Prescriptive Model Research Articles

Incidence of V˙O2max Responders to Personalized versus Standardized Exercise Prescription.

Despite knowledge of cardiorespiratory fitness (CRF) training responders and nonresponders, it is not well understood how the exercise intensity prescription affects the incidence of response. The purpose of this study was to determine CRF training responsiveness based on cohort-specific technical error after 12 wk of standardized or individually prescribed exercise and the use of a verification protocol to confirm maximal oxygen uptake (V˙O2max). Sedentary adult participants (9 men, 30 women; 48.2 ± 12.2 yr) completed exercise training on 3 d·wk for 12 wk, with exercise intensity prescribed based on standardized methods using heart rate reserve or an individualized approach using ventilatory thresholds. A verification protocol was used at baseline and 12 wk to confirm the identification of a true V˙O2max and subsequent relative percent changes to quantify CRF training responsiveness. A cohort-specific technical error (4.7%) was used as a threshold to identify incidence of response. Relative V˙O2max significantly increased (P < 0.05) from 24.3 ± 4.6 to 26.0 ± 4.2 and 29.2 ± 7.5 to 32.8 ± 8.6 mL·kg·min for the standardized and individualized groups, respectively. Absolute V˙O2max significantly increased (P < 0.05) from 2.0 ± 0.6 to 2.2 ± 0.6 and 2.4 ± 0.8 to 2.6 ± 0.9 L·min for the standardized and individualized groups, respectively. A significant difference in responsiveness was found between the individualized and standardized groups with 100% and 60% of participants categorized as responders, respectively. A threshold model for exercise intensity prescription had a greater effect on the incidence of CRF training response compared with a standardized approach using heart rate reserve. The use of thresholds for intensity markers accounts for individual metabolic characteristics and should be considered as a viable and practical method to prescribe exercise intensity.

DEVELOPING DATA DRIVEN PRODUCTS IN THE EMERGING MARKETS

The expansion of the digital economy and the rapid developments of technology induced the creation of new products and industries and drove a significant increase in data resources. The information products industry, including products based on data, information and knowledge, is intensely dynamic in terms of growth and the pace of new product introduction. The complexity in the variety of product offerings and the number of firms offering those products in this industry is increasing exponentially every day. Data-driven innovation forms a key pillar in the 21 century sources of growth. Large data sets are becoming a core asset in the economy, fostering new industries, processes, and products and creating significant competitive advantages (OECD, 2015). The past two decades have brought several reconfigurations of the information and knowledge economy. The recent technological breakthroughs have driven the emergence and the exponential growth of a digital economy with vast data assets. The changes have been accompanied by ongoing attempts to make sense of all the data through the use of analytics. Analytics add substantial value to intangible assets by making them easier to understand and apply. In a world in which information alone has become ubiquitous and somewhat commoditized, analytics provide a means of making information more useful and valuable. In this paper, we focus on new analytical capabilities and data assets that together form value-added information product offerings and new possibilities for emerging markets. These offerings are often called data products. In general, a data product is digital information that can be purchased. Data products incorporate data science into the operation of a product or service, using data in smart ways to provide value. In research, a data product is a large data set in a format that requires little or no processing or programming. Typical data products are predictive, descriptive or prescriptive models, as well as insights. The future of new product development reflects both developing new innovative products and data driven products typical for emerging markets that are large economies. For any organization, there is not only revenue, but competitive advantage to be gained in developing data products and new innovative products. Creating an effective development process for data products requires following well-established steps and data analytics helps adding a few new ones, which are explained in the paper. Further, this paper will focus on three important decisions for innovative process: decision 1- key enablers of emerging market innovation (R&amp;D capability, market opportunity and executive champions), decision 2 - what product to develop (market need, portfolio fit, and product-capability fit) and decision 3 - how to develop the product (the decision matrix and bootstrapping). Organizations can follow several approaches to monetize their data like improving internal business processes and decisions, focusing information around core products and services, and selling information offerings to emerging and existing markets. Few remarks for our country will be made as further paths of development.

A latent growth curve analysis of precursor cognitive abilities and academic achievement.

