Добірка наукової літератури з теми "Non-parametric direct data-driven control design"

SUMMARYAchieving the asymptotic tracking control of electrically driven robot manipulators is a challenging problem due to approximation/modelling error arising from parametric and non-parametric uncertainty. Thanks to the specific property of Taylor series systems as they are universal approximators, this research outlines two robust control schemes using an adaptive Taylor series system for robot manipulators, including actuators' dynamics. First, an indirect adaptive controller is designed such as to approximate an uncertain continuous function by using a Taylor series system in the proposed control law. Second, a direct adaptive scheme is established to employ the Taylor series system as a controller. In both controllers, not only a robustifying term is constructed using the estimation of the upper bound of approximation/modelling error, but the closed-loop stability, as well as the asymptotic convergence of joint-space tracking error and its time derivative, is ensured. Due to the design of the Taylor series system in the tracking error space, our technique clearly has an advantage over fuzzy and neural network-based control methods in terms of the small number of tuning parameters and inputs. The proposed methods are simple, model free in decentralized forms, no need for uncertainty bounding functions and perfectly capable of dealing with parametric and non-parametric uncertainty and measurement noise. Finally, simulation results are introduced to confirm the efficiency of the proposed control methods.

The aim of this study was to investigate the enhancement of the mathematical understanding ability of pre-service mathematics teachers through Activity-Class Discussion-Exercise (ACE) teaching cyclic based on APOS theory. This study used a quasi-experiment method with non-equivalent pre-post test control group design. The subjects of this study were 120 pre-service mathematics teachers from two universities in Palembang, Indonesia. The subjects were divided into two class, that is, experiment and control class. Experiment class was a class that is applied ADE teaching cyclic based on APOS theory, whereas control class was a class that is applied direct learning. The subjects were also divided into three groups of mathematical initial ability, that is, high, average, and low. The Instruments used in this study were mathematical initial ability test, mathematical understanding ability test, observation, and interview. Data analysis tests used in this study were statistic test of parametric and non-parametric. The results of data analysis showed that 1) there is no significant difference between the improvement of mathematical understanding ability of pre-service mathematics teachers applied ACE teaching cyclic based on APOS theory and direct learning in terms of overall and the group of mathematical initial ability, 2) there is no interaction between learning factors (APOS and direct learning) and the group of mathematical initial ability (high, average, and low) to the improvement of mathematical understanding ability of pre-service mathematics teachers.

The process of ensuring automatic operation for industrial appliances using both supervision and control techniques is a challenging task. Therefore, this article focuses on implementing Supervisory Control and Data Acquisition (SCADA) for controlling all industrial appliances. The design process of implementation case is performed using an analytical framework by examining the primary energy sources at the initial state; thus, a smart network is supported. The designed mathematical model is integrated with a learning technique that allocates resources at proper quantities. Further, the complex manual tuning of individual appliances is avoided in the projected method as the input variables are driven in a direct way at reduced loss state. In addition, the data processing state of individual appliances is carried out using central data controller where all parametric values are stored. In case any errors are observed, then SCADA network fixes the error in an automated way, reducing end-to-end delays in all appliances. To validate the effectiveness of the proposed method, five scenarios are examined and simulated where outcomes prove that SCADA network using learning models provides optimal results on an average of 84 percent as compared to the existing models without learning algorithm.

This paper describes an ejector model for the prediction of on-design performance under available conditions. This is a direct method of calculating the optimal ejector performance (entrainment ratio or ER) without the need for iterative methods, which have been conventionally used. The values of three ejector efficiencies used to account for losses in the ejector are calculated by using a systematic approach (by employing CFD analysis) rather than the hit and trial method. Both experimental and analytical data from literature are used to validate the presented analytical model with good agreement for on-design performance. R245fa working fluid has been used for low-grade heat applications, and Engineering Equation Solver (EES) has been employed for simulating the proposed model. The presented model is suitable for integration with any thermal system model and its optimization because of its direct, non-iterative methodology. This model is a non-dimensional model and therefore requires no geometrical dimensions to be able to calculate ejector performance. The model has been validated against various experimental results, and the model is employed to generate the ejector performance curves for R245fa working fluid. In addition, system simulation results of the ejector refrigeration system (ERS) and combined cooling and power (CCP) system have been produced by using the proposed analytical model.

Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of noise statistics that result from the direct sensor readings. Despite this, the availability of RAW images is limited in comparison with the abundance and diversity of available RGB data. Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping: handcrafted model-based methods that are interpretable and controllable usually require manual parameter fine-tuning, while end-to-end learnable neural networks require large amounts of training data, at times with complex training procedures, and generally lack interpretability and parametric control. Towards addressing these existing limitations, we present a novel hybrid model-based and data-driven ISP that builds on canonical ISP operations and is both learnable and interpretable. Our proposed invertible model, capable of bidirectional mapping between RAW and RGB domains, employs end-to-end learning of rich parameter representations, i.e. dictionaries, that are free from direct parametric supervision and additionally enable simple and plausible data augmentation. We evidence the value of our data generation process by extensive experiments under both RAW image reconstruction and RAW image denoising tasks, obtaining state-of-the-art performance in both. Additionally, we show that our ISP can learn meaningful mappings from few data samples, and that denoising models trained with our dictionary-based data augmentation are competitive despite having only few or zero ground-truth labels.

Small piped water supply systems are often unable to provide reliable, microbiologically safe, and sustainable service over time, and this has direct impacts on public health. Circuit Rider (CR) post-construction support (PCS) addresses this through the provision of technical, financial, and operational assistance to these systems. CRPCS operates in low and high-income countries; yet, no rigorous studies of CRPCS exist. We measured the impact of CRPCS on ‘water quality’ and ‘sustainability’ indicators (technical and administrative capacity, and water supply protection) in El Salvador. In this field-based study, a case-control design was utilized in 60 randomly selected case (28 CR) and comparable control (32 noCR) communities. Microbiological water quality tests and pre-tested structured key-informant interviews were conducted. The operational costs of CRPCS were also assessed. Data were compared using parametric and non-parametric statistical methods. We found communities with CRPCS had significantly lower microbiological water contamination, better disinfection rates, higher water fee payment rates, greater transparency (measured by auditable banking records), greater rates of household metering, and higher spending for repairs and water treatment than comparable control communities. CRPCS is also a low-cost (<$1 per household/year in El Salvador) drinking water intervention.

This study aims to determine the effect of cooking class activities on the cognitive abilities of group A children in Cluster IV, Gedangan Sidoarjo District. This study uses a quasi-experimental design through a quantitative approach. The experimental design used was nonequivalent control group design. The subjects in this study were the children of group A RA Al Islah Karangbong with a total of 51 children as the experimental group and PAUD Tashwirul Afkar Karangbong with a total of 30 children as the control group. Data analysis techniques in this study used non-parametric statistics with the Mann Whitney Test. The results of this study indicate that "there is an effect of cooking class activities on the cognitive abilities of Group A kindergarten children in Cluster IV Gedangan Sidoarjo District" with a statistical value of Z = -4.076 with a significance level of 0.000 or less than 5%. This shows that there is an effect of cooking class activities on the cognitive abilities of Group A kindergarten children. Cooking class activities can be used as an interesting and fun learning reference for children in improving children's cognitive abilities because children get direct experience.

SummaryAcute coronary syndrome (ACS) is nowadays one of the leading causes of morbid-mortality in HIV-infected population, but innate and adaptive immune mechanisms preceding this event are unknown. In this work we comprehensively and longitudinally observed, by multi-parametric flow cytometry and following a case-control design, increased CCR5+CD8+ T-cells levels and monocytes expressing activation and adhesion markers in HIV-infected patients who are going to suffer ACS. In addition, we found direct associations between activated CD8+ T-cells and myeloid cells that were only statistically significant in the group of patients with ACS and in the follow up time point just before the ACS. Our data highlight the important role of CCR5 in the onset of ACS and suggest this receptor as a marker of cardiovascular risk and potential therapeutic target to prevent the development of such non-AIDS-related event in HIV-infected patients.Note: This work was presented at the CROI 2016 conference (Boston, USA in February 2016).

