Search

G networks can offer network slices customized according to the demands of the services to enhance the quality of their users' experience. The time for selecting an appropriate network slice to facilitate traffic flow between users and services by the core network functions in 5G networks is crucial for services such as emergency service and ultra-reliable low latency services. Therefore, we propose a distributed slice selection architecture for 5G and beyond networks to reduce the waiting time for starting services for users. The proposed architecture distributes slice selection function (SSF) to the edge of the network. The networks have to ensure stealthy slice selection attack (S3 attack) free operation, as moving the SSF to the edge increases attack surface. Attackers can launch S3 attack by manipulating the slice selection decisions of SSFs distributed in the network edge. The S3 attacker intentionally maps service requests from users to inappropriate network slices to damage cloud radio access network utilization and the quality of experience of users. In this article, we also present a countermeasure to tackle the S3 attack using a novel protocol called VACCINE (verifiable privacy-preserving protocol for slice selection in 5G and beyond networks). VACCINE also ensures privacy-preserving slice selection by the SSFs to prevent traffic analysis attacks. We prove the chosen-ciphertext attack security strength of VACCINE and also compare the computational cost of VACCINE with other related protocols. [ABSTRACT FROM AUTHOR]

This study presents an improved understanding of the stress distribution in the joint region of weak-axis connections in steel MRFs, and based on this understanding, connection configurations have been proposed for the column-to-foundation connections Columns in MRFs are subjected to lateral forces along their strong and weak axes. To ensure ductile response, analysis, and requisite provisions of column foundation connections along the strong axis have been dealt with in detail. In this study, preliminary inelastic finite element analyses are conducted to study the flow of forces near the weak axis column-to-foundation connection. Theoretical axial load P in the column that produces the worst V-M (shear-moment) loading is identified. Based on these loads a cover plated and ribbed configuration is proposed for the weak- axis column-base (WACB) connection. Nonlinear finite element analyses are performed under monotonic, and FEMA recommended multi-cycle loadings using ABAQUS software to evaluate the performance of the proposed connection configurations and the associated design procedures. The performance of the new weak-axis connections (WACs) is compared with that of the existing connections in terms of von Mises stress, shear stress, normal stress, load deformation response, energy dissipated, and cumulative plastic strain excursion. It was observed that WACB connections in steel MRFs need to be reinforced to keep the stress in CJP welds connecting the column to the base plate below the yield stress even at the lateral drift levels much lower than those expected of about 4–6% lateral drifts. The proposed configuration results in increased moment of inertia of the column about the weak axis and hence enhances the seismic response. [ABSTRACT FROM AUTHOR]

Background: Primary acquired Cholesteatoma is a complex issue for otolaryngologists, with its development mechanisms still unclear due to the intricate anatomy of this region. It's aetiopathogenesis remains poorly understood and this aggressive clinical condition often leads to various complications. Recent research explores myofibroblast and fibronectin's potential roles in pathomechanisms of Cholesteatoma. Objective: To determine and analyze the role of myofibroblast and fibronectin in the aetiopathogenesis of Cholesteatoma. Methodology: In a cross-sectional study at a tertiary care hospital, 30 patients with chronic suppurative otitis media with cholesteatoma were surgically treated, and intraoperative biopsy specimens were collected. These specimens were processed and subjected to histopathological examination, including immunohistochemical staining with Alpha-smooth muscle actin and anti-fibronectin antibody to identify myofibroblast and fibronectin presence. The data were then analyzed to investigate the aetiopathogenesis of cholesteatoma in this cohort. Results: On immunostaining, 25 blocks (83.33%) were positively stained for Alpha-SMA (p-value—0.0007), whereas 29 blocks (96.67%) were positively stained for fibronectin (p-value < 0.0001), suggesting a statistically significant association between the presence of both myofibroblast and fibronectin with cholesteatoma perimatrix. Additionally, a statistically significant association was noted between complications and positive staining for myofibroblast (p-value − 0.0415) and positive staining for fibronectin (p-value—0.0254). Conclusions: Our study indicates that Cholesteatoma retraction and progression are driven by myofibroblast and fibronectin mechanisms, and also links them to disease severity. This understanding opens avenues for innovative diagnostics and treatments targeting these biomarkers. [ABSTRACT FROM AUTHOR]

A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 attributed to severe acute respiratory syndrome has been implicated with life threatening opportunistic infections like mucormycosis. COVID-19 is a hyperferritinemic syndrome and emerging data project the role of iron in the susceptibility and pathogenesis of mucormycosis but whether high ferritin is an indicator of severity of mucormycosis is debated. The study aimed to determine the relationship between serum ferritin levels and the extent of involvement of COVID-19 associated mucormycosis. A hospital based observational study was conducted with a sample size of 70. All biopsy confirmed cases of COVID-19 associated mucormycosis were included. Retrospective data from hospital records prepared at the time of patient admission were retrieved. The imaging data was used to determine the extent of disease involvement and serum ferritin values were analysed. During the study period 40 patients had mild extent mucormycosis and 30 had severe extent. A statistically significant difference was seen in levels of serum ferritin between mild extent mucormycosis and severe extent involvement (p < 0.01). COVID-19 associated Mucormycosis patients tend to have higher serum ferritin values especially in severe extent disease and with active COVID-19 infection along with diabetes mellitus as a potent aggravating factor. [ABSTRACT FROM AUTHOR]

The purpose of the study was to offer a guideline for the treatment of chronic suppurative otitis media (CSOM) with empirical antibiotics as well as to evaluate the aerobic bacteria that were implicated as well as their antibiotic sensitivity pattern in patients who had been diagnosed with the condition chronic suppurative otitis media (CSOM). According to our research, Staphylococcus aureus, Pseudomonas aeruginosa, Proteus species, Klebsiella species and E. coli are the most frequently occurring pathogens that cause CSOM. In order to establish a guideline for empirical antibiotic therapy, it is essential necessary to have an understanding of the local pattern of infection. This information is vitally necessary. The purpose of this research is to determine the antimicrobial sensitivity pattern of the microbes that are responsible for chronic suppurative otitis media as well as the local pattern of aerobic bacteria that are causing the condition. The treating physicians will be able to use this information to identify which antibiotics have the best chance of being effective against the particular microorganisms in question. [ABSTRACT FROM AUTHOR]

