Search

Facial swapping technology is a rapidly growing area of research with a wide range of applications, including entertainment, security, and healthcare. In this project, a deep learning approach was used to achieve highly accurate and realistic face swaps. Specifically, a CNN encoder and decoder network was trained using a large dataset of facial images, and facial clusters were generated using k-means clustering. Computer vision methodologies were also employed to accurately detect and align facial landmarks. The resulting model achieved impressive accuracy of 97% to 99.48% in different epochs, demonstrating its potential for various applications. The system configuration for executing the project included an 11th generation Intel Core i7 processor and 16GB RAM, which provided sufficient computational resources for the task at hand. Overall, this project highlights the power and potential of deep learning techniques for generating highly accurate and realistic facial swaps.

Cloud computing (CC) is an easy way to access computer resources, which many businesses increasingly use to outsource their data. Nonetheless, privacy and security are significant concerns in CC when sharing and storing data in a distributed setting. Potential dangers to cloud data security would cause significant disruptions. This paper discusses security challenges and feasible encryption approaches. The workings of a few encryption schemes are also discussed. By identifying and mitigating potential threats, it is possible to reduce the risk of a data breach. In this connection, a case study based on threat model simulations is presented. The CC’s future is bright and hopeful, albeit a few loose ends exist. Building an integrated security architecture that can regulate all cloud layers is an immediate requirement for providing highly safe cloud services. A few recommendations to protect cloud data are provided and CC's future research directions are also emphasized.

The storage of data with access controllability in sharing data between multiple users in a dispersed environment is now the most pressing issue in cloud computing. Cloud computing's unique feature allows its subscribers to exchange and manage their data with one another safely. However, privacy is essential for all cloud users to communicate freely and openly, as data is accessed by unwanted parties in the cloud. Cloud services have employed numerous security-related techniques for efficient and safe user data exchange. These methods provide efficient, flexible, and reliable access control rules between users when exchanging data. But, they each have advantages and disadvantages concerning key generation and data security. To address the need for adaptable, scalable, and trustworthy access control while exchanging data across a dispersed network, this research proposes a novel access control-based privacy-preserving approach. In terms of cipher text and critical policy security, this method is an extension of attribute-based encryption and the only difference is that with cloud computing, users hierarchically share data, and access control policy amongst shared users is evaluated efficiently. Our method includes sophisticated security features like the revocation of users and access rights of users for outsourced data in the cloud. It also provides scalability and dependability in producing critical structures with dynamic qualities. Experimental results demonstrate that the suggested method improves the current system in terms of cloud data sharing efficiency, scalability, and dependability.

Background: Premenstrual dysphoric disorder is a mood disorder that has been recognized in the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition (DSM-5) as a subcategory of depressive disorders. The disorder has a high prevalence rate, and yet no uniform measure is standardized on the Indian population. Aim: This study aimed to develop a scale to assess premenstrual dysphoric disorder that can be used for screening and for clinical use and that is standardized on the Indian population. Methods: This study used cross-sectional research design and collected data through social media platforms from a sample of 300 females, out of which 38 were excluded for pregnancy, mental health conditions, substance use, and oral contraceptive use. Results: The scale was analyzed using SPSS 21.0 for descriptive statistics, internal consistency, and convergent, content (factor analysis), and divergent validity. Results showed a high reliability of 0.90 and a concurrent validity of 0.76 and 0.64 against PSTT and BDI. Factor analysis revealed four factors, namely, affective and cognitive functioning, daily life and occupational functioning, somatic symptoms, and interpersonal anger. The sensitivity and specificity of the scale were found to be 86 (86%) and 66 (66%), respectively. Conclusion: The scale developed in this study was found reliable and valid for use on the Indian population. It has high sensitivity and can be used as a screening measure for premenstrual dysphoric disorder.

