Analisis Kebutuhan Siswa serta Kesiapan Belajar Siswa Melalui Pendekatan Berdiferensiasi dalam Pembelajaran pada Siswa

Tujuan penelitian ini adalah untuk mengetahui kesiapan dan kebutuhan belajar siswa. Temuan dari penelitian ini akan digunakan untuk mengidentifikasi elemen spesifik dari pendekatan pembelajaran yang dibedakan untuk diintegrasikan ke dalam kurikulum. Metode deskriptif kualitatif digunakan dalam penelitian ini, yang melibatkan pengumpulan data melalui observasi, wawancara, dan dokumentasi. Penelitian difokuskan pada siswa SDK Mataia sebagai subjek penelitian. Hasil penelitian menunjukkan bahwa kebutuhan siswa tergolong tinggi dengan pencapaian rata-rata sebesar 81%, sedangkan kesiapan belajar berada pada tingkat baik dengan pencapaian rata-rata sebesar 80%. Menganalisis kebutuhan dan kesiapan siswa untuk belajar sangat penting bagi guru ketika memilih materi dan pendekatan pembelajaran yang sesuai. Memanfaatkan pendekatan yang berbeda merupakan cara yang paling tepat untuk memenuhi kebutuhan dan kesiapan belajar siswa. Penggunaan pendekatan yang berbeda dapat secara efektif memenuhi kebutuhan dan kesiapan belajar siswa, yang pada akhirnya meningkatkan kualitas proses pembelajaran

Effect of soil temperature changes on geogrid strains

Abstract: Temperatures were measured along instrumented geogrids to determine thermal strains and their changes with seasonal temperatures. It was observed that the application of temperature correction to the measured strain values by electrical wire resistance (EWR) strain gauges to compensate for temperature-induced strains is not correct. Because of the effects of soil confinement, the geogrids confined with soil do not undergo thermal expansion or contraction from temperature change if slippage between the soil and geogrid cannot occur. Instead of thermal strains, thermal stress or thermal force will be developed in the geogrids with the magnitude depending on the elastic properties, temperature change, and linear coefficient of thermal expansion

Building digital twins of the human immune system: toward a roadmap

Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement

EFECT -- A Method and Metric to Assess the Reproducibility of Stochastic Simulation Studies

Reproducibility is a foundational standard for validating scientific claims in computational research. Stochastic computational models are employed across diverse fields such as systems biology, financial modelling and environmental sciences. Existing infrastructure and software tools support various aspects of reproducible model development, application, and dissemination, but do not adequately address independently reproducing simulation results that form the basis of scientific conclusions. To bridge this gap, we introduce the Empirical Characteristic Function Equality Convergence Test (EFECT), a data-driven method to quantify the reproducibility of stochastic simulation results. EFECT employs empirical characteristic functions to compare reported results with those independently generated by assessing distributional inequality, termed EFECT error, a metric to quantify the likelihood of equality. Additionally, we establish the EFECT convergence point, a metric for determining the required number of simulation runs to achieve an EFECT error value of a priori statistical significance, setting a reproducibility benchmark. EFECT supports all real-valued and bounded results irrespective of the model or method that produced them, and accommodates stochasticity from intrinsic model variability and random sampling of model inputs. We tested EFECT with stochastic differential equations, agent-based models, and Boolean networks, demonstrating its broad applicability and effectiveness. EFECT standardizes stochastic simulation reproducibility, establishing a workflow that guarantees reliable results, supporting a wide range of stakeholders, and thereby enhancing validation of stochastic simulation studies, across a model's lifecycle. To promote future standardization efforts, we are developing open source software library libSSR in diverse programming languages for easy integration of EFECT.Comment: 25 pages, 4 figure

Change Point Estimation in Monitoring Survival Time

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered

Economic evaluation of three populational screening strategies for cervical cancer in the county of Valles Occidental: CRICERVA clinical trial

Copyright @ 2011 Acera et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.A high percentage of cervical cancer cases have not undergone cytological tests within 10 years prior to diagnosis. Different population interventions could improve coverage in the public system, although costs will also increase. The aim of this study was to compare the effectiveness and the costs of three types of population interventions to increase the number of female participants in the screening programmes for cancer of the cervix carried out by Primary Care in four basic health care areas.Fondo de Investigación Sanitaria del Instituto Carlos III de Madri

Navigating the Demands of Tenure-Track Positions

Navigating a tenure-track position can sometimes feel like walking the high wire, teetering from side to side wondering when that next overload course, research paper, or service project will topple you from your scholarly perch. Many of these positions lack significant formalized mentorship and guidance to help navigate and balance the workload of academia. Even with experience, the tenure and promotion process can be ambiguous. Workload balance is imperative to achieve tenure and promotion. Once you are in a tenure-track position, it is important to balance and understand the tenure and promotion process and its value. We provide a roadmap for early career academic professionals on how to balance their teaching, research, and service to obtain tenure and promotion. We inform the next generation of academicians about how researchers address public health problems through teaching, scholarship, and service. Finally, we explore five critical areas relevant to successful tenure and promotion: (a) classification and organizational culture, (b) the “Big Three” (teaching, research, and service), (c) professional development and network, (d) mentorship, and (e) work–life balance. </jats:p