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Neuroleadership strategies: Elevating motivation and engagement among employees
In the ever-evolving landscape of the modern era, organizations face the ongoing challenge of maintaining motivated and engaged employees. Despite the substantial body of research on this topic, many organizations still struggle to effectively promote engagement and motivation among their employees. This research aims to investigate the application of neuroleadership strategies in addressing this issue. The SCARF model, based on neuroscience principles, provides a valuable framework for understanding neuroleadership strategies which address social and emotional triggers that impact engagement and motivation. It can be effectively used to drive motivation and engagement in the workplace by addressing the fundamental social and emotional needs of employees. This study employs a quantitative approach which assesses the 321 employees from different organizations in India. The results of the study would provide leaders with practical insights to boost motivation and engagement in organizations and thereby improve the effectiveness of the organization. 2024, IGI Global. All rights reserved. -
IoT Based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa Technology
Extracting coal from Underground mine is a hazardous and tough job that needs continuous monitoring of environmental conditions to protect workers health and safety. Though some research works have explored wireless monitoring devices for underground mining, such as ZigBee and Wi-Fi technologies, they come with inherent restraints for instance restricted coverage, susceptibility to interference, reliability issues, security concerns, and high-power consumption. An Enhanced Safety Monitoring System for coal extraction from Underground Mines, employing LoRa communication technology for the effectual transmission of collected data to overcome existing challenges is discussed in this paper. The proposed system consists of two subsystems, one for monitoring the status of miners and another for comprehensive monitoring. LoRaWAN (Long Range Wide Area Network) is a multipoint protocol and this media access control (MAC) enables low-power devices to establish communication with Internet of Things (IoT) applications over extended wireless connections for long-range networks. LoRaWAN operates on lower radio frequencies, thereby providing a longer range of communication. This technology is known for its efficiency in optimizing LPWAN, offering extended range, extended battery life, robustness, and cost-effectiveness, making it highly suitable for industrial mining applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Smart Embedded Framework of Real-Time Pollution Monitoring and Alert System
The sustainability and progress of humanity depend on a clean, pollution-free environment, which is essential for good health and hygiene. Huge indoor auditorium does not have proper ventilation for air flow so when the auditorium is crowded the carbon di-oxide is emitted and it stays there for many days this may be a chance to spreading of COVID-19 and other infectious diseases. Without proper ventilation virus may present in the indoor auditorium. In the proposed system, emissions are detected by air, noise, and dust sensors. If the signal limit is exceeded, a warning is given to the authorities via an Android application and WiFi, and data is stored in cloud networks. In this active system, CO2 sensor, noise sensor, dust sensor, Microcontroller and an exhaust fan are used. This ESP-32 based system is developed in Arduino Integrated Development Environment (Aurdino IDE) to monitor air, dust and noise pollution in an indoor auditorium to prevent unwanted health problems related to noise and dust. More importantly, using IoT Android Application is developed in Embedded C, which continuously records the variation in levels of 3 parameters mentioned above in cloud and display in Android screen. Also, it sends an alert message to the users if the level of parameters exceeds the minimum and maximum threshold values with more accuracy and sensitivity. Accuracy and sensitivity of this products are noted which is very high for various input values. 2022 IEEE. -
Hyperledger Fabric as a Secure Blockchain Solution for Healthcare 4.0 Framework
The healthcare sector deals with extremely sensitive information that must be administered in a safe and confidential way. The objective of the proposed framework is to utilize Blockchain Technology (BT) for tracking medical prescriptions and the implementation is carried out using the Hyperledger Fabric platform, an enterprise-grade open-source distributed ledger technology platform designed for Bigdata applications. Multiple entities, including patients, e-pharmacies, pharmacies, doctors and hospitals can establish connections by introducing several nodes in the Fabric chain. A web-centered application is provided for doctors, connecting them with participating pharmacies, hospitals and e-pharmacies through which, they can share patient prescription. Pharmacies and e-pharmacies have access to this data and can notify patients about the availability of prescribed medicines. Additionally, reminders for refills, such as heart medication, can be sent for patients requiring long-term medication. Patients can also try with nearby pharmacies and the availability of their prescribed medicines. The inclusion of a wallet feature in the application enables patients to use mobile tokens for making purchases. Patient data is treated with the utmost confidentiality, kept private, and accessed only upon request and with the consent of the concerned parties. This privacy is ensured through the use of zero-knowledge proof. Patients retain access to their complete medical history, facilitating interactions with doctors without the need for repetitive information sharing. 2023 IEEE. -
