Total and Edge Domination on Anti Fuzzy Line Graph

In this paper, the total dominating set, edge dominating set and domination number (TDN & EDN) for anti fuzzy line graph is computed by maxima method algorithm (MMA). This calculation is executed using strong adjacency matrix of anti fuzzy graphs and its line graph

Multi-Objective Optimization of Construction Project Time-Cost-Quality Trade - off Using Differential Evolution Algorithm

Time and cost are among the important aspects considered for every construction project. Many research approaches have been followed to model time-cost relationship. There is a constant rise in the use of innovative contract methods which provide incentives for maximizing quality. There is an increasing pressure to improve the project performance due to the innovative contracting methods which necessitate developing models incorporating quality along with time and cost. A main contractor normally subcontracts most of the tasks of a project for improving project performance. It is always a complex and challenging task for a main contractor, to choose a correct bid which satisfies the time, cost and quality requirements of a project. In the present study, a differential evolution algorithm is used to solve this multi-objective time-cost-quality optimization problem. Two case studies are analyzed and the results obtained compared with the existing approaches to test the applicability and efficiency of the algorithm. It is evident from the results that the differential evolution algorithm performs efficiently in locating the optimal solution with minimum function evaluation

Achieve Secure Handover Session Key Management via Mobile Relay in LTE-Advanced Networks

Internet of Things is increasing the network by group action immense quantity of close objects which needs the secure and reliable transmission of the high volume knowledge generation, and also the mobile relay technique is one among the economical ways in which to satisfy the on-board knowledge explosion in LTE-Advanced (LTE-A) networks. However, the observe of the mobile relay can cause potential threats to the knowledge security throughout the relinquishing method. Therefore, to handle this challenge, during this paper, we have a tendency to propose a secure relinquishing session key management theme via mobile relay in LTE-A networks. Specifically, within the planned theme, to realize forward and backward key separations, the session key shared between the on-board user instrumentality (UE) and also the connected donor evolved node B (DeNB) is initial generated by the on-board UE then firmly distributed to the DeNB. moreover, to cut back the communication overhead and also the process complexness, a unique proxy re-encryption technique is used, wherever the session keys at the start encrypted with the general public key of the quality management entity (MME) are going to be re-encrypted by a mobile relay node (MRN), so alternative DeNB will later rewrite the session keys with their own non-public keys whereas while not the direct involvement of the MME. elaborated security analysis shows that the planned theme will with success establish session keys between the on-board UEs and their connected DeNB, achieving backward and forward key separations, and resisting against the collusion between the MRN and also the DeNB because the same time. Additionally, performance evaluations via in depth simulations area unit applied to demonstrate the potency and effectiveness of the planned theme

A Clinical Profile of Coma

INTRODUCTION: Impaired consciousness is among the most difficult and dramatic of clinical problems. The brain tolerates only limited physical or metabolic injury, so that impaired consciousness is often a sign of impending irreparable damage to the brain. Stupor and coma imply advanced brain failure, just as, for example, uremia means renal failure, and the longer such brain failure lasts the narrower the margin between recovery and the development of permanent neurologic injury. The limited time for action and the multiplicity of potential causes of brain failure challenge the physician. The physician must organize available information to anticipate as accurately as possible the likelihood that the patient will either recover or remain permanently disabled. Prospective studies of prognosis in adults and children indicate that within a few hours or days after the onset of coma, neurologic signs and electrophysiologic markers in many patients differentiate, with a high degree of probability, the extremes of no improvement or good recovery. Unfortunately, radiologic and biochemical indicators have generally provided less accurate predictions of outcome, with some exceptions. AIM OF THE STUDY: To determine the incidence, etiology, and outcome of coma, to determine the commonest causes of coma, to recognize unusual presentations and to assess the prognosis in these patients. MATERIALS AND METHODS: A total of 100 consecutive patients admitted in a comatose state to the IMCU/medical ward in Government Stanley Medical College, were enrolled in this study after prior written and informed consent. Period of the Study: February 2010 To April 2010. METHODOLOGY: A detailed case history was taken.Depression of consciousness was assessed by the GCS. A detailed clinical examination was done.Investigations were guided by the clinical presentation. Neurological outcomes were determined as intact(normal or no change from pre-morbid condition), impaired (alteration of tone,power or reflexes,cranial nerve dysfunction, ataxia, seizures, persistent vegetative state) or death. Inclusion Criteria: • Cases with coma &focal signs, • Those without focal signs, • Cases with coma & fever. Exclusion Criteria: • Head injury. • Toxin ingestion. CONCLUSION: The GCS is a valuable tool but it may not account for the localized motor, speech and language deficits associated with a focal lesion and hence its accuracy may be affected which has an impact on its prognostic value. The presence of pupillary /eye signs may not portend a poor prognosis in all cases especially in hypoxic injury. The initial GCS may be deceptively low in alcoholics. Caution is required while interpreting the clinical findings in alcoholics. There may be difficulties in differentiating the more serious injuries from reversible signs. The presence of seizures in a comatose patient may not always signify a poor prognosis in all cases. The limitations of our study are that it is an observational study and the number of patients analyzed may not be significant to apply the results to a larger population. The observed proportion might represent the lower limit of a confidence interval rather than the mean. Also, actual outcome from the illness in many instances could be worse than the best neurologic state because some patients who recovered temporarily (48 hrs) died of complications later. A multimodal work up including brain CT, MRI, EEG, somatosensory evoked potentials and clinical examination might be useful in predicting accurate prognosis than any of them alone

