Catalytic enhancement in the performance of the microscopic two-stroke heat engine.

We consider a model of heat engine operating in the microscopic regime: the two-stroke engine. It produces work and exchanges heat in two discrete strokes that are separated in time. The working body of the engine consists of two d-level systems initialized in thermal states at two distinct temperatures.

Methods Used in Co-Creation Within the Health CASCADE Co-Creation Database and Gray Literature: Systematic Methods Overview.

Background: Co-creation is increasingly recognized for its potential to generate innovative solutions, particularly in addressing complex and wicked problems in public health. Despite this growing recognition, there are no standards or recommendations for method use in co-creation, leading to confusion and inconsistency. While some studies have examined specific methods, a comprehensive overview is lacking, limiting the collective understanding and ability to make informed decisions about the most appropriate methods for different contexts and research objectives.

Embroidered Transmission Lines with Conductive Yarns: Challenges, Modeling, Fabrication, and Experimental Performance Assessment.

This paper presents an enhanced measurement technique for evaluating embroidered transmission lines (TLs), based on a TL characterization method. The evaluation metric is the "pure" losses of the embroidered TL excluding mismatch losses. Enhanced mechanical stability and removability of embroidered samples under a test is supported by a specially designed measurement setup.

Stakeholder-centric explanations for black-box decisions: an XAI process model and its application to automotive goodwill assessments.

Machine learning has made tremendous progress in predictive performance in recent years. Despite these advances, employing machine learning models in high-stake domains remains challenging due to the opaqueness of many high-performance models. If their behavior cannot be analyzed, this likely decreases the trust in such models and hinders the acceptance of human decision-makers.

Pyrimidine Azepine Targets the Complex and Displays Multistage Antimalarial Activity.

Malaria control and elimination efforts would benefit from the identification and validation of new malaria chemotherapeutics. Recently, a transgenic line was used to perform a series of high-throughput in vitro screens for new antimalarials acting against the parasite sexual stages. The screens identified pyrimidine azepine chemotypes with potent activity.

A novel 4-aminoquinoline chemotype with multistage antimalarial activity and lack of cross-resistance with PfCRT and PfMDR1 mutants.

Artemisinin-based combination therapy (ACT) is the mainstay of effective treatment of Plasmodium falciparum malaria. However, the long-term utility of ACTs is imperiled by widespread partial artemisinin resistance in Southeast Asia and its recent emergence in parts of East Africa. This underscores the need to identify chemotypes with new modes of action (MoAs) to circumvent resistance to ACTs.

Deep Learning-Adjusted Monitoring of In-Hospital Mortality after Liver Transplantation.

: Surgeries represent a mainstay of medical care globally. Patterns of complications are frequently recognized late and place a considerable burden on health care systems. The aim was to develop and test the first deep learning-adjusted CUSUM program (DL-CUSUM) to predict and monitor in-hospital mortality in real time after liver transplantation.

Effect of Age and Education on Discourse Cohesion.

Background: Several studies have proven the presence of cohesion difficulties in neurogenic communication disorders. However, we still have very little information about discourse cohesion in the intact adult population and the factors that influence it. The aim of the present study is to provide additional information on this topic and to assess the effect of age and education on discourse cohesion.

ChatGPT's quality: Reliability and validity of concept inventory items.

Introduction: The recent advances of large language models (LLMs) have opened a wide range of opportunities, but at the same time, they pose numerous challenges and questions that research needs to answer. One of the main challenges are the quality and correctness of the output of LLMs as well as the overreliance of students on the output without critically reflecting on it. This poses the question of the quality of the output of LLMs in educational tasks and what students and teachers need to consider when using LLMs for creating educational items.

Short-term neurologic outcomes in pediatric extracorporeal membrane oxygenation are proportional to bleeding severity graded by a novel bleeding scale.

Introduction: This study aimed to characterize the severity of bleeding and its association with short-term neurologic outcomes in pediatric ECMO.

Methods: Multicenter retrospective cohort study of pediatric ECMO patients at 10 centers utilizing the Pediatric ECMO Outcomes Registry (PEDECOR) database from December 2013-February 2019. Subjects excluded were post-cardiac surgery patients and those with neonatal pathologies.

Slowly progressive autosomal dominant Alport Syndrome due to COL4A3 splicing variant.

Alport syndrome is a rare genetic kidney disease caused by variants in the COL4A3/A4/A5 genes. It's characterised by progressive kidney failure, though therapies targeting Renin-Angiotensin System can delay its progression. Additionally, extrarenal manifestations may sometimes coexist.

Sequence Flow: interactive web application for visualizing partial order alignments.

Background: Multiple sequence alignment (MSA) has proven extremely useful in computational biology, especially in inferring evolutionary relationships via phylogenetic analysis and providing insight into protein structure and function. An alternative to the standard MSA model is partial order alignment (POA), in which aligned sequences are represented as paths in a graph rather than rows in a matrix. While the POA model has proven useful in several applications (e.

AI-Based Forecasting of Polymer Properties for High-Temperature Butyl Acrylate Polymerizations.

High-temperature polymerizations involving self-initiation of the monomer are attractive because of high reaction rate, comparable lower viscosities, and no need for an additional initiator. However, the polymers obtained show a more complex microstructure, e.g.

Pangenome comparison via ED strings.

Introduction: An elastic-degenerate (ED) string is a sequence of sets of strings. It can also be seen as a directed acyclic graph whose edges are labeled by strings. The notion of ED strings was introduced as a simple alternative to variation and sequence graphs for representing a pangenome, that is, a collection of genomic sequences to be analyzed jointly or to be used as a reference.

Intrusion detection using metaheuristic optimization within IoT/IIoT systems and software of autonomous vehicles.

The integration of IoT systems into automotive vehicles has raised concerns associated with intrusion detection within these systems. Vehicles equipped with a controller area network (CAN) control several systems within a vehicle where disruptions in function can lead to significant malfunctions, injuries, and even loss of life. Detecting disruption is a primary concern as vehicles move to higher degrees of autonomy and the possibility of self-driving is explored.

Laser-Induced Periodic Surface Structures and Their Application for Gas Sensing.

Semiconducting metal oxides are widely used for solar cells, photo-catalysis, bio-active materials and gas sensors. Besides the material properties of the semiconductor being used, the specific surface topology of the sensors determines device performance. This study presents different approaches for increasing the sensing area of semiconducting metal oxide gas sensors.

Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects.

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns.

User interface design in mobile learning applications: Developing and evaluating a questionnaire for measuring learners' extraneous cognitive load.

Mobile learning is increasingly popular due to its flexibility in timing and location. However, challenges such as small screen sizes and poor user interface design can elevate learners' cognitive load, especially extraneous cognitive load, which hinders learning. Extraneous cognitive load, stemming from user interface design complexity, must be minimized to enhance learning focus.

Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data.

Aims: Coronary artery disease (CAD) is a highly prevalent disease with modifiable risk factors. In patients with suspected obstructive CAD, evaluating the pre-test probability model is crucial for diagnosis, although its accuracy remains controversial. Machine learning (ML) predictive models can help clinicians detect CAD early and improve outcomes.

Exploring the impact of sequence context on errors in SNP genotype calling with whole genome sequencing data using AI-based autoencoder approach.

A critical step in the analysis of whole genome sequencing data is variant calling. Despite its importance, variant calling is prone to errors. Our study investigated the association between incorrect single nucleotide polymorphism (SNP) calls and variant quality metrics and nucleotide context.