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In present digital world, the data transmitted over network has become worldwide. The information compiled, processed and communicated in Bits and bytes of digital format over communication channels. For secure communication an advanced encryption Standard (AES) cryptographic algorithm is employed to avoid the risk of secret information tampering. This Research article predominantly focuses on low power and low area hardware architecture designed using Field Programmable Gate array. The novel architecture is derived by employing composite field arithmetic for substitute byte and inverse substitute bytes operation. By this approach the unwanted delay occurs during the Look up table (LUT) method has been averted. Optimized Positive polarity reed Muller (OPPRM) architecture is introduced in order to minimize the hardware utilization, eventually subpipeling technique has brought in, to enhance the speed of the architecture. The Performance metrics of OPPRM-AES is evaluated with respect to Bonded IPADS, Slice, LUTS, Number of slices, Static as well as Dynamic Power. The performance of a proposed architecture is compared with AES-PPRM, AES-CFA and AES-ECG.The architecture is implemented onto Zynq ZC702 Evaluation Board and the simulation is performed through the vivado 2019.1 software.

BACKGROUND: Health care environment relies to a great extent on positive nursing behaviors. So in this study, individual difference factors were taken into account in explaining organizational citizenship behavior by integrating trait theory, conservation of resource theory, and affective event theory. OBJECTIVE: The present study examines whether work burnout mediates the relationship between the extraversion trait and organizational citizenship behavior, then investigating whether the mediating effect is moderated by emotional contagion. METHOD: The study employs cross-sectional design and questionnaire-based survey was used to collect the data from 311 nurses working in private hospitals. RESULTS: The findings reveals that extraversion trait is positively related to organizational citizenship behavior. Then, work burnout partially mediates the relationship between extraversion and organizational citizenship behavior. Further, moderated mediation analysis discloses the indirect effect of extraversion on organizational citizenship behavior through work burnout is a decreasing function of emotional contagion. CONCLUSION: The study reports extravert nurses are likely to exhibit more organizational citizenship behavior through reduced work burnout. However, nurses should maintain low level of emotional contagion to have stronger mediating effects. Training should be provided to nursing staff to understand and handle the level of emotional contagion exist among them unconsciously.

PurposeHigher education management institutions play a vibrant role in imparting managerial skills to the students to face the corporate world. Performance evaluation of such institutions is mandate to ensure the outcome quality. To establish this, the present research explored the critical performance indicators of management institutions using the balanced scorecard (BSC) approach.Design/methodology/approachThis research explored the critical performance indicators of public, private and standalone management institutions in India. Data were collected from the representative sample of all the stakeholders in those management institutions. A specific vision was created and a systematic procedure was employed to arrive at the objectives, measures and metrics of the scorecard specific to the vision. Confirmatory factor analysis was used to perform analysis on the collected data. For the objectives and measures that evolved from confirmatory factor analysis, metrics were formulated based on the expert opinion.FindingsThe study resulted in 16 objectives, 46 measures and 54 metrics encompassing all the four perspectives of BSC. This paper has contributed a concrete, concise, comprehensive and context specific framework.Research limitations/implicationsThe nature of the BSC framework paves the way for continuous assessment and eventually helps the institutions to attain sustainable growth. This research contributes to the literature of balanced scorecard and also to the performance assessment of the management institutions.Originality/valueBSC-based benchmarking is a unique contribution to the academia of management education to precisely measure the performance of institutions. The model comprehensively includes the indicators from all the perspectives of stakeholders in terms of objectives, measures and metrics, thus proposing a holistic assessment.

The main objective of the present research is to conduct a numerical study for the nanoparticle enhanced energy storage material in solar desalination application. Paraffin is selected as the phase change material because of their stable thermo-physical properties as compared to other energy storage materials used in solar desalination (like lauric acid, stearic acid, etc.). Titanium oxide nanoparticles were impregnated in paraffin and tested for various thermophysical properties like thermal conductivity, latent heat, melting and solidifying characteristics, etc. The test results were used as input for various parameters during the numerical investigation. The overall cumulative yield per day was enhanced to 6.6 L/m2/day when titanium oxide enhanced paraffin was used instead of using fossil paraffin. It is inferred that the titanium oxide enhanced paraffin yields better productivity as compared to the solar still with virgin paraffin as energy storage material. Hence it is concluded that nanoparticle enhanced energy storage material is the next stage of research and development in the area of solar desalination integration with phase change materials as it yields better productivity as compared to unblended paraffin because of their enhanced thermo-physical properties.

