Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues

From Big Data To Knowledge – Good Practices From Industry

Recent advancements in data gathering technologies have led to the rise of a large amount of data through which useful insights and ideas can be derived. These data sets are typically too large to process using traditional data processing tools and applications and thus known in the popular press as ‘big data’. It is essential to extract the hidden meanings in the available data sets by aggregating big data into knowledge, which may then positively contribute to decision making. One way to engage in data-driven strategy is to gather contextual relevant data on specific customers, products, and situations, and determine optimised offerings that are most appealing to the target customers based on sound analytics. Corporations around the world have been increasingly applying analytics, tools and technologies to capture, manage and process such data, and derive value out of the huge volumes of data generated by individuals. The detailed intelligence on consumer behaviour, user patterns and other hidden knowledge that was not possible to derive via traditional means could now be used to facilitate important business processes such as real-time control, and demand forecasting. The aim of our research is to understand and analyse the significance and impact of big data in today’s industrial environment and identify the good practices that can help us derive useful knowledge out of this wealth of information based on content analysis of 34 firms that have initiated big data analytical projects. Our descriptive and network analysis shows that the goals of a big data initiative are extensible and highlighted the importance of data representation. We also find the data analytical techniques adopted are heavily dependent on the project goals

Re-examining interpretations of non-ideal behavior during diagnostic fracture injection tests

AbstractDiagnostic fracture injection tests (DFITs) are performed in low permeability formations to estimate the minimum principal stress, formation pressure, permeability, and other parameters. G-function derivative plots are used for diagnosing fracture closure and “non-ideal” reservoir processes. In this study, we use a discrete fracture network hydraulic fracturing simulator to investigate non-ideal DFIT mechanisms. The simulator fully couples fluid flow with the stresses induced by fracture deformation. DFITs are simulated for six different scenarios: a single hydraulic fracture, multiple fracture strands, opening of transverse fractures, near-wellbore complexity, far-field complexity, and height recession. The results indicate that pressure transient behavior commonly ascribed to “fracture height recession,” “closure of transverse fractures,” and “fracture tip extension” are likely to be misinterpreted by conventional techniques. In previous studies, we found that a curving upward G×dP/dG plot is caused by changing fracture stiffness after closure and that the closure pressure is best picked when G×dP/dG begins to deviate upward. In contrast, the commonly used “tangent” method can significantly underestimate the minimum principal stress. The results of this study confirm those prior results. The results suggest that in most cases, it should be possible to use pump-in/flowback tests to confirm estimates of the minimum principal stress. However, if a flow bottleneck occurs at the wellbore due to near-wellbore complexity, the pump-in/flowback test may be uninterpretable

Patterns of historical and future urban expansion in Nepal

Globally, urbanization is increasing at an unprecedented rate at the cost of agricultural and forested lands in peri-urban areas fringing larger cities. Such land-cover change generally entails negative implications for societal and environmental sustainability, particularly in South Asia, where high demographic growth and poor land-use planning combine. Analyzing historical land-use change and predicting the future trends concerning urban expansion may support more effective land-use planning and sustainable outcomes. For Nepal's Tarai region-a populous area experiencing land-use change due to urbanization and other factors-we draw on Landsat satellite imagery to analyze historical land-use change focusing on urban expansion during 1989-2016 and predict urban expansion by 2026 and 2036 using artificial neural network (ANN) and Markov chain (MC) spatial models based on historical trends. Urban cover quadrupled since 1989, expanding by 256 km2 (460%), largely as small scattered settlements. This expansion was almost entirely at the expense of agricultural conversion (249 km2). After 2016, urban expansion is predicted to increase linearly by a further 199 km2 by 2026 and by another 165 km2 by 2036, almost all at the expense of agricultural cover. Such unplanned loss of prime agricultural lands in Nepal's fertile Tarai region is of serious concern for food-insecure countries like Nepal

Comparative Analysis of Plastid Genomes Using Pangenome Research ToolKit (PGR-TK)

Plastid genomes (plastomes) of angiosperms are of great interest among biologists. High-throughput sequencing is making many such genomes accessible, increasing the need for tools to perform rapid comparative analysis. This exploratory analysis investigates whether the Pangenome Research Tool Kit (PGR-TK) is suitable for analyzing plastomes. After determining the optimal parameters for this tool on plastomes, we use it to compare sequences from each of the genera - Magnolia, Solanum, Fragaria and Cotoneaster, as well as a combined set from 20 rosid genera. PGR-TK recognizes large-scale plastome structures, such as the inverted repeats, among combined sequences from distant rosid families. If the plastid genomes are rotated to the same starting point, it also correctly groups different species from the same genus together in a generated cladogram. The visual approach of PGR-TK provides insights into genome evolution without requiring gene annotations.Comment: 15 pages, 4 figure

The 2015 Gorkha Nepal Earthquake: Insights from Earthquake Damage Survey

The 2015 Gorkha Nepal earthquake caused tremendous damage and loss. To gain valuable lessons from this tragic event, an earthquake damage investigation team was dispatched to Nepal from 1 May 2015 to 7 May 2015. A unique aspect of the earthquake damage investigation is that first-hand earthquake damage data were obtained 6–11 days after the mainshock. To gain deeper understanding of the observed earthquake damage in Nepal, the paper reviews the seismotectonic setting and regional seismicity in Nepal and analyzes available aftershock data and ground motion data. The earthquake damage observations indicate that the majority of the damaged buildings were stone/brick masonry structures with no seismic detailing, whereas the most of RC buildings were undamaged. This indicates that adequate structural design is the key to reduce the earthquake risk in Nepal. To share the gathered damage data widely, the collected damage data (geo-tagged photos and observation comments) are organized using Google Earth and the kmz file is made publicly available

Insights into frequent asthma exacerbations from a primary care perspective and the implications of UK National Review of Asthma Deaths recommendations

The United Kingdom National Review of Asthma Deaths (NRAD) recommends that patients who require ≥3 courses of oral corticosteroids (OCS) for exacerbations in the past year or those on British Thoracic Society (BTS) Step 4/5 treatment must be referred to a specialist asthma service. The aim of the study was to identify the proportion of asthma patients in primary care that fulfil NRAD criteria for specialist referral and factors associated with frequent exacerbations. A total of 2639 adult asthma patients from 10 primary care practices in Glasgow, UK were retrospectively studied between 2014 and 2015. Frequent exacerbators and short-acting β2-agonist (SABA) over-users were identified if they received ≥2 confirmed OCS courses for asthma and ≥13 SABA inhalers in the past year, respectively. Community dispensing data were used to assess treatment adherence defined as taking ≥75% of prescribed inhaled corticosteroid (ICS) dose. The study population included 185 (7%) frequent exacerbators, 137 (5%) SABA over-users, and 319 (12%) patients on BTS Step 4/5 treatment. Among frequent exacerbators, 41% required BTS Step 4/5 treatment, 46% had suboptimal ICS adherence, 42% had not attended an asthma review in the past year and 42% had no previous input from a specialist asthma service. Older age, female gender, BTS Step 4/5, SABA over-use and co-existing COPD diagnosis increased the risk of frequent exacerbations independently. Fourteen per 100 asthma patients would fulfil the NRAD criteria for specialist referral. Better collaboration between primary and secondary care asthma services is needed to improve chronic asthma care