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This paper aims to provide a comprehensive understanding of the big data–performance relationship based on the existing empirical evidence. Using a meta-analysis approach, big data adoption (BDA) related 446 effect sizes reported in 133 prior empirical studies were gathered from 118848 informants in more than 30 countries. Results confirm ten significant antecedents and eight outcomes of BDA based on identified literature. We further estimate the heterogeneity based on subgroup analysis by considering two types of moderators as (a) economic regions (developed vs developing), and (b) type of industry. We find that organizations in developed countries adopt big data largely due to environmental and organizational factors. Further, developed countries can harness the potential of big data for better performance (e.g., supply chain integration, collaboration, customer relationship management, and innovation). This study provides multifaceted insights for practitioners and academia alike regarding the use of big data.

Abstract The main challenges for utilizing daily evapotranspiration (ET) estimation in the study area revolve around the need for accurate and reliable data inputs, as well as the interpretation of ET dynamics within the context of local agricultural practices and environmental conditions. Factors such as cloud cover, atmospheric aerosols, and variations in land cover pose challenges to the precise estimation of ET from remote sensing data. This research aimed to utilize Landsat 8 and 9 datasets from the 2022–23 period in the Udham Singh Nagar district to apply the modified Priestley-Taylor (MPT) model for estimating ET. An average ET was estimated 1.33, 1.57, 1.70, 2.99, and 3.20 mm day−1 with 0.29, 0.33, 0.41, 0.69, and 1.03 standard deviation for December, January, February, March, and April month, respectively. In the validation phase, a strong correlation was found between the evaporative fraction derived from MPT and that observed by lysimeter, with R2 = 0.71, mean biased error = 0.04 mm day−1, root mean squared error = 0.62 mm day-1 and agreement index of 0.914. These results collectively support the effectiveness of the MPT model in accurately estimating ET across Udham Singh Nagar district. In essence, this research not only confirms the MPT model’s capability in ET estimation but also offers detailed insights into the spatial and temporal fluctuations of energy fluxes and daily ET rates.

Background and Aims: Assessment of the severity of substance use disorders (SUDs) in a culture-sensitive manner can help gauge the current condition of the substance user and assess change with time. The present study aimed to develop a scale for the assessment of the severity of SUDs in the Indian clinical context. Methods: Based upon the review of literature on previously available instruments and a consultative meeting of experts, a clinician-rated scale was developed that finally comprised 41 items. A briefer 5-item scale with current and lifetime versions was also developed. The scales were applied to patients with SUDs at six different clinical sites. Results: The instrument was applied to 720 patients (98.2% males, mean age: 34.6 years). The Cronbach’s alpha of the full scale was 0.852. The inter-rater reliability Pearson correlation coefficient of the full-scale was r = 0.821 (P < 0.001), and the intra-class correlation coefficient single measure was 0.800 (95% confidence interval: 0.724–0.956). A four-factor solution was suggested to be the most tenable. The mean application duration of the full scale was 13.4 minutes, and that of the briefer version was 2 minutes. Conclusion: This validated scale could be a potentially useful assessment measure for the severity of SUDs in the Indian context. The utility lies in the simplicity of administration and scoring and the balance between brevity and thorough assessment.

Abstract Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India.

Monitoring vegetation phenology is essential for understanding the impacts of climate change on agricultural production. This study leverages Sentinel-2 data to develop an algorithm in Google Earth Engine (GEE) for calculating phenological metrics of rice crop cultivated over the hilly area, allowing for high-resolution, efficient, and large-scale analysis without the need for data download. The study focuses on key metrics, including the start of the season and end of the season , length of growing season derived from various vegetation indices. The results demonstrate that NDVI-based phenological metrics closely align with the observed values at the experimental site, Malan. Moreover, the relationship of NDVI based length of growing season with the rice crop yield was found stronger with a R2 value of 0.68, depicting the capability of the satellite-based phenology metrics to estimate the rice crop yield in hilly region of Himachal Pradesh.

Timely and accurate crop mapping plays an important role in food security, economic and environmental policies. Crop maps are also utilized for agro-environmental assessments and crop water usage monitoring. Because it provides periodic large-scale observations of ground objects, satellite remote sensing has been regarded as an advanced tool to characterize crop types and their distributions on a regional scale. High-resolution, multispectral images of October 13, 2021, December 7, 2021 and March 6, 2022 of sentinel-2 satellite released by the European Space Agency (ESA) have been used for classification. Ground truth points have been collected manually with the android app ‘Mapmarker’ and Google Earth. Further, pre-processing of satellite imageries such as resampling, mosaicking and sub-setting have been done with the Sentinel Application Platform (SNAP) software. Crop classification and acreage estimation was conducted using Artificial Neural Network. It is the first time an attempt was made to estimate cropping intensity using geospatial technology in the upper Gangetic plains of Uttarakhand state. Rice and sugarcane areas of 108,884 ha and 11,479 ha, respectively, were estimated from the October 13, 2021 image. Pea crop area was estimated as 6227 ha from December 7, 2021 image. Using March 6, 2022 image, wheat and mustard crop areas were estimated as 105,334 ha and 2018 ha, respectively. The estimated area of each major crop was further utilized to calculate Multiple Cropping Index which was found to be 174.4 %.

The present study was undertaken on 164 bovine milk samples (cow = 113 and Buffalo = 51) collected from various dairy farms in and around Rewa (Madhya Pradesh, India) and only 24 samples (6.83%) were found to be positive for mastitis when screened through California Mastitis Test (CMT). The prevalence of subclinical mastitis in cattle was 21%. All 17 isolates were phenotypically characterized by mannitol fermentation, biochemical tests (acetoin production by Voges-Proskauer test), beta-galactosidase test susceptibility to novobiocin (5 µ µµg disk), resistance to polymyxin B (300 µg disk). We also observed the biofilm formation ability of all the Staphylococcus aureus strains (n=17) by Congo Red Agar (CRA), Microtiter plate (using crystal violet), and light microscopy method, 90% sensitivity was seen through microtiter plate method. Antibiofilm activity of garlic oil was undertaken on positive isolates (3%, 2%, and 1% concentration) along with positive and negative control in every microtiter plate assay. Maximum inhibition was observed at 3% concentration with O.D values ranging from 0.021±0.007 to 0.291 ± 0.005 in all the samples with percent inhibition (50 to 60%). Multi-drug resistance profiles of biofilm-producing isolates were also undertaken against polymyxin, novobiocin, tetracyclines, cotrimoxazole, clindamycin, cefoxitin, and cefoperazone. Eighty-two (82) percent of isolates showed resistance against polymyxin, 52% against novobiocin and erythromycin, and all the isolates showed 100% sensitivity against tetracyclines and cefoxitin.

