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Introduction Amyotrophic lateral sclerosis (ALS) is a fatal neurogenerative disease with no effective treatment to date. Despite numerous clinical trials, the majority of studies have been futile in their effort to significantly alter the course of the disease. However, these studies may still provide valuable information for identifying patient subgroups and generating new hypotheses for future research. Additionally, synthesising evidence from these studies may help overcome the limitations of individual studies. Network meta-analysis may refine the assessment of efficacy in specific patient subgroups, evaluate intervention characteristics such as mode of administration or biological mechanisms of action, and rank order promising therapeutic areas of interest. Therefore, we aim to synthesise the available evidence from ALS clinical trials.Methods and analysis We will conduct a systematic review to identify all clinical trials that assessed disease-modifying pharmaceutical therapies, cell therapies, or supplements in patients with ALS. Outcomes of interest are clinical disease progression outcomes and survival. We will conduct this search in the period Q4 2024 in three databases: PubMed, Embase and ClinicalTrials.gov for studies from 1999 to 2023. Individual patient data and aggregate data will be collected and subsequentially synthesised in meta-analytical models. The final model will be presented as an open-source web application with biannual updates of the underlying data, thereby providing a ‘living’ overview of the ALS clinical trial landscape.Ethics and dissemination No ethics approvals are required. Findings will be presented at relevant conferences and submitted to peer-reviewed journals. Data will be stored anonymously in secure repositories.

Background: During takotsubo syndrome (TS), QTc prolongation is common, reflecting repolarization injury and providing the substrate for torsades de pointes (TdP). TdP has been reported sporadically in TS, yet QTc prolongation and TdP risk are often overlooked during management. Objectives: In TS patients, we sought to document TdP incidence, characteristics of patients with TdP, and association of QTc with postdischarge survival. Methods: Among consecutive TS patients at a single institution, we documented admission and discharge QTc, TdP incidence, and postdischarge 1-year mortality from 2006 to 2019. For perspective regarding TdP-TS risk, we characterized all published TdP cases from 2003 to 2022. Results: Of 259 patients, median age was 68 (range: 59-77) years; 92% were female. The QTc interval was prolonged (≥460 ms) on admission in 129 (49.8%) patients and at discharge in 140 (54%) patients. QTc was ≥500 ms either on admission or at discharge in 98 (37.8%) patients. In-hospital TdP incidence was 0.8%. Postdischarge mortality was associated with admission but not discharge, QTc: 98% survival at 1-year postdischarge.

Abstract The timing and fitness effect of somatic copy number alterations (SCNA) in cancer evolution remains poorly understood. Here we present a framework to determine the timing of a clonal SCNA that encompasses multiple gains. This involves calculating the proportion of time from its last gain to the onset of population expansion (lead time) as well as the proportion of time prior to its first gain (initiation time). Our method capitalizes on the observation that a genomic segment, while in a specific copy number (CN) state, accumulates point mutations proportionally to its CN. Analyzing 184 whole genome sequenced samples from 75 patients across five tumor types, we commonly observe late gains following early initiating events, occurring just before the clonal expansion relevant to the sampling. These include gains acquired after genome doubling in more than 60% of cases. Notably, mathematical modeling suggests that late clonal gains may contain final-expansion drivers. Lastly, SCNAs bolster mutational diversification between subpopulations, exacerbating the circle of proliferation and increasing heterogeneity.

Network meta-analysis (NMA) is an established method for assessing the comparative efficacy and safety of competing interventions. It is often the case that we deal with interventions that consist of multiple, possibly interacting, components. Examples of interventions' components include characteristics of the intervention, mode (face-to-face, remotely etc.), location (hospital, home etc.), provider (physician, nurse etc.), time of communication (synchronous, asynchronous etc.) and other context related components. Networks of multicomponent interventions are typically sparse and classical NMA inference is not straightforward and prone to confounding. Ideally, we would like to disentangle the effect of each component to find out what works (or does not work). To this aim, we propose novel ways of visualizing the NMA results, describe their use, and illustrate their application in real-life examples. We developed an R package "viscomp" to produce all the suggested figures.

Rotating systems are essential components and play a critical role in many industrial sectors. Unbalance is a very common and serious fault that can cause machine downtime, unplanned maintenance, and potential damage to vital rotating machines. Accurately estimating unbalance in rotor–bearing systems is crucial for ensuring the reliable and efficient operation of machinery. This research paper presents a novel approach utilizing artificial neural networks (ANNs) to estimate the unbalance masses in a multidisk system based on simulation data from a nonlinear rotor–bearing system. Additionally, this study explores the effect of various operating parameters on oil film stability and vibration response through a combination of bifurcation diagrams, spectrum cascades, Poincare maps, and orbit and FFT plots. This study demonstrates the effectiveness of ANNs for unbalance estimation in a fast and accurate way and discusses the potential of ANNs in smart online condition monitoring systems.

Summary: Lymphatic endothelial cells (LECs) of the lymph node (LN) parenchyma orchestrate leukocyte trafficking and peripheral T cell dynamics. T cell responses to immunotherapy largely rely on peripheral T cell recruitment in tumors. Yet, a systematic and molecular understanding of how LECs within the LNs control T cell dynamics under steady-state and tumor-bearing conditions is lacking. Intravital imaging combined with immune phenotyping shows that LEC-specific deletion of the essential autophagy gene Atg5 alters intranodal positioning of lymphocytes and accrues their persistence in the LNs by increasing the availability of the main egress signal sphingosine-1-phosphate. Single-cell RNA sequencing of tumor-draining LNs shows that loss of ATG5 remodels niche-specