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Disaster is a natural or man- made event that negatively affects life, property, livelihood or industry Disaster preparedness and planning is an inter-sectoral exercise. It is the responsibility of the health care professionals to stimulate and co-ordinate. In December 30th 2012 last thane cyclone which left 7 persons dead in Pondicherry and several homeless. Conceptual frameworks act like a map that gives coherence to empirical inquiry. It is a set of coherent ideas or concepts organized in a manner that makes them easy to communicate to others. The objectives of the study were to assess the knowledge, attitude and practice of women before and after STP, to find the effectiveness of STP and associate pretest knowledge, attitude and practice scores with selected demographic variables and develop the conceptual model. A one group pre-test and post-test research design was used with non randomized quota sampling technique. Data collection was done using a structured interview to assess the level of knowledge, attitude and practice regarding disaster preparedness and triage. The mean score obtained for overall knowledge was 5.4 SD 0.4 which shows the subjects had poor knowledge on disaster preparedness and triage. The mean score for pre-test attitude is 3.2 and SD 4.3 which shows unfavourable attitude towards disaster preparedness and triage. Wilcoxon signed rank test was computed for knowledge and attitude, pre-test and post-test. The obtained Z value -4.716, -4.410 were significant at P significant (Z= -1.301, P= 0.191). The findings led to the conceptual framework based on Modified General System Theory by J W Kenny. Keywords: Conceptual framework, STP on disaster management, KAP.

The SARS-CoV-19 coronavirus pandemic has had a huge economic impact around the world. As a result, the behaviour of all financial instruments, including cryptocurrencies, was significantly affected. Following the COVID-19 market crash, cryptocurrency derivative markets have seen a massive increase in popularity as speculators try to profit from the price and trading volume instability. The fluctuations in the virtual currency market during this time span seem to be a reflection of shifts in other capital and commodity markets. During this challenging era, the demand has remained relatively stable. It's yet another example of how cryptocurrencies can be viewed as a fully developed financial tool. Despite mediocre trade volume, the number of markets available increased significantly in the run-up to this case.

The purpose of the present study was to determine the offect on water intake on thermoregulatory and cardiovascular functions during a round of golf (18 holes) in the heat (30-33℃, 55-70% RH). Ten middle aged male subjects participated in two separate golf. During the first round of golf, the subjects played without fluid replacement(D) while playing with fluid replacement during the second round of golf (R). With D, body weight loss of 3.8% was accompanied by a 13.3% reduction in calculated plasma volume. With R on the other hand, little change of body weight loss was found and was accompanied by a 4.6% increase in the plasma volume. Both rectal temperatures and heart rates during golf were significantly higher in D than in R. The rectal temperature and the heart rate at the end of the game was 39.4℃ and 145bpm, and 116bpm, in D and R, respectively. It was concluded that the frequent fluid replacement during golf in high environmental temperatures was required to reduce thermal stresses on golfers., 9, KJ00004207155, 論文, Article

In this paper we discuss theoretical foundations and a practical realization of a circular traffic sign detection and recognition system operating on board of a vehicle. To initially detect sign candidates in the scene, we utilize the circular Hough transform with an appropriate post-processing in the vote space. Track of an already established candidate is maintained using a function that encodes the relationship between a unique feature representation of the target object and the affine transinformation it is subject to. This function is learned on-the-fly via regression from random distortions applied to the last stable image of the sign. Finally, we adopt a novel AdaBoost algorithm to learn a sign similarity measure from example image pairs labeled either "same" or "different". This enables construction of an efficient multi-class classifier. Prototype implementation has been evaluated on a video captured in crowded street scenes. Good detection and recognition performance was achieved for a 14 class problem which reveals a high potential of our approach.