Climate change has a considerable influence on agricultural output, raising farmers’ production risk. Nevertheless, the risk can be mitigated by selecting stable genotypes. In countries such as India, where significant proportions of farmers are smallholders or operate on marginal land, the minimization of risk is of paramount importance. Existing methods of stability measures often result in low-yielding varieties. Consequently, there is a need to develop more effective stability strategy to solve this problem without reducing yield. In light of the preceding, the Rank Based Stability Index (RSI) has been proposed for choosing genotypes based on the rank of interaction residuals to mitigate the influence of climatic changes without compromising yield. Through statistical analyses, the RSI approach demonstrates its ability to discern stable genotypes resilient to environmental fluctuations. By evaluating genotype performance across multiple environments and seasons, RSI identifies cultivars with consistent yield performance, thus offering a valuable tool for enhancing crop resilience and ensuring food security. The effectiveness of the proposed RSI approach for selecting stable genotypes from groundnut (Arachis hypogaea L.) data has been notably demonstrated in comparison to other methods. RSI emerges as a promising methodology for genotype selection in groundnut, offering a robust framework for mitigating the influence of climatic changes on crop yields.

BackgroundCataract is a leading cause of global blindness, affecting around 33% of blind individuals worldwide. It significantly impacts individuals’ well-being, independence, and quality of life, posing a substantial economic burden. India’s rapidly ageing population necessitates an examination of cataract prevalence and treatment disparities. No attempts have been made to address socioeconomic variation in treatment disparities of effective cataract treatment coverage among older adults in India.Data and methodThis study utilises data from the Longitudinal Ageing Study of India (LASI) conducted in 2017–18, that covered, 73,396 individuals aged 45 and above. Logistic regression, univariate, and bivariate analyses were employed to understand the variation of cataract and their associations with various demographic factors. Visual acuity tests and self-reported cataract data were used.ResultsThe prevalence of cataract among older adults in India was 14.25%, with higher rates among females and the older adult. Socioeconomic disparities werelarge, with lower prevalence among those with higher education and urban residence. Despite the effectiveness of cataract surgery, disparities in treatment access and effective coverage persisted. Approximately 27.52% of older adults did not receive cataract treatment, and those who received out of them 28% did not receive effective treatment. The effective treatment was lower among female, less educated, and poor.ConclusionCataract remains a significant public health concern in India, particularly among older adults. The study highlights the importance of addressing socioeconomic disparities in cataract treatment access and quality of care. Targeted interventions are needed to bridge these gaps, ultimately improving visual health outcomes and well-being among older adults in India.

Background: Nomophobia is a situational phobia evoked by unavailability of smart phone or the thought of the possibility of not having it, not being able to use it and losing it. Currently used instruments for assessment of severity of nomophobia offers challenges of administration and have limited applicability in the Indian setting. Therefore, this study was aimed to depict and understand the lived experience of college students with nomophobia and making sense of it. Methods: This interpretative phenomenological analysis research design study was carried out on 17 undergraduate students belonging to different academic streams including Science, Social science and Commerce from the three study sites situated in different locations of India. An in-depth interview guide was prepared. The students who scored more than 90 on nomophobia questionnaire (NMP-Q Questionnaire) were included in the study. The data was recorded in audio and video format, it was transcribed, and translated from Hindi to English language. Coding was done and the theme were extracted. Results: The findings identified six superordinate themes: Digital Obsession, Digital Compulsion, Approval Motivation, Digital Intensement, Digital Well-being and Insight. Conclusion: The lived experiences of the students with nomophobia had explicitly shown a strong inclination towards the smartphone. They also focused on some of the significant aids provided by the smartphone. Further the intensive use of the smartphone was posing major challenges to the students when they were away from it.

Abstract Lead-free, silicon compatible materials showing large electromechanical responses comparable to, or better than conventional relaxor ferroelectrics, are desirable for various nanoelectromechanical devices and applications. Defect-engineered electrostriction has recently been gaining popularity to obtain enhanced electromechanical responses at sub 100 Hz frequencies. Here, we report record values of electrostrictive strain coefficients (M 31 ) at frequencies as large as 5 kHz (1.04×10−14 m2/V2 at 1 kHz, and 3.87×10−15 m2/V2 at 5 kHz) using A-site and oxygen-deficient barium titanate thin-films, epitaxially integrated onto Si. The effect is robust and retained upon cycling upto 6 million times. Our perovskite films are non-ferroelectric, exhibit a different symmetry compared to stoichiometric BaTiO3 and are characterized by twin boundaries and nano polar-like regions. We show that the dielectric relaxation arising from the defect-induced features correlates well with the observed giant electrostriction-like response. These films show large coefficient of thermal expansion (2.36 × 10−5/K), which along with the giant M 31 implies a considerable increase in the lattice anharmonicity induced by the defects. Our work provides a crucial step forward towards formulating guidelines to engineer large electromechanical responses even at higher frequencies in lead-free thin films.

The present experiment was conducted during winter (rabi) seasons of 2019–20 and 2020–21 at the ICAR-Indian Grassland and Fodder Research Institute, Jhansi, Uttar Pradesh to study the genetic diversity amongst oat (Avena sativa L.) lines for micronutrients and agro-morphological traits. For study, 150 oat accessions collected from different sources were evaluated for two years and four micronutrients (Zn, Cu, Fe and Mn) and 9 agro-morphological traits were recorded. Genotypes IG02122 (464.0 mg/kg), IG02156 (48.1 mg/kg), IG03271 (136.0 mg/kg), and IG03213 (22.0 mg/kg) had maximum Fe, Zn, Mn and Cu content in fodder (harvested at 50% flowering). Genotype IG0280 had both high Zn (36.97 mg/kg) and Mn (114.33 mg/kg); IG03233 had high Cu (18.0 mg/kg) and Mn (124.0 mg/kg); and IG02131 had high Cu (18.33 mg/kg) and Fe (369.0 mg/kg) content. Analysis of variance (ANOVA) highlighted significant genotypic differences (P

Asparagus racemosus (Shatavari), a member of the Asparagaceae family, holds a special position in Ayurveda medicine because of its adaptability in preventing and healing a wide range of ailments. In various vernacular languages, it is also reckoned as Satawar, Satamuli, Satavari. The plant generally cultivated at low altitudes across India. It is popular among all therapeutic plants due to the presence of various bioactive chemicals such as steroidal glycosides, saponins (primarily Shatavarins I, II, III, and IV), polyphenols, flavonoids, alkaloids (racemosol), and vitamins. Data repository on different extracts of shatavari plant- roots, leaves, blossoms, and stems have illustrated their phytopharmaceutical effects that alleviate issues related to female reproductive system and a variety of ailments such as dyspepsia, neurological disorders, cough bronchitis, throat infections, TB, COVID-19 and so on. Several medications based on shatavari extracts are commercially available as nutraceutical and consumed by mass of people. Hence, the present review article attempted to report an insightful facts and scientific information about the plant profile, detailed phytochemistry, and medicinal properties of different extracts of shatavari. The potential application in food and future scopes are also summarized.

