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Promoting photocatalytic hydrogen evolution rates in layered graphitic carbon nitride through integrated non-noble CoB co-catalyst
Despite being one of the most widely studied metal-free semiconductors, graphitic carbon-nitride (gC3N4) shows meaningful photocatalytic activities only when loaded with noble-metal co-catalysts. The present work reports an alternative to noble metals in the form of cobalt boride (CoB) co-catalyst that can be easily integrated within the gC3N4 framework with facile fabrication strategies. The optimized CoB-gC3N4 composite showed ?60 times higher hydrogen generation rate compared to bare gC3N4 nanosheets, with good stability. Detailed morphological, structural, chemical, electrochemical and spectroscopic investigations revealed the key aspects of CoB-gC3N4 composite that unanimously led to higher photocatalytic activity. Computational investigations not only corroborated the experimental results but also established that the surface Co and B sites in CoB provided the most energetically favoured sites for hydrogen evolution reaction. Based on the experimental and computational investigations, a generic reaction mechanism was formulated that will prove as a guiding light for future studies on similar photocatalytic systems. 2024 The Authors -
Flexible Nanogenerators Based on Enhanced Flexoelectricity in Mn3O4 Membranes
Atomically thin, few-layered membranes of oxides show unique physical and chemical properties compared to their bulk forms. Manganese oxide (Mn3O4) membranes are exfoliated from the naturally occurring mineral Hausmannite and used to make flexible, high-performance nanogenerators (NGs). An enhanced power density in the membrane NG is observed with the best-performing device showing a power density of 7.99mWm?2 compared to 1.04Wm?2 in bulk Mn3O4. A sensitivity of 108mVkPa?1 for applied forces <10N in the membrane NG is observed. The improved performance of these NGs is attributed to enhanced flexoelectric response in a few layers of Mn3O4. Using first-principles calculations, the flexoelectric coefficients of monolayer and bilayer Mn3O4 are found to be 50100 times larger than other 2D transition metal dichalcogenides (TMDCs). Using a model based on classical beam theory, an increasing activation of the bending mode with decreasing thickness of the oxide membranes is observed, which in turn leads to a large flexoelectric response. As a proof-of-concept, flexible NGs using exfoliated Mn3O4 membranes are made and used in self-powered paper-based devices. This research paves the way for the exploration of few-layered membranes of other centrosymmetric oxides for application as energy harvesters. 2023 Wiley-VCH GmbH. -
Multiwavelength spectral modelling of the candidate neutrino blazar PKS 0735+178
The BL Lac object PKS 0735+178 was in its historic ?-ray brightness state during 2021 December. This period also coincides with the detection of a neutrino event IC 211208A, which was localized close to the vicinity of PKS 0735+178. We carried out detailed ?-ray timing and spectral analysis of the source in three epochs: (a) quiescent state (E1), (b) moderate-activity state (E2), and (c) high-activity state (E3) coincident with the epoch of neutrino detection. During the epoch of neutrino detection (E3), we found the largest variability amplitude of 95 per cent. The ?-ray spectra corresponding to these three epochs are well fit by the power-law model and the source is found to show spectral variations with a softer when brighter trend. In epoch E3, we found the shortest flux doubling/halving time of 5.75 h. Even though the spectral energy distribution in the moderate-activity state and in the high-activity state could be modelled by the one-zone leptonic emission model, the spectral energy distribution in the quiescent state required an additional component of radiation over and above the leptonic component. Here, we show that a photomeson process was needed to explain the excess ?-ray emission in the hundreds of GeV that could not be accounted for by the synchrotron self-Compton process. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
Study of hybrid nanofluid flow in a stationary cone-disk system with temperature-dependent fluid properties
Cone-disk systems find frequent use such as conical diffusers, medical devices, various rheometric, and viscosimetry applications. In this study, we investigate the three-dimensional flow of a water-based Ag-MgO hybrid nanofluid in a static cone-disk system while considering temperature-dependent fluid properties. How the variable fluid properties affect the dynamics and heat transfer features is studied by Reynoldss linearized model for variable viscosity and Chiams model for variable thermal conductivity. The single-phase nanofluid model is utilized to describe convective heat transfer in hybrid nanofluids, incorporating the experimental data. This model is developed as a coupled system of convective-diffusion equations, encompassing the conservation of momentum and the conservation of thermal energy, in conjunction with an incompressibility condition. A self-similar model is developed by the Lie-group scaling transformations, and the subsequent self-similar equations are then solved numerically. The influence of variable fluid parameters on both swirling and non-swirling flow cases is analyzed. Additionally, the Nusselt number for the disk surface is calculated. It is found that an increase in the temperature-dependent viscosity parameter enhances heat transfer characteristics in the static cone-disk system, while the thermal conductivity parameter has the opposite effect. The Author(s) 2024. -
