Siksha O Anusandhan University
  • Bhubaneshwar, Odisha, India
Recent publications
The formalization of a stable water quality index (WQI) from measured hydrogeochemical parameters is essential for the identification and classification of water resources. In the principal component analysis (PCA) based WQI approach, the parameter weight is derived using either PC loading or rotated factor loading from a large number of samples pooled for WQI measurement. The PCA-based approach is paradoxical, as the calculated WQI rating of a sample would rather be dependent on the size, and composition of the population. Though this issue is well anticipated, no attempt has been made to regularize or measure the extent of WQI disagreement. In the present study, the WQI of 106 groundwater samples analyzed for 12 different hydrochemical parameters were modelled using PC loading or rotated factor loading (referred to as PCQ-1, PCQ-2, respectively) approach. Analysis reveals PCQ-1 to be positively biased in 78 % of samples and rating disagreements were evident in 9.43 % of samples. WQI of the data set was estimated using repeated (1000) random non-overlapping 2 to 5-fold data partitioning (containing 21 to 83 samples in each fold) adopting either an in-sample (test set) or out-sample (train set) modelling approach. The mean of WQI deviations in repeated resampling from the reference (i.e., using the entire dataset) has been positive in most of the samples using the PCQ-1 model, irrespective of the fold partition size. The median root mean square deviation values of the data set increased with the number of fold partitioning for in-sample calibration for both PCQ-1 and PCQ-2 approaches. The exclusion of a single water quality parameter from the PCA model can cause up to a 60 % deviation of the WQI score in some water samples. The cross-validation and Monte Carlo resampling approach can serve as a framework to test the stability of PCA-based WQI.
Mineral industry environment is an interlinked chain of activities. Identification of major activities of mineral industry, termed as critical success factors and their relationship structure, will be approachable, productive & efficiency-oriented for the industry to manage. The aim of the paper is to develop a relationship structure consisting of critical success factors influencing the mineral industry. 16-such operational influencing critical success factors (CSF) are identified through broad literature review and subsequent dialogue held with mineral field experts. The interpretive structural modelling approach is applied for analyzing critical success factors and finding a relationship structure. The study is carried out in Odisha, Chhattisgarh, and Jharkhand, mineral-rich states of India. Identification of critical success factors and their relationship is carried out in nine phases sequentially to arrive at the latent structure. This is the only study to develop relationships of critical success factors for any mineral industry in India. Findings of the study will provide an insight for relationship structure of CSFs of mineral industry operation and be helpful for improvisation in addressing operational difficulties for corporate as well as academics. Among the 16-CSFs identified, ‘Training & Skill Level’, 'infrastructure' and ‘political influence’ have maximum driving forces and least dependent. The projected model is helpful in understanding the relationship among CSFs and the business operation as a whole.
The objective of the current article is to incorporate the concepts of convexity and Jensen-Mercer inequality with the Caputo-Fabrizio fractional integral operator. Moreover, we present new midpoint versions of Hermite-Hadamard-Mercer (H-H-M) type inequalities for convex functions and the product of two convex functions on fractional integrals. Also, we consider a new identity for differentiable mappings in the context of the Caputo-Fabrizio fractional integral operators. Then, considering this identity as an auxiliary result, some new related H-H-M type inequality with the assistance of Hölder, power-mean, Young, and Hölder-İşcan inequality are presented. Finally, we give some applications of modified Bessel functions and matrices, and we also discuss some future scopes.
Background Radiology is among India's top five speciality choices pursued by meritorious medical graduates. With the advent of the subspecialization degree courses, fellowships, the requirement of senior residency as faculty eligibility criteria, and the lucrative option of private practice, the final-year postgraduates are given opportunities to choose from but with little guidance on what and how to choose. This study aims to analyze the views of the final-year radiology postgraduate residents in India regarding their understanding of how to proceed in their professional life with options available. Materials and Methods A cross-sectional, questionnaire-based study was conducted online via Google forms distributed via email and social media platforms. The questionnaire was prepared after going through previous literature, video blogs, and media available on the Internet and was further validated for content. Results About half (48%) of them wished to pursue higher studies in the form of Doctorate of Medicine (DM) degree courses or fellowships. Almost three-quarters of the participants preferred short-term subspecialization via fellowships over 3-year DM courses (28%). Regarding clinical practice, 61.9% preferred to take up senior residency, while slightly over one-third (35.7%) expressed their will to move on to private practice. Conclusion A relative conundrum was observed in the decision to take up senior residency or private practice or to go for DM but had to pursue a fellowship due to limited choice in topics and seats available in the country for subspecialization.
