Traditionally, rice leaf disease identification relies on a visual examination of abnormalities or an analytical result obtained by growing bacteria in the research lab. This method of visual evaluation is qualitative and error-prone. On the other hand, an artificial neural network system is fast and more accurate. Several pieces of research using traditional machine learning and deep convolution neural networks (CNN) have been utilized to overcome the issues. Still, these methods need more semantic contextual global and local feature extraction. Due to this, efficiency is less. Hence, in the present study, a multi-scale feature fusion-based RDTNet has been designed. The RDTNet contains two modules, and the first module extracts feature via three scales from the local binary pattern (LBP), gray, and a histogram of orient gradient (HOG) image. The second module extracts semantic global and local features through the transformer and convolution block. Furthermore, the computing cost is reduced by dividing the query into two parts and feeding them to convolution and the transformer block. The results indicate that the proposed method has a very high average precision, f1-score, and accuracy of 99.55%, 99.54%, and 99.53%, respectively. It is suggestive of improved classification accuracy using multi-scale features and the transformer. The model has also been validated on other datasets confirming that the present model can be used for real-time rice disease diagnosis. In the future, such models can be used for monitoring other crops, including wheat, tomato, and potato.

Leukemia is a cancer of white blood cells characterized by immature lymphocytes. Due to blood cancer, many people die every year. Hence, the early detection of these blast cells is necessary for avoiding blood cancer. A novel deep convolutional neural network (CNN) 3SNet that has depth-wise convolution blocks to reduce the computation costs has been developed to aid the diagnosis of leukemia cells. The proposed method includes three inputs to the deep CNN model. These inputs are grayscale and their corresponding histogram of gradient (HOG) and local binary pattern (LBP) images. The HOG image finds the local shape, and the LBP image describes the leukaemia cell's texture pattern. The suggested model was trained and tested with images from the AML-Cytomorphology_LMU dataset. The mean average precision (MAP) for the cell with less than 100 images in the dataset was 84%, whereas for cells with more than 100 images in the dataset was 93.83%. In addition, the ROC curve area for these cells is more than 98%. This confirmed proposed model could be an adjunct tool to provide a second opinion to a doctor.

In recent years deep learning (DL) models have obtained great success in hyperspectral image classification (HSIC) with commendable performance and especially convolutional neural networks (CNNs) have attracted huge attention due to the exceptional performance demonstrated in this area. However, most of the CNN-based models have to suffer with low classification performance due to non-availability of abundant training samples. Recently, hybrid-CNN models, utilizing both spectral and spatial features unitedly, have exhibited remarkable classification accuracy. Yet, the hybrid-CNN models have been adopted in very limited research works due to a very high computational complexity. Furthermore, huge dimensional HSIs contain highly correlated but irrelevant bands. The selection of most relevant spectral bands affects the performance and computational complexity of HSIC models very profoundly. To address these issues, the authors have proposed a binary grey wolf optimization-based hybrid CNN (BGWOHCNN) framework for HSIC, in this paper. In the hybrid framework, a 3-D CNN has been employed to exploit spatial and spectral features jointly along with a 2-D CNN utilizing the spatial features and reducing the computational complexity of the overall design. In addition, binary grey wolf optimization (BGWO) technique has been adapted to select the most relevant spectral bands for HSIC. As per the best of authors’ knowledge and belief, the BGWO technique has been investigated for the first time with hybrid CNN for HSIC in the current work. Further, the experiments are conducted to investigate the performance and superiority of the proposed framework over three very popular datasets viz. Salinas, Indian Pines and Pavia University. The obtained results are compared with the six state-of-art DL models, in terms of average accuracy, overall accuracy and Cohen’s kappa coefficient, to show that the proposed framework provides very promising results to HSIC tasks.