Human activity recognition using wearable sensors
The advancement of the internet coined a new era for inventions. Internet of Things (IoT) is one such example. IoT is being applied in all sectors such as healthcare, automobile, retail industry etc. Out of these, Human Activity Recognition (HAR) has taken much attention in IoT applications. The prediction of human activity efficiently adds multiple advantages in many fields. This research paper proposes a HAR system using the wearable sensor. The performance of this system is analyzed using four publicly available datasets that are collected in a real-time environment. Five machine learning algorithms namely Decision tree (DT), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (kNN), and Support Vector Machine (SVM) are compared in terms of recognition of human activities. Out of this SVM responded well on all four datasets with the accuracy of 77%, 99%, 98%, and 99% respectively. With the support of four datasets, the obtained results proved that the performance of the proposed method is better for human activity recognition. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A Review on Fish Skin-Derived Gelatin: Elucidating the Gelatin PeptidesPreparation, Bioactivity, Mechanistic Insights, and Strategies for Stability Improvement
Fish skin-derived gelatin has garnered significant attention recently due to its abundant availability and promising bioactive properties. This comprehensive review elucidates various intricacies concerning fish skin-derived gelatin peptides, including their preparation techniques, bioactive profiles, underlying mechanisms, and methods for stability enhancement. The review investigates diverse extraction methods and processing approaches for acquiring gelatin peptides from fish skin, emphasizing their impact on the peptide composition and functional characteristics. Furthermore, the review examines the manifold bioactivities demonstrated by fish skin-derived gelatin peptides, encompassing antioxidant, antimicrobial, anti-inflammatory, and anticancer properties, elucidating their potential roles in functional food products, pharmaceuticals, and nutraceuticals. Further, mechanistic insights into the functioning of gelatin peptides are explored, shedding light on their interactions with biological targets and pathways. Additionally, strategies aimed at improving the stability of gelatin peptides, such as encapsulation, modification, and integration into delivery systems, are discussed to extend the shelf life and preserve the bioactivity. Overall, this comprehensive review offers valuable insights into using fish skin-derived gelatin peptides as functional ingredients, providing perspectives for future research endeavors and industrial applications within food science, health, and biotechnology. 2024 by the authors. -
Barriers to Smart Home Technologies in India
Smart home technologies (SHT) are critical for effectively managing homes in a digital society. However, SHTs face challenges related to their limited use in developing country contexts. This study investigates the factors that act as barriers to SHT adoption among individuals in Bengaluru, India. The roles of perceived risk, performance and after-sale service, and demographics in using smart home technologies (SHT). This study used the data from the primary survey of 133 respondents. The collected data were analyzed using regression analysis. The results supported five of the proposed hypotheses, namely, perceived performance risk, perceived financial risk, perceived psychological risk, and technological uncertainty, which influence the Behavioral intention to adopt SHT. However, service intangibility is influenced by performance risk. Income and age influence the psychological risk and adoption of SHT. The study identifies the barriers to SHT adoption. The supportive environment for SHT needs to be strengthened to reduce the associated risks. IFIP International Federation for Information Processing 2024. -
Value addition to international students' exchange programs through engagement in services
Social responsibility has been an emerging concept in Higher Educational Institutions in India. Promoting social responsibility through international students' exchange programs helps students' capacity to improve their cultural, social and service knowledge to bring about sustainable and meaningful development. This chapter looks at the impact of the interventions of international students in slum communities, especially working with children and women for their academic, health and economic empowerment. This was a qualitative study using a self-structured interview schedule. Data were collected from twenty international students from universities of Norway and the Netherlands who were placed in urban slums for five years and thirty children and women from urban slums of Bangalore who benefitted from this program. A purposive sampling method was used, and the data were analyzed using thematic analysis. This chapter reveals the development of children and women through international students' programs and helps showcase further planning for innovative programs for vulnerable populations. Attitudes of both groups towards cultural differences and the expectation and effectiveness of the exchange program may also be described in this chapter. This chapter intends to help plan international exchange programs from different dimensions benefiting the slum communities for their development and sensitizing cultural differences from different perspectives. 2024 Nova Science Publishers, Inc. -
