Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail

We are motivated by the problem of impromptu or as- you-go deployment of wireless sensor networks. As an application example, a person, starting from a sink node, walks along a forest trail, makes link quality measurements (with the previously placed nodes) at equally spaced locations, and deploys relays at some of these locations, so as to connect a sensor placed at some a priori unknown point on the trail with the sink node. In this paper, we report our experimental experiences with some as-you-go deployment algorithms. Two algorithms are based on Markov decision process (MDP) formulations; these require a radio propagation model. We also study purely measurement based strategies: one heuristic that is motivated by our MDP formulations, one asymptotically optimal learning algorithm, and one inspired by a popular heuristic. We extract a statistical model of the propagation along a forest trail from raw measurement data, implement the algorithms experimentally in the forest, and compare them. The results provide useful insights regarding the choice of the deployment algorithm and its parameters, and also demonstrate the necessity of a proper theoretical formulation.Comment: 7 pages, accepted in IEEE MASS 201

QoS Constrained Optimal Sink and Relay Placement in Planned Wireless Sensor Networks

We are given a set of sensors at given locations, a set of potential locations for placing base stations (BSs, or sinks), and another set of potential locations for placing wireless relay nodes. There is a cost for placing a BS and a cost for placing a relay. The problem we consider is to select a set of BS locations, a set of relay locations, and an association of sensor nodes with the selected BS locations, so that number of hops in the path from each sensor to its BS is bounded by hmax, and among all such feasible networks, the cost of the selected network is the minimum. The hop count bound suffices to ensure a certain probability of the data being delivered to the BS within a given maximum delay under a light traffic model. We observe that the problem is NP-Hard, and is hard to even approximate within a constant factor. For this problem, we propose a polynomial time approximation algorithm (SmartSelect) based on a relay placement algorithm proposed in our earlier work, along with a modification of the greedy algorithm for weighted set cover. We have analyzed the worst case approximation guarantee for this algorithm. We have also proposed a polynomial time heuristic to improve upon the solution provided by SmartSelect. Our numerical results demonstrate that the algorithms provide good quality solutions using very little computation time in various randomly generated network scenarios

A new synthetic approach to 8-aza analogs of prostaglandins

A new synthetic approach to (dl)-8-aza-13,14-dihydroprostanoic acid and its corresponding ll-hydroxy derivative is described

SUPPLEMENTATION OF Α-LIPOIC ACID IN DIABETIC PERIPHERAL NEUROPATHY: A PROSPECTIVE OPEN LABEL RANDOMIZED CONTROLLED TRIAL

Abstract Objective: Diabetic peripheral neuropathy is the most common long term complications associated with reduced nerve conduction and blood flow. The present study was designed to investigate the effect of oral supplementation of α-lipoic acid (600 mg/day) on peripheral, sensory and motor nerve conduction and glycaemic control in type 2 diabetes mellitus with peripheral neuropathy. Methods: A total of 20 patients were enrolled in this study, then randomly allocated to two groups control (n=10) and intervention group (n=10). Patients in control group received only oral hypoglycaemic treatment and in intervention group received α-lipoic acid (600 mg/day) oral supplementation along with their oral hypoglycaemic treatment for a period of 3 months. Nerve conduction and glycaemic control were measured at the base line and at the end of 3 months by using specific methods. Results: In intervention group α-lipoic acid supplementation significantly improves 6 of 15 electrophysiological parameters of nerve conduction. Distal latency of peroneal (mean ± SD 5.13 ± 0.52 vs 4.92±0.55; p<0.02), median (mean ± SD 3.66 ± 0.76 vs 3.53±0.63; p<0.03) & ulnar motor nerves (mean ± SD 2.91 ± 0.32 vs 2.82±0.36; p<0.01), and Nerve Conduction Velocity of peroneal (mean ± SD 42.0 ± 3.07 vs 43.4±2.13; p<0.03), median (mean ± SD 51.4 ± 3.31 vs 52.2±3.59; p<0.01) & ulnar motor nerves (mean ± SD 51.0 ± 5.84 vs 52.1±5.46; p<0.03) shows significant improvement. Conclusion: Oral supplementation of α-lipoic acid was found to be effective in improving motor nerve conduction of upper and lower extremities in patients with diabetic peripheral neuropathy