Gastrointestinal (GI) diseases are one of the rising issues in medical field from the past two decades. The incidence and prevalence rates have shown a notable rise in these conditions, thereby leading to an increase in morbidity and mortality rate. The principle aim of this study is to design and develop a system that can acquire signals precisely and non-invasively from the abdominal skin over the stomach and share the obtained results to the patient mobile wirelessly. This technique enables the user to monitor their gastric condition effortlessly. This standalone system, for detecting various abdominal disorders incorporates the technique of Electrogastrography (i.e.,) recording gastric signals non-invasively using surface electrodes. The proposed real-time gastric recording and monitoring system is designed using Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software in addition to the initial analog circuitries. The entire system is enclosed in an elastic belt to make it wearable for different abdominal torsos. The real-time data acquired is shared with the patient mobile using National Instruments (NI) Data dashboard application mobile application. On the other hand, the data recorded from the system is analyzed in Matrix Laboratory (MATLAB) using moving average algorithm to obtain precise electrogastrographic envelopes. The gastric signal recording is obtained from ten healthy individuals by following the developed experimental protocol in postprandial session. The data obtained is used in evaluating and quantifying the gastric slow wave propagation in the individuals by extracting the signal features in temporal and frequency domains. The obtained results prove the efficiency of the proposed design, with the fixed electrode position making the system user-friendly.

Despite broad evidence in the past demonstrating the relationships between personality traits and organizational citizenship behavior, research on organizational citizenship behavior from the perspective of emotional contagion has not received deserving attention to date. Drawing on the trait theory and affective event theory as an overarching reference, this study theorizes emotional contagion as a moderator by arguing that it improves the above relationship incrementally. This study conducted hierarchical multiple regression analysis to test the hypotheses and adopted the Johnson–Neyman method using SPSS PROCESS Macro to probe the pattern of the significant interactions. A total of 320 bank employees took part in the survey. The findings revealed, agreeableness and conscientiousness as significant predictors of organizational citizenship behavior. Furthermore, the above association was enhanced for employees at the conditional level of high emotional contagion. The study provides insight into recognizing the significance of employees’ emotional levels and their contribution to organizational growth.

Incorporating corporate social responsibility (CSR) initiatives in an organization is identified as a strategic tool for achieving sustainable performance. The objective of this research was to examine the conceptual framework that relates adoption of CSR initiatives, green practices both product and process related practices and the effect of these practices on performance and employees' pro-environmental behavior (PEB) at work. After a review of the literature, seven hypotheses have been proposed and tested with green practices as a mediating variable. Adoption of green practices was found to partially mediate the relationship between CSR and performance while the mediating relationship of green practices between CSR and employees’ PEB at work was found to be insignificant. This research extends to the body of extant literature by establishing the results of the conceptual framework and in addressing the implications for India. The results also highlight how organizations can include CSR initiatives and green practices synergistically to enhance performance and PEB at the workplace for corporate sustainability. Further study is required to explore how the constructs are related at two different time periods and across the dimensions.

Rapid globalization and industrialization is witnessed across the globe. This has led to a rapid increase in energy demand. In India, it is found that energy consumption is growing exponentially. A sustainable energy model that helps in optimizing the energy consumption is the need of the hour. The model should help policymakers to put proactive plans in place. A two-step process has been carried out. A demand prediction model is proposed to forecast the fossil based energy demand for India upto 2040-41. 92 econometric models have been were developed and the best fit was found. The requirement was found to be coal and lignite = 32203.42 pJ oil = 20396.92 PJ, natural gas = 3460.77 pJ and electricity = 9887.71 pJ. The total energy requirement in 2040-41 will be 65948 pJ in 2040-41. Using constraints such as energy return on investment, acceptability, reliability and CO2 emissions a multi-objective optimization model that maximizes efficiency and minimizes cost is developed. Renewable energy substitution scenarios are developed. Solar energy is expected to dominate and polices have to designed to ensure that the naturally available solar energy is being utilized to a larger extent. Smart grid technology is at its nascent stage of development in India. Research and development has to be done to enhance the use of smart grid so that decentralized energy generation from renewable energy can be increased.

