Prescriptive Model Research Articles

R&D innovation under uncertainty: a framework for empirical investigation of knowledge complementarity and goal congruence

PurposeThis study aims to introduce a new prescriptive model to aid both managers and researchers in partner selection for innovation-orientated collaboration. This framework demonstrates how prospective partner firms’ complementing bodies of knowledge and goal alignment interact to affect the success of a collaboration.Design/methodology/approachThe authors use geometric modeling to represent the interrelationships among knowledge similarity/dissimilarity, goal congruence, knowledge complementarity (KC) and innovation in alliance formation. Using this model as a framework, the authors derive relationships among predictors of innovation success and determine how they affect the nature of partnerships under varying conditions of KC.FindingsThis research shows how innovation success is strongly determined by partner selection. Specifically, the authors examine the influence of KC and partner goals on three aspects of a potential research and development (R&D) alliance – the potential level of innovation outcome for the alliance, the boundaries of knowledge sharing and limitations arising from knowledge and goal incongruence and the nature of cooperation.Originality/valueAlthough there is broad empirical support that innovation success is influenced by the similarity of R&D partners’ knowledge, further research is still needed to model the relationship more precisely between partner KC and goal alignment. The authors address this gap by developing a model that is both prescriptive and predictive of how innovation success can be achieved in the context of disparate but complementing knowledge and goal sets. The authors conclude with practical implications for practice and future research directions.

Hearing Aid User Perspectives: Reasons and Recommendations for Prescription and Over-The-Counter Device Uptake

Hearing Aid User Perspectives: Reasons and Recommendations for Prescription and Over-The-Counter Device Uptake

Decision-Analytic, Simulation-Based Model for Plantation Management Under Uncertainty in a Competitive Environment

This work takes a decision-analytic approach to develop a prescriptive model for three essential plantation management decisions: the size of the cultivated area, the method of cultivation, and the price at which the produce will be sold. These decisions are key to plantation performance and are made difficult by uncertainty and the fact that they are neither made simultaneously nor at the same informational state, aspects that the presented approach explicitly includes. The model is developed by addressing the management of a hypothetical plantation. Decision trees are used to represent the structure of the problem, and simulation is employed to calculate the plantation's expected sales given the market conditions. The simulation relies on a customer preference model that fulfills Keeney's Value-Focused Thinking (VFT) requirements for a proper value representation. The results are presented as recommendations for planted areas and cultivation methods, while the implemented selling price depends on the conditions observed at harvest (market and produce characteristics). According to the results, the presented approach is successful in guiding plantation management decisions and may be useful in increasing plantation competitiveness.

Cognitive Adaptability and Sales Growth of Selected Small and Medium Scale Enterprises in Lagos States, Nigeria

Stament of the Problem: Small and Medium Enterprises (SMEs) contribute immensely to the development of any nation’s GDP, of which Nigeria is not exempt. However, the survival rate has become very low, and the reason for the high mortality rate has been attributed to the volatility, uncertainty, complexity, and ambiguity of the dynamic operating environment. Responding with strategic decisions to changing environmental factors is a function of how cognitively adaptable the decision-makers are. Objective: Hence, the study investigated the effect of cognitive adaptability and business survival on selected small-scale enterprises in Lagos State, Nigeria. Methodology: The study adopted a survey research design. and simple ransampling technique. . The study population was 42,067 owners/managers of the registered SMEs in Lagos State, Nigeria. The sample size was 770 respondents, and this was determined using the Cochran sample size formula. A structured, adapted and validated questionnaire was administered. Cronbach’s alpha coefficient for the construct ranged from 0.725 to 0.923. Data were analysed using the descriptive and inferential (Multiple linear and hierarchical regression) statistics Findings: The regression model indicates that if cognitive adaptability factors were held constant at zero, the sales growth of the selected Small and Medium Scale Enterprises in Lagos State, Nigeria would be 8.479. In the predictive model it is seen that one of the variables of cognitive adaptability is insignificant, therefore the management of small and medium scale enterprises can downplay the variable that is why it is not included in the prescriptive model. From the analysis, the prescriptive models show that dimensions of cognitive adaptability (goal orientation, metacognitive knowledge, metacognitive control, and monitoring) except metacognitive experience have a significant effect on sales growth. This means that companies in the SMEs in Lagos State should pay close attention to the three components of cognitive adaptability in order to enhance sales growth. Conclusion: The study concluded that the owner and manager should take cognizance of cognitive adaptability, which is tutorial in nature, to enable them to acquire needed skills and plan their tasks creatively. Primarily to develop the capacity to respond with effective and strategic decisions that will aid their survival in the disruptive and dynamic business environment.

Can Orchestration Teaching be improved?

The fundamentals of mastering orchestration are a well-grounded knowledge of the instruments, their mechanics, techniques, and their phenomenology. To succeed in instrumentation and orchestration praxis, students must commonly 1) massively memorize examples of successful orchestration available in treatises on orchestration; 2) analyze as many as possible orchestral scores, and 3) orchestrate excerpts proposed by their instrumentation teachers. We aimed to conduct an exploratory study to detect possible voids in orchestration teaching. For this purpose we recruited 16 participants for this aim. Eight were Music Pedagogy students in their senior academic year, and the other eight were Composition undergraduates. The participants of the Pedagogy group completed one course in instrumentology, while the composers-students had a background of at least one course in instrumentation. Due to the differences in training, Pedagogy students instrumented an excerpt from a song by Ibert, and Composition students orchestrated an excerpt transcribed for piano from “Romeo and Juliet” (overture-fantasia) composed by Tchaikovsky. Also, we asked the participants to write down all their decisions and choice-making during the task to obtain more information for a deeper insight. As a result, we observed that the participants of both groups showed poor performance and low-quality products, except for those who conducted large ensembles. Both groups presented relevant similarities concerning orchestration-related concepts and procedures. Therefore, we can conclude that the general trend of the handbooks on instrumentation and orchestration is to work from a prescriptive model. We attribute the low-quality results of the composers’ group (comparable to non-composers) to the quantity of information to be integrated into the frame of such a model. Rimsky-Korsakov inaugurated the transition to a nomothetic model concerning the tembrotechnonic musical structure. But still, major understanding must be achieved regarding the timbre.

Stochastic optimization model for ship inspection planning under uncertainty in maritime transportation

<abstract><p>Maritime transportation plays a significant role in international trade and global supply chains. Ship navigation safety is the foundation of operating maritime business smoothly. Recently, more and more attention has been paid to marine environmental protection. To enhance maritime safety and reduce pollution in the marine environment, various regulations and conventions are proposed by international organizations and local governments. One of the most efficient ways of ensuring that the related requirements are complied with by ships is ship inspection by port state control (PSC). In the procedure of ship inspection, a critical issue for the port state is how to select ships of higher risk for inspection and how to optimally allocate the limited inspection resources to these ships. In this study, we adopt prediction and optimization approaches to address the above issues. We first predict the number of ship deficiencies based on a k nearest neighbor (kNN) model. Then, we propose three optimization models which aim for a trade-off between the reward for detected deficiencies and the human resource cost of ship inspection. Specifically, we first follow the predict-then-optimize framework and develop a deterministic optimization model. We also establish two stochastic optimization models where the distribution of ship deficiency number is estimated by the predictive prescription method and the global prescriptive analysis method, respectively. Furthermore, we conduct a case study using inspection data at the Hong Kong port to compare the performances of the three optimization models, from which we conclude that the predictive prescription model is more efficient and effective for this problem.</p></abstract>

A deep learning method to detect opioid prescription and opioid use disorder from electronic health records

ObjectiveAs the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD. Materials and MethodsWe developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes. ResultsOur deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008. DiscussionOur model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior. ConclusionAlgorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.

Perancangan Mesin Pengupas Kulit Ari Kopi dengan Model Preskiptif Pahl & Beitz

<p><em>The process of stripping the epidermis of coffee beans is an important process before the coffee bean roasting process is carried out. One of the post-harvest machines for coffee beans is a huller. As the coffee bean huller developed, there were many variations in the process. In this study, a coffee huller was designed using the Pahl & Beitz prescriptive model. The design stage of the Pahl & Beitz model includes initial planning, conceptual design, embodiment design, and detail design. The results of the design include 3D models using the SolidWorks application. The calculation results produce machine specifications in the form of motor design power of 0,5 kW; transmission using V-belt number A53 (L = 1346 mm), the number of belts are 2, pulley diameter 95mm, pulley width 35mm, adjustment distance (504 —564) mm; shaft diameter 25mm; as well as the width and height of the stake 7mm.</em></p><p><em> </em></p><strong><em>Keywords:</em></strong><em> Coffee, Design, Machine Design, Post-Harvest </em>

Analysis of The Best Strategy for Great Education Policy Using Prescriptive Analytics (Indonesian School Experience)

This paper discusses an analytical prescriptive approach to analyzing education policy. This study uses an appropriate literature review method so that the results of the study are in the form of an overview based on a critical analysis of the subject matter of the benefits of prescriptive analytics in the field of Islamic education management. The main issues discussed in this study include a prescriptive analytics approach as an educational policy analysis approach, a prescriptive approach model in education policy analysis, and the advantages and disadvantages of a prescriptive approach in education policy analysis. This study finds that a project manager must undertake prescriptive analytics today and in the Industry 4.0 era. However, managers have a weakness in skills in this area because the future of data analysis is prescriptive, despite the impact of the obligation to change management. Research advises educationmanagers to realize that prescriptive analytics is not always correct. Prescriptive analytics products can only be applied to one educational institution. However, the domino effect of prescriptive analytics has many benefits for educational institutions,both tangible and intangible.

Fundamental challenge and solution methods in prescriptive analytics for freight transportation

Prescriptive analytics, in which some parameters are predicted using statistical or machine learning models and then input into an optimization model, is often used to prescribe recommended solutions to freight transportation problems. The effectiveness of the optimal decision prescribed by prescriptive analytics is typically evaluated through a comparison with the results of the current decision model using predicted data. However, such comparisons are often flawed because of insufficient and uncertain data. We use four freight transport examples to illustrate this fundamental challenge in prescriptive analytics modeling. Furthermore, we propose three solutions to fully or partially overcome this challenge and fairly compare the optimal decisions generated by prescriptive analytics and the current approach. The three solutions involve using sufficient historical data, constructing new test sets, and generating synthetic data. We show how these solutions address the challenges in the four examples and are suitable for different problems considering data availability. The proposed solutions allow for a more comprehensive, accurate, and fair comparison of the optimal decisions to validate those generated by prescriptive analytics. This improves the effectiveness of the prescriptive analytics paradigm and can promote its application in freight transport and other disciplines.

A Personalized Opioid Prescription Model for Predicting Postoperative Discharge Opioid Needs.

Opioid overprescribing after surgery is common. There is currently no universal predictive tool available to accurately anticipate postdischarge opioid need in a patient-specific manner. This study examined the efficacy of a patient-specific opioid prescribing framework for estimating postdischarge opioid consumption. A total of 149 patients were evaluated for a single-center retrospective cohort study of plastic and reconstructive surgery patients. Patients with length of stay of 2 to 8 days and quantifiable inpatient opioid consumption (n = 116) were included. Each patient's daily postoperative inpatient opioid consumption was used to generate a personalized logarithmic regression model to estimate postdischarge opioid need. The validity of the personalized opioid prescription (POP) model was tested through comparison with actual postdischarge opioid consumption reported by patients 4 weeks after surgery. The accuracy of the POP model was compared with two other opioid prescribing models. The POP model had the strongest association (R2 = 0.899; P < 0.0001) between model output and postdischarge opioid consumption when compared to a procedure-based (R2 = 0.226; P = 0.025) or a 24-hour (R2 = 0.152; P = 0.007) model. Accuracy of the POP model was unaffected by age, gender identity, procedure type, or length of stay. Odds of persistent use at 4 weeks increased, with a postdischarge estimated opioid need at a rate of 1.16 per 37.5 oral morphine equivalents (P = 0.010; 95% CI, 1.04 to 1.30). The POP model accurately estimates postdischarge opioid consumption and risk of developing persistent use in plastic surgery patients. Use of the POP model in clinical practice may lead to more appropriate and personalized opioid prescribing.

Failed Tech Deployment Initiatives: Is Poor IT Governance to Blame?

Information Technology (IT) solutions have become a critical enabling tool in businesses and communities. As a result, organisations invest heavily in technology solutions to transform businesses. Motivated by rapid technological advancements, there have been radical changes in IT infrastructure management, IT applications, and IT services delivery. The recent COVID-19 pandemic also encouraged the rapid adoption of technology solutions. However, despite numerous prescriptive models and ‘best practice frameworks’ available in the field, and an increased uptake of ITG in organisations, achieving key ITG outcomes is consistently ranked as one of the top concerns of management. After establishing a theoretical foundation for effective ITG practices (via literature review), this study begins a pilot examination to determine if tech deployment failure can be attributed to poor ITG.

PUBLIC RELATIONS EDUCATION

In this article the question is posed to what extent the prescriptive model for public relations education formulated by the International Public Relations Association (IPRA) can be accepted as a role model for South Africa. This model, known as The Wheel of Education, was proposed by IPRA to suggest generally accepted educational standards. The model takes the form of a recommended curriculum accompanied by a set of general re~ommendations pertaining to education and research. In this article, an attempt is made to test the validity of this model by reviewing it in terms of both international and South African literature. It is suggested that the model has international validity but lacks sufficient emphasis on international public relations. It is also suggested that the model cannot be applied strictly as a blueprint for public relations education in South Africa, but needs to be adapted to the development needs of this country.

Development of knowledge based model for the diagnosis of sorghum diseases using Rule-Base approach

The nuances of semantics, diction, and intent are too broad for a computer to understand directly. Instead, humans give the computer a set of rules which define the way to process incoming information. Hence, expertise in this particular field is apparently essential to help diagnose crop pest and disease whilst recommending a proper treatment or control procedure to farmers affected by the incident. Damage occurs, both on the field during the cultivation process and warehouse storage. These conditions will significantly affect the income of farmers and the world’s food supply. The aim of this research is to develop a knowledge-based model for prediction and prescription of sorghum diseases using the concept of Machine learning techniques (K- nearest neighbor, KNN) algorithm. The specific objectives include: To seek, create and preprocess dataset, To develop the front end of the system using Java programming language and the back end using PHP, To Develop and implement the K-nearest Neighbour algorithm, Train and test the Model, To validate the Model using Confusion matrix in order to determine the prediction accuracy, The software will bring an end to the manual method of application which leads to applying the wrong insecticide, fungicide etc. to the wrong crop and in a wrong quantity, It will be beneficial to the farmers in Adamawa State and their experts. Plant diseases are the most important reason that leads to the destruction of plants and crops. Detecting that disease at early stages will enable us to overcome and treat them appropriately. The process requires an expert to identify the disease, describe the method of treatment and protection. Identifying the treatment accurately depends on the method that is used in diagnosing the diseases. An expert system was developed using two different methods of plant diagnosis: Step by step description and graphical representational methods. The simulation was carried out using JAVA Net beans for windows.

Rollout-based routing strategies with embedded prediction: A fish trawling application

In an effort to replenish stocks after years of overfishing, the European Union has been implementing policies to restrict the catch in all fisheries including in the Baltic Sea. These regulations incentivize fishermen to seek operational efficiency to counter lower catch limits. In this study, we address the fish trawling problem as a stochastic dynamic orienteering problem and propose rollout-based routing policies. Using a prescriptive analytic approach, we propose several prediction models and combine them with dynamic routing policies, the combination of which forms a prescriptive model. We evaluate and validate the effectiveness of these prescriptive models using a Baltic dataset on the fish species cod (Gadus). We propose a spatiotemporal cross-validation procedure to fairly assess different prescriptive models. Our findings show that reoptimization-based rollout strategies coupled with simple prediction methods such as nearest neighbors perform better than prescriptive models that use complex spatiotemporal smoothing techniques.

Generic model for vocationally oriented public relations education in globalised contexts

This article reports on a prescriptive generic Globalisation Model that was developed forvocationally oriented public relations education in globalised contexts. The model isbased on a literature study that pointed out that the public relations profession is bothaffected by the forces of globalisation, and it has the potential to play a role in themanagement of globalisation towards a constructive outcome. The model consists ofa recommended curriculum and recommendations pertaining to the functioning of publicrelations education departments in the context of globalisation. It is accompanied byan annexure indicating how the generic Globalisation Model should be adapted forapplication in South Africa.