VALIDATION OF STABILITY INDICATING ULTRA-FAST LIQUID CHROMATOGRAPHY METHOD FOR SIMULTANEOUS ESTIMATION OF ATENOLOL and NIFEDIPINE IN BOTH BULK AND PHARMACEUTICAL DOSAGE FORMS

Objective: The study depicts improvement of ensuing validation of a stability indicating technique for the simultaneous estimation of Atenolol and Nifedipine using Ultra-fast liquid chromatographic method (UFLC).Methods: The analysis is performed on Phenomenex Kinetex C18, (150 × 4.6 mm, 5μm) column using methanol and 0.1%ortho-phosphoric acids (75:25 v/v) as mobile phase with a flow rate of 1.3 ml/min. The eluents were checked with PDA detector at 237 nm.Results: In this optimized conditions Atenolol and Nifedipine elutes at a retention time of 2.79 and 4.50 min respectively individually the considered optimized condition is having linearity in the range from 10 to 50µg/ml of Atenolol and 4-20µg/ml of Nifedipine. The method was validated by following the ICH guidelines and their combination drug yield was exposed to acid and base stress, thermal stress, photolytic stress, hydrolytic stress, and oxidative stress conditions. All samples were studied by the given optimized method. In this Calibration curves were linear over studies ranges with correlation coefficient found between the ranges of 0.99 to 1.00.Conclusion: The proposed method was found to be accurate, precise, and specific and suitable for determination of both the drugs

EEG-metric based mental stress detection

Mental stress level is a vital parameter affecting physical well-being, cognition, emotions, and professional efficiency. With growing adversities in modern living standards, causing abnormal mental stress, it is necessary to measure to cure it. Regular personal stress profile generated can be used as neurofeedback for the clinical as well as personal assessment. This paper describes a method to detect mental stress level based on physiological parameters. In this method, an electroencephalogram (EEG)-metric parameters based binary and ternary stress classifier is developed. This is validated through probabilistic stress profiler of differential stress inventory (a questionnaire based evaluation). Nine channel EEG is used to extract physiological signal. EEG-metric based cognitive state and workload outputs are generated for 41 healthy volunteers (37 males and 4 females, age; 24±5 years). All subjects were guided to perform three simple tasks of closed eye, focusing vision on a red dot on center of dark screen and focusing on a white screen. Central tendencies (mean, median and mode) and standard deviation were extracted of EEG-metric (sleep onset, distraction, low engagement, high engagement and cognitive states) as features. Either of the two or three classes of stress are evaluated from probabilistic stress profiler of differential stress inventory and used as training output classes. A supervisory training of multiple layer perceptron based binary support vector machine classifier was used to detect stress class one by one. 40 subject's samples were used for training and interchanging one-by one 41th subjects stress class is determined from the designed classifier. Out of 41 subjects, stress level of 30 subjects is correctly identified by binary classifier and stress level of 26 subjects is correctly identified by ternary classifier, using multi-layer perceptron kernel based SVM

Herbicide sequence for weed management in direct seeded rice

An experiment was conducted during Kharif 2014 and 2015 at Agricultural Research Station, Dhadesu-gur, University of Agricultural Sciences, Raichur, Karnataka, India, to know the herbicide sequence for weed man-agement in direct seeded rice. The dominant weeds in direct seeded rice were Echinochloa sp, Panicum repens, Cynodon doctylon, Leptochloa chinensis, Bracharia sp. Ludwigia parviflora, Commelena sp. and Cyperus sp. Pooled data revealed that, application of pyrazosulfuron ethyl 10 % WP at 20 g a.i./ha as pre-emergent herbicide followed by the application of Bispyribac sodium 10 % SC @ 250 ml/ha at 20 to 25 days after sowing as post-emergent herbi-cide in direct seeded rice was most effective in controlling of grasses, broad leaf weeds and sedges and increased the rice grain yield (5583 kg/ha) without any phytotoxic effect and which was onpar with the application of Pendime-thalin 30 EC @ 1 kg a.i. /ha as pre-emergent herbicide followed by one hand weeding at 30 days after sowing and weed free check .Therefore, the application of pre emergent herbicides followed by the post emergent herbicide application can reduce the weed problem in direct seeded rice