Reinforcement Learning-Based Control for Collaborative Robotic Brain Retraction.

In recent years, the application of AI has expanded rapidly across various fields. However, it has faced challenges in establishing a foothold in medicine, particularly in invasive medical procedures. Medical algorithms and devices must meet strict regulatory standards before they can be approved for use on humans.

Characterisation at Cryogenic Temperatures of an Attenuator for an Application of Astrophysical Instrumentation with MKIDs.

The use of non-cryogenic certified commercial electronics for cryogenic applications may be attractive due to their cost and availability, but it also carries risks related to reliability, performance and thermal compatibility. The decision to use commercial components that are not certified for cryogenics instead of components specifically designed for such applications must be carefully weighed based on specific project needs and risk tolerances. This work presents the characterisation of an attenuator circuit at cryogenic temperatures used in a microwave kinetic inductance detector (MKID) readout system.

A Spatial Crowdsourcing Engine for Harmonizing Volunteers' Needs and Tasks' Completion Goals.

This work addresses the task allocation problem in spatial crowdsensing with altruistic participation, tackling challenges like declining engagement and user fatigue from task overload. Unlike typical models relying on financial incentives, this context requires alternative strategies to sustain participation. This paper presents a new solution, the Volunteer Task Allocation Engine (VTAE), to address these challenges.

Personalized Clustering for Emotion Recognition Improvement.

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements.

Enhancing Greenhouse Efficiency: Integrating IoT and Reinforcement Learning for Optimized Climate Control.

Automated systems, regulated by algorithmic protocols and predefined set-points for feedback control, require the oversight and fine tuning of skilled technicians. This necessity is particularly pronounced in automated greenhouses, where optimal environmental conditions depend on the specialized knowledge of dedicated technicians, emphasizing the need for expert involvement during installation and maintenance. To address these challenges, this study proposes the integration of data acquisition technologies using Internet of Things (IoT) protocols and optimization services via reinforcement learning (RL) methodologies.

Soil Water Status Monitoring System with Proximal Low-Cost Sensors and LoRa Technology for Smart Water Irrigation in Woody Crops.

Weather and soil water dictate farm operations such as irrigation scheduling. Low-cost and open-source agricultural monitoring stations are an emerging alternative to commercially available monitoring stations because they are often built from components using open-source, do-it-yourself (DIY) platforms and technologies. For irrigation management in an experimental vineyard located in Quiroga (Lugo, Spain), we faced the challenge of installing a low-cost environmental and soil parameter monitoring station composed of several nodes measuring air temperature and relative humidity, soil temperature, soil matric potential, and soil water content.

Implementation and Experimental Application of Industrial IoT Architecture Using Automation and IoT Hardware/Software.

The paradigms of Industry 4.0 and Industrial Internet of Things (IIoT) require functional architectures to deploy and organize hardware and software taking advantage of modern digital technologies in industrial systems. In this sense, a lot of the literature proposes and describes this type of architecture with a conceptual angle, without providing experimental validation or with scarce details about the involved equipment under real operation.

Effectiveness of a Telerehabilitation-Based Exercise Program in Patients with Chronic Neck Pain-A Randomized Clinical Trial.

Background: Non-specific chronic neck pain is a prevalent musculoskeletal disorder with a significant impact on individuals' quality of life. The lack of consensus on effective therapeutic management complicates the establishment of standardized treatment protocols. Home exercise programs have yielded positive results.

Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress.

This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally.

Digital Twin-Assisted Lightpath Provisioning and Nonlinear Mitigation in C+L+S Multiband Optical Networks.

Multiband (MB) optical transmission targets increasing the capacity of operators' optical transport networks. However, nonlinear impairments (NLI) affect each optical channel in the C+L+S bands differently, and, therefore, the routing and spectrum assignment (RSA) problem needs to be complemented with fast and accurate tools to consider the quality of transmission (QoT) within the provisioning process. This paper proposes a digital twin-assisted approach for lightpath provisioning to provide a complete solution for the RSA problem that ensures the required QoT in MB optical networks.

