Survey and assessment of the design and evaluation techniques of cumulus cloud modification experiments for rain enhancement

Physical basis of cloud seeding and meteorological considerations involved in the efficient design of experiments for rain enhancement are discussed. An analysis of thevarious designs and statistical techniques currently being employed for evaluating the experiments is presented. The limitations of available statistical methods, when used alone, to establish significantly the changes in rainfall caused by cloud seeding within a reasonable period of experimentation are broughtout. The manner in which remarkable increase is produced in the power efficiency of some tests when applied along with the simultaneous measurements of physical covariates or predictor variables and proper stratification of data is elucidated. The scope of anlayses based on postfactum stratifications or partitioning of data and the problems of multiplicity of analyses are discussed

Automating the parallel processing of fluid and structural dynamics calculations

The NASA Lewis Research Center is actively involved in the development of expert system technology to assist users in applying parallel processing to computational fluid and structural dynamic analysis. The goal of this effort is to eliminate the necessity for the physical scientist to become a computer scientist in order to effectively use the computer as a research tool. Programming and operating software utilities have previously been developed to solve systems of ordinary nonlinear differential equations on parallel scalar processors. Current efforts are aimed at extending these capabilities to systems of partial differential equations, that describe the complex behavior of fluids and structures within aerospace propulsion systems. This paper presents some important considerations in the redesign, in particular, the need for algorithms and software utilities that can automatically identify data flow patterns in the application program and partition and allocate calculations to the parallel processors. A library-oriented multiprocessing concept for integrating the hardware and software functions is described

Assessment of a 2D CFD model for a single phase natural circulation loop

The use of passive safety systems are more and more diffused in many technological fields. Natural circulation is probably one of the main phenomenon applied in this kind of systems: indeed, as known, by means of gravity and buoyancy forces, the fluids can circulate without any external power sources. In this paper a preliminary analysis (also by comparisons between experimental tests and numerical simulations) of a natural circulation based loop (namely a natural circulation based facility installed at University of Genova) is presented. Starting from some experimental results, the data deriving from CFD loop simulations (both in steady and in unsteady conditions) are used for a first preliminary validation, mainly in order to have a computational tool reliable and able to computationally simulate motion inversions related phenomena. The physical inversions phenomena are very well reproduced also by the a simplified numerical 2D model of the loop, and the physical considerations related to the temperature and velocity fluctuations during the transient simulations, are in agreement with the well-known observations formulated by Welander on the basis of a simple point source analysis scheme

A Deep Learning based Detection Method for Combined Integrity-Availability Cyber Attacks in Power System

As one of the largest and most complex systems on earth, power grid (PG) operation and control have stepped forward as a compound analysis on both physical and cyber layers which makes it vulnerable to assaults from economic and security considerations. A new type of attack, namely as combined data Integrity-Availability attack, has been recently proposed, where the attackers can simultaneously manipulate and blind some measurements on SCADA system to mislead the control operation and keep stealthy. Compared with traditional FDIAs, this combined attack can further complicate and vitiate the model-based detection mechanism. To detect such attack, this paper proposes a novel random denoising LSTM-AE (LSTMRDAE) framework, where the spatial-temporal correlations of measurements can be explicitly captured and the unavailable data is countered by the random dropout layer. The proposed algorithm is evaluated and the performance is verified on a standard IEEE 118-bus system under various unseen attack attempts

Variety Concept in Designing A Trading Area (Case Study: Dauh Puri Kangin, Denpasar City, Indonesia)

Variety is the extent to which design can give a choice of activities, types of users, functions, and meanings that occur in an environment. Feasibility of variety is fundamental in supporting the design quality of the trading area, especially in regions that have priority in developing the trading area, such as in Denpasar City. The economy of Denpasar City depends mostly on the trading sector. However, Dauh Puri Kangin, as the trade center area in the city, still has physical and non-physical problems and has not been well developed.  In getting a solution, this place needs research to find design criteria that can increase environmental variety. The study used cognitive mapping methods for data collection and qualitative assessment techniques for analysis methods. The analysis process results in the conditions of the problem and the potential related to variations in the study site. Then, it becomes a consideration in the design criteria of the trading area. From the results of the analysis, the problem that occurs in the corridor facilities is have not been able to accommodate the activities of all types of users, both regular users and users with special needs. Based on the results of the analysis, some considerations for the redesign of the area proposed several design proposals related to architectural design, landscape planning, and pedestrian facilities

The importance of an organic process in ethnographic research: Working with children in a physical activity setting

Background: In comparison to adult-centred research in physical activity, there are far fewer studies which concentrate on hearing children’s voices in physical activity research (Noonan et al. 2016). Additionally, despite a number of studies which utilise a child-centred approach, the number of papers which concentrate on the complexities when conducting research with young people are extremely limited. Purpose: To consider power relationships between adult researchers and young participants. Also, to provide empirical examples of considerations related to an organic research process and the complexities that may arise in research with children. Data Collection and Analysis: An ethnographic approach is deemed useful when conducting research with children (Davis and Watson 2017), but particular considerations need to be taken into account as an adult conducting research with young people. The data for this paper was drawn from a year-long ethnographic study with junior korfball players (aged 11-13 years of age). The study involved participant observation where the researcher’s role was ‘coaches’ help’. Nine semi-structured interviews took place 10-months into the study, and numerous informal conversations occurred throughout the research. Some of Foucault’s ideas related power were utilised to discuss the way relationships were negotiated with children to maintain a child-centred approach to the research. Final Thoughts: Adopting an organic approach to research may help reduce young participants’ perceptions of adult power. Adopting a Foucauldian lens can also heighten awareness of power divisions and aid the researcher’s sensitivity to their own use of techniques of power whilst in the field. Additionally, an organic approach can also help facilitate child-centred research which empowers participants and supports their voices being heard

Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to coping with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations of stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal of identifying or improving the model dynamics, building a surrogate or reduced model, or producing forecasts solely from observations of the physical model

Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to coping with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations of stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal of identifying or improving the model dynamics, building a surrogate or reduced model, or producing forecasts solely from observations of the physical model

Physical fitness, absenteeism and workers’ compensation in smoking and non-smoking police officers

Background: Employers seek to minimize business costs by creating conditions of employment. Relying on the presumably negative effects of smoking on variables such as workers’ compensation claims, absenteeism and physical fitness scores, they seek a rational basis for requirements that employees refrain from smoking. No research has been found on police officer smoking rates relating to physical fitness, and the resulting economic variables of workers’ compensation claims and absenteeism rates. Aims: To compare police officer non-smoker and smoker physical fitness, absenteeism rates and workers’ compensation claims. Methods: The sample included 514 officers of a metropolitan police department. A physical fitness test was ad- ministered. Smoking status, yearly absenteeism rates and workers’ compensation claims were collected. Results: Male smokers were significantly older than non-smokers. An analysis of covariance controlling for sex and age indicated that smokers had significantly (P = 0.05) lower fitness scores in sit and reach flexibility, sit-ups endurance, bench press strength and bicycle ergometer cardiovascular endurance. When neither age nor sex was controlled in males, a similar trend continued. However, in females only the sit and reach and sit-up tests demonstrated statistically significant differences. Fat percentage, step-test scores, absenteeism rates and workers’ compensation claims were not statistically different. Conclusion: These data do not provide a rational basis for the requirement that officers refrain from smoking when considering body fat and the economic savings of lower absenteeism rates and workers’ compensation. To some extent, smoking policies can be justified by officers’ physical fitness but there are age, gender and test protocol considerations