Analyzing blockchain-based supply chain resilience strategies: resource-based perspective
Purpose: This research tries to find the blockchain-based resilience strategies that can help the supply chains of micro, small, and medium-sized enterprises (MSMEs) to recover from the disruptions and work effectively in a resource-based view perspective. Design/methodology/approach: Eight broad strategies and 32 sub-strategies are identified from the literature review. Delphi study was carried out, and detailed discussion with 16 experts helped in finalizing these strategies. Further, the best-worst method (BWM) prioritized these strategies. Findings: The findings suggests that building social capital, improving coordination capabilities, sensitivity towards market, flexibility in process and production, reduction in process and lead time,and having a resource efficiency and redundancy are the top strategies on which the top management should focus to overcome the situations of disruptions and enhance performance of MSMEs. Practical implications: The blockchain-based strategies will enable the companies in tracing the products from the company to customers. Further, the customers will be able to identify their manufacturers, the raw materials used in manufacturing, and the life and quality of raw used materials. Altogether the textile industry will become more sensitive toward environmental practices. Originality/value: The previous research has not identified and evaluated the blockchain-based resilience strategies, and therefore this study tries to fill this gap. This study used a smaller sample from the experts, so the results may vary if the larger data set is used and hypothesis testing can be done. 2023, Emerald Publishing Limited. -
Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model
A crucial natural resource that directly affects the ecology is forests. Forest fires have become a noteworthy problem recently as a result of both natural and man-made climatic changes. A smart city application that uses a forest fire discovery technology based on artificial intelligence is provided in order to prevent significant catastrophes. A major danger to the environment, animals, and human lives is posed by forest fires. The early detection and suppression of these fires is crucial. This work offers a thorough method for detecting forest fires using advanced deep learning (DL) algorithms. Preprocessing the forest fire dataset is the initial step in order to improve its relevance and quality. Then, to enable the model to capture the dynamic character of forest fire data, long short-term memory (LSTM) networks are used to extract useful feature from the dataset. In this work, weight optimisation in LSTM is performed using a Modified Firefly Algorithm (MFFA), which enhances the model's performance and convergence. The Variational Autoencoder Generative Adversarial Networks (VAEGAN) model is used to classify the retrieved features. Furthermore, every DL model's success depends heavily on hyperparameter optimisation. The hyperparameters of an VAEGAN model are tuned in this research using the Waterwheel Plant Optimisation Algorithm (WWPA), an optimisation technique inspired by nature. WPPA uses the idea of plant growth to properly tune the VAEGAN's parameters, assuring the network's peak fire detection performance. The outstanding accuracy (ACC) of 97.8%, precision (PR) of 97.7%, recall (RC) of 96.26%, F1-score (F1) of 97.3%, and specificity (SPEC) of 97.5% of the suggested model beats all other existing models, which is probably owing to its improved architecture and training techniques. Copyright: 2024 The authors. This piece is published by IIETA and is approved under the CC BY 4.0 license. -
3D Face Reconstruction with Feature Enhancement using Bi-FPN for Forensic Analysis
The representation of facial features in three-dimensional space plays a pivotal role in various applications such as facial recognition, virtual reality, and digital entertainment. However, achieving high-fidelity reconstructions from two-dimensional facial images remains a challenging task, particularly in preserving fine texture details. This research addresses this problem by proposing a novel approach that leverages a combination of advanced techniques, including Resnet, Flame model, Bi-FPN, and a differential render architecture. The primary objective of this study is to enhance texture details in reconstructed 3D facial images. The integration of Bi-FPN (Bi-directional Feature Pyramid Network) enhances feature extraction and fusion across multiple scales, facilitating the preservation of texture details across different regions of the face. The objective is to accurately represent facial features from 2D images in three-dimensional space. By combining these methods, the proposed framework achieves significant improvements in preserving fine texture details and overall facial structure. Experimental results demonstrate the effectiveness of the approach, suggesting its potential for various applications such as virtual try-on and facial animation. 2024 The Authors. -