Objective: Determine the impact of the COVID-19 pandemic on the changes in training, sleep, diet, and mental health of cross-country runners. Participants: Cross-country runners from NCAA Division II institutes. Methods: Wilcoxon signed-rank test was performed to analyze survey responses to ordinal questions on the survey while Spearman's rank correlation analysis (?) was used to calculate correlation between after the start of pandemic questions. Data was marked significant at p < 0.05. Results: Analysis of the survey responses revealed that cross-country runners were more likely to experience feelings of depression (p < 0.001), lack of motivation (p < 0.001), and higher daily stress (p < 0.001) after the start of the pandemic. After the start of the pandemic, runners running less days per week were more likely to report an increased feeling of depression (?=-0.315, p = 0.008) and lack of motivation (?=-0.458, p < 0.001). Conclusions: The present study underscores the importance of training, sleep, diet, and mental health amongst cross-country runners.

o-Hydroxyphenylthiourea (HPTU) undergoes oxidative dimerization to form a yellow-colored disulfide in the presence of Iridium(III), a process significantly enhanced by the activator 1,10-phenanthroline. This catalytic oxidation was monitored spectrophotometrically at 416 ​nm under ambient conditions, employing both tangent and fixed-time methods. The method's performance was optimized by investigating factors like pH, reagent concentration, temperature, and potential interferents. A linear relationship between the rate of reaction and Iridium(III) concentration was observed in the range of 0.001 ​ng–5.0 ​mg of Iridium per mL, enabling the detection of Iridium(III) down to 0.001 ​ng/mL at pH 11.7. The method's simplicity, cost-effectiveness, and sensitivity make it suitable for the determination of Iridium(III) in real-time samples, eliminating the need for additional reagents, complex procedures, or preconcentration steps.

Metal-matrix composites (MMCs) and hybrid metal-matrix composites (HMMCs) are novel materials that possess distinctive mechanical characteristics, making them well-suited for industrial applications. This research implements an experimental study of the mechanical properties of HMMCs, where weight percentages (2, 3, and 4 wt. %) of zirconium dioxide (ZrO2) and graphite powder (Gr) are used as reinforcements and the aluminum alloy 7475 (AA7475) is used as a matrix. In this investigation, the stir casting method was used to prepare the composite by using AA7475 alloy with pre-heated reinforced particles of ZrO2 and graphite with various reinforcement weight percentages. Among these combinations, AA with 4 wt. % ZrO2 and 2 wt.% Gr showed enhanced mechanical properties. The mechanical properties of the ultimate tensile strength, yield strength, and hardness were evaluated. A Scanning Electron Microscope analysis was conducted to study the reinforcement distribution in the matrix. The input parameters for wire-cut electrical discharge machining (WCEDM) are pulse-on time (TON), pulse peak current (IP), and flushing pressure (PF). The material removal rate and surface roughness are the output responses. Determining the ideal set of process parameters in WCEDM for HMMC also involved the use of the Taguchi design technique known as the L9 orthogonal array. The peak current for the MRR and flushing pressure for the SR are the most prominent parameters.

In 2001, Virchenko et al. published a paper on a new generalization of Gauss hypergeometric function, namely Wright-type generalized hypergeometric function. Present work aims to define the q-analogue generalized hypergeometric function, which reduces to generalized hypegeometric function by letting q tends to one, and study some new properties. Convergence of the series defining generalized q-hypergeometric function and properties including certain differentiation formulae and integral representations have been deduced.

Guillain–Barré syndrome (GBS) is an autoimmune polyneuropathy characterized by hyporeflexic neuromuscular paralysis and albuminocytologic dissociation in the cerebrospinal fluid. It is a postinfectious disorder. The most common antecedent illnesses are respiratory tract infection and Campylobacter jejuni infection. After the antecedent infection, specific antibodies are generated that cross-react with gangliosides in the host culminating in demyelination of the peripheral nerves or nerve roots. Complement activation also contributes to nerve degeneration. Bilateral symmetrical progression of the limb weakness occurs over a period of a few days followed by a plateau phase, after which a recovery phase follows. Generalized hypotonia and hyporeflexia characterize the limb weakness. Cerebrospinal fluid analysis shows albuminocytologic dissociation. About one-third of patients develop respiratory failure. Neuropathic pain is a disturbing symptom in GBS. Dysautonomia is very characteristic of GBS. Erasmus GBS respiratory insufficiency score predicts the need for mechanical ventilation. The weaning process from mechanical ventilation mainly depends on the recovery of vital capacity and inspiratory force. The definitive treatment for GBS consists of plasma exchange or intravenous immunoglobulin therapy, both of which are equally efficacious. Seasonal variation has been observed in the occurrence and recovery of GBS. Prognosis of GBS varies widely. Erasmus GBS outcome scale scoring system predicts the ability of the patient to walk independently after 6 months. Several GBS cases have been reported globally during recent pandemic of coronavirus disease 2019. Though GBS is a self-limiting disease, there are quite a few research questions that still remain to be answered.