The most common type of chronic and life-threatening disease is cardiovascular disease (CVD). For the early prediction of arrhythmia, electrocardiogram (ECG) is recorded from the patients, non-invasively using surface electrode. In this approach, Empirical Mode Decomposition (EMD) is performed for noise removal followed by Pan Tompkins algorithm for feature extraction. To reduce the amount of signal characteristics and computation time, Principal Component Analysis (PCA) is utilized. Finally, two classifiers, The Support Vector Machine (SVM) and the Naive Bayes (NB) classifier is used to determine the cardiac abnormality from the ECG signal. The comparison is made between the two classifiers and their accuracy will be analysed. We obtained 89% accuracy for SVM and 99% for NB classifier. Lakhs of samples will be available in the Physionet. The amplitude of the signal is 0.1 Mv and time period (T) is 10ms and the frequency of 100Hz. The Confusion Matrix can then be used to assess how well an ECG signal is performing. A MATLAB program is used which has the capacity to observe the ECG bio-signal on a computer.

The use of an automated system in the X-ray image processing is very important to radiologists. To end this, many approaches for detecting lung diseases have been developed, and many imaging techniques such as computerized tomography (CT) and chest radiography (X-ray) have been employed. In this paper, an algorithm to detect pneumonia the chest X-ray is considered. Pneumonia, inflammatory lung disease appears as white patches of cloud on the chest X-ray images. On chest X-ray images, an automated system has been implemented using both with pathology and without pathology using Kaggle dataset. This paper aims to help efficiently detect pneumonia by segmenting the lung area and by extracting features. The lung area is segmented using the watershed algorithm and graph cut process. Gray Level Co-Occurrence Matrix (GLCM) features are extracted and fed to classifier for identifying the diseased lung from the healthy lung. The classification accuracy of 92% is accomplished by K-Nearest Neighbor (K-NN) classifier. The system has been tested with 300 chest X-ray images and has achieved sensitivity of 89%, specificity of 93% in classifying the lung diseases.

In the past, attention has been created to use solar energy due to increased environmental pollution. Solar energy utilization through photovoltaic (PV) and thermal technologies is required to replace the conventional use of fossil fuels across the globe. Different types of solar PV (SPV) technologies utilizing the photons as input are driving the life of people. On the other hand, utilizing the solar heat for various applications is categorized as the solar thermal application which includes desalination, heating, cooling, cooking and power generation. Hence the objective of this work is to discuss the fundamentals, recent advancements and applications of different solar utilization technologies. The chapter is categorized into two major sections namely solar PV techniques and solar thermal techniques. In the first section SPV techniques, the principle, operation and recent advancements in the SPV system have been covered. In the second section solar thermal techniques, the principle, construction, working mechanism and current state of the art in recent research on solar collectors, solar cooling techniques, solar pond, solar cooking techniques and solar desalination have been addressed.

Giving blind people with great accessibility to their environment is of great demand. People with visual impairments experience a lot of problem in using the modern assistive device that limits their daily basic activities. The level of assistance provided of these special aids does not meet the consumer requirements and not affordable by the every section of the society. To overcome the some of the limitations of the existing visual aids, in this paper we present the work that helps the visually impaired person with smart glasses to identify the medicine. The name of the medicine can be read by the smart glass system that provides audio signal through the ear phones. The smart glass system reads the medicine name using Convolutional neural network

In recent years latent heat energy storage (LHES) has received a worldwide focus in the area of solar still applications. Among various techniques available to improve the productivity of solar stills, heat storage is one of the proverbial areas in the present scenario. LHES surpasses sensible heat energy storage (SHES) in solar still applications owing to its properties like latent heat, thermal conductivity, etc. Focus has to be given in the selection of suitable LHES material since its properties are very significant during the performance and operation of solar stills. Though previous reviews on LHES in solar stills focus only on the productivity enhancement, this paper presents an up to date comprehensive review exclusively about LHES in solar stills focusing on its productivity, properties of LHES materials and the mechanisms behind the variation in properties of LHES materials. Further, in addition to this, a detailed analysis is also presented in this paper by employing analytical hierarchy process (AHP) which enable the researchers to prioritize the properties during the selection of LHES in solar still applications. Besides AHP analysis, we also evaluate the relative efficiency of the available LHES materials for solar still application by data envelopment analysis (DEA) to find the frontier LHES materials thereby suggesting the methods to augment the properties of the frontier material (identified through DEA analysis). Thus, this paper may be a reference guide in the near future for the academicians, researchers to get an overview about the various LHES materials used in solar stills and significance of their properties for improving the yield in solar still applications.

