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The conversion of forest to agricultural land has been a major worldwide concern and a cause of deterioration of soil quality. The study examined the influence of land uses on the soil quality index (SQI) using minimum datasets (MDS) constructed from soil profiles through two methods namely, additive (SQIa) and weighted (SQIw). In this study, 29 geo-referenced soil profiles (116 layers) up to a depth of 150 cm were collected in Northeastern India, representing forest, jhum, jhum fallow, and agricultural land uses. Principal component analysis (PCA) with multiple correlation analysis is found to be a suitable statistical method for selecting the key soil quality indicators. From PCA, organic carbon (OC), sand, clay, available P and available S were selected as soil quality indicators in MDS. Our results showed that these MDS significantly varied within the profiles depending on the land-uses. The estimated values of SQIs showed that SQIa was higher than SQIw across the soil depths and land-uses. For the surface soil, SQIa values were in the order of forest (2.97) > jhum fallow (2.82) > agriculture (2.68) > jhum land (1.97) and for profile soil jhum fallow (2.38) > forest (2.37) > agriculture (2.33) > jhum (1.88). Similarly, SQIw followed the trend of forest > jhum fallow > agriculture > jhum for all the three studied depths. The statistical analysis showed that the surface, subsurface, and profile soil had no significant impact on SQIw for agriculture, fallow jhum, and forest. The overall data indicated that jhum land had a significantly lower SQI than other land-uses for all soil depths. From this study, the information generated can be useful to assess the soil quality in different land-uses for managing soil health in similar areas of the Northeastern region of India. The findings are also useful in the ecosystems in which soils develop in an acid edaphic environment. [ABSTRACT FROM AUTHOR]

Hardware Trojan (HT) is the most critical threat due to outsourcing of Integrated circuit designing phases. Existing machine learning-based HT detection techniques at the pre-silicon IC phase use the structural or SCOAP gate-level netlist features for detection. However, these techniques either fail to detect the always-on-Trojans or low SCOAP Trojans, thus provides large false positives/negatives. Moreover, they fail to interpret the model prediction locally due to model-specific feature importances, identify the best feature subset in large retraining rounds, and also drop some relevant features. Therefore, to tackle these limitations, this paper proposes a new technique that utilizes structural and SCOAP features to detect HT from the gate-level netlist. The proposed technique utilizes the fastest model Light Gradient Boosting that uses gradient-based one-side sampling and exclusive feature bundling to reduce the computational time. Further, a model agnostic Shapley additive explanations (SHAP) is employed to identify each feature global and local impact on model prediction, thus making the prediction transparent. Moreover, a quartile-based feature selection method is proposed, which uses SHAP to identify the optimal feature set by keeping low retraining rounds. Experimental results show that the proposed technique accurately detects always-on-Trojans and HT nets from Trust-Hub, DeTrust, DeTest and MIMIC based Trojan benchmarks. [ABSTRACT FROM AUTHOR]

Hardware Trojan (HT) intrusion at different integrated circuit (IC) phases is the most important concern for the semiconductor industries. Recently, machine learning (ML) models have been used to detect HT from the pre-silicon IC phase, which utilizes either structural or SCOAP gate level netlist features. However, the main concern is that an adversary may poison the training dataset by flipping the target labels to malign the ML model training, which further provides an incorrect prediction on the test dataset. Thus, due to the malicious training of ML models, the Trojan-inserted ICs are missed out and can easily perform their malicious activities. Hence, it is of utmost importance to scan the training dataset and identify the poisoned input samples before applying ML models for HT detection. Therefore, this paper proposes a new technique that first identifies the poisoned training samples, which consist of SCOAP features, and then detects HTs from the unseen gate-level netlist. The proposed technique employs a robust ensemble Categorical Boosting (CatBoost) model, which avoids the problem of target leakage by using the concept of ordered boosting. Further, a label flipping poisoning attack based on a stochastic hill-climbing search is proposed, which flips the labels of the handful of samples that maximizes the validation dataset loss by deteriorating the model performance. Moreover, a defense method is proposed which utilizes CatBoost object importance and k-nearest neighbor to detect malicious training samples and restore their original labels. Finally, the CatBoost model is trained on the clean dataset to detect the HT nets from the unseen gate-level netlist accurately. Experimental results shows that the proposed attack method increases the on-an-average loss up to 58 % and 54 % on Trust-Hub and DeTrust benchmarks. Whereas the proposed defense method accurately identifies the poisoned input labels from the training dataset with on-an-average 99 % accuracy on these benchmarks. [ABSTRACT FROM AUTHOR]

