Simulation-based multi-criteria decision making: an interactive method with a case study on infectious disease epidemics

Whenever a system needs to be operated by a central decision making authority in the presence of two or more conflicting goals, methods from multi-criteria decision making can help to resolve the trade-offs between these goals. In this work, we devise an interactive simulation-based methodology for planning and deciding in complex dynamic systems subject to multiple objectives and parameter uncertainty. The outline intermittently employs simulation models and global sensitivity analysis methods in order to facilitate the acquisition of system-related knowledge throughout the iterations. Moreover, the decision maker participates in the decision making process by interactively adjusting control variables and system parameters according to a guiding analysis question posed for each iteration. As a result, the overall decision making process is backed up by sensitivity analysis results providing increased confidence in terms of reliability of considered decision alternatives. Using the efficiency concept of Pareto optimality and the sensitivity analysis method of Sobol’ sensitivity indices, the methodology is then instantiated in a case study on planning and deciding in an infectious disease epidemic situation similar to the 2020 coronavirus pandemic. Results show that the presented simulation-based methodology is capable of successfully addressing issues such as system dynamics, parameter uncertainty, and multi-criteria decision making. Hence, it represents a viable tool for supporting decision makers in situations characterized by time dynamics, uncertainty, and multiple objectives.

Conceptual Analysis

Chapter 8 argues that neo-positivists can endorse scientistic views of conceptual-analysis questions—i.e., questions like ‘What is free will?’, ‘What is a person?’, and so on. Very roughly, scientism about a question Q is the view that Q is an ordinary empirical-scientific question about some aspect of physical reality. This chapter argues for scientism about conceptual-analysis questions by arguing that these questions are completely settled by physical-empirical facts about us—in particular, by psychological facts about what we mean by our words. This is an important part of the neo-positivist argument; for in connection with almost all metaphysical questions, one of the main subquestions that neo-positivists need to address is (or is something like) a conceptual-analysis question. So if neo-positivists can endorse scientistic views of all conceptual-analysis questions, then this simplifies things for them considerably (it makes it much easier for them to motivate neo-positivist views of specific metaphysical questions).

Assessment Question Characteristics Predict Medical Student Performance in General Pathology

Context.— Evaluation of medical curricula includes appraisal of student assessments in order to encourage deeper learning approaches. General pathology is our institution's 4-week, first-year course covering universal disease concepts (inflammation, neoplasia, etc). Objective.— To compare types of assessment questions and determine which characteristics may predict student scores, degree of difficulty, and item discrimination. Design.— Item-level analysis was employed to categorize questions along the following variables: type (multiple choice question or matching answer), presence of clinical vignette (if so, whether simple or complex), presence of specimen image, information depth (simple recall or interpretation), knowledge density (first or second order), Bloom taxonomy level (1–3), and, for the final, subject familiarity (repeated concept and, if so, whether verbatim). Results.— Assessments comprised 3 quizzes and 1 final exam (total 125 questions), scored during a 3-year period (total 417 students) for a total 52 125 graded attempts. Overall, 44 890 attempts (86.1%) were correct. In multivariate analysis, question type emerged as the most significant predictor of student performance, degree of difficulty, and item discrimination, with multiple choice questions being significantly associated with lower mean scores (P = .004) and higher degree of difficulty (P = .02), but also, paradoxically, poorer discrimination (P = .002). The presence of a specimen image was significantly associated with better discrimination (P = .04), and questions requiring data interpretation (versus simple recall) were significantly associated with lower mean scores (P = .003) and a higher degree of difficulty (P = .046). Conclusions.— Assessments in medical education should comprise combinations of questions with various characteristics in order to encourage better student performance, but also obtain optimal degrees of difficulty and levels of item discrimination.

