Integrated Optics Waveguides and Mesoporous Oxides for the Monitoring of Volatile Organic Compound Traces in the Mid-Infrared.

Volatile organic compounds (VOCs) are an ever-growing hazard for health and environment due to their increased emissions and accumulation in the air. Quantum cascade laser-based infrared (QCL-IR) sensors hold significant promise for gas monitoring, thanks to their compact, rugged design, high laser intensity, and high molecule-specific detection capabilities within the mid-infrared spectrum's fingerprint region. In this work, tunable external cavity QCLs were complemented by an innovative germanium-on-silicon integrated optics waveguide sensing platform with integrated microlenses for efficient backside optical interfacing for the tunable laser spectrometer.

Optically Pumped and Electrically Switchable Microlaser Array Based on Elliptic Deformation and Q-Attenuation of Organic Droplet Oscillators.

Conventional laser panel displays are developed through the mass integration of electrically pumped lasers or through the incorporation of a beam steering system with an array of optically pumped lasers. Here a novel configuration of a laser panel display consisting of a non-steered pumping beam and an array of electrically Q-switchable lasers is reported. The laser oscillator consists of a robust, self-standing, and deformable minute droplet that emits laser through Whispering-Gallery Mode resonance when optically pumped.

Exploring RF-EMF levels in Swiss microenvironments: An evaluation of environmental and auto-induced downlink and uplink exposure in the era of 5G.

The advancement of cellular networks requires updating measurement protocols to better study radiofrequency electromagnetic field (RF-EMF) exposure emitted from devices and base stations. This paper aims to present a novel activity-based microenvironmental survey protocol to measure environmental, auto-induced downlink (DL), and uplink (UL) RF-EMF exposure in the era of 5G. We present results when applying the protocol in Switzerland.

Guiding light with surface exciton-polaritons in atomically thin superlattices.

Two-dimensional materials give access to the ultimate physical limits of photonics with appealing properties for ultracompact optical components such as waveguides and modulators. Specifically, in monolayer semiconductors, a strong excitonic resonance leads to a sharp oscillation in permittivity from positive to even negative values. This extreme optical response enables surface exciton-polaritons to guide visible light bound to an atomically thin layer.

An analysis of physical activity coping plans: mapping barriers and coping strategies based on user ratings.

Introduction: Personalising recommendations for physical activity coping plans can help bridging the physical activity intention-behaviour gap. Data-driven 'black-box' approaches result in recommendations that prove difficult to explain, and may have undesired consequences. This study aimed to explicitly link barriers and coping strategies using end-user input.

b-move: Faster Lossless Approximate Pattern Matching in a Run-Length Compressed Index.

Background: Due to the increasing availability of high-quality genome sequences, pan-genomes are gradually replacing single consensus reference genomes in many bioinformatics pipelines to better capture genetic diversity. Traditional bioinformatics tools using the FM-index face memory limitations with such large genome collections. Recent advancements in run-length compressed indices like Gagie et al.

Emergent Self-Adaptation in an Integrated Photonic Neural Network for Backpropagation-Free Learning.

Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt, and process information similarly to the way biological brains do. In this work, these properties occurring in arrays of photonic neurons are experimentally demonstrated. Importantly, this is realized autonomously in an emergent fashion, without the need for an external controller setting weights and without explicit feedback of a global reward signal.

"There is more that unites us than divides us". Optimizing talent transfer processes by clustering 34 sports by their task, individual and environmental similarities.

Sports are characterized by unique rules, environments, and tasks, but also share fundamental similarities with each other sport. Such between-sports parallels can be vital for optimizing talent transfer processes. This study aimed to explore similarities between sports to provide an objective basis for clustering sports into families by means of machine learning.

A hierarchical active inference model of spatial alternation tasks and the hippocampal-prefrontal circuit.

Cognitive problem-solving benefits from cognitive maps aiding navigation and planning. Physical space navigation involves hippocampal (HC) allocentric codes, while abstract task space engages medial prefrontal cortex (mPFC) task-specific codes. Previous studies show that challenging tasks, like spatial alternation, require integrating these two types of maps.

Action potential threshold variability for different electrostimulation models and its potential impact on occupational exposure limit values.

Occupational exposure limit values (ELVs) for body internal electric fields can be derived from thresholds for action potential generation. These thresholds can be calculated with electrostimulation models. The spatially extended nonlinear node model (SENN) is often used to determine such thresholds.

A comprehensive evaluation of an artificial intelligence based digital pathology to monitor large-scale deworming programs against soil-transmitted helminths: A study protocol.

Background: Manual screening of a Kato-Katz (KK) thick stool smear remains the current standard to monitor the impact of large-scale deworming programs against soil-transmitted helminths (STHs). To improve this diagnostic standard, we recently designed an artificial intelligence based digital pathology system (AI-DP) for digital image capture and analysis of KK thick smears. Preliminary results of its diagnostic performance are encouraging, and a comprehensive evaluation of this technology as a cost-efficient end-to-end diagnostic to inform STH control programs against the target product profiles (TPP) of the World Health Organisation (WHO) is the next step for validation.