In this work, we demonstrate the strategic incorporation of p-type NiO and f-MWCNTs into n-type TiO2-active layer that leads to significant augmentation in the power conversion efficiency (PCE) of dye-sensitized solar cells (DSSCs). The NiO@f-MWCNT composites were prepared by in situ precipitation method with different concentrations of MWCNTs. The presence of NiO as and MWCNTs has been confirmed by scanning electron microscopy (SEM), transmission electron microscope (TEM), Fourier transform infrared (FTIR) and Raman spectroscopy. The structural orientation was also confirmed by X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) measurements. The optical measurements like UV–Vis absorbance and photoluminescence spectra confirmed the band gap, absorption and electronic transitions. Fluorescence resonance energy transfer (FRET) mechanism exists in between N719 dye and NiO. The electrochemical measurements confirmed the facile charge transfer kinetics within all the samples and delivered superior PCE values of composites. However, the highest PCE values of 6.50% were observed in 20% composite due to maximum charge transfer kinetics and least recombination processes. The improvement in PCE values due to enhanced light-absorbing ability as well as superior charge conducting ability, lower internal resistance and lower charge recombination rate of NiO@f-MWCNT perfectly matching with N719 dye. As a strategic incorporation, nearly 34% enhancement was noticed in PCE values over bare TiO2 photoanode, opening new horizons for photoanode modification using uniquely functionalized MWCNT. [ABSTRACT FROM AUTHOR]

Anaplasma genus has been classified under the family Anaplasmataceae of order Rickettsiales, which contains the spp. Anaplasma marginale, Anaplasma bovis, Anaplasma centrale, Anaplasma platys and Anaplasma phagocytophilum is accountable for a broad range of diseases in both man and animal medicine around the globe. A multiplex PCR procedure was designed to identify A. marginale, A. bovis, and A. platys simultaneously by employing species-specific primers targeting the msp4 (Anaplasma marginale), 16S rRNA (Anaplasma bovis), and groEL (Anaplasma platys) genes. The sensitivity of the assay was ascertained by tenfold dilutions of DNA obtained from cattle blood infected with A. marginale, A. bovis, and A. platys was used to evaluate the test sensitivity. A total of 31 Anaplasma genus positive samples were subjected to mPCR by using species specific primers for simultaneous detection of all the three species. All the 31 samples showed amplification of 753 bp fragment specific for A. marginale and 61.29% of the samples showed amplification of 547 bp fragment specific for A. bovis. The 470 bp DNA fragment specific for A. platys was found in 96.7% of samples. The lower limit of detection of the method for A. platys, A. marginale and A. bovis was found to be 4.4, 0.44 and 0.044 ng/µl, respectively. [ABSTRACT FROM AUTHOR]

Early-season agricultural drought is frequent over South Asian region due to delayed or deficient monsoon rainfall. These drought events often cause delay in sowing and can even result in crop failure. The present study focuses on monitoring early-season agricultural drought in a semi-arid region of India over 5-year period (2016–2020). It utilizes hydro-climatic and biophysical variables to develop a combined drought index (CDI), which integrates anomalies in soil moisture conditions, rainfall, and crop-sown area progression. Synthetic aperture radar (SAR)–based soil moisture index (SMI) represents in situ measured soil moisture with reasonable accuracy (r=0.68). Based on the highest F1-score, SAR backscatter in VH (vertical transmit-horizontal receive) polarization with specific values for parameter threshold (−18.63 dB) and slope threshold (−0.072) is selected to determine the start of season (SoS) with a validation accuracy of 73.53%. The CDI approach is used to monitor early-season agricultural drought and identified drought conditions during June–July in 2019 and during July in 2018. Conversely, 2020 experienced consistently wet conditions, while 2016 and 2017 had near-normal conditions. Overall, the study highlights the use of SAR data for early-season agricultural drought monitoring, which is mainly governed by soil moisture–driven crop-sowing progression. The proposed methodology holds potential for effective monitoring, management, and decision-making in early-season agricultural drought scenarios. [ABSTRACT FROM AUTHOR]

Breast cancer is very prevalent and because of its death rate is taken into deliberation to be the second dangerous disease in the world. There is a relentless effort to create more effective techniques for an early and reliable diagnosis. Classical approaches require oncologists to investigate breast lesions to detect and classify different cancer stages. Such manual attempts in many cases are time-consuming and inefficient. Hence there is a requirement for effective methods to diagnose cancer cells with high accuracy without human involvement. A "Moon Phase Wavelet Chain Rule Model" has been proposed in this research we introduced Moon Light Dimming Illumination Technique and Smart Recon Techniques. Thus, it overcomes the dense mass accumulation by providing a clear view of fat density, heterogeneous density, tumour size and thus it reduces the beam hardening problems. Our work has initiated Modified Segmented Stationary Wavelet Transform and Multivariable Chain Rule-Based Back Propagation Neural Network, to improvised the features extraction and classifying the phases of breast cancer by avoiding the under and overfitting problems. The proposed model reduces the dense mass accumulation, beam hardening, and obtains a segmented feature image for feature extraction.The Accuracy, Sensitivity, Specificity, Recall, Precision, prevalence performances of 98.62%, 98.25%, 97.52%, 98.25%, 97.25%, and 25.03% respectively. Hence, the outcome of the proposed model has been showing that our system is a promising and robust method for detecting breast cancer. [ABSTRACT FROM AUTHOR]

The primary interest of this study is to investigate the results of magnetohydrodynamics Casson and Carreau fluid heat via a porous medium at the aspect of a stretching sheet. Variable thermal physical phenomena and suction/injection parameter results are also taken into account. Similarity transformations are applied to convert the governing equations of the magnetohydrodynamics Casson and Carreau fluid glide model into dimensionless non-linear normal differential equations. The results on velocity, concentration, temperature profiles, surface drag, Nusselt number, and Sherwood number are discussed for various flow parameters and presented the results through plots and tables. The fluid flow is analyzed for every suction and injection case. From the analysis it is noted that the velocity profile reduces by increasing the magnetic parameter and the reverse trend is observed for increasing values of Weissenberg number in the case of suction and injection. The temperature space and heat transfer rate enhances by increasing the variable thermal conductivity and more in the case of suction. [ABSTRACT FROM AUTHOR]

This work probes the polysulfone/azide-functionalized multi-walled carbon nanotubes mixed matrix membrane where the azide groups were converted into triazole rings. This modification was confirmed from X-ray photoelectron spectroscopy studies. Post modification, unlike the polyethersulfone based mixed matrix membranes, these membranes show improved heavy metal rejection. Membrane morphology by atomic force microscopy and scanning electron microscopy images shows membrane bulk morphology does not change after the modification. Also, the modified membrane showed better antifouling property with high flux recovery ratio and lower irreversible fouling as compared to the polyethersulfone-based membrane. This modified membrane shows smaller pore size as compared to modified polyethersulfone mixed matrix membrane due to their different morphology. Surface energy provides additional information such as the wetting and adhesion characteristics of the membranes. These results indicate that the pristine membrane morphology has profound influence on the final morphology of mixed matrix membrane it becoming more hydrophilic and permeable post modification. Surface modified membrane via click reaction gives nearly 99% rejection for Cu(II), 90% rejection for Pb(II) and 95% rejection for Cr(VI), which is higher than the unmodified and pristine polysulfone membranes. [ABSTRACT FROM AUTHOR]

