xMEN: a modular toolkit for cross-lingual medical entity normalization.

Objective: To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English.

Materials And Methods: We propose xMEN, a modular system for cross-lingual (x) medical entity normalization (MEN), accommodating both low- and high-resource scenarios. To account for the scarcity of aliases for many target languages and terminologies, we leverage multilingual aliases via cross-lingual candidate generation.

Enhancing Perioperative Outcomes of Pancreatic Surgery with Wearable Augmented Reality Assistance System: A Matched-Pair Analysis.

Objective: The present study aimed to evaluate the safety of the first wearable augmented reality assistance system (ARAS) specifically designed for pancreatic surgery and its impact on perioperative outcomes.

Background: Pancreatic surgery remains highly complex and is associated with a high rate of perioperative complications. ARAS, as an intraoperative assistance system, has the potential to reduce these complications.

MLR-predictor: a versatile and efficient computational framework for multi-label requirements classification.

Introduction: Requirements classification is an essential task for development of a successful software by incorporating all relevant aspects of users' needs. Additionally, it aids in the identification of project failure risks and facilitates to achieve project milestones in more comprehensive way. Several machine learning predictors are developed for binary or multi-class requirements classification.

Unsupervised SapBERT-based bi-encoders for medical concept annotation of clinical narratives with SNOMED CT.

Objective: Clinical narratives provide comprehensive patient information. Achieving interoperability involves mapping relevant details to standardized medical vocabularies. Typically, natural language processing divides this task into named entity recognition (NER) and medical concept normalization (MCN).

Trainees' perspectives and recommendations for catalyzing the next generation of NeuroAI researchers.

At this critical juncture in the development of NeuroAI, we outline challenges and training needs of junior researchers working across AI and neuroscience. We also provide advice and resources to help trainees plan their NeuroAI careers.

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses.

Remaining useful life prediction of Lithium-ion batteries using spatio-temporal multimodal attention networks.

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage. The prediction of the remaining useful life (RUL) of batteries is crucial for ensuring reliable and efficient operation, as well as reducing maintenance costs. However, determining the life cycle of batteries in real-world scenarios is challenging, and existing methods have limitations in predicting the number of cycles iteratively.

Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents.

Origami single-end capacitive sensing for continuous shape estimation of morphing structures.

In this work, we propose a novel single-end morphing capacitive sensing method for shape tracking, FxC, by combining Folding origami structures and Capacitive sensing to detect the morphing structural motions using state-of-the-art sensing circuits and deep learning. It was observed through embedding areas of origami structures with conductive materials as single-end capacitive sensing patches, that the sensor signals change coherently with the motion of the structure. Different from other origami capacitors where the origami structures are used in adjusting the thickness of the dielectric layer of double-plate capacitors, FxC uses only a single conductive plate per channel, and the origami structure directly changes the geometry of the conductive plate.

Avoidance of specific calibration sessions in motor intention recognition for exoskeleton-supported rehabilitation through transfer learning on EEG data.

Exoskeleton-based support for patients requires the learning of individual machine-learning models to recognize movement intentions of patients based on the electroencephalogram (EEG). A major issue in EEG-based movement intention recognition is the long calibration time required to train a model. In this paper, we propose a transfer learning approach that eliminates the need for a calibration session.

Embedding textile capacitive sensing into smart wearables as a versatile solution for human motion capturing.

This work presents a novel and versatile approach to employ textile capacitive sensing as an effective solution for capturing human body movement through fashionable and everyday-life garments. Conductive textile patches are utilized for sensing the movement, working without the need for strain or direct body contact, wherefore the patches can sense only from their deformation within the garment. This principle allows the sensing area to be decoupled from the wearer's body for improved wearing comfort and more pleasant integration.

A review of machine learning in scanpath analysis for passive gaze-based interaction.

The scanpath is an important concept in eye tracking. It refers to a person's eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.

Beyond the visible: preliminary evaluation of the first wearable augmented reality assistance system for pancreatic surgery.

Purpose: The retroperitoneal nature of the pancreas, marked by minimal intraoperative organ shifts and deformations, makes augmented reality (AR)-based systems highly promising for pancreatic surgery. This study presents preliminary data from a prospective study aiming to develop the first wearable AR assistance system, ARAS, for pancreatic surgery and evaluating its usability, accuracy, and effectiveness in enhancing the perioperative outcomes of patients.

