Realistic Scenarios Research Articles

Virtual reality tools for training in gastrointestinal endoscopy: A systematic review

BACKGROUND Virtual reality (VR) has emerged as an innovative technology in endoscopy training, providing a simulated environment that closely resembles real-life scenarios and offering trainees a valuable platform to acquire and enhance their endoscopic skills. This systematic review will critically evaluate the effectiveness and feasibility of VR-based training compared to traditional methods. AIM To evaluate the effectiveness and feasibility of VR-based training compared to traditional methods. By examining the current state of the field, this review seeks to identify gaps, challenges, and opportunities for further research and implementation of VR in endoscopic training. METHODS The study is a systematic review, following the guidelines for reporting systematic reviews set out by the PRISMA statement. A comprehensive search command was designed and implemented and run in September 2023 to identify relevant studies available, from electronic databases such as PubMed, Scopus, Cochrane, and Google Scholar. The results were systematically reviewed. RESULTS Sixteen articles were included in the final analysis. The total number of participants was 523. Five studies focused on both upper endoscopy and colonoscopy training, two on upper endoscopy training only, eight on colonoscopy training only, and one on sigmoidoscopy training only. Gastrointestinal Mentor virtual endoscopy simulator was commonly used. Fifteen reported positive results, indicating that VR-based training was feasible and acceptable for endoscopy learners. VR technology helped the trainees enhance their skills in manipulating the endoscope, reducing the procedure time or increasing the technical accuracy, in VR scenarios and real patients. Some studies show that the patient discomfort level decreased significantly. However, some studies show there were no significant differences in patient discomfort and pain scores between VR group and other groups. CONCLUSION VR training is effective for endoscopy training. There are several well-designed randomized controlled trials with large sample sizes, proving the potential of this innovative tool. Thus, VR should be more widely adopted in endoscopy training. Furthermore, combining VR training with conventional methods could be a promising approach that should be implemented in training.

Laser-Induced Graphene-Based Fabry-Pérot Cavity Label-Free Immunosensors for the Quantification of Cortisol

There is a strong and growing need to monitor stress biomarkers in human beings and animals for real-time wellness assessment, which is a key indicator of health. Toward this, we report an optical fiber immunosensor based on laser-induced graphene (LIG) for the potential quantification and monitoring of cortisol hormone. The developed sensors were created using the Fabry-Pérot cavity principle. The cavities were filled with polyimide (PI) and, in some cases, PI mixed with gold nanoparticles. The interferometers were tested for temperature and refractive index, and an increase of two times in the sensitivity to the refractive index was observed due to the addition of the gold nanoparticles. In addition, the PI cavity was partially transformed into LIG using a CO2 20 kHz pulsed laser. The resulting LIG morphology was quite porous and presented a leafy texture. The presence of LIG creates a second cavity within the interferometer, and as a consequence, the spectral response of the interferometers resembled the Vernier effect. The refractive index behavior of the LIG-modified interferometers shows improvements in sensitivity up to 15.3 times due to the creation of the LIG cavity. The interferometers were then functionalized with anti-cortisol to promote an affinity for cortisol. Excellent sensitivities of up to -34.7±0.7 nm/log(ng/mL) were achieved within few ng/mL concentration ranges (within the 0.5 to 3 ng/mL). The results obtained show an increase of up to 50 times in sensitivity when compared with other cortisol sensors. The achieved limit of detection was 0.1 ng/mL for the proposed sensors. The sensors’ responses were also shown to be immune to the interference from substances such as glucose, sucrose, fructose, and ascorbic acid. This study lays the foundation for new cortisol biosensors demonstrating a great potential for detection in a simple and highly sensitive way, opening its path for relevant biomedical and environmental monitoring applications in real samples. As a future outline, selectivity studies employing the detection of cortisol in the presence of interfering agents, both using synthetic and real samples, will be accomplished in order to fully investigate the potential of these sensing tools in real-life scenarios.

High-Performance Ionogel-Embedded electroluminescent device reliably operating in harsh environments

Recently, there have been notable strides in stretchable, adaptable displays capable of conforming to curved surfaces, including human interfaces, architecture, and vehicles. However, prior research mostly focused on coplanar structures, where the light-emitting layer is sandwiched between facing electrodes, leading to limited performance. This study introduces a breakthrough approach by embedding electrodes within the light-emitting layer and utilizing polymer electrolytes to increase lighting volume, resulting in superior brightness (3,260 cd/m2), stretchability (500 %), durability (5,000 cycles) and doubled power efficiency. Furthermore, the device with buried electrodes proves reliable even in harsh environments, such as ice or boiling water, commonly encountered in real-life scenarios. This concept paves the way for a new era of ACEL applications, including shape-adaptable outdoor lighting and displays, light-emitting soft robotics, and actuators capable of operating in challenging environmental conditions previously unattainable with other types of light-emitting devices.