The current 3D object detection methods have achieved promising results for conventional tasks to detect frequently occurring objects like cars, pedestrians and cyclists. However, they require many annotated boundary boxes and class labels for training, which is very expensive and hard to obtain. Nevertheless, detecting infrequent occurring objects, such as police vehicles, is also essential for autonomous driving to be successful. Therefore, we explore the potential of few-shot learning to handle this challenge of detecting infrequent categories. The current 3D object detectors do not have the necessary architecture to support this type of learning. Thus, this paper presents a new method termed few-shot single-stage network for 3D object detection (FS-3DSSN) to predict infrequent categories of objects. FS-3DSSN uses a class-incremental few-shot learning approach to detect infrequent categories without compromising the detection accuracy of frequent categories. It consists of two modules: (i) a single-stage network architecture for 3D object detection (3DSSN) using deformable convolutions to detect small objects and (ii) a class-incremental-based meta-learning module to learn and predict infrequent class categories. 3DSSN obtained 84.53 mAP3D\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\textrm{mAP}_{\textrm{3D}}$$\end{document} on the KITTI car category and 73.4 NDS on the nuScenes dataset, outperforming previous state of the art. Further, the result of FS-3DSSN on nuScenes is also encouraging for detecting infrequent categories while maintaining accuracy in frequent classes. [ABSTRACT FROM AUTHOR]

In recent years, many sustainable farming approachbased on natural and organicinputs are gaining popularityacross the world. However, information on its impacts on soil health is quite scanty. Therefore, in present study we evaluated the effect of nature-based farming and different nutrient management practices on soil chemical, biological and crop physiological attributes after three years of adopting various nutrient management practices includingorganic farming during the kharif season of 2021 and 2022. The treatments comprised of: T1—control, T2—nutrient supplement through indigenous natural preparation, T3—organic farming, T4—integrated crop management + natural pest control, and T5—integrated crop management with chemical pesticides (ICM-P). In terms of soil nutrients status, integrated crop management) with need-based chemical pesticides had the highest available macro and micronutrients, followed by ICM with natural-basedfarming. The organic farming practice had the lowest soil pH, EC and the highest soil organic carbon. The higher soil enzymatic and biochemical activity was found under the organic farming and natural preparation. Enzymatic indices, including the biological activity index and the geometric mean of enzyme activities were greater in organic farming and natural preparation compared to control. At higher soil depth, nutrient availability and biological activity declined. Plant total chlorophyll and nitrate reductase activity found highest under ICM-P at 60 days after sowing under the ICM-P treatment; however, it declined thereafter. Soil enzymatic activity and all available nutrients are positively correlated. Integrating nutrient sources through nature-basedand organic farming can benefit soil fertility, biological activity, and overall system sustainability. [ABSTRACT FROM AUTHOR]

Amelioration of salt affected soils aims to reduce exchangeable sodium percentage (ESP), improve crop growth and yield. A field study was conducted with soybean-mustard cropping system for four consecutive years (2016–2019) to study the effect of soil amendments on crop yield, nutrient use efficiency and soil properties in degraded vertisol of North Western India. Treatments consist of various combinations of gypsum, farm yard manure (FYM) and crop residue (CR) with recommended fertilizer doses (RNPK), were laid in random block design with three replications. Application of gypsum alone and combined with organic amendments, showed pronounced decrease in ESP, slight reduction in pH, increased bioavailability of nutrients, as reflected by higher nutrient uptake and crop yields. Grain yield improvement in soybean (37–41%) and mustard (58–63%) was recorded in plots receiving RNPK + Gypsum + CR + FYM, RNPK + Gypsum + CR or FYM over only chemical fertilizer treatments. Combined application of gypsum with organic amendments increased N, P, K and S uptake by 75, 76, 84 and 114%, respectively over RNPK alone in soybean-mustard system. Highest values of apparent recovery efficiency and agronomic efficiency of N, P, K, S was registered under RNPK + Gypsum and RNPK + Gypsum + CR/FYM. Highest physiological and internal efficiency of nutrients was registered under RNPK and decreased with amendment application. This study revealed the combined use of FYM (10 t ha−1), CR (3 and 1.5 t ha−1), gypsum (2.5 t ha−1) with fertilizers as a sustainable technology for reclaiming soil sodicity, improving nutrient use efficiency and crop productivity under soybean-mustard system. [ABSTRACT FROM AUTHOR]

