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

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.

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.

Background: Alopecia areata (AA) is an autoimmune disease of hair loss affecting people of all ages. Alopecia totalis (AT) and universalis (AU) involve scalp and total body hair loss, respectively. AA significantly affects quality of life, but evidence on the economic burden in adolescents is limited. Objectives: To assess healthcare resource utilization (HCRU) and all-cause direct healthcare costs, including out-of-pocket (OOP) costs, of US adolescents with AA. Methods: IBM MarketScan® Commercial and Medicare databases were used to identify patients aged 12-17 years with ≥2 claims with AA/AT/AU diagnosis (prevalent cases), from October 1, 2015, to March 31, 2018, enrolled for ≥12 months before and after the first AA diagnosis (index). Patients were matched 1:3 to non-AA controls on index year, demographics, plan type, and Charlson Comorbidity Index. Per patient per year HCRU and costs were compared post-index. Results: Patients comprised 130 AT/AU adolescents and 1105 non-AT/AU adolescents (53.8% female; mean age, 14.6 years). Post-index, AT/AU vs controls had more outpatient (14.5 vs 7.1) and dermatologist (3.6 vs 0.3) visits, higher mean plan costs ($9397 vs $2267), including medical ($7480 vs $1780) and pharmacy ($1918 vs $487) costs, and higher OOP costs ($2081 vs $751) (all P<.001). The non-AT/AU cohort vs controls had more outpatient (11.6 vs 8.0) and dermatologist (3.4 vs 0.4) visits, higher mean plan costs ($7587 vs $4496), and higher OOP costs ($1579 vs $805) (all P<.001). Discussion: This large-sample, real-world analysis found that adolescents with prevalent AA had significantly higher HCRU and all-cause costs than matched controls. The greater burden was driven by more frequent outpatient visits, and higher payer medical and pharmacy costs in comparison with controls. Oral corticosteroid use was higher among patients with AT/AU; topical and injectable corticosteroid use was higher for non-AT/AU. Although the data preclude the identification of AA-attributable costs, the matched-control design allows an estimation of incremental all-cause costs associated with AA. Conclusions: Adolescents with AA incurred substantial incremental healthcare costs, with greater costs incurred among those with AT/AU. Study findings suggest that AA incurs costs as a medical condition with a high burden on adolescent patients and health plans.

In this work, an enhanced data-driven optimal iterative learning control (eDDOILC) is proposed for nonlinear nonaffine systems where a new iterative sliding mode surface (ISMS) is designed to replace the traditional tracking error in the controller design and analysis. It is the first time to extend the sliding mode surface to the iteration domain for systems operate repetitively over a finite time interval. By virtual of the new designed ISMS, the control design becomes more flexible where both the time and the iteration dynamics can be taken into account. Before proceeding to the controller design, an iterative dynamic linear data model is built between two consecutive iterations to formulate the linear input-output data relationship of the repetitive nonlinear nonaffine discrete-time system. The linear data model is virtual and does not have any physical meanings, which is very different to the traditional mechanism mathematical model. In the sequel, the eDDOILC is proposed by designing an objective function with respect to the proposed two-dimensional ISMS. Rigorous proof is provided to show the convergence of the proposed eDDOILC method. Furthermore, the results have been extended to a multiple-input multiple-output (MIMO) nonaffine nonlinear discrete-time repetitive system. In general, the proposed eDDOILC is data-driven where no explicit model information is included. It is illustrated that the presented eDDOILC is effective when applied to the nonlinear nonaffine uncertain systems.

We have used the hypothalamo-pituitary disconnected (HPD) sheep model to investigate direct pituitary actions of cortisol to suppress LH secretion in response to exogenous GnRH. We previously observed that, during the non-breeding season, treatment with cortisol did not suppress the LH response to GnRH in HPD gonadectomised rams or ewes.1 In the present experiment, we tested the effect of cortisol on the LH response to exogenous GnRH in gonadectomised HPD sheep during the breeding season. Using a cross-over design, HPD gonadectomised Romney Marsh rams (n = 6) and ewes (n = 5) received a saline or cortisol (250 μg/kg/h) infusion for 30 h on each of two days, one week apart. All animals were treated with 125 ng i.v. injections of GnRH every 2 h during a 6h control period preceding the infusion and during the infusion. Jugular blood samples were taken during the control period and the first 6 h and last 6 h of the infusion (over 3 LH pulses). Mean plasma concentrations of LH and LH pulse amplitudes, driven by programmed GnRH injections, were similar in gonadectomised rams and ewes and there were no significant effects of saline infusion between the control periods or the saline infusion in either sex. The amplitude of LH pulses was significantly (P < 0.05) reduced in rams during the first 6 h of the cortisol infusion compared to the control period, but there were no effects of the cortisol infusion in ewes. These data show that, in the absence of sex steroids, there is a sex difference in the mechanism by which cortisol acts at the pituitary to reduce LH secretion in response to exogenous GnRH in HPD gonadectomized sheep during the breeding season. We conclude that the effect of cortisol to reduce secretion of LH involves an action on the pituitary, at least in gonadectomised rams. (1)Stackpole CA, Turner AI, Clarke IJ and Tilbrook AJ (2003) Biology of Reproduction 36(Supplement 1), 288.

Purpose The purpose of this paper is to develop and test theory-driven hypotheses on the influence of corruption and gender inequality on logistics performance. Design/methodology/approach This paper develops hypotheses based on a review of the literature and theory linking corruption, gender inequality and logistics performance. Testing the hypotheses draws on the following secondary data sources: the World Bank Logistics Performance Index, Transparency International’s Corruption Perceptions Index and the United Nations Development Programme Gender Inequality Index. Regression analysis is used to test the hypotheses. Findings A significant direct effect is evident between corruption perceptions and perceived logistics performance. Corruption is detrimental to logistics. Further, there is evidence of an indirect effect, via gender inequality. Gender inequality is also linked directly to lower logistics performance. Gross domestic product/capita enters the analysis as a control variable. Research limitations/implications While the analysis uses secondary data, sources are credible and their methods – while not perfect – are logical and appear to be reasonable. It is possible that excluded variables could further explain the relationships under study. This implies future research opportunities, perhaps involving case studies of specific nations. Practical implications The results should inspire businesses, non-governmental organizations and governments to invest in, aid, advocate for and legislate toward greater gender equality – and against corruption. Logistics educators have an important role in disseminating this message. Social implications Gender inequality and corruption are current, global social issues. Moving forward toward equality and away from corruption are the right moves. Such moves appear to also yield better logistics. Originality/value This paper is among the first linking corruption and gender inequality to logistics performance. It shows how social issues impact logistics performance at a national level.

This paper analyses the multi-objective design of an inductor for a DC-DC buck converter. The core volume and total losses are the two competing objectives, which should be minimised while satisfying the design constraints on the required differential inductance profile and the maximum overheating. The multi-objective optimisation problem is solved by means of a population-based metaheuristic algorithm based on Artificial Immune Systems (AIS). Despite its effectiveness in finding the Pareto front, the algorithm requires the evaluation of many candidate solutions before converging. In the case of the inductor design problem, the evaluation of a configuration is time-consuming. In fact, a non-linear iterative technique (fixed point) is needed to obtain the differential inductance profile of the configuration, as it may operate in conditions of partial saturation. However, many configurations evaluated during an optimisation do not comply with the design constraint, resulting in expensive and unnecessary calculations. Therefore, this paper proposes the adoption of a data-driven surrogate model in a pre-selection phase of the optimisation. The adopted model should classify newly generated configurations as compliant or not with the design constraint. Configurations classified as unfeasible are disregarded, thus avoiding the computational burden of their complete evaluation. Interesting results have been obtained, both in terms of avoided configuration evaluations and the quality of the Pareto front found by the optimisation procedure.