Green Wheels, Greener Wallets: Economic Viability of Last-Mile Delivery Fleet Electrification in Case of Latvia

The analysis of internal combustion engine vehicle (ICEV) usage habits in the DPD Latvija company and the study of the specifics of its last-mile delivery transport business will allow to understand whether electromobility solutions can make the company more efficient. The question addressed in the research is whether the electrification of the delivery vehicle fleet can reduce transport expenses for the company and under what conditions. For estimation of direct and indirect costs of transport acquisition and usage Total Cost of Ownership (TCO) is used. The company’s daily operation parameters and technical data of the used vehicles VW e-Crafter and VW Crafter 35 Kasten/long base are considered in the study. Net Present Value (NPV) and Internal Rate of Return (IRR) calculation methods are used for analyzing the investment project. The expense of the creation of electrical vehicle charging infrastructure is not considered, because the company’s daily routine allows to cover all charging processes overnight by using domestic sockets together with Mode 2 chargers. All technical data and calculations were made for deliveries in a city cycle. In financial terms of owning delivery transport, the company is using a 5-year operational lease with 0% first payment. The obtained results in terms of TCO comparison showed a slight advantage by using EV after 5 years of last-mile delivery service per 1 vehicle – 706.98 Euro at 31200km a year or 100km in the working day of 6-day working week (156000km in 5-year is under 160000 warranty condition). The calculation is performed for DPD Latvija 213 ICE vans of a total 240-vehicle fleet. Results of NPV, NPVI, and IRR with a comparison rate of 10.6% for EV case clearly show a sufficiently good return. The Net Present Value of the solution is 89271.92 Euro and the Internal rate of return is 48.05%. The study proves that the electrification of the last-mile delivery company's fleet is economically justified and within 5 years. In the process of electrification, the company will reduce its expenses – by 1.45%, which increases the net annual profit by 0.13%. The main conclusion is that the company will not incur losses by switching to an environmentally friendly vehicle, the capital investment in a realistic scenario pays off already from the 3rd year. Nevertheless, the authors of the study recommend evaluating delivery vehicles with higher driving range parameters, because during peak delivery and seasons with cold weather, the company may experience unplanned downtime and expenses due to the need for additional charging time at public charging networks on the road, that could lead to financial losses and need for TCO calculation corrections.

Two dimensional simulations to study the relationship between settling velocity and flexibility of a particle

In various industrial and real-life scenarios, sedimentation, whether involving flexible fibres, permeable structures, or a combination of both, plays a pivotal role. Its impact spans from influencing paper properties to waste water treatment and microorganism transport dynamics. Understanding sedimentation is crucial for optimizing processes like flocculation, organic matter removal, and particulate material management. Settling velocity, a key metric, is vital in designing instruments and formulating optimization strategies across environmental engineering and sediment transport. Despite extensive research on settling velocity correlations with viscosity, structure density, and permeability, the relationship with structural flexibility remains unexplored. This study employs the Immersed Boundary (IB) method, utilizing a MATLAB code to numerically investigate the correlation between settling velocity and the flexibility of settling structures, addressing a gap in prior research. The results demonstrate a robust correlation between settling velocity and flexibility, supported by high R-squared values (ranging from 0.9979 to 1) for exponential fits across all discussed cases. The R-squared value, a statistical measure assessing model accuracy, reinforces the superiority of the exponential fit in describing the settling velocity-flexibility relationship. To confirm the optimal fit, we conducted fitting attempts with various curve types using MATLAB, encompassing polynomial, Fourier, and smooth spline curves for both impermeable and permeable structures. The exponential curve consistently emerged as the most fitting model in this context.In our recent research, we conducted a sensitivity analysis focusing on the time-step to validate the robustness of our findings. The investigation encompassed both impermeable and permeable scenarios for the structures under study. The time-step was systematically varied across a specified range, revealing a notable outcome: the results demonstrated a consistent independence from the chosen time-step values.

Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.

Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpretable AI system and provide some valuable insights regarding short, and long-term mortality. We propose the RIAS framework, an end-to-end framework that is designed with reliability and interpretability at its core and automatically optimizes the given model. Using RIAS, clinicians get accurate and reliable predictions which can be used as likelihood, with global and local explanations, and "what if" scenarios to achieve desired outcomes as well. We apply RIAS to AMI prognosis prediction data which comes from the Korean Acute Myocardial Infarction Registry. We compared FT-Transformer with XGBoost and MLP and found that FT-Transformer has superiority in sensitivity and comparable performance in AUROC and F1 score to XGBoost. Furthermore, RIAS reveals the significance of statin-based medications, beta-blockers, and age on mortality regardless of time period. Lastly, we showcase reliable and interpretable results of RIAS with local explanations and counterfactual examples for several realistic scenarios. RIAS addresses the "black-box" issue in AI by providing both global and local explanations based on SHAP values and reliable predictions, interpretable as actual likelihoods. The system's "what if" counterfactual explanations enable clinicians to simulate patient-specific scenarios under various conditions, enhancing its practical utility. The proposed framework provides reliable and interpretable predictions along with counterfactual examples.

Dynamic feature-based deep reinforcement learning for flow control of circular cylinder with sparse surface pressure sensing

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor information, taking deep reinforcement learning (DRL) as the starting point. The DRL performance is significantly improved by lifting the sensor signals to dynamic features (DFs), which predict future flow states. The resulting DF-based DRL (DF-DRL) automatically learns a feedback control in the plant without a dynamic model. Results show that the drag coefficient of the DF-DRL model is 25 % less than the vanilla model based on direct sensor feedback. More importantly, using only one surface pressure sensor, DF-DRL can reduce the drag coefficient to a state-of-the-art performance of approximately 8 % at Reynolds number $(Re) = 100$ and significantly mitigates lift coefficient fluctuations. Hence, DF-DRL allows the deployment of sparse sensing of the flow without degrading the control performance. This method also exhibits strong robustness in flow control under more complex flow scenarios, reducing the drag coefficient by 32.2 % and 46.55 % at $Re =500$ and 1000, respectively. Additionally, the drag coefficient decreases by 28.6 % in a three-dimensional turbulent flow at $Re =10\,000$ . Since surface pressure information is more straightforward to measure in realistic scenarios than flow velocity information, this study provides a valuable reference for experimentally designing the active flow control of a circular cylinder based on wall pressure signals, which is an essential step toward further developing intelligent control in a realistic multi-input multi-output system.

Reducing overdose deaths among persons with opioid use disorder in connecticut

BackgroundPeople in Connecticut are now more likely to die of a drug-related overdose than a traffic accident. While Connecticut has had some success in slowing the rise in overdose death rates, substantial additional progress is necessary.MethodsWe developed, verified, and calibrated a mechanistic simulation of alternative overdose prevention policy options, including scaling up naloxone (NLX) distribution in the community and medications for opioid use disorder (OUD) among people who are incarcerated (MOUD-INC) and in the community (MOUD-COM) in a simulated cohort of people with OUD in Connecticut. We estimated how maximally scaling up each option individually and in combinations would impact 5-year overdose deaths, life-years, and quality-adjusted life-years. All costs were assessed in 2021 USD, employing a health sector perspective in base-case analyses and a societal perspective in sensitivity analyses, using a 3% discount rate and 5-year and lifetime time horizons.ResultsMaximally scaling NLX alone reduces overdose deaths 20% in the next 5 years at a favorable incremental cost-effectiveness ratio (ICER); if injectable rather than intranasal NLX was distributed, 240 additional overdose deaths could be prevented. Maximally scaling MOUD-COM and MOUD-INC alone reduce overdose deaths by 14% and 6% respectively at favorable ICERS. Considering all permutations of scaling up policies, scaling NLX and MOUD-COM together is the cost-effective choice, reducing overdose deaths 32% at ICER $19,000/QALY. In sensitivity analyses using a societal perspective, all policy options were cost saving and overdose deaths reduced 33% over 5 years while saving society $338,000 per capita over the simulated cohort lifetime.ConclusionsMaximally scaling access to naloxone and MOUD in the community can reduce 5-year overdose deaths by 32% among people with OUD in Connecticut under realistic budget scenarios. If societal cost savings due to increased productivity and reduced crime costs are considered, one-third of overdose deaths can be reduced by maximally scaling all three policy options, while saving money.

