Causality Analysis for Concurrent Reactive Systems (Extended Abstract)

In a previous paper [1], the analysis of pump suction pressure decay utilizing the varying condensate temperature approach was first introduced. Although the paper provided power plant engineers with an accurate analytical method for determining the magnitude of feedwater pump suction pressure decay, no protective method to prevent or alleviate the pressure decay under instant turbine load rejection was mentioned. As unit size has rapidly increased in recent years, the power plant deaerator requires not only a larger storage capacity but also must be elevated higher to protect the boiler feed pumps against suction pressure decay under load rejection. Since this trend is expected to continue, the cost of supporting the power plant deaerator may soon become prohibitive. Several protective methods have been previously introduced. However, the evaluation of the methods was invariably based on the constant-condensate-temperature approach, which is not suitable for central station regenerative cycle units. This paper reevaluates those methods in general and recommends the on-off type deaerator bypass system as the most effective and economical method. The use of this protective method will not only reduce deaerator storage capacity to the absolute minimum as required solely for surge, but will also largely preclude the provision of additional static head for instant load reduction. As a result, enormous savings can be expected both from the deaerator itself and its supporting structures. The magnitude of savings in supporting structures alone may amount to more than a half million dollars for a 750 MW fossile unit. Based on the varying condensate temperature approach, mathematical equations expressing the deaerator pressure decay as well as design parameters required for designing the protective system have been derived. The paper also presents a detailed description of the recommended protective system and discusses some advantages of this system over the others. Finally, a few engineering examples are included to illustrate the application in system design.

The title of this paper provides an answer to the question posed by Larry King in his panel paper titled "Do formal methods really belong in the toolbox of the practicing engineer?" Yes, formal methods do indeed belong to the toolbox of the practicing engineer. The capabilities of today's computers are truly impressive and as a result have opened door to the design and development of increasingly ambitious systems. An almost unavoidable by-product of these systems is the growing consequences associated with their failure. Another attribute shared by most of these systems is that their complexity is of an unprecedented nature. Thus, these systems live in the cross section (or shall I say cross-hairs) of high consequence and high complexity. For these reasons, attaining and developing the ability to construct highly reliable software is be coming a significant concern. Furthermore, software manufacturers as well as consumers are getting more and more fed-up with missed deadlines, bugs crashing and freezing hardware, and in the worst case, causing havoc to organizations, as bugs/incompleteness lead to allowing trojan horses for hackers through viruses and attacks. This is leading to attempts to replace ad hoc techniques in system design by more systematic methods. What is the role that formal methods, as a field of study, can play in addressing these issues? If by formal methods, one is exclusively referring to formal verification methods for software, such tools will probably not be part of a practicing engineer's tool in the near future. King's note reflects the prevailing confusion about formal methods, what can be achieved using them, and what cannot be achieved using them, and their overall role in system design and development.

In this article, we provide an evidence-based primer of current tools and evolvingconcepts in the area of intraprocedural artificial intelligence (AI) methods incolonoscopy and laparoscopy as a ‘procedure companion’, with specific focus on colorectalcancer recognition and characterisation. These interventions are both likely beneficiariesfrom an impending rapid phase in technical and technological evolution. The domains whereAI is most likely to impact are explored as well as the methodological pitfalls pertainingto AI methods. Such issues include the need for large volumes of data to train AI systems,questions surrounding false positive rates, explainability and interpretability as well asrecent concerns surrounding instabilities in current deep learning (DL) models. The areaof biophysics-inspired models, a potential remedy to some of these pitfalls, is exploredas it could allow our understanding of the fundamental physiological differences betweentissue types to be exploited in real time with the help of computer-assistedinterpretation. Right now, such models can include data collected from dynamicfluorescence imaging in surgery to characterise lesions by their biology reducing thenumber of cases needed to build a reliable and interpretable classification system.Furthermore, instead of focussing on image-by-image analysis, such systems could analysein a continuous fashion, more akin to how we view procedures in real life and makedecisions in a manner more comparable to human decision-making. Synergistical approachescan ensure AI methods usefully embed within practice thus safeguarding against collapse ofthis exciting field of investigation as another ‘boom and bust’ cycle of AI endeavour.

As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.

As of today, model-based testing (MBT) is considered as leading-edge\ntechnology in industry. We sketch the different MBT variants that - according\nto our experience - are currently applied in practice, with special emphasis on\nthe avionic, railway and automotive domains. The key factors for successful\nindustrial-scale application of MBT are described, both from a scientific and a\nmanagerial point of view. With respect to the former view, we describe the\ntechniques for automated test case, test data and test procedure generation for\nconcurrent reactive real-time systems which are considered as the most\nimportant enablers for MBT in practice. With respect to the latter view, our\nexperience with introducing MBT approaches in testing teams are sketched.\nFinally, the most challenging open scientific problems whose solutions are\nbound to improve the acceptance and effectiveness of MBT in industry are\ndiscussed.\n

We present a novel framework for symbolically exploring very large or infinite state spaces of concurrent reactive object-oriented systems. In our framework we apply A* directed search algorithms for performing an efficient heuristic state space exploration towards user-defined search goals. The used heuristics exploit semantical knowledge about a system when searching for specific properties. The knowledge is extracted prior to the search process by computing abstractions of the considered systems. Based on an abstraction and a search goal we generate a heuristic function which can be used during state space exploration of the concrete system. The heuristics can be generated fully automatically and with different precision. Our approach to state space exploration has been implemented in a tool set called HORSE. The tool set can work in combination with an industrial UML case tool, and we show the applicability of our approach by applying the tool set to several sample models.Keywordsheuristic searchsymbolic executionabstractionsUML

Formal models of communicating and concurrent systems are one of the most important topics in formal methods, and process calculus is one of the most successful formal models of communicating and concurrent systems. In the previous works, the author systematically studied topology in process calculus, probabilistic process calculus and pi-calculus with noisy channels in order to describe approximate behaviors of communicating and concurrent systems as well as randomness and noise in them. This article is a brief survey of these works.