ABSTRACTPolyphenol oxidase (PPO) activity is a primary cause of the development of unattractive dark brown discoloration of wheat-based end products. The present study aims to evaluate a set of 41 diverse wheat genotypes grown at three different locations in India for grain phenol color reaction, PPO activity and molecular marker-based characterization of alleles of PPO genes. Relationships among these parameters were analyzed along with the effects of grain PPO activity on dough and chapati color at different time intervals. The mean PPO activity ranged from 7.42 to 27.57 min−1 g−1 10−3 among the genotypes and it showed a significant negative correlation with color brightness (L*) of dough rested for 0 min (r = -0.406), 15 min (r = -0.406), 2 h (r = -0.502) and 4 h (r = -0.551) and whole wheat flour-derived chapati rested for 2 h (r = -0.267) and 4 h (r = -0.424). The overall quality color score was negatively correlated with PPO activity (r = -0.863) and showed a positive correlation with both dough and chapati visual color measured at different time intervals. PPO activity in the genotypes carrying different alleles was found to be Ppo-A1a>Ppo-A1b; Ppo-B2d>Ppo-B2a; and Ppo-D1b>Ppo-D1a. The allelic constitution Ppo-A1bPpo-B2aPpo-D1a and Ppo-A1bPpo-B2dPpo-D1 was found to produce the lowest PPO activity, and thus these alleles are recommended to be used in marker assisted breeding for low PPO activity genotypes to minimize the discoloration of wheat-based end-products.

Litchi (Litchi chinensis) plantations in new areas without any land suitability assessment putting small farmers at risk of economic loss. The unique capability of the Geographical information system (GIS) for integration, analysis, and interpretation of spatial data has been leveraged for the identification, and mapping of potential litchi cultivation zones in India to help farmers and policymakers make informed decisions. Five climate-derived parameters including the number of days per year having a minimum temperature

Abstract Background Though over three-fourths of all births receive medical attention in India, the rate of cesarean delivery (22%) is twice higher than the WHO recommended level. Cesarean deliveries entail high costs and may lead to financial catastrophe for households. This paper examines the out-of-pocket expenditure (OOPE) and distress financing of cesarean deliveries in India. Methods We used data from the latest round of the National Family Health Survey conducted during 2019–21. The survey covered 636,699 households, and 724,115 women in the age group 15–49 years. We have used 159,643 births those delivered three years preceding the survey for whom the question on cost was canvassed. Descriptive analysis, bivariate analysis, concentration index (CI), and concentration curve (CC) were used in the analysis. Result Cesarean deliveries in India was estimated at 14.08%, in private health centres and 9.96% in public health centres. The prevalence of cesarean delivery increases with age, educational attainment, wealth quintile, BMI and high for those who had pregnancy complications, and previous birth as cesarean. The OOPE on cesarean births was US$133. It was US$498 in private health centres and US$99 in public health centres. The extent of distress financing of any cesarean delivery was 15.37%; 27% for those who delivered in private health centres compared to 16.61% for those who delivered in public health centres. The odds of financial distress arising due to OOPE on cesarean delivery increased with the increase of OOPE [AOR:10.00, 95% CI, 9.35–10.70]. Distress financing increased with birth order and was higher among those with low education and those who belonged to lower socioeconomic strata. Conclusion High OOPE on a cesarean delivery leads to distress financing in India. Timely monitoring of pregnancy and providing comprehensive pregnancy care, improving the quality of primary health centres to conduct cesarean deliveries, and regulating private health centres may reduce the high OOPE and financial distress due to cesarean deliveries in India.

Precise estimation of evapotranspiration is crucial for optimizing crop water uses particularly in the context of agriculture and horticultural production. In this study, various machine learning techniques was used to determine reference evapotranspiration by leveraging historical weather data. The models tested include artificial neural networks (ANN), Lasso, Ridge, Random Forest, LGBM regressor, and Gradient boosting regressor. LGBM regressor emerged as the top-performing model, exhibiting exceptional accuracy with a testing R-squared of 1.0. ANN also demonstrated notable performance, achieving a testing R-squared of 0.99. Moreover, the Random Forest and Gradient boosting regressor models showcased strong predictive capabilities, with R2 values of 0.99 and 0.98, respectively. These models offer valuable alternatives for estimating evapotranspiration, providing robustness and adaptability to diverse environmental datasets.

This review delves into the utilization of non-conventional starch sources through a specific cultural lens, focusing on the practices and significance of Arenga obtusifolia Griff., commonly referred to as the sugar palm, and its derivative product called Tassey. The tassey is widely popular in the Nyishi tribe of Arunachal Pradesh, India, a region renowned for its biodiversity and abundant palm tree varieties, including the sugar palm. Highlighting the multifaceted dimensions of the sugar palm, this review expounds upon Tassey's creation, consumption, and versatile applications. Tassey, a substance extracted from the sugar palm's stem, emerges as a pivotal element of the Nyishi tribe's cultural heritage and sustenance strategies, especially during times of scarcity and natural calamities. This review sheds light on the integral role played by Nyishi women in the cultivation and preparation of tassey, underscoring their vital contribution to upholding this time-honoured practice. Furthermore, the economic ramifications of tassey production and its commercial viability are explored, elucidating its role in bolstering the tribe's livelihood. Extending beyond its dietary role, tassey exhibits a diverse array of applications, spanning from human consumption to its incorporation in the crafting of traditional alcoholic beverages and even as a therapeutic resource for livestock. By offering insights into the profound relationship between the Nyishi tribe and the sugar palm, this review enriches our comprehension of traditional ecological wisdom and sustainable exploitation of resources within a culturally vibrant backdrop. Ultimately, this comprehensive exploration contributes to a nuanced appreciation of the intricate interplay between heritage, environment, and innovation in this distinctive region.

Diabetes is a global public health challenge, particularly in India, affecting millions. Among diabetic patients, lean type 2 diabetes is a severe subtype with higher microvascular complication risks. While studies on the prevalence, variations and risk factors of diabetes are increasingly available, there has been limited research on the prevalence, variations, and socioeconomic disparities of lean diabetes in India. This study used NFHS-5 microdata, and lean diabetes is defined as those with a BMI level of under 25 and random blood glucose levels of over 200 or under diabetic medication. Descriptive and multivariate analyses were conducted to understand lean diabetes variations and related factors. Socioeconomic disparities were measured using concentration curves and the concentration index. The study unveiled important insights into lean diabetes in India. 8.2% of men and 6.0% of women had elevated blood glucose levels, indicating a significant diabetes burden. Notably, 2.9% of men and 2.4% of women were diagnosed with lean diabetes. Among type 2 diabetics, 52.56% of males and 43.57% of females had lean type 2 diabetes. Lean diabetes prevalence varied from 11.6% in the poorest quintile to 1.1% in the richest. The odds of lean type 2 diabetes among those in the poorest quintile was 6.7 compared to the richest quintile. The concentration index of lean type 2 diabetes was -0.42 for men and -0.39 for women, suggesting a disproportionate impact on lower socioeconomic groups. This study advances our understanding of the complex interplay between socioeconomic factors and lean type 2 diabetes in India. To address the rising burden of lean diabetes among lower socioeconomic strata, policymakers and healthcare professionals must prioritise initiatives enhancing healthcare access, promoting healthy lifestyles, and ensuring effective diabetes management. By addressing socioeconomic disparities and implementing interventions for vulnerable populations, India can reduce diabetes-related mortality and enhance its citizens' overall health.

Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results.

Stimulated Raman scattering (SRS) microscopy is a high-speed imaging modality based on intrinsic molecular vibrations, producing chemical maps in living systems. Such capability, allowing for direct visualization without the perturbation of biological processes, has enabled a plethora of biological and medical applications. In this review, after introducing the basic theory and competitive effects of SRS, some crucial features for SRS microscopy implementations, such as noise, spectral bandwidth, speed, chemical sensitivity, spatial resolution, and quantum enhancement, are discussed. Finally, some SRS applications in biological and medical imaging are described. Even if certainly not exhaustive, we aimed to offer a broad overview, providing guidance for newcomers and hinting at a more detailed investigation to interested researchers in this rapidly growing field.

In the realm of food safety, dried foods have traditionally been perceived as having a lower risk profile. However, recent reports have highlighted concerning levels of mycotoxin contamination in dried food products, such as dried fruits, vegetables, and spices, exceeding permissible limits. This contamination not only compromises shelf life but also poses a significant threat to consumers, while depleting the bioactive components of these products. The food industry grapples with considerable challenges arising from the substantial risk of mycotoxin contamination in dried foods. To meet the demands of a disc; erning food market, there is an imperative need for sustainable mycotoxin-degrading technologies. These technologies must be non-thermal and chemical-free, minimizing thermal damage to treated food to preserve essential nutrients, ultimately enhancing food quality and safety. Developing non-thermal decontamination technologies for dried foods holds enormous economic and public health significance.This review centers its focus on the decontamination of dried foods, proposing a green approach through the utilization of Cold Plasma Technology (CPT). We delve into the working principles and applications of CPT in dried foods, highlight the influential factors affecting its efficacy, and acknowledge the limitations of this innovative technique. Furthermore, we pinpoint and explore emerging research opportunities in the decontamination of dried foods, particularly in conjunction with other complementary methods. CPT, an evolving and environmentally friendly technology, exhibits promising capabilities in controlling pathogens in dried food products and extending their shelf life. Critical determinants encompass the parameters of cold plasma, operational conditions, food-related variables, and microbiological factors, which collectively influence the efficacy of CPT. We advocate for future research dedicated to assessing the impact of CPT on various mycotoxins, food composition, and its interaction with food-related factors. In this vein, it is imperative to emphasize the scaling up of this technology for practical commercial applications, addressing the growing need for enhanced food safety in dried products.

Effective control and monitoring the spread of foot-and-mouth disease (FMD) relies upon rapid and accurate laboratory detection of FMD virus (FMDV). Therefore, in this report, a multiplex TaqMan probe-based one-step RT-qPCR assay simultaneously targeting FMDV 5′UTR and 3Dpol regions, and 18S rRNA housekeeping gene (as an internal control) in a single reaction tube was developed and evaluated. The multiplex one-step RT-qPCR assay specifically detected viral genome in both FMDV-infected cell culture suspensions and clinical samples collected from known-FMD infected animals. The assay could detect FMDV RNA in the archived FMDV cell culture isolates (n = 120) collected during the last two decades in India. In addition, the new assay could also detect viral RNA in the FMD suspected clinical samples (n = 740) collected from various field outbreaks. At a cut-off Ct-value of

Because of inadequate hardness, low resistance to wear, and excess friction coefficient of titanium, and its alloys are limited in their applicability. Cladding, a type of surface modification process, is used to create layers on titanium and its alloys that have superior mechanical qualities, wear characteristics, oxidation resistance at high temperatures, and good biocompatibility. Material selection is critical for achieving the increased qualities mentioned above, in addition to various cladding techniques and associated process parameters. A review of the outcomes of various common wear-resistant cladding techniques applied to the titanium alloy surface is the subject of this study. The most important functional claddings in this domain are also presented and investigated in depth. The present issues and future initiatives are also discussed, with an emphasis on identifying knowledge and technological gaps as well as attempting to establish future research possibilities. On this foundation, it is suggested that in the coming years, resistant-to-wear cladding with significant improvements in toughness and hardness should progress on the path of smart manufacturing techniques, optimising and precisely customising microstructural configurations, and developing numerical simulation techniques of cladding.

Introduction: Pregnancy is often associated with a number of oral manifestations. There is a change in lifestyle factors and dental care during pregnancy. Aim: We conducted this cross-sectional study to determine how lifestyle factors affect oral health-related quality of life (OHRQOL) in pregnant women residing in Bero block, Jharkhand. Methods: The study included a total of 400 pregnant women aged 18–45. The assessment of lifestyle factors and OHRQOL was done using Health Practice Index (HPI) Index and Oral Health Impact Profile-14 (OHIP-14), respectively. Data was collected through face-to-face interviews. Results: Forty percent of the pregnant women showed low OHRQOL. The majority of them were not using mouthwash and were brushing their teeth using faulty techniques. The results of logistic regression analysis showed that women with poor lifestyle scores (Odds Ratio [OR] =3.8, P-value

Abstract Context Chronic diseases are growing in India and largely affecting the middle-aged and elderly population; many of them are in working age. Though a large number of studies estimated the out-of-pocket payment and financial catastrophe due to this condition, there are no nationally representative studies on productivity loss due to health problems. This paper examined the pattern and prevalence of productivity loss, due to chronic diseases among middle-aged and elderly in India. Methods We have used a total of 72,250 respondents from the first wave of Longitudinal Ageing Study in India (LASI), conducted in 2017-18. We have used two dependent variables, limiting paid work and ever stopped work due to ill health. We have estimated the age-sex adjusted prevalence of ever stopped working due to ill health and limiting paid work across MPCE quintile and socio- demographic characteristics. Propensity Score Matching (PSM) and logistic regression was used to examine the effect of chronic diseases on both these variables. Findings We estimated that among middle aged adults in 45–64 years, 3,213 individuals accounting to 6.9% (95%CI:6.46–7.24) had ever-stopped work and 6,300 individuals accounting to 22.7% (95% CI: 21.49–23.95) had limiting paid work in India. The proportion of ever-stopped and limiting work due to health problem increased significantly with age and the number of chronic diseases. Limiting paid work is higher among females (25.1%), and in urban areas (24%) whereas ever-stopped is lower among female (5.7%) (95% CI:5.16–6.25 ) and in urban areas (4.9%) (95% CI: 4.20–5.69). The study also found that stroke (21.1%) and neurological or psychiatric problems (18%) were significantly associated with both ever stopped work and limiting paid work. PSM model shows that, those with chronic diseases are 4% and 11% more likely to stop and limit their work respectively. Regression model reveals that more than one chronic conditions had a consistent and significant positive impact on stopping work for over a year (increasing productivity loss) across all three models. Conclusion Individuals having any chronic disease has higher likelihood of ever stopped work and limiting paid work. Promoting awareness, screening and treatment at workplace is recommended to reduce adverse consequences of chronic disease in India.

