Determination of rainfall thresholds for landslide prediction using an algorithm-based approach: Case study in the Darjeeling Himalayas, India

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Landslides are one of the most devastating and commonly recurring natural hazards in the Indian Himalayas. They contribute to infrastructure damage, land loss and human casualties. Most of the landslides are primarily rainfall-induced and the relationship has been well very well-established, having been commonly defined using empirical-based models which use statistical approaches to determine the parameters of a power-law equation. One of the main drawbacks using the traditional empirical methods is that it fails to reduce the uncertainties associated with threshold calculation. The present study overcomes these limitations by identifying the precipitation condition responsible for landslide occurrence using an algorithm-based model. The methodology involves the use of an automated tool which determines cumulated event rainfall–rainfall duration thresholds at various exceedance probabilities and the associated uncertainties. The analysis has been carried out for the Kalimpong Region of the Darjeeling Himalayas using rainfall and landslide data for the period 2010–2016. The results signify that a rainfall event of 48 h with a cumulated event rainfall of 36.7 mm can cause landslides in the study area. Such a study is the first to be conducted for the Indian Himalayas and can be considered as a first step in determining more reliable thresholds which can be used as part of an operational early-warning system

DANTE: Deep AlterNations for Training nEural networks

We present DANTE, a novel method for training neural networks using the alternating minimization principle. DANTE provides an alternate perspective to traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convexity to cast training a neural network as a bi-quasi-convex optimization problem. We show that for neural network configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations effectively in this formulation. DANTE can also be extended to networks with multiple hidden layers. In experiments on standard datasets, neural networks trained using the proposed method were found to be promising and competitive to traditional backpropagation techniques, both in terms of quality of the solution, as well as training speed.Comment: 19 page

ARTIFICIAL NEURAL NETWORKS: FUNCTIONINGANDAPPLICATIONS IN PHARMACEUTICAL INDUSTRY

Artificial Neural Network (ANN) technology is a group of computer designed algorithms for simulating neurological processing to process information and produce outcomes like the thinking process of humans in learning, decision making and solving problems. The uniqueness of ANN is its ability to deliver desirable results even with the help of incomplete or historical data results without a need for structured experimental design by modeling and pattern recognition. It imbibes data through repetition with suitable learning models, similarly to humans, without actual programming. It leverages its ability by processing elements connected with the user given inputs which transfers as a function and provides as output. Moreover, the present output by ANN is a combinational effect of data collected from previous inputs and the current responsiveness of the system. Technically, ANN is associated with highly monitored network along with a back propagation learning standard. Due to its exceptional predictability, the current uses of ANN can be applied to many more disciplines in the area of science which requires multivariate data analysis. In the pharmaceutical process, this flexible tool is used to simulate various non-linear relationships. It also finds its application in the enhancement of pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its applications in pharmaceutical research, medicinal chemistry, QSAR study, pharmaceutical instrumental engineering. Its multi-objective concurrent optimization is adopted in the drug discovery process, protein structure, rational data analysis also

Role of ferric carboxymaltose in the treatment of postpartum anemia in a tertiary care hospital in Andhra Pradesh

Background: Postpartum iron deficiency anemia is common in India as a consequence of postpartum hemorrhage.  Recent studies have evaluated the use of parenteral iron as a better tolerated treatment modality. Compared with oral iron supplements, parenteral iron is associated with a rapid rise in serum ferritin and hemoglobin and improved maternal fatigue scores in the postpartum period.  Parenteral iron may be considered for the treatment of postpartum anemia. The objective of the study was to evaluate the efficacy, safety, and tolerability of intravenous ferric carboxymaltose, in women with postpartum anemia.Methods: A clinical observational study was undertaken in a tertiary care hospital,  50 women within six weeks of delivery with Hb ≥6 gm/dl and ≤10 gm/dl received 1000 mg/week,  over 15 minutes or less, repeated weekly to a calculated replacement dose (maximum 2500 mg) . Hemoglobin and serum ferritin levels were recorded prior to treatment and on day 21 after completion of treatment.Results: Ferric carboxymaltose-treated subjects achieved a hemoglobin greater than 12 gm/dL in a short time period (21 days), achieve a hemoglobin rise of ≥3 gm/dL more quickly, and attain higher serum ferritin levels. It is also associated with better patient compliance, and shorter treatment period. Drug-related adverse events occurred less frequently with ferric carboxymaltose. The only noted disadvantage was that it is more expensive when compared to other iron preperations.Conclusions: Intravenous ferric carboxymaltose was safe and well tolerated with good efficacy and better patient compliance in the treatment of postpartum iron deficiency anemia