Turning Data into Actionable Policy Insights

It is becoming clearer that data-supported input is essential in the policy making process. But at which point of the process, and in which format, can data aid policy making? And what does an organisation need to turn data into relevant insights? This paper explores the role of data from two perspectives. In the first part, data and data analysis are situated in the policy making process by mapping them onto the data supported policy making model and highlighting the different roles they can assume in each stage and step of the process. The second part discusses a practical framework for policy-oriented data activities, zooming in on the data-specific actions and the actors performing them in each data-supported step of the policy making process. We observe that a close collaboration between the policy maker and data scientist in the framework of an iterative approach permits to transform the policy question into a suited data analysis question and deliver relevant insights with the flexibility desired by decision makers. In conclusion, for data to be turned into actionable policy insights it is vital to set up structures that ensure the presence and the collaboration of policy-oriented and data-oriented competences.

A Study on The Second Year Students’ Ability to Comprehend Reading Text Based on The Level of Knowledge Questions at SMAN 1 Sijunjung

This study was designed to describe the ability of the second year students to comprehend reading text according to the levels of question at SMAN 1 Sijunjung. To be more specific it aimed at describing students’ ability to comprehend reading text according to the levels of knowledge question, comprehension question, application question and analysis question. This research was descriptive in nature. The population of this research was the second year students at SMAN 1 Sijunjung. The number of members of population was 165 students. The researcher took 80 students as the sample. In selecting sample, the researcher used stratified cluster random sampling technique. The data of this research were the students’ scores on comprehending text according to the levels of question. To take the data, the researcher used reading test. In analyzing the data, there were some steps she used: the first was presentation of raw scores and calculation of Mean and Standard Deviation. The second was classifying the students’ ability into high, moderate, and low ability. The third was determining the percentage of the students who had high, moderate, and low ability. The result of the data analysis showed that the ability of the second year students to comprehend reading text according to the levels of question at SMAN 1 Sijunjung was moderate. It was proved by the fact that 41 students (51.25%) had moderate ability. Specifically, the ability of the students to comprehend reading text at the level of knowledge question was moderate. It was proved by the fact that 53 students (66.25%) had moderate ability. Based on the findings above, it was suggested that the teachers give more exercises, beginning from the low levels of question to the high levels of question. It was also suggested to the teachers that they must promote a discussion to answer the questions on the texts being taught.

Using COVID-19 Narratives to Think Deeply About Physicians’ Conflicting Roles and Responsibilities

Background: In recent decades, many physicians have abandoned the private practice model of medicine, electing instead to work as hospital employees. For the most part, hospitals and physician employees enjoy a synergistic relationship. In other cases, conflicts can arise between the two parties. This paper explores a subset of such conflicts, those which transpired within the context of the COVID-19 pandemic. Methods: We utilized the study of Narrative, a Medical Humanities discipline, as an analytical tool. First, we compiled narratives detailing disagreements between various clinicians and their respective employers’ COVID-19 response. Second, we employed literary analysis question prompts to explore the ethical implications of, and potential solutions to, such conflicts. Results: Physicians, like all individuals, have multiple roles within society. In today’s healthcare landscape, many physicians concurrently assume the roles of caregiver and employee. The moral priorities, values, and obligations inherent to these two roles can sometimes be misaligned, creating ethical dilemmas for physicians and their employers. Conclusion: Physicians must recognize that their primary professional responsibility is to patients. When the welfare or safety of patients appears to be at risk, physicians have a duty to advocate on their behalf. However, physicians and hospital administration alike must seek out synergies and minimize potential conflict. Good hospitals should have forums, communication lines, and organizational cultures that allow clinicians to openly voice concerns and feedback. Good physicians should voice dissenting opinions in a diplomatic, stepwise, and cooperative manner. If patient welfare remains at risk, other recourses are available, but teamwork and collaboration should be the initial focus. Impact and Implications: This discussion can help physicians think deeply about their different roles and responsibilities in society and how to ethically balance those roles. It can also inform various topics within healthcare ethics, including care transformation, resource allocation, organizational ethics, and patient advocacy.