An empirical analysis of sustainability of public debt among BRICS nations
The main objective of this paper is to verify the sustainability of public debt among Brazil, Russia, India, China and South Africa (BRICS) in a political economy framework. Annual panel data have been used for BRICS countries from World Development Indicators of World Bank for the period 19802017 for the analysis. Bohn's sustainability framework is used to examine the sustainability of public debt in BRICS nations and verify the influence of political economic variables such as election year, coalition dummy, ideology of the government and unemployment on public debt sustainability. The results suggest that public debt sustainability is weak for BRICS as a whole. China and India have a better public debt sustainability coefficients compared to the same for Brazil, Russia and South Africa. Structural change dummy included in the model suggests that debt sustainability is severely affected after the 2008 crisis period. Political factors have influence on debt sustainability in BRICS. Electoral cycle year and coalition dummy variables adversely affect public debt sustainability in BRICS. While centrist political ideology is found to be significant and negative, left and right ideologies are not significant for debt sustainability. Since debt sustainability is found to be weak in BRICS, countries in the region need to adopt necessary measures to improve their primary balance through appropriate fiscal and debt management. Besides, it is important for the governments to prioritize fiscal prudence irrespective of their ideologies and political compulsions. 2020 John Wiley & Sons, Ltd -
Effect of Subtitles on Gaze Behavior during Shot Changes: An Eye-tracking Study; [Efecto de los subtulos en el comportamiento de la mirada durante los cambios de plano: un estudio de seguimiento ocular]
The study provides a comprehensive picture of the effect of subtitles on the gaze behavior of the participants while watching continuity editing and discontinuity editing style cinema. Three video clips (with English subtitles and without subtitles) of continuity editing and discontinuity editing styles were presented to participants. The video clips came from English movies and the participants were not native English speakers. Entry time, dwell time, first fixation time, scan path, and average fixation duration were taken as dependent variables in this within-group study. The eye-tracking data gathered were subjected to repeated measures of two-way ANOVA and paired t-test. Results revealed that the appearance of subtitles at the bottom of the screen changed the eye movement pattern of the participants during the shot changes. Timing of the subtitle starting point (before the cut or after the cut) also affected the gaze behavior. The editing style, however, did not make any difference in the gaze behavior of participants while watching subtitled video clips. Further, participants preferred reading subtitles to seeing visual images even if the subtitles were presented during the shot changes. 2023. International Journal of Psychological Research provides open access to all its contents under the terms of the license creative commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) -
Learning analytics for academic management system enhancement: A participatory action research in an Indian context
A common thread noted in many academic management system implementations was the stagnation and deterioration of their usage after the initial hype. This action research study was aimed at addressing this decline in a higher education institute after undertaking a reflective analysis of the waning usage patterns and taking appropriate initiatives to enhance their usage. The authors have attempted this as participants driving the change. As change agents, the academic management system implementation was advanced to move closer to a stage where the committed use occurred and intended benefits were realised. Several initiatives were introduced to propel this change. The scope of this article was confined to gauging the impact of the trigger factors; memos, and training on the academic management system usage. Their effects were measured by applying learning analytics to various sources of usage and performance data. This further led to analysing the relationship between usage and performance of the stakeholders; students, and faculty. 2021 British Educational Leadership, Management & Administration Society (BELMAS). -
Innovative constraints formulation in timetable planning for efficient resource allocation in academic institutions
Many developing nations still rely on manual timetable scheduling in academic institutions, leading to inefficiencies. However, advancements in technology have introduced software solutions such as FET (free evolutionary timetabling) to automate the process. In a study, the authors successfully implemented FET to automate timetable creation at a university, reducing the time required from days to seconds. Scheduling in universities with elective courses poses challenges, includingavailable rooms, faculty availability, and guest lecturers. The authors propose a unique timetable generation process that considers post-pandemic social distancing measures. This process addresses various complex constraints faced by academic institutions and holds potential for reopening institutions in a cautious manner following the pandemic. 2023, IGI Global. All rights reserved. -
