A two-year field study was conducted during Rabi 2018–2019 and 2019–20 to find out the influence of different residue and weed management practices on weed dynamics, growth, yield, energetics, carbon footprint, economics and soil properties in zero-tilled sown wheat at Research Farm, AICRP-Weed management, SKUAST-Jammu. The experiment with four rice residue management practices and four weed management practices was conducted in a Strip-Plot Design and replicated thrice. The results showed that residue retention treatments recorded lower weed density, biomass and higher wheat growth, yield attributes and yields of wheat as compared to no residue treatment. The magnitude of increase in wheat grain yield was 17.55, 16.98 and 7.41% when treated with 125% recommended dose of nitrogen + residue + waste decomposer (RDN + R + WD), 125% RDN + R, and 100% RDN + R, respectively, compared to no residue treatment. Further, all three herbicidal treatments decreased weed density and biomass than weedy treatments. Consequently, a reduction of 29.30, 28.00, and 25.70% in grain yield were observed in control as compared to sulfosulfuron + carfentrazone, clodinafop-propargyl + metasulfuron, and clodinafop-propargyl + metribuzin, respectively. Moreover, 125% RDN + R + WD obtained significantly higher energy output (137860 MJ ha −1 ) and carbon output (4522 kg CE/ha), but 100% RDN had significantly higher net energy (101802 MJ ha −1 ), energy use efficiency (7.66), energy productivity (0.23 kg MJ −1 ), energy profitability (6.66 kg MJ −1 ), carbon efficiency (7.66), and less carbon footprint (7.66) as compared to other treatments. Despite this, treatments with 125% RDN + R + WD and 125% RDN + R provided 17.58 and 16.96% higher gross returns, and 24.45% and 23.17% net outcomes, respectively, than that of control. However, compared to the control, sulfosulfuron + carfentrazone showed considerably higher energy output (140492 MJ ha −1 ), net energy (104778 MJ ha −1 ), energy usage efficiency (4.70), energy productivity (0.14 kg MJ −1 ), energy profitability (3.70 kg MJ −1 ), carbon output (4624 kg CE ha −1 ), carbon efficiency (4.71), and lower carbon footprint (0.27). Furthermore, sulfosulfuron + carfentrazone, clodinafop-propargyl + metasulfuron, and clodinafop-propargyl + metribuzin recorded 29.29% and 38.42%, 27.99%, and 36.91%, 25.69% and 34.32% higher gross returns and net returns over control treatment, respectively. All three herbicides showed higher gross returns, net returns, and benefit cost ratio over control. The soil nutrient status was not significantly affected either by residue or weed management practices. Therefore, based on present study it can be concluded that rice residue retention with 25% additional nitrogen and weed management by clodinafop-propargyl + metasulfuron herbicide found suitable for zero tillage wheat.

Background: Balancing productivity, profitability and environmental health is a key challenge for maintaining agricultural sustainability. The use of locally available agro-inputs in agriculture by avoiding or minimizing the use of synthetically compounded agro-chemicals appears to be one of the probable options to sustain the agricultural productivity. Widespread use of herbicides has resulted in purported environmental and health problems as well as residual toxicity issues in succeeding crops. At present, the awareness on safe food is increasing. So, keeping this point in view the present investigation was carried out to evaluate the influence of organic sources of nutrients and weed management on growth, yield and weed flora of Frenchbean. Methods: A field experiment was conducted during summer season of 2016 and 2017. The present investigation was laid out in split plot design with six sources of nutrients in main plot and four weed management treatments in sub plots. Result: Application of treatment T5 recorded significantly higher growth parameters, yield attributes, fresh pod yield, net returns and B:C ratio of French bean which was statistically at par with T3 and T2. Amongst the weed management treatments, treatment W1 resulted in significantly lowest species wise and total weed density and biomass, highest weed control efficiency, lowest weed index, highest growth, yield attributes and fresh pod yield of French bean which was statistically at par with W2 and W0. However, the highest net returns and B:C ratio were obtained in weed free plots.

