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Ni-Co MOF Flowers/ZnO NRs Mediated Electrochemical Sensor for Rapid and Ultrasensitive Detection of Neotame in Food Samples
This study focuses on the bioreduction of waste-derived graphite rods into reduced graphene oxide(rGO), followed by the fabrication with Ni-Co metal-organic flowers and Zinc oxide nanorods(ZnO NRs) using Nafion, for sensitive detection of neotame. The Ni-Co metal-organic flowers and ZnO NRs were synthesized using solvothermal synthesis and Azadirachta indica leaf extract, respectively. Additionally, Nafion polymer enhances the stability and conductivity of the nanocomposite. The nanocomposite was characterized using UVvis, Fourier transform infrared spectorscopy, X-ray diffraction, Raman spectroscopy, Dynamic light scattering, X-ray photoelectron spectroscopy, Field-emission scanning electron microscopy, Energy-dispersive X-ray analsysis, Transmission electron microscopy, and Atomic force microscopy. The electrochemical studies were carried out using Electrochemical impedance spectroscopy and Cyclic voltammetry. The modified electrode (rGO/Nafion/Ni-Co MOF/ZnO NRs) demonstrated improved electrochemical activity (34.01 ?A) for neotame with an enhanced peak current at +0.73 V. The LOD and LOQ values were calculated and found to be 0.32 and 0.99 ?M with a recovery (%) ranging from 94.50 to 101.34%. The outcome of this study identifies the morphological and electrochemical factors as major contributors to the adsorption affinities and catalytical activities, with promising possibilities for the design of electrochemical sensing of artificial sweeteners. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Navigating AI and chatbot applications in education and research: a holistic approach
Purpose: This study aimed to identify factors influencing AI/chatbot usage in education and research, and to evaluate the extent of the impact of these factors. Design/methodology/approach: This study used a mixed approach of qualitative and quantitative methods. It is based on both primary and secondary data. The primary data were collected through an online survey. In total, 177 responses from teachers were included in this study. The collected data were analyzed using a statistical package for the social sciences. Findings: The study revealed that the significant factors influencing the perception of the academic and research community toward the adoption of AI/interactive tools, such as Chatbots/ChatGpt for education and research, are challenges, benefits, awareness, opportunities, risks, sustainability and ethical considerations. Practical implications: This study highlighted the importance of resolving challenges and enhancing awareness and benefits while carefully mitigating risks and ethical concerns in the integration of technology within the educational and research environment. These insights can assist policymakers in making decisions and developing strategies for the efficient adoption of AI/interactive tools in academia and research to enhance the overall quality of learning experiences. Originality/value: The present study adds value to the existing literature on AI/interactive tool adoption in academia and research by offering a quantitative analysis of the factors impacting teachers' perception of the usage of such tools. Furthermore, it also indirectly helps achieve various UNSDGs, such as 4, 9, 10 and 17. 2024, Abhishek N., Sonal Devesh, Ashoka M.L., Neethu Suraj, Parameshwara Acharya and Divyashree M.S. -
Construction of Sorafenib Tosylate and Etoposide-loaded Liposomes: A Path to Precision Liver Cancer Therapy and its Apoptosis Induction
Nanotechnology is an effective tool in fighting against cancer, playing a crucial role in investigating and fabricating novel anticancer drugs. Recognizing the worldwide prevalence of cancer, we combined sorafenib tosylate (ST) and etoposide (ETP) within liposomes. We assessed their ability to kill human umbilical vein endothelial cells (HUVECs) and HepG2 liver cancer cells. The liposomes effectively contained ST and ETP, exhibiting a particle size distribution below 180nm, a polydisperse index (PDI) below 0.2, a spherical shape, a strong negatively charged zeta potential, and encapsulation efficiencies of 59% for ST, 88% for ETP, and 57% for ST combined with 87% for ETP. The FTIR analysis indicates that the drugs were incorporated within liposomes. Encapsulation of the drugs in liposomes resulted in a more significant cytotoxic impact on HepG2 cells and a reduced cytotoxic impact on HUVECs. The morphological assessment of the HepG2 liver cancer cells was investigated using AO-EB and Hoechst 33258 staining methods. Apoptosis mechanisms of HepG2 cells were examined by Annexin V and PI dual staining. Furthermore, the coadministration of ST and ETP, which were enclosed in liposomes, resulted in a synergistic impact on the drugs, leading to cell death by apoptosis. 2024 Wiley-VCH GmbH. -