Abstract Hyperthermia, the procedure of raising the temperature of a part of the entire body above normal for a defined period of time, is applied alone or as an adjunctive treatment to various established cancer treatment modalities such as radiotherapy and chemotherapy. Whole-Body Hyperthermia (WBHT), in contrast to local or regional hyperthermia, represents the only hyperthermia modality available for patients with disseminated malignancies. The biological rationale for the treatment of malignant disease by heat is driven by a number of reasons; a) the survival of cells depends on the temperature and duration of heating in a predictable and repeatable way; b) the tumor cell environment (such as hypoxia, poor nutrition, and low pH) that negatively influences the tumor cell killing by ionizing radiation and some chemotherapy regimens, is beneficially influenced by heat therapy; c) the differential sensitivity of normal and tumor cells to heat is dependent on cell type and environmental conditions; d) heat treatment enhances the biological effect of both radiation and chemotherapy agents. The biological rationale is based on a direct cell-killing effect at temperatures in the range of 41– 42°C. A systematic review of van der Horst et al, 2018, addressed clinical trials that used local or whole-body hyperthermia treatment (at variable temperatures) in pancreatic cancer patients. In those described trials, the weighted estimate of the treated population median overall survival was 11.7 compared to 5.6 for the control cohorts. In addition, locoregional hyperthermia (42-44°C) clinical trials showed that the weighted estimate median overall survival of the treated population was 15 months compared to 9 months in control cohorts. The MATTERS trial is a first in-human clinical investigation in advanced solid cancer patients or pancreatic adenocarcinoma patients (TxNxM1). The justification of the design is based on evaluation of pre-clinical data and clinical evaluation of clinical data, safety and/or performance of similar devices/therapies. The study is a mono-centric, non-randomized trial in which the safety and preliminary efficacy of whole-body hyperthermia will be evidenced. Well designed and performed early-stage correlative studies have the potential to strongly influence further clinical development of oncology clinical trials, and correlative data obtained from early stage trials has the potential to provide important guidance on the design and ultimate success of later stage trials. Blood samples will be collected for analysis of immunological panels (e.g. cytokines, chemokines), exosome research, RNA expression profiles. Urine will be collected for analysis of exosome research. The samples will be collected during different timepoints (before, during and after treatment). Citation Format: Ivana Gorbaslieva, Dana Mustafa, Robin Colenbier, Marc Peeters, Dirk Ysebaert, Vera Saldien, Luigi Brancato, Oleg Rudenko, Johan Van den Bossche, John Paul Bogers. First in-human, safety and preliminary efficacy study of (neo)adjuvant, model-based, whole-body hyperthermia treatment in advanced solid cancer patients or stage IV (TxNxM1) metastatic pancreatic adenocarcinoma patients: Liquid biopsies [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A023.

Abstract Introduction People with palliative care needs often use prescription medication to achieve symptom control. Prescribing and medication use are complex, multi-step processes influenced by prescribers, patients, carers, and contexts. They are shaped by historically established divisions of labour and normative rules. However, in the care of palliative patients, approximately 20% of NHS serious incident reports involve prescription medications with causes and contributing factors poorly understood (1). Understanding the mismatches between what is intended to happen and what is experienced in practice is key to improving safety and confidence in palliative care. Phase one of this study focuses on how the processes of prescribing and medication use are intended to happen, and comprises stakeholder engagement (presented here) and a scoping review. Aim To develop a model of intended processes of prescribing and medication use for symptom control in palliative care across the contexts of home, hospital, and hospice. Methods Stakeholder engagement was undertaken with a purposive sample of participants who had an interest in, or direct experience of, palliative medications. Invitations to participate were sent to professional networks and a patient and public involvement group. Informal conversations with a topic guide including known steps in the processes of prescribing and medication use were held either via video-call (recorded with permission) or by email. Conversations were examined for content relating to steps in the processes, and for themes that ran across multiple steps. An iterative cycle of listening, identifying steps, theme attribution and development of an intended processes model was undertaken with each recording. Results Twenty video and one email conversations were conducted with 7 family carers, 4 pharmacists/pharmacy researchers, 4 specialist palliative care professionals, 3 non-clinical researchers and 3 general practitioners. Different steps in the intended processes of prescribing and medication use at home and in hospital were identified. Themes of decision-making, prescribing, monitoring/supply, use/administration, and stopping/disposal were all represented. Further themes of deprescribing, anticipatory medicines, and contexts of practice - including out-of-hours care - were all raised. The processes model that resulted was not linear and incorporated complex feedback loops that facilitated ‘tinkering’, safe-to-fail experiments, and negotiations balancing ‘gut feeling’ with protocols and patients’ wishes. Conclusion Engaging with carer and professional stakeholders as a preliminary step in our study provided opportunity for clarification of key concepts, exploration of the nature and diversity of experiences, and identification of knowledge gaps. Whilst our engagement work may have been limited with the inclusion of only one nursing representative and little discussion of the hospice context, these aspects together with the content and themes identified will inform the design of our scoping review using the Joanna Briggs Institute framework. Data from the stakeholder engagement and the scoping review will be combined to finalise a model of intended processes in prescribing and medication use in symptom control in palliative care. Phase two of this study is a focussed ethnography to develop an experienced processes model. Synthesising the intended (phase one) and experienced (phase two) processes models using activity theory (2) will lead to when, how, and why processes in prescribing and medication use are misaligned and to ways in which systems can be improved to support patients and their families. References (1) Yardley I, Yardley S, Williams H, Carson-Stevens A, Donaldson LJ. Patient safety in palliative care: A mixed-methods study of reports to a national database of serious incidents. Palliat Med. 2018;32(8):1353–62. (2) Engeström Y, Davydov V, Toulmin S, Lektorsky V, Tolman C, Cole M, et al. Theoretical Issues. In: Engeström Y, Miettinen R, Punämaki R-L, editors. Perspectives on Activity Theory. Cambridge: Cambridge University Press; 1999. p. 1–146.