The present study was conducted purposively in Jammu and Samba districts because these two districts were having the maximum area under knol-khol cultivation in Jammu region. A proportionate random sampling procedure, based on the area under knol-khol cultivation in these districts was employed for the selection of villages. The area under knol- khol cultivation in Jammu district and Samba district was 599 ha and 121 ha, respectively. A list of knol-khol growing villages was prepared and 12 villages from Jammu district and 2 villages from Samba district were randomly selected. A list of knol-khol growing farmers with a minimum of 1 kanal (1/20 ha) area under vegetable cultivation was prepared from the selected villages during the year 2020 and 10 farmers from each selected village were randomly selected, The KVK Samba carried out front line demonstrations (FLDs) of the university released G 40 variety of knol-khol. The KVK laid 9 demonstrations on the farmers’ fields, each in 2017-18 and 2018-19. In the year 2019-20 KVK samba again laid the FLDs for 20 farmers. Thus, to study the adoption and adoptability of university released G 40 knol-khol variety, 38 farmers of Samba district were selected as final respondents. Thus, a total sample of 178 (140+ 38) farmers were taken as respondents for the present study. The result showed that majority of the respondents fell under middle age category. Literacy rate of the respondents were quite good. Social participation of the respondents was low. Average operational land holding of the respondents was 1.24 ha and that majority of the respondents were fell under small (1-2 ha) and marginal (<1 ha) category of farmers. Cow was possessed by majority of the respondents.

A field study was conducted during the summer seasons of 2015 and 2016 at the Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Chatha, Jammu and Kashmir, to study the effect of pre- and post-emergence application of herbicides on nutrients uptake by weeds and blackgram or urdbean [Vigna mungo (L.) Hepper] crop. A significant reduction in weed density and weed biomass was observed with 2 hoeing. Further, among the herbicidal treatments, imazethapyr + pendimethalin @ 1,000 g/ha as pre-emergence and imazethapyr + imazamox 80 g/ha as post-emergence significantly reduced the weed density and weed biomass. The lowest nutrient depletion by weeds, highest grain and stover yields and nutrient uptake by urdbean crop were recorded with pre- emergence application of imazethapyr + pendimethalin @ 1,000 g/ha. Thus, pre-emergence application of imazethapyr + pendimethalin @ 1,000 g/ha may be used for effective weed-management for achieving the higher seed yield (786 kg/ha and 743 kg/ha) in urdbean crop, as it provided higher net returns of (`45,205/ha and 41,364/ ha) and benefit: cost ratio of (2.56 and 2.29) to resourceful farmers of sub-tropical conditions of Jammu region.

A field experiment was conducted during 2015 16 and 2016 17 under different organic sources of nutrients and weed management in rice ( L.) -potato ( L.)-frenchbean ( ) cropping system with six sources of nutrients as Oryza sativa Solanum tuberosum Phaseolus vulgaris main-plot treatments and four weed management practices as sub-plot treatments.Among the organic sources of nutrients, application of 100% organics (100% recommended N through different organic sources each equivalent to 1/3 of recommended N i.e.FYM+ vermicompost + non-edible oil cake) + VAM recorded significantly maximum individual crop yield and rice equivalent yield (REY).The highest apparent balance of nitrogen and phosphorus was recorded with the application of 100 % recommended NPK + secondary and micronutrients based on soil test through inorganic fertilizer and 50 % recommended N through vermicompost + biofertilizers for N + rock phosphate to substitute the P requirement + PSB.Whereas, the balance of available potassium in soil was negative with the application of different sources of nutrients.Application of mustard seed meal @ 5 t/ha registered significantly higher yield of crops, REYand apparent balance of nitrogen.However, the balance of available phosphorous and potassium in soil was recorded highest with the application of rice bran @ 4 t/ha.

A field experiment was conducted during Kharif (rainy) seasons of 2015 and 2016 to study the response of varying organic and inorganic sources of nutrients and weed management on weed flora, basmati rice growth and yield. Application of 100% organics + vesicular-arbuscular mycorrhiza (VAM) recorded significantly higher values of growth parameters, yield attributes, grain yield, net returns and B:C ratio of rice which was statistically at par with 100% organics + marigold for potato on border as trap crop and bottle guard as trap crop for french bean and 100% organics (100% recommended nitrogen using different organic sources each equivalent to 1/3 of recommended nitrogen i.e. farm yard manure (FYM)+ vermicompost + non edible oil cake). Amongst the weed management treatments, application of mustard seed meal 5 t/ha resulted in significantly lowest weed density and biomass (species wise and total); highest weed control efficiency; lowest weed index; highest growth, yield attributes and grain yield of basmati rice which was statistically at par with application of rice bran 4 t/ha and weed free treatment. However, the highest net returns and B:C ratio were obtained in weed free plots.

With the increase in popularity of the internet and android operating system, the number of active internet user and their daily activity on android devices is also increasing. So, that's the reason malware writers are targeting android devices more and more. The quickly creating malware is a major issue, and there is a requirement for discovery of android malware to secure the framework. Signature-based technologies work efficiently for known malware but fail to detect unknown malware or new malware. Academia is continuously working on machine learning and deep learning techniques to detect advanced malware in today's scenario. For machine learning, feature vector and sufficient dataset are very important. In this paper, we will develop and implement an approach for the detection of unknown malware with a high detection rate.