Employee performance attainment is a pervasive issue in the workplace and is increasingly becoming an important problem for effective human resource management. A review of the extant literature on perceived organizational support (POS) and performance suggests that there is a dearth of research aimed at examining the underlying mechanisms and the boundary conditions of the relationship between POS and performance. One of the objectives of this study is to examine the mediating role of psychological capital on the relationship between POS and performance. Furthermore, this study investigates the moderating role of organizational justice perception in said indirect relationship. Study 1 included a sample of 465 employees from both large private life insurance and telecom organizations. Study 2 was conducted on a sample of 216 employees from a large steel manufacturing firm. Findings suggest that psychological capital mediated the relationship between POS and performance. The indirect relationship of POS and performance via psychological capital was moderated by organizational justice. However, there is a counter-intuitive finding in this research. It was observed that at a high level of organizational justice, it had a smaller effect on performance in contrast to low level of organizational justice. Finally, theoretical contributions and managerial implications are discussed.
Electricity from renewable energy is certainly the most prominent alternative to deliver power to remote locations; however, its reliability is affected by the intermittency of the renewable source. A smart load technology called ‘electric spring (ES)’ can compensate for the intermittency and thereby maintain the voltage level constant for critical loads with an efficient and reliable control approach. This paper suggests wind energy-fed generation as the only and primary source for an isolated remote microgrid. The voltage level of the system remained unaltered by deploying a voltage source converter-based ES with a novel artificial neural network-based fuzzy controller. The novelty of the system is that it can operate significantly under varying load, variable torque, and varying wind speed conditions by exchanging power between critical and non-critical loads. Further simulation results ensure the credibility of the electric spring–artificial neural network-based fuzzy controller system in terms of stability and performance parameters such as settling time, rise time, and maximum overshoot. Additionally, the total harmonic distortion of the system is found to be well within the boundary of the acceptable range that proclaims its feasibility and applicability in a real-world scenario.
Advances in nanoscience and technology acquired the significance of the nanofluid in novel functional polymers like fibre insulation, geothermal system and chemical catalytic reactors. Inspired by the above applications, an innovative mathematical model is established for radiative nanoliquid flow and is engendered due to stretching sheet with inclined magnetic field which is immersed with nanoparticles. Joule dissipation and exponentially-based heat source/sink effects are employed in the present phenomenon under the heat constraints. The governing equations, which describe the flowing nanofluid, are transformed into invariant dimensionless equations with suitable similarity quantities. With the adoption of a shooting scheme with Runge–Kutta-45, the resultant equations are numerically simplified. The impact of several converted dimensionless elements on physically interesting values is depicted visually. The current analysis is validated through comparison with some selected related literature, which shows a positive correlation. The nanoparticle thermal conductivity is raised for an increased value of the thermal radiation, thermal viscosity and heat source to propel temperature profiles. The heat flux gradient significantly affects the heat propagation all over the flow regime.
Temperatures have risen at a faster rate across various mountainous regions around the world. A study of the changing rainfall patterns and their spatiotemporal variability is critical for agricultural, water resource, and hydrological planning and management. The goal of this research is twofold: first, using functional data analysis climate change hotspots are determined, which in turn identified most temperature increases in high elevations. Second, these hotspots are utilized to investigate the impact of temperature changes on the frequency of extreme rainfall events in this Northwestern Himalaya region. Our study observed that the frequency of rainfall extremes, such as, at the 90th, 95th, and 99.99th percentile values, is found to be increasing mostly in lower Himalayan regions. A warmer atmosphere leads to heavy precipitation events as the capacity of air to hold moisture increases with the increase in temperature. The continuous temperature rise possibly indicates an increase in extreme rainfall events in the region. Further, this study reports an interesting finding that can help us better understand the impact of climate change in the region: the frequency of shorter wet spells has increased around the recent mean‐change year, whereas the frequency of long spells has reduced. The frequency of longest wet spells shows a decrease across all seasons, leading to drought in the higher altitude region, whereas the short‐wet spell frequency has increased in the lower altitude regions. Insight into the changing patterns of rainfall is crucial for hydrological and agricultural management.