Perspective: new insights from loss function landscapes of neural networks

Abstract: We investigate the structure of the loss function landscape for neural networks subject to dataset mislabelling, increased training set diversity, and reduced node connectivity, using various techniques developed for energy landscape exploration. The benchmarking models are classification problems for atomic geometry optimisation and hand-written digit prediction. We consider the effect of varying the size of the atomic configuration space used to generate initial geometries and find that the number of stationary points increases rapidly with the size of the training configuration space. We introduce a measure of node locality to limit network connectivity and perturb permutational weight symmetry, and examine how this parameter affects the resulting landscapes. We find that highly-reduced systems have low capacity and exhibit landscapes with very few minima. On the other hand, small amounts of reduced connectivity can enhance network expressibility and can yield more complex landscapes. Investigating the effect of deliberate classification errors in the training data, we find that the variance in testing AUC, computed over a sample of minima, grows significantly with the training error, providing new insight into the role of the variance-bias trade-off when training under noise. Finally, we illustrate how the number of local minima for networks with two and three hidden layers, but a comparable number of variable edge weights, increases significantly with the number of layers, and as the number of training data decreases. This work helps shed further light on neural network loss landscapes and provides guidance for future work on neural network training and optimisation

Participatory management for conservation of seahorses in the Gulf of Mannar, South-east coast of India

The Inception Workshop of the research project “Participatory management for conservation of seahorses in the Gulf of Mannar, south-east coast of India” was organized on 27 December 2014 at the Mandapam Regional Centre of the Central Marine Fisheries Research Institute (ICAR), Mandapam Camp, Tamil Nadu. The workshop was aimed to mark the beginning of the project activities in the Gulf of Mannar and also to bring to one place all the concerned stakeholders who are directly or indirectly associated with sustainable management of seahorse fishery and conservation, and to inform them about the project, its objectives and the expectations. The workshop was also aimed to discuss and get feedback on the methodology and approach for implementation of the project activities such as field surveys and interviews with the stakeholders. The workshop also focused to gather valuable inputs from various stakeholders, which are vital for the on-going project

Robust Monocular Localization of Drones by Adapting Domain Maps to Depth Prediction Inaccuracies

We present a novel monocular localization framework by jointly training deep learning-based depth prediction and Bayesian filtering-based pose reasoning. The proposed cross-modal framework significantly outperforms deep learning-only predictions with respect to model scalability and tolerance to environmental variations. Specifically, we show little-to-no degradation of pose accuracy even with extremely poor depth estimates from a lightweight depth predictor. Our framework also maintains high pose accuracy in extreme lighting variations compared to standard deep learning, even without explicit domain adaptation. By openly representing the map and intermediate feature maps (such as depth estimates), our framework also allows for faster updates and reusing intermediate predictions for other tasks, such as obstacle avoidance, resulting in much higher resource efficiency