Abstract Background The present study determines the prevalence and correlates of falls, multiple falls, and injuries, focusing on visual impairment among the older adult and elderly population in India. Additionally, owing to the higher prevalence of falls and visual impairment among women, a sex-stratified analysis has also been done in the present study. Methods The study utilized the data from the first wave of the Longitudinal Ageing Study in India (LASI wave-1), conducted during 2017-2018. Bivariate with chi-square and multivariate analyses were performed to fulfill the objective. Results Around 34% of population had low vision (male:30% and female: 38%), while blindness prevalence was 1.63% (males: 1.88% and females: 1.41%). The fall was higher among females and increased across the gender with increasing visual impairment and blindness levels. The unadjusted odds of falls were 16% higher among individuals with low vision and 40% higher among individuals with blindness than with normal vision, and comparatively higher odds among females than males. Conclusion In summary, falls and visual impairment is public health challenge and needs to be addressed. Visual impairment is preventable in most cases, so it may be a modifiable target for reducing the risk of falls.

Landslide susceptibility mapping (LSM) is essential for land-use planning, as it helps to identify areas at risk of landslides and enables effective prevention measures to be taken. Various statistical and machine learning (ML) models are used in LSM, including SVM and ANFIS, which have shown promising results. However, determining which model performs better remains a key challenge. To address this issue, this paper aims to compare six hybrid models constructed with two well-known and powerful ML models, namely SVM and ANFIS, and three meta-heuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Cultural Algorithm (CA), for LSM in a case study in western Serbia. In the process of building the models, 359 landslide sites and 14 determinants were used. The accuracy of the models was evaluated using several indexes, including Root Mean-Squared Error (RMSE), coefficient of determination (R2), and Area under the Receiver Operating Characteristic Curve (AUROC). The modeling results showed that the SVM-GA model has the highest accuracy (AUROC = 0.78) in predicting landslide incidence, followed by the ANFIS-GA (AUROC = 0.775), SVM-CA (AUROC = 0.773), ANFIS-DE (AUROC = 0.771), SVM-DE (AUROC = 0.76), and ANFIS-CA (AUROC = 0.65) models in validation phase. Therefore, the study suggested that SVM-based hybrid models are more accurate than ANFIS-based models for LSM, and thus, modelers may use SVM-based hybrid models for such applications. This study provides valuable insights into identifying the most appropriate and effective models for LSM, which can help to mitigate the risks associated with landslides and ensure sustainable land-use practices.

Biaxial tensile tests were carried out on cruciform specimens of composite solid propellants. Digital Image Correlation (DIC) was used to measure the strain in the gage section. Experiments were conducted at displacement rates of 1, 50 and 1000 mm/min (Y direction) to study the effect of the displacement rate on the material response. Two displacement rate ratios of 1:1 and 0.5:1 (X:Y directions) are considered to understand the influence of biaxiality of loading on the mechanical response. The biaxial experiments were also conducted at temperatures of 20 °C and 55 °C to understand the effect of temperature on the material response. The stress–strain response under biaxial loading was observed to be non-linear and dependent on the displacement rate. The slope of the stress–strain response in the linear region and the yield stress increased with an increase in the displacement rate for all the loading situations. There was no significant change observed in the slope of the stress–strain response in the linear region and yield stress with variation in temperature from 20 to 55 °C. However, a significant decrease in failure strain was observed with an increase in temperature from 20 °C to 55 °C. The mechanical response under equi-biaxial (displacement rate ratio — X:Y of 1:1) loading is compared with the uniaxial loading responses. The yield stress was observed to be 25%–47% higher during equi-biaxial tests as compared to uniaxial tests. However, the failure strain was 50%–70% lower in the equi-biaxial loading compared to uniaxial loading. The fractography and elemental mapping using Energy Dispersive Spectroscopy (EDS) of the fractured surfaces were carried out. A large number of cavities which are bigger in size and depth were observed on the fractured surface of the uniaxially loaded specimens as compared to equi-biaxially loaded specimens.

Abstract Background In the last two decades, cesarean section (CS) deliveries in India have increased by six-fold and created economic hardship for families and households. Although several schemes and policies under the National Health Mission (NHM) have reduced the inequality in the use of maternal care services in India, the distributive effect of public health subsidies on CS deliveries remains unclear. In this context, this paper examines the usage patterns of CS delivery and estimates the share of public health subsidies on CS deliveries among mothers by different background characteristics in India. Data Data from the fourth round of the National Family Health Survey (NFHS-4) was used for the study. Out-of-pocket (OOP) payment for CS delivery was used as a dependent variable and was analyzed by level of care that is, primary (PHC, UHC, other) and secondary (government/municipal, rural hospital). Descriptive statistics, binary logistic regression, benefit incidence analysis, concentration curve and concentration index were used for the analysis. Results A strong economic gradient was observed in the utilization of CS delivery from public health facilities. Among mothers using any public health facility, 23% from the richest quintile did not pay for CS delivery compared to 13% from the poorest quintile. The use of the public subsidy among mothers using any type of public health facility for CS delivery was pro-rich in nature; 9% in the poorest quintile, 16.1% in the poorer, 24.5% in the middle, 27.5% among richer and 23% in the richest quintile. The pattern of utilization and distribution of public subsidy was similar across the primary and secondary health facilities but the magnitude varied. The findings from the benefit-incidence analysis are supported by those obtained from the inequality analysis. The concentration index of CS was 0.124 for public health centers and 0.291 for private health centers. The extent of inequality in the use of CS delivery in public health centers was highest in the state of Mizoram (0.436), followed by Assam (0.336), and the lowest in Tamil Nadu (0.060), followed by Kerala (0.066). Conclusion The utilization of CS services from public health centers in India is pro-rich. Periodically monitoring and evaluating of the cash incentive schemes for CS delivery and generating awareness among the poor would increase the use of CS delivery services in public health centers and reduce the inequality in CS delivery in India.