Methods: We developed ARAS as a two-phase system for a wearable AR device to aid surgeons in planning and operation.

Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study.

Background: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods.

Draft genome of the 24Kpn33 and complete sequence of its pCOL-1, a plasmid related to the acquisition in .

We report the draft genome of a clinical multi-resistant (24Kpn33) isolate, whose genome (5.7 Mbp) harbored 17 antibiotic resistance genes, including . Notably, this gene was mobilized within the IncP-6 pCOL-1 plasmid, the first genetic platform related to the acquisition and dissemination of the in .

Long-term continuous instrumented insole-based gait analyses in daily life have advantages over longitudinal gait analyses in the lab to monitor healing of tibial fractures.

Monitoring changes in gait during rehabilitation allows early detection of complications. Laboratory-based gait analyses proved valuable for longitudinal monitoring of lower leg fracture healing. However, continuous gait data recorded in the daily life may be superior due to a higher temporal resolution and differences in behavior.

SHAP value-based ERP analysis (SHERPA): Increasing the sensitivity of EEG signals with explainable AI methods.

Conventionally, event-related potential (ERP) analysis relies on the researcher to identify the sensors and time points where an effect is expected. However, this approach is prone to bias and may limit the ability to detect unexpected effects or to investigate the full range of the electroencephalography (EEG) signal. Data-driven approaches circumvent this limitation, however, the multiple comparison problem and the statistical correction thereof affect both the sensitivity and specificity of the analysis.

QAL-BP: an augmented Lagrangian quantum approach for bin packing.

The bin packing is a well-known NP-Hard problem in the domain of artificial intelligence, posing significant challenges in finding efficient solutions. Conversely, recent advancements in quantum technologies have shown promising potential for achieving substantial computational speedup, particularly in certain problem classes, such as combinatorial optimization. In this study, we introduce QAL-BP, a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation designed specifically for bin packing and suitable for quantum computation.

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.

Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompasses three image collections wherein rodent neuronal cell nuclei and cytoplasm are stained with diverse markers to highlight their anatomical or functional characteristics. Specifically, we release 1874 high-resolution images alongside 750 corresponding ground-truth annotations for several learning tasks, including semantic segmentation, object detection and counting.

Designing for passengers' information needs on fellow travelers: A comparison of day and night rides in shared automated vehicles.

Shared automated mobility-on-demand promises efficient, sustainable, and flexible transportation. Nevertheless, security concerns, resilience, and their mutual influence - especially at night - will likely be the most critical barriers to public adoption since passengers have to share rides with strangers without a human driver on board. Prior research points out that having information about fellow travelers could alleviate the concerns of passengers and we designed two user interface variants to investigate the role of this information in an exploratory within-subjects user study (N=24).

Social processing modulates the initial allocation of attention towards angry faces: evidence from the N2pc component.

Previous research has shown that attentional bias towards angry faces is moderated by the activation of a social processing mode. More specifically, reliable cueing effects for angry face cues in the dot-probe task only occurred when participants performed a task that required social processing of the target stimuli. However, cueing effects are a rather distal measure of covert shifts in spatial attention.

Smart greenhouse construction and irrigation control system for optimal Brassica Juncea development.

This paper contributes to smart greenhouses and IoT (Internet of Things) research. Our pioneering achievement centers on successfully designing, constructing, and testing a 30m2 smart greenhouse, explicitly focusing on the cultivation and development of Brassica Juncea, a mustard variety commonly grown in Vietnam. The construction phase entailed the meticulous integration of diverse IoT technologies and systems, culminating in the creation of a finely tuned environment to meet the unique needs of Brassica Juncea cultivation.

Generation climate crisis, COVID-19, and Russia-Ukraine-War: global crises and mental health in adolescents.

Climate change, COVID-19, and the Russia-Ukraine War are some of the great challenges of our time. These global crises affect young people in a particularly vulnerable phase of their lives. The current study aimed to assess the impact of these crises on mental health (depression, anxiety, and health-related quality of life) in secondary school students in Germany.