Developing professional capital through technology-enabled university-school-enterprise collaboration: an innovative model for C-STEAM preservice teacher education in the Greater Bay area

Purpose Preservice teacher (PST) professional development programs are crucial for cultivating high-quality STEAM teachers of the future, significantly impacting the quality of regional STEAM education. The Guangdong-Hong Kong-Macao Greater Bay Area, as a region of cross-border cooperation, integrates the resources and advantages of Guangdong, Hong Kong, and Macao, possessing rich cultural heritage and innovative capabilities. Transdisciplinary Education for Cultural Inheritance (C-STEAM) is an effective approach to promoting educational collaboration within the Greater Bay Area, facilitating the integration of both technological and humanities education. This study aims to develop a Technology-Enabled University-School-Enterprise (T-USE) collaborative education model and implement it in the Greater Bay Area, to explore its role as a support mechanism in professional development and its impact on C-STEAM PSTs' professional capital. Design/methodology/approach Adopting a qualitative methodology, the study interviewed PSTs who participated in a C-STEAM teacher education course under the T-USE model. Thematic coding is used to analyze their knowledge acquisition, interaction benefits with community members, and autonomous thinking and decision-making in theoretical learning and teaching practice. Findings The findings show that the T-USE model significantly enhanced the PSTs' human capital, including teaching beliefs, knowledge, and skills. In terms of social capital, PSTs benefited from collaboration with PST groups, university teaching teams, in-service teachers, and enterprises, though challenges such as varying levels of expertise among in-service teachers and occasional technical instability emerged. For decisional capital, the T-USE model provided opportunities for autonomous thinking and promoted teaching judgment skills through real teaching challenges and scenarios. Reflective practice activities also supported PSTs' professional growth. Originality/value This study reveals the effectiveness and internal mechanism of the T-USE model in C-STEAM PST training, offering significant theoretical and practical references for future PST education.

Imidazole[1,5-a]pyridine derivatives as EGFR tyrosine kinase inhibitors unraveled by umbrella sampling and steered molecular dynamics simulations

Although the use of the tyrosine kinase inhibitors (TKIs) has been proved that it can save live in a cancer treatment, the currently used drugs bring in many undesirable side-effects. Therefore, the search for new drugs and an evaluation of their efficiency are intensively carried out. Recently, a series of eighteen imidazole[1,5-a]pyridine derivatives were synthetized by us, and preliminary analyses pointed out their potential to be an important platform for pharmaceutical development owing to their promising actions as anticancer agents and enzyme (kinase, HIV-protease,…) inhibitors. In the present theoretical study, we further analyzed their efficiency in using a realistic scenario of computational drug design. Our protocol has been developed to not only observe the atomistic interaction between the EGFR protein and our 18 novel compounds using both umbrella sampling and steered molecular dynamics simulations, but also determine their absolute binding free energies. Calculated properties of the 18 novel compounds were in detail compared with those of two known drugs, erlotinib and osimertinib, currently used in cancer treatment. Inspiringly the simulation results promote three imidazole[1,5-a]pyridine derivatives as promising inhibitors into a further step of clinical trials.

Compliance of Legal Restrictions in Potable Water Abstraction in Rajshahi WASA Area: An Overview

The ground water level in Barind area Rajshahi rise and falls continuously with the advance of wet and dry season. A growing population, urbanization, increasing pressure on land and water resources by different competing usage and degradation of scarce resources challenge the extraction, management and protection of the water resources of Rajshahi as well as all over the Bangladesh. Agriculture is the major water using sector for ground water irrigation with rice cultivation, the single most important activity in the economy. Industry is also liable for large scale water withdrawal. Due to water level depletion ‘deep tube well’ especially submersible pump is randomly used in many of the houses in Rajsahi WASA area. Apart from agriculture, presently uncontrolled water abstraction for domestic or household purposes causes serious threats to sustainable ground water management. Due to this excessive stress upon the ground water resource, the yield capacity is being reduced and the need to conserve them is now generally recognized. Some of the legislations have clear guidelines and restrictions on personal source of ground water abstractions. National water policy, 1999 stressed the need for the protection of surface water and groundwater resources. WASA Act, 1996; Water Act, 2013; Local Govt. Act 2009 are the major instruments in this respect. This study has been an attempt to review the relevant laws regarding restrictions on domestic water abstraction and to examine through field survey the real scenario how far these are being complied with.

