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Executive functions and psychological flourishing in public sector employees
The present study examined the relationship between executive functions and psychological flourishing. Executive functions are based on three broad brain capacities namely: inhibitory control, working memory and cognitive flexibility. The participants for the study comprised 99 executives working in public sector organizations in India. Correlational analysis was computed to examine the relationship between executive functions and psychological flourishing. Multiple regression was used to find out if executive functions predicted psychological flourishing. The significant positive association of psychological flourishing with self-restraint, working memory, emotional control, focus, task initiation, planning/prioritization, organization, time management, defining and achieving goals, flexibility and observation was observed. The results of the multiple regression indicated that working memory, focus and observation predicted psychological flourishing. 2021 Ecological Society of India. All rights reserved. -
Advancements in optical steganography for secure medical data transmission in telehealth systems
Secure medical data transfer technologies have advanced as a result of the brisk growth of telehealth services. This study provides a thorough review of the most up-to-date research on using optical steganography to conceal medical records from prying eyes. Data concealing capacity has been increased without sacrificing picture quality using new techniques that make it difficult for unauthorised parties to access hidden information. Using adaptive steganography methods, medical data may be encoded in images in a way that makes it impossible to detect or extract by prying eyes. By concealing information over many picture layers, multi-layer steganography adds an extra degree of protection from prying eyes. The development of steganographic techniques has been spurred on by the use of machine learning and artificial intelligence to enhance steganalysis and the use of quantum characteristics to offer an extra layer of security in quantum steganography. Combining this with cryptographic safeguards like encryption provides an additional layer of security. In order to successfully safeguard sensitive medical data during transmission, standardisation and compliance in optical steganography are becoming more important as telehealth systems become more widespread. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Work-Cognition and Subjective Well-being Among Female Professional Educators During the COVID-19 Pandemic: Moderating Role of Resilience
Teaching demands educators to be both caretakers and educators, leading to significant cognitive and emotional strain. The pandemic has worsened these challenges, requiring teachers to seek psychological and professional support. Online teaching has added to these difficulties, with educators having to adapt to new technologies while managing virtual classrooms and addressing the unique needs of students in remote learning environments. This present study sought to understand the relationship between work cognition (WCog) and subjective well-being (SWB) among female professional educators during the COVID-19 pandemic. The study also examines the moderating effect of Resilience (RSL) on the association between WCog and SWB. Using a purposive sampling technique, data was collected through an online survey from 181 female professional educators in India. Female professional educators reported moderate levels of WCog, high levels of RSL, and high levels of SWB, which indicates that the female professional educators faced cognitive and emotional strain during online teaching but demonstrated resilience and maintained positive well-being. RSL moderated the relationship between WCog and SWB, highlighting its influence on educators cognitive management and well-being during online teaching. These insights have implications for support system to enhance the well-being of educators and promoting their professional development. The Author(s) 2023. -
Development of the House of Collaborative Partnership to overcome supply chain disruptions: evidence from the textile industry in India
Collaboration in a supply chain becomes a significant competitive weapon for member firms in an uncertain business environment. The present study develops a model of supply chain collaboration named as House of Collaborative Partnership (HCP) and includes the enablers and impeders of a successful Collaborative Partnership (CP). Model development follows a three-phase process. The first phase consists of the identification of enablers and impeders of CP based on the literature review and experts opinions. The second phase applies Total Interpretive Structural Modelling (TISM) as a tool to construct hierarchical structures of the enablers and impeders of CP. The third phase deals with the development of HCP based on the hierarchical structures of enablers and impeders. The HCP is then validated with two case studies in the Indian textile industry. Eight enablers and seven impeders were identified in the first phase. After analyzing these factors with TISM, the HCP was developed consisting of four parts: Foundation, Columns, Beam, and Roof. The existence of trust, commitment to long-term collaboration, top management support, adequate financial support, ability to deal with technological changes, and providing regular training to employees constitute the HCP Foundation to achieve supply chain collaboration. The study concludes with the managerial implications of HCP to help supply chain partners in becoming resilient during an uncertain business environment. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Does energy transition reduce carbon inequality? A global analysis