A Real-Time Quantitative PCR Method Specific for Detection and Quantification of the First Commercialized Genome-Edited Plant

Discussion regarding the regulatory status of genome-edited crops has focused on precision of editing and on doubts regarding the feasibility of analytical monitoring compliant with existing GMO regulations. Effective detection methods are important, both for regulatory enforcement and traceability in case of biosafety, environmental or socio-economic impacts. Here, we approach the analysis question for the first time in the laboratory and report the successful development of a quantitative PCR detection method for the first commercialized genome-edited crop, a canola with a single base pair edit conferring herbicide tolerance. The method is highly sensitive and specific (quantification limit, 0.05%), compatible with the standards of practice, equipment and expertise typical in GMO laboratories, and readily integrable into their analytical workflows, including use of the matrix approach. The method, validated by an independent laboratory, meets all legal requirements for GMO analytical methods in jurisdictions such as the EU, is consistent with ISO17025 accreditation standards and has been placed in the public domain. Having developed a qPCR method for the most challenging class of genome edits, single-nucleotide variants, this research suggests that qPCR-based method development may be applicable to virtually any genome-edited organism. This advance resolves doubts regarding the feasibility of extending the regulatory approach currently employed for recombinant DNA-based GMOs to genome-edited organisms.

Static Mantle Density Distribution 3 Dimpling and Bucking of Spherical Crust

This paper is the third step of project “Static mantle distribution, Equation, Solution and Application”. It consists of < Static Mantle Distribution 1 Equation>, <Static Mantle Density Distribution 2 Improved Equation and Solution>, and this paper. Our result on shape of core is a “X type”, which differs from the traditional view that core is a sphere. Which one is correct? or, both are not correct? The aim of thispaper is to study dimpling and bucking of the spherical crust under mantle loading. Dimpling analysis depends on the outer solution of non-homogeneous non-linear D.E., while bucking analysis depends on non-linear Eigen value of the homogeneous D. E The results based on two models and governing equations show that crust dimpled at poles is proved theoretically and numerical result well consists with pole radius, while the non-linear bucking Eigen value boundary problem is solved by decomposition method. The results show that bucking can occur, and the un-continuity of internal force per unit length causes un-continuity of masses by mantle material emitting to crust at turning point of “X”. The growing of Tibet high-land might be viewed as an evidence of the mass ms(θ0)increasing due to mantle emission. Both poles radius and equatorial radius have been used to support our analysis. Question: how the nature makes cold at poles?

A comparative review on deep learning models for text classification

<p>Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.</p>

Analisis Deskriptif Soal Ujian Nasional dan Cambridge Checkpoint Pelajaran Matematika Tingkat Sekolah Menengah Pertama Tahun Ajaran 2014/2015 Berdasarkan Tipe Penyajian Soal dan Ruang Lingkup Materi

National Examination and Cambridge Checkpoint are the instrument for evaluating the standard competence ofstudent which organized in Secondary Level. National Examination’s questions based on the National Curriculum ofIndonesia but Cambridge Checkpoint’s questions taken based on Cambridge Curriculum. The aims of this researchis analyzing the type of each question and distribution of each strands in the National Mathematics Examination 2015and Mathematics of Cambridge Checkpoint for Secondary Level 2015. This type of research is a descriptive studywith a qualitative approach. National Mathematics Examination 2015 has one paper only but Mathematics ofCambridge Checkpoint for Secondary Level 2015 has 2 papers for the test. It can be concluded that all question’stype of the National Mathematics Examination for Secondary Level 2015 are multiple choice questions. OnMathematics of Cambridge Checkpoint for Secondary Level 2015, there are various types of questions which consistof 11,43% short-answer question; 68,57% analysis question; 8,57% completing question; and 11,43% match questionfor paper 1, but 22,22% short-answer question; 58,33% analysis question; 11,11% completing question; 2,78% matchquestion; 2,78% multiple choice question; and 2,78% yes/no question for paper 2. Based on strands analyzing result,It can be determined that National Mathematics Examination for Secondary Level 2015 contain of 22,25% number;27,5 algebra; 40% geometry and measurement; 10% statistic and probability. On Mathematics of CambridgeCheckpoint for Secondary Level 2015, It can be explained that 45,72% number; 20% algebra; 17,14% geometry andmeasurement; and 17,14% statistic and probability for paper 1, and 33,33% number; 19,45% algebra; 25% geometryand measurement; and 22,22% statistic and probability for paper 2.