Assessment of groundwater depletion–induced land subsidence and characterisation of damaging cracks on houses: a case study in Mohali-Chandigarh area,India

Bulletin of Engineering Geology and the Environment - The present study identified groundwater depletion–induced land subsidence by spaceborne differential interferometric SAR (DInSAR)...     

Markov Chain Based Roaming Schemes for Honeypots

The paper proposes a reactive roaming scheme for honeypots. The main aim of a honeypot is to capture the activities of the attacker. If the attacker detects honeypot on a system, its value drops. So, the concept of roaming honeypots is being proposed, to prevent the attacker from detecting the honeypot, which in turn increases the efficiency of honeypot and allows collecting rich data about activities of active attackers. The honeypot is shifted to another system which is most probable to be attacked within the network. The concept of Markov chain analysis is being used to detect the most probable system to be attacked based on the current status of the network. Further, using IP shuffling and services on/off concepts, honeypots roam on the network to the most probable system to be attacked using the threat score. Snort is used to capture data about the number of attacks on each of the nodes of the network and the data collected is then used as an input for Markov chain analysis to identify the most probable system where honeypot can be roamed/moved. The roaming scheme has been implemented for both high interaction honeypots and low interaction honeypots. The high interaction implementation helps in capturing in depth information on a shorter range of IP addresses, whereas the low interaction implementation is efficient in capturing information on a large range of IP addresses. The main advantage of this approach is that it predicts the frequency of attacks on the nodes of a particular network and takes a reactive step by starting the honeypot services on that particular node/system on the network.     

Cyclic learning rate based HybridSN model for hyperspectral image classification

Classification of remotely sensed hyperspectral images (HSI) is a challenging task due to the presence of a large number of spectral bands and due to the less available data of remotely sensed HSI. The use of 3D-CNN and 2D-CNN layers to extract spectral and spatial features shows good test results. The recently introduced HybridSN model for the classification of remotely sensed hyperspectral images is the best to date compared to the other state-of-the-art models. But the test performance of the HybridSN model decreases significantly with the decrease in training data or number of training epochs. In this paper, we have considered cyclic learning for training of the HybridSN model, which shows a significant increase in the test performance of the HybridSN model with 10%, 20%, and 30% training data and limited number of training epochs. Further, we introduce a new cyclic function (ncf) whose training and test performance is comparable to the existing cyclic learning rate policies. More precisely, the proposed HybridSN(ncf ) model has higher average accuracy compared to HybridSN model by 19.47%, 1.81% and 8.33% for Indian Pines, Salinas Scene and University of Pavia datasets respectively in case of 10% training data and limited number of training epochs.

     

Preparation of mechanically strong poly (ether block amide)/Mercaptoethanol breathable membranes for biomedical applications

Poly (ether block amide) (PEBA) was modified with Mercaptoethanol (ME) to introduce crosslinks in its polymeric structure and emphasis was laid on obtaining non-porous breathable membranes with improved mechanical properties which can be used for various biomedical applications. Pebax MH 1657 (mentioned as PEBA throughout the text) was cast polymerized with ME and the effect of ME on the properties of the membranes (such as water absorption, permeability, tensile strength, elongation & tear strength) was studied. Fourier Transform Infrared Spectroscopy- Attenuated Total Reflectance (FTIR-ATR), Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA) and Differential Thermogravimetry (DTG) techniques were used to characterize the membranes. Different amount of ME (10 to 40% w/w) was added to PEBA and 30% of ME was found to be most effective in increasing the mechanical properties of the membrane. Sulfhydryl group played an important role in enhancing mechanical strength of the membranes. PEBA/ME based non porous breathable membrane with excellent mechanical strength is a novel material that can be used for various biomedical applications.     

Prediction of Permeate Flux During Ultrafiltration of Polysaccharide in a Stirred Batch Cell

Ultrafiltration of polysaccharide macromolecule has been carried out in a stirred batch cell using a fully retentive membrane over a wide range of operating conditions. An unsteady state mass transfer model in conjunction with resistance-in-series model is developed using film theory. The proposed model is used to predict the transient permeate flux decline behavior in gel controlled ultrafiltration. This model is also able to quantify the variation of bulk volume as well as the bulk concentration of solute with time. The variation of viscosity as a function of solute concentration is included in the model. A model parameter is used in this model and is evaluated by optimizing the experimental flux profiles with calculated flux profiles. The model predictions are successfully compared with the experimental data. A parametric study has been performed to observe the effect of different process parameters on the filtration performance in terms of transient permeate flux decline behavior. The proposed model in general will provide a better understanding of gel controlled ultrafiltration.