Energy transition from fossil fuels to renewables is instrumental in mitigating climate change. Low-income countries have a higher share of renewable energy in their total energy consumption than rich countries (WDI, 2023). Thus, it is imperative to examine the role of energy transition in affecting relative CO2 emissions between rich and poor sections of the societies across income groups of the countries. In this context, our study contributes by constructing the carbon inequality models with renewable and non-renewable energy consumption as prime explanatory variables separately for 114 countries over a data period 19902019. The models are estimated individually for high-middle-low-income countries by controlling for foreign direct investment (FDI), economic growth, and innovations. Starting with preliminary econometric operations, we employ the dynamic simulated panel autoregressive distributed lag approach and Driscoll-Kraay standard error regression for empirical investigation. We find that energy transition reduces carbon inequality globally. Innovation has a negative impact, economic growth has a positive impact on carbon inequality, and FDI has an asymmetric impact based on the income level of the countries. The crucial global policy implications are discussed. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Human behavior analysis on political retweets using machine learning algorithms
The exponential rise in the use of social media has resulted in a massive increase in the volume of unstructured text created. This content is presented through messages, conversations, postings, and blogs. Microblogging has become a popular way for people to share what they are thinking. Many people express their thoughts on various issues relating to their hobbies. As a result, microblogging websites have become a valuable resource for opinion mining and sentiment research. Twitter is a well-known microblogging network, with over 500 million new tweets posted daily. The goal of this study was to mine tweets for political sentiments. The extraction of tweets relating to India's well-known political leaders of different states & parties in India and applying the polarity detection analysis of human behavior on the retweeted messages As a result, the sentiment classification algorithm is designed to determine whether tweets are more likely to predict the popularity of certain politicians among the general public. The subjectivity and polarity present in the tweets of political leaders are compared. The engagements of these leaders are then taken into account to determine their popularity. All these comparisons are then portrayed using data visualizations. 2023 The Authors -
Porous carbon nanoparticles dispersed nematic liquid crystal: influence of the particle size on electro-optical and dielectric parameters
Porous carbon nanoparticles (PCNPs) of four different sizes (~180nm, ~51nm, ~41 and ~25nm) were dispersed into a nematic liquid crystal (NLC) in 0.25wt% concentration. PCNPs were derived from biowaste materials and pyrolysed at elevated temperatures to get the porous structure. Polarising optical microscopic observations were carried out in dark and bright states on both the pure NLC as well as NLC-PCNPs composites. Homogeneous alignment was well maintained in all the composites except the one with the highest sized (~180nm) PCNPs. Birefringence, relative permittivity and dielectric anisotropy, increases as the size of the PCNPs is decreased in the composites. The threshold voltage was also found to decrease with the decrease in the size of the PCNPs. Such investigations may be useful for the fabrication of display devices such as flat panel displays (FPDs) and phase shifters. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Impact of porous nanoparticles on the electro-optical and dielectric parameters of nematic liquid crystals for display applications: Cost effective approach
In this study, several vital electro-optical and dielectric properties of nematic liquid crystal (NLC) dispersed with porous carbon nanoparticles (PCNP) with three different concentrations were measured. NLCs are birefringent materials. Increased birefringence was observed for NLC-PCNP composites. Dielectric study was also performed for NLC dispersed with PCNPs. Dielectric anisotropy was found to be increased for PCNP dispersed NLC system. Contrast ratio was also measured for NLC dispersed with PCNP, and it is found to be enhanced. Decreased threshold voltage was observed after dispersing PCNP into NLC. High birefringence reduces the cell gap so this work may be applicable in the making of flat panel displays (FPDs). 2024 Taylor & Francis Group, LLC. -
Influence of composite mixtures between nematic liquid crystal and porous carbon nanoparticles towards photoluminescence and UV absorbance
The optical parameters of the liquid crystalline materials can be tuned by the dispersion of nanoparticles. Concentration of dopant in the host LC material affects its optical properties significantly, which makes the dispersed system suitable for LC-based devices. In the present investigation, we have studied the effect of different concentrations of nanoparticles on the optical properties of LC, as a guesthost system, where PCNP is guest material and NLC is host material. Porous carbon nanoparticles (PCNPs) were dispersed into the nematic liquid crystal (NLC) in three different concentrations. Optical parameters were measured for pure NLC and NLC-PCNP composites. Photoluminescence (PL) study was performed and it was found that the PL intensity increased for the PCNP dispersed system. High photoluminescence has much importance in the luminescent displays. Full width half maxima (FWHM) were also determined by the Gaussian fitting of PL intensity data. UV absorbance was also measured which gets increased for the PCNP dispersed NLC system when compared to pure NLC. Optical bandgap was found to be reduced after the dispersion of PCNP into NLC. Several other parameters such as absorption coefficient and optical density were also determined. The proposed work may be significant for the liquid crystal displays (LCDs) and other devices which require less bandgap materials. This work may also put some light on the effect of dopants on the LC material in the research based on guesthost system. Increasing the photoluminescence and creating less bandgap materials using carbon nanoparticles is a real challenge, and porous nanoparticles used here overcome this challenge. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. -