Disentangling the association of PAH molecules with star formation
Context. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous complex molecules in the interstellar medium and are used as an indirect indicator of star formation. On the other hand, the ultraviolet (UV) emission from young massive stars directly traces the star formation activity in a galaxy. The James Webb Space Telescope (JWST), along with the UltraViolet Imaging Telescope (UVIT), opened up a new window of opportunity to better understand the properties of PAH molecules that are associated with star-forming regions. Aims. We investigate how the resolved scale properties of PAH molecules in nearby galaxies are affected by star formation. Methods. We analyzed the PAH features observed at 3.3, 7.7, and 11.3 m using F335M, F770W, and F1130W images obtained from the JWST. These images helped us identify and quantify the PAH molecules. Additionally, we used UVIT images to assess the star formation associated with these PAH-emitting regions. Our study focused on three galaxies, namely NGC 628, NGC 1365, and NGC 7496, which were selected based on the availability of both JWST and UVIT images. Bright PAH emission regions were identified in the JWST images, and their corresponding UV emission was estimated using the UVIT images. We quantified the star formation properties of these PAH emitting regions using the UVIT images. Furthermore, we investigated the relation between the star formation surface density (?SFR) and the PAH ratios to better understand the impact of star formation on the properties of PAH molecules. Results. Based on the resolved scale study of the PAH-bright regions using JWST images, we found that the fraction of ionized PAH molecules is high in the star-forming regions with high ?SFR. We observed that emission from smaller PAH molecules is higher in star-forming regions with higher ?SFR. Conclusions. Our study suggests that the PAH molecules excited by the photons from star-forming regions with higher ?SFR are dominantly smaller and ionized molecules. UV photons from the star-forming regions could be the reason for the higher fraction of the ionized PAHs. We suggest that the effect of the high temperature in the star-forming regions and the formation of smaller PAH molecules in the star-forming regions might also result in the higher emission in the F335MPAH band. The Authors 2024. -
Uplifting and Uncanny Conversations Around Death and Dying: Qualitative Study Among Indian Adolescents and Emerging Adults
This study explores perspectives of adolescents and emerging adults on having conversations around death and dying, if there is a value in discussing death early in life, and to explore the views on likelihood of introducing death education in Indian curriculum. Using constructivist grounded theory of qualitative research, the study inquired the perspectives of adolescents and emerging adults employing semi-structured interviews. All participants showed interest in discussing the topic; they actively participated in sharing their views, something that they heard, and inquiring about cultural practices. In analyzing the interview data, mainly three themes emerged: 1. Understanding death in relation to shadow and spirit stories; 2. Existential view on death and managing grief and anxiety; 3. Social and cultural narratives into death education. This study sets out to address a gap in research among adolescents and emerging adult attitudes and opinions toward death. However, there is a need to understand barriers in normalizing conversations around death and dying in wider communities in India and further research is essential. The Author(s) 2024. -
Factors Influencing Data Utilization and Performance of Health Management Information Systems: A Case Study
The Healthcare Management Information System (HCMIS) is a comprehensive collection of data systematically gathered at healthcare institutions to fulfill the requirements for statistical information on medical services. This research aimed to assess the use of HCMIS information and identify the elements that impact the efficiency of the medical system at the district and primary medical institution levels in Tanzania as a case study. This research was conducted in 11 districts in Tanzania and included 115 healthcare institutions. It was cross-sectional research. The data were gathered via a semi-structured survey given to healthcare professionals at the institution and district stages. The information was then recorded utilizing an observational checklist. The researchers used an analytical technique for thematic content to combine and validate the replies and findings and gather essential data. 93 healthcare institution personnel and 13 district authorities were surveyed. Approximately 61% of the facility participants said they utilized the HCMIS information, but only 39% of the district participants acknowledged consistently analyzing HCMIS information. Out of the participants from nine districts, 68% said that they regularly get feedback on the quality of their work from authority figures monthly and quarterly. The patient workload was often shown to significantly impact the efficiency of staff members in data collection and administration. Insufficient analysis and subpar use of information were prevalent in most districts and healthcare institutions in Tanzania. Inadequate human and financial resources, absence of rewards and monitoring, and lack of standard processes for data handling significantly hindered the HCMIS efficiency in Tanzania. The Research Publication, www.trp.org.in. -