A sequence of ABA-type polystyrene-b-poly(dimethylsiloxane)-b-polystyrene (PS-b-PDMS-b-PS) triblock copolymers were synthesized by ATRP (Atom Transfer Radical Polymerization) after appropriate end functionalization of vinyl terminated PDMS. Vinyl terminated PDMS was reacted with a 3-chloropropane thiol to synthesize a halo terminated macroinitiator. Subsequently, this macroinitiator was used to synthesize ABA-type block copolymers. The resulting block copolymers were characterized to confirm the presence of PDMS & PS blocks. The theoretical molecular weight and GPC was used to verify the high molecular weight of the ultimate block copolymer. The results show that the PS chains were successfully bound to the PDMS backbone and molecular weight linearly increased with time during the controlled polymerization. The functionality of the given polymer was confirmed by FT-IR analysis, 1H NMR, and 13C NMR spectra. The thermal stability increased after the addition of PS with PDMS. Additionally, the EDS analysis confirms the halogen content. DLS verified the particle size, whereas SEM was used for the morphology of the nanoaggregates. This triblock has good moisture resistance & thermal stability as determined by thermogravimetric analysis. The formation of PDMS blocks increased the hydrophobicity of the PS and contact angle analysis confirmed this. [ABSTRACT FROM AUTHOR]

Soybean mapping in Indian context is challenging owing to its short growing period coinciding with the monsoon clouds, inter-cropping and smallholders' land. This study proposes an approach for mapping soybean by integrating object-based image analysis with machine learning (ML) based classification using temporal Sentinel-1 SAR (S-1) and Sentinel-2 optical (S-2) data. Field objects were delineated with scale-optimized multi-resolution segmentation using historical S-2 data. Object-based temporal VH-backscatter and NDVI were extracted for training, validation and testing of the three ML models. Validation results showed the outperformance of Extreme gradient boosting (OB-XGBoost) over Random Forest (OB-RF) and Support vector machine (OB-SVM) with an overall accuracy (OA) of 92.50, 91.08 and 90.1, respectively. Testing of OB-XGBoost model resulted in OA, kappa statistics, and F-score (soybean) of 86.12%, 0.82, and 87.23%, respectively. The soybean map produced by the proposed methodology has shown better representation in terms of homogeneity and uniformity than the pixel-based classification. [ABSTRACT FROM AUTHOR]

Assessing the extent of hailstorm-affected crop is one of the thrust areas for quantifying mid-season adversities under crop insurance values chain. This study evaluated the pre- and post-hailstorm responses of potato crop, over different spectral bands and vegetation indices derived from Sentinel-2 data, towards assessment of damage severity classes. The potato crop was mapped using pre-event satellite data with overall accuracy of 88% (k = 0.82). Pair-wise Games–Howell t-test showed significant differences among the post-hailstorm potato severity classes in red, near infrared and short-wave infrared (SWIR) bands and normalized vegetation indices. Percentage change (from pre- to post-event) in band reflectance and vegetation indices showed a better sensitivity in differentiating damage severities. Differential behaviour of SWIR-1 (Band-11) and SWIR-2 (Band-12) was observed within severely affected potato crop under dry and wet soil conditions. Decision matrix based on percentage change in normalized difference vegetation index (ΔNDVI) and normalized difference tillage index (ΔNDVI) could able to capture the damage severity classes with an overall accuracy of 86.7%. Higher proportion of affected area was found to be associated with larger percentage of potato yield reduction based on measured yield data at insurance unit level. The proposed methodological approach could be adopted for operational assessment of the impact of hailstorm events on other crops with required modifications. [ABSTRACT FROM AUTHOR]

Pre-harvest crop mapping, the fundamental requirement for many of the crop management decisions, continues to be challenging either due to cloud cover in satellite images or due to spectral separability issues. These limitations are overcome in this study by using Synthetic Aperture Radar (SAR) data and deep learning technique. Two-dimensional convolutional neural network (2D-CNN) architecture is applied on multi-temporal SAR data of Sentinel-1 to classify soybean, jowar, cotton and sugarcane crops in a large geographic area located in a Central Indian state. Classification experiments are conducted with full season data from nine overpasses in three modes namely VH alone, VH and VH/VV ratio, and VH and VV, and these experimental results reveal that VH and VV combination performed better with an overall accuracy of 91.75% as compared to 84.96% and 88.75% respectively by others. Classification performances with three sets of temporal data, covering part of the season, reveal that crop map can be generated as early as 27th August (i.e. roughly 45 days prior harvesting) with an accuracy of 89.15% slightly less than the mapping accuracy achieved with full season data (i.e. till 14th October). 2D-CNN algorithm has performed better than SVM and RF techniques. This methodology can be extended to similar agro-ecological regions of the country. Richly available SAR data from Sentinel-1 and the potential of deep learning techniques for recognising complex phenological patterns offer immense opportunities for early-season crop mapping even during monsoon season in tropical countries like India. [ABSTRACT FROM AUTHOR]

Over the last couple of decades, several copula based methods have been proposed in the literature to test for independence among several random variables. But these existing tests are not invariant under monotone transformations of the variables, and they often perform poorly if the dependence among the variables is highly non-monotone in nature. In this article, we propose a copula based measure of dependency and use it to construct some distribution-free tests of independence. The proposed measure and the resulting tests, all are invariant under permutations and strictly monotone transformations of the variables. Our dependency measure involves a kernel function with an associated bandwidth parameter. We adopt a multi-scale approach, where we look at the results obtained for several choices of the bandwidth and aggregate them judiciously. Large sample properties of the dependency measure and the resulting tests are derived under appropriate regularity conditions. Several simulated and real data sets are analyzed to compare the performance of the proposed tests with some popular tests available in the literature. [ABSTRACT FROM AUTHOR]

Email services have become an integral aspect of modern communication. Emails can be transmitted digitally without the adequate authentication of the sender. As a result, there has been a considerable surge in security threats coming from email communication, such as phishing, spear phishing, whaling, and malware deposition through emails where recipients can be duped into acting. Authorship assertion of the sender can prevent several security issues, particularly in an organizational setting where an employee's trust can be compromised by faking an email from a colleague or senior without exposing any specific system weakness. A psychometric approach to determining the authorship of an email in an organization is proposed in this research. Machine learning (ML) models have been developed using four classification algorithms. The performance of these ML models has been compared. [ABSTRACT FROM AUTHOR]