In medical science, imaging is the most effective diagnostic and therapeutic tool. Almost all modalities have transitioned to direct digital capture devices, which have emerged as a major future healthcare option. Three diseases such as Alzheimer's (AD), Haemorrhage (HD), and COVID‐19 have been used in this manuscript for binary classification purposes. Three datasets (AD, HD, and COVID‐19) were used in this research out of which the first two, that is, AD and HD belong to brain Magnetic Resonance Imaging (MRI) and the last one, that is, COVID‐19 belongs to Chest X‐Ray (CXR) All of the diseases listed above cannot be eliminated, but they can be slowed down with early detection and effective medical treatment. This paper proposes an intelligent method for classifying brain (MRI) and CXR images into normal and abnormal classes for the early detection of AD, HD, and COVID‐19 based on an ensemble deep neural network (DNN). In the proposed method, the convolutional neural network (CNN) is used for automatic feature extraction from images and long‐short term memory (LSTM) is used for final classification. Moreover, the Hill‐Climbing Algorithm (HCA) is implemented for finding the best possible value for hyper parameters of CNN and LSTM, such as the filter size of CNN and the number of units of LSTM while fixing the other parameters. The data‐set is pre‐processed (resized, cropped, and noise removed) before feeding the train images to the proposed models for accurate and fast learning. Forty‐five MR images of AD, Sixty MR images of HD, and 600 CXR images of COVID‐19 were used for testing the proposed model ‘CNN‐LSTM‐HCA’. The performance of the proposed model is evaluated using six types of statistical assessment metrics such as; Accuracy, Sensitivity, Specificity, F‐measure, ROC, and AUC. The proposed model compared with the other three types of hybrid models such as CNN‐LSTM‐PSO, CNN‐LSTM‐Jaya, and CNN‐LSTM‐GWO and also with state‐of‐art techniques. The overall accuracy of the proposed model received was 98.87%, 85.75%, and 99.1% for COVID‐19, Haemorrhage, and Alzheimer's data sets, respectively.
Dextromethorphan (DM) and its metabolite Dextrorphan (DX) continue to draw the attention of researchers to their diverse pharmacodynamics. Thus, there are possibilities for repurposing DM. Most of the pharmacodynamics of DM needs further validation in different preclinical models. Also, it is necessary to correlate pharmacodynamics with relevant pharmacokinetics data. Multiple bioanalytical techniques developed for this purpose primarily use a high sample processing volume. Since sample volume is a limiting factor for many preclinical models; an effort was taken to develop an alternative method suitable for handling low sample processing volume. Efficient solid phase extraction technique, robust liquid chromatographic (LC) separation and highly sensitive Tandem mass spectrometric detection (MS/MS) showed suitability for use of a 30μL sample processing volume. This led to the development of a highly specific, selective, accurate and precise‐bio‐analytical method for simultaneous quantification of DM and DX in rat plasma. The validated method was linear in the range of 0.196 ng/mL to 403.356 ng/mL for DM and 0.102 ng/mL to 209.017 ng/mL for DX. The application of the method was demonstrated through the estimation of pharmacokinetic parameters that showed good congruence with earlier studies.
Asian tiger shrimp Penaeus monodon (P. monodon) of Chilika lagoon, India was studied regarding the metal accumulation and its associated human health risks. It showed a tendency of metal accumulation in the following order: Zn > Ni > Cu > Co > Cr > Pb > Cd. A two-way ANOVA indicated the metal accumulation was insignificant with respect to season (n = 421, p = 0.59) and sector (n = 32, p = 0.61). The estimated daily intake (EDI), targeted hazard quotient (THQ) and hazard index (HI), and carcinogenic risks (CR) revealed no potential human health risks and were safe for consumption. The pollution load index (PLI) of <1, Geo-accumulation index (Igeo,) and contamination factor (CF) indicated that the study area was unpolluted. This pioneering study highlighted that P. monodon was nurtured well in the healthy habitat of Chilika lagoon and the fair level of metal content made it an excellent source of dietary components.