The advent of high-spatial-resolution hyperspectral imagery from unmanned aerial vehicles (UAVs) made a breakthrough in the detailed retrieval of crop traits for precision crop-growth monitoring systems. Here, a hybrid approach of radiative transfer modelling combined with a machine learning (ML) algorithm is proposed for the retrieval of the leaf area index (LAI) and canopy chlorophyll content (CCC) of wheat cropland at the experimental farms of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India. A hyperspectral image captured from a UAV platform with spatial resolution of 4 cm and 269 spectral bands ranging from 400 to 1000 nm was processed for the retrieval of the LAI and CCC of wheat cropland. The radiative transfer model PROSAIL was used for simulating spectral data, and eight machine learning algorithms were evaluated for hybrid model development. The ML Gaussian process regression (GPR) algorithm was selected for the retrieval of crop traits due to its superior accuracy and lower associated uncertainty. Simulated spectra were sampled for training GPR models for LAI and CCC retrieval using dimensionality reduction and active learning techniques. LAI and CCC biophysical maps were generated from pre-processed hyperspectral data using trained GPR models and validated against in situ measurements, yielding R2 values of 0.889 and 0.656, suggesting high retrieval accuracy. The normalised root mean square error (NRMSE) values reported for LAI and CCC retrieval are 8.579% and 14.842%, respectively. The study concludes with the development of optimized GPR models tailored for UAV-borne hyperspectral data for the near-real-time retrieval of wheat traits. This workflow can be upscaled to farmers’ fields, facilitating efficient crop monitoring and management.

Abstract Background During the COVID-19 lockdown period many education institutions have shifted their focus from the traditional face-to-face education to online instruction mainly through various social media (SM) tools. However, it is not known if these results can be generalized across locations where infrastructure facilities are unevenly distributed. Further, no previous work has explored the role played by SM tools in knowledge translation. The objectives of this work are 1. To evaluate the students perceptions on the accessibility and acceptability of SM tools via an anonymous online survey and 2. Assess the efficacy of SM tools as an educational medium in imparting knowledge change. An online survey using an anonymous web-based questionnaire was conducted to assess the student’s accessibility and acceptability of SM tools as a direct information sharing pathway between the faculty and students. A randomized comparative design was utilized to evaluate knowledge change via an online examination administered 10 min before and after an online class delivered via 2 different SM platforms (Google meet, YouTube) and e-mail. Results Data were obtained from 627 participants through a survey. Though 71.1% of the respondents believed online classes have helped them in their study, only 21.4% and 22.6% of the participants strongly agreed that social networking platforms are helpful for teaching and will be used for teaching/learning in the future respectively. The ANOVA responses to evaluate knowledge transfer from 224 participants who were randomized to receive course content through Google meet, YouTube, and e-mail showed no significant differences in outcomes before and after the delivery of contents. Conclusion Our findings suggest that multiple external and internal factors need to be addressed before substituting classroom teaching with online teaching, especially during emergencies.

Aim: The aim of this study is to elucidate the patterns of characteristic hypometabolism on 18F-Fluoro Deoxy-glucose (18F-FDG) Positron Emission Tomography/Computed Tomography (PET/CT) in the variants of Progressive supranuclear palsy (PSP) and its correlation with their core clinical features. Material and Methods: A retrospective analysis of 88 subjects with clinically suspected PSP was done. An institutional informed consent to participate in the study was taken from all the subjects. All the subjects had undergone a prior 99mTechnetium labeled Tropane derivative of dopamine transporter Single Photon Emission Computed Tomography (99mTc TRODAT-1 SPECT) study and had abnormal scans to confirm degenerative parkinsonism. The subjects were clinically examined by the neurologists using the Progressive Supranuclear Palsy Rating Scale proposed by the Movement Disorder Society and were further clinically segregated into eight subtypes. All the included subjects further underwent a detailed clinical analysis to obtain their individual Schwab and England activities of daily living scale and Modified Hoehn and Yahr staging by a neurologist. All the subjects underwent 18F-FDG PET/CT scan after adequate preparation. The scans were analyzed both qualitatively (visually) and quantitatively using Statistical Parametric Mapping. Results: The frontal, limbic, and sensorimotor cortices represented the common areas of hypometabolism among all the subtypes of PSP. The subcortical regions showing the most significant hypometabolism were the thalami, mid-brain, nucleus accumbens, caudate, globus pallidus, and putamen in descending order. Multiple cortical and subcortical regions of hypometabolism were identified in different subtypes of PSP. Conclusion: The characteristic patterns of hypometabolism observed in the different subgroups were more apparent on quantification and based on visual analysis alone, it may not be possible to differentiate the different subtypes of PSP. A good correlation was seen between some of the core clinical features and hypometabolic clusters.

The present study intended to prepare stuffed red chilli pickles using a traditional recipe that is commonly followed in the northern part of India. A fuzzy approach was applied to characterise the sensory attributes of prepared pickles during storage. The physicochemical properties of fresh chilli, edible mustard oil, stuffing material, and prepared pickles were analysed. The stuffed chilli pickle showed an increase in acidity and total soluble solids, while pH decreased during storage. In the pickle, nine phenolic compounds such as gallic acid (154.85 mg/kg), p-coumaric acid (1.78 mg/kg), 4-hydroxybenzoic acid (0.36 mg/kg), caffeic acid (26.19 mg/kg), trans-ferulic acid (0.05 mg/kg), syringic acid (130.62 mg/kg), sinapic acid (68.36 mg/kg), vanillic acid (0.49 mg/kg), and chlorogenic acid (48.63 mg/kg) were identified. The pickles were analysed for sensory attributes during storage (0–90 days) with an interval of 30 days using fuzzy logic analysis. It was observed that sample 4 (stored for 90 days) secured rank 1 among all the four samples and was accepted by the sensory panels. Besides these, the effect of storage on chemical and bioactive compounds (total phenolic content, total antioxidant activity, and radical scavenging activity) of chilli was determined at an interval of 15 days, which was further modelled using order kinetics. It was observed that the changes in red chilli pickles follow the first-order rate equation during storage. According to the findings of this study, this is the first study to fill a gap in the literature regarding the documentation of a traditional pickle recipe and the kinetic evaluations of phytochemicals during storage.

In an agriculturally dependent nation like India, accurate and effective rainfall forecasting methods are crucial for assessing agrometeorological risk. Forecasting rainfall is perhaps one amongst the most arduous tasks in this context due to the prevalence of a non-linear pattern. One of the most promising and frequently employed approaches for forecasting rainfall data is the Time-Delay Neural Network (TDNN) model. TDNN's non-parametric, data-driven, and self-adaptive characteristics make it increasingly attractive for modelling nonlinear dynamics and generating nonlinear forecasts. Nevertheless, since the conventional TDNN uses the sigmoid activation function, there is always a chance that the training process may converge to local minima. This study addresses the usage of a Time-delay wavelet neural network (TDWNN), which employs a TDNN architecture with a hidden layer activation function derived from the orthonormal wavelet family, in order to circumvent this issue. TDWNN has been empirically demonstrated using annual rainfall data from two districts in the Indian state of West Bengal. According to the findings of this study, the TDWNN model is superior than the conventional TDNN method to assess agrometeorological risk.

