A hyperbox classifier model for identifying secure carbon dioxide reservoirs

An important stage in the development of living systems on Earth was the formation of RNA-like molecules capable of self-transcripting and self-replicating. In this paper, the authors attempt to develop a simple, flexible and accurate computer model of nucleotide interactions that lead to the non-enzymatic transcription of an oligonucleotide that acts as a template to catalyze the formation of a suite of oligonucleotides. The authors' computer model is cellular automata based and allows nucleotides to experience random movement and interact locally to associate with a template and/or oligomerize with other nucleotides according to a set of rules. To test the simulation method, results were compared to specific laboratory experimental results. The hypotheses were that the best set of rules developed would be able to produce results which were: 1. More similar to the laboratory experiment's results than random rules; 2. More similar to the laboratory experiment's results than a set of rules which is chemically realistic but has random probabilities; and 3. Statistically similar to the laboratory experiment's results. The test for determining whether the results were statistically similar was done using a regression analysis. At the a=0.05 level: the first two hypothesis were supported, and the third hypothesis has not yet been statistically supported. >

Life cycle carbon efficiency of Direct Air Capture systems with strong hydroxide sorbents

BackgroundDengue is the most common arboviral disease of humans, with more than one third of the world’s population at risk. Accurate prediction of dengue outbreaks may lead to public health interventions that mitigate the effect of the disease. Predicting infectious disease outbreaks is a challenging task; truly predictive methods are still in their infancy.MethodsWe describe a novel prediction method utilizing Fuzzy Association Rule Mining to extract relationships between clinical, meteorological, climatic, and socio-political data from Peru. These relationships are in the form of rules. The best set of rules is automatically chosen and forms a classifier. That classifier is then used to predict future dengue incidence as either HIGH (outbreak) or LOW (no outbreak), where these values are defined as being above and below the mean previous dengue incidence plus two standard deviations, respectively.ResultsOur automated method built three different fuzzy association rule models. Using the first two weekly models, we predicted dengue incidence three and four weeks in advance, respectively. The third prediction encompassed a four-week period, specifically four to seven weeks from time of prediction. Using previously unused test data for the period 4–7 weeks from time of prediction yielded a positive predictive value of 0.686, a negative predictive value of 0.976, a sensitivity of 0.615, and a specificity of 0.982.ConclusionsWe have developed a novel approach for dengue outbreak prediction. The method is general, could be extended for use in any geographical region, and has the potential to be extended to other environmentally influenced infections. The variables used in our method are widely available for most, if not all countries, enhancing the generalizability of our method.

Knowledge base for a compositional expert system consists of a set of IF THEN rules with uncertainties expressed as weights. During consultation for a particular case, all aplicable rules are combined and weighhs of goals (final diagnoses or recommendations) are computed. The main problem when eliciting such knowledge base from an expert is the question of “correct” weights of rules.

The scheduling of batch processes is a widely researched field of chemical engineering. Over the last few decades, a great number of tools have been developed for industrial examples and literature problems. These methods vary not only in their applied model, but in their representation of the problem inputs as well. The most well-known representations are the State-Task Network, the Resource-Task Network, the State Sequence Network, and the S-graph. While the latter also serves as the mathematical model for the related optimization approaches, the others only act as an intermediate model between the raw problem data and the model used for optimization, e.g., a mixed-integer linear programming model. The eS-graph model is a generalization of the S-graph, where the one-to-one relation between nodes and tasks has been relaxed, allowing a much wider range of scheduling problems to be tackled. Processes may simultaneously occupy several units, and release them at different stages of execution, and certain stages of separate processes can be forced to overlap in time. As in the case of the S-graph, the eS-graph models can be solved to optimality by specially designed combinatorial algorithms or serve as a basis for precedence based linear programming formulations. In this work, the modelling capacities of the eS-graph framework are illustrated via a Polymer production case study, where complex timing constraints are present.

Inducing general functions from specific training examples is a central problem in the machine learning. Using sets of If-then rules is the most expressive and readable manner. To find If-then rules, many induction algorithms such as ID3, AQ, CN2 and their variants, were proposed. Sequential covering is the kernel technique of them. To avoid testing all possible selectors, Entropy gain is used to select the best attribute in ID3. Constraint of the size of star was introduced in AQ and beam search was adopted in CN2. These methods speed up their induction algorithms but many good selectors are filtered out. In this work, we introduce a new induction algorithm that is based on enumeration of all possible selectors. Contrary to the previous works, we use pruning power to reduce irrelative selectors. But we can guarantee that no good selectors are filtered out. Comparing with other techniques, the experiment results demonstrate that the rules produced by our induction algorithm have high consistency and simplicity.

