Green gutter as a Nature-based Solution for mitigation and adaptation strategy in urban environments.

The goal of this paper is to conduct an experimental study to evaluate the efficiency of an innovative green infrastructure named green gutter made of mineral wool as a prospective green wall to delay stormwater. The pilot is formed by an unplanted prismatic vertical column of metal filled with mineral wool with 4 meters in height, width 0,6 m, and depth 0,15 m. Along its elevation, 8 moisture sensors divided in 2 lines of 4 are embedded within the filling material to assess saturation levels, flow meters are installed in the inlet and outlet of the system.

The Role of Stable Anatomical Landmarks in Automated 3D Model Superimposition: A Closer Look.

Objective: To evaluate the concordance of automated 3D superimposition methods applied to digital models, with a focus on methods that consider stable palatal regions as geometric reference landmarks versus those that do not.

Design And Setting: This was a prospective, cross-sectional study using digital model files of patients undergoing orthodontic treatment in a university clinical setting.

Participants: Sixty-one patients were prospectively enrolled and divided into three groups based on the type of orthodontic treatment they received: (20) non-extractive orthodontic treatment without intermaxillary elastics, (21) intermaxillary elastics, and (20) control subjects with no orthodontic movement.

Muscle synergies and metabolic adaptations during perturbed walking in older adults.

Walking is an accessible and beneficial form of physical activity for older adults. However, when performed under perturbed conditions, it can place greater demands on both neuromuscular control and metabolic efficiency, highlighting the need to better understand the underlying adaptations. We compared stable and perturbed walking on a treadmill at three different speeds in 16 healthy older adults.

Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications.

Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review examines the landscape of deep learning-based multimodal AI applications across the medical domain, analyzing 432 papers published between 2018 and 2024.

Influence of surface oxidation on the catalytic activity of topological quantum materials.

Topological quantum materials (TQMs) have been extensively studied due to their exotic properties, as predicted by theory and confirmed by experiments. These properties, particularly protected surface states and high charge carrier mobility, make them promising candidates for applications where electron transport is crucial, such as catalysis. Recently, various TQMs have been experimentally demonstrated to enhance water splitting reactions, specifically the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), by establishing a correlation between topological surface states and catalytic activity.

Synergism or mirage: Current progress and an empirical approach for elucidating combination drug effects.

In the context of the multitarget paradigm, fixed-dose combination (FDC) drugs, that is, compounds that synergistically target multiple sites when combined, have gained attention for treating complex diseases like cancers and viral infections, as traditional single-drug approaches are often inadequate. This review examines current methods for evaluating and predicting drug synergism in advancing combination drug discovery, highlighting their limitations and providing a unified mathematical framework. Additionally, we present a novel solution to resolve these limitations and improve synergism evaluation, demonstrated through a case study with 20 pairs of FDA-approved chemotherapy drugs for colorectal cancer.

The geometry of efficient codes: How rate-distortion trade-offs distort the latent representations of generative models.

Living organisms rely on internal models of the world to act adaptively. These models, because of resource limitations, cannot encode every detail and hence need to compress information. From a cognitive standpoint, information compression can manifest as a distortion of latent representations, resulting in the emergence of representations that may not accurately reflect the external world or its geometry.

Effect of Implant Scan Body type, Intraoral scanner and Scan Strategy on the accuracy and scanning time of a maxillary complete arch implant scans: an in vitro study.

Objectives: This study aimed to investigate the influence of three Implant Scan Bodies (ISBs), four intraoral scanners (IOSs), and two scan strategies on the trueness of complete arch implant scans.

Methods: A reference digital cast of an all-on-six maxillary rehabilitation was obtained using a coordinate measuring machine. Three ISBs - AQ (ISB-AQ), One-shot, Elos (ISB-PEEK) - four IOSs - Trios3POD (TRIOS), Medit i700 (MED), iTero Element 5D (iTe), Primescan (PRIME) - and two scan strategies - Zig-zag (ZZ), One-shot (OS) - were used for evaluation.

Bowel urgency in ulcerative colitis assessed from patients' reports: Results of a telemonitoring programme.

Background: Bowel urgency is common in ulcerative colitis (UC), but which patients are affected, and how, has not been fully studied.

Objective: We explored associations of bowel urgency with the clinical characteristics of unselected outpatients.

Design: The study included adults with UC already enrolled in a telemonitoring programme using the IBD Tool platform.

A waveform engineering approach for class F operation in a class C biased peaking branch of GaN MMIC Doherty power amplifiers.

This paper presents a novel design strategy to enhance the performance of a Doherty Power Amplifier using a nonlinear driver stage in the Peaking branch. To implement a class F harmonic termination for the peaking final stage, a third harmonic voltage component is injected at the input by a driver stage, allowing the phase of the third harmonic current at the output of the final stage to be reversed compared to its normal evolution. This enables a class F design strategy for a class C biased device.

Comparison of brain volume increase in patients with intracranial hypertension after decompressive craniectomy and expansion craniotomy.

Intracranial hypertension is a critical condition often resulting from traumatic brain injury or malignant middle cerebral artery ischemia, necessitating surgical intervention when conservative treatments fail. This study compares the efficacy of decompressive craniectomy (DC) and expansion craniotomy (EC) in managing cerebral edema. Sixty-three patients, 52 undergoing DC and 11 undergoing EC, were evaluated for changes in brain volume, midline shift, and other key parameters using pre- and post-operative CT scans.

Electronic Band Structure of Gallium Sulfide (GaS) with Thickness Reduction Unveiling Parabolic and Pudding Mold Band Dispersion.