Non-contact photoacoustic imaging with a silicon photonics-based Laser Doppler Vibrometer.

Photoacoustic imaging has emerged as a powerful, non-invasive modality for various biomedical applications. Conventional photoacoustic systems require contact-based ultrasound detection and expensive, bulky high-power lasers for the excitation. The use of contact-based detectors involves the risk of contamination, which is undesirable for most biomedical applications.

Experimental Demonstration of the High Alignment-Tolerant Behavior of a Mid-Infrared Waveguide Platform for Evanescent Field Sensing.

Alignment tolerant coupling interfaces are an important feat for mid-IR waveguides when moving closer to real-world sensing applications, as they allow for an easy and fast replacement of waveguides. In this work, we demonstrate the alignment tolerant behavior of a germanium-on-silicon trenched waveguide platform with monolithically integrated microlenses using backside coupling of an expanded beam for evanescent field sensing between 6.5 and 7.

SCIP: A scalable, reproducible and open-source pipeline for morphological profiling of image cytometry and microscopy data.

Imaging flow cytometry (IFC) provides single-cell imaging data at a high acquisition rate. It is increasingly used in image-based profiling experiments consisting of hundreds of thousands of multi-channel images of cells. Currently available software solutions for processing microscopy data can provide good results in downstream analysis, but are limited in efficiency and scalability, and often ill-adapted to IFC data.

50 Hz Temporal Magnetic Field Monitoring from High-Voltage Power Lines: Sensor Design and Experimental Validation.

A low-cost, tri-axial 50 Hz magnetic field monitoring sensor was designed, calibrated and verified. The sensor was designed using off-the-shelf components and commercially available coils. It can measure 50 Hz magnetic fields originating from high-voltage power lines from 0.

Innovative Integration of Dual Quantum Cascade Lasers on Silicon Photonics Platform.

For the first time, we demonstrate the hybrid integration of dual distributed feedback (DFB) quantum cascade lasers (QCLs) on a silicon photonics platform using an innovative 3D self-aligned flip-chip assembly process. The QCL waveguide geometry was predesigned with alignment fiducials, enabling a sub-micron accuracy during assembly. Laser oscillation was observed at the designed wavelength of 7.

Harnessing artificial intelligence microscopy to improve diagnostics for soil-transmitted helminthiasis and schistosomiasis: a review of recent advances and future pathways.

Purpose Of Review: This opinion piece aims to explore the transformative potential of integrating artificial intelligence with digital microscopy to enhance diagnostics for soil-transmitted helminthiasis (STH) and schistosomiasis (SCH), two pervasive neglected tropical diseases (NTDs). By aligning innovative artificial intelligence-driven solutions with WHO's strategic objectives and calls for better, more accessible, and more integrated diagnostics, we highlight the latest advancements that may support improved health outcomes in affected communities.

Recent Findings: The review covers recent advancements in artificial intelligence-based diagnostic technologies, emphasizing automated egg detection and quantification.

Partial coherence enhances parallelized photonic computing.

Advancements in optical coherence control have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores.

Mitigating data quality challenges in ambulatory wrist-worn wearable monitoring through analytical and practical approaches.

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts.

Solution-Processed Pb(Zr,Ti)O Thin Films with Strong Remnant Pockels Coefficient.

In contrast to the widely studied electrical properties of Pb(Zr,Ti)O thin films, which have led to their applicability in various application areas such as thin film capacitors, microelectronics, and ferroelectric memories, the electro-optic (EO) properties are far less studied, which hinders the applicability of Pb(Zr,Ti)O films for EO applications such as heterogeneously integrated phase modulators in silicon (Si) photonics. Therefore, the EO properties of Pb(Zr,Ti)O films need to be further investigated to pave the way for the applicability of Pb(Zr,Ti)O films in EO applications. As the EO properties of ferroelectric thin films strongly depend on their crystal phase and texture, which in turn are influenced by the method of film fabrication.

Monolayer Semiconductor Superlattices with High Optical Absorption.

Optical absorption plays a central role in optoelectronic and photonic technologies. Strongly absorbing materials are thus needed for efficient and miniaturized devices. A uniform film much thinner than the wavelength can only absorb up to 50% of the incident light when embedded in a symmetric and homogeneous environment.

Goats on the Move: Evaluating Machine Learning Models for Goat Activity Analysis Using Accelerometer Data.

Putting sensors on the bodies of animals to automate animal activity recognition and gain insight into their behaviors can help improve their living conditions. Although previous hard-coded algorithms failed to classify complex time series obtained from accelerometer data, recent advances in deep learning have improved the task of animal activity recognition for the better. However, a comparative analysis of the generalizing capabilities of various models in combination with different input types has yet to be addressed.

Reactive shepherding along a dynamic path.

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill employed in the herding of animals, crowd control, and evacuation operations. Integrating shepherding capabilities into robots holds promise to perform such tasks with increased efficiency and reduced labor costs. To date, robotic shepherds have only been designed to steer a herd towards a predetermined goal location without constraints on the trajectory.