Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, clean water availability, air pollution, traffic congestion leading to delays in vehicular transportation, etc. Transportation and network modeling through transportation indices have been widely used to understand transportation problems in the recent past. This necessitates predicting transportation indices to facilitate sustainable urban planning and traffic management. Recent advancements in deep learning research, in particular, Generative Adversarial Networks (GANs), and their modifications in spatial data analysis such as CityGAN, Conditional GAN, and MetroGAN have enabled urban planners to simulate hyper-realistic urban patterns. These synthetic urban universes mimic global urban patterns and evaluating their landscape structures through spatial pattern analysis can aid in comprehending landscape dynamics, thereby enhancing sustainable urban planning. This research addresses several challenges in predicting the urban transportation index for small and medium-sized Indian cities. A hybrid framework based on Kernel Ridge Regression (KRR) and CityGAN is introduced to predict transportation index using spatial indicators of human settlement patterns. This paper establishes a relationship between the transportation index and human settlement indicators and models it using KRR for the selected 503 Indian cities. The proposed hybrid pipeline, we call it RidgeGAN model, can evaluate the sustainability of urban sprawl associated with infrastructure development and transportation systems in sprawling cities. Experimental results show that the two-step pipeline approach outperforms existing benchmarks based on spatial and statistical measures

An Interpretable Probabilistic Autoregressive Neural Network Model for Time Series Forecasting

Forecasting time series data presents an emerging field of data science that has its application ranging from stock price and exchange rate prediction to the early prediction of epidemics. Numerous statistical and machine learning methods have been proposed in the last five decades with the demand for generating high-quality and reliable forecasts. However, in real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable, and therefore, hybrid solutions are needed to bridge the gap between classical forecasting methods and scalable neural network models. We introduce an interpretable probabilistic autoregressive neural network model for an explainable, scalable, and "white box-like" framework that can handle a wide variety of irregular time series data (e.g., nonlinearity and nonstationarity). Sufficient conditions for asymptotic stationarity and geometric ergodicity are obtained by considering the asymptotic behavior of the associated Markov chain. During computational experiments, PARNN outperforms standard statistical, machine learning, and deep learning models on a diverse collection of real-world datasets coming from economics, finance, and epidemiology, to mention a few. Furthermore, the proposed PARNN model improves forecast accuracy significantly for 10 out of 12 datasets compared to state-of-the-art models for short to long-term forecasts

Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The unavailability of specific drugs and ready-to-use vaccines to prevent most of these epidemics makes the situation worse. These force public health officials and policymakers to rely on early warning systems generated by reliable and accurate forecasts of epidemics. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyse a wide variety of epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it EWNet model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with several statistical, machine learning, and deep learning models. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods

Antibiotic sensitivity pattern of bacterial isolates from urine samples of admitted patients with urinary tract infection in a tertiary care teaching hospital of Tripura, India: a hospital record based study

Background: Urinary tract infection (UTI) being one of the most common and a serious health problem both in the community and hospital settings each year worldwide, the emergence of antibiotic resistance in the management of UTI is a serious public health issue. The present study will analyse the antimicrobial sensitivity pattern of pathogens isolated from the urine samples of admitted patients suffering from UTI in Tripura Medical College and Dr. B.R. Ambedkar Memorial Teaching Hospital (TMC).Methods: This was a hospital record-based study. The urine samples of clinically diagnosed UTI patients admitted in various departments of the hospital during the study period were included. The reports of culture and sensitivity testing of the samples were collected. The results were interpreted according to the guidelines of the Clinical and Laboratory Standards Institute (CLSI).Results: During the 12-month study period, a total of 752 urine samples were analysed. Enterococcus (43.75%) was the most frequently isolated bacteria, followed by E. coli (28.45%) and Klebsiella (14.89%). Enterococcus was highly sensitive (p<0.001) to vancomycin (95.33%), E. coli was mostly sensitive to nitrofurantoin (83.65%) and Klebsiella mainly sensitive to imipenem (75.49%).Conclusions: The study showed that positive urine culture with the antibiotic sensitivity of the isolates is very important for antimicrobial therapy, as antibiotic resistance is a worldwide problem which causes ineffectiveness of treatment

Knowledge, attitude and practice of generic medicines among doctors in a tertiary care teaching hospital of Tripura, India

Background: The assessment of doctors’ perceptions and understanding about generic medicines may help in recognizing possible barriers to greater generic medicine usage. The primary objective of this study was to explore the knowledge, attitude, and practice (KAP) of doctors toward generic medicines.Methods: A questionnaire based cross-sectional study was carried out in a tertiary-care teaching hospital of Tripura. The questionnaire was designed to assess the KAP about generic medicines. The doctors working in this institute during the study period were included. All data were analysed using statistical software for epidemiology (EPI6). P <0.05 was considered statistically significant.Results: 67.5% doctors agreed to the fact that generic medicines were intended to be interchangeable with a branded drug (p=0.0003). Among the doctors, 95% were aware that generic drug manufacturers need to conduct studies to show bioequivalence between the generic medicine and their branded counterparts (p <0.0001). Majority of the doctors (82.5%) were of the view that generic medicines were as safe as that of branded drugs (p <0.0001). 97.5% of the doctors agreed that importance of generic medicines should be taught in early part of internship. 75% doctors did not think that switching a patient from a brand-name to generic drug may change the outcome of the therapy (p <0.0001). 92.5% doctors said that they prescribe generic medicines (p <0.0001).Conclusion: The study showed that the doctors were well aware of generic medicines and Jan Aushadhi scheme of Govt. of India. It was also observed that efficacy, safety and quality profile of the medicine were the most important factors considered by doctors when they prescribe drugs