The high spatial resolution satellite images covering multiple objects of the urban area are visually complex in nature. This visual complexity causes ambiguity during segmentation of such images when the targets are unknown. In such case, reference data are required to assess the segmentation methods. Due to excellence of humans in visual analysis, the presented work has attempted for a psycho-visual approach to prepare the reference segmented complex HRS images. The reference data have has prepared through the correlation of the quantified eye-tracking data (metrics) and corresponding concurrent think-aloud (CTA) data for each of the created segments. Segments get updated based on Gestalt principles and Gibson's theory. Those segments having the best correlation between metrics and CTA data have been opted as the final output. The results suggest that the functional grouping of objects while preferring perceptual grouping for segments drawing conforms the most to the participants' verbal response. The final results have been compared with the existing notion of full segmentation used for complex images. The comparison also proffers the superiority of the proposed segmentation over full segmentation to be used as reference. In the future, a large number of images may be used to prepare better reference data.

End Stage Kidney Disease (ESKD) in patients with Sickle Cell Nephropathy is marked with an inexorable clinical deterioration with an increase in frequency of painful crisis, transfusion requirement and hypertension. This makes Kidney Transplant in these set of patients quite challenging.

Android-based smart devices are exponentially growing, and due to the ubiquity of the Internet, these devices are globally connected to the different devices/networks. Its popularity, attractive features, and mobility make malware creator to put a number of malicious apps in the market to disrupt and annoy the victims. Although to identify the malicious apps, time-to-time various techniques are proposed. However, it appears that malware developers are always ahead of the anti-malware group, and the proposed techniques by the anti-malware groups are not sufficient to counter the advanced malicious apps. Therefore, to understand the various techniques proposed/used for the identification of Android malicious apps, in this paper, we present a survey conducted by us on the work done by the researchers in this field.

An investigation was conducted at Advance Centre for Rainfed Agriculture, Rakh Dhiansar, SKUAST-Jammu during kharif season of 2015. The experiment was laid out in randomized block design with three replications. The nine treatments viz. sole blackgram, sole sesame, blackgram + sesame (1 row of sesame in 2 rows of blackgram) additive series, blackgram + sesame (1:1) replacement series, black + sesame (3:1) replacement series, blackgram + sesame (5:1) replacement series, blackgram + sesame (1:3) replacement series, blackgram + sesame (1:5) replacement series and blackgram + sesame (seed mix) were taken for study. The soil of experimental field was sandy loam in texture, slightly acidic in reaction, low in organic carbon and available nitrogen and medium in available phosphorus and potassium. The experimental results revealed that among the different intercropping systems blackgram + sesame (5:1) replacement series recorded highest blackgram equivalent yield (BEY) 7.01 q ha-1 which was statistically at par with blackgram + sesame (3:1) replacement series, blackgram + sesame (1:1) replacement series and blackgram + sesame (1:1) additive series and significantly higher than other intercropping systems. Also blackgram + sesamum (5:1) row ratio gave highest value of land equivalent ratio, aggressivity, area time equivalent ratio, net returns, B:C ratio, energy output, energy use efficiency, net energy return, energy productivity and energy intensity followed by blackgram + sesame (3:1) replacement series.

An important feature of malware is that it can self-replicate. It is not known who created the first self-replicating program in the world, but it is clear that the first malware/virus (Creeper) was created by the Bible Broadcasting Network engineer Robert (Bob) H. Thomas, probably around 1970, and since then malware are evolving continuously to evade the known detection techniques from early-day signature-based to the date machine learning methods. The complexity of the malware is continuously growing using sophisticated obfuscation techniques not only to attack individual computational devices but also for the military espionage, to disrupt industries, ransomware, etc. Thus researchers are motivated to find an effective anti-malware to detect the known as well as new or previously unseen malware. Hence, time-to-time to defend the attacks/threats from the advanced malware, a number of static and dynamic methods are proposed by the researchers. Therefore, in order to understand various techniques proposed/used for the detection of new or previously unseen Windows Desktops malware in this paper, we present the survey conducted by us on the work done by the researchers in this field.

