Adverse drug reactions due to cancer chemotherapy in a tertiary care hospital in south Karnataka: a prospective observational study

Background: Cancer is a multi-cellular disease which can arise from any cell type and organs. Adverse drug reactions (ADR) are undesirable consequence of cancer chemotherapeutic drugs. A great importance has to be given for their assessment, detection, monitoring, reporting and preventing these ADR for the beneficial effects of the patients. So the present study was undertaken for the purpose of detecting and quantifying those adverse reactions which is of some importance in therapeutic setting.Methods: A prospective observational study conducted in chemotherapy ward, male and female patients of any age receiving cancer chemotherapy and presenting with ADR’s in duration of 3 months.Results: 160 patients were observed. Out of 160 patients 123 presented with ADR’s. Most common ADR’s were loss of appetite (67.6), diarrhea (61.8%), vomiting (21.5%), nausea (17.7%), anemia (24.7%). Cisplatin, paclitaxel, oxaliplatin, doxorubicin, gefitinib are common drugs causing ADR’s.Conclusions: Cancer chemotherapeutic drugs are associated with various adverse reactions. This study shows the importance of active monitoring of these reactions and measures to prevent their effects early in the treatment of cancer

GESTURE BASED TOUCHPAD SECURITY SYSTEM WITH DESIGN OF TOUCH SCREEN CONTROLLER

The purpose of the project is to present a new approach on the design of security systems by using a touch sensitive device. Security is a permanent concern in a variety of environments ranging from physical access restriction in home and industrial settings to information security in digital systems. Numeric passwords, fingerprint recognition, and many other techniques have been extensively implemented in the past but they present certain drawbacks. The proposed technique makes use of a touch device to recognize symbols as passwords and takes time into account to add a new dimension and prevent password theft. We implemented a prototype that demonstrates the capabilities of the proposed security approach by showing one direct application in physical access restriction systems

One-Pot Synthesis of 5,6-Dihydro-4H-1,2-Oxazines by Cyclization of Ketoximes with Derivatives of Allylbenzene

A new series of 5,6-dihydro-4H-1,2-oxazines were synthesized via hetero Diels-Alder reaction of  α-nitrosolefins with derivatives of allylbenzene. α-Nitrosoolefins were generated from ketoximes by the action of chloramine-T and triethylamine

Structural, Morphological and 1/f noise Properties of ITO/TiO2 thin Films by e-beam Evaporation System for Optoelectronic Device Applications

In the present research study, ITO/TiO2 thin films were prepared on a glass substrate by using an electron beam (e-beam) evaporation system at different annealing temperatures (300, 350, 400 and 450 °C). The amorphous and crystalline natures of ITO/TiO2 structure were analyzed by X-ray diffraction study. As the grain size becomes larger, indirectly it will develop the crystalline quality of the TiO2 films studied from AFM. The surface of TiO2 films and the crystalline size of the sample were increased gradually with respect to a temperature that is observed in SEM. The elemental composition determined by the energy dispersive analysis of EDAX showed that TiO2 thin films were highly stoichiometric. Further, the higher optical transmittance (93%) was obtained with 450 °C annealed ITO/TiO2 film. The optical band gap increased along with annealing temperatures (300, 350, 400 and 450 °C). All the above results of this present work can be utilized for solar cell and optoelectronic device applications. © 2020 Author(s)

Construction of Data Driven Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network for IoT Healthcare Applications

The architecture of IoT healthcare is motivated towards the data-driven realization and patient-centric health models, whereas the personalized assistance is provided by deploying the advanced sensors. According to the procedures in surgery, in the emergency unit, the patients are monitored till they are stable physically and then shifted to ward for further recovery and evaluation. Normally evaluation done in ward doesn’t suggest continuous parameters monitoring for physiological condition and thus relapse of patients are common. In real-time healthcare applications, the vital parameters will be estimated through dedicated sensors, that are still luxurious at the present situation and highly sensitive to harsh conditions of environment. Furthermore, for real-time monitoring, delay is usually present in the sensors. Because of these issues, data-driven soft sensors are highly attractive alternatives. This research is motivated towards this fact and Auto Encoder Deep Neural Network (AutoEncDeepNN) is proposed depending on Health Framework in the internet assisting the patients with trigger-based sensor activation model to manage master and slave sensors. The advantage of the proposed method is that the hidden information are mined automatically from the sensors and high representative features are generated by multiple layer’s iteration. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Hierarchical Extreme Learning Machine (HELM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). It is found that the proposed AutoEncDeepNN method achieves 94.72% of accuracy, 41.96% of RMSE, 34.16% of RAE and 48.68% of MAE in 74.64 ms