Plectranthus amboinicus (Lour.) Spreng, known as the Indian borage or Mexican mint, is one of the most documented species in the family Lamiaceae for its therapeutic and pharmaceutical values. It is found in the tropical and subtropical regions of the world. The leaf essential oil has immense medicinal benefits like treating illnesses of the skin and disorders like colds, asthma, constipation, headaches, coughs, and fevers. After analyzing earlier reports with regard to the quantity and quality of leaf oil yield, we discovered that the germplasm taken from Odisha is preferable to other germplasms. The objective of the present work is to evaluate the free radical scavenging activity and bactericidal effect of leaf essential oil (EO) of Plectranthus amboinicus (Lour.) Spreng collected from the state of Odisha, India. The hydro distillation technique has been used for essential oil extraction. Upon GC/MS analysis, approximately 57 compounds were identified with Carvacrol as the major compound (peak area=20.25%), followed by p-thymol (peak area= 20.17%), o-cymene (peak area=19.41%) and carene (peak area= 15.89%). On evaluation of free radical scavenging activity, it was recorded that the best value of inhibitory concentration, was for DPPH with IC50 = 18.64 ppm and for H2O2 with IC50 = 9.35 ppm. The EO showed efficient bactericidal effect against both gram positive (Mycobacterium smegmatis, Staphylococcus aureus, Enterococcus faecium) and gram negative (Escherichia coli, Vibrio cholerae, Klebsiella pneumoniae) bacteria studied through well diffusion method. Fumigatory action of the essential oil was found against M. smegmatis, the model organism for tuberculosis study. Alamar Blue assay, gave a result with MIC value for M. smegmatis i.e., 0.12 µg/ml and the MBC value of 0.12 µg/ml. Hence, P. amboinicus found in Odisha can be suggested as an elite variety and should be further investigated for efficient administration in drug formulation.
The emission of solar energy is one of the greatest challenges due to its global impact nowadays. Further, the improved heat transport properties of the nanofluids have also important aspects in industries as well as engineering equipment and any others. Therefore, the present analysis motivates the enhancement in the heat transport enrichment of Sakiadis nanofluid by imposing Koo–Kleinstreuer–Li (KKL) model conductivity along with dusty CuO nanoparticles in water. Dust particles in conjunction with the magnetic effect and the radiative heat explore its greater role in the enhancement of the heat transport phenomena. Suitable transformation is proposed for the governing equations to get its non-dimensional form with significant parameters to characterize the flow properties. Moreover, numerical treatment using Runge–Kutta shooting is a good approach to handle the complex nonlinear system equipped with nanofluid phase and dusty phase liquids. Several factors impacting the flow phenomena have been examined graphically. In careful observation, it reveals that the augmented thermal radiation along with the heat source encourages the nanofluid phase temperature but the reverse impact is rendered in the dust phase.
Leptospirosis, caused by the pathogenic Leptospira, is an emerging zoonotic disease affecting over one million people annually. The existence of a large number of serovars, different reservoir hosts, and common disease symptoms account for the difficulty in early diagnosis, prophylaxis, and treatment. Post-translational modification plays a significant regulatory role in both eukaryotic and prokaryotic organisms. Therefore, the study of post-translational modification may help in better understanding the pathogenesis of the bacterial disease. Acetylation at lysine residue was found to be involved in regulating bacterial pathogenesis. This study aims to identify protein lysine acetylation patterns among groups of pathogenic and saprophytic species of Leptospira, by screening the leptospiral proteome using a robust proteomics approach. In this study, a total of 15, 78,796 acetylated proteins with 83, 65,945 acetylation sites were identified among 469 strains of Leptospira to predict the pathogenesis pattern and signature peptide sequence, which was conserved among pathogenic Leptospira species, that can be used as a novel vaccine candidate. A similar pattern of acetylation was observed among the pathogenic and intermediate groups while different in the saprophytic group of Leptospira. Consequently, a common signature peptide was observed among pathogenic strains of Leptospira. Acetylated proteins were found to be primarily involved in metabolic processes. As a result, this is the first study to analyze proteome-wise strain specific lysine acetylation of Leptospiral proteins which may constitute a valuable resource for in-depth studies of the impact of lysine acetylation in the pathogenesis of Leptospira.
