Model Clinical Research

Since the conception of pedagogical content knowledge (PCK) in the 1980s, the majority of research on PCK has focused on particular subjects, chemistry, physics, mathematics, and others, with fewer studies shedding light on the nature of PCK needed for integrated STEM (iSTEM) education. This study investigates the development of PCK among early career teachers tasked to introduce robotics to secondary school learners. An exploratory case study within the interpretive paradigm was employed to investigate teachers’ pedagogical content knowledge for navigating through the educational robotics curriculum in a model school in Rwanda. Three STEM teachers and nine students were conveniently selected to constitute a team that represented the school in a district robotics competition. Data was generated through observations, semi-structured interviews, and document study. Content analysis is employed to generate themes and ordinal categories that are used to report the findings. The findings revealed that the Education Robotics trainers’ application of composite STEM-PCK ranged from low to moderate. On one hand, the educators demonstrated limitations regarding the contextualization of curricular knowledge to align with local environments, while on the other, applying cross-disciplinary pedagogical knowledge and technological integration knowledge moderately. The study concluded that an iSTEM-PCK demanded shifts from being teachers to facilitators who possess knowledge of engineering design, project-based learning, and the capabilities to steer self-regulated learning.

AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess whether an evaluation worked as intended. Researchers have started developing methods for log analysis, but a standardised approach is still missing. Here we suggest a pipeline based on current best practices. We illustrate it with concrete code examples in the Inspect Scout library, provide detailed guidance on each step, and highlight common pitfalls. Our framework provides researchers with a foundation for rigorous and reproducible log analysis.

For patients with complex health needs, the periods between clinical encounters are times of significant vulnerability, during which unobserved risks can escalate into acute events. The Waymark community-based care management program was designed to reduce this vulnerability for the patients it serves who are enrolled in Medicaid programs, but the organization faced the challenge of processing thousands of unstructured daily encounter notes from its field-based teams. To meet this challenge in a scalable way, the authors developed and implemented an artificial intelligence (AI) oracle, a system that continuously analyzes these notes. The oracle has three functions: (1) to identify urgent safety red flags; (2) to surface opportunities for preventive care; and (3) to generate monthly, strengths-based feedback to foster a learning health system. Following a phased implementation that began with a silent validation period, the system now delivers real-time, actionable alerts directly into the care teams’ messaging system. Iterative refinement of the AI prompts, driven by clinical leadership review, improved the actionable true-positive rate of safety alerts from 68% of 147 to over 95% of 203 over 12 weeks. The system’s value lies in its ability to synthesize disparate data streams — such as community health worker observations; pharmacy data; rising risk scores; and hospital admission, discharge, and transfer feeds — to reveal complex risks that could be missed by manual, siloed review. The AI oracle has become an integrated, “over-the-shoulder” digital coach, transforming a reactive care model into a proactive, learning health system and offering a scalable solution to improve outpatient safety for vulnerable populations.

Exposure human tissue to electromagnetic radiation (EM) from radio wireless frequencies causes many negative health effects. The assessment of the absorbed EM by human tissue depends on the Specific Absorption Rate (SAR) factor. In this paper, a square patch antenna (SPA) is designed to be a source of EM radiation, and optimized to operate at several applicable frequencies, such as GSM 1800, IEEE 802.11 WLAN standard 2.4 GHz and 5.3 GHz bands, and 3.2 GHz WiMAX band. The radiated EM by the SPA antenna is evaluated in a 3D human head model or Specific Anthropomorphic Model (SAM), which consists of two layers, the outer shell (1.5 mm thickness) and filled with tissue simulating liquid (TSL). The investigation involved four aspects, first the distance between the SAM and the EM source has been moved between 0 mm to 50 mm, second for the specific distances (0 mm, 15 mm, 30 mm, and 45 mm) the frequency of EM source has been changed among 1.8 GHz, 2.4 GHz, 3.2 GHz, and 5.3 GHz. Third, the tilt angle (θ) between the SAM and the antenna has been shifted from 00to 900. Finally, the antenna encasement (2 mm thickness plastic material) was removed and the procedure in the first step is repeated to investigate the effect of encasement on the SAR reducing. The results reveal that there is an inversely proportional relation between SAR and distance, SAR and tilt angle. Besides, the antenna encasement has a large impact on attenuating SAR value, while the SAR is directly proportional to frequency. All SAR evaluations were performed by CST-2014 Microwave studio simulator which is built on the Finite-Difference-Time-Domain (FDTD) principle. All calculations are achieved over 1 g and 10 g of mass tissue averaging and according to IEEE/IEC 62704-1 standards.

Real-world studies may be distinguished from traditional randomized controlled trials (RCTs) based on the study population and the setting in which the research is conducted. While RCTs are the gold standard for evaluating the efficacy and safety of new therapeutic interventions, they are typically conducted in specialized environments that may lack the everyday reality of clinical, home, and community settings. There is often a gap between efficacy shown in RCTs and the effectiveness observed in real-world settings. Real-world studies and resultant real-world data can be used to develop real-world evidence (RWE). RWE adds to the evidence from RCTs by providing additional results obtained from a broader patient population outside the constraints of RCTs to evaluate what is happening in usual clinical practice. Thus, RWE is useful in determining the effectiveness and safety of an intervention in clinical practice, and it can also provide information on health care resource utilization and costs, which are not typically evaluated in RCTs.

Karapinar, halicilik adina onemli eserler uretmis dokuma merkezlerimizden birisidir. Karapinar halilari, halilara islenmis motiflere gore cesitli isimler almislardir. Bunlar arasinda en bilinenleri, Direkli Karapinar, Gobekli Karapinar ve Karapinar'in Ladik modelidir. Gobekli Karapinar halilarinda, zeminde bir ya da uc tane gobek gorulur. Bu gobeklerin zemine islenisi, cilt sanatinda gorulen semse ve salbek duzenlemesini andirir. Halilari cogunlukla tarak motifinin islendigi tek bir kusak cevreler. Halinin kisa kenarlarinda ise ev tasvirinin islendigi ince seritler yer alir. 18. yuzyil Karapinar halilarinda kompozisyonda genel bir sadelik hakimdir. 19. yuzyil halilarinin gobek ve kusaklari kalabaliklasir ve farkli yorelerin desenleri gorulmeye baslar. 19. yuzyildan itibaren diger yorelerimizin pek cogunda oldugu gibi ozelliklerini yitirmeye baslamis, gunumuzde ise yok olma noktasina gelmistir.