Spatial and seasonal association study between PM2.5 and related contributing factors in India
Global environmental pollution and rapid climate change have become a serious matter of concern. Remarkable spatial and seasonal variations have been observed due to rapid industrialization, urbanization, different festive occasions, etc. Among all the existing pollutants, the fine airborne particles PM2.5 (with aerodynamic equivalent diameter ?2.5?m) and PM10 (with aerodynamic equivalent diameter ?10?m) are associated with chronic diseases. This leads to carry out the study regarding the varying relationship between PM2.5 and other associated factors so that its concentration level might be under control. Existing literature has explored the geographical association between the pollutants and a few other important factors. To address this problem, the present study aims to explore the wide spatio-temporal relationships between the particulate matter (PM2.5) with the other associated factors (e.g., socio-demographic, meteorological factors, and air pollutants). For this analysis, the geographically weighted regression (GWR) model with different kernels (viz. Gaussian and Bisquare kernels) and the ordinary least squares (OLS) model have been carried out to analyze the same from the perspective of the four major seasons (i.e., autumn, winter, summer, and monsoon) in different districts of India. It may be inferred from the results that the local model (i.e., GWR model with Bisquare kernel) captures the spatial heterogeneity in a better way and their performances have been compared in terms of R2 values (>0.99 in all cases) and corrected Akaike information criterion (AICc) (maximum value -618.69 and minimum value -896.88). It has been revealed that there is a strong negative impact between forest coverage and PM pollution in northern India during the major seasons. The same has been found in Delhi, Haryana, and a few districts of Rajasthan during the 1-year cycle (October 2022September 2023). It has also been found that PM concentration levels become high over the specified period with the temperature drop in Delhi, Uttar Pradesh, etc. Moreover, a strong positive association is visible in PM pollution level with the total population. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Maximizing Bifunctionality for Overall Water Splitting by Integrating H2 Spillover and Oxygen Vacancies in CoPBO/Co3O4 Composite Catalyst
In the pursuit of utilizing renewable energy sources for green hydrogen (H2) production, alkaline water electrolysis has emerged as a key technology. To improve the reaction rates of overall water electrolysis and simplify electrode manufacturing, development of bifunctional electrocatalysts is of great relevance. Herein, CoPBO/Co3O4 is reported as a binary composite catalyst comprising amorphous (CoPBO) and crystalline (Co3O4) phases as a high-performing bifunctional electrocatalyst for alkaline water electrolysis. Owing to the peculiar properties of CoPBO and Co3O4, such as complementing Gibbs free energy values for H-adsorption (?GH) and relatively smaller difference in their work functions (??), the composite exhibits H2 spillover (HS) mechanism to facilitate the hydrogen evolution reaction (HER). The outcome is manifested in the form of a low HER overpotential of 65 mV (at 10 mA cm?2). Moreover, an abundant amount of surface oxygen vacancies (Ov) are observed in the same CoPBO/Co3O4 composite that facilitates oxygen evolution reaction (OER) as well, leading to a mere 270 mV OER overpotential (at 10 mA cm?2). The present work showcases the possibilities to strategically design non-noble composite catalysts that combine the advantages of HS phenomenon as well as Ov to achieve new record performances in alkaline water electrolysis. 2024 The Author(s). Small Science published by Wiley-VCH GmbH. -
Literary Cartography of Performance Ecologies in Sheela Tomys Valli
The shift towards posthumanism is characterized by blurring boundaries between humans and other species alongside emerging narratives centred on climate catastrophes and ecological crises. Sheela Tomys Valli (2022) is one of the most recent works of Indian fiction that actively promotes ecological consciousness. Set against the picturesque landscape of Wayanad, Valli intricately captures the essence of the indigenous community, weaving their stories into its narrative. The paper suggests that reading Valli through a cartographic lens transforms the narrative into an intelligent discourse on spatial politics. The performances in Valli are understood through the lens of performance ecology (Jeff Grygny), reflecting ongoing contemporary ecological debates. Their interrelation is explored by mapping spatial memory and schema of the characters, based on Robert T. Tallys theory of literary cartography (2013). Additionally, the paper will provide an overview of the ecopolitics of Wayanad, with a specific focus on the socio-political conditions of the Paniyar and Kuruchiyar scheduled tribes from which the characters are drawn. The study will underscore the triad of space, performance, and ecology in Valli, invoking a sense of ecoprecarity essential for rethinking and potentially expanding our notion of sustainability. 2024, University of Malaya. All rights reserved. -
