Limosilactobacillus fermentum IKP 111 reduces pathogen load and improves immunity of broilers when challenged with Salmonella enteritidis.

In this study, we checked the effectiveness of L. fermentum IKP 111 in treating S. enteritidis infection in an in vivo study. Its oral administration to broiler chicks significantly reduced the colonization of S. enteritidis in the gut and there was a lower bacterial count of S. enteritidis in the droppings after infection. The administration of the probiotic L. fermentum IKP 111 also led to increase in weight gain in the broiler chicks as well as their immunomodulation against avian influenza virus (AIV) and Newcastle disease virus (NDV) as compared to the chicks challenged only with S. enteritidis. Our study provides evidence that the probiotic strain L. fermentum IKP 111 could be an alternate for controlling S. enteritidis infection while enhancing the gut health as well as the immune response of broiler chickens against viral infections.

Towards deep learning based smart farming for intelligent weeds management in crops.

Introduction: Deep learning (DL) is a core constituent for building an object detection system and provides a variety of algorithms to be used in a variety of applications. In agriculture, weed management is one of the major concerns, weed detection systems could be of great help to improve production. In this work, we have proposed a DL-based weed detection model that can efficiently be used for effective weed management in crops. Methods: Our proposed model uses Convolutional Neural Network based object detection system You Only Look Once (YOLO) for training and prediction. The collected dataset contains RGB images of four different weed species named Grass, Creeping Thistle, Bindweed, and California poppy. This dataset is manipulated by applying LAB (Lightness A and B) and HSV (Hue, Saturation, Value) image transformation techniques and then trained on four YOLO models (v3, v3-tiny, v4, v4-tiny). Results and discussion: The effects of image transformation are analyzed, and it is deduced that the model performance is not much affected by this transformation. Inferencing results obtained by making a comparison of correctly predicted weeds are quite promising, among all models implemented in this work, the YOLOv4 model has achieved the highest accuracy. It has correctly predicted 98.88% weeds with an average loss of 1.8 and 73.1% mean average precision value. Future work: In the future, we plan to integrate this model in a variable rate sprayer for precise weed management in real time.

On Zagreb coindices and Mostar index of [Formula: see text] nanotubes.

Topological indices are valuable tools in predicting properties of chemical compounds. This study focuses on degree-based topological indices, which have shown strong correlations with various physico-chemical properties such as boiling points and strain energy. Specifically, we applied these indices to titania nanotubes [Formula: see text] and explored the vertex and edge versions of the Mostar index. These findings provide insights into the properties of [Formula: see text] nanotubes and contribute to the development of topological indices for predicting the behavior of other chemical compounds.

A mobile-based system for maize plant leaf disease detection and classification using deep learning.

Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.

Thermodynamic Analysis of Group-III-Nitride Alloying with Yttrium by Hybrid Chemical Vapor Deposition.

Group-IIIb-transition-metal-alloyed wurtzite Group-IIIa-nitride (IIIb-IIIa-N) thin films have higher piezoelectric characteristics than binary IIIa-N for a broad range of applications in photonic, electronic, sensing, and energy harvesting systems. We perform theoretical thermodynamic analysis for the deposition and epitaxial growth of Y-alloyed GaN and AlN films by a newly introduced growth technique of hybrid chemical vapor deposition (HybCVD), which can overcome the limitations of the conventional techniques. We investigate the equilibrium vapor pressures in the source zones to determine the dominant precursors of cations for the input of the mixing zone. Then, we study the driving force for the vapor-solid phase reactions of cation precursors in the growth zone to calculate the relationship between the solid composition of YxGa1-xN and YxAl1-xN and the relative amount of input precursors (Y vs. GaCl and AlCl3) in different deposition conditions, such as temperature, V/III precursor input ratio, and H2/inert-gas mixture ratio in the carrier gas. The xY composition in YAlN changes nearly linearly with the input ratio of cation precursors regardless of the growth conditions. However, YGaN composition changes non-linearly and is also substantially affected by the conditions. The thermodynamic analysis provides insight into the chemistry involved in the epitaxial growth of IIIa-IIIb-N by the HybCVD, as well as the information for suitable growth conditions, which will guide the way for ongoing experimental efforts on the improvement of piezoelectricity of the lead-free piezoelectric materials.

Synthesis of nanomaterials using various top-down and bottom-up approaches, influencing factors, advantages, and disadvantages: A review.

Nanotechnology is one of the emerging fields of the 21st Century. Many new devices and patentable technology is based on nanomaterials (NMs). One of the dominant factors in the use of nanomaterials and their applications in various fields is the synthesis and growth mechanism of nanostructures and nanomaterials. A nanostructured material may have been a good candidate in one application but could be more useful in a different application if synthesized by a different mechanism and technique. Similarly, the structure and morphology of a nanomaterial also depend upon the method of growth and synthesis. For example, it is easy to grow and synthesize amorphous nanostructured thin film using the plasma magnetron sputtering technique, but it may be difficult to obtain a similar structure using the thermal evaporation process due to the nature of the technique itself. In this study, the Top-down and Bottom-up methods and techniques of synthesizing nanostructured materials are reviewed, compared, and analyzed. Both approaches are critically analyzed, and the influencing factors on the synthesis of different nanomaterials, the advantages, and disadvantages of each technique are reported. This review also provides a step-by-step analysis of the choice of method for the synthesis of namomaterials for specific applications.

Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic.