FORMULATION AND EVALUATION OF RAMIPRIL MOUTH DISSOLVING FILMS

Objective: The present investigation was aimed at preparation and evaluation of mouth dissolving films (MDFs) of Ramipril to enhance patient convenience, compliance and to improve bioavailability. Methods: MDFs with 0.5% w/w Ramipril were prepared by a solvent casting method using a wet film applicator. The effects of film formers, wetting/solubilizing, saliva stimulating agents and film modifiers on the physicomechanical and in vitro Ramipril release from MDFs were evaluated. Results: The MDFs prepared were transparent, smooth and showed no re-crystallization upon storage. MDFs casted with hydroxypropyl methylcellulose (HPMC) E3 as film former and polyethylene glycol (PEG-400) as plasticizer showed superior Ramipril release rates and good physicomechanical properties when compared to MDFs with E5 and E15 as film formers. HPMC E3 MDFs with polyvinyl pyrrolidone K30 (PVP K30) and sodium lauryl sulphate (SLS) gave superior drug release properties than MDFs without PVP K30 and SLS. The HPMC E3 MDFs with citric acid (CA) as saliva stimulating and xylitol as soothing agent gave significantly superior in vitro drug release than the MDFs without CA and xylitol. Release kinetics data reveals diffusion as a drug release mechanism. Conclusion: From the obtained results, it can be concluded that the administration of Ramipril as MDF may provide a quick onset of action with enhanced oral bioavailability and therapeutic efficacy

Microdrone-Based Indoor Mapping with Graph SLAM

Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with six laser rangefinders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph optimization. It performs loop-closure detection and correction to recognize previously visited places, and to correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multilayer LiDAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicate that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31-m-long acquisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multilayer LiDAR-based macrodrone, given the low deviation between the point clouds built by both drones. Approximately 85 % of the cloud-to-cloud distances were less than 10 cm

TRANSDERMAL DELIVERY OF CALCIUM CHANNEL BLOCKER: DEVELOPMENT AND CHARACTERIZATION

  Objective: Felodipine, a BCS class II calcium channel blocker, is used in the management of hypertension and angina pectoris. Due to the poor solubility and low bioavailability of the drug, there is a necessity to design an alternative route to achieve a constant plasma concentration of felodipine for its maximum therapeutic utility and can be achieved by transdermal route.Methods: In this study, matrix type transdermal patches were prepared using different combinations of hydrophilic polymer, namely, polyvinylpyrrolidone (PVP) and hydrophobic polymer, namely, ethyl cellulose (EC) by solvent evaporation technique and were subjected for characterization.Results: The Fourier transform infrared studies confirmed the compatibility between drug and polymers. Hydrophilic nature of the polymers greatly influenced physical characteristics and dissolution rate. Equal percentage of PVP and EC yielded patches with good folding endurance. The concentration of plasticizer present in the patches gave them desired folding endurance, and it increased with the presence of hydrophilic polymer. The formulation with highest PVP concentration, F3, exhibited a maximum drug release of 96.23% for 24 hrs. While the formulation with highest EC concentration, F5, exhibited only 74.45% drug release for 24 hrs.Conclusion: From the data, formulation F2 (PVP/EC, 2:1) can be concluded as best formulation due to its desired physical characteristics, good initial drug release, sustained release behavior, and good in vitro permeation. This formulation can be further studied in a clinical scenario

SCREENING AND OPTIMIZATION OF VALACYCLOVIR NIOSOMES BY DESIGN OF EXPERIMENTS

Objective: The objective of the study was to perform a screening, optimization of valacyclovir niosomal formulation to achieve a sustained release of drug using the design of experiments by 32 full factorial design.Methods: Valacyclovir loaded niosomes were prepared using thin film hydration method by varying the ratio of Span 60 and Cholesterol. The prepared niosomes were evaluated for vesicle size, entrapment efficiency, cumulative drug release, fourier transformed infrared spectroscopy (FTIR), zeta potential and surface morphology by field emission scanning electron microscopy (FESEM).Results: The valacyclovir was successfully encapsulated and its entrapment efficiency ranged from 36.70 % to 50.62 %. The average vesicle size of the niosomes was found to be 431 to 623 nm. At 8th hour the drug release varied from 77.50% to 96.31 %. The optimized niosomes were multilamellar with a surface charge potential of about-43.2 mV. The studies revealed that the interaction of cholesterol and surfactant had a substantial effect on vesicle size, entrapment efficiency and drug release from the niosomes. The release kinetics of the optimized niosomes followed zero order kinetics with fickian diffusion controlled mechanism. The stability studies were performed for the optimized formulation and found that the formulation is stable at 4°C ± 2°C.Conclusion: Model equations were developed for the responses. No significant difference was observed between the predicted and observed value, showing that the developed model is reliable