TCAD Simulation of Two Photon Absorption-Transient Current Technique Measurements on Silicon Detectors and LGADs.

Device simulation plays a crucial role in complementing experimental device characterisation by enabling deeper understanding of internal physical processes. However, for simulations to be trusted, experimental validation is essential to confirm the accuracy of the conclusions drawn. In the framework of semiconductor detector characterisation, one powerful tool for such validation is the Two Photon Absorption-Transient Current Technique (TPA-TCT), which allows for highly precise, three-dimensional spatially-resolved characterisation of semiconductor detectors.

A Second-Order True-VCO ADC Employing a Digital Pseudo-DCO Suitable for Sensor Arrays.

This paper explores the implementation of a VCO-based ADC, achieving an ENOB of 12 bits with 1 MHz of a sampling rate in the audio bandwidth. The solution exploits the scalability and PVT invariance of a novel digital-to-frequency converter to reduce the size and consumed power. The architecture has been validated in a 130 nm CMOS technology node displaying a power consumption of 105.

Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers.

Interferometric radiometers operating at L-band, such as ESA's SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors' fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions.

Vertex-Oriented Method for Polyhedral Reconstruction of 3D Buildings Using OpenStreetMap.

This work presents the mathematical definition and programming considerations of an efficient geometric algorithm used to add roofs to polyhedral 3D building models obtained from OpenStreetMap. The algorithm covers numerous roof shapes, including some well-defined shapes that lack an explicit reconstruction theory. These shapes include gabled, hipped, pyramidal, skillion, half-hipped, gambrel, and mansard.

Towards Accurate Photogrammetry Using Molded Markers.

Traditional marker-based photogrammetry systems often require the attachment and removal of a sticker for each measured point, involving labor-intensive manual steps. This paper presents an innovative approach that utilizes raised, cross-shaped markers, referred to as 'molded markers', directly embedded into composite pieces. In this study, these markers, commonly employed in other industrial processes, serve as fiducial markers for accurate photogrammetry.

Non-Invasive Malaria Detection in Sub-Saharan Africa Using a DNA-Based Sensor System.

Malaria poses a serious global health problem, with half the world population being at risk. Regular screening is crucial for breaking the transmission cycle and combatting the disease spreading. However, current diagnostic tools relying on blood samples face challenges in many malaria-epidemic areas.

Extended Reality to Assess Short-Term Spatial Memory-A Comparative Study of Mixed Reality, Augmented Reality, and Virtual Reality.

A Mixed Reality (MR) application using an optical see-through headset was developed to assess short-term spatial memory. A study with 29 participants was conducted. Data from this study were compared to two previous studies using mobile Augmented Reality (AR) and Virtual Reality (VR) with headsets.

Comparison of Mean Values and Entropy in Accelerometry Time Series from Two Microtechnology Sensors Recorded at 100 vs. 1000 Hz During Cumulative Tackles in Young Elite Rugby League Players.

Several microtechnology devices quantify the external load of team sports using Global Positioning Systems sampling at 5, 10, or 15 Hz. However, for short, explosive actions, such as collisions, these sample rates may be limiting. It is known that very high-frequency sampling is capable of capturing changes in actions over a short period of time.

The Validity and Reliability of the My Jump Lab App for the Measurement of Vertical Jump Performance Using Artificial Intelligence.

The countermovement jump (CMJ) is a widely used test to assess lower body neuromuscular performance. This study aims to analyze the validity and reliability of an iOS application using artificial intelligence to measure CMJ height, force, velocity, and power in unloaded and loaded conditions. Twelve physically active participants performed 12 CMJs with external loads ranging from 0% to 70% of their body mass while being simultaneously monitored with a pair of force platforms and the My Jump Lab application.

