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A study was planned to investigate the effects of different doses of ethanol on body organs of Japanese quails. A total of 120 quails were randomly divided into five groups, A, B, C, D and E. Quails of groups A, B, C and D were given ethanol at concentrations of 2, 4, 8 and 16%, respectively in drinking water for four weeks, while birds of group E served as untreated control. The results at the end of 4th week revealed a significant effect on relative weight of heart, kidney and lungs in most treated groups. The increase in heart and lung weight was significant (P<0.05) in quail given 4% and higher ethanol, of kidney given 2 to 8% ethanol, while statistically no effect was observed on relative weight of liver. The relative weight of the proventriculus and the intestine at 4th week also showed statistically no difference compared to control group. However, the weight of the gizzard at 4th week increased significantly (P0.05) in groups given 8 to 16% ethanol and the increase was 42% in these groups compared with control group. The lymphoid organs at the end of 4th week revealed significant difference in weight of the bursa of Fabricius in quails given 16% ethanol and of the thymus in quails given 4 to 16% ethanol. Statistically, no difference was observed in spleen weight of treated groups compared to control group. The gross and light microscopic examination failed to reveal significant changes in these organs with routine methods of examination. Ethanol showed a significant effect on feed conversion ratio which was poor in ethanol treated groups; at the end of 4th week, it varied from 232 to 442% in groups given 8 and 16% ethanol, respectively. These data suggest that ethanol has significant effects on relative weight of heart, kidney, lungs, thymus, and on feed conversion ratio in the Japanese quails.

In this paper the unified Bond Graph model of the left ventricle ejection phase is presented, simulated and validated. The integro-differential and ordinary differential equations obtained from the bond graph models are simulated using ODE45 (Ordinary Differential Equation Solver) on MATLAB and Simulink. The results, thus, obtained are compared with CVS (Cardiovascular System) physiological data present in Simbiosys (a software for simulating biological systems) and also with the CVS Wiggers diagram of heart cycle. As the cardiac activity is a multi domain process that includes mechanical, hydraulic, chemical and electrical events; therefore, for modeling such systems a unified modeling approach is needed. In this paper the unified Bond Graph model of the left ventricle ejection phase is proposed. The Bond Graph conventionalism approach is a graphical method principally powerful to portray multi-energy systems, as it is formulated on the portrayal of power exchanges. The model takes into account a simplified description of the left ventricle which is close to the medical investigation promoting the apperception and the dialogue between engineers and physiologists.

Healthcare service centres equipped with electronic health systems have improved their resources as well as treatment processes. The dynamic nature of healthcare data of each individual makes it complex and difficult for physicians to manually mediate them; therefore, automatic techniques are essential to manage the quality and standardization of treatment procedures. Exploratory data analysis, patternanalysis and grouping of data is managed using clustering techniques, which work as an unsupervised classification. A number of healthcare applications are developed that use several data mining techniques for classification, clustering and extracting useful information from healthcare data. The challenging issue in this domain is to select adequate data mining algorithm for optimal results. This paper exploits three different clustering algorithms: DBSCAN (Density-Based Clustering), agglomerative hierarchical and k-means in real transactional healthcare data of diabetic patients (taken as case study) to analyse their performance in large and dispersed healthcare data. The best solution of cluster sets among the exploited algorithms is evaluated using clustering quality indexes and is selected to identify the possible subgroups of patients having similar treatment patterns