Balancing work and life inacademia: unraveling theemployee engagement mystery
Purpose: This study aims to further the understanding of employees engagement by explaining their organizational commitment through their perception of the availability of work-life benefits in the organization. This study also investigates the mediating role of job satisfaction in this context. Design/methodology/approach: The model was tested on the primary data collected in two phases from 270 teaching professionals in higher education institutes in Northern India. Barren and Kennys algorithm and hierarchical regression analysis were used to test the hypotheses. Findings: The results reveal that employees perception of work-life benefits strongly influences their organizational commitment. Also, the results support that employees job satisfaction mediates the above-mentioned relationship. Research limitations/implications: Self-reported data could be considered as a key limitation of this study and for more accurate results supervisors (line managers) perspective could also be included in future studies. Also, in addition to perceived work-life benefits, supervisors support could also have an impact on employees commitment, thus its inclusion in the model could draw a clearer picture. Originality/value: This research has two key contributions: first, it adds to the limited literature examining the employees engagement issues in the academic sector. Second, this research is one of, if not the first, to investigate perceived work-life benefits among third-level teaching staff in India to explain employees commitment to their organizations. 2024, Emerald Publishing Limited. -
Enhanced power quality control of a photo voltaic power plant integrated with multiple electric vehicle
As there is a great need for high-quality electricity on the distribution side, distribution side generation (DSG) has become increasingly important. The increased weight of EVs on the distribution side is the cause of this. There are numerous power quality mitigation techniques employed to address this type of issue, but many of the solutions suggest the usage of a separate device, such as an active power filter. But while construction the DSG the solution to this problem may be addressed using the proposed solution in this paper. Power quality (PQ) problems are being caused by the grids integration of Photo-Voltaic (PV) and its application to all connected loads. With the aid of Direct Quardrature (DQ) controller and Multicarrier Space Vector Pulse Width Modulation (SVPWM) technology, the overall power quality disturbance is decreased. A Simulink model for the PV-EV-Grid system was built to measure voltage and current Total Harmonic Distortion (THD) percentages under linear, non-linear, and Plug in Hybrid Vehicle (PHEV) load situations. The model shows that the THD values are well within the IEEE 519. Indian Academy of Sciences 2024. -
Gut Microbiome Characterisation of Chrysomya megacephala: Isolation, Identification, Antibiotic Profiling, and Initial Documentation of Leclercia adecarboxylata from the Fly
Chrysomya megacephala, known for its vector potential, harbors a diverse microbiota crucial in understanding disease transmission dynamics. Herein, we report the first documentation of Leclercia adecarboxylata isolated from C. megacephala. L. adecarboxylata is an Enterobacteriaceae, gram-negative bacillus that cause infections in human and animals. Additionally, we have reported the presence of Pseudomonas aeruginosa and Enterococcus faecalis from C. megacepahala. The study carried out the antibiotic profiling and hemolytic assays, which revealed distinct resistance patterns and virulence characteristics, shedding light on potential public health implications. L. adecarboxylata, Pseudomonas aeruginosa and Enterococcus faecalis showed positive result for hemolysis and in terms of antibiotic resistance P. aeruginosa strains showed resistance to Amoxicillin, Ampicillin and Tetracycline while, E. faecalis showed resistance towards Streptomycin and Tetracycline. However, L. adecarboxylata showed sensitivity to all antibiotics. This study was conducted from Kozhikode, Kerala, India, and this is the first of its kind of study from the region to analyse the vector potential of C. megacephala. These findings underscore the significance of comprehensive microbiological investigations in vector-borne disease surveillance and management strategies. The Author(s) 2024. -