Genetic improvement of temperate fruit and nut crops through conventional breeding methods is not sufficient alone due to its extreme time-consuming, cost-intensive, and hard-to-handle approach. Again, few other constraints that are associated with these species, viz., their long juvenile period, high heterozygosity, sterility, presence of sexual incompatibility, polyploidy, etc., make their selection and improvement process more complicated. Therefore, to promote precise and accurate selection of plants based on their genotypes, supplement of advanced biotechnological tools, viz., molecular marker approaches along with traditional breeding methods, is highly required in these species. Different markers, especially the molecular ones, enable direct selection of genomic regions governing the trait of interest such as high quality, yield, and resistance to abiotic and biotic stresses instead of the trait itself, thus saving the overall time and space and helping screen fruit quality and other related desired traits at early stages. The availability of molecular markers like SNP (single-nucleotide polymorphism), DArT (Diversity Arrays Technology) markers, and dense molecular genetic maps in crop plants, including fruit and nut crops, led to a revelation of facts from genetic markers, thus assisting in precise line selection. This review highlighted several aspects of the molecular marker approach that opens up tremendous possibilities to reveal valuable information about genetic diversity and phylogeny to boost the efficacy of selection in temperate fruit crops through genome sequencing and thus cultivar improvement with respect to adaptability and biotic and abiotic stress resistance in temperate fruit and nut species.
The letter looks into India’s preventive measures and control strategies to prevent the spread of the 2 new emerging Omicron subvariants (XBB.1.5 and BF.7) of the COVID-19 virus.
A particularly successful type of cybercrime that allows criminals to trick people and steal crucial data is phishing. Phishing is currently one of the most common online scams. Phishing assaults may result in significant losses for sensitive information, identity theft, businesses, and the government are just some of their victims. Phishing websites are widespread entrance points for social engineering attacks carried out online, including countless website frauds. When such attacks occur, by copying the acts of reputable websites, the attacker(s) create website pages and send the URL(s) to the targeted recipients via social networking, texting, or spam messaging. However, the number of victims is rising exponentially as a result of inadequate security technologies. Studies have categorized phishing attacks by basic phishing methods and defenses, ignoring the significance of the end-to-end phishing lifecycle. This article offers a fresh, in-depth model of phishing that takes into account attack stages, different types of attackers, threats, targets, attack channels, and attack tactics. The proposed anatomy will also make it easier for readers to understand how long a phishing effort lasts, increasing knowledge of these attacks and the techniques used as well as aiding in the creation of a comprehensive anti-phishing system. Due to the anonymity and lack of regulations on the Internet, phishing attacks are more likely to be successful. The effectiveness of the phishing detection system is limited, according to existing research. To protect consumers from cyberattacks, a clever strategy is needed. A detection method used in this work is based on artificial intelligence-based LSTM which has produced satisfactory performance and accuracy.
The switched reluctance motors (SRM) technology has existed since 1838. Cheaper construction, high efficiency, and reliability have been the major factors for its renewed interest in the current technological trends. However, despite of the advanced motor designs, the primary concerns to adopting SRM drives on a large scale in existing drive applications have been due to high acoustic noise and high torque ripple observed in machines with primitive control strategies. In this paper, a comparative evaluation of the stator-rotor configurations of three types of SRM drives, namely 6/4, 8/6, and 10/8 stator/rotor pole configurations, have been modeled in MATLAB/SIMULINK environment, and the performance of the same has been evaluated and compared with each configuration type. The standard SRM control loop is used with the standard SRM drive configuration to observe the torque ripple characteristics of different SRM configurations.
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1,196 members
Swagatika Panda
  • Department of Oral Pathology and Microbiology
Lala Behari Sukla
  • Biofuels & Bioprocessing Research Center
Shakti Mishra
  • Department of critical care medicine
Jayashankar Das
  • Institute of Medical Sciences and SUM Hospital
Sandeep Kumar Panigrahi
  • Community Medicine
Khandagiri Square, 751030, Bhubaneshwar, Odisha, India
Head of institution
Prof.(Dr) Khageswar Pradhan