Artificial Intelligence (AI) has revolutionized the biomedical sector in advanced diagnosis, treatment, and personalized medicine. While these AI-driven innovations promise vast benefits for patients and service providers, they also raise complex intellectual property (IP) challenges due to the inherent nature of AI technology. In this review, we discussed the multifaceted impact of AI on IP within the biomedical sector, exploring implications in areas like drug research and discovery, personalized medicine, and medical diagnostics. We dissect critical issues surrounding AI inventorship, patent and copyright protection for AI-generated works, data ownership, and licensing. To provide context, we analyzed the current IP legislative landscape in the United States, EU, China, and India, highlighting convergences, divergences, and precedent-setting cases relevant to the biomedical sector. Recognizing the need for harmonization, we reviewed current developments and discussed a way forward. We advocate for a collaborative approach, convening policymakers, clinicians, researchers, industry players, legal professionals, and patient advocates to navigate this dynamic landscape. It will create a stable IP regime and unlock the full potential of AI for enhanced healthcare delivery and improved patient outcomes.

The rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a machine learning (ML) approach for rolling element bearing failure identification under a variety of circumstances. This study presents, a new method called Composite coyote optimized resilient linear regression (CCO-RLR) for defect recognition and classification in rolling element bearings. Early rolling bearing failure diagnosis is a crucial and time-sensitive operation that guarantees the dependability and security of mechanical fault systems. Initially, the rolling element bearings dataset is collected and preprocessed using Min-max normalization. For extracting the feature, Fourier transform (FT) is employed. The result shows that the CCO-RLR accuracy is 97.8% when compared with those existing methods. Our suggested method offers an effective means of quantifying flaws and significantly improving classification effectiveness.

Abstract Background B-type lamins are critical nuclear envelope proteins that interact with the three-dimensional genomic architecture. However, identifying the direct roles of B-lamins on dynamic genome organization has been challenging as their joint depletion severely impacts cell viability. To overcome this, we engineered mammalian cells to rapidly and completely degrade endogenous B-type lamins using Auxin-inducible degron technology. Results Using live-cell Dual Partial Wave Spectroscopic (Dual-PWS) microscopy, Stochastic Optical Reconstruction Microscopy (STORM), in situ Hi-C, CRISPR-Sirius, and fluorescence in situ hybridization (FISH), we demonstrate that lamin B1 and lamin B2 are critical structural components of the nuclear periphery that create a repressive compartment for peripheral-associated genes. Lamin B1 and lamin B2 depletion minimally alters higher-order chromatin folding but disrupts cell morphology, significantly increases chromatin mobility, redistributes both constitutive and facultative heterochromatin, and induces differential gene expression both within and near lamin-associated domain (LAD) boundaries. Critically, we demonstrate that chromatin territories expand as upregulated genes within LADs radially shift inwards. Our results indicate that the mechanism of action of B-type lamins comes from their role in constraining chromatin motion and spatial positioning of gene-specific loci, heterochromatin, and chromatin domains. Conclusions Our findings suggest that, while B-type lamin degradation does not significantly change genome topology, it has major implications for three-dimensional chromatin conformation at the single-cell level both at the lamina-associated periphery and the non-LAD-associated nuclear interior with concomitant genome-wide transcriptional changes. This raises intriguing questions about the individual and overlapping roles of lamin B1 and lamin B2 in cellular function and disease.