Maize is a short duration crop. Accurate and up-to-date assessment of the spatial distribution of cultivated area and its production is a key information requirement for all stake holders including policy makers, farmers and consumers. The study utilized Sentinel-2A/2B satellite based NDVI (Normalized Difference Vegetation Index) data to study yield variability of maize in Siddipet district of Telangana state, in 2019. NDVI is a quantitative measurement of crop vigour, which denotes the crop biomass and health status. A regression analysis was developed between observed yield from crop cut experimental plots and seasonal maximum NDVI of maize area. Results showed that there was statistically significant relation (R2=0.87) between NDVI and observed yield at respective field plots. Validation of yield model with Root Mean Squared Error (RSME) computed for all observed and predicted yields was 0.50 explaining higher accuracy of the yield model. The average seasonal rainfall received in different mandals of Siddipet district was between 727 to 799 mm and during the crop growth period the yields were recorded in the range of 4.2 to 5.0 t ha-1. This showed that the distribution of rainfall has played a major role in yield prediction. The 31st Standard Week Rainfall (SWR) coinciding with the peak vegetative growth showed a significant positive correlation (r=0.68***) with maize yield data at p<0.001. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is scaling rapidly to billions of low power devices, with diverse radio technologies sharing common unlicensed spectrum. Inevitably, this results in rampant cross-technology collisions between the devices that lead to wasteful re-transmissions, draining the battery life of low-power devices significantly. We present CharIoT, the first cross-technology distributed MIMO receiver system that exploits the potential of distributed MIMO to facilitate better co-existence and decoding of a large number of simultaneous low power uplink transmissions from unmodified low-power clients. CharIoT is a recovery-based system that intelligently collects radio samples from teams of light-weight IoT gateways and streams them to the cloud to effectively resolve collisions. At the cloud, CharIoT develops a suite of technology-specific software filters that decouple collisions across diverse technologies, facilitating seamless co-existence across low power radios. An implementation of CharIoT on inexpensive RTL-SDR gateways connected to Raspberry Pis decode collisions of four popular IoT technologies in the 868MHz ISM bands – LoRa, XBee, Z-Wave, and SIGFOX showing gains in throughput of up to 4× and battery life of up to 3.5 years. [ABSTRACT FROM AUTHOR]

Present study is a maiden attempt to assess net ecosystem exchange (NEE) of carbon dioxide (CO2) flux from jute crop (Corchorus olitorius L.) in the Indo-Gangetic plain by using open-path eddy covariance (EC) technique. Diurnal variations of NEE were strongly influenced by growth stages of jute crop. Daytime peak NEE varied from − 5 µmol m−2 s−1 (in germination stage) to − 23 µmol m−2 s−1 (in fibre development stage). The ecosystem was net CO2 source during nighttime with an average NEE value of 5–8 μmol m−2 s−1. Combining both daytime and nighttime CO2 fluxes, jute ecosystem was found to be a net CO2 sink on a daily basis except the initial 9 days from date of sowing. Seasonal and growth stage-wise NEEs were computed, and the seasonal total NEE over the jute season was found to be − 268.5 gC m−2 (i.e. 10.3 t CO2 ha-1). In different jute growth stages, diurnal variations of NEE were strongly correlated (R2 > 0.9) with photosynthetic photon flux density (PPFD). Ecosystem level photosynthetic efficiency parameters were estimated at each growth stage of jute crop using the Michaelis–Menten equation. The maximum values of photosynthetic capacity (Pmax, 63.3 ± 1.15 µmol CO2 m−2 s−1) and apparent quantum yield (α, 0.072 ± 0.0045 µmol CO2 µmol photon−1) were observed during the active vegetative stage, and the fibre development stage, respectively. Results of the present study would significantly contribute to understanding of the carbon flux from the Indian agro-ecosystems, which otherwise are very sparse. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to assess the level of social support among Pre-Retiree and Retiree Teachers at different stages of retirement among 200 teachers (100 Pre-Retiree and 100 Retired) from government educational institutions. The Social Support Inventory for the Elderly (SSI) by Ramamurti and Jamuna (1991) was administered to assess 'social support' among these teachers. The analysis showed no significant difference between Pre-Retiree and Retired teachers in the perception of social support. There was no significant difference in the perception of social support among the two groups of Pre-Retiree (6 months and 3 years) and Retired (6 months and 3 years) teachers. Implications were discussed. [ABSTRACT FROM AUTHOR]

Sulfonation of polyethersulfone (SPES) was carried out using chlorosulfonic acid and the modified polymer was then used as a matrix for flat sheet mixed matrix membranes (MMMs). Second component in the MMMs was functionalized multi-walled carbon nanotubes (f-CNTs). The zeta potential study of the cast membranes shows high negative charge on the surface of the membranes containing f-CNTs in comparison to the pristine sulfonated polyethersulfone (SPES) membranes. The addition of carbon nanotubes (CNTs) reduced the roughness parameter of the membranes as revealed by atomic force microscopy study. This also reflected in the reduced irreversible fouling (10.5%). The largest pore size of the membranes having 1.0 wt.% of azide functionalized multi-walled carbon nanotubes was 13.7 nm which classifies them as NF membranes. The highest rejection percentage (91.0%) obtained was for Cu(II) metal by azide functionalized multi-walled carbon nanotubes incorporated membranes. The mechanical stability, in terms of weight loss as observed from thermogravimetric analysis, was the highest as compared to the existing similar membranes with a matching increase in the tensile strength amon g the membranes of its class. [ABSTRACT FROM AUTHOR]

A study was carried out using the temporal Sentinel-1B microwave data (June to November at 12 days interval) and Sentinel-2A/2B optical data (June to November) to discriminate the maize crop from other competing crops rice and cotton in Siddipet district, Telangana state, India during kharif, 2019 (June to November). The study utilized the data from multiple sources such as Multitemporal VH backscatter intensity from Sentinel-1B SAR and NDVI values from Sentinel-2A/2B in combination with field data to discriminate the maize crop. Synchronous to satellite pass, ground truth data on crop parameters viz., crop stage, crop vigour, biomass, plant height, plant density, soil moisture, LAI and chlorophyll content were collected. Multi-temporal VH backscatter intensity and Normalized Difference Vegetation Index (NDVI) data were used to characterize backscatter and greenness behaviour of the maize crop. The backscatter intensity (dB) for maize crop ranged from -21.83 (the lowest backscatter values) at planting to -12.52 (the highest backscatter values) at peak growth stage. The NDVI values during vegetative and reproductive stages (August and September) were >0.6 and during senescence to harvesting the values were less than or equal to 0.52. The increase in backscatter intensity values from initial vegetative stage to peak stage was due to increased volume scattering of the maize crop canopy and a continuous decline in backscatter intensity values of VH band at maturity stage, was due to decrease in greenness and moisture content in leaves of the maize crop helped in maize crop discrimination from other dominant kharif crops in the study area. [ABSTRACT FROM AUTHOR]