The 2030 agenda for sustainable development calls for a holistic approach. It needs to tackle multidimensional aspects such as poverty, inequality, inclusivity, skill development to name a few. In this paper, a multi expert multi criteria decision making approach is adopted using fuzzy AHP, VIKOR and DEA. The weights of the criteria are determined using fuzzy AHP and it is found that basic amenities is the most important (0.21) followed by environmental concern (0.18). Fuzzy VIKOR early defuzzification is used to rank the various sectors based on the importance of sustainability criteria. Science & technology is ranked first followed by rural development. Considering the investment made in each sector, fuzzy AHP DEA indicates water resources, river development & environment lies in the frontier efficiency region. The DEA analysis reveals that basic amenities, environment concern and governance system need to be focused upon for improving the efficiency score of science & technology from 92.2% to reach the frontier line efficiency. The integrated fuzzy AHP VIKOR presents the ranking considering only the sustainability criteria while fuzzy AHP DEA highlights whether the national investment is in tune with the sustainability criteria for sustainable development.

Purpose Second-hand/used goods channels compete with existing traditional channels to satisfy consumers’ needs that are unmet by traditional retail networks. However, most studies on online shopping have largely ignored online second-hand/used good purchases. This study aims to use Thaler’s mental accounting model, principal–agent perspective and contamination theory to highlight the differences in the value sought by online new goods and second-hand shoppers. Design/methodology/approach A conceptual framework linking perceived uncertainty, perceived acquisition value and e-loyalty was developed and tested using structural equation modelling. The moderating effects of product type (new vs second-hand) and frugality were also included. Findings The paper found strong support for the model. Results showed that online second-hand shoppers were more uncertain and perceived lesser levels of acquisition value when compared to new goods shoppers. They were also less frugal. Online shoppers are also more likely to buy products with sensory attributes (experience goods) in new goods websites and products with non-sensory attributes (search goods) from second-hand websites. The authors recommend various ways in which managers can increase perceived value for the online shopper. Research limitations/implications Future studies can extend this investigation by including transaction value or other hedonic values to verify their impact on acquisition value and e-loyalty. While the authors found support for the notion that consumers who buy used goods online are less frugal, there is some research that could point to the opposite. Hence, research can investigate this topic in depth in more countries to throw more light on this. Practical implications To sustain themselves in a competitive online market, retailers need to understand the value sought by consumers. This study provides empirical evidence of the importance of acquisition value for new goods and second-hand shoppers. Originality/value No recent research has compared the value sought by online second-hand and new goods shoppers. This study contributes to the understanding of the acquisition value perceived by consumers in online new goods and second-hand shopping channels.

Identification and retention of talented workforce is of utmost importance in the current career scenario. This research studied mediating role of individual career self-management and tested a theoretical model examining direct and indirect relationships among perceived organizational support (POS), psychological empowerment (PE), individual career self-management (ICM) and affective commitment (AC). Data was collected from a sample of 270 IT employees designated as ‘talent’ in their respective organizations from the southern part of India. We employed structural equation modeling technique to test proposed research hypotheses. The result supported the idea that perceived organizational support and psychological empowerment positively affects individual career self-management. Individual career self-management was the significant antecedent of affective commitment. Regarding mediation hypothesis, individual career self-management was found to fully mediate the relationship between psychological empowerment and affective commitment. Further, it acted as partial mediator between perceived organizational support and affective commitment. Implication for future research and practices are discussed.