Pharmacodynamic Model of the Hemodynamic Effects of Propofol and Remifentanil and Their Interaction with Noxious Stimulation.

: Despite the known impact of propofol and remifentanil on hemodynamics and patient outcomes, there is a lack of comprehensive quantitative analysis, particularly in surgical settings, considering the influence of noxious stimuli. The aim of this study was to develop a quantitative semi-mechanistic population model that characterized the time course changes in mean arterial pressure (MAP) and heart rate (HR) due to the effects of propofol, remifentanil, and different types of noxious stimulation related to the clinical routine. : Data from a prospective study were used; the study analyzed the effects of propofol and remifentanil general anesthesia on female patients in physical status of I-II according to the American Society of Anesthesiologists (ASA I-II) undergoing gynecology surgery.

Pharmacogenetics of Neoadjuvant MAP Chemotherapy in Localized Osteosarcoma: A Study Based on Data from the GEIS-33 Protocol.

Osteosarcoma is a rare disease, but it is the most frequent malignant bone tumor. Primary treatment consists of preoperative MAP (methotrexate (MTX), doxorubicin and cisplatin) chemotherapy followed by surgery and adjuvant chemotherapy. Pathological response to preoperative chemotherapy is one of the most important prognostic factors, but molecular biomarkers are lacking.

Nanomedicines for Pulmonary Drug Delivery: Overcoming Barriers in the Treatment of Respiratory Infections and Lung Cancer.

The pulmonary route for drug administration has garnered a great deal of attention in therapeutics for treating respiratory disorders. It allows for the delivery of drugs directly to the lungs and, consequently, the maintenance of high concentrations at the action site and a reduction in systemic adverse effects compared to other routes, such as oral or intravenous. Nevertheless, the pulmonary administration of drugs is challenging, as the respiratory system tries to eliminate inhaled particles, being the main responsible mucociliary escalator.

Long-Term Effectiveness and Safety of Proactive Therapeutic Drug Monitoring of Infliximab in Paediatric Inflammatory Bowel Disease: A Real-World Study.

Background: This study evaluated the long-term effectiveness and safety of a multidisciplinary early proactive therapeutic drug monitoring (TDM) program combined with Bayesian forecasting for infliximab (IFX) dose adjustment in a real-world dataset of paediatric patients with inflammatory bowel disease (IBD).

Methods: A descriptive, ambispective, single-centre study of paediatric patients with IBD who underwent IFX serum concentration measurements between September 2015 and September 2023. The patients received reactive TDM before September 2019 (n = 17) and proactive TDM thereafter (n = 21).

Personalized Secukinumab Treatment in Patients with Plaque Psoriasis Using Model-Informed Precision Dosing.

Patient care and control of inflammatory disorders, such as psoriasis, can be improved by model-informed precision dosing (MIPD) techniques based on population pharmacokinetic/pharmacodynamic (PK/PD) models. Clinical dose selection decisions based on MIPD strategies need to take account of the uncertainty associated with the individual PK/PD model parameters, which is determined by the quantity of individual observational data collected in clinical practice. The aim of this study was to propose an approach for personalized dosage regimens of secukinumab (SCK) in 22 Spanish patients with plaque psoriasis, whose severity level was considered moderate to severe, taking into account the uncertainty associated with individual parameters in a population-based PK/PD model.

Description and Modeling of Relevant Demographic and Laboratory Variables in a Large Oncology Cohort to Generate Virtual Populations.

: Pathophysiological variability in patients with cancer is associated with differences in responses to pharmacotherapy. In this work, we aimed to describe the demographic characteristics and hematological, biochemical, and coagulation variables in a large oncology cohort and to develop, optimize, and provide open access to modeling equations for the estimation of variables potentially relevant in pharmacokinetic modeling. : Using data from 1793 patients with cancer, divided into training ( = 1259) and validation ( = 534) datasets, a modeling network was developed and used to simulate virtual oncology populations.