The present study was conducted to examine the arrivals and prices behaviour of pigeon pea in Karnataka using the secondary data from January 2007 to December 2020. Kalaburagi, Raichur and Bidar markets for the pigeon pea were selected for the study and computed trend in arrivals and prices with seasonal variations. The result revealed that annual increase in price of pigeon pea was observed to be high in the case of Kalaburagi market with Rs.328.34 per quintal followed by Raichur market with Rs.239.04 per quintal and Bidar market with Rs.203.2 per quintal. The reason for this behavior in arrivals in the selected markets is due to huge transaction in the selected markets. It is because of the selected market doesn't depend on the districts in which highest area under pigeon pea is observed. The prices of pigeon pea showed increasing trend in all the selected markets which may be due to increase in consumption, which in turn increases the demand for pigeon pea coupled with the inflationary trend in the economy. Thus, a positive trend in all the markets under study was observed. Farmers developed a positive attitude and realized the benefits of regulated markets by selling produce in the regulated markets due to higher price realization. [ABSTRACT FROM AUTHOR]

The present study is a maiden attempt to assess jute crop lodging due to super cyclone Amphan (20 May 2020) by synergistic use of Sentinel-2 (optical) and Sentinel-1 (SAR) data over part of West Bengal, India. Pre-event Sentinel-2 data (9 April, 14 May) along with the ground information were used to map the jute crop of the affected districts with accuracy of 85%. The cross-polarized backscatter (σ0VH) of Sentinel-1 was found to be sensitive to the sudden change in the canopy structure due to lodging and partial flooding. ▵ σ VH 0 (σ0VH_22 May − σ0VH_16 May) indicating post-event damage was > 2.5 dB over the affected jute crop and δ σ VH 0 (σ0VH_22 May − σ0VH_28 May) representing post-event recovery showed > 1.5 dB for recovered crop, depending on the crop vigor/height. Decision matrix was prepared combining ▵ σ VH 0 and δ σ VH 0 for NDVI-based crop vigor strata (low, medium, and high) to classify the area into affected, marginally affected and normal. Overall accuracy of the classified map was found to be 84.12% with kappa coefficient of 0.74. Nearly, 12.5% of the jute area, i.e., 38,119 ha was found to be either affected or marginally affected due to Amphan and distributed in the southern part of Murshidabad, north-eastern Nadia, northern 24 Paraganas (N), and middle region of Hooghli district. Geospatial map of block-wise affected jute area was prepared to facilitate informed decision making. The study demonstrated an operational methodology for assessing crop lodging due to natural calamities to support relief management and crop insurance. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the authors present different methods for reconfigurable finite impulse response (RFIR) filter design. Distributed arithmetic (DA)-based reconfigurable FIR filter design is suitable for software-defined radio (SDR) applications. The main contribution of reconfiguration is reuse of registers, multipliers, adders and to optimize various parameters such as area, power dissipation, speed, throughput, latency and hardware utilizations of flip-flops and slices. Therefore, effective design of building blocks will be optimized for RFIR filter with all the above parameters. Design/methodology/approach: The modified, direct form register structure of FIR filter contributes the reuse concept and allows utilization of less number of registers and parallel computation operations. The disadvantage of DA and other conventional methods is delay increases proportionally with filter length. This is due to different partial products generated by adders. The usage of adder and multipliers in DA-FIR filter restricts the area and power dissipation because of their complexity of generation of sum and carry bits. The hardware implementation time of an adder can be reduced by parallel prefix adder (PPA) usage based on Ling equation. PPA uses shift-add multiplication, which is a repetitive process of addition, and this process is known as Bypass Zero feed multiplicand in direct multiplication, and the proposed technique optimizes area-power product efficiently. The modified DA (MDA)-based RFIR filter is designed for 64 taps filter length (N). The design is developed by using Verilog hardware description language and implemented on field-programmable gate array. Also, this design validates SDR channel equalizer. Findings: Both RFIR and SDR are integrated as single system and implemented on Artix-7 development board of XC7A100tCSG324 and exploited the advantages in area-delay, power-speed products and energy efficiency. The theoretical and practical comparisons have been carried out, and the results are compared with existing DA-RFIR designs in terms of throughput, latency, area-delay, power-speed products and energy efficiency, which are improved by 14.5%, 23%, 6.5%, 34.2% and 21%, respectively. Originality/value: The DA-based RFIR filter is validated using Chipscope Pro software tool on Artix-7 FPGA in Xilinx ISE design suite and compared constraint parameters with existing state-of-art results. It is also tested the filtering operation by applying the RFIR filter on Audio signals for removal of noisy signals and it is found that 95% of noise signals are filtered effectively. [ABSTRACT FROM AUTHOR]

A stereoselective method has been developed for the chiral separation of the enantiomers of eight antiretroviral (ARV) drugs under subcritical conditions using a supercritical fluid chromatography apparatus. The ARVs: abacavir, tenofovir alafenamide, lamivudine, efavirenz, atazanavir, emtricitabine, dolutegravir and darunavir, are used to treat the human immunodeficiency virus (HIV). The antiretroviral therapy has been planned to manage and control the severity of HIV and prolong the patient's life. The use of multiple drugs that act on different viral targets is known as highly active antiretroviral therapy, which slows down the development of AIDS and prevents opportunistic infections that often lead to death. The immobilized meta-substituted polysaccharide-derived carbamate columns (CSPs) have been chosen for chiral SFC separation of selected ARVs. The ARVs enantioseparation was achieved on CSPs by using different alcohol co-solvents (methanol, ethanol and isopropyl alcohol) with diethylamine (DEA) as an additive to the mobile phase. These CSPs exhibit an exceptional level of complementary chiral recognition characteristics by applying different screening and method optimization strategies. The cellulose tris(3,5-dichlorophenylcarbamate) immobilized on silica gel was found to be useful for the chiral SFC analysis of abacavir, lamivudine, efavirenz combination ARV analysis, and the method was optimized by studying the effects of different mobile phase additives, column temperatures and flow rates. The amylose tris(3-chloro, 5-methylphenyl carbamate) immobilized on silica gel was found to be useful for the chiral SFC analysis of emtricitabine, abacavir and dolutegravir combination ARV drugs. [ABSTRACT FROM AUTHOR]