Abstract We present green synthesis of silver nanoparticles in water using unirradiated and Ag $$^{15+}$$ 15 + ion irradiated phytoextracts of Bergenia Ciliata leaf, Eupatorium adenophorum leaf, Rhododendron arboreum leaf and flower. The use of different plant extracts and their subsequent ion irradiation allow for successful refinement of nanoparticle size and morphology. Due to changes in reducing and capping agents the nanoparticle surface functionalization also varies which not only controls the morphology but also allows for surface oxidation and aggregation processes. In this work, we have synthesized silver nanoparticles which exhibit sizes in the range from 13 to 24 nm and having shapes like spherical, quasispherical, trigonal, hexagonal, cylindrical, dendritic assemblies, and porous nanoparticles. Owing to changes in the size and shape of the nanoparticles, their direct bandgap (2.05 eV - 2.48 eV) and local surface plasmon resonance (420 nm - 490 nm) could also be tuned. These nanoparticles are examined as SERS substrates, where their enhancement factors, limit of detection for methylene blue, and SERS substrate homogeneity have been tested. It has been observed the nanoparticles synthesized using unirradiated plant extracts present an enhancement factor of 10 $$^6$$ 6 with a limit of detection 10 $$^{-8}$$ - 8 M. Whereas nanoparticles with refined morphology and shapes upon irradiation present high enhancement factors of >10 $$^7$$ 7 and detection limit down to 10 $$^{-9}$$ - 9 M. In addition, uniformity in Raman spectra over the SERS substrates has been obtained for selected Ag NPs substrates synthesized using irradiated extracts with minimum relative standard deviation in enhancement factor < 12%.

Background: Depression among people living with human immunodeficiency virus (PLHIV) is highly prevalent and it is associated with increased morbidity, poor adherence to antiretroviral therapy, and poor psychosocial outcomes. To address this, integrated counselling and testing centres (ICTC) counsellors provide psychosocial support to PLHIV. Materials and Methods: This descriptive study aims to assess the awareness and knowledge of ICTC counsellors about depression and its management. A total of 338 (n = 452) ICTC counsellors participated in the study. A demographic data sheet and a semi-structured questionnaire were used to collect data. Results: More than half of the participants reported that biochemical imbalances cause depression. 71.60% and 79.59% of participants reported that depression was common among PLHIV and required immediate attention. 92.60% of counsellors reported that a combination of counselling and medication would be effective to treat depression. 86.98% and 81.95% of counsellors were confident and actively screened for depression among PLHIV, and 78.11% of counsellors had access to a psychiatrist. In contrast. One-third of participants had difficulties working with PLHIV, and 55.56% of participants expressed that addressing issues of PLHIVs' depression to be left to mental health professionals. Conclusion: ICTC counsellors had adequate knowledge about depression and its symptoms. However, lack of knowledge on intervention strategies, time constraints and work targets are significant barriers. These findings suggest that training on mental illness screening; brief intervention strategies may help counsellors to assist PLHIV in overcoming depression complications.

We present the Monsoon Mission Coupled Forecast System version 2.0 (MMCFSv2) model, which substantially upgrades the present operational MMCFSv1 (version 1) at the India Meteorology Department. The latest 25 years (1998–2022) of retrospective seasonal coupled hindcast simulations of the Indian summer monsoon with April initial conditions from Coupled Forecast System Reanalysis are discussed. MMCFSv2 simulates the tropical wind, rainfall, and temperature structure reasonably well. MMCFSv2 captures surface winds well and reduces precipitation biases over land, except over India and North America. The dry bias over these regions remained like in MMCFSv1. MMCFSv2 captures significant features of the Indian monsoon, including the intensity and location of the maximum precipitation centers and the large-scale monsoon circulation. MMCFSv2 improves the phase skill (anomaly correlation coefficient) of the interannual variation of Indian summer monsoon rainfall (ISMR) by 17 % and enhances the amplitude skill (normalized root mean square error) by 20 %. MMCFSv2 shows improved teleconnections of ISMR with the equatorial Indian and Pacific oceans. This 25-year hindcast dataset will serve as the baseline for future sensitivity studies of MMCFSv2.

Machine learning, a rapidly growing field, has attracted numerous researchers for its ability to automatically learn from and make predictions based on data. This manuscript presents an innovative approach to estimating the covariance matrix of noise in radar measurements for target tracking, resulting from collaborative efforts. Traditionally, researchers have assumed that the covariance matrix of noise in sonar measurements is present in the vast majority of literature related to target tracking. On the other hand, this research aims to estimate it by employing deep learning algorithms with noisy measurements in range, bearing, and elevation from radar sensors. This collaborative approach, involving multiple disciplines, provides a more precise and accurate covariance matrix estimate. Additionally, the unscented Kalman filter was combined with the gated recurrent unit, multilayer perceptron, convolutional neural network, and long short-term memory to accomplish the task of 3D target tracking in an airborne environment. The quantification of the results was achieved through the use of Monte Carlo simulations, which demonstrated that the convolutional neural network performed better than any other approach. The system was simulated using a Python program, and the proposed method offers higher accuracy and faster convergence time than conventional target tracking methods. This is a demonstration of the potential that collaboration can have in research.