Language-based cognitive fitness describes the ability to carry out cognitive tasks with vigour and alertness, to learn, and to adapt. The purpose of this research was to test the efficacy of a brain fitness training programme to better understand the role played by three precursor abilities for language-based cognitive development-verbal reasoning, visual synthesis, and active analysis-and their impact on academic achievement. The sample included 92 children from a private school designed to deliver a prescriptive educational model for the spectrum of challenged, average, and gifted students. Students completed cognitive tests at entry to the program and at the end of each academic year. Students completed daily brain-based exercises. Associative latent variable growth curve modelling using Mplus was used to test growth in all variables. Bayesian estimation analysis was used for missing data imputation. Age and baseline cognitive ability were included as covariates. Model fit was excellent, χ2 (104)=115.98, p=.20, (χ2 /df)=1.11, CFI=0.98, TLI=0.98, RMSEA=0.04, SRMR=0.05. Students who started with the lowest ability levels initially grew faster than those students with higher initial two-wave performance levels consistent with a closing of an ability gap. This had a lagged effect on achievement that accelerated in growth after the first 2years. Results provide evidence for the importance of interventions that focus on strengthening precursor cognitive abilities directly involved with language and mathematics achievement.

Design of Laboratory Management Information System: Case Study National Narcotics Board Republic of Indonesia

Requirement design for the laboratory information management system at the National Narcotics Boards Republic of Indonesia (BNN-RI) has been developed by applying a modified Rational Unified Process methodology that is more adaptive to changing needs than prescriptive process models but still with relatively little cost because not all activities are performed. The design of appropriate requirements of the system is expected to produce useful information that can accelerate laboratory testing services business processes that help Drugs Testing Laboratory in providing service in accordance with the promise of service. The design is done by following a workflow in business modeling and requirements discipline on RUP methodology. The results of this study are business modeling and list of needs contain functional and non functional requirement, which will be implemented in laboratory management information systems. In designing the requirement for the Drugs Testing Laboratory BNN-RI, re-engineering and reconfiguration processes on RUP methodology has been conducted to suit the scale of organization and reduce the required costs.

Vehicle Modification Prescription: Australian Occupational Therapy Consensus-Based Guidelines.

Vehicle modifications are often essential to ensure that drivers with disabilities can drive independently and safely. Occupational therapy driver assessors and driver rehabilitation specialists routinely prescribe vehicle modifications on the basis of individualized evaluations and support by rehabilitation interventions. After a literature review and consultation with stakeholders, we identified a resource gap in information to support an evidence-based process for vehicle modification prescription. Using an action research approach, we established content validity for draft guideline items and developed consensus-based draft guidelines for vehicle modification prescription that are ready for trialing by occupational therapy driver assessors. We also propose a practice model for vehicle modification prescription that is applicable to international contexts.

PriMa: a prescriptive maintenance model for cyber-physical production systems

ABSTRACTCyber-physical production systems (CPPS), as an emerging Industry 4.0’s technology, trigger a paradigm shift from descriptive to prescriptive maintenance. In particular, maintenance management approaches nowadays are more and more transformed to (semi-) automated knowledge-based decision support systems. This paper is intended to examine existing approaches and challenges towards rethinking maintenance in the context of Industry 4.0 and thus contributes to the literature of production management and planning, by introducing a novel prescriptive maintenance model (PriMa). PriMa is comprising of four layers (i.e. data management, predictive data analytic toolbox, recommender and decision support dashboard as well as an overarching layer for semantic-based learning and reasoning). The integrated approach of PriMa enhances two functional capabilities, namely i) efficiently processing large amount of multi-modal and heterogeneous data collected from multidimensional data sources and ii) effectively generating decision support measures and recommendations for improving and optimising forthcoming maintenance plans correlated with production planning and control (PPC) systems. An industry-oriented proof-of-concept study has been conducted to explore the feasibility of applying PriMa in real production systems by implementing a decision support solution and achieving a significant reduction of downtime. Finally, future research directions in this area are outlined.

Sample Size Requirements for Multivariate Models to Predict Between-Patient Differences in Best Treatments of Major Depressive Disorder

Clinical trials have documented numerous clinical features, social characteristics, and biomarkers that are “prescriptive” predictors of depression treatment response, that is, predictors of which types of treatments are best for which patients. On the basis of these results, research is actively under way to develop multivariate prescriptive prediction models to guide precision depression treatment planning. However, the sample size requirements for such models have not been analyzed. We present such an analysis here. Simulations using realistic parameter values and a state-of-the-art cross-validated targeted minimum loss-based prescription treatment response estimator show that at least 300 patients per treatment arm are needed to have adequate statistical power to detect clinically significant underlying marginal improvements in treatment response because of precision treatment selection. This is a considerably larger sample size than in most existing studies. We close with a discussion of practical study design options to address the need for larger sample sizes in future studies.

多世界パラダイムに基づく算数授業における社会的相互作用の規範的モデルの開発研究（III）― 第４学年「分数」の授業による検証 ―

多世界パラダイムに基づく算数授業における社会的相互作用の規範的モデルの開発研究（III）― 第４学年「分数」の授業による検証 ―

Conceptual Design of an Integrated Autonomous Production Control Model in association with a Prescriptive Maintenance Model (PriMa)

Autonomous production control models facilitate coping with co-occurrent and ever-growing production system complexity. The choice of the appropriate control model and objective function in manufacturing industry depends on several interrelated factors such as machine and process flexibility, plant availability, process stability, resource utilization, environmental and economic sustainability. Numerous theoretical models and approaches for autonomous production control have been proposed. Yet, it is hard to determine the effectiveness of efforts in real-life applications, due to neglecting or incomplete consideration of the aforementioned factors. This paper concentrates on the correlative relation between production planning and control (PPC) and maintenance planning. An integrated approach is presented that establishes the link between autonomous production control and prescriptive maintenance model (PriMa). Traditionally, PPC and maintenance planning are carried out apart from one another. In the light of PriMa, various aspects of data-driven maintenance planning and the missing link with autonomous production control are discussed. To this end, this paper presents a conceptual model, which encompasses three sub-systems, namely i) maintenance system, ii) autonomous production control system, and iii) production planning. The proposed model is intended to reduce planning complexity and at the same time achieve an improved plant availability and resource efficiency.

Knowledge Management in Graduate Programs: A Prescriptive Model

La investigacion trata sobre la Gestion del Conocimiento (GC) en el ambito especifico de los programas universitarios de postgrado. Propone, fundamenta y valida un modelo teorico cuyas prescripciones permiten desarrollar e implementar sistemas de GC (SGC) para programas de doctorado, maestria y especializacion, tomando como casos de estudio programas correspondientes a esos tres niveles en la Universidad Simon Bolivar (Venezuela). La investigacion, mixta o multimetodo, corresponde a un diseno exploratorio secuencial, con una fase inicial de recoleccion y analisis de datos cualitativos seguida de otra donde se recabaron y analizaron datos cuantitativos. En la fase cualitativa se formulo un modelo teorico prescriptivo, y en la cuantitativa, se evaluo el SGC resultante en usuarios adscritos a los tres tipos de programas mencionados, para cuatro dimensiones: Aceptacion del Sistema, Calidad del Sistema, Utilidad del Sistema, y Satisfaccion del Usuario, lo que permitio validar el modelo teorico propuesto. La investigacion contribuye de esta manera a reducir brechas de la teoria en el contexto de la Gestion del Conocimiento en programas de postgrado, aporta soluciones al problema de la ralentizacion de las iniciativas de Gestion del Conocimiento en las universidades, genera un modelo que permite desarrollar SGC aplicables en otros programas de postgrado, y muestra metodos de evaluacion de SGC generalizables y replicables, con potenciales beneficios para el Capital Intelectual de las instituciones de educacion superior.