An equivalent circuit approach for the analysis of cold circuit parameters such as dispersion and interaction impedance characteristics was used for the design of staggered double slot square-shaped coupled-cavity slow wave structure at millimeter waveband and the results were validated against numerical analysis at Ka-band and $W$ -band. The derived lumped parameters of the square-shaped coupled-cavity slow wave structure (CC-SWS) is different from the reported cylindrical CC-SWS. A typical structure was fabricated at Ka-band and cold test measurement was carried and the results were compared against the proposed equivalent circuit approach. The comparison of results shows that the equivalent circuit is simple but accurate to predict within 3% and 10% accuracy against numerical simulation and cold test measurement for dispersion and interaction impedance characteristics, respectively. [ABSTRACT FROM AUTHOR]

The purpose of the present study is to assess the level of anxiety among Pre-retiree and Retired Bank employees at different stages of retirement among of 200 bank employees (100 Pre-Retiree and 100 Retired) from public sector banks. The State Trait Anxiety Test (STAT) by Sanjay Vohra (1993) was used to assess ‘anxiety’ among these employees. The analysis showed a significant difference between Pre-retiree and Retired employees with higher level of anxiety among Pre-retiree employees. There was a significant difference in the level of anxiety among the two groups of Pre-retiree (6 months & 3 years) employees in certain dimensions and no significant difference among the groups of retired (6 months & 3 years) employees within. Implications are discussed. [ABSTRACT FROM AUTHOR]

Context: Many contaminants are routinely encountered in cytopathology practice. However, because of lack of familiarity and experience with them, many are unnoticed, neglected, or confused with other structures of major relevance. Aims: The purpose of this study was to intentionally introduce contaminants into the smears and to provide distinctive morphological criteria required for the microscopist to identify them confidently to avoid possible confusion. Settings and Design: Prospective cross-sectional study. Methods and Material: This study included smears prepared from the buccal mucosa of healthy volunteers. Common contaminants were deliberately introduced into the smears, fixed using 90% ethanol, and stained with Hematoxylin and Eosin stain (H and E) and Papanicolaou stain (PAP). The study also included smears from leftover cerebrospinal fluid (CSF) and wet mount preparations. The morphology of these contaminants was studied and the results were tabulated. Statistical Analysis Used: Nil. Results: The vivid morphological appearance of these commonly encountered contaminants were described and many of these mimicked structures of major relevance. Conclusions: Contaminants and mimickers can make the evaluation of cytologic specimens challenging and may necessitate secondary review by another pathologist or further workup. Knowledge and familiarity of these commonly encountered extraneous substances will help to prevent misinterpretation. [ABSTRACT FROM AUTHOR]

Mobile Network Operators perceive Spectrum Slicing and Sharing as an efficient and viable option to meet the growing mobile user data demands in the upcoming 5G network deployments. Efficient spectrum sharing requires optimized spectrum partitioning, i.e., slicing of the spectrum for sharing among operators, to improve spectrum utilization, offer services with high quality of experience, and increase their revenues. This paper proposes Spectrum Slicing Coalition Formation (SSCF) protocol, a novel solution to address effective spectrum slicing required to meet the dynamic data demands at every 5G base station. The base stations’ cooperation is formulated as a coalition formation game with overlapping coalitions, where a base station's contribution is not restricted to a single coalition, instead a base station participates intelligently in multiple coalitions. The SSCF protocol enables a base station to dynamically slice and share its allocated spectrum to service its load based spectral requirements, a key requirement for effective radio access network softwarization. The study explores operator base stations’ options to determine with whom, when, and how much to slice and share the spectrum and the payoffs. Extensive simulations using real-world operator base station deployments highlight that SSCF solution brings in operational efficiencies, improved users’ quality of experience, and revenue maximization. [ABSTRACT FROM AUTHOR]

There is growing concern regarding the quality of higher education. With growing diversity in the student profile and phenomenal technological advancement, the challenges facing our education system are unprecedented. At the centre of this cauldron of expectation is the teacher. To meet these challenges there is a need for teachers who view teaching not just as job or career, but the very purpose or meaning of their lives. Calling work orientation refers to a focus on enjoyment of fulfilling, socially useful work irrespective of the financial gains. The present study measures the 'presence of calling work orientation among Higher Secondary, Academic under graduation degree and Professional under graduation degree college teachers. The sample consisted of 565 teachers (85 Higher Secondary, 123 Academic undergraduate degree and 357 Professional undergraduate degree college teachers) from Mysore. The Calling and Vocational Questionnaire (CVQ) by Dik et al (2012) was used to determine the 'presence of calling' among these teachers. The results indicated that Professional college degree teachers had the highest incidence of calling orientation. The results showed that there was no significant difference among the 3 groups of teachers in 'presence of calling' orientation. The effects of demographic variables such as gender, marital status and years of teaching experience have been explored. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B viral infection is the most common cause of hepatitis, and it leads to serious liver diseases such as cirrhosis and hepatocellular carcinoma. Aim: The aim of the study is to differentiate acute hepatitis B and chronic hepatitis B (CHB) among patients seropositive for hepatitis B surface antigen (HBsAg). Materials and Methods: This study was carried out in the Department of Microbiology, Chettinad Hospital and Research Institute, Kelambakkam, Tamil Nadu, India, for a period of 6 months (January 2018–June 2018). Blood samples were collected from 87 patients for the detection of hepatitis B virus (HBV) serological markers. HBsAg, hepatitis B e antigen (HBeAg), anti-HBc total, anti-HBc IgM, and antibody to hepatitis B surface antigen were screened using the ELISA method. Detailed demographic profile including history of previous hepatitis infection, previous blood transfusion, and other related details were collected and documented using a structured questionnaire. Results: A total of 87 patients were HBsAg seropositive; among them, 55 (63.2%) were male and 32 (36.9%) were female. Based on the serological markers tested, 24 and 63 were suffering from acute and chronic HBV infections, respectively. Among the acute hepatitis B patients, all samples were seropositive for HBsAg, anti-HBc total, and anti-HBc IgM. HBeAg seromarker was found in 15 patients (62.5%). Among the CHB patients, all samples were seropositive for HBsAg and anti-HBc total. HBeAg seromarker was found in 28 patients with 44.4%. Alcohol consumption was the major risk factor for the transmission of HBV infection. Conclusion: An increased sample size and detailed study of high-risk behavior will provide an alarming awareness of their association. [ABSTRACT FROM AUTHOR]

