments. Graphical abstract

Rising travel distances and worsening urban air quality underscore the need for improved route choices to reduce energy consumption and exposure. Whereas traditional route-planning systems focus mainly on distance and time, this study develops a methodology that provides users with wider choices in route selection. The paper presents a web-based tool designed to assist commuters in selecting eco-friendly routes. This tool combines a React.js frontend for intuitive user input with a Go back end that processes real-time data to rank and deliver route suggestions based on air pollution and energy consumption, in addition to distance and time. Real-time application programming interfaces from Mapbox are used for traffic conditions, and the World Air Quality Index is used for air-quality metrics, which are integrated into the GraphHopper multimodal routing engine. As a result, the tool offers five route choices: the shortest route, the fastest route, the route with the least exposure to air pollution (LEAP), the route with the least energy-consumption route (LECR), and a suggested route that considers all four parameters. Applied to Delhi, the tool demonstrates that the LEAP route can reduce exposure by up to 53%, although it may lead to a 42% increase in travel time in Central Delhi. This study is the first to showcase the LECR in India, demonstrating that selecting this route can save up to 28% of energy in South Delhi. Daily variability analysis conducted at different hours revealed that peak-hour times correlate with increased exposure and energy consumption. Although these results are initial and could be further enhanced with better data, the preliminary results are encouraging.