Desalination using solar stills is an ancient economic method for water desalination. Over the years, research and development in the area of solar still has resulted in increased distillate yield by means of integration of PCM (phase change material), photo-voltaic thermal (PVT), etc with the still. Nano-PCM is an upcoming technology which modifies the thermal performance of PCM. The aim of this research is to analyze the efficiency of 20 solar stills including nano-PCM based solar stills considering various input and output criteria using integrated fuzzy analytical hierarchy process (AHP) and data envelopment analysis (DEA). The efficiency derived here is relative with regard to the parameters and stills considered in this study. The result infers that, even though the productivity of stepped solar still with sun tracking system was high, but when techno-economic aspects were considered it is not among the top solar stills. The analysis indicated pyramid type solar still, single slope solar still with PVT, solar still with NPCM (paraffin + copper oxide), solar still with NPCM (paraffin + titanium dioxide) and solar still with PCM (paraffin) occupies the top five positions with relative efficiency of 100, 100, 88.47, 88.46 and 76.93% respectively.

A smart city is defined as a sustainable and efficient city center providing high quality of life by optimally using its resources. Managing its resources especially energy is a very crucial factor owing to the complexity of various interlinked parameters. In this paper, the importance of various criteria for sustainability in a smart city are determined using fuzzy and fuzzy-AHP method. The sustainability indicators for designing a smart city in a developing country has been identified. DEA:AR-CCR model is adopted to determine the relative efficiency of each of the sustainability indicators for a smart city in the context of input and output criteria. The decomposition efficiency measures clearly highlight which sustainability indicator the country needs to focus based on the importance of the input criteria to achieve the desired outputs. It was found policy makers and administrators have to design policies for economic development (0.85) and energy (0.82) for achieving economic prosperity of the nation.

Developing economies need to invest in energy projects. Because the gestation period of the electric projects is high, it is of paramount importance to accurately forecast the energy requirements. In the present paper, the future energy demand of the state of Tamil Nadu in India, is forecasted using an artificial neural network (ANN) optimized by particle swarm optimization (PSO) and by General Algorithm (GA). Hybrid ANN Models have the potential to provide forecasts that perform well compared to the more traditional modelling approaches. The forecasted results obtained using the hybrid ANN-PSO models are compared with those of the ARIMA, hybrid ANN-GA, ANN-BP and linear models. Both PSO and GA have been developed in linear and quadratic forms and the hybrid ANN models have been applied to five-time series. Amongst all the hybrid ANN models, ANN-PSO models are the best fit models in all the time series based on RMSE and MAPE.

Whereas previous researchers analyzed the thermal behavior of paraffin waxes impregnated with graphene oxide nanoparticles (P-GONP) at high mass fraction ( > 1%), this paper analyzes behavior and stability at only 0.3% mass fraction. GONP was prepared by Hummer’s method. The morphology was studied using scanning electron microscope (SEM), transmission electron microscope (TEM), X-Ray diffraction (XRD) and Fourier Transformation-Infrared (FT-IR) Spectrometer and the thermal properties were measured using laser flash analyser (LFA), differential scanning calorimetry (DSC), thermo-gravimetric analysis (TGA) and thermal cycling. LFA showed a 101.2% and 94.5% increase in the thermal conductivity of P-GONP compared to pure paraffin (P) in solid and liquid state respectively. Melting and solidifying temperatures and latent heat were found to be 63.5, 59 °C & 102 kJ/kg and 57.5, 56 °C & 64.7 kJ/kg for P and P-GONP respectively. Thermal cycling over 4000 cycles showed that P-GONP was 27% more stable than P. The latent heat was 64.7 kJ/kg, a 36.5% deterioration compared to virgin paraffin. Compared against higher mass fraction impregnation, lower mass fraction P-GONP was found to have almost equivalent thermo-physical properties (namely thermal conductivity, melting and solidifying characteristics, thermo-chemical stability and reliability) while providing considerable cost saving.

Practicing yoga has become more effective among all kind of age groups, but researches focusing on the movement related yoga postures are limited. This work analysis the effect of therapeutic yoga on biosignals. Physiological parameter variations between yoga trainers and untrained subjects while performing the selected asanas including pranayama, dwipadapitham, apanasana, jathara parivrtti has been studied. ECG and EMG are acquired simultaneously before, during and after the specially framed yoga practice sequence. These signals are processed suitably to measure the time and frequency domain features and analyze their variations over the period of recording. HRV analysis shows variation of MHR, LF/HF and SD1/SD2 with increase during posture and decrease with rest. pNN50 shows decrease during posture and increase during rest. Further study will helpful in designing special yoga course for individuals based on their requirements and thus complementing their medical support as well.