Enhancing Cardiac Arrest Skills: Exploring Student Nurses’ Journey through Mental Simulation to Self-efficacy

Objective: This study explores mental simulation (MS) as a potential tool for enhancing skill acquisition and readiness among student nurses in life-saving procedures, particularly in high-stress, time-sensitive situations like cardiac arrest. It aims to investigate the impact of MS on learning life support skills and creating self-officious students in nursing education. Methods: The study combines a theoretical framework grounded in social constructionism with qualitative research methods. We utilize a qualitative reflexive thematic analysis approach. Eleven pre-registration student nurses participated in interview-based investigations after engaging in a 4-week MS program. Data collection utilized open-ended questions, allowing participants to express their experiences and perceptions regarding MS for life support skill acquisition. Results: The research identified six key themes and sub-themes: “Motivation to learn”, “emulating reality”, “emulating real-world intensity”, “emotional resonance”, “harmonizing reflective practice”, and “empowering confidence”. Participants reported using MS to address self-efficacy challenges, creating vivid simulated experiences, inducing real-world intensity, personalizing visualizations, fostering reflective practice, enhancing team understanding, and demystifying cardiac arrest skills, ultimately empowering confidence in employing their cardiac arrest skills. Conclusion: The findings underscore MS’s potential as a valuable training tool in nursing education. MS not only bridges knowledge gaps but also prepares student nurses for real-life scenarios, demystifying cardiac arrests and enhancing self-efficacy. The study advocates for the integration of MS alongside traditional training methods to enhance preparedness and confidence among student nurses in critical life support situations. Further research could explore the broader implications of MS across various healthcare contexts.

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

Epidemiological impact of Neisseria gonorrhoeae and Chlamydia trachomatis screening in men having sex with men: a modelling study

ObjectivesThe impact of the systematic screening of Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) in men having sex with men (MSM) on these pathogens’ epidemiology remains unclear. We conducted a...

MOLO-SLAM: A Semantic SLAM for Accurate Removal of Dynamic Objects in Agricultural Environments

Visual simultaneous localization and mapping (VSLAM) is a foundational technology that enables robots to achieve fully autonomous locomotion, exploration, inspection, and more within complex environments. Its applicability also extends significantly to agricultural settings. While numerous impressive VSLAM systems have emerged, a majority of them rely on static world assumptions. This reliance constrains their use in real dynamic scenarios and leads to increased instability when applied to agricultural contexts. To address the problem of detecting and eliminating slow dynamic objects in outdoor forest and tea garden agricultural scenarios, this paper presents a dynamic VSLAM innovation called MOLO-SLAM (mask ORB label optimization SLAM). MOLO-SLAM merges the ORBSLAM2 framework with the Mask-RCNN instance segmentation network, utilizing masks and bounding boxes to enhance the accuracy and cleanliness of 3D point clouds. Additionally, we used the BundleFusion reconstruction algorithm for 3D mesh model reconstruction. By comparing our algorithm with various dynamic VSLAM algorithms on the TUM and KITTI datasets, the results demonstrate significant improvements, with enhancements of up to 97.72%, 98.51%, and 28.07% relative to the original ORBSLAM2 on the three datasets. This showcases the outstanding advantages of our algorithm.

Experimental and Numerical Investigation of Single-Phase Liquid Cooling for Heterogeneous Integration Multichip Module

Abstract The variety of new electronic packaging technologies has grown significantly over the last 20 years as a result of market demands for higher device performance at lower costs and in less space. Those demands have pushed for heterogeneous packaging, where computer chips with different stack heights are closely packed, creating nonuniform heat flux and temperature and additional challenges for thermal management. Without implementing an appropriate thermal management strategy for heterogeneous packages, large temperature gradients can be observed within the package, which would increase the thermal stresses on the chip and raise reliability issues. To mimic this real-life scenario of such packaging, an experimental setup was designed and built. The design of the new experimental setup consists of four identical 1.2 cm × 1.2 cm ceramic heaters, each of which is connected to a separate power supply and can reach a heat flux of 140 W/cm2. Accordingly, this mock package is capable of delivering different power levels to mimic different multicore microprocessor conditions. To give the heater the ability to move precisely in the x-, y-, and z-directions, each heater is mounted to an XYZ linear stage. Deionized water (DI) was used as the working fluid, and a pin-fin heat sink was used to run the initial steady-state tests on the experimental rig. The tests showed how different flow rates at a constant fluid temperature and input power affect the temperatures of the heaters and the thermohydraulic performance of the heat sink. In addition, a three-dimensional numerical model has been developed and validated with experimental data in terms of heat sink pressure drop and the temperatures of the heaters.