Using Holden’s taxonomy of domestic violence (DV) exposure as a guiding framework, the current study examined young adults’ diverse DV exposure experiences. Twenty-five young adults (ages 19-25) exposed to father-perpetrated DV during their childhood and adolescence were interviewed using a qualitative descriptive design. Data analyses focused on coercive control exposure through reports of non-physical abuse tactics, types of exposure (e.g., direct, indirect), physical violence exposure (e.g., severity, frequency), and child abuse and harsh parenting practices. DV-exposed young adults were directly and indirectly exposed to physical violence and an array of non-physical abuse tactics toward their mothers. Young adults categorized as having been exposed to coercive controlling violence reported exposure to ongoing, non-physical abuse tactics and more frequent and severe physical violence. These young adults were also more likely to intervene and become victimized during physical violence and reported repeated episodes of child abuse and harsh parenting. Although coercive control appeared to be associated with physical violence and child abuse, generalizations should be made with caution as a few participants exposed to situational conflict were exposed to frequent and severe DV. The findings suggest that DV exposure should be measured in methodologically sophisticated ways to capture the heterogeneity in experiences, with the goal of promoting empirically driven intervention and prevention initiatives that are tailored to individual and family needs.

PurposeThe purpose of this work is twofold. First, looks to identify the main homogenous groups of companies after environmental, social, economic and governance (ESEG) disclosures, non-financial statement and earnings per share (EPS), and second investigates the connection between variables.Design/methodology/approachUsing financial and non-financial information from annual reports of private listed companies, the authors performed two-step cluster analysis (TSCA) in the first stage of the research, followed by parametric, nonparametric correlation analysis, as well as regression analysis based on panel data, in the second stage.FindingsResults of TSCA revealed a cluster of companies with good financial and non-financial outcomes and a cluster of companies with poor performance. The performance dynamics showed a slight improvement during the period for few companies and composition analysis of clusters by industries through Kruskal–Wallis test highlighted differences between clusters, only for 2017. The main findings confirm a direct, although weak in intensity but statistically significant correlation between ESEG disclosure index, its sustainability component and financial performance (FP), valid for the entire period. Also, the results showed a direct link of low intensity to average, but statistically significant between the non-financial statement and EPS, valid only for 2017 and 2018.Research limitations/implicationsThe results indicate mixed findings which invites further in-depth research. Limits of the study can be found in selected indicators and the short period of time analyzed. However, the practical implications are worth considering from the perspective of finding new managerial tools that can better shape the relationship between ESEG disclosures and FP.Practical implicationsESEG Dindx can be an instrument for managers that can optimize the link between the FP of companies and its sustainable development.Social implicationsESEG Dindx measures the disclosure degree of ESEG information by the companies listed on Bucharest Stock Exchange (BSE). The main findings of the work confirm a direct, although weak in intensity but statistically significant correlation between ESEG disclosure index, its sustainability component and FP, valid for the entire period.Originality/valueThis study adds value to the existing literature by the proposed research framework, design of ESEG Dindx and the way correlations between variables were investigated.

Thermodynamic studies provide information that is necessary in order to understand the forces that drive the formation of ligand-macromolecule complexes. Knowledge of the energetics of these interactions is also indispensable for characterization of functionally important structural changes that occur within the studied complexes. Quantitative examination of the equilibrium interactions are designed to provide the answers to the questions: What is the stoichiometry of the formed complexes? How strong or how specific are the interactions? Are there any cooperative interactions among the binding sites and/or the bound ligand molecules? Are the binding sites intrinsically heterogeneous? What are the molecular forces involved in the formation of the studied complexes, or, in other words, how do the equilibrium binding and kinetic parameters depend on solution variables (temperature, pressure, pH, salt concentration, etc.)? Equilibrium isotherms for the binding of a ligand to a macromolecule represent the relationship between the degree of ligand binding (moles of ligands bound per mole of a macromolecule) and the free ligand concentration. A true thermodynamic binding isotherm is model-independent and reflects only this relationship. Only then, when such an isotherm is obtained, can one proceed to extract physically meaningful interaction parameters that characterize the free energies of interaction. This is accomplished by comparing the experimental isotherms to theoretical predictions based on specific binding models that incorporate known molecular aspects, such as intrinsic binding constants, cooperativity parameters, allosteric equilibrium constants, discrete character of the binding sites or overlap of potential binding sites, etc. (see below). Any method used to quantitatively study ligand binding to a macromolecule must relate the extent of the complex formation to the free ligand concentration in solution. Numerous techniques have been developed to study equilibrium properties of specific and non-specific ligand-macromolecule interactions in which binding is directly monitored, including equilibrium dialysis, ultrafiltration, column chromatography, filter binding assay and gel electrophoresis (1-6). These direct methods are very straightforward; however, they are usually time consuming and some, like filter binding or gel shift assays, are non-equilibrium techniques which require many controls before the reliable equilibrium binding data can be obtained.

A cohort study is one in which the outcome (usually disease status) is ascertained for groups of individuals defined on the basis of their exposure. At the time exposure status is determined, all must be free of the disease. All eligible participants are then followed up over time. Since exposure status is determined before the occurrence of the outcome, a cohort study can clarify the temporal sequence between exposure and outcome, with minimal information bias. The historical and the population cohort study (Box 9.1) are efficient variants of the classical cohort study described above, which nevertheless retain the essential components of the cohort study design. The exposure can be dichotomous [i.e. exposed (to obstetric complications at birth) vs. not exposed], or graded as degrees of exposure (e.g. no recent life events, one to two life events, three or more life events). The use of grades of exposure strengthens the results of a cohort study by supporting or refuting the hypothesis that the incidence of the disease increases with increasing exposure to the risk factor; a so-called dose–response relationship. The essential features of a cohort study are: ♦ participants are defined by their exposure status rather than by outcome (as in case–control design); ♦ it is a longitudinal design: exposure status must be ascertained before outcome is known. The classical cohort study In a classical cohort study participants are selected for study on the basis of a single exposure of interest. This might be exposure to a relatively rare occupational exposure, such as ionizing radiation (through working in the nuclear power industry). Care must be taken in selecting the unexposed cohort; perhaps those working in similar industries, but without any exposure to radiation. The outcome in this case might be leukaemia. All those in the exposed and unexposed cohorts would need to be free of leukaemia (hence ‘at risk’) on recruitment into the study. The two cohorts would then be followed up for (say) 10 years and rates at which they develop leukaemia compared directly. Classical cohort studies are rare in psychiatric epidemiology. This may be in part because this type of study is especially suited to occupational exposures, which have previously been relatively little studied as causes of mental illness. However, this may change as the high prevalence of mental disorders in the workplace and their negative impact upon productivity are increasingly recognized. The UK Gulf War Study could be taken as one rather unusual example of the genre (Unwin et al. 1999). Health outcomes, including mental health status, were compared between those who were deployed in the Persian Gulf War in 1990–91, those who were later deployed in Bosnia, and an ‘era control group’ who were serving at the time of the Gulf war but were not deployed. There are two main variations on this classical cohort study design: they are popular as they can, depending on circumstances, be more efficient than the classical cohort design. The population cohort study In the classical cohort study, participants are selected on the basis of exposure, and the hypothesis relates to the effect of this single exposure on a health outcome. However, a large cohort or panel of subjects are sometimes recruited and followed up, often over many years, to study multiple exposures and outcomes. No separate comparison group is required as the comparison group is generally an unexposed sub-group of the panel. Examples include the British Doctor's Study in which over 30,000 British doctors were followed up for over 20 years to study the effects of smoking and other exposures on health (Doll et al. 1994), and the Framingham Heart Study, in which residents of a town in Massachusetts, USA have been followed up for 50 years to study risk factors for coronary heart disease (Wolf et al. 1988). The Whitehall and Whitehall II studies in the UK (Fuhrer et al. 1999; Stansfeld et al. 2002) were based again on an occupationally defined cohort, and have led to important findings concerning workplace conditions and both physical and psychiatric morbidity. Birth cohort studies, in which everyone born within a certain chronological interval are recruited, are another example of this type of study. In birth cohorts, participants are commonly followed up at intervals of 5–10 years. Many recent panel studies in the UK and elsewhere have been funded on condition that investigators archive the data for public access, in order that the dataset might be more fully exploited by the wider academic community. Population cohort studies can test multiple hypotheses, and are far more common than any other type of cohort study. The scope of the study can readily be extended to include mental health outcomes. Thus, both the British Doctor's Study (Doll et al. 2000) and the Framingham Heart Study (Seshadri et al. 2002) have gone on to report on aetiological factors for dementia and Alzheimer's Disease as the cohorts passed into the age groups most at risk for these disorders. A variant of the population cohort study is one in which those who are prevalent cases of the outcome of interest at baseline are also followed up effectively as a separate cohort in order (a) to study the natural history of the disorder by estimating its maintenance (or recovery) rate, and (b) studying risk factors for maintenance (non-recovery) over the follow-up period (Prince et al. 1998). Historical cohort studies In the classical cohort study outcome is ascertained prospectively. Thus, new cases are ascertained over a follow-up period, after the exposure status has been determined. However, it is possible to ascertain both outcome and exposure retrospectively. This variant is referred to as a historical cohort study (Fig. 9.1). A good example is the work of David Barker in testing his low birth weight hypothesis (Barker et al. 1990; Hales et al. 1991). Barker hypothesized that risk for midlife vascular and endocrine disorders would be determined to some extent by the ‘programming’ of the hypothalamo-pituitary axis through foetal growth in utero. Thus ‘small for dates’ babies would have higher blood pressure levels in adult life, and greater risk for type II diabetes (through insulin resistance). A prospective cohort study would have recruited participants at birth, when exposure (birth weight) would be recorded. They would then be followed up over four or five decades to examine the effect of birth weight on the development of hypertension and type II diabetes. Barker took the more elegant (and feasible) approach of identifying hospitals in the UK where several decades previously birth records were meticulously recorded. He then traced the babies as adults (where they still lived in the same area) and measured directly their status with respect to outcome. The ‘prospective’ element of such studies is that exposure was recorded well before outcome even though both were ascertained retrospectively with respect to the timing of the study. The historical cohort study has also proved useful in psychiatric epidemiology where it has been used in particular to test the neurodevelopmental hypothesis for schizophrenia (Jones et al. 1994; Isohanni et al. 2001). Jones et al. studied associations between adult-onset schizophrenia and childhood sociodemographic, neurodevelopmental, cognitive, and behavioural factors in the UK 1946 birth cohort; 5362 people born in the week 3–9 March 1946, and followed up intermittently since then. Subsequent onsets of schizophrenia were identified in three ways: (a) routine data: cohort members were linked to the register of the Mental Health Enquiry for England in which mental health service contacts between 1974 and 1986 were recorded; (b) cohort data: hospital and GP contacts (and the reasons for these contacts) were routinely reported at the intermittent resurveys of the cohort; (c) all cohort participants identified as possible cases of schizophrenia were given a detailed clinical interview (Present State examination) at age 36. Milestones of motor development were reached later in cases than in non-cases, particularly walking. Cases also had more speech problems than had noncases. Low educational test scores at ages 8,11, and 15 years were a risk factor. A preference for solitary play at ages 4 and 6 years predicted schizophrenia. A health visitor's rating of the mother as having below average mothering skills and understanding of her child at age 4 years was a predictor of schizophrenia in that child. Jones concluded ‘differences between children destined to develop schizophrenia as adults and the general population were found across a range of developmental domains. As with some other adult illnesses, the origins of schizophrenia may be found in early life’. Jones' findings were largely confirmed in a very similar historical cohort study in Finland (Isohanni et al. 2001); a 31 year follow-up of the 1966 North Finland birth cohort (n = 12,058). Onsets of schizophrenia were ascertained from a national hospital discharge register. The ages at learning to stand, walk and become potty-trained were each related to subsequent incidence of schizophrenia and other psychoses. Earlier milestones reduced, and later milestones increased, the risk in a linear manner. These developmental effects were not seen for non-psychotic outcomes. The findings support hypotheses regarding psychosis as having a developmental dimension with precursors apparent in early life. There are many conveniences to this approach for the contemporary investigator. ♦ The exposure data has already been collected for you. ♦ The follow-up period has already elapsed. ♦ The design maintains the essential feature of the cohort study, namely that information bias with respect to the assessment of the exposure should not be a problem. ♦ As with the Barker hypothesis example, historical cohort studies are particularly useful for investigating associations across the life course, when there is a long latency between hypothesized exposure and outcome. Despite these important advantages, such retrospective studies are often limited by reliance on historical data that was collected routinely for other purposes; often these data will be inaccurate or incomplete. Also information about possible confounders, such as smoking or diet, may be inadequate.