Systematics of proton decay of actinides

255-262The phenomenon of proton emission from nuclear ground states limits the possibilities of the creation of more exotic proton rich nuclei that are usually produced by fusion-evaporation nuclear reactions. In the energy domain of radioactivity, proton can be considered as a point charge having highest probability of being present in the parent nucleus. Conclaves et al.1 studied the two-proton radioactivity of nuclei of mass number Aet al.2 reviewed the theories of proton emission to analyse the properties of nuclear matter. Maglione et al.3 analysed the proton emission from the some deformed nuclei. We have studied proton decay in almost all actinide nuclei. We have calculated the energy released during the proton decay (QP), penetration factor (P), and half-lives of proton decay. Proton decay half-lives are also longer than that of other decay modes such as alpha decay and spontaneous fission. To check the Geiger-Nuttal law for proton decay in actinide nuclei, we have plotted the logarithmic proton decay half-lives versus 1/sqrt(Q). The competition of proton decay with different decay modes such as alpha decay and spontaneous fission are also studied. We have also highlighted possible proton emitters with the corresponding energies and half-lives in the actinide region

Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India

Accurate rainfall forecasting is crucial for effective disaster preparedness and mitigation in the North-East region of India, which is prone to extreme weather events such as floods and landslides. In this study, we investigated the use of two data-driven methods, Dynamic Mode Decomposition (DMD) and Long Short-Term Memory (LSTM), for rainfall forecasting using daily rainfall data collected from India Meteorological Department in northeast region over a period of 118 years. We conducted a comparative analysis of these methods to determine their relative effectiveness in predicting rainfall patterns. Using historical rainfall data from multiple weather stations, we trained and validated our models to forecast future rainfall patterns. Our results indicate that both DMD and LSTM are effective in forecasting rainfall, with LSTM outperforming DMD in terms of accuracy, revealing that LSTM has the ability to capture complex nonlinear relationships in the data, making it a powerful tool for rainfall forecasting. Our findings suggest that data-driven methods such as DMD and deep learning approaches like LSTM can significantly improve rainfall forecasting accuracy in the North-East region of India, helping to mitigate the impact of extreme weather events and enhance the region's resilience to climate change.Comment: Paper is under review at ICMC 202

A different approach to soil analysis: Indicative studies

Soil analysis is a tool that has been employed with the primary goal of providing recommendations for soil rectification, crop productivity and for soil health management. Time tested methods like ammonium acetate extraction and diethylene triamine penta acetic acid (DTPA) are commonly used for analysis of bioavailable nutrients. However, there are some limitations to these methods as both extraction fluids are buffered to neutral or near-neutral pH. Hence extracted nutrients represent a “potential or ideal-case” fertility status of soil instead of an “actual” field status. In the ‘Regular methods’, we are overlooking the role of pH, the master variable, in determining the availability of nutrients. Hence, in ‘Modified methods’, the extraction fluid is buffered to actual soil pH. Results obtained with over 150 random samples representing a range of pH, have indicated a difference in values between regular and modified extraction methods. The modified methods (MM) of ammonium acetate and DTPA extraction adjusted to soil pH were found to be better than regular method (RM) for estimation of calcium, magnesium with ammonium acetate and iron and manganese with DTPA in alkaline soils above pH 8.0. For a complete picture of soil health, productivity and fertility, microbiological and enzymatic analysis of soils were included in the present study. Soil solution equivalent medium (SSE) was found to be the appropriate culture medium for microbial counts. A linear relationship was found between urease activity and available nitrogen of soil