Programmable metasurfaces with high angular stability for arbitrarily-polarized obliquely-incident waves

Abstract Programmable metasurfaces show enormous potential in real-time electromagnetic (EM) manipulation and their stable responses for variable EM waves including incident angle and polarization changes is crucial for related applications. However, the demonstrated programmable metasurfaces are commonly considered in normal-incident wave with fixed polarization; how to achieve stable performance in more realistic scenarios of wide-angle and full-polarized wave incidences is still a challenge. Here, we propose and realize a wide-angle and full-polarization programmable metasurface, which can generate stabilized amplitude and phase responses at both transverse electric (TE) and transverse magnetic (TM) oblique incidences. These are achieved by introducing the metallic walls around the metasurface element to reduce element coupling, which greatly improves the angular stability of its reflection responses. The mechanisms of angular insensitivity are analyzed comprehensively, and as a proof of concept, the obliquely-incident beam steering, large-angle beam scanning and oblique vortex beam emitting are demonstrated on this programmable metasurface. Both the simulation and experimental results demonstrate that the programmable metasurface can operate well at both TE and TM wide-angle oblique incidences in the upper half space. Our work offers an actual route for improving the angular stability and polarization insensitivity of metasurface, which could push it one step closer towards more complicated applications.

Quantifying anisotropy parameters in weakly anisotropic media through diffraction traveltime parameter clusters

The seismic response datasets obtained from anisotropic media present several challenges for established seismic processing methods. To determine whether wavefront interference stems from anisotropic effects, velocity model heterogeneity, or both remains a key challenge. While reflection signatures may be insufficient for distinguishing these attributes, diffraction information in seismic datasets often provides richer insights into subsurface structures. In response to these challenges, we propose a framework that explores the feasibility of using diffraction traveltime parameters as indicators of anisotropy. By introducing an average measurement velocity derived from a cluster of diffraction traveltime responses, defined as a function of the traveltime slopes estimated from the dataset, we aim to discern the prevalence of anisotropy, heterogeneity, or both within a target region. Experiments conducted with synthetic data designed to simulate realistic scenarios have yielded promising results.

Development and validation of immediate self-feedback very short answer questions for medical students: practical implementation of generalizability theory to estimate reliability in formative examination designs

BackgroundVery Short Answer Questions (VSAQs) reduce cueing and simulate better real-clinical practice compared with multiple-choice questions (MCQs). While integrating them into formative exams has potential, addressing marking time and ideal occasions and items is crucial. This study gathers validity evidence of novel immediate self-feedback VSAQ (ISF-VSAQ) format and determines the optimal number of items and occasions for reliable assessment.MethodsNinety-four third-year pre-clinical students took two ten-item ISF-VSAQ exams on cardiovascular drugs. Each question comprised two sections: (1) Questions with space for student responses and (2) a list of possible correct answers offering partial-credit scores ranging from 0.00 to 1.00, along with self-marking and self-feedback options to indicate whether they fully, partially, or did not understand the possible answers. Messick’s validity framework guided the collection of validity evidence.ResultsValidity evidence included five sources: (1) Content: The expert reviewed the ISF-VSAQ format, and the question was aligned with a standard examination blueprint. (2) Response process: Before starting, students received an example and guide to the ISF-VSAQ, and the teacher detailed the steps in the initial session to aid self-assessment. Unexpected answers were comprehensively reviewed by experts. (3) Internal structure: The Cronbach alphas are good for both occasions (≥ 0.70). A generalizability study revealed Phi-coefficients of 0.60, 0.71, 0.76, and 0.79 for one to four occasions with ten items, respectively. One occasion requires twenty-five items for acceptable reliability (Phi-coefficient = 0.72). (4) Relations to other variables: Inter-rater reliability between self-marking and teacher is excellent for each item (rs(186) = 0.87–0.98,p = 0.001). (5) Consequences: Path analysis revealed that the self-reflected understanding score in the second attempt directly affected the final MCQ score (β = 0.25,p = 0.033). However, the VSAQ score did not. Regarding perceptions, over 80% of students strongly agreed/agreed that the ISF-VSAQ format enhances problem analysis, presents realistic scenarios, develops knowledge, offers feedback, and supports electronic usability.ConclusionElectronic ISF-VSAQs enhanced understanding elevates learning outcomes, rendering them suitable for formative assessments with clinical scenarios. Increasing the number of occasions effectively enhances reliability. While self-marking is reliable and may reduce grading efforts, instructors should review answers to identify common student errors.

Learning with sparse reward in a gap junction network inspired by the insect mushroom body.

Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.