Advances in thermal level measurement techniques using mathematical models, statistical models and decision support systems in blast furnace

The estimation of thermal level in blast furnace is of utmost importance, because the processes occurring inside the blast furnace are complex in nature and any drift in thermal level could lead to abnormal furnace state. The present review is made to understand the methods for estimating thermal level in blast furnace, and the drift in estimation of the thermal level. The thermal level estimation is divided into 3 categories, viz. mathematical models, statistical models and decision support systems. The mathematical models are based on the first principle of thermodynamics and give an estimate of the thermal level in blast furnace. On the other hand, the statistical models are mainly the data-based approach that uses the historical data to predict the instability in blast furnace. Lastly, the decision support systems are the prescriptive models that give the recommendations for making the necessary corrections in the process parameters to avoid occurrence of abnormality in blast furnace. Further, the drifts in estimation of thermal level by these techniques are identified and recommendations are made to improve the accuracy of thermal level estimation. The recommendations to control thermal level in blast furnace are provided which when applied in the industrial blast furnace, can avoid the occurrence of catastrophic condition.

Main Elements of National Model of Electronic Prescription System from Physicians' Point of View: A Case Study in a Developing Country.

Identifying the factors and components of an electronic prescription system is of utmost importance in effective designing and implementation of this system. In this regard, the current study was conducted to determine the main factors affecting the national model of electronic prescription from the physicians' point of view. This is a cross-sectional, descriptive-analytical research carried out in 2015. Based on the census sampling method, 104 members of the boardofdirectors of the Iraniangeneral practitioners' associations, general practitioners' alumni association of Iran, and physicians owner of a website or weblog were selected as samples for this study. Data were collected using a valid and reliable questionnaire. After analyzing the data with SPSS software (v.16), a model was proposed using a regression algorithm. The findings indicated that accessing the current medication data and medication history of patients during prescription, and also creating the electronic patient medication record (ePMR) are the most important selective components for physicians with frequency percent of 92.1%. Moreover, from the physicians' viewpoint, the method of "transmission of prescriptions to the central national database and retrieving prescriptions information from the selected pharmacy of the patient" had the highest priority (weight coefficient) in the model of the national electronic prescriptionsystem. Therefore, the Iranian prescription system is required to be developed based on the centralized architecture and national electronic prescription database.

A Dynamic Prescriptive Maintenance Model Considering System Aging and Degradation

This paper develops a dynamic maintenance strategy for a system subject to aging and degradation. The influence of degradation level and aging on system failure rate is modeled in an additive way. Based on the observed degradation level at the inspection, repair or replacement is carried out upon the system. Previous researches assume that repair will always lead to an improvement in the health condition of the system. However, in our study, repair reduces the system age but on the other hand, increases the degradation level. Considering the two-fold influence of maintenance actions, we perform reliability analysis on system reliability as a first step. The evolution of system reliability serves as a foundation for establishing the maintenance model. The optimal maintenance strategy is achieved by minimizing the long-run cost rate in terms of the repair cycle. At each inspection, the parameters of the degradation processes are updated with maximum a posteriori estimation when a new observation arrives. The effectiveness of the proposed model is illustrated through a case study of locomotive wheel-sets. The maintenance model considers the influence of degradation and aging on system failure and dynamically determines the optimal inspection time, which is more flexible than traditional stationary maintenance strategies and can provide better performance in the field.

Quality of resistance training description in COPD trials: study protocol for a systematic review

IntroductionLimb muscle dysfunction is a common manifestation in patients with chronic obstructive pulmonary disease (COPD). Optimising of limb muscle function is therefore an important goal during pulmonary rehabilitation of patients...

University Spaces for Entrepreneurship: A Process Model

This paper explores trends in entrepreneurship spaces developed by universities to support entrepreneurship education. It identifies characteristics that make a space conducive to innovation and explains whether current spaces adequately conform to those characteristics. More generally, this paper seeks to clarify what is being built, for which purposes, and with what results. Given the novelty of this research, the paper uses a multiple-method approach to allow for an iterative examination between theory and data. Multiple data and methods were used, including an action research method, a systematic survey of 57 entrepreneurship spaces at U.S. universities, and a thematic and content analyses of interviews carried out with individuals directly involved in the functioning of such spaces. The paper presents a prescriptive model aimed at guiding the practitioner in the design of an entrepreneurship space. It identifies five types of entrepreneurship spaces that differentially support entrepreneurial activities and rely on different characteristics. These characteristics are centrally important for innovation and entrepreneurship spaces. There are a number of practical implications from the work. It identifies key challenges in the design of entrepreneurship spaces and shows which questions to consider in the decision-making process. The paper advances research on entrepreneurship spaces, an important yet poorly understood phenomenon. It reviews and introduces the literature on how space can support innovation, entrepreneurship education and entrepreneurial ‘spirit’, and proposes a typology of entrepreneurship spaces, providing a path towards more robust and comprehensive theory building.