Experimental Investigation of Physical and Mechanical Properties of Al-Cu-ZrO2-TiO2 Composites

Composite Materials plays a vital role in the formation of mankind, ranging from the habitation of early civilisation to enable future modernisation. In this present work titania and zirconia reinforced Al6063-Cu composite has been developed by using a new casting process comprising of clay soil and sand mold. On top of that stirrer mechanism has also been used to mix the hybrid abrasive particles of Single Particle Size (SPS), Double Particle Size (DSP) and Triple Particle Size (TPS) in the Al-Cu alloy which is a completely new approach. Taguchi L18 orthogonal arrays have been used in this study to reduce the number of tests. For this present research work, 5%, 7.5% and 10% of hybrid abrasive have been mixed in the alloy to investigate its different properties like elastic modulus, ductility and density. It has been observed that in addition of the reinforcements in the newly developed composites elastic modulus, ductility and density have been improved to 31.42%, 40% and 19.81% respectively. Due to this improved properties this composite can be used to manufacture bearing, valve, aircraft electronics and many other appliances. The experimental results were analyzed by the SN ratio plot and an RSM model was used to predict these properties and to estimate the affecting factors for each property. Further, the RSM models have been corroborated by performing the experiments on the newly manufactured composites for elastic modulus, ductility and density. The result obtained from RSM model shows good similarity with the experimental results

An Insight into the Gelatinization Properties Influencing the Modified Starches Used in Food Industry: A review

AbstractNative starch is subjected to various forms of modification to improve its structural, mechanical, and thermal properties for wider applications in the food industry. Physical, chemical, and dual modifications have a substantial effect on the gelatinization properties of starch. Consequently, this review explores and compares the different methods of starch modification applicable in the food industry and their effect on the gelatinization properties such as onset temperature (To), peak gelatinization temperature (Tp), end set temperature (Tc), and gelatinization enthalpy (ΔH), studied using differential scanning calorimetry (DSC). Chemical modifications including acetylation and acid hydrolysis decrease the gelatinization temperature of starch whereas cross-linking and oxidation result in increased gelatinization temperatures. Common physical modifications such as heat moisture treatment and annealing also increase the gelatinization temperature. The gelatinization properties of modified starch can be applied for the improvement of food products such as ready-to-eat, easily heated or frozen food, or food products with longer shelf life

Bioequivalence study of two formulations containing 400 mg dexibuprofen in healthy Indian subjects

Objective: This study presents the results of two-period, two-treatment crossover investigations on 24 healthy Indian male subjects to assess the bioequivalence of two oral formulations containing 400 mg of dexibuprofen (CAS 51146-56-6). An attempt was also made to study the pharmacokinetics of dexibuprofen in the local population of Indian origin.Method: Both of the formulations were administered orally as a single dose separated by a one-week washout period. The concentration of dexibuprofen in plasma was determined by a validated HPLC method with UV detection using carbamazepine as internal standard. The formulations were compared using the parameters area under the plasma concentration-time curve (AUC0-t), area under the plasma concentration-time curve from zero to infinity (AUC0-∞), peak plasma concentration (Cmax), and time to reach peak plasma concentration (tmax).Results: The results of this investigation indicated that there were no statistically significant differences between the logarithmically transformed AUC0-∞ and Cmax values of the two preparations. The 90 % confidence interval for the ratio of the logarithmically transformed AUC0-t, AUC0-∞ and Cmax were within the bioequivalence limit of 0.8-1.25 and the relative bioavailability of the test formulation was 99.04 % of that of reference formulationjok?.Conclusion: Thus, these findings clearly indicate that the two formulations are bioequivalent in terms of rate and extent of drug absorption. Both preparations were well tolerated with no adverse reactions observed throughout the study

Combined Photoredox and Iron Catalysis for the Cyclotrimerization of Alkynes

This is the peer reviewed version of the following article: Neumeier, M., Chakraborty, U., Schaarschmidt, D., de la Pena O'Shea, V., Perez¿Ruiz, R., & Jacobi von Wangelin, A. (2020). Combined photoredox and iron catalysis for the cyclotrimerization of alkynes. Angewandte Chemie International Edition, 59(32), 13473-13478, which has been published in final form at https://doi.org/10.1002/anie.202000907. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Successful combinations of visible-light photocatalysis with metal catalysis have recently enabled the development of hitherto unknown chemical reactions. Dual mechanisms from merging metal-free photocatalysts and earth-abundant metal catalysts are still in their infancy. We report a photo-organo-iron-catalyzed cyclotrimerization of alkynes by photoredox activation of a ligand-free Fe catalyst. The reaction operates under very mild conditions (visible light, 20 degrees C, 1 h) with 1-2 mol % loading of the three catalysts (dye, amine, FeCl2).This work was supported by the Fonds der Chemischen Industrie (to M.N.) and the European Research Council (CoG 683150). We thank Luana Cardinale for technical support.Neumeier, M.; Chakraborty, U.; Schaarschmidt, D.; De La Pena O'shea, V.; Pérez-Ruiz, R.; Jacobi Von Wangelin, A. (2020). Combined Photoredox and Iron Catalysis for the Cyclotrimerization of Alkynes. Angewandte Chemie International Edition. 59(32):13473-13478. https://doi.org/10.1002/anie.2020009071347313478593