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Background: Briquettes is more efficient than conventionally applied nutrients as it reduce the loss of nutrients by obtaining higher yield and decreased the expenditure cost of fertilizers. Indian soils are 49 per cent deficient in zinc which is characterized by widespread Zn deficiency in human beings. Methods: Three different zinc fortified briquettes viz., UB-DAP, UB-suphala and UB-KAB were tested in field experiments during kharif season of 2016-19 to compare and calculate variations in yield, yield attributing characters and nutrients uptake by rice (Oryza sativa L.). Result: The results showed that treatment UB-KAB fortified with 10 kg ZnSO4.7H2O ha-1 (T8) registered higher growth, yield parameters and Zn concentration in grain indicating zinc-sulfate-heptahydrate (ZSHH) offers vital solution to curtail Zn malnutrition. Further, UB-KAB fortified through ZSHH (T8) significantly enhanced the grain (55.2 q ha-1) and straw (63.6 q ha-1) yield of rice over RDF and absolute control. Application of UB-KAB fortified with 10 kg ZnSO4.7H2O ha-1 (T8) significantly increased nutrient uptake of N (97.2 kg ha-1), P (20.2 kg ha-1), K (95.5 kg ha-1), Zn (262.4 g ha-1) and S (53.9 kg ha-1) over fertilizer control (75% RDF) and absolute control. The applications of KAB fortified with zinc sulphate assume great significance in improving rice productivity. [ABSTRACT FROM AUTHOR]

Terminalia bellirica is one of the high traded medicinal plant species, mainly known for its fruits which are bestowed with numerous biological activities and used in treatment of various ailments. The fruits are one of three ingredients of well-known Ayurvedic formulation 'TRIPHLA'. The purpose of this study was to determine the variations for Gallic acid (GA), a chemical marker compound, as well as for key morphological traits (height, girth at breast height, clear bole height, fruit size) in trees from central Indian states viz. Madhya Pradesh, Chhattisgarh and Maharashtra. In this investigation, we also explored the correlation between the chemical marker and morphological features. The study suggested the maximum GA content (0.98±0.42%) in populations of Keregaon range of Dhamtari forest division of Chhattisgarh state which can be considered as superior chemotypes/populations. The correlation analysis exhibited the positive association between fruit size and GA content. [ABSTRACT FROM AUTHOR]

This paper is concerned with transient thermoelastic problem in which we need to determine the temperature distribution, displacement function and thermal stresses of a semi- infinite rectangular beam when the boundary conditions are known. Integral transform techniques are used to obtain the solution of the problem. [ABSTRACT FROM AUTHOR]

This paper is concerned with steady-state as well as transient thermoelastic problem in which we need to determine the temperature distribution, displacement function and thermal stresses of a thin rectangular plate when the boundary conditions are known. Integral transform techniques are used to obtain the solution of the problem. [ABSTRACT FROM AUTHOR]

In this paper, an alternative approach to the Simplex method for game theory problem is suggested. Here we proposed a new approach based on the iterative procedure for the solution of a game theory problem by alternative simplex method. The method sometimes involves less or at the most an equal number of iteration as compared to computational procedure for solving NLPP. We observed that the rule of selecting pivot vector at initial stage and thereby for some NLPP it takes more number of iteration to achieve optimality. Here at the initial step we choose the pivot vector on the basis of new rules described below. This powerful technique is better understood by resolving a cycling problem. [ABSTRACT FROM AUTHOR]

This paper is concerned with transient thermoelastic problem in which we need to determine the temperature distribution, displacement function and thermal stresses of a thin annular disc due to partially distributed heat supply, when the boundary conditions are known. Integral transform techniques are used to obtain the solution of the problem. . [ABSTRACT FROM AUTHOR]

In this paper, an attempt has been made to study thermoelastic response of a direct thermoelastic problem of a hollow cylinder occupying the space D = {(x, y, z) ∈ R³: a ≤ (x² + y²)1/2 ≤ b, 0 ≤ z ≤ h} with radiation type boundary conditions. We apply integral transform technique to find the thermoelastic solution. [ABSTRACT FROM AUTHOR]

Public key cryptography is one of the most important fields in computer security. . It is a method of transferring private information and data through open network communication, so only the receiver who has the secret key can read the encrypted messages which might be documents, phone conversations, images or other form of data. To implement privacy simply by encrypting the information intended to remain secret can be achieved by using methods of Public key cryptography. The information must be scrambled, so that other users will not be able to access the actual information. In this study, we propose new public key cryptographic technique based on Suzuki 2- group. We demonstrated the security of proposed public key cryptographic technique in the chosen plaintext attack in the random oracle model. [ABSTRACT FROM AUTHOR]