Impact of NiO/CuO as additives on the pseudocapacitive performance of SiO2-GO composite: Insights from experimental investigation
Recent interest in pseudocapacitive materials faces challenges like degradation and high costs, while low-cost carbon materials suffer from low capacitance. SiO2-GO composites, despite their potential, remain unexplored for pseudocapacitors. The present study addresses this gap by focusing on the synthesis and characterization of SiO2-GO composites, both in their pure form and doped with NiO and/or CuO. These materials are subsequently investigated for their suitability as electrode materials in supercapacitor applications. The obtained results have been comprehensively analyzed with respect to the bonding interactions and morphological characteristics of each material variant. This analysis aims to elucidate how the incorporation of NiO and/or CuO influences the structural integrity, surface chemistry, and electrochemical performance of the SiO2-GO composites. By investigating these aspects, we aim to contribute new insights that could lead to the development of efficient and cost-effective pseudocapacitive electrode materials. 2024 Elsevier Ltd -
The use of augmented reality in assessing and training children with attention deficit hyperactivity disorder
Attention deficit hyperactivity disorder (ADHD) is a serious issue that must be addressed in the modern world. Treatment for ADHD is challenging because it is costly, has adverse effects, might not be successful, and is not considered an emergency. The reason that ADHD is hard to manage is because it causes people-especially children-to make impulsive decisions that hinder their ability to succeed in school, the workplace, and other areas of life. As an alternative approach, neurofeedback therapy or play therapy, which relies on real-time feedback of an individual's brainwave activity typically collected through electroencephalogram (EEG), has demonstrated promising outcomes in the treatment of mental disorders and enhancing cognitive capabilities. On the other hand, prolonged exposure to repetitive feedback might result in lower engagement since people may become disinterested in the process and find it difficult to continue participating. An extensive assessment on the use of augmented reality (AR) in the context of pediatric ADHD has been carried out, with an emphasis on the benefits of creating games specifically for kids with ADHD. By using AR technology in a group of children, the goal of this study was to investigate the basic characteristics of AR systems that aid in the identification and treatment of ADHD in children. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Challenging the dichotomy: Examining parent socialization goals and behaviors regarding positive affect in Bengaluru, India
Parents' responses to youth positive affect (PA) have been dichotomized as enhancing and dampening. This dichotomy may not fit with cultural scripts about emotion in communities where a balance between positive and negative emotions is preferred. To assess parents' PA socialization in a culturally relevant manner for urban, middle-class families in India, we developed a new measure of parental goals about happiness and adapted the Responses to Adolescent Happy Affect Scale (RAHAS). We tested the psychometric properties of these measures and assessed relations among parental socialization goals and behaviors across 5 months. Our sample included 377 adolescent (84.4% girls; Mage = 14.47) and parent (63.9% mothers) dyads, primarily Hindu, in Bengaluru, India. Two parental goals factors emerged: Balancing and Controlling and Maximizing and Sharing happiness. Three factors emerged for the adapted RAHAS. Two factors were the same as the original RAHAS: (a) Enhancing strategies to upregulate PA and (b) Dampening strategies to downregulate PA. A third factor emerged: (c) Balancing strategies, which were culturally salient for families in India and aimed for moderation. Among socialization behaviors, Enhancing and Dampening were inversely related, while Balancing related positively to each. Balancing and Controlling goals were only correlated to Balancing behaviors. Maximizing and Sharing goals were correlated positively with Enhancing and inversely with Dampening. Longitudinally, Maximizing and Sharing and Balancing and Controlling goals were related to a significant increase and marginal decrease in Dampening, respectively. Challenging the dichotomy, our findings highlight the relevance of balancing to theories of PA socialization. 2024 The Author(s). Journal of Research on Adolescence published by Wiley Periodicals LLC on behalf of Society for Research on Adolescence. -
Exploring the potential of Andrographis paniculata for developing novel HDAC inhibitors: an in silico approach