The education system worldwide has been affected by the Corona Virus Diseases 2019 (COVID-19) pandemic, resulting in the interruption of all educational institutions. Moreover, as a precautionary measure, the lockdown has been imposed that has severely affected the learning processes, especially assessment activities, including exams and viva. In such challenging situations, E-learning platforms could play a vital role in conducting seamless academic activities. In spite of all the advantages of remote learning systems, many hurdles and obstacles, like a selection of suitable learning resources/material encounter by individual users based on their interests or requirements. Especially those who are not well familiar with the internet technology in developing countries and are in need of a platform that could help them in resolving the issues related to the online virtual environment. Therefore, in this work, we have proposed a mechanism that intelligently and correctly predicts the appropriate preferences for the selection of resources relevant to a specific user by considering the capabilities of diverse perspectives users to provide quality online education and to make work from home policy more effective and progressive during the pandemic. The proposed system helps teachers in providing quality online education, familiarizing them with advanced technology in the online environment. It also semantically predicts the preferences for virtual assistance of those users who are in need of learning the new tools and technologies in short time as per their institutional requirements in order to meet the quality standards of online education. The experimental and statistical results have demonstrated that the proposed virtual personalized preferences system has improved overall academic activities as compared to the current method. The proposed system enhanced user's learning abilities and facilitated them in selecting short courses while using different online education tools adopted/suggested by the institutions to conduct online classes/seminars/webinars etc., as compared to the conventional classes/activities.

Physiological insights into sulfate and selenium interaction to improve drought tolerance in mung bean.

The present study involved two pot experiments to investigate the response of mung bean to the individual or combined SO4 2- and selenate application under drought stress. A marked increment in biomass and NPK accumulation was recorded in mung bean seedlings fertilized with various SO4 2- sources, except for CuSO4. Compared to other SO4 2- fertilizers, ZnSO4 application resulted in the highest increase in growth attributes and shoot nutrient content. Further, the combined S and Se application (S + Se) significantly enhanced relative water content (16%), SPAD value (72%), photosynthetic rate (80%) and activities of catalase (79%), guaiacol peroxidase (53%) and superoxide dismutase (58%) in the leaves of water-stressed mung bean plants. Consequently, the grain yield of mung bean was markedly increased by 105% under water stress conditions. Furthermore, S + Se application considerably increased the concentrations of P (47%), K (75%), S (80%), Zn (160%), and Fe (15%) in mung bean seeds under drought stress conditions. These findings indicate that S + Se application potentially increases the nutritional quality of grain legumes by stimulating photosynthetic apparatus and antioxidative machinery under water deficit conditions. Our results could provide the basis for further experiments on cross-talk between S and Se regulatory pathways to improve the nutritional quality of food crops. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-021-00992-6.

Sulfate-mediated Drought Tolerance in Maize Involves Regulation at Physiological and Biochemical Levels.

Restriction in nutrient acquisition is one of the primary causes for reduced growth and yield in water deficient soils. Sulfur (S) is an important secondary macronutrient that interacts with several stress metabolites to improve performance of food crops under various environmental stresses including drought. Increased S supply influences uptake and distribution of essential nutrients to confer nutritional homeostasis in plants exposed to limited water conditions. The regulation of S metabolism in plants, resulting in synthesis of numerous S-containing compounds, is crucial to the acclimation response to drought stress. Two different experiments were laid out in semi-controlled conditions to investigate the effects of different S sources on physiological and biochemical mechanisms of maize (Zea mays L. cv. P1574). Initially, the rate of S application in maize was optimized in terms of improved biomass and nutrient uptake. The maize seedlings were grown in sandy loam soil fertigated with various doses (0, 15, 30 and 45 kg ha-1) of different S fertilizers viz. K2SO4, FeSO4, CuSO4 and Na2SO4. The optimized S dose of each fertilizer was later tested in second experiment to determine its role in improving drought tolerance of maize plants. A marked effect of S fertilization was observed on biomass accumulation and nutrients uptake in maize. In addition, the optimized doses significantly increased the gas exchange characteristics and activity of antioxidant enzymes to improve yield of maize. Among various S sources, application of K2SO4 resulted in maximum photosynthetic rate (43%), stomatal conductance (98%), transpiration rate (61%) and sub-stomatal conductance (127%) compared to no S supply. Moreover, it also increased catalase, guaiacol peroxidase and superoxide dismutase activities by 55, 87 and 65%, respectively that ultimately improved maize yield by 33% with respect to control under water deficit conditions. These results highlight the importance of S fertilizers that would likely be helpful for farmers to get better yield in water deficient soils.

Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs.

Road transportation is the backbone of modern economies, albeit it annually costs 1.25 million deaths and trillions of dollars to the global economy, and damages public health and the environment. Deep learning is among the leading-edge methods used for transportation-related predictions, however, the existing works are in their infancy, and fall short in multiple respects, including the use of datasets with limited sizes and scopes, and insufficient depth of the deep learning studies. This paper provides a novel and comprehensive approach toward large-scale, faster, and real-time traffic prediction by bringing four complementary cutting-edge technologies together: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). We trained deep networks using over 11 years of data provided by the California Department of Transportation (Caltrans), the largest dataset that has been used in deep learning studies. Several combinations of the input attributes of the data along with various network configurations of the deep learning models were investigated for training and prediction purposes. The use of the pre-trained model for real-time prediction was explored. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for smart cities, big data, high performance computing, and their convergence.