How people deal with …............................ outliers

AbstractPeople regularly make sense of distributions that are complicated by noise. How do individuals determine whether an outlying observation should be incorporated into one's understanding of the true distribution of the population or considered a fluke that ought to be disregarded? In a simple prediction task, we examine how individuals incorporate outliers and compare their behavior to various prescriptive models (e.g., averaging and tests of discordancy). We find that, on average, individuals do discount outlying values and that their outlier detection strategies approximate approaches that statisticians have recommended for Gaussian distributions, even when the observed distributions are not Gaussian. However, there are notable differences in treatment of outliers across individuals.

Application of a person-centered prescription model improves pharmacotherapeutic indicators and reduces costs associated with pharmacological treatment in hospitalized older patients at the end of life.

ObjectiveThis study sought to investigate whether applying an adapted person-centered prescription (PCP) model reduces the total regular medications in older people admitted in a subacute hospital at the end of life (EOL), improving pharmacotherapeutic indicators and reducing the expense associated with pharmacological treatment.DesignRandomized controlled trial. The trial was registered with ClinicalTrials.gov (NCT05454644).SettingA subacute hospital in Basque Country, Spain.SubjectsAdults ≥65 years (n = 114) who were admitted to a geriatric convalescence unit and required palliative care.InterventionThe adapted PCP model consisted of a systematic four-step process conducted by geriatricians and clinical pharmacists. Relative to the original model, this adapted model entails a protocol for the tools and assessments to be conducted on people identified as being at the EOL.MeasurementsAfter applying the adapted PCP model, the mean change in the number of regular drugs, STOPPFrail (Screening Tool of Older Persons' Prescriptions in Frail adults with limited life expectancy) criteria, drug burden index (DBI), drug–drug interactions, medication regimen complexity index (MRCI) and 28-days medication cost of chronic prescriptions between admission and discharge was analyzed. All patients were followed for 3 months after hospital discharge to measure the intervention's effectiveness over time on pharmacotherapeutic variables and the cost of chronic medical prescriptions.ResultsThe number of regular prescribed medications at baseline was 9.0 ± 3.2 in the intervention group and 8.2 ± 3.5 in the control group. The mean change in the number of regular prescriptions at discharge was −1.74 in the intervention group and −0.07 in the control group (mean difference = 1.67 ± 0.57; p = 0.007). Applying a PCP model reduced all measured criteria compared with pre-admission (p < 0.05). At discharge, the mean change in 28-days medication cost was significantly lower in the intervention group compared with the control group (−34.91€ vs. −0.36€; p < 0.004).ConclusionApplying a PCP model improves pharmacotherapeutic indicators and reduces the costs associated with pharmacological treatment in hospitalized geriatric patients at the EOL, continuing for 3 months after hospital discharge. Future studies must investigate continuity in the transition between hospital care and primary care so that these new care models are offered transversally and not in isolation.

Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics

Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on what-if modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the what-if modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on what-if counterfactuals can be used to automate student feedback through prescriptive analytics.

MUSIC COMPOSITION AND EPISTEMIC INJUSTICE

AbstractThis article considers the implications of the consideration of epistemic justice within modes of composition pedagogy in higher education and is in part a manifesto, in part a reflection on my experiences of teaching composition in this setting. I ask how composition education can become, as described in 2015 by the North Macedonian dramatist and creative educator Goran Stefanovski, ‘the politics of the impossible’. I question how composition education could function without a canon of examples or assumed master–apprentice hierarchies and frame this as a question of epistemic justice, one that considers the composers themselves as individuals prior to the technical exercises that they may undertake. I describe why I believe that epistemic justice is a concept that is worthy of consideration in creative education in composition alongside the ways that current models of composition pedagogy might unintentionally cause students to experience epistemic injustice within their education experiences. Rather than a prescriptive model, I propose challenges that I hope can influence my educational approach now and in the future and conclude with some suggestions about what a model of hermeneutic epistemic justice might look like as a pedagogic model for music composition.