Narrowing and heuristic search for symbolic reachability analysis of concurrent object-oriented systems

This paper investigates the simultaneous use of in-situ hydrologic measurements in combination with two different artificial intelligent (AI) methods, namely, Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), for developing enhanced long-term streamflow forecasting models. To enhance the reliability of the proposed models’ outputs, a sub-basin method using the regionalization approach is proposed. Furthermore, to accelerate the training process and achieve more accurate handling of seasonal changes, a parameter representing seasonal variations is introduced. The models are applied to the mountainous Talezang basin, southwestern Iran, for which there is a 14-year series of monthly in-situ data records and snow cover area data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the use of the sub-basin approach significantly improves both AI methods’ performances. Moreover, it is deduced that the use of seasonal information and satellite data has a great impact on the model performance and accuracy. Comparing the long-term flow forecasts of both models showed that the ANFIS model is superior to the ANN.

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.

<p>Artificial intelligence (AI) methods, especially machine learning and natural language processing, are increasingly affecting health professions education (HPE), including the medical school application and selection processes, assessment, and scholarship production. The rise of large language models over the past 18 months, such as ChatGPT, has raised questions about how best to incorporate these methods into HPE. The lack of training in AI among most HPE faculty and scholars poses an important challenge in facilitating such discussions. In this commentary, the authors provide a primer on the AI methods most often used in the practice and scholarship of HPE, discuss the most pressing challenges and opportunities these tools afford, and underscore that these methods should be understood as part of the larger set of statistical tools available.Despite their ability to process huge amounts of data and their high performance completing some tasks, AI methods are only as good as the data on which they are trained. Of particular importance is that these models can perpetuate the biases that are present in those training datasets, and they can be applied in a biased manner by human users. A minimum set of expectations for the application of AI methods in HPE practice and scholarship is discussed in this commentary, including the interpretability of the models developed and the transparency needed into the use and characteristics of such methods.The rise of AI methods is affecting multiple aspects of HPE including raising questions about how best to incorporate these models into HPE practice and scholarship. In this commentary, we provide a primer on the AI methods most often used in HPE and discuss the most pressing challenges and opportunities these tools afford.</p> <p><br></p> <p><br></p>

This paper focused on the adsorption behavior of Fe (III) in aqueous solution on melamine. The effects of experimental conditions including dosage of melamine, reaction time and reaction temperature were investigated. The results showed that nearly 99% Fe (III) was adsorbed under the optimal conditions: melamine dosage (mole ratio) at n(C3H6N6)/n(Fe) = 3.5:1, reaction time of 60 min and reaction temperature of 90 °C. The optimal processing factors were obtained from response surface methodology and the effects of processing parameters on the removal efficiency of Fe (III) followed the order: mole ratio (n(C3N6H6):n(Fe)) > reaction temperature > reaction time. The adsorption kinetics behavior was fitted well with the pseudo-second-order model. The thermodynamic study showed that the adsorption process was unspontaneous and endothermic. The value of free energy change and standard enthalpy change disclosed that the mechanism of adsorption onto melamine was physisorption. The results will be useful for further applications of system design in the treatment of practical waste effluents.

Simulation of swine herd population dynamics

Era Society 5.0, requires the logistics agents to adapt and follow technology development to create supply chain efficiency. One of the ways is to apply the IoT to warehousing activities, creating a system design Warehouse Tracking app that works to increase order picking time efficiency. This system design application method is using the System Development Live Cycle (SDLC) and follows Object-oriented programming (OOP). This final result study is a system design application based on the IoT and QR codes for operating order-tracking goods in a warehouse. With this Tracker Stuff (T-UFF) app, the time it takes for order picking will be decreased so that it can create activity-efficient logistics for the company.

This paper is mainly focused on the study of the techniques available for the safety analysis of critical systems. It is never possible to build a completely safe system. There is a possibility to bring the behavior of these systems within acceptable limits. For safety evaluation of such systems both formal and informal techniques are available. Both techniques have their own prospects and consequences. Informal techniques are simpler to learn and easier to interpret and have more space for creativity and imagination of the analyst. Formal techniques due to their rigorousness ensure completeness. In this paper, we have analyzed both techniques after defining few parameters. Our study found it that formal techniques are better but usage of informal techniques can never be overlooked. Some approaches combine formal and informal techniques to reap the benefits of both. In some cases, informal techniques can be used as pre-requisite to narrow down the input of minimal critical set for formal techniques and reduce the effort required for formalization of the entire system.