Abstract This paper presents a framework to develop data-driven parametric reduced order models (PROMs) for aeroelastic (AE) analysis of flexible vehicles within a broad flight envelop. It is based on the separate domain and mode shape perturbation method. The flight envelop is first partitioned by multiple grid points, on each of which an aerodynamic ROM (AeroROM) is constructed using system identification (SYSID) techniques to capture dependence of the generalized aerodynamic force on the generalized displacement using data generated by high-fidelity CFD simulation. Then AeroROMs not on the grid point are obtained by interpolating those at neighboring grid points. Two interpolation schemes, i.e., the output-based interpolation and coefficient interpolation are developed. The parametric AeroROM is then coupled with the mode-based structural ROMs to enable integrated AE analysis under various flight conditions. The state consistence enabled by different SYSID techniques and performance of both ROM interpolation methods are also investigated. For the first time, it is found that the autoregressive exogenous ROM allows state consistence and direct model coefficient interpolation. The ROMs exhibit excellent agreement with CFD simulations (< 3% relative error) and orders of magnitudes speedup. The effort opens up new opportunities for parametric AE analysis and flight control design.

Abstract In many-query applications such as design, optimization, and model-predictive control, reduced order models (ROMs) have the potential to significantly decrease the computational cost. In contrast to intrusive projection-based ROMs which require direct access to the high-fidelity model (HFM) operators, non-intrusive ROMs can be constructed in a data-driven fashion using the input-output data generated by the HFM. When used in the prediction of the system response in unseen parameter regimes, however, generalization capabilities have to be carefully assessed. In this study, we pursue a two-step approach to construct non-intrusive parametric ROMs: The first step involves the extraction of low-dimensional latent spaces using proper-orthogonal decomposition (POD) and convolutional neural network-based autoencoders (CNN-AE); and the second step uses regression for the latent variables in parameter space. We adapt a unique decomposition approach named Split-POD to enrich the low-dimensional subspace of the parametric ROM. The proposed methods are used to predict the flow field over a vehicle geometry as a function of vehicle speed and internal fan speed. Through orders of magnitude reduction in the degrees of freedom, the non-intrusive ROMs enable flow field prediction in real-time and bypass hours of computations by the HFM. The results show that the nonlinear encoding in CNN-AE can enhance ROM predictions given adequate data. POD-based ROMs are more robust and efficient, and in particular more accurate than CNN-AE in under-sampled regions of the parameter space. We further enhance the utility of non-intrusive ROMs by formulating a training process that minimizes the required number of high-fidelity simulations using Gaussian process regression to adaptively sample the input parameter space and optimize the estimated variance of the predictions.

Abstract The analyses of parent-child well performance is a complex problem depending on the interplay between timing, completion design, formation properties, direct frac-hits and well spacing. Assessing the impact of well spacing on parent or child well performance is therefore challenging. A naïve approach that is purely observational does not control for completion design or formation properties and can compromise well spacing decisions and economics and perhaps, lead to non-intuitive results. By using concepts from causal inference in randomized clinical trials, we quantify the impact of well spacing decisions on parent and child well performance. The fundamental concept behind causal inference is that causality facilitates prediction; but being able to predict does not imply causality because of association between the variables. In this study, we work with a large dataset of over 3000 wells in a large oil-bearing province in Texas. The dataset includes several covariates such as completion design (proppant/fluid volumes, frac-stages, lateral length, cluster spacing, clusters/stage and others) and formation properties (mechanical and petrophysical properties) as well as downhole location. We evaluate the impact of well spacing on 6-month and 1-year cumulative oil in four groups associated with different ranges of parent-child spacing. By assessing the statistical balance between the covariates for both parent and child well groups (controlling for completion and formation properties), we estimate the causal impact of well spacing on parent and child well performance. We compare our analyses with the routine naïve approach that gives non-intuitive results. In each of the four groups associated with different ranges of parent-child well spacing, the causal workflow quantifies the production loss associated with the parent and child well. This degradation in performance is seen to decrease with increasing well spacing and we provide an optimal well spacing value for this specific multi-bench unconventional play that has been validated in the field. The naïve analyses based on simply assessing association or correlation, on the contrary, shows increasing child well degradation for increasing well spacing, which is simply not supported by the data. The routinely applied correlative analyses between the outcome (cumulative oil) and predictors (well spacing) fails simply because it does not control for variations in completion design over the years, nor does it account for variations in the formation properties. To our knowledge, there is no other paper in petroleum engineering literature that speaks of causal inference. This is a fundamental precept in medicine to assess drug efficacy by controlling for age, sex, habits and other covariates. The same workflow can easily be generalized to assess well spacing decisions and parent-child well performance across multi-generational completion designs and spatially variant formation properties.