Cancer is one of the dreaded diseases of the twentieth century, emerging the major global causes of human morbidity. Cancer research in the last 15 years has provided unprecedented information on the role of epigenetics in cancer initiation and progression. Histone deacetylases (HDACs) are recognized as important epigenetic markers in cancer, whose overexpression leads to increased metastasis and angiogenesis. In the current study, thirty-four (34) compounds from Andrographis paniculata were screened for the identification of potential candidate drugs, targeting three Class I HDACs (Histone deacetylases), namely HDAC1 (PDB id 5ICN), HDAC3 (PDB id 4A69) and HDAC8 (PDB id 5FCW) through computer-assisted drug discovery study. Results showed that some of the phytochemicals chosen for this study exhibited significant drug-like properties. In silico molecular docking study further revealed that out of 34 compounds, the flavonoid Andrographidine E had the highest binding affinities towards HDAC1 (?9.261 Kcal mol?1) and 3 (?9.554 Kcal mol?1) when compared with the control drug Givinostat (-8.789 and ?9.448 Kcal mol?1). The diterpenoid Andrographiside displayed the highest binding affinity (-9.588 Kcal mol?1) to HDAC8 compared to Givinostat (-8.947 Kcal mol?1). Statistical analysis using Principal Component Analysis tool revealed that all 34 phytocompounds could be clustered in four statistical groups. Most of them showed high or comparable inhibitory potentials towards HDAC target protein. Finally, the stability of top-ranked complexes (Andrographidine E-HDAC1 and HDAC3; Andrographiside-HDAC8) at the physiological condition was validated by Molecular Dynamic Simulation and MM-PBSA study. Communicated by Ramaswamy H. Sarma. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
A framework for natural resource management with geospatial machine learning: a case study of the 2021 Almora forest fires
Background: Wildfires have a substantial impact on air quality and ecosystems by releasing greenhouse gases (GHGs), trace gases, and aerosols into the atmosphere. These wildfires produce both light-absorbing and merely scattering aerosols that can act as cloud condensation nuclei, altering cloud reflectivity, cloud lifetime, and precipitation frequency. Uttarakhand province in India experiences frequent wildfires that affect its protected ecosystems. Thus, a natural resource management system is needed in this region to assess the impact of wildfire hazards on land and atmosphere. We conducted an analysis of a severe fire event that occurred between January and April 2021 in the Kumaun region of Uttarakhand, by utilizing open-source geospatial data. Near-real-time satellite observations of pre- and post-fire conditions within the study area were used to detect changes in land and atmosphere. Supervised machine learning algorithm was also implemented to estimate burned above ground biomass (AGB) to monitor biomass stock. Results: The study found that 21.75% of the total burned area burned with moderate to high severity, resulting in a decreased Soil Adjusted Vegetation Index value (> 0.3), a reduced Normalized Differential Moisture Index value (> 0.4), and a lowered Normalized Differential Vegetation Index (> 0.5). The AGB estimate demonstrated a significant simple determination (r2 = 0.001702) and probability (P < 2.2 10?16), along with a positive correlation (r ? 0.24) with vegetation and soil indices. The algorithm predicted that 17.56 tonnes of biomass per hectare burned in the Kumaun forests. This fire incident resulted in increased emissions of carbon dioxide (CO2; ~ 0.8 10?4kgcarbonh?1), methane (CH4; ~ 200 10?9mol fraction in dry air), carbon monoxide (CO; 2000 1015moleculescm?2 total column), and formaldehyde (HCHO; 3500 1013moleculescm?2 total column), along with increased aerosol optical thickness (varying from 0.2 to 0.5). Conclusions: We believe that our proposed operational framework for managing natural resources and assessing the impact of natural hazards can be used to efficiently monitor near-real-time forest-fire-caused changes in land and atmosphere. This method makes use of openly accessible geospatial data that can be employed for several objectives, including monitoring carbon stocks, greenhouse gas emissions, criterion air pollution, and radiative forcing of the climate, among many others. Our proposed framework will assist policymakers and the scientific community in mitigating climate change problems and in developing adaptation policies. The Author(s) 2024. -
Unveiling the Dual Potential of the MoS2@VS2 Nanocomposite as an Efficient Electrocatalyst for Hydrogen and Oxygen Evolution Reactions