The current pandemic caused by a novel coronavirus (SARS‐CoV‐2) has underlined the importance of emerging diseases of zoonotic importance. Along with human beings, several species of wild and pet animals have been demonstrated to be infected by SARS‐CoV‐2, both naturally and experimentally. In addition, with constant emergence of new variants, the species susceptibility might further change which warrants intensified screening efforts. India is a vast and second most populated country, with a habitat of a very diverse range of animal species. In this study we place on record of SARS‐CoV‐2 infections in three captive Asiatic lions. Detailed genomic characterization revealed involvement of Delta mutant (Pango lineage B.1.617.2) of SARS‐CoV‐2 at two different locations. Interestingly, no other feline species enclosed in the zoo/park were found infected. The epidemiological and molecular analysis will contribute to the understanding of the emerging mutants of SARS‐CoV‐2 in wild and domestic animals. [ABSTRACT FROM AUTHOR]

Background: Information on the prevalence of infectious agents in dairy farms forms the basis for formulating a suitable control strategy; especially in endemic situations. Aims: A cross-sectional study was undertaken to determine the prevalence of six economically important bovine diseases, causing reproductive disorders including bovine abortion in organized dairy herds in India. Methods: A total of 1,075 animals (cattle and buffaloes) from 09 dairy farms were screened by ELISA tests. Results: Bovine viral diarrhoea (BVD) was the most prevalent (56.5%) disease followed by infectious bovine rhinotracheitis (IBR) (45.4%). Prevalence of Q-fever (5.4%) and neosporosis (6.1%) were less on the farms. Although 16.3% of the samples turned positive for brucellosis, the contribution of calf-hood vaccination (B. abortus S19 vaccine) to the prevalence of antibodies cannot be ruled out. The overall prevalence of bovine anaplasmosis, known to cause sporadic abortions in dairy herds, was 34.1% in the 9 farms with a prevalence of less than 20% in 5 farms. Infection of multiple abortifacient (seroprevalence to more than two pathogens) was recorded in 56.8% of animals. A very strong association was observed between BVD and brucellosis (Odds ratio 14.2; P<0.001). Further, a positive association was also seen between seroprevalence of IBR and anaplasmosis, and neosporosis and Q fever (P<0.05). Conclusion: Viral diseases were found to be more common in the dairy herds than bacterial and protozoan diseases. Increased susceptibility of IBR seropositive cows to other bacterial and viral infections was observed. [ABSTRACT FROM AUTHOR]

Mulberry (Morus indica L.) is a foliage crop cultivated extensively in the North-Eastern India under rainfed condition for rearing silkworm (Bombyx mori L.). An attempt was made through field experimentation to develop a soil-cum-nutrient management package for mulberry cultivation in acid soil under rainfed condition. Effects of different treatment combinations comprising of NPK, farmyard manure (FYM), lime and boron (B) on important soil properties, yield and quality of mulberry (Morus indica L. var. S1635) leaf were assessed to identify the best soil-cum-nutrient management package. Results indicated that fertilizer dosage based on ready-reckoner soil test value was not effective for the production of high-quality leaf with specific targeted yield of the crop (4.5 t/ha/crop-cutting). Integrated soil application of NPK @ 150:50:50 kg ha−1, FYM@10 t ha−1, lime as per requirement (3t ha−1 in present case) and foliar application of boric acid solution (0.1% w/v) was the most effective soil-cum-nutrient management package for the production of higher leaf yield which is comparable with the targeted yield. As far as leaf quality is concerned, increased moisture content, total N, P, K and B content of leaf are also attributed to the package. The package resulted in increased pH, organic carbon, dehydrogenase activity, available N, P, K and decreased exchangeable aluminum, exchangeable acidity and total potential acidity of soil. The study indicates the usefulness of soil-cum-nutrient management package for improved soil health of acid soils as well as achieving high yield and quality of mulberry leaf in such acid soils of the North-Eastern India and elsewhere. [ABSTRACT FROM AUTHOR]