In past few years, hyperspectral image classification (HSIC) has been one of the most sparkling fields of research in the area of remote sensing. The presence of very complex characteristics and nonlinearity in hyperspectral images (HSIs), makes the classification task very crucial. Recently, capsule networks (CapsNets) have drawn huge attention in HSIC and demonstrated remarkable performance with transcendent classification accuracy. However, the availability of very limited training samples makes the HSIC task more challenging for existing CapsNet-based models. Also, the utilization of spectral–spatial features efficiently, is considered to be very important in improving the classification performance. To address these issues, the authors have proposed a spectral–spatial three-dimensional convolutional capsule (SS-3D-ConvCapsule) network model in this article. In the proposed work, a three-dimensional convolutional capsule layer based upon a three-dimensional dynamic routing algorithm is utilized to exploit spectral–spatial features for the classification task. Furthermore, the principal component analysis (PCA), as preprocessing technique, is utilized for dimensionality reduction. Moreover, a very limited number of trainable parameters are utilized to train the SS-3D-ConvCapsule network model in order to avoid network design complexities and overfitting problem. Furthermore, the experiments are conducted over three well known HSI datasets viz. Pavia University, Salinas and Indian Pines to investigate the performance of proposed network along with eight state-of-art deep learning models. The experimental results are compared in terms of kappa coefficient, overall accuracy and average accuracy. The comparison reveals that the proposed model has clearly outperformed all of the state-of-art models in terms of classification accuracy.

Paddy is a very important crop in Asian agriculture. Rice is a staple food in Asian countries because most people rely on it for their food. Paddy will be commonly affected by many diseases like Brown spot, Leaf blast, Bacterial blight Leaf smut, etc. Because there are few professionals available, identifying paddy leaf diseases takes a long time. Even though experts are accessible in some sectors, the naked sight might lead to inappropriate acts. So, by using a CNN algorithm this research study attempts to detect which disease is effective and also suggest the proper treatment to cure that disease so that it can help to take appropriate actions within the time. For the data present in the paddy leaf disease, features are extracted and classified by using ResNet 50 and VGG16 model. By comparing both models which give high accuracy, the model will be selected for detecting the paddy leaf disease.

In this paper, the physical properties of ZnGeN2 and GaN compound semiconductors are calculated under different pressures using the density functional theory calculations. The lattice parameters and energy bandgap are studied at ambient conditions. Further, the energy bandgap of ZnGeN2 and GaN under different pressures has been calculated. The bandgap values show that ZnGeN2 is a direct bandgap up to 100 GPa and becomes an indirect bandgap at 110 GPa. GaN is a direct bandgap up to 150 GPa and turns out to be an indirect bandgap semiconductor at 160 GPa. The elastic parameters, i.e., elastic stiffness coefficients, have been estimated in the range of 0−190 GPa pressures. Results show that ZnGeN2 and GaN are stable up to 180 and 150 GPa pressures, respectively. Comparative results show that the physical properties of ZnGeN2 have a resemblance with GaN up to 100 GPa pressure and can be a potential candidate in place of GaN in various technological applications.

Single point incremental forming is an emerging die-less forming technique that has been evolved in the beginning of current century. The relevance and suitability of this process have been proved for various applications to fill the needs of customized production. This technique can trigger the revolution in the field of sheet material forming as it exempts the use of costly dies and punches that is otherwise essential part of conventional forming techniques. The prediction and measurement of forming forces during SPIF process determine the size of forming machinery and additional hardware along with preventing the failures of facilities. In this work, maximal axial forming forces have been investigated under the effects of interactions of significant input variables like step size, wall angle and spindle speed. Results showed that components could be formed with minimal axial peak force (863 N, in this case) when the combination of lower step size (0.2 mm) and lower wall angle (60°) was employed. On the other hand, very high axial peak force was obtained (1458 N, in this case) when the combination of higher step size (1.2 mm) and higher wall angle (68°) was employed. Moreover, employment of higher wall angle and step size resulted in the fracture of sheet material well before achieving the designed height of conical frustums.

Hyperspectral image (HSI) classification is one of the important topic in the field of remote sensing. In general, HSI has to deal with complex characteristics and nonlinearity among the hyperspectral data which makes the classification task very challenging for traditional machine learning (ML) models. Recently, deep learning (DL) models have been very widely used in the classification of HSIs because of their capability to deal with complexity and nonlinearity in data. The utilization of deep learning models has been very successful and demonstrated good performance in the classification of HSIs. This paper presents a comprehensive review of deep learning models utilized in HSI classification literature and a comparison of various deep learning strategies for this topic. Precisely, the authors have categorized the literature review based upon the utilization of five most popular deep learning models and summarized their main methodologies used in feature extraction. This work may provide useful guidelines for the future research work in this area.