Cost effective porous areca nut carbon nanospheres for adsorptive removal of dyes and their binary mixtures
Novel porous nanospheres from areca nuts (ACNPs) were synthesized via one-step pyrolysis without the use of any chemical treatment and the materials were used as adsorbents for the removal of cationic methylene blue (MB) and anionic methyl orange (MO) as well as their binary mixtures. Around, 67 tonnes of areca nut biowaste is generated every year which are then burnt due to their slow rate of decomposition resulting in higher carbon footprints. Biosorbents are generally a preferable alternative for dye adsorption but involve chemical modification for surface enhancement and complex sample treatment. In this work, ACNPs, were investigated for their efficiency in the raw form and were characterized by SEM, EDS, FTIR, XRD, and BET techniques before and after subjecting to the dye adsorption studies. The BET analysis of the adsorbents showed a high specific surface area of 693.8 m2/g when prepared at 1000 C, while the N2 adsorption-desorption plot showed type-IV isotherm, suggesting the microporous nature of the carbon matrix. Batch equilibrium studies showed the removal efficiency of >95% for both the dyes and their binary mixtures under the optimum conditions of 0.15 g/L dosage, 10 ?M concentration and contact time of 70 min. Due to the synergistic effects of the binary dyes, higher removal efficiency of MB compared to MO was observed in the binary mixture. Adsorption results were tested using Langmuir, Freundlich, Temkin, Redlich-Peterson, and Elovich isotherms to assess the best fit of the models. The qm value of MB was found to be 97.37 mg/g, while that of MO was 71.22 mg/g which is higher compared to individual dye components having lower values of 86.12 mg/g and 50.35 mg/g, respectively. Extended Langmuir and Jain and Snoeyink isotherms were used for binary data interpretation. The kinetic results showed good agreement with the Pseudo-second order equation, indicating internal diffusion. The possible mechanism involved electrostatic and ?-? interactions between the dye molecules and ACNPs. This approach is comprehensible and cost effective and can be utilized for dye removal in textile industries. 2023 Elsevier Inc. -
Garlic peel based mesoporous carbon nanospheres for an effective removal of malachite green dye from aqueous solutions: Detailed isotherms and kinetics
Biowaste based nanoadsorbents have gained much attention in the recent times for wastewater decolourization owing to their low cost, high surface area and high adsorption capacities. In the present research, garlic peel based nanoparticles (GCNP) were synthesized at different temperatures by a one step pyrolytic green approach for the effective removal of cationic dye, malachite green from the aqueous medium. The surface properties of Garlic nanoparticles were elucidated by N2 adsorption- desorption and all the GCNP samples were found to exhibit Type IV(a) isotherm indicating the presence of mesopores in carbon matrix. Using BET calculations, highest surface area (380 m2/g) was obtained for GCNP synthesized at 1000 ?C. Characterization of nanoparticles was done by XRD, EDAX, SEM and FTIR studies before and after the dye treatment. Adsorption studies conducted using different parameters like contact time, concentration and pH and dosage of adsorbent showed removal efficiency above 90% for the contact time of 70 min. Best adsorption experimental results were obtained for GCNP synthesized at 1000 C ascribable to its high surface area, higher total pore volume (0.26 cm2/g) and higher carbon content. Four adsorption isotherm models were used to validate batch equillibrium studies and the results showed data in good agreement with Langmuir and Freundlich isotherms with maximum Langmuir adsorbtion capactiy to be 373.7 mg/g. Kinetic modelling of the data showed best fit with the Pseudo second order model with rate constant value of 48.726 g mg?1 min?1. Regenerative studies were conducted conducted upto 6 cycles. Also the GC nanoparticles were tested for their compatibility in membrane form wherein, removal efficiency results were obtained for GCNP anchored in polyvinyl difluoride (PVDF) and polysulfone (PSF) membrane matrix for dye adsorption. 2022 Elsevier B.V. -
Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture
Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days. To overcome such issues in e-Healthcare systems, Internet of Things (IoT) applications have been deployed using cloud computing (CC) approaches. There are still a number of disadvantages to using CC, including latency, bandwidth, energy usage, and security and privacy concerns. Fog computing (FC), a CC development, may be able to overcome these obstacles. DiaFog enabling remote users for real-time diagnosis of diabetic mellitus disease (DMD) has been proposed in this study, which is based on the combined ideas of IoT, cloud, and fog computing, as well as an ensemble deep learning (EDL) technique. The proposed system is trained with EDL approaches on the integrated dataset of two diabetes mellitus disease datasets (DMDDs), namely, Pima Indians Diabetes Dataset (PIDD) and Hospital Frankfurt Germany Diabetes Dataset (HFGDD), obtained from the UCI-ML and Kaggle repository, respectively, and the integrated dataset of these two. The suggested system has been used to demonstrate accuracy, precision, recall, F-measure, latency, arbitration time, jitter, processing time, throughput, energy consumption, bandwidth utilization, network utilization, scalability, and more. In the remote instantaneous diagnosis of diabetic patients, the integration of IoT-fog-cloud is useful. The results of the trials show the value of employing FC principles and their applicability for speedy diabetic patient remote diagnosis. PACS-key is describing text of that key PACS-key describing text of that key. 2022 Abhilash Pati et al. -
Comparative analysis and suggestion of architectures for reduction of road accidents
As Road Accidents are increasing all over the world, it is very important to save peoples lives. With the advancement in technology we can make use of various real time sensors and technology to save peoples lives. This paper focuses on comparing various architectures which consists of various real time sensors like Eye blink sensor, Alcohol sensor, Speed sensor, load sensor, tilt and turning sensor and various technologies like GPS, GSM. After comparison paper suggests which architecture should be used in the vehicle based on certain attributes. For E.g. If the car always travels outside the city then this paper suggests the architecture which has Eye blink sensor, Speed Sensor GPS and GSM. IAEME Publication. -
Big data-Industry 4.0 readiness factors for sustainable supply chain management: Towards circularity
Big data-Industry 4.0 interaction is expected to revolutionize the existing supply chains in recent years. While increased operational efficiency and enhanced decision-making are the primary advantages studied widely, the sustainable aspects of digital supply chain in the circular economy era have received limited attention. The previous literature rarely explores the industry readiness for a digital supply chain. Thus, the present study objectives to explore Big data-Industry 4.0 readiness factors for sustainable supply chain management. A detailed literature analysis was performed to identify a total of seventeen readiness factors for sustainable supply chain management. A team of six experts were consulted to perform the pairwise comparison for the identified potential readiness factors. This study adopts a fuzzy best-worst method to prioritize the readiness factors according to their degree of influence. The results from the study reflect that readiness towards information system infrastructure, Internet stability for developing I4.0 infrastructure, and circular process and awareness are the most significant readiness factors. The potential recommendation of this study includes the increased attention from sustainable supply chain stakeholders on developing infrastructure, including knowledge building exercise and training process focused on circular economy process. The findings from the study will assist sustainable supply chain stakeholders to frame strategies and action plans during the digitalization of supply chains. 2023 Elsevier Ltd -
An integrated framework for digitalization of humanitarian supply chains in post COVID-19 era
Digital Supply Chains (DSCs) are transforming industries across various domains. Digitalization can improve coordination, increase data collection and retention capacities, enhance funding mechanisms, and improve operational performance and resource utilization. However, DSC adoption is constrained by lack of funding, operational complexities, infrastructure issues, etc. Thus, the need emerges to explore the digitalization of the Humanitarian Supply Chain (HSC) and provide solutions that can ease the adoption of DSC. In this study, a framework is created to facilitate the digitalization process of HSC in post COVID-19 era. Nineteen related drivers are identified with the potential to digitalize the HSC. The drivers are identified from the previous literature and finalized with the assistance of HSC stakeholders. A Principal Component Analysis is carried out to discover the most pertinent drivers from the identified list of drivers. A Kappa analysis is adopted to perfect the priority map of the digitalization drivers. Further, the neutrosophic DEMATEL methodology is adopted to prioritize the potential drivers and find their dependency on each other. The results from the study indicate that the most influential drivers fall under the operational and technological categories. However, the social drivers have the potential to play a significant contribution in an effort to HSC digitalization. In addition, the study presents strategies for enhancing funds collection and data management using emerging technologies. These strategies can assist HSC decision-makers in formulating relevant policies and strategic interventions. 2023 Elsevier Ltd -
Digital twins' readiness anditsimpacts on supply chaintransparency andsustainable performance