A Smart Internet of Things (IoT) Enabled Agricultural Farming System
Industry 4.0 has brought about a profound revolution in recent times. This advancement profoundly impacted technology usage in every aspect and has significantly improved businesses. Agriculture is one of the evergreen economic contributors to Indias GDP. With improvements in adaptability in this sector, the time is ripe for instituting IoT (Internet of Things)-based smart agriculture. Water scarcity and drastic climate change are real issues affecting crop yields, leading to the failure in the timely fulfillment of market demand (Nawandar 2019). The authors have collaborated to address these concerns by creating a system comprising a functional hardware prototype and an android application for regulating irrigation and temperature. The introduction of IoT (Internet of Things) automates crop monitoring and reduces labor costs. By using IoT, (Internet of Things) an earmarked agricultural field is covered with sensors. The sensors are concealed so as not to be affected by the bleakness of the external environment. These sensors work in tandem with drip irrigation following the sensed climatic conditions. The water is pumped directly to the root zone in an optimally sensed manner. The authors developed and tested the system successfully in a greenhouse system. The process initially aims to extract the values of soil parameters by using IoT (Internet of Things) sensors and appropriately control the watering of crops, thus enabling the cultivation of crops even in a hot and dry climate. Crops can be irrigated from a remote location and their temperature can be meticulously regulated to ensure they remain within an optimal range. Water utilization for agricultural crops is optimized with the use of automated irrigation systems that use W.S.N (Wireless - Sensor-Networks) and G.P.R.S (General-Packet-Radio-Service) modules. The algorithm employed in the system to control water usage is based on the needs of the crop and the terrain. The entire system is powered by photovoltaic panels, which are useful in rural and isolated areas without electricity (Raut and Shere 2014). A cellular network is used for duplex communication. Continuous monitoring and irrigation schedule programming are used by web apps to manage irrigation. This is also possible using a browser and web pages. A system with three identical automatic irrigation systems can save water use by up to 90%. 2024 by Nova Science Publishers, Inc. All rights reserved. -
Biosynthesized AG nanoparticles: A promising pathway for bandgap tailoring
The unrivaled features and prospective applications promote graphene as a potent contender for next-generation nanodevices. But the realization of a tunable bandgap structure for zero-bandgap graphene at all times persists as a dilemma. In this work, a green approach is adopted for the bandgap modulation of graphene oxide (GO). The biosynthesized silver nanoparticles (AgNPs) were introduced into the graphene matrix, and hence the bandgap was tailored for the formation of a semiconductor composite. The bare GO that has got a bandgap of 3.41 eV was tuned to 2.33 eV on the addition of AgNPs. The preparation of AgNPs using fruit extract of cyanococcus make the process greener, safer, and cost-effective. This paper intends to open a new venture towards the environment safe synthesis of semiconductor nanocomposite necessitate for optoelectronic and photovoltaic technologies. 2020 by the authors. -
Deep CNN Based Interpolation Filter for High Efficiency Video Coding
Video coding is a current focus in research area as the world focus more on multimedia transfer. High Efficiency Video Coding (HECV) is prominent among existing one. The interpolation in HEVC with fixed half-pel interpolation filter uses fixed interpolation filter derived from traditional signal processing methods. Some research came up with CNN based interpolation filter too, here we are proposing a deep learning-based interpolation filter to perform interpolation in inter prediction in HEVC. The network extracts the low-resolution image and extract the patch and feature in that to predict a high-resolution image. The network is trained to predict the HR image for the given patch, it can be repeated to generate the full frame in the HEVC. The system uses cleave approach to reduce the computational complexity. The trained network is validated and tested for different inputs. The results show an improvement of 2.38% in BD-bitrate saving for low delay configuration. 2024 IEEE. -
Deep Convolutional Neural Network Driven Interpolation Filter for High Efficiency Video Coding