Developing economies need to invest in energy projects. Because the gestation period of the electric projects is high, it is of paramount importance to accurately forecast the energy requirements. In the present paper, the future energy demand of the state of Tamil Nadu in India, is forecasted using an artificial neural network (ANN) optimized by particle swarm optimization (PSO) and by Genetic Algorithm (GA). Hybrid ANN Models have the potential to provide forecasts that perform well compared to the more traditional modelling approaches. The forecasted results obtained using the hybrid ANN-PSO models are compared with those of the ARIMA, hybrid ANN-GA, ANN-BP and linear models. Both PSO and GA have been developed in linear and quadratic forms and the hybrid ANN models have been applied to five-time series. Amongst all the hybrid ANN models, ANN-PSO models are the best fit models in all the time series based on RMSE and MAPE.

Purpose Previous green research shows conflicting results regarding the relationship between environmental concern and persuasion. It has also largely overlooked the role of situational involvement. The purpose of this paper is to aim to show that message involvement influences attitude towards green advertisements. Design/methodology/approach To test the proposed model, an experimental study based on a sample of young adults in an emerging economy was used to investigate if situational involvement can produce favourable attitudes towards green advertisements. Findings Using PLS, it was found that fear and response efficacy increased message involvement which in turn was a significant predictor of attitude towards the advertisement. The findings show that advertisers can use these variables to increase message involvement. Originality/value This research extends previous studies on message involvement and expands current knowledge by showing that situational involvement predicts attitude towards green advertising.

The demand for fresh water production is growing day by day with the increase in world population and with industrial growth. Use of desalination technology is increasing to meet this demand. Among desalination technologies, solar stills require low maintenance and are readily affordable; however their productivity is limited. This paper aims to give a detailed review about the various types of solar stills, covering passive and active designs, single- and multi-effect types, and the various modifications for improved productivity including reflectors, heat storage, fins, collectors, condensers, and mechanisms for enhancing heat and mass transfer. Photovoltaic-thermal and greenhouse type solar stills are also covered. Material advances in the area of phase change materials and nanocomposites are very promising to enhance further performance; future research should be carried out in these and other areas for the greater uptake of solar still technology.

Energy is found to be a vital input for the economic growth of any nation. In India, which is a developing country, energy is found to be one of the major drivers of economic development. Review indicates energy consumption is linked to socioeconomic variables like GNP, price of energy, and population. In this article, econometric models are developed to study the influence of the socioeconomic variables on energy consumption. The best fit is determined using R2, SE, and Durbin Watson (DW) statistic. ‘t’ test is conducted to find the significant variables influencing energy demand. It is found that coal price and population influence the demand for coal, whereas GNP per capita influences the demand for oil. Electricity demand is dependent on the GNP and electricity price. The coal, oil, natural gas, and electricity requirement for India during 2030–2031 is forecasted and the total energy requirement is found to be 22.944 × 1015 kJ.

Over the years, large scale production and consumption have played havoc on the ecosystem. Organizations and consumers are often blamed for their excessiveness since it leads to resource depletion which in turn causes the ecosystem imbalance. Both the inhabitants and organizations have to take the responsibility of preserving the ecosystem while living within its premises. Review of the literature indicated that though researchers have worked on how workplace spirituality has helped employees and the organizations to improve their performance not much research has been done on ecospirituality. This research gap is being addressed in this work wherein the relationship of ecospirituality with organizational performance and employees’ pro-environment behavior is examined with corporate social responsibility as the mediating variable. The conceptual model was tested using 527 clean responses. Corporate social responsibility partially mediated the relation between ecospirituality and performance. Also it was found that the income of the employees acted as a moderating variable. Organizations need to instil a reverential attitude towards the environment among the employees’ belonging to the low_income group. The research also throws open a plethora of options, organizations need to take for its sustainability and for preserving the planet earth.