Between Floods and Climate Change: Revisiting the Mishing Community of Majuli Island, Northeast India
The transformation of monsoon rainfall patterns in India, largely attributed to climate change, is leading to more frequent and severe floods. These escalating challenges underscore the imperative of prioritising adaptive measures, given the intrinsic link between humans and climate change. This research conducted in Majuli Island, a highly vulnerable region in Indias northeast, aims to understand current adaptive strategies and assess potential risks from impending physical exposures. Empirical evidence was collected using purposive sampling in two flood-prone villages. The objective was to revisit the Mishing communitys experiences with annual flooding and climate challenges. Thematic analysis interpreted the qualitative findings. Implications for community-based adaptation and sustainable practices are discussed for future flood and climate challenges. The study emphasises strengthening ecosystem-based adaptation through multi-sectoral networking in Majuli Island, Northeast India. 2024 IOS Press BV. All rights reserved. -
Quadratic convection on the radiative flow of ternary nanofluid Gr-Ag-TiO2-H2O subjected to velocity slip and temperature jump
This paper deals with the analysis of the flow and heat transfer performance of ternary nanofluid flowing past a stretching sheet under the influence of quadratic convection. The ternary nanofluid is formed by suspending three dierent nanoparticles, namely, titanium dioxide (TiO2), silver (Ag) and graphene (Gr) in the base fluid water (H2O). Thus, the ternary nanofluid obtained is Gr-Ag-TiO2/H2O where the hybrid nanofluid Ag-TiO2/H2O forms the base fluid for the resulting ternary nanofluid. The addition of TiO2 nanoparticles enhances the photocatalytic nature of the base fluid and makes it useful in various applications concerning the medicinal field. The presence of Gr helps in intensifying the thermal conductivity of water while the suspension of silver nanoparticles ensures chemical stability. Meanwhile, the thermophysical properties of the ternary nanofluid are mathematically defined and the system of equations that describe the flow of a ternary nanofluid past a stretching sheet is framed using dierential equations. The outcomes of this study are interpreted through graphs for velocity and temperature profiles of the ternary nanofluid. It was mainly observed that the thermal conductance of ternary nanofluid was higher than the monophase and hybrid nanofluid. Also, the presence of quadratic convection had a prominent impact on the ternary nanofluid flow. The Nusselt number was found to be greater for spherical nanoparticles and it was found to be least for blade-shaped nanoparticles. 2024 World Scientific. All rights reserved. -
Photovoltaic Power Plant Performance Improvement with Electric Vehicle Integration: Integrated Control Strategies
The combination of Photovoltaic (PV) systems and Electric Vehicles (EVs) holds enormous promise in an era characterized by growing environmental consciousness and sustainable energy solutions. PV technology is a clean, sustainable energy source that produces electricity by utilizing solar energy. Concurrently, EVs electrification of transportation is a critical step in the direction of lower greenhouse gas emissions and more energy efficiency. Through the use of advanced control systems, this research aims to push the boundaries of current practice in the area of PV and EV integration. Specifically, it focuses on the Icos? controller and dq controller to regulate voltage, minimize Total Harmonic Distortion (THD), and facilitate bidirectional power flow. A thorough Simulink model is created, simulating a complicated PV-EV-grid system, in order to evaluate the effectiveness of different control mechanisms. This model accommodates the unique characteristics of Plug-in Hybrid Electric Vehicles (PHEVs) and enables a detailed assessment of the percentages of voltage and current THD under different operating situations. It can handle both linear and non-linear loads. Most importantly, the studys findings showthattheTHDvaluesmeetthestrictrequirementsoutlinedinIEEE519, highlighting the efficiency of the integrated control approaches. The research not only contributes to the advancement of PV and EV technologies but also paves the way for grid-compatible, high-quality power distribution. This endeavor facilitates sustainable energy integration while simultaneously reducing the environmental footprint, making substantial strides toward a greener and more energy-efficient future. 2024 Seventh Sense Research Group -