This paper presents Transistor Clamped H-Bridge (TCHB) inverter-based Electric Spring (ES), a demand side management device that enhances the voltage stability of the critical load connected to the grid through Renewable Energy Sources (RES). This configuration of ES provides the solution to voltage sag/swell for reduced size, %THD and input power required. To validate the effectiveness of the system performance of TCHB-based ES, detailed simulation studies are performed using MATLAB/Simulink environment.

In the development of automated manipulators for fruit and vegetable picking technologies, the challenge of ensuring an efficient, stable, and loss-free picking process has been a complex problem. In such an environment, manipulators require the most efficient and robust control for effective operations. In this paper, a serial 9-DOF redundant manipulator (1P8R) is proposed with various controllers for trajectory tracking problems in agricultural applications. The dynamic analysis of redundant manipulator has been carried out using the Recursive Newton-Euler method. The joint configurations of the robot are determined using optimization techniques for specific Task Space Locations (TSLs) by avoiding obstacles. The process of generating joint trajectories has been implemented by considering the cubic polynomial function. For the task of controlling the robot trajectory tracking in the virtual agricultural environment, different combinations of Proportional (P), Integrative (I), Derivative (D), and Feed-Forward (FF) controllers are employed, and a comparative analysis has been performed among these controllers. To verify the performance of the manipulator, simulations are carried out in a virtual environment using Simulink software. Results show that the robot is able to reach specific TSL accurately with better control and it is found that the implementation of Feed-Forward and PID-CTC controllers has better performance in a complex environment.

A field experiment was conducted at Agricultural college, Naira, Srikakulam, Andhra Pradesh, India during early summer (January–April) of 2021 to know about the incidence of leaf webber and sucking pests viz., leafhopper (Hishimonus phycitis Distant) and whitefly (Bemisia tabaci Gennadius) its relation with main weather factors. Results revealed that a peak incidence of mean capsule borer population per plant were recorded in 9th and 11th standard weeks, respectively. The correlation study revealed that the Antigastra larvae had significantly positive correlation with maximum temperature (r=0.48) and significantly negative correlation with relative humidity (r=- 0.71). The regression analysis revealed that these factors accounted for approximately 59.8% of the variation in the capsule borer population (R2=0.598). The incidence of leafhopper (Hishimonus phycitis Distant) and whitefly (Bemisia tabaci Gennadius) were started during 5th and 4th SMW attained their peaks during 11th an 12th SMW respectively. The correlation study revealed that the leafhopper showed significant positive correlation with maximum temperature (r=0.623) and minimum temperature (r=0.283) and significantly negatively correlated with morning and evening relative humidity (r=-0.165, r=-0.122) respectively. While the correlation study between whitefly population and weather parameters revealed that the it showed positive correlation with maximum (r=0.821) and minimum (r=0.120) temperature and negatively correlated with morning and evening relative humidity (r=-0.097, r=-0.182) respectively whereas, rainfall showed non-significant correlation (r=0.00) with the both sucking pests.

Abstract Nuclear compartments are prominent features of 3D chromatin organization, but sequencing depth limitations have impeded investigation at ultra fine-scale. CTCF loops are generally studied at a finer scale, but the impact of looping on proximal interactions remains enigmatic. Here, we critically examine nuclear compartments and CTCF loop-proximal interactions using a combination of in situ Hi-C at unparalleled depth, algorithm development, and biophysical modeling. Producing a large Hi-C map with 33 billion contacts in conjunction with an algorithm for performing principal component analysis on sparse, super massive matrices (POSSUMM), we resolve compartments to 500 bp. Our results demonstrate that essentially all active promoters and distal enhancers localize in the A compartment, even when flanking sequences do not. Furthermore, we find that the TSS and TTS of paused genes are often segregated into separate compartments. We then identify diffuse interactions that radiate from CTCF loop anchors, which correlate with strong enhancer-promoter interactions and proximal transcription. We also find that these diffuse interactions depend on CTCF’s RNA binding domains. In this work, we demonstrate features of fine-scale chromatin organization consistent with a revised model in which compartments are more precise than commonly thought while CTCF loops are more protracted.