The frequently used aerospace materials, i.e., ultra-high-strength maraging steel (250) and corrosion-resistant 13-8 Mo stainless steel in the solution-annealed and cold-worked condition, have been joined by three fusion welding processes, namely interpulse TIG welding, and high energy density fusion processes like electron beam welding (EBW) and laser beam welding (LBW). The interpulse TIG welding process was carried out by using W2 grade maraging steel filler wire. The dissimilar joints were welded by EBW and LBW processes without any filler wire. All the dissimilar welded joints were characterized by microstructural observations and validated by mechanical properties in the as-welded as well as precipitation-hardened conditions after welding. The weld microstructures and microhardness profiles were correlated to the tensile strength of weld. Electron beam welded joint with precipitation hardening after welding, i.e., soaking at 510 °C and subsequent air cooling, demonstrated the superior mechanical properties among all the welds. [ABSTRACT FROM AUTHOR]

To overcome the limitation of polymeric and ceramic membranes in terms of performance and durability for water treatment, mixed-matrix membranes are being increasingly studied. In our studies, mixed matrix membranes of functionalized multiwalled carbon nanotubes (MWCNTs) with polyether sulfone as the membrane matrix showed reduced porosity. The pore dimensions, when studied by small angle neutron scattering and capillary flow porometry techniques, showed smaller pores. The cross sectional image of cold fractured membrane obtained from scanning electron microscopy showed that they retained their original asymmetric nature when cast by the phase inversion technique. The thermogravimetric analysis of mixed-matrix membranes showed enhanced thermal properties and better stability to degradation than the pristine membranes with no loss in the stability. These membranes showed better separation characteristics and enhanced selectivity for metal ion separation. Interestingly, the conductivity in dry state of the mixed-matrix membranes showed perfectly semicircular plots which shows conducting nature of the membranes which is attributed to the presence of functionalized nanotubes in the otherwise insulating polymer matrix. [ABSTRACT FROM AUTHOR]

India is one of the leading rice producing countries in the world. In eastern part of the country, after harvesting of kharif rice crops, large area remains fallow, mostly due to unavailability of irrigation facility. Approximately 30 percent of total kharif rice area (i.e. around 12 Mha) is left fallow during rabi season. Government of India, in order to improve the land productivity, cropping intensity and food security, has taken up various initiatives to grow pulses in these areas. A project was launched under the National Food Security Mission for use of satellite remote sensing for suitability analysis towards crop intensification in 6 eastern Indian states. Before execution of the project an exploratory study was carried out for 4 selected districts of 2 major rice-fallow states of the country. The selected districts were Raipur and Surguja districts of Chhattisgarh state and Mayurbhanj and Balasore districts of Odisha state. Kharif rice area was mapped using RISAT-1 SAR data, while rabi fallow area was mapped using Resourcesat-2 AWiFS and Landsat-8 OLI data. Suitable area for growing pulses were analysed with respect to various soil, weather and land parameters such as, mean monthly air temperature (°C), Slope (%), Surface wetness, Plant available water capacity (mm) and proximity to drainage network. The post kharif rice-rabi fallow area, in these districts ranged between 20–40% of the geographical area, while the suitable area was found to be in range of between 8–12%. Thus, this exploratory study could show, with remote sensing and other agro-physical parameters, it is possibly not only to map the rice–fallow area, but also to assess the suitability of growing short duration rabi crops. [ABSTRACT FROM AUTHOR]

The classification of agricultural crop types is an important application of remote sensing. With the improvement in spatial, temporal and spectral resolution of satellite data, a complete seasonal crop growth profile and separability between different crop classes can be studied by using ensemble-learning techniques. This study compares the performance of Random Forest (RF), which is a decision tree based ensemble learning method and Naïve Bayes (a probabilistic learning technique) for crop classification of Lekoda gram panchayat, Ujjain district, using multi-temporal Sentinel 2 of Rabi 2017–18. The study area contains seven different classes of crop types, and in each class, we have used 65% of the ground data for training and 35% to test the classifier. The performance of RF classifier was found to be better than NB classifier. Kappa coefficient of RF classifier in mid of the crop season (December–January) was found to be 0.93. This result indicates that an accurate in-season crop map of the study area can be generated through integrated use of Sentinel 2 temporal data and RF classifier. [ABSTRACT FROM AUTHOR]

Limited national fossil fuel resources and sustained increases in energy prices have resulted in nationwide efforts to study and deploy alternative energy sources. Despite high prospect, biomass resources has not been effectively utilized in India due to the lack of information on amount, type and time of its availability on a geospatial frame work to facilitate its transportability, establishment of bio-fuel plants tailor made for specific crop residues. Hence, a comprehensive approach towards geospatial mapping of bio-energy potential from surplus crop residues of selected crops (rice. wheat, cotton and sugarcane) over the Haryana state of India is implemented by utilizing a hybrid model combining both statistical and remote sensing technique. Bioenergy potential was calculated from crop production statistics collected at district level. The grain production data were converted into gross residue potential using residue production ratio. The crop residue was further converted into collectable crop residue using collectable coefficient. To generate the spatial map of the selected crops, potential crop masks were prepared using multi-temporal satellite data. These crop masks were then converted to crop fraction at 1 km grid level. MODIS NPP data product was then processed and converted into same 1 km to account the spatial variability of biomass potential. Using these crop fractions as independent variables, relationship was established with NPP as dependent variable using a machine learning technique (Random Forest algorithm). These crop specific response curves (crop fraction vs NPP) were utilized as a weight to disaggregate district level gross biomass potential to 1 km grid level. The spatial map thus generated provided spatial details of the type and amount surplus crop residues and could be vital input for planning and policy making of utilization of the surplus biomass resources of India. [ABSTRACT FROM AUTHOR]

The Indian and French Space Agencies, ISRO and CNES, have conceptualized a space-borne Thermal Infrared Reflectance (TIR) mission, TRISHNA (Thermal infRared Imaging Satellite for High-resolution Natural Resource Assessment). The primary design drivers of TRISHNA are the monitoring of (i) terrestrial water stress and use, and of (ii) coastal and continental water. A suit of four TIR bands and six optical bands is planned. The TIR bands will be centred at 8.6 μm, 9.1 μm, 10.3 μm and 11.5 μm to provide noon-night global observations at 57m nadir resolution over land and coastal regions. The field of view (FOV) is ±34° and the orbit of 761 km altitude was designed to allow 3 sub-cycle acquisitions during the 8-day cycle. The optical bands correspond to blue, green, red, and NIR plus two SWIR bands at 1.38 μm and 1.61 μm. The green, red, NIR and the 1.61 μm SWIR bands will have better radiometry quality than those of AWiFS. ISRO and CNES will develop optical and TIR payloads, respectively. Assessing evapotranspiration and furthermore Gross and Net Primary Productivity (GPP and NPP) will in turn assist in quantifying water use in rainfed and irrigated agriculture, water stress and water use efficiency, with expected applications to agricultural drought and early warning, crop yield prediction, water allocation, implementation of water rights, crop insurance business and agro-advisories to farmers. The other scientific objectives of TRISHNA are also briefly described. TRISHNA instrument will fly aboard a ISRO spacecraft scheduled to be launched from 2024 for a minimum period of 5 years' mission lifetime. [ABSTRACT FROM AUTHOR]