Background: Malnutrition is estimated to account for more than one third of all child deaths, although it is rarely listed as a direct cause. Hypnoparenting is an attempt by parents to bring their children into the subconscious. This study aimed to determine the effect of hypnoparenting on nutritional status in preschool age children at Al-Fathir kindergarten, Tangerang, Banten. Subjects and Method: This was a quasi-experiment with a non-randomized pretest posttest design was conducted at Al-Fathir Kindergarten, Tangerang City, Banten Province from April to August 2020. A sample of 36 selected by purposive sampling. This study was divided into 2 groups: (1) 18 respondents were given hypnoparenting treatment and for the second group; and (2) 18 respondents were not given hypnoparenting. The dependent variable was nutritional. The independent was hypnoparenting. The data were analyzed by non-parametric tests. Results: There was a significant difference between nutritional status before hypnoparenting (p= 0.293) and nutritional status after hypnoparenting (p= 0.406) but there was no effect between changes in nutritional status made in the intervention group (Mean= 16.25; SD= 2.46; p= 0.222) and the control group (Mean= 16.18; SD= 3.88; p= 0.150). Conclusion: Hypnoparenting has effect in nutritional status of preschool age children, but it is statistically non-significant. Next, researchers will conduct further research on overcoming problems in children, especially nutritional problems, and the parents/guardians of students to be more effective in doing hypnoparenting to be maximized. Keyword: BMI, age, pre-school, hypnoparenting Correspondence: Diani Maryani, Academy of Midwifery Karya Bunda Husada, Tangerang, Banten. E-mail: dhianeyrs@gmail.com. Mobile: 081381234008. DOI: https://doi.org/10.26911/the7thicph.02.23

An analytical model of a double cardan shaft driveline is presented. The model has been derived by using a Lagrangian dynamics formulation and it includes description of the torsion flexibility of the shafts. The cardan shaft model is used in a coupled simulation with a direct torque controlled permanent magnet synchronous motor. Mechanical and electrical non-idealities affecting the dynamics of the driveline are studied. The modeled non-idealities include unequal joint angles, incorrect phasing of the cardan joints and current measurement errors in the supply of the electric motor. The results show that the control system of the electric drive tries to correct the transmission error caused by the mechanical non-idealities. This in turn leads to fluctuating torque and undesirable dynamic loads.

Abstract This paper addresses the issue of modeling a system with parametric uncertainty via a reduced-order interval model. A system with parametric uncertainty is represented by intervals of transfer function coefficients obtained directly from time-domain response data. An approach based on the sensitivity of a performance specification to the identified uncertain parameters is developed for efficiently analyzing the identified interval model. This approach can significantly reduce the computational effort required by interval system techniques and hence simplify its robust control design. A torque converter clutch system with friction uncertainty is given to fix ideas.

This paper presents a full parametric model of a turbomachinery blade. The model forms the geometric backbone of a new aerodynamic design suite, which aims at speeding up the coupled 2D/3D aerodynamic design process. The approach employed here follows the basic design concepts of turbomachinery blades, which are typically defined by a series of cross-sectional aerofoils stacked at their radial location to a three-dimensional blade. Unlike the geometry management in current design systems, the paradigm of a CAD based Master Model has been incorporated in the geometry definition and the corresponding software architecture. Therefore all blade features have been modelled as computational components in the flexible object-oriented software environment. The blade parameterisation enables the 2D aerofoil construction either from the common superposition of the camber line and thickness distribution or the direct control of the suction and pressure side. The sensitivity of the aerofoil aerodynamic performance with respect to a design parameter can be quickly assessed with a fast 2D flow solver. The parameters of the radial stacking line define the axial and tangential shift of each section. The parametric concept facilitates the inclusion of specific shape control techniques such as curvature manipulation and surface smoothing. Furthermore the use of optimisation methods is greatly simplified by the modular program structure, which allows to access single modules of the blade design tool in a batch job. Since the blade design process involves different coordinate systems, unique mapping functions are essential for a consistent update of the blade geometry during a design cycle. The interface to the CAD system is based on the standard data exchange format STEP. The CFD interface makes use of the NetCDF data format for automatic grid generation.

Abstract Demands for rotorcraft with increased flight speed, improved operational performance and reduced environmental impact have led to a drive in research and development of alternative concepts. Compound rotorcraft overcome the flight speed limitations of conventional helicopters with additional lifting and propulsive components. Further to operational benefits, these augmentations provide additional flight control parameters, resulting in control redundancy. This work aims to investigate the impact of optimal control strategies for a generic coaxial compound rotorcraft, equipped with turboshaft engines, targeting the minimization of mission fuel burn and gaseous emissions. The direct redundant controls considered are: (a) main rotor speed, (b) propeller speed, and (c), fuselage pitch attitude. A simulation tool for coaxial compound rotorcraft analysis has been developed and coupled to a zero-dimensional engine performance model and a stirred-reactor combustor model. Firstly, experimental and flight test data were used to provide extensive validation of the developed models. A parametric analysis was then carried out to gain insight into the effect of the redundant controls. This was followed by the derivation of a generalized set of optimal redundant control allocations using a surrogate-assisted genetic algorithm. Application of the optimal redundant control allocations during realistic operational scenarios has demonstrated reductions in fuel burn and NOx of up to 6.93% and 8.74% respectively. The developed method constitutes a rigorous approach to guide the design of control systems for future advanced rotorcraft.