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 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.

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.

Iterative learning control is an adaptive, feedforward control technique traditionally used to improve the performance of systems that execute a task repetitively. While generally applied to systems driven by temporal dynamics, there exist applications, such as additive manufacturing, for which spatial dynamics play a particularly important role in determining system behavior. To ensure high fidelity functionality for these application spaces, this paper presents a spatial learning framework for optimizing multiple performance metrics simultaneously. Utilizing a one-step optimization approach enables direct evaluation of design trade-offs over a broad range of potential solutions. The multi-objective spatial learning framework, along with stability and convergence analysis is presented. Simulation results validate the control framework.

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.

Based on the continuity and motion equations of fluid, the direct problem is settled by using iterative calculation for two kinds of stream surfaces to obtain the flow field of the mixed-flow pump impeller. The inverse problem of the mixed-flow pump impeller is solved by employing point-by-point integration method to draw the blade shape, thickening the blade and smoothing the leading edge of the blade by conformal mapping. The meridional velocity will not be determined until the iterative calculation of direct and inverse problems is converged. Based on the meridional velocity field, the impeller performance is predicted with loss models. Then an optimal design model of the mixed-flow pump impeller is developed. This model, which applies genetic algorithm, takes the highest efficiency and the velocity moment distribution as the optimum objective and the optimum parameter, respectively. The obtained velocity moment distribution is then used in the next iterative calculation of direct and inverse problems. The impeller will not be finally determined until the difference between the velocity moment distributions, which are obtained from two consecutive iterative calculations, can meet the set requirements. Based on SIMPLEC algorithm, the three-dimensional turbulent flow field of the mixed-flow pump impeller is obtained by solving the Reynolds averaged Navier-Stokes equation and RNG k-ε turbulent model equation. The simulation results show that the impeller designed by this method has higher hydraulic efficiency, the pressure distribution is well uniform and the flow is steady without separation. The characteristic of this method is to determine blade shape by satisfying both the continuity and motion equations of fluid, so it can lead to high accuracy of impeller design. Meanwhile, the blade has the smooth surface and the complete data, which is convenient for blade manufacturing with numerical control machines.

Iterative learning control (ILC) is an effective technique to improve the tracking performance of systems through adjusting the feedforward control signal based on the memory data. It is critically important to design the learning filters in the ILC algorithm that assures the robust stability of the convergence of tracking errors from one iteration to next. The design procedure usually involves lots of tuning work especially in high-order ILC. To facilitate this procedure, this paper proposes an approach to design learning filters for an arbitrary-order ILC with guaranteed convergence and ease of tuning. The filter design problem is formulated into an H∞ optimal control problem. This approach is based on an infinite impulse response (IIR) system and conducted directly in iteration-frequency domain. Important characteristics of the proposed approach are explored and demonstrated on a simulated wafer scanning system.

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).