Purpose: Production systems occupy geographically dispersed organizations with limited visibility and transparency. Such limitations create operational inefficiencies across the Supply Chain (SC). Recently, researchers have started exploring applications of Digital Twins Technology (DTT) to improve SC operations. In this context, there is a need to provide comprehensive theoretical knowledge and frameworks to help stakeholders understand the adoption of DTT. This study aims to fulfill the research gap by empirically investigating DTT readiness to enable transparency in SC. Design/methodology/approach: A comprehensive literature survey was conducted to develop a theoretical model related to Supply Chain Transparency (SCT) and DTT readiness. Then, a questionnaire was developed based on the proposed theoretical model, and data was collected from Indian manufacturers. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural EquationModelling (SEM) to confirm the proposed relationships. Findings: The findings from the study confirmed a positive relationship between DTT implementation and SCT. This study reported that data readiness, perceived values and benefits of DTT, and organizational readiness and leadership support influence DTT readiness and further lead to SCT. Originality/value: This study contributes to the literature and knowledge by uniquely mapping and validating various interactions between DTT readiness and sustainable SC performance. 2024, Emerald Publishing Limited. -
Acute Leukemia Subtype Recognition in Blood Smear Images with Machine Learning
Acute leukemia is a swiftly progressing blood cancer affecting white blood cells which poses a significant threat to the immune system and often leads to fatal outcomes if not detected and treated promptly. The current manual diagnostic method, being time-consuming and prone to errors, necessitates an urgent shift toward a comprehensive automated system. This paper presents an innovative approach to automatically identify acute leukemia cells and their subtypes by analyzing microscopic blood smear images. The proposed methodology involves the segmentation of clustered lymphocytes, isolation of nuclei, and extraction of diverse features from each nucleus. A random forest classifier is then trained to categorize nuclei into healthy or cancerous, with further precision in classifying cancerous nuclei into specific subtypes. The method achieves an impressive 97% accuracy across all evaluations, holding profound implications for pathologists and medical practitioners in their decision-making processes. 2024, J.J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved. -
Quantitative assessment of blockchain applications for Industry 4.0 in manufacturing sector
Blockchain is one of the emerging digital technologies that will play a role in the breakthroughs of the fourth industrial revolution. The use of blockchain technology has the potential to greatly benefit businesses of all sizes by increasing their data's integrity, privacy, and openness. The term Industry 4.0? refers to the amalgamation of recent advances in manufacturing technology that have helped businesses cut production times significantly. The industrial and supply chain industries can benefit from these technological advancements in a number of ways. Increased efficiency in production and a more stable supply chain are just two of the many benefits that blockchain promises to bring to the manufacturing industry. The study focuses on Blockchain's huge potential in the context of Industry 4.0. Understanding the role of Blockchain technology in Industry 4.0 is examined, along with its various drivers, enablers, and associated capabilities. The several sub-domains of Industry 4.0 that can benefit from the implementation of Blockchain technology are also covered. The present research is primary and exploratory in nature. The sample size of the study is 256. The responses obtained from workers working in manufacturing sector in Delhi/NCR. The responses from workers obtained through structured questionnaire. The several sub-domains of Industry 4.0 found that can benefit from the implementation of Blockchain technology. At last, the existing study found the most important uses of Blockchain technology in the fourth industrial revolution. 2023 -
Enhanced radial basis function neural network for tomato plant disease leaf image segmentation
Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work. 2022 Elsevier B.V. -
Plant Leaf Disease Classification Using Optimal Tuned Hybrid LSTM-CNN Model
Tomatoes are widely cultivated and consumed worldwide and are susceptible to various leaf diseases during their growth. Therefore, early detection and prediction of leaf diseases in tomato crops are crucial. Farmers can take proactive measures to prevent the spread and minimize the impact on crop yield and quality by identifying leaf diseases in their early stages. Several Machine Learning (ML) and Deep Learning (DL) frameworks have been developed recently to identify leaf diseases. This research presents an efficient deep-learning approach based on a hybrid classifier by optimizing the CNN and LSTM models, which helps to enhance classification accuracy. Initially, Median Filtering (MF) is used for leaf image pre-processing. Then, an improved watershed approach is used for segmenting the leaf images. Subsequently, enhanced Local Gabor Pattern (LGP) and statistical and color features are extracted. An optimized CNN and LSTM are used for classification, and the weights are tuned using the SISS-OB (Self Improved Shark Smell With Opposition Behavior) algorithm. Finally, we have analyzed the performance using various measures. Since we have done segmentation, feature extraction, and optimization improvisations, our proposed methodology results are higher than other available methods and existing works. The results obtained at Learning Percentage (LP) is 90% which is far superior to those obtained at other LPs. The FNR (False Negative Rate) is much lower (0.05) at the 90th LP. The proposed model achieved better classification performance in terms of Accuracy of 97.13%, Sensitivity of 95.09%, Specificity of 95.24%, Precision of 94.31%, F measure of 96.71% and MCC 87.34%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.