Building trust in policing: challenges and strategy; [Construindo confian no polia: desafios e estratia]
In recent years, trust has gained significant importance when discussing the evolution of policing. This shift in focus has been acknowledged by scholars, policymakers, and law enforcement officials who are responsible for ensuring public safety. Unlike the traditional emphasis on crime reduction, there is now a shared recognition that building trust is a fundamental objective in the relationship between policing agencies and the communities they serve. This article discusses three commonly employed methods by policing agencies and their personnel to enhance public trust in the police: policy changes, police training, and citizen oversight boards. Further, it focuses to a less conventional avenue for change, which involves re-evaluating the laws enforced by the police. To achieve meaningful transformation within the police system, it is necessary not only to modify how officers perform their duties but also to examine and potentially revise the laws they are obligated to enforce. 2024 Centro Universitario de Brasilia. All rights reserved. -
Management and Sales Forecasting of an E-commerce Information System Using Data Mining and Convolutional Neural Networks
The exponential development of e-commerce in recent decades has enhanced convenience for individuals. Compared to the conventional business environment, e-commerce is characterized by increased dynamism and complexity, resulting in several obstacles. Data mining assists individuals in effectively addressing these difficulties. Traditional data mining cannot efficiently use big data in the power provider industry. It heavily relies on time-consuming and labor-intensive feature engineering, and the resulting model could be more easily scalable. Convolutional Neural Networks (CNN) can efficiently use vast amounts of data and autonomously extract valuable elements from the original input, resulting in increased effectiveness. This article utilizes a CNN to extract valuable insights from e-commerce information to forecast commodities sales accurately and proposes a CNN-based Sales Forecasting Model (CNN-SFM). The findings indicate that using data mining and CNN yields a high level of precision in forecasting forthcoming people buying capacity data. The correlation variable between actual usage information and projected usage information was 0.98, and the highest mean error was just 1.78%. Data mining can effectively extract hidden relevant information and forecast future consumption habits for e-commerce systems. CNN demonstrates proficiency in accurately predicting forthcoming consumption power and trends. The Research Publication. -
Energy-efficient low-power LED-mediated effective photodegradation of cationic and anionic dyes by phthalocyanine-based COF sensitized ZnO photoactive material
The present work involves the synthesis of 2D covalent organic framework involving zinc phthalocyanine and perylene systems (2DZnPc) as low-power LED visible-light sensitizer for ZnO nanomaterial. The synthesized materials and their composites (2DZnPc@ZnO) were characterized by Fourier transform infrared spectroscopy (FTIR), powder X-ray diffraction (XRD), Scanning electron microscopy (SEM), and Solid-state diffuse reflectance spectrophotometer to understand the structure, size, morphology, and optical properties. To examine the photodegradation competence of ZnO alone and its four different composites (2DZnPc@ZnO5, 2DZnPc@ZnO10, 2DZnPc@ZnO15, and 2DZnPc@ZnO20) in the presence of the energy-efficient low-power white LED (16 W) as source of visible light, and as modular pollutants, cationic methylene violet (MV) and anionic Eosin Y (EY) dyes were employed. The effect of different parameters on photocatalytic activity such as photocatalyst dosage, interaction time, pH of dye solution, and photocatalyst re-usage is examined. The results indicate that photosensitizing ZnO with 2DZnPc improved photocatalytic performance for the photodegradation of MV and EY dyes substantially. In an optimized environment, the removal efficiency of MV and EY was found to be 98 and 92 % respectively in 90 min. 2024 Elsevier Ltd -
Coset component signed graph of a group
In this paper, the notion of a newly derived signed graph called a coset component graph, based on cosets of subgroups of a group is introduced. Let G be a group and H be its subgroup. Then, the coset component graph of H in G, denoted by ?cc, is a simple graph with the vertex set consisting of elements of G and two vertices say, a, b ? ?cc are adjacent if either aH = bH or Ha = Hb. A coset component signed graph of H in G is a signed graph whose edges get the sign in accordance with their inclusion in the edge set of the corresponding coset component graph. The structure and important properties of the coset component signed graphs are determined in this paper. 2024 World Scientific Publishing Company. -
Estimation of state of charge considering impact of vibrations on traction battery pack