Soybean, a high value oilseed crop, is predominantly grown in the rainfed agro-ecosystem of central and peninsular India. Accurate and up-to-date assessment of the spatial distribution of soybean cultivated area is a key information requirement of all stakeholders including policy makers, soybean farmers and consumers. A methodology for timely assessment with high precision of soybean crop using satellite data is yet not operational in India. In this scenario, synthetic aperture radar (SAR) has been shown to be a reliable form of gathering crop information, especially during monsoon season. In this work, repeat coverage from the C-band Sentinel-1 satellite over Ujjain district, Madhya Pradesh is used for in-season soybean crop mapping along with other agricultural land-cover types. The data were processed through four steps: (a) data preprocessing, (b) constructing smooth time series backscatter data, (c) soybean crop classification using knowledge-based decision rule classifier and support vector machines (SVM) and (d) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of soybean crop growing in the study region. Phenological characteristics were also derived from the smoothed S-1 VH backscatter time series to segregate early and late sown soybean. This information was used as an input to a decision-rule classifier and SVM in order to classify the input data into soybean and other crops. An overall accuracy of more than 80% using SVM and 75% using rule based classifier, in Ujjain district was achieved. These results demonstrate the scope for using time-series S-1 VH data to develop an operational soybean crop-monitoring framework. [ABSTRACT FROM AUTHOR]

Crop yield maps are very crucial inputs for different practical applications like crop production estimation, pay-out of crop insurance, yield gap analysis etc. Satellite derived vegetation indices across different electromagnetic region has the ability to explain the variation in crop yield and can be used for prediction of yield before harvesting. This study utilised indices derived from multi-temporal Optical, Thermal and Radar data for developing model for Wheat (Triticum aestivum) grain yield using Machine learning approaches i.e., Random Forest Regression (RFR). Time series of Sentinel-2 derived Normalized difference vegetation index (NDVI), Normalized difference water Index (NDWI), Landsat-8 derived GPP using LST-EVI relationship (Temparature-Greeness model) and Sentinel-1 derived cross-polarization backscatter ratio (σVH/σVV) were used as predictor for wheat yield estimation. Actual grain yield measurements at ground were carried out at the end of the season over 178 locations. Seventy five percent of ground yield data were used for training of the model and rest twenty five percent data were used for its validation. All the datasets were grouped into ten fortnightly datasets ranging from November 2017 to March 2018. Through the random forest regression using time-series of NDVI alone, wheat grain yields were estimated with an RMSE of 9.8 Q ha−1. Subsequently by adding the multi-temporal NDWI, GPP and σVH/σVV led to the improvement of RMSE to 8.7, 7.6 and 7.4 Q ha−1 respectively. Variable importance based on the out of box error showed the significance of NDVI, NDWI and GPP during Dec-Jan and σVH/σVV during Feb for wheat grain estimation. It was concluded that the RFR algorithm together with the indices from optical, thermal and microwave satellite data can able to produced significantly accurate estimates of wheat grain yield. [ABSTRACT FROM AUTHOR]

Integration of remote sensing derived biophysical parameters with process based crop growth simulation model is an emerging technology with diversified application for crop insurance as well as precision farming. Basically the crop growth simulation models are point based which simulate crop growth and yield as a function of soil, weather and crop management factors at a daily time scale. The temporal dimension of the crop growth model is supplemented by the spatial information on crop coverage and condition generated from remote sensing satellite derived biophysical parameters. In the present study an attempt is made to simulate pixel wise rice crop growth, condition and yield using Sentinel1 SAR, AWiFS/Landsat8 OLI and MODIS data with CERES rice growth simulation model on DSSAT platform. Temporal SAR data provides pixel wise start of season for rice crop at a spatial resolution of 10 m. Spectral indices derived from Landsat8/AWiFS and MODIS optical images are used for characterisation of rice growth environment and at the same time these indices are used to generate crop management information like irrigation and sowing dates. Bhadrak district of Odisha in the eastern coast of India is selected as the study area based on the prevalence of diversified rice growing environments. Sufficient in season field data are collected for checking the accuracy of rice map as well as for calibration and validation of the crop growth model. In season rice growth is initialised using SAR derived staggered sowing dates along with daily weather data and soil inputs. As CERES crop growth models are running well on DSSAT environment, rice crop growth simulation is carried out using multi date SAR images on DSSAT platform. The output of this study is maps depicting the spatial variability in rice area, staggered sowing dates and irrigation in the study region. These in season information are crucial for decision making particularly for crop insurance related activities. [ABSTRACT FROM AUTHOR]

Maraging steel (250) and 13-8 Mo stainless steel plates were joined by gas tungsten constricted arc welding (GTCAW) process in similar and dissimilar metal combinations using 13-8 Mo stainless steel filler wire. The similar and dissimilar metal welds made in solutionized condition were subjected to standard post weld hardening treatments direct ageing at 485 °C, soaking for 3½ hours followed by air cooling (ageing treatment of maraging steel) and direct ageing at 510 °C, soaking for 4 h followed by air cooling (ageing treatment of 13-8 Mo stainless steel). The joint characterization studies include microstructure examination, microhardness survey across the weldments and transverse weld tensile test. Similar and dissimilar metal weldments responded to both the post weld ageing treatment. After post weld aging, increase in yield strength, UTS and slight reduction in % elongation of similar and dissimilar metal were observed. The observed tensile properties were correlated with microstructure and hardness distribution across the welds. [ABSTRACT FROM AUTHOR]

An algorithm for density-based subspace clustering of given data is proposed here. Unlike the existing density-based subspace clustering algorithms which find clusters using spatial proximity, existence of common high-density regions is the condition for grouping of features here. The proposed method is capable of finding subspace clusters based on both linear and nonlinear relationships between features. Unlike existing density-based subspace clustering algorithms, the values of parameters for density estimation need not be provided by the user. These values are calculated for each pair of features using data distribution in space corresponding to the particular pair of features. This allows proposed approach to find subspace clusters where relationship between different features exists at different scales. The performance of proposed algorithm is compared with other subspace clustering methods using artificial and real-life datasets. The proposed method is seen to find subspace clusters embedded in 5 artificial datasets with greater G score. It is also seen that the proposed method is able to find subspace clusters corresponding to known classes in 4 real-life datasets, with greater accuracy. [ABSTRACT FROM AUTHOR]