Predictions about solar power will have a significant impact on large-scale renewable energy plants. Photovoltaic (PV) power generation forecasting is particularly sensitive to measuring the uncertainty in weather conditions. Although several conventional techniques like long short-term memory (LSTM), support vector machine (SVM), etc. are available, but due to some restrictions, their application is limited. To enhance the precision of forecasting solar power from solar farms, a hybrid machine learning model that includes blends of the K-Nearest Neighbor (KNN) machine learning technique with the SVM to increase reliability for power system operators is proposed in this investigation. The conventional LSTM technique is also implemented to compare the performance of the proposed hybrid technique. The suggested hybrid model is improved by the use of structural diversity and data diversity in KNN and SVM, respectively. For the solar power predictions, the suggested method was tested on the Jodhpur real-time series dataset obtained from the data centers of weather stations using Meteonorm. The data set includes metrics such as Hourly Average Temperature (HAT), Hourly Total Sunlight Duration (HTSD), Hourly Total Global Solar Radiation (HTGSR), and Hourly Total Photovoltaic Energy Generation (HTPEG). The collated data has been segmented into training data, validation data, and testing data. Furthermore, the proposed technique performed better when evaluated on the three performance indices, viz., accuracy, sensitivity, and specificity. Compared with the conventional LSTM technique, the hybrid technique improved the prediction with 98 % accuracy.

Background: In terms of connectivity and at hand accessibility to the ocean of information, smartphones could be considered a boon to the students. Yet, due their lucrative benefits, addictive behaviour is surfacing, hampering the sleep quality and mental wellbeing among individuals. Aim: To assess the influence of smartphone addiction on sleep and mental wellbeing among undergraduate dental students of Hyderabad and Ranga Reddy districts. Methods: A cross sectional study was conducted to assess the smartphone addiction (SAS-SV), sleep quality (PSQI) and mental wellbeing (WHO-5 Well Being Index), among 427 undergraduate dental students. Mean values and frequency distributions were used to assess the addiction levels, sleep quality and wellbeing. Multiple regression analysis was performed to assess the influence of independent SAS-SV components and smartphone related variables on sleep and mental wellbeing. Results: Smartphone addiction was seen among 42.60 % (n = 190) of students. Majority of the smartphone addicts used the phone during the night and approximately for 6–8 hs/day. Also, smartphone addicts reported poorer sleep quality (8.16 ± 3.74; p = 0.00) and wellbeing (12.46 ± 4.94; p = 0.00). The components of SAS-SV ‘Daily life disturbance’ influenced PSQI (p = 0.000) and WHO-5 WBI scores (p = 0.009), whereas, ‘Withdrawal’ and ‘Tolerance’ had influenced the PSQI scores (p = 0.000; p = 0.00) alone. Smartphone related variables had no influence on sleep quality and wellbeing (p > 0.01) Conclusion: Findings of the study showed that 42 % of the students were addicted to smartphone. Smartphone addiction demonstrated a positive correlation with poor sleep quality and negative correlation with mental wellbeing among this sample.

Introduction: In Ayurvedic classics, the potential of herbal medicine is expressed through various dosage forms having multilevel actions. Ksheerapaka is one of the unique preparations of Ayurveda pharmaceutics, where milk is used as the medium for formulation. In general, Ksheerapaka is prepared by boiling the drug with a specific quantity of milk and water and reducing it to quantity. The preparation of ksheerapaka is time-consuming. As milk is the medium of preparation, the stability of the product is low and not travel-friendly. Due to these flaws, a new patient-friendly formulation with less bulk and better stability is intended to be prepared by value addition. Methods: A literature review was done on commonly used ksheerapaka and the value addition of milk products. Three commonly used Ksheerapakas, namely, Arjuna, Sthira, and Lasuna, were selected and prepared by the classical method. Value addition was done by trial and error using different techniques in three different centers. Result and Conclusion: Ksheerapaka prepared by spray drying had the same flavor and taste as the classical preparation and was free-flowing without any grittiness. The appearance and taste were appreciable and easily dissolvable. The product was also stable at room temperature. Spray drying can be recommended for the commercial preparation of Ksheerapaka powder considering the product's cost-effectiveness and efficacy. Unlike other value-added preparations of classical Ayurvedic formulations, Ksheerapaka powder also satisfies the mode of administration like the classical preparation.