Interest towards electric vehicle adoption is on the rise due to the lower running and maintenance cost it offers, along with zero tailpipe emissions. Range anxiety is one of the only concern that affects the adoption of electric vehicles. The state of charge of the traction battery pack has to be accurately determined and provided to the user to avoid range anxiety. Minute battery parameters has to be considered to improve the accuracy of the state of charge determination. In order to overcome the problem of range anxiety, an innovative strategy that takes into account how vibrations affect the performance of EV batteries is developed in this research. By doing this, the state of charge estimation precision is improved and thereby raises the drivers faith in electric vehicles. The impacts and vibrations felt on the traction battery pack during driving would lead to heat generation. The heat generated is found to be highest when the vibrations resonate at the natural frequencies of the traction battery pack. The natural frequency of the battery pack is considered when the battery is kept in the battery chamber of the two-wheeler electric vehicle. The vibrations at natural frequency produces heat which is accounted for when the state of charge is determined. To obtain accurate state of charge estimation, a Kalman filter-based approach is used. The Kalman filter-based estimation uses the conventional methods which are the open circuit voltage method and the Coulomb counting method to improve the estimation process along with the consideration of the heat component due to vibrations and impact. The vibration analysis is performed using MATLAB, while the state of charge determination is implemented in hardware and the Kalman estimation done using Python. The system is modelled on an electric two-wheeler platform and the testing is done to compare the state of charge accuracy of the open circuit voltage method, the Coulomb counting method and the Kalman filter-based estimation approach. The inclusion of the vibrational heat analysis for State of Charge estimation in the hardware testing of the electric two-wheeler provides an accurate state of charge value. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Corporate social responsibility assurance, board characteristics and social performance disclosure. Evidence of listed firms in India
The study examines board characteristics, corporate social responsibility (CSR) assurance and social performance disclosure of listed firms before and after mandatory CSR reporting in India. We used the Indian stock market as the testing grounds and applied panel regression and difference-in-differences to analyse 960 firm-year observations between 2010 and 2021. The first findings show that independent board directors and total board size are insignificant in CSR assurance engagement in a mandatory CSR policy period. However, CEO duality is less than likely causing CSR assurance engagement. The second findings show that CSR assurance engagement more than likely causes an increase in social performance disclosure before mandatory CSR policy implementation and increases social performance after policy implementation. The third findings show that the interactive effect of board characteristics (independent directors, total board size and CEO duality) and CSR assurance engagement causes an increase in social performance disclosure. The study sought clarity on the impact of CSR assurance and mandatory CSR reporting on information asymmetry problems to stakeholders. The study also contributes new knowledge on the influence of the interactive effect of board characteristics and CSR assurance on the social performance disclosure of listed firms in India. 2022 John Wiley & Sons Ltd. -
Exploring the applications and security threats of Internet of Thingin the cloud computing paradigm: A comprehensive study on the cloud of things
The term Internet of Things (IoT) represents a vast interconnected network comprising ordinary objects enhanced with electronics like sensors, actuators, and wireless connectivity. These augmentations enable seamless communication and data sharing among devices. IoT constitutes an ecosystem of programs, systems, and technologies that operate across diverse communication services. In our rapidly evolving world, cutting-edge technologies are swiftly gaining prominence. IoT, in particular, strives to proliferate innovative applications, programs, and communication amalgamating the virtual and physical realms. The bedrock of this communication is the machine-to-machine communication paradigm. IoT encompasses a spectrum of technologies encompassing smart vehicles, efficient vehicle parking management systems, and roadway-embedded sensors, culminating in the vision of a smart city. Such integration has the potential to alleviate traffic congestion and curb energy consumption. The impact of IoT extends to power generation and business operations, promising transformative outcomes. Furthermore, the synergy between IoT and cloud computing plays a pivotal role in the wireless communication domain. This paper offers a comprehensive insight into IoT's functionalities and underscores the associated security threats. The study aims to equip academia with an enhanced understanding of diverse IoT applications, particularly their integration with cloud computing, referred to as the Cloud of Things.. 2023 John Wiley & Sons, Ltd.