La pollution de l'air et l'ozone atmosphérique peuvent causer des dommages à la santé humaine et à l'environnement. Cette étude explore l'approche potentielle du modèle de réseau neuronal artificiel (RNA) et le compare à un modèle de régression pour prédire la concentration d'ozone à l'aide de différents paramètres et fonctions mesurés par le Climate Prediction Center du National Weather Service des États-Unis. En outre, cette étude a comparé la viabilité économique de l'ANN et d'autres méthodes de mesure. Les résultats ont montré que le modèle basé sur l'ans présentait de meilleures performances. De tels types de modèles peuvent être bénéfiques pour les agences gouvernementales. En prédisant la concentration d'ozone, les organismes gouvernementaux peuvent prendre des mesures préventives pour éviter des effets importants sur la santé, protéger les populations locales et aider à préserver un environnement durable. La contaminación del aire y el ozono atmosférico pueden causar daños a la salud humana y al medio ambiente. Este estudio explora el enfoque potencial del modelo de red neuronal artificial (ANN) y lo compara con un modelo de regresión para predecir la concentración de ozono utilizando diferentes parámetros y funciones medidas por el Centro de Predicción Climática del Servicio Meteorológico Nacional de EE. UU. Además, este estudio ha comparado la viabilidad económica de ANN y otros métodos de medición. Los resultados mostraron que el modelo basado en Ann mostró un mejor rendimiento. Tales tipos de modelos pueden ser beneficiosos para las agencias gubernamentales. Al predecir la concentración de ozono, las agencias gubernamentales pueden tomar medidas preventivas para evitar efectos significativos en la salud, proteger a las poblaciones locales y ayudar a preservar un medio ambiente sostenible. Air pollution and atmospheric ozone can cause damages to human health and to the environment. This study explores the potential approach of the artificial neural network (ANN) model and compares it with a regression model for predicting ozone concentration using different parameters and functions measured by the Climate Prediction Center of US National Weather Service. In addition, this study has compared the economic viability of ANN and other measuring methods. Results showed that the ANN-based model exhibited better performance. Such model types can be beneficial to government agencies. By predicting ozone concentration government agencies can take preventive measures to avoid significant health effects, protect local populations, and help preserve a sustainable environment. يمكن أن يتسبب تلوث الهواء والأوزون الجوي في إلحاق أضرار بصحة الإنسان والبيئة. تستكشف هذه الدراسة النهج المحتمل لنموذج الشبكة العصبية الاصطناعية (ANN) وتقارنه بنموذج الانحدار للتنبؤ بتركيز الأوزون باستخدام معلمات ووظائف مختلفة يقيسها مركز التنبؤ بالمناخ التابع للخدمة الوطنية الأمريكية للطقس. بالإضافة إلى ذلك، قارنت هذه الدراسة الجدوى الاقتصادية لـ ANN وطرق القياس الأخرى. أظهرت النتائج أن النموذج القائم على ANN أظهر أداءً أفضل. يمكن أن تكون هذه الأنواع النموذجية مفيدة للوكالات الحكومية. من خلال التنبؤ بتركيز الأوزون، يمكن للوكالات الحكومية اتخاذ تدابير وقائية لتجنب الآثار الصحية الكبيرة وحماية السكان المحليين والمساعدة في الحفاظ على بيئة مستدامة.

The breast cancer detection and diagnosis is a critical and complex procedure that demands high degree of accuracy. In computer aided diagnostic systems, the breast cancer detection is a two stage procedure. First, to classify the malignant and benign mammograms, while in second stage, the type of abnormality is detected. In this paper, we have developed a novel architecture to enhance the classification of malignant and benign mammograms using multi-classification of malignant mammograms into six abnormality classes. DWT (Discrete Wavelet Transformation) features are extracted from preprocessed images and passed through different classifiers. To improve accuracy, results generated by various classifiers are ensembled. The genetic algorithm is used to find optimal weights rather than assigning weights to the results of classifiers on the basis of heuristics. The mammograms declared as malignant by ensemble classifiers are divided into six classes. The ensemble classifiers are further used for multiclassification using one-against-all technique for classification. The output of all ensemble classifiers is combined by product, median and mean rule. It has been observed that the accuracy of classification of abnormalities is more than 97% in case of mean rule. The Mammographic Image Analysis Society dataset is used for experimentation.

The article reviews the issue of climate change and health in the Indian context. The importance of climatechange leading to estimated loss of above 2.5 million DALYs in southeast Asia, mortality due to heat waves, andthe importance of air quality related respiratory diseases, disasters due to excessive floods, malnutrition due toreduction in rice, maize and sorghum crops etc. Latest work undertaken in India, vis-a-vis current scenario andneed for further work has been discussed. There is felt need of further studies on assessing the impact on dengueand chikungunya as the transmission dynamics of these diseases involve water availability, storage and lifestyle, etc. Uncertainties and knowledge gaps identified in the studies undertaken so far have also been highlighted.As regards to vector borne diseases, there is a need to concentrate in the areas which are presently free frommalaria and with use of best available tools of interventions in already disease endemic areas like northeasternstates, the risk of climate change impacts can be minimized.