A two years study (2012-2014) on conservation agriculture was conducted at the research farm, FSR, Centre, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Main Campus, Chatha, Jammu, India. The experiment was laid out in split- plot design with two crop establishment methods (Minimum / Zero tillage and conventional tillage) and three cropping systems (Rice-Wheat, Rice-Marigold-French bean and Maize + soybean -Wheat) and two fertilizer rates (Rec. Dose of Fertilizer and 75% RDF + 25%N through FYM) with and without mulching in sub-plots under clay loam soil having alkaline in reaction (pH-8.1), medium in soil organic carbon( 0.55%) available P( 19.20Kgha-1& K(122.0Kgha-1 ) and low in available N(221.12 Kgha-1 ) with three replication. Among system based performance, Rice (Oryza sativa)-Marigold (Tagetes erecta)-Frenchbean (Phaselous vulgaris) recorded higher rice equivalent yield (REY) of (22.4 t/ha-1 and 19.48t/ha-1 in 1st and 2nd year of study). The maximum net return of Rs. 240372 ha-1 and Rs. 239015 ha-1 was recorded under Rice- Marigold- French bean cropping system with B:C ratio of 2.18 and 2.03 in 1st and 2nd year of study. Soil organic carbon content showed 6-9%& 9-11% enhancement over initial value in integrated nutrient management treatment where 75% RDF + 25% N through FYM was applied and in mulched treatment during first and second year respectively. Application of 75% RDF + 25% N through FYM and mulching with paddy straw @ 5t/ha also contributed a positive improvement in soil bulk density. The available NPK was slightly build up (4-12%, 14-30%, 10-14%) in all the treatments except rice –wheat cropping system (varied -1.36 to 22% N, 0.52 to 1.4%% and 1.64 to 4.9% K, respectively). However, NPK uptake (132.85, 45.47, 183.78 kgha-1) was recorded maximum in Rice-Marigold – French bean cropping system due to 300% cropping intensity of the system during both the years. With regards to microbial population, the maximum population of bacteria, fungi and actinomycetes was recorded in minimum tillage over conventional tillage. However maximum population of bacteria (17.56 x 106 CFU/g soil), fungi (22.72 x 103 CFU/g soil) and Actinomycetes (21.25 x 104 CFU/g) was recorded in second year of study. However, among the different cropping systems maize + soybean – wheat recorded more number of bacteria and fungi in first year and second year, while actinomycetes was recorded maximum in Rice-marigold-French bean, respectively. Moreover mulched and INM treatment plots also recorded higher microbial count during both the years.Based on the two years of investigation it can be concluded that application of paddy straw as mulch @ 5 ton ha-1 during Rabi season with INM under conventional method of sowing to Rice-Marigold-French bean yielded maximum REY of 21.09 t/ha-1with net returns (Rs 239693ha-1) and B:C ratio of 2.10.

AbstractEarly cases of ankylosing spondylitis do not have the typical presentation of radiographic sacroiliitis but present as non-radiographic axial spondyloarthritis (nr-axSpA) where both are varieties of axSpA. Homoeopathic prescription based on totality of symptoms offers a promising relief to nr-axSpA where peripheral joints are also affected. Here, a 27-year-old male patient presented with bilateral heel pain, pain in low back, pain on base of right toes, pain in neck with stiffness for 1 year had refractive response to conventional medication. Diagnosis was confirmed by Assessment of SpondyloArthritis International Society diagnostic criteria for axSpA. Single medicine and minimum dose of Silicea showed its effectiveness on the symptoms' improvement and Bath Ankylosing Spondylitis Disease Activity Index score whereby score of 8.8 (active disease) before treatment changed to 1.2 (inactive or mild disease) after treatment. Various other parameters were assessed accordingly before and after treatment. This case report encourages further exploring the beneficial effects of homoeopathic treatment in clinical condition like axSpA utilising validated scale.

AbstractThe Human Microbiome Project (HMP) launched in 2008 by the National Institute of Health (NIH) fascinated microbiologists with discoveries of micro-organisms inside and outside of human beings. Their correlation with health and disease brings a new insight to preventive and therapeutic measures. At present, focus is more on the micro-organisms residing in the gut and various factors capable of altering their composition. The conclusion made by Dr. Edward Bach regarding the ability of homoeopathic potencies to alter bowel flora and its relation with chronic diseases was investigated and experimented way back. The present review attempts to correlate gut microbiota with the art and science of homoeopathy.