In India, electric rickshaws (ERs) prove to be a sustainable and affordable paratransit mode for the public. The ER service is also creating employment and income opportunities for the unskilled labor force. Many Indian state governments are proposing ER services to cater to first and last-mile trips. Therefore, planners must identify the significant factors that can affect ER services' adoption and expansion. Because of this, the present study investigates the perceptions of drivers of three prominent paratransit modes toward ERs. The research also provides a better understanding of the socio-economic conditions of these paratransit drivers. The study utilizes data of 620 paratransit drivers obtained from a face-to-face survey conducted in Delhi, India. Grey relation analysis is applied to assess the three paratransit driver groups' perceptions about ERs. The data reveals that paratransit drivers belong mostly to low financial and educational backgrounds. Male drivers constitute many paratransit drivers in the city. The lack of facilities such as designated parking and fixed charging stations and fare regulation is perceived as critical ER services barriers. Similarly, the absence of separate lanes and unsafe overtaking attempts by fast-moving vehicles are the major causes of accidents involving ERs. Further, the light body of ERs and careless driving and traffic rule violations of drivers in mixed traffic conditions are the primary safety issues. The study also highlights potential policy recommendations to design an efficient electric version of the paratransit service for achieving sustainable development goals.

Irrigation is one of the largest consumers of fresh water in the agricultural sector. Surface irrigation is one the most widely adopted method of irrigation due to its simplicity and low energy requirement. Although, it is mostly associated with poor irrigation application efficiency mainly due to deep percolation and uneven distribution of irrigation water. A study was conducted in closed-end level basin layout in loamy soil to establish and test an Internet of Things (IoT) based system through a wireless connection between the automatic check gate and soil moisture sensors which can be operated remotely based on real-time soil moisture status. An attempt had been made to identify the suitable location of the sensor in the basin layout for improving irrigation application efficiency. An aluminium automatic-check gate fitted in an iron frame was placed in the concrete water supply channel to control the water flow. Three capacitance-based soil moisture sensors were placed at 25%, 50% and 75% of field length (i.e., 15 m, 30 m and 45 m) at 37.5, 15 cm and 7.5 cm depth, respectively. A wireless communication network was established between capacitance-based soil moisture sensors and an automatic check gate through the LoRa module and GSM module. The software used for automatic control of the check gate in this study was Croplytics® by Agsmartic. The system was tested for nine irrigation events under bare soil conditions. Three different operational schedules based on different soil moisture sensor placement had been tested under 40%, 30% and 20% soil moisture deficit conditions. The results suggested that the best location of sensors for closing the system would be at 37.5 cm depth placed at 25% length from the inlet in higher soil moisture deficit condition and at 7.5 cm depth placed at 75% length in low moisture deficit condition. Overall, the irrigation application efficiency was improved up to 86.6% with automation.

Crop wild relatives (CWRs) belonging to the Brassicaceae family possess extensive genetic diversity and have frequently been utilized in the enhancement of cultivated Brassica species. However, their tolerance to glyphosate, a widely used herbicide, has remained unknown. Our study examined the glyphosate response of 20 genotypes from the Brassicaceae family, which included genotypes within the U triangle and their wild relatives. We evaluated their behaviour based on morpho-biochemical responses, specifically focusing on the traits of germination percentage, root length, and survival percentage. By calculating the mean membership function value (MFV) for each genotype’s response to these traits, we classified them into three distinct groups: susceptible, moderately tolerant, and tolerant. Among these genotypes, Brassica rapa (NRCPB rapa 8) demonstrated tolerance to glyphosate, as indicated by their mean MFV value of 0.68. Moderate tolerance to glyphosate was observed in Brassica juncea (Pusa Jaikisan) with a mean MFV of 0.52. Conversely, Diplotaxis catholica, Diplotaxis muralis, and Enarthrocarpus lyratus were susceptible, with mean MFV values of 0.37, 0.35, and 0.34, respectively. These findings revealed varying levels of response to glyphosate among these genotypes, with some displaying significant tolerance. The study provides valuable insights into the herbicide tolerance of Brassica CWRs and emphasizes the potential use of phenotypic and biochemical markers in evaluating herbicide tolerance.

The consequences of sandstone reservoir rock failure may lead to sand production. This phenomenon can have negative impact on lifting cost and economic of any field development. Metal erosion due to sanding can lead to loss of integrity and hydrocarbon leakage. Poor decision on the type of completion can risk the viability of the field. To facilitate best sand management over the life of a field and to maintain economical productivity, accurate prediction of sand production volume/rates is needed to increase both productivity and the ultimate recovery of the hydrocarbon while keeping the operating cost low. This paper summarizes the sand production modeling for onset and volume of sand namely technology that required to improve understanding on sand production and mitigation. Three main questions will be answered, why industry needs to worry about sand production, what are the available technologies to predict sanding volume/rates finally, how the current technologies can be improved to estimate sand production volume/rates.

Background: Comparable estimates of household health spending and out-of-pocket (OOP) payment on health care in India are a daunting task. Often these estimates are provided for specific services such as maternal care, type of disease, hospitalisation, outpatient care, and an episode of hospitalisation. This paper presents comprehensive and comparable estimates of health spending and out-of-pocket payment on hospitalisation and outpatient care in India over the past 15 years. Methods: A total of 73,868 households in 2004, 65,932 households in 2014, and 113,823 households in 2018 surveyed in the 60th, 71st, and 75th rounds of NSSO health surveys, respectively, were used in the analysis. Descriptive statistics, concentration index, two-part regression, and logistic regression were used in the analysis. Results: The utilisation of hospitalisation services has increased over time. During 2004–18, the mean adjusted OOP payment on hospitalisation at 2018 prices was 308 US$ in 2004,353 US$ in 2014 and 332 US$ by 2018. Reimbursement on medical spending was only 2%, 7% and 11% of medical spending in 2004, 2014 and 2018 respectively. Both medical spending and OOP on inpatient and outpatient care showed large inter-state variations. The OOP payment on inpatient and outpatient care was significantly higher among richer households in urban households, households without health insurance, households having an elderly member and female-headed households. Conclusion: Though the households OOP for outpatient and inpatient, in recent years had declined, OOP as a share of medical expenditure remained high over time and majority of the households are not yet covered under any health protection scheme in India.