Metal monochalcogenides (MXs) have attracted significant interest due to their unique electronic properties, which can be tuned by varying the thickness. Gallium sulfide (GaS) stands out among MX compounds for its potential in photocatalysis, thanks to its bandgap within the visible range. However, the theoretical predictions of its band structure have not been experimentally validated until now.

Robust power management capabilities of integrated energy systems in the smart distribution network including linear and non-linear loads.

This research presents the best power management of flexible-renewable integrated energy systems (FRIESs) with smart distribution networks (SDNs) by taking nonlinear load harmonic compensation into account. A deterministic model that optimizes for three distinct goals serves as the foundation for the proposed system. The goal is to minimize the combined impact of the network's operational costs, energy losses, and voltage harmonic distortion, taking into account their respective weights.

Raman spectroscopies for cancer research and clinical applications: a focus on cancer stem cells.

Over the last 2 decades, research has increasingly focused on cancer stem cells (CSCs), considered responsible for tumor formation, resistance to therapies, and relapse. The traditional "static" CSC model used to describe tumor heterogeneity has been challenged by the evidence of CSC dynamic nature and plasticity. A comprehensive understanding of the mechanisms underlying this plasticity, and the capacity to unambiguously identify cancer markers to precisely target CSCs are crucial aspects for advancing cancer research and introducing more effective treatment strategies.

Proposal of a Machine Learning Based Prognostic Score for Ruptured Microsurgically Treated Anterior Communicating Artery Aneurysms.

Aneurysmal subarachnoid hemorrhage (aSAH) carries significant mortality and disability rates, with rebleeding posing a grave risk, particularly in anterior communicating artery (AcoA) aneurysms. This retrospective study aims to analyze preoperative and intraoperative variables of patients with ruptured AcoA aneurysms, evaluating the association of these variables with patient outcomes using machine learning techniques, proposing a prognostic score. : A retrospective study was conducted on 50 patients who underwent microsurgical clipping for a ruptured AcoA aneurysm at San Giovanni Bosco Hospital, Turin, Italy.

Endovascular robotics: technical advances and future directions.

Endovascular interventions excel in treating cardiovascular diseases in a minimally invasive manner, showing improved outcomes over open techniques. However, challenges related to precise navigation - still relying on 2D fluoroscopy - persist. This review examines the role of robotics, highlighting commercial and research platforms, while exploring emerging trends like MRI compatibility, enhanced navigation, and autonomy.

Performance Restoration of Chemically Recycled Carbon Fibres Through Surface Modification with Sizing.

The recycling of Carbon Fibre-Reinforced Polymers (CFRPs) is becoming increasingly crucial due to the growing demand for sustainability in high-performance industries such as automotive and aerospace. This study investigates the impact of two chemical recycling techniques, chemically assisted solvolysis and plasma-enhanced solvolysis, on the morphology and properties of carbon fibres (CFs) recovered from end-of-life automotive parts. In addition, the effects of fibre sizing are explored to enhance the performance of the recycled carbon fibres (rCFs).

CAD-CAM complete digital dentures: An improved clinical and laboratory workflow.

The purpose of this article was to present a novel clinical workflow for the fabrication of complete dentures using computer-aided design and computer-aided manufacturing (CAD-CAM) technology. The dental technique consists of 3 clinical steps and 2 laboratory phases that result in the production of 2 CAD-CAM milled complete denture bases with prefabricated teeth. The integration of analog and digital procedures and materials maximizes their benefits in the planning and fabrication of complete dentures, with the goal of improving clinical outcomes.

The Rapidly Evolving Scenario of Acoustic Voice Analysis in Otolaryngology.

The field of voice analysis has experienced significant transformations, evolving from basic perceptual assessments to the incorporation of advanced digital signal processing and computational tools. This progression has facilitated a deeper understanding of the complex dynamics of vocal function, particularly through the use of acoustic voice analysis within a multidimensional evaluation framework. Traditionally, voice analysis relied on parameters such as fundamental frequency, jitter, shimmer, and noise-to-harmonic ratio, which, despite their utility, have faced criticism for variability and lack of robustness.

Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies.

This perspective article explores the convergence of advanced digital technologies, including high-performance computing (HPC), artificial intelligence, machine learning, and sophisticated data management workflows. The primary objective is to enhance the accessibility of multiscale simulations and their integration with other computational techniques, thereby advancing the field of nanomaterials technologies. The proposed approach relies on key strategies and digital technologies employed to achieve efficient and innovative materials discovery, emphasizing a fully digital, data-centric methodology.

Dynamics of salt precipitation at pore scale during CO subsurface storage in saline aquifer.

CO storage in deep saline aquifers is an effective strategy for reducing greenhouse gas emission. However, salt precipitation triggered by evaporation of water into injected dry CO causes injectivity reduction. Predicting the distribution of precipitated salts and their impact on near-well permeability remains challenging.

Numerical and experimental analysis of thermal behaviour of high voltage power cable in unfilled ducts.

The work addresses the topic of the thermal study of high-voltage power cables installed inside plastic pipes in the absence of filling. The presence of air inside the pipe creates an insulating layer that does not favor heat exchange and makes the calculation of the flow rate more complex, as it is necessary to take into account the thermal phenomena of natural convection and radiation between the surface of the cable and the internal surface of the tube. The numerical model based on the finite element calculation was compared with the experimental results obtained on a simulacrum in which the temperatures on the different layers of the cable were measured.

Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field.

Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging (MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and prognostic information. We comprehensively overviewed the currently available DL applications, critically examining the limitations that hinder their broader adoption in clinical practice and molecular research. Technical limitations to the routine application of DL include the qualitative heterogeneity of MRI, related to different machinery and protocols, and the absence of informative sequences, possibly compensated by artificial image synthesis.