EXEMPLARY PROGRAMMING IN RITA

Maintaining proper operation of adaptive protection schemes is one of the main challenges that must be considered for smart grid deployment. The use of reliable cyber detection and protection systems boosts the preparedness potential of the network as required by National Infrastructure Protection Plans (NIPPS). In an effort to enhance grid cyber-physical resilience, this paper proposes a tool to enable attack detection in protective relays to tackle the problem of compromising their online settings by cyber attackers. Implementing the tool first involves an offline phase in which Monte Carlo simulation is used to generate a training dataset. Using rough set classification, a set of If-Then rules is obtained for each relay and loaded to the relays at the initialization stage. The second phase occurs during online operation, with each updated setting checked by the corresponding relay’s built-in tool to determine whether the settings received are genuine or compromised. A test dataset was generated to assess tool performance using the modified IEEE 34-bus test feeder. Several assessment measures have been used for performance evaluation and their results demonstrate the tool’s superior ability to classify settings efficiently using physical properties only.

On sets of graded attribute implications with witnessed non-redundancy

This paper proposes machine learning techniques to discover knowledge in a dataset in the form of if-then rules for the purpose of formulating queries for validation of a Bayesian belief network model of the same data. Although do-main expertise is often available, the query formulation task is tedious and laborious, and hence automation of query formulation is desirable. In an effort to automate the query formulation process, a machine learning algorithm is lev-eraged to discover knowledge in the form of if-then rules in the data from which the Bayesian belief network model under validation was also induced. The set of if-then rules are processed and filtered through domain expertise to identify a subset that consists of “interesting” and “significant” rules. The subset of interesting and significant rules is formulated into corresponding queries to be posed, for validation purposes, to the Bayesian belief network induced from the same dataset. The promise of the proposed methodology was assessed through an empirical study performed on a real-life dataset, the National Crime Victimization Survey, which has over 250 attributes and well over 200,000 data points. The study demonstrated that the proposed approach is feasible and provides automation, in part, of the query formulation process for validation of a complex probabilistic model, which culminates in substantial savings for the need for human expert involvement and investment.

A cognitive simulator to animate behavior of a group of car drivers is developed. First, car driving is discussed from the point of view of intentions and actions of offline and online natures. Second, driving is discussed in terms of information used. Third, a cognitive structure of a driver is modeled by a tree which consists of situations such as physical environments, mental intentions and physical actions. Each transition between an upper level situation to an adjacent lower level on the tree is expressed by a set of if-then rules. A typical animation output from the cognitive simulator is demonstrated. >

A fuzzy diagnosis method in process systems with feedback control loops

This paper presents an on-line prediction technique of the photovoltaic output power under cloudy skies based on artificial intelligence. The technique takes advantage of the fact that fuzzy logic can accurately simulate nonlinear phenomena using a set of IF-THEN rules. These rules are fired partially and in-parallel. This means that, at any time, only a very limited subset of the rules is used. This enables a quick, on-line estimation of the output power of the photovoltaic cells based on the analysis of the image of the cloudy sky.

Linear models dominate quantitative thinking in investments. The weighted sum of a relevant set of factors, typically determined from a regression or discriminant analysis, determines model expectation. This approach is quite different from the heuristic style of decision making people tend to prefer. For example, when a patient is admitted to a trauma center, the attending physician determines the course of treatment for the emergency by going through a set of hierarchical decisions. Some things are more important than others, and they get considered first. Is there a pulse? is clearly a question to be asked first. Then follow questions of decreasing significance in deciding the course of treatment for the patient. The decision model for the physician is a decision tree, with an appropriate set of if-then rules. The decision tree is determined in a heuristic fashion based on experience and is preferred to a quantitative, weighted-sum approach, as in regression or discriminant analysis. The best investment decision making is more along the lines of using the right decision tree than using the right linear model.

The optimal design and operation of an offshore wind powered direct air capture (DAC) system is complex owing to the intermittent energy supply and the modularity of the units. A solid amine DAC process involves multiple individual units which undergo periodic loading to capture carbon dioxide (CO2) from ambient air, followed by regeneration to produce pure CO2 for utilisation or sequestration. The modular nature of a solid DAC process is exploited in this study to investigate the optimal design and coordinated operation of multiple DAC units mounted on a single 15 MW offshore wind turbine platform, with battery energy storage for additional short term power buffering. Important design parameters considered include the number of independently controllable units, the cyclic capacity of each unit (proportional to the amount of adsorbent) and the battery capacity and maximum power ratings. The design study results highlighted the diminishing returns to the CO2 capture rate with scaling, with a full design optimisation based upon cost estimations left for future work as the technology matures. It was found the optimal configuration was 14 DAC units, each with a cyclic capacity of 2000 kg CO2 , giving a total annual capture rate of 45 600 ton yr−1 and a wind utilisation factor of 96.6%. Furthermore, it was found that a rules-based control strategy based on high and low loading limits was competitive with a machine learning based controller and outperformed a model predictive control scheme.