Remote sensing is being increasingly used in stress management in different agricultural practices. It is useful for real time analysis for crop stress which is not possible for visual observation alone. Rice blast caused by fungus Pyricularia Oryzae is a serious constrain in rice production in India. There is hardly any basic information available for spectral characteristics of rice blast disease for its real-time detection and management. Present study is to characterize spectral reflectance of blast affected rice in order to identify the sensitive spectral range. Disease severity of 10 different genotypes of rice was graded 0 to 9 based on the extent of host organ covered by symptom or lesion. Result shows that severely infected plant (score 9) have higher reflectance at visible region and lower reflectance at NIR region. Change in the reflectance for the infected plant as compare to the healthy plant was more pronounced in the VNIR, 550 to 760 nm and 1140 and 1300 nm having correlation coefficient above 0.6. The study of change in the reflectance with the change in wavelength (1st derivative) revealed that VNIR region have high correlation with the disease severity. Maximum rate of change value at red edge position (REP) is called as red edge value (REV) which has good relation with disease severity levels. Amplitude of the red edge peak decreases with the increase in severity levels. Amplitude of score 0 and 9 was 0.00929 and 0.002301, respectively for upland land condition whereas the amplitude of the score 0 and 9 was 0.010421 and 0.00193, respectively for upland land rice. This study identifies that VNIR and red edge region are sensitive for detecting rice blast, which could be utilized to aerial or satellite based monitoring blast affected rice cropping region.

Background: Prospective recipients of liver transplant (LT) have a high prevalence rate of coronary artery disease (CAD) requiring revascularization. In patients of Child Turcot Pugh Class B and C performing LT prior to cardiac revascularization on cardiopulmonary bypass leads to a high risk of major adverse cardiovascular events (MACE). Whereas, isolated cardiac surgery prior to LT has perioperative risk of coagulopathy, sepsis, and hepatic decompensation. We present four cases of end stage liver disease who underwent concomitant living donor liver transplant (LDLT) with off pump coronary artery bypass graft (OPCAB) in an effort to decrease the morbidity and mortality. Methods: The cases were performed in a tertiary care centre over two years. Four patients scheduled for LDLT, who were diagnosed with significant CAD, underwent single sitting OPCAB and LDLT. Cardiac surgery was performed first and once patient was stable, it was followed by LDLT. The morbidity parameters in terms of duration of intubation, blood transfusion, hospital stay, ICU stay, requirement of dialysis, atrial fibrillation and sepsis was compared with similar studies. Results: The blood transfusion requirement (median 8 units PRBC), incidence of atrial fibrillation (25%), sepsis (25%), and renal dysfunction (0%) was less than the combined surgery conducted on cardiopulmonary bypass. The rate of median intubation time, length of ICU stay, hospital stay, and one year mortality rate was comparable with other studies. Conclusions: Morbidity with combined OPCAB and LDLT is less than combined on pump coronary artery bypass surgery with LDLT. Combined CABG with LDLT may be performed with acceptable outcomes in CTP class B and C cirrhosis.

Aim: To elucidate the patterns of characteristic hypometabolism on 18F- fluorodeoxyglucose( 18F-FDG) positron emission tomography/computed tomography (PET/CT) in multisystem atrophy (MSA) and their correlation with the patterns of uptake on dopamine transporter imaging with 99mTc TRODAT-1 SPECT. Material and Methods: A retrospective analysis of 67 patients with clinically diagnosed MSA was performed. All the subjects underwent 99mTc TRODAT-1 SPECT and 18F-FDG PET/CT scanning on two separate days. The 99mTc-TRODAT-1 scans were analyzed visually for asymmetry and rostro-caudal gradient. The FDG uptake patterns were recorded, and areas of hypometabolism that were two standard deviations from the mean were considered abnormal. Results: All the subjects had an abnormal pattern of FDG uptake on PET scan, both on a visual inspection and semiquantitative analysis. In MSA-P subjects (n = 29), diffuse predominant hypometabolism of the globus pallidus-putamen complex was noted, with relative sparing of the caudate nuclei. In MSA-C subjects (n = 25), characteristic hypometabolism was noted in the cerebellum and brainstem. In mixed subtypes (n = 13), variable involvement of the basal ganglia, cerebellum, and brainstem was noted with frontoparietal hypometabolism. A statistically significant difference between MSA-P and MSA-C for gradient reduction and asymmetry with gradient reduction was observed. Conclusion: Dopamine transporter imaging with 99mTc TRODAT-1 SPECT not only helps in confirmation of parkinsonian disorders but also demonstrates varying patterns of distribution in different subtypes of MSA. Characteristic patterns of hypometabolism in 18F-FDG PET may help in the differentiation of the subtypes of MSA in the presence of clinically overlapping symptoms.

The Beijing Xiangshan Forum (BXF), earlier known as “Xiangshan Forum” is emerging as an important element in China’s diplomatic toolbox. At a time when Asian states, to varying degrees, are receptive to various Chinese initiatives, and Beijing considers itself better resourced to implement its own regional initiatives, the forum is expected to play a larger role in regional military diplomacy and security dialogue. The evolution of the Xiangshan Forum has been a measure of China’s changing self-perception and expanding strategic role in the shifting regional security landscape. As a newly created security forum, the forum can and should coexist with existing regional institutions and complement other stakeholders’ efforts to build an inclusive, robust, and resilient security architecture in the Asia-Pacific.

Metal-enhanced bioluminescence (MEB) is a complex photophysical event which manifests itself in manifold luminescence enhancement and depends on many parameters. The parameters include the distance between the luminescent source and the nanomaterial surface, the size and shape of nanoparticles, localized surface plasmon resonance (LSPR) peaks of the nanomaterial and the dielectric constant of the surrounding medium. Studying distance-dependent MEB in the presence of linkers of specified length may provide new insights into luminescence enhancement. In this regard, the present investigation is aimed to understand the role of ethylene diamine as a potential linker in model systems for distance-dependent metal-enhanced bioluminescence (MEB) with gold nanoparticles (AuNPs). Four different types of AuNPs (AuNP1, AuNP2, AuNP3, AuNP4) were synthesized by varying the trisodium citrate (TC) and silver nitrate (AgNO3) concentrations with maximum absorbance values of 0.99, 1.24, 1.21 and 1.38 respectively and the corresponding LSPR peaks (λmax) of 520 nm, 535 nm, 525 nm, and 525 nm. Luminescence enhancement up to 1.44-fold was observed when ethylene diamine (ED) was used as a linker in the presence of 1-N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide (EDC), ATP and AuNP1. The sample consisting of FMN, EDC and AuNP1 showed 1.3-fold luminescence enhancement. It was noted that AuNPs synthesized using AgNO3 as an additional component did not enhance luminescence in all the investigations. The suggested technique of using linkers of predetermined lengths may also prove fairly effective for studying other parameters which can influence MEB and cause sensitivity enhancement of luminescence-based biosensors