Background: Despite the poor outcomes related to the presence of pulmonary hypertension, it often goes undiagnosed in part because of low suspicion and screening tools not being easily accessible such as echocardiography. A new readily available screening tool to identify elevated pulmonary artery systolic pressures is needed to help with the prognosis and timely treatment of underlying causes such as heart failure or pulmonary vascular remodeling. We developed a deep learning-based method that uses phonocardiograms (PCGs) for the detection of elevated pulmonary artery systolic pressure, an indicator of pulmonary hypertension., Methods: Approximately 6000 PCG recordings with the corresponding echocardiogram-based estimated pulmonary artery systolic pressure values, as well as ≈169 000 PCG recordings without associated echocardiograms, were used for training a deep convolutional network to detect pulmonary artery systolic pressures ≥40 mm Hg in a semisupervised manner. Each 15-second PCG, recorded using a digital stethoscope, was processed to generate 5-second mel-spectrograms. An additional labeled data set of 196 patients was used for testing. GradCAM++ was used to visualize high importance segments contributing to the network decision., Results: An average area under the receiver operator characteristic curve of 0.79 was obtained across 5 cross-validation folds. The testing data set gave a sensitivity of 0.71 and a specificity of 0.73, with pulmonic and left subclavicular locations having higher sensitivities. GradCAM++ technique highlighted physiologically meaningful PCG segments in example pulmonary hypertension recordings., Conclusions: We demonstrated the feasibility of using digital stethoscopes in conjunction with deep learning algorithms as a low-cost, noninvasive, and easily accessible screening tool for early detection of pulmonary hypertension.

Background: In closed-loop on-demand control (ODC) of deep brain stimulation (DBS), stimulation is applied only when symptoms appear. Following stimulation of a fixed duration, DBS is switched off until the symptoms reappear. By repeating these demand-driven cycles, the amount of stimulation delivered can be decreased, thereby reducing DBS side-effects and improving battery-life of the pulse-generator. This article introduces R o metric for quantification of degree of benefit of ODC and explores candidate selection in tremor-dominant Parkinson's disease (PD)., Method: The study was performed on nine PD patients previously implanted with Medtronic DBS systems. Accelerometer sensor was placed on the tremor-dominant hand to detect onset of tremor. Fixed duration of stimulation (DS) of 20-80 sec was applied. Once the tremor was observed, stimulation was switched on. These trials were repeated during resting, postural, and kinetic conditions. R o metric was calculated as the ratio of stimulation-off tremor-free period to the DS. R o calculated at different DS were compared for each patient., Results: We found that for each patient, R o varied with DS and an optimal DS * gave a higher percentage of stimulation-off time. Average R o at DS * varied from 0.554 to 4.24 for eight patients giving 35%-80% stimulation-off time., Conclusions: R o values can be used for selection of optimal DS * in ODC. Three of nine patients were found to be tremor-free without stimulation for >50% of total time with even up to 80% in one patient. Patients with low R o may not benefit from ODC in DBS, where the trade-off between having side-effects and using ODC system will need to be assessed., (© 2018 International Neuromodulation Society.)

The results presented in this paper indicate that future on-demand Deep Brain Stimulation (DBS) systems for chronic use in patients with movement disorders should continuously and adaptively "learn" in order to maintain high symptom control efficacy. In this work, two machine learning algorithms-Decision Tree and LArge Memory STorage And Retrieval (LAMSTAR) neural network, both with surface Electromyography and accelerometry as control signals-are used to predict onset of tremor after DBS has been switched off in two patients, one suffering from Parkinson's disease and the other from essential tremor. The novelty of this work is that training and testing are done by using different data recorded during sessions at least one week apart. The question is whether the applied algorithms are robust to long-term operation (as patient's control signal may change over time due to disease progression, displacement of the wearable sensor, etc.). Various metrics are used to compare the performance of the proposed approach to those available in the literature, where training and testing are done on data from the same recording session. It is shown that a 100% sensitivity is achieved for training and testing over the same session; however, the sensitivity reduces when tested over a different session. The ratio of predicted stimulation-off time to observed stimulation-off time value is also found to be lower when training and testing on data from separate sessions. These results point to the need of adaptive learning in on-demand DBS systems.

This paper describes the application of the LAMSTAR (LArge Memory STorage and Retrieval) neural network for prediction of onset of tremor in Parkinson's disease (PD) patients to allow for on-off adaptive control of Deep Brain Stimulation (DBS). Currently, the therapeutic treatment of PD by DBS is an open-loop system where continuous stimulation is applied to a target area in the brain. This work demonstrates a fully automated closed-loop DBS system so that stimulation can be applied on-demand only when needed to treat PD symptoms. The proposed LAMSTAR network uses spectral, entropy and recurrence rate parameters for prediction of the advent of tremor after the DBS stimulation is switched off. These parameters are extracted from non-invasively collected surface electromyography and accelerometry signals. The LAMSTAR network has useful characteristics, such as fast retrieval of patterns and ability to handle large amount of data of different types, which make it attractive for medical applications. Out of 21 trials blue from one subject, the average ratio of delay in prediction of tremor to the actual delay in observed tremor from the time stimulation was switched off achieved by the proposed LAMSTAR network is 0.77. Moreover, sensitivity of 100% and overall performance better than previously proposed Back Propagation neural networks is obtained.