Introduction: COVID-19 has led to an increase in burden on health-care workers. Postgraduate residents are at the forefront of the pandemic. Understanding its impact and the perspectives of postgraduate residents could provide better solutions to their problems. Objectives: 1. To assess the psychological impact of the COVID-19 pandemic on postgraduate doctors. 2. To assess the perception of postgraduate doctors regarding the medical profession and its association to the psychological impact of the COVID-19 pandemic. Methodology: This cross-sectional study recruited 65 postgraduate medical resident doctors working in teaching hospitals involved in the care of COVID-19 patients. They were requested to complete the questionnaire consisting of qualitative as well as quantitative questions. For an objective assessment of anxiety, the Generalized Anxiety Disorder-7 (GAD-7) scale was used. Relevant statistical methods were employed and statistical analysis was done using IBM SPSS software version 20.0. Results: The qualitative data showed themes of concern regarding social awareness about COVID-19 (hygiene, masks, and social distancing), working conditions of doctors, and increased stress and burnout. 47.54% (n = 29) of respondents said that they would reconsider their profession/specialty, out of which 21 said that they would switch to another profession. The resident doctors who would reconsider their choice of profession/specialty had significantly higher (P < 0.05) scores for anxiety (GAD-7). Conclusion: Owing to the significant association between anxiety and desire to change their profession altogether, greater psychological support for doctors appears to be a pressing need. It is important that the concerns of the postgraduate residents are addressed promptly and holistically.

Background: A Medicine and Bachelor of Surgery (MBBS) graduate will gain the required skills and competencies under supervision during the internship training. Many factors influence the competency levels. Coronavirus disease-19 (COVID-19) pandemic could be one of those factors. Objectives: The objectives were to assess the competency levels among medical interns, postinternship, during the COVID pandemic and also to assess the association between the competency levels with their final year results. Subjects and Methods: A cross-sectional study was conducted among 113 interns using a self-assessment questionnaire. Self-perception about the competencies was graded into three categories as low, moderate, and high. The Chi-square test was used for analysing statistical association between self-perception and MBBS final year part II results. Results: The level of self-perception about the competencies among the interns was found to be high in-analysis, display and interpretation of information; hypothesis formulation and decision-making (45.1%) and interpersonal communication, management, organizing health care system and professionalism (42.5%), whereas it was low for obtaining information from the patients and their families (29.2%) and procedural skills (18.6%). About 75.2% and 76.1% of the interns had low self-perception for endo-tracheal intubation and lumbar puncture, respectively. High level of self-perception was noted for urethral catheterization (84.2%) and intramuscular drug administration (76.1%). It was found that none of the competencies were statistically associated with the grade based on marks obtained in final year part II. Conclusion: COVID pandemic has affected the interns training to a significant extent. There was no statistically significant association between final year grades and the competencies.

We report on the development and characterization of superconducting damascene tantalum nitride (TaN) nanowires, 100 nm–3 μm wide, with TaN thicknesses varying from 5 to 35 nm, using 193-nm optical lithography and chemical mechanical planarization among other 300-mm wafer-scale processes. The TaN film composition chosen for nanowire fabrication was informed by a detailed study of unpatterned TaN films with varying nitrogen to tantalum ratios, formed by reactive sputtering. We also discuss the influence of encapsulation by copper and disordered atomic layer deposited TaN on the critical current of superconducting nanowires. Superconducting critical current density (measured at 12 mK) ranges from 0.12 to 0.85 MA/cm2 depending on nanowire width and film thickness. The potential of ultrathin TaN nanowires at 300-mm scale is discussed in the context of applications such as on-chip integration for readout of superconducting qubits, in single-photon detection for quantum computing, as well as in large single-photon detecting focal plane arrays for cosmology in a broader range of wavelengths.