Adaptive Management of Coastal Aquifers Using Entropy-Set Pair Analysis–Based Three-Dimensional Sequential Monitoring Network Design

A three-dimensional compliance monitoring network design methodology is presented to develop an adaptive and sequentially modified management policy that intends to improve optimal and justifiable use of groundwater resources in coastal aquifers. In the first step, an ensemble metamodel-based multiobjective prescriptive model is developed using a coupled simulation-optimization approach to derive a set of Pareto optimal groundwater extraction strategies. Prediction uncertainty of metamodels is addressed by utilizing a weighted average ensemble using set pair analysis. In the second step, a monitoring network is designed for evaluating the compliance of the implemented strategies with the prescribed management goals due to possible uncertainties associated with field-scale application of the proposed management policy. Optimal monitoring locations are obtained by maximizing Shannon's entropy between the saltwater concentrations at the selected potential locations. Performance of the proposed three-dimensional sequential compliance monitoring network design is assessed for an illustrative multilayered coastal aquifer study area. The performance evaluations show that sequential improvements of optimal management strategy is possible by using saltwater concentrations measured at the proposed optimal compliance monitoring locations. Therefore, the salinity concentration data collected at the designed compliance monitoring wells can be used to collect feedback information in terms of salinity concentrations. This feedback information can be applied to improve the initially prescribed optimal groundwater extraction patterns while keeping the original management goal intact.

Modeling in measurement science: from the prescriptive model to the descriptive model for the practical implementation of the former

Modeling in measurement science: from the prescriptive model to the descriptive model for the practical implementation of the former

What Are (English) Lessons For?

ABSTRACTThis article takes as its starting-point four moments that occurred in the course of a year of teaching A-level English Literature in an inner-London sixth-form college. It argues that these moments represented forms of learning and experience that were valuable, but that fell outside the prevailing version of what (English) lessons are for. It uses these moments to challenge the current prescriptive and reductive models of what learning looks like, focussing on the relationship between knowledge and interpretation (with specific reference to Shakespeare), on the social dimension of learning, and on the relationship between learning and time. It concludes that the best lessons are potentially open-ended, unpredictable and unrepeatable.

The Mauritian Novel: Fictions of Belonging - Conclusion: Over the Rainbow

In the conclusion, I reflect that a common concern with the problem of belonging unites all of the novels of my corpus. Despite the great diversity of forms of belonging imagined, there also emerge areas of striking convergence: the trope of the journey of discovery; moments of rapturous connection with the island’s natural beauty; the haunting figure of the stateless migrant; and a desire to connect with Creole characters and hence with a long-suppressed, locally-rooted ‘Creole’ culture that endures in the interstices of Mauritius’s dominant, diasporic ‘community system’. When combined with the novels’ common resistance to prescriptive identitarian models, these convergences betray a potentially unifying, future-orientated commitment to common values, to the ongoing project of Mauritian nationhood and to the tantalising fiction of belonging.

Prescriptive Data-Analytical Modeling of Laser Powder Bed Fusion Processes for Accuracy Improvement

Laser powder bed fusion (LPBF) has the ability to produce three-dimensional lightweight metal parts with complex shapes. Extensive investigations have been conducted to tackle build accuracy problems caused by shape complexity. For metal parts with stringent requirements, surface roughness, laser beam positioning error, and part location effects can all affect the shape accuracy of LPBF built products. This study develops a data-driven predictive approach as a promising solution for geometric accuracy improvement in LPBF processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.