In the fast-growing smart devices, Android is the most popular OS, and due to its attractive features, mobility, ease of use, these devices hold sensitive information such as personal data, browsing history, shopping history, financial details, etc. Therefore, any security gap in these devices means that the information stored or accessing the smart devices are at high risk of being breached by the malware. These malware are continuously growing and are also used for military espionage, disrupting the industry, power grids, etc. To detect these malware, traditional signature matching techniques are widely used. However, such strategies are not capable to detect the advanced Android malicious apps because malware developer uses several obfuscation techniques. Hence, researchers are continuously addressing the security issues in the Android based smart devices. Therefore, in this paper using Drebin benchmark malware dataset we experimentally demonstrate how to improve the detection accuracy by analyzing the apps after grouping the collected data based on the permissions and achieved 97.15% overall average accuracy. Our results outperform the accuracy obtained without grouping data (79.27%, 2017), Arp, et al. (94%, 2014), Annamalai et al. (84.29%, 2016), Bahman Rashidi et al. (82%, 2017)) and Ali Feizollah, et al. (95.5%, 2017). The analysis also shows that among the groups, Microphone group detection accuracy is least while Calendar group apps are detected with the highest accuracy, and with the highest accuracy, and for the best performance, one shall take 80-100 features.

This paper reports an empirical study which inspects the human segmentation process for high-resolution satellite (HRS) images in the absence of target objects. The lack of standard guidelines for eye-tracking data analysis in the above-mentioned case motivates this study. Since a careful selection of area of interest (AOI) is necessary for meaningful interpretation of gaze data, this study represents a way to draw AOIs which metrics correlate with the human verbal response. This study formulates a hypothesis for AOI drawing and updates it until the metrics of the hypothesis based AOIs matches with the verbal response. The outputs of this study suggest the grouping of objects during AOI-based analysis of gaze data and provides some rules for the same. The AOIs drawing for hypothesis validation also highlights the dominance of perceptual grouping over the functional grouping of objects. Developing an autonomous system for segmentation and reference data creation are some possible future scopes of this study.

Sterculia foetida seeds are rich in various secondary metabolites such as fatty acids, alkaloids, flavonoids, and saponins, and possess multidisciplinary anti-inflammatory, anti-microbial, anti-diabetic, and anti-obesity pharmacological activities. The present study described the phytochemical compounds along with, anti-microbial, anti-oxidant, and first time in vitro anti-osteosarcoma effects from these seeds. The seeds were collected, authenticated, dried, ground into crude powder, and the secondary metabolites extracted with ethanol. Further, gas chromatography and mass spectrophotometry (GC-MS) analysis was carried out to determine the presence of SFE phytochemical compounds. Anti-microbial activity was confirmed against selected bacterial strains by agar well diffusion and anti-oxidant activity was investigated through 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging method. The anti-proliferative and apoptotic activity was analyzed by 3-[4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide (MTT) colorimetric assay, reactive oxygen species (ROS) generation, decrease in the mitochondrial membrane potential (MMP), and nuclear fragmentation assay. The results showed that SFE contained 35 active phytochemical and the secondary metabolites, alkaloids, glycosides, flavonoids, phenols, saponins, terpenoids, and tannins, which demonstrated significant anti-microbial and anti-oxidant potential. The results for SFE anti-cancerous activity on MG-63 osteosarcoma cells were reduced cell viability, generated excessive ROS, induced nuclear fragmentation and decreased MMP in a concentration-dependent manner. It was concluded that SFE had potent anti-oxidant, anti-microbial, and anti-cancerous activity on the MG-63 cells. However, further studies are needed to validate SFE efficacy for prevention and management of osteosarcoma.

It is now recognised that one of the most far reaching developments in the management of diabetes (Diabetes mellitus) is the targeted delivery of herbal nanoparticles using nano-pumps, smart cells, nanorobots, and nanotized herbal drugs (NHDs). The design, development and targeted delivery of NHDs has now become a frontier research area. Previous research on nano-herbal medicines such as the methanolic extract of Triphala churn nanoparticle, Costus pictus D. Don (insulin plant) silver nanoparticles, and Talinum portulacifolium solid lipid nanoparticles indicated the possibility of overcoming the drawbacks of conventional management of glucose and controlling the complications of diabetes such as delayed wound healing and the side effects of synthetic drugs. Curcumin loaded poly (caprolactone) nanofibers were also found to have beneficial effects for treating diabetic ulcers; hence, the scientific data available to support the exploration of various nanotized herbal drugs or phyto-constituents which are effective in diabetes treatment and have wound healing capability. This paper focuses on the integration of nanotechnology with herbal remedies for the effective management of diabetes.

In today’s world, information is one of the most valuable assets, but there is a major threat to it by the evolving second-generation sophisticated malware, because it can enter the networks, quietly take the confidential data/information from the computational devices, and can cripple the infrastructures, etc. To detect these malware, time-to-time various techniques are proposed. These methods range from the early day signature-based detection to machine/deep learning techniques. Therefore, to understand the evolution of malware and its detection technique, this paper presents an overview of the evolution of malware and it’s detection techniques. It discusses in details the various type of second-generation malware and the popular detection techniques used to detect it, viz. signature matching, heuristic methods, normalization, and machine/deep learning techniques.