Community contributions are often required as part of community-driven development (CDD) programmes, with payment encouraged through matching grants. However, little remains known about the impact of matching grants, or the implications of requiring community contributions in order for communities to receive development funding. This paper describes research where we partner with two non-governmental organisations (NGOs) – one international and one Somali – and undertake a randomised control trial of a CDD matching grant programme designed to incentivise informal contributions for local public goods in Gedo region in south-central Somalia. We rely on household survey data collected from 1,297 respondents in 31 treatment and 31 control communities, as well as surveys of village leaders and data on informal contributions from the mobile money platform used by community leaders to collect revenue. Two key findings emerge. First, our research shows that working with communities and incentivising informal revenue generation can be an effective way to deliver public goods and to support citizens and communities. Second, building on research exploring the potential for development interventions to spur virtuous or adverse cycles of governance, we show that development partners may work directly with community leaders and informal taxing institutions without necessarily undermining – and indeed perhaps strengthening – state legitimacy and related ongoing processes of statebuilding in the country. Indeed, despite playing no direct role in the matching grant programme, taxpayer perceptions of the legitimacy of the local government improved as a result of the programme. These findings deepen our understanding of how community contributions may be incentivised through matching grant programmes, and how they may contribute to CDD and public goods provision in a context of weak institutional capacity.

Voltage-gated sodium channels (Navs) play a pivotal role in excitability and are a prime target of insecticides like pyrethroids. Yet, these insecticides are non-specific due to conservation of Navs in animals, raising risks to the environment and humans. Moreover, insecticide overuse leads to resistance buildup among insect pests, which increases misuse and risks. This sad reality demands novel, more selective, insect killers whose alternative use would avoid or reduce this pressure. As highly selective insect toxins exist in venomous animals, why not exploit this gift of nature and harness them in insect pest control? Many of these peptide toxins target Navs, and since their direct use via transformed crop plants or mediator microorganisms is problematic in public opinion, we focus on the elucidation of their receptor binding sites with the incentive of raising knowledge for design of toxin peptide mimetics. This approach is preferred nowadays by agro-industries in terms of future production expenses and public concern. However, characterization of a non-continuous epitope, that is the channel receptor binding site for such toxins, requires a suitable experimental system. We have established such a system within more than a decade and reached the stage where we employ a number of different insect-selective toxins for the identification of their receptor sites on Navs. Among these toxins we wish to focus on those that bind at receptor site-3 and inhibit Nav inactivation because: (1) We established efficient experimental systems for production and manipulation of site-3 toxins from scorpions and sea anemones. These peptides vary in size and structure but compete for site-3 on insect Navs. Moreover, these toxins exhibit synergism with pyrethroids and with other channel ligands; (2) We determined their bioactive surfaces towards insect and mammalian receptors (see list of publications); (3) We found that despite the similar mode of action on channel inactivation, the preference of the toxins for insect and mammalian channel subtypes varies greatly, which can direct us to structural features in the basis of selectivity; (4) We have identified by channel loop swapping and point mutagenesis extracellular segments of the Navinvolved with receptor site-3. On this basis and using channel scanning mutagenesis, neurotoxin binding, electrophysiological analyses, and structural data we offer: (i) To identify the residues that form receptor site-3 at insect and mammalian Navs; (ii) To identify by comparative analysis differences at site-3 that dictate selectivity toward various Navs; (iii) To exploit the known toxin structures and bioactive surfaces for modeling their docking at the insect and mammalian channel receptors. The results of this study will enable rational design of novel anti-insect peptide mimetics with minimized risks to human health and to the environment. We anticipate that the release of receptor site-3 molecular details would initiate a worldwide effort to design peptide mimetics for that site. This will establish new strategies in insect pest control using alternative insecticides and the combined use of compounds that interact allosterically leading to increased efficiency and reduced risks to humans or resistance buildup among insect pests.

Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).