Clean and reliable energy sources are essential amidst growing environmental concerns and impending energy shortages. Creating efficient and affordable catalysts for water splitting is a challenging yet viable option for renewable energy storage. Traditional platinum-based catalysts, while highly active, are quite expensive. Our study introduces two-dimensional (2D) MoS2@VS2 nanocomposites, developed using hydrothermal technique, as a bifunctional catalyst for the electrolysis of water into valuable products. Structural studies revealed the formation of MoS2@VS2 nanocomposites with a nanoflake-like structure, where MoS2 nanosheets grow on the VS2 surface. This 2D-based electrocatalyst demonstrated exceptional reaction kinetics, with low overpotentials of 265 mV for the hydrogen evolution reaction (HER) and 300 mV for the oxygen evolution reaction (OER) at 10 mA/cm2. Furthermore, the electrocatalyst displayed small Tafel slopes of 65 mV/dec and 103 mV/dec for HER and OER, respectively, along with excellent stability. The unprecedented catalytic activity stems from the synergistic effect between semiconducting MoS2 and metallic VS2. Density functional theory calculations confirmed that this synergy enhances the electrical conductivity, facilitating efficient electron transfer during the reaction and providing an abundance of exposed active sites. These results mold MoS2@VS2 nanocomposites as promising electrocatalysts for overall water splitting, paving the way for sustainable energy future. 2024 American Chemical Society. -
Deciphering the non-linear nexus between government size and inflation in MENA countries: an application of dynamic-panel threshold model
Contradictory to conventional economic theory, which foresees any increase in the size of government as inflationary, this article provides evidence that the reaction of price levels to changes in the size of government is nonlinear. The price levels do not necessarily increase in response to a rise in the size of the government but only up to a certain threshold or optimal level. Accordingly, this paper utilizes the dynamic panel threshold model to examine the threshold effects of government size (measured as government final consumption expenditure as a proportion of GDP) on inflation using a sample of 10 selected MENA countries from 1980 to 2019. The findings of this study stand out in several ways. First, the results support the nonlinear relationship between government size and inflation in the study area. Second, the government sizes estimated threshold level is equivalent to 12.46%. Third, government size negatively impacts inflation in the regime of small governments up to the threshold level. The impact turns positive once the government size goes beyond the threshold level in a regime of large size of government. These findings have ramifications for the conduct of fiscal policy. Policymakers in the MENA region can increase the size of government till it reaches the threshold level without exerting any upward pressure on price levels. The Author(s) 2024. -
Multi-Model Traffic Forecasting in Smart Cities using Graph Neural Networks and Transformer-based Multi-Source Visual Fusion for Intelligent Transportation Management
In the intelligent transportation management of smart cities, traffic forecasting is crucial. The optimization of traffic flow, reduction of congestion, and improvement of theoverall transportation systemefficiency all depend on accurate traffic pattern projections. In order to overcome the difficulties causedby the complexity and diversity of urban traffic dynamics, this research suggests a unique method for multi-modal traffic forecasting combining Graph Neural Networks (GNNs) and Transformer-based multi-source visual fusion. GNNs are employed in this method to capture the spatial connections betweenvarious road segments and to properly reflect the basic structure of the road network. The model's ability to effectively analyse traffic dynamics and relationships between nearby locations is enhanced by graphsrepresenting the road layout, which also increases theoutcome of traffic predictions. Recursive Feature Elimination (RFE) is employed to improve the model's feature selection process and choose the most pertinent features for traffic prediction, producing forecasts that are more effective and precise. Utilizing real-time data, the performance of the suggested strategywasassessed, enabling it to adjust to shifting traffic patterns and deliver precise projections for intelligent transportation management. The empirical outcomes show exceptional results ofperformance metrics for the proposed approach, achieving anamazing accuracy of 99%. The resultsshow that the suggested techniques findings have the ability to anticipate traffic and exhibit a superior level of reliability whichsupports efficient transportation management in smart cities. The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2024. -
Detection and identification of un-uniformed shape text from blurred video frames