Introduction: The most widely accepted and used method of blood typing is the agglutination test with the antibodies A and B for identifying ABO blood group and a separate antibody D is used for giving Rh (Rhesus) type of the sample under study. The problem faced by this method is that it consumes more time and technician’s alertness in case of large number of samples. Thus, optical ABO blood grouping system with appropriate wavelength will improve the blood grouping with better accuracy and time efficiency. Aim: To determine the ideal wavelength of visible spectrum which has maximum absorbance with the agglutinated blood sample using spectrophotometry so that the estimated wavelength can be implemented as light source in an optical blood typing system for better results. Materials and Methods: An observational study has been performed on nine subjects which was conducted at Sri Sivasubramaniya Nadar College of Engineering (SSNCE) Tamil Nadu, India. The subject’s blood were collected and mixed with saline. Antibodies A, B, D are added and the data of specific blood absorbance values were measured using Spectrometry. Obtained data were tabulated and graphically represented using Microsoft Excel. Results: The highest absorbance of sample corresponds to the wavelength in the range of 425-475 nm. Thus, this wavelength range is most suitable and ideal visible spectrum range for optimal ABO blood grouping using spectrophotometer. Significant difference of absorbance between various types of positive and negative samples is obtained. Conclusion: A difference in absorbance of 0.3 is seen between negative and positive samples. With further research and study, it is possible to separate the specific blood group after identification of the positive and negative types.

Sleep apnea is a breathing disorder which occurs during sleep. Sleep apnea causes more health-threatening problems such as daytime sleepiness, fatigue and cognitive problems, coronary arterial disease, arrhythmias, and stroke. However, there is an extremely low public consciousness about this disease. The most common type of sleep apnea is obstructive sleep apnea (OSA). Polysomnography (PSG) is the widely used technique to detect OSA. Obstructive sleep apnea is extremely undiagnosed due to the inconvenient and costly polysomnography (PSG) testing procedure at hospitals. Moreover, a human expert has to monitor the patient overnight. Hence, there is a requirement of new method to diagnose sleep apnea with efficient algorithms using noninvasive peripheral signal. This work is basically aimed at detection of sleep apnea using a physiological signal electrocardiogram (ECG) alone which is taken from free online apnea ECG database provided by PhysioNet/PhysioBank. This database consists of 70 ECG recordings. A detailed time- and frequency-domain features and nonlinear features extracted from the RR interval of the ECG signals for observing minutes of sleep apnea are occurred in this work. Time-domain features mean HR (P = 0.0093, r = 0.3593) and RR interval mean (ms) (p = 0.0003, r = 0.376), frequency-domain features VLF power (%) (P = 0.00659, r = 0.1081) and HF power (%) (P = 0.00135, r = 0.41138), and nonlinear analysis feature SD1 (P = 0.00039, r = 0.18998), significantly different for normal and apnea ECG. Further supervised learning algorithms have been used to classify ECG signal to differentiate normal and apnea data. The overall efficiency is 90.5%. Algorithms which deal ECG signal along with respiratory signal will give more incite about apnea disease and also the classification accuracy may be improved.

Purpose The purpose of this paper is to understand the impact of transformational leadership (TL) on follower emotional intelligence (EI) and examine the potential mediation role played by follower EI in the relationship between TL and follower outcomes (i.e. growth satisfaction in the job and job stress (JS)). Design/methodology/approach Data were obtained through survey using questionnaire collected from 908 employees who worked across six different sectors, i.e. manufacturing, IT, healthcare, hospitality, educational and public services in Southern India. The mediation model proposed in this study was tested using structural equation modelling and bootstrapping method. Findings The relationship between TL and Follower EI was significant. Follower EI was found to partially mediate the relationship between TL and followers’ growth satisfaction in job. Contrary to expectations, the follower EI did not significantly predict JS in this study and hence the follower EI did not mediate in the proposed model. However, follower EI and growth satisfaction in the job jointly mediated the relationship between TL and follower JS fully. Research limitations/implications Self-report bias about supervisors’ TL behaviours and followers’ own EI assessment and collection of data from the mono-source (subordinate self-report) might have impacted the results of this study. Moreover, some items were negatively worded and reverse coded as cognitive speed bumps to restrain the respondent’s tendency to rush through answering the survey questionnaire. Practical implications This study established a partial and joint mediation of follower EI on the relationship between TL and follower outcomes. Basing on these findings, this study highlights the need for the practitioners to better understand the importance of EI training for the leaders in the organisations for obtaining better outcomes in the followers. Social implications The study establishes the fact that the attunement of transformational leaders’ EI and follower EI help leaders as well as followers to guide their behaviour towards positive outcomes. Originality/value This study is among the first to examine the impact of TL on follower EI and the potential mediation of follower EI between TL and follower outcomes. From a theoretical perspective, this study is one step closer to fully understand the intervening process between TL and follower outcomes.

This paper investigates the effects of nanoparticle-enhanced phase change material (NPCM) on solar still operation and performance. Technical and economic aspects were considered, to show an advance on earlier works using virgin phase-change materials (PCM). Three types of nanoparticle (TiO2, CuO and GO) were impregnated individually at 0.3 weight% in paraffin to form NPCM-1, NPCM-2 and NPCM-3 respectively. Experiments were conducted with four solar stills (SS) each of 0.5 m2 area using respectively paraffin (SSPCM), paraffin-TiO2 (SSNPCM-1), paraffin-CuO (SSNPCM-2) and paraffin-GO (SSNPCM-3). There was observed an increase in thermal conductivity and a reduction in melting and solidification temperatures, with NPCM compared to PCM. The effects of NPCM on water temperature, storage temperature, hourly and annual productivity were determined. SSPCM, SSNPCM-1, SSNPCM-2 and SSNPCM-3 yielded 3.92, 4.94, 5.28 and 3.66 l/m2/day respectively, corresponding to 26 and 35% increases in productivity of SSNPCM-1 and 2 respectively over SSPCM. Economic analysis showed cost per liter (CPL) of water of $0.035, $0.028, $0.026 and $0.13 for SSPCM, SSNPCM-1, 2 and 3 respectively. Considering the advantages in productivity and CPL, SSNPCM-2 can be recommended as the best solar still compared to SSPCM, SSNPCM-1 and 3, providing clean water at less than half the cost of bottled water in India.

The solarized steam injection gas turbine (STIG) cycle uses both the fuel and solar heat simultaneously for power generation. The annual thermodynamic performances of the cycle for sites in India with local climatic conditions such as ambient temperature, relative humidity and availability of direct normal irradiance (DNI) to the solar concentrators under two modes of constant and variable power are presented in this paper. The results reveal that the solar to electricity efficiency of solar hybrid STIG plant with a simple parabolic trough collector (PTC) is similar to existing solar thermal technologies, and also higher solar share is obtained. The study also reveals that the annual CO 2 emission is similar to combined cycle plants and lower than gas turbine technologies. The incremental CO 2 avoidance is also computed due to solar participation. The annual values of exergetic solar fraction and exergetic efficiency at Indore are higher than Jaipur. Results of an improved economic assessment show that the levelized tariff (LT) of solar hybrid STIG plant is 0.2–0.23 $/kWh and the levelized tariff (solar only) or solar levelized tariff (SLT) of solar STIG plant ranges from 0.11 to 0.27 $/kWh for both constant and variable power scenarios.

A hybrid solar linear concentrator (HSLC) based on photovoltaic linear concentrator technology is capable of delivering both electricity and hot water for satisfying urban energy needs. The net output of such a system plays a major role in determining the benefit to cost ratio of such a system. Different approaches were tried in this article to consider the thermal energy in equivalent electrical energy terms to determine the net output of the system under select meteorological conditions. A sensitivity-based study is conducted based on these approaches considering the maintenance cost, payback, net present values and benefit to cost ratios. The capital cost of the system at Rs 200,000 to Rs 300,000 yielded a benefit to cost ratio of 1.79 to 3, respectively whereas the same cost structures had an internal rate of return of 10.4% and 5.2%, respectively. When compared to the actual market price of the HSLC system, carbon credits earned during the lifetime operation of the HSLC system were useful for...

In recent years, with the advent of globalization, the world is witnessing a steep rise in its energy consumption. The world is transforming itself into an industrial and knowledge society from an agricultural one which in turn makes the growth, energy intensive resulting in emissions. Energy modeling and energy planning is vital for the future economic prosperity and environmental security. Soft computing techniques such as fuzzy logic, neural networks, genetic algorithms are being adopted in energy modeling to precisely map the energy systems. In this paper, an attempt has been made to review the applications of fuzzy logic based models in renewable energy systems namely solar, wind, bio-energy, micro-grid and hybrid applications. It is found that fuzzy based models are extensively used in recent years for site assessment, for installing of photovoltaic/wind farms, power point tracking in solar photovoltaic/wind, optimization among conflicting criteria. The review indicates that fuzzy based models provide realistic estimates.