Advanced science workflows are orchestrated over complex ecosystems consisting of computing platforms and physical instruments connected over wide-area networks. The availability of physical facilities of these ecosystems may be limited during typically long software development periods, due to the expense, disruption risk and limited readiness during the design and construction stages. We present a Virtual Infrastructure Twin (VIT) of such an ecosystem to support the software development and testing for multi-site computing-instrument ecosystems. A VIT is implemented using virtual hosts connected over emulated local-area networks at sites, which are in turn connected over an emulated wide-area network. We describe the design and implementation details of a VIT including: (i) host and network emulations and the incorporation of instrument simulators, (ii) services for remote instrument steering, and (iii) performance analytics based on network throughput and disk input/output measurements. We illustrate a use case by emulating an ecosystem of four geographically distributed laboratory sites with services for accessing and steering instruments and estimating throughput profiles using physical testbed measurements.

Recent decades have seen dramatic development and adoption of digital technology. This technological advancement generates a large amount of critical data that must be safeguarded. The security of confidential data is one of the primary concerns in fog computing. As a result, achieving a reliable level of security in the fog computing environment is crucial. In this context, 3D point and mesh fog data are becoming increasingly popular among the various types of data stored in the fog. Data encryption using chaotic behavior is one of the preferred research areas due to its unique properties, such as randomness, determinism, sensitivity to initial conditions, and ergodicity. In this paper, we have taken advantage of this chaotic behavior to achieve higher security. This study presents a novel approach for protecting the privacy of 3D point and mesh fog data. Initially, the fog data coordinates are transformed using the sequence generated by the chaotic behavior. Then, bifurcation analysis is used to depict the enhanced scope of the proposed map. The quality of the proposed chaotic system is assessed using metrics such as the Lyapunov exponent and approximate entropy. Results show that the proposed encryption framework performs superior when subjected to brute-force and statistical attacks. Further, the designed framework produces better results than the prior literature.

Bacteria are commonly found in various environmental sources, such as soil and water. Among the different bacterial species, Pseudomonas aeruginosa is particularly known for causing infections in humans and can easily colonize surfaces. To address this issue, one approach is to modify the surface with antibacterial coatings. Thus, here, we investigate the effectiveness of Cu–Ti oxide coatings by fabricating Cu1−xTixO2 (0.25 < x < 0.75) thin films on glass substrates using the pulsed laser deposition technique. Microstructural analysis revealed that the films were amorphous and exhibited a smooth surface. In addition, the contact angle measurements demonstrated high hydrophilicity, as indicated by values below 90°. Subsequently, we examined the biofilm formation of Pseudomonas aeruginosa bacteria on both Cu1−xTixO2 coated and uncoated glass surfaces. The results revealed a 20% reduction in bacterial growth on Cu0.75Ti0.25O2-coated samples, as determined by calculating the biomass of the biofilm. These findings were further discussed in relation to the copper content present on the surface of the coating. Ultimately, the results contribute to our understanding of bacterial development on various surfaces. Consequently, this research may have practical implications for enhancing the antibacterial properties, preventing bacterial infections associated with different materials and surfaces, and improving implant performance.

Sun-as-a-star spectroscopic characteristics of solar flares can be used as a benchmark for the detection and analysis of stellar flares. Here, we study the Sun-as-a-star properties of an X1.0 solar flare, using high-resolution spectroscopic data obtained by the Chinese H α Solar Explorer (CHASE). A noise reduction algorithm based on discrete Fourier transformation is first employed to enhance the signal-to-noise ratio of the space-integral H α spectrum, with a focus on its typical characteristics. For the flare of interest, we find that the average H α profile displays a strong emission at the line center and an obvious line broadening. It also presents a clear red asymmetry, corresponding to a redshift velocity of around 50 km s ^−1 that slightly decreases with time, consistent with previous results. Furthermore, we study how the size of the space-integral region affects the characteristics of the flare's Sun-as-a-star H α profile. It is found that although the redshift velocity calculated from the H α profile remains unchanged, the detectability of the characteristics weakens as the space-integral region becomes larger. An upper limit on the size of the target region where the red asymmetry is detectable is estimated. It is also found that the intensity in H α profiles, measured by the equivalent widths of the spectra, are significantly underestimated if the H α spectra are further averaged in the time domain.