Research in video coding has gained significant importance in recent years, driven by the increasing demand for multimedia transmission. High Efficiency Video Coding (HEVC) has emerged as a prominent standard in this field. Interpolation is a crucial aspect of HEVC, particularly when using fixed half-pel interpolation filters derived from traditional signal processing techniques. In recent times, there has been an exploration of interpolation filters that are based on Convolutional Neural Networks (CNNs). Conventional signal processing techniques are used in traditional HEVC methods to employ fixed half-pel interpolation filters. Recent advancements have delved into the application of Convolutional Neural Networks (CNNs) to enhance interpolation performance. Our proposed method utilises a sophisticated CNN architecture specifically crafted to extract valuable features from low-resolution image patches and accurately predict high-resolution images. The network consists of multiple layers of CNN blocks, which utilise 1 and 3 convolutional kernels to enable efficient and thorough feature extraction through parallel processing. This architecture improves computational efficiency and greatly enhances prediction accuracy The suggested interpolation filter shows a 2.38% enhancement in bitrate savings, as evaluated by the BD-rate metric, specifically in the low delay P configuration. This highlights the potential of deep learning techniques in improving video coding efficiency. 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). -
Excited-state intramolecular proton transfer (ESIPT) salicylaldehyde Schiff bases: ratiometric sensing of ammonia and biologically relevant ions in solution and solid state
The intricate molecular architecture of ESIPT salicylaldehyde Schiff bases facilitates dynamic processes, inducing tunable photoluminescent properties. Notably, their halochromic nature, exhibiting colour changes in response to external stimuli, adds a vibrant dimension to their molecular repertoire. This sensitivity extends to environmental factors, making them valuable indicators for alterations in surroundings. The compound (E)-N-(3,5-dibromo-2-hydroxybenzylidene)-4-methylbenzohydrazide (PTBH) demonstrates exceptional sensitivity to ammonia, enabling real-time detection (LOD = 0.14 nM) in both solution (ratiometric) and the solid state. Moreover, their metal chelation capability allows simultaneous sensing of Mg2+ and Fe2+ ions, addressing environmental hazards. Exploiting molecular recognition, the fluorescent probe serves as sensors for amino acids, opening new avenues in biomedical diagnostics. The study introduces a novel solid-state emissive Schiff base, highlighting its stimuli-responsive photoluminescent properties and diverse applications, emphasising its potential in intelligent fluorescent materials for analytical and sensing technologies. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
UV-Promoted Metal- and Photocatalyst-Free Direct Conversion of Aromatic Aldehydes to Nitriles
Abstract: An efficient, simple, and catalyst-free UV-induced functional group transformation of aromatic aldehydes to nitriles has been reported. The developed strategy delivers various functionalized aromatic nitriles with high yields and purity. The UV irradiation activates the carbonyl group of aldehydes and leads to the formation of aldoxime intermediate, further resulting in the generation of nitriles. The striking highlights of the reported methodology are simple reaction conditions, good yields, UV-promoted transformation, and catalyst-free synthesis. Due to the above-mentioned advantages, the methodology provides a whip hand toward environmentally friendly chemical synthesis. 2022, Pleiades Publishing, Ltd. -
Templating motifs of molecular axles in hydrogen bonding [2]rotaxanes: Synthesis and applications
In [2]rotaxanes, hydrogen bonding interactions are found to be one of the prominent factors driving the templation of the macrocycle to the molecular axle. Several hydrogen bonding templating moieties like amides, hydrazones, nitrones, and squaraines have been incorporated into the molecular axle and the macrocycle for generating hydrogen bonding [2]rotaxanes. This review focuses on the design and synthetic strategy adopted in rendering various molecular axles, which can be used to generate [2]rotaxanes for numerous applications. Moreover, a detailed description is provided with suitable mechanistic insights about the utilization of hydrogen bonding [2]rotaxanes in applications like molecular motors, organic synthesis, and catalysis. 2022 Elsevier Ltd -
Fungi-Templated Silver Nanoparticle Composite: Synthesis, Characterization, and Its Applications
The self-assembly of nanoparticles on living bio-templates is a promising synthetic methodology adopted for synthesizing nano/microstructures with high efficiency. Therefore, the method of bio-templating offers various advantages in controlling the geometries of nano/microstructures, thereby increasing the efficiency of the synthesized material towards various functional applications. Herein, we utilized a filamentous fungus (Sclerotium rolfsii) as a soft bio-template to generate silver nanoparticle (AgNP) microtubules adhering to the fungal hyphae. The resulting composite combines the unique properties of silver nanoparticles with the biological activity of the fungi. The 3D fungal hyphaesilver nanoparticle (FH-AgNP) composite was characterized using SEM, elemental analysis, and the X-ray diffraction technique. Additionally, to highlight the functional application of the synthesized composite, dye degradation studies of methylene blue under visible light was effectuated, and a percentage degradation of 67.86% was obtained within 60 min, which highlights the potent catalytic activity of FH-AgNPs in dye degradation. Further, the antibacterial study of the composite was carried out against the bacterium Escherichia coli, and it was found that 200 ?g of the composite exhibited maximum antibacterial properties against Gram positive (Staphylococcus aureus) and Gram negative (Escherichia coli) bacteria. Overall, fungi-templated silver nanoparticle composites are a promising area of research due to their combination of biological activity and unique physical and chemical properties. 2023 by the authors.