The identification and recognition of text from video frames have received a lot of attention recently, that makes many computer vision-based applications conceivable. In this study, we modify the picture mask and the original identification of the mask region convolution neural network and permit detection in three levels, including holistic, sequence, and at the level of pixels. To identify the texts and determine the text forms, semantics at the pixel and holistic levels can be used. With masking and detection, existences of the character and the word are separated and recognised. In addition, text detection using the results of 2-D feature space instance segmentation is done. Moreover, we explore text recognition using an attention-based optical character recognition (OCR) method with mask region convolution neural networks (R-CNN) to address and detect the problem of smaller and blurrier texts at the sequential level. Using attribute maps of the word occurrences in sequence to seq, the OCR method calculates the character sequence. At last, a fine-grained learning strategy is proposed to constructs models at word level using the annotated datasets, resulting in the training of a more precise and reliable model. The well-known benchmark datasets ICDAR 2013 and ICDAR 2015 are used to test our suggested methodology. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
EDSSR: a secure and power-aware opportunistic routing scheme for WSNs
Motivated by the pivotal role of routing in Wireless Sensor Networks (WSNs) and the prevalent security vulnerabilities arising from existing protocols, this research tackles the inherent challenges of securing WSNs. Many current WSN routing protocols prioritize computational efficiency but lack robust security measures, making them susceptible to exploitation by malicious actors. The prevalence of reactive protocols, chosen for their lower bandwidth consumption, exacerbates security concerns, as proactive alternatives require more resources for maintaining network routes. Additionally, the ad hoc nature and energy constraints of WSNs render conventional security models designed for wired and wireless networks unsuitable. In response to these limitations, this paper introduces the Secured Energy-Efficient Opportunistic Routing Scheme for Sustainable WSNs (EDSSR). EDSSR is designed to enhance security in WSNs by continuously updating neighbor information and validating the legitimacy of standard routing parameters. Critically, the protocol is power-aware, recognizing the vital importance of energy considerations in the constrained environment of WSNs. To assess the efficacy of EDSSR in mitigating WSN vulnerabilities, simulation experiments were conducted, evaluating the protocols performance on key metrics such as throughput, average End-to-End delay (delay), energy consumption (EC), network lifetime (alive nodes), and malware detection rate. The results demonstrate that the EDSSR protocol significantly improves performance. It shows substantial gains in sum goodput relative to throughput, average delay, EC, and alive nodes. Specifically, the EDSSR protocol is 23% faster than DLAMD and 1013% faster than EEFCR. Additionally, the malware detection rate increases by 23%. The Author(s) 2024. -
Bio-Inspired Energy Storage Electrode: Utilizing Co3O4 Hollow Spheres Derived from Sugarcane Bagasse Extract Synthesis Via Hydrothermal Route
Recent research has explored the utilization of sugarcane bagasse, a bio-industrial waste, to fabricate energy storage devices due to ecofriendly nature, low cost with industrial scale production. In this investigation, cobalt oxide hollow spheres (Co3O4 HSs) were synthesized from waste sugarcane bagasse extract with the carbon spheres (CSs) act as template. The main component of sucrose (C12H22O11) linked with cellulose fibers and other oxygenic functional groups were used to prepare CSs. Previously, a metal precursor (Co(NO3)2.6H2O) was mixed with sugarcane bagasse extract and subjected to a hydrothermal process, resulting in uniform-sized metal CSs. The uniform sized Co3O4 HSs were formed by calcined metal CSs. The calcination temperature plays a crucial role to eliminating implanted carbon material on inter surface area of the metal oxide, shaping the Co3O4 HSs. Structural, vibrational, morphology and elemental analyses were confirmed by X-ray diffraction (XRD), Fourier transformed infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), respectively. Electrochemical tests show improved ion transport and low resistance, leading to high capacitance in asymmetric supercapacitor (ASC) devices. Subsequently, for asymmetric supercapacitor (ASC) devices, using with Co3O4 HSs has function of cathode and activated carbon (AC) as anode, the devices demonstrated impressive results of 33.1 Fg? 1 at 1 Ag? 1, 86.8% retention after 4,000 cycles, as well as the energy density and power density of 5.9W h kg? 1 at 1500W kg? 1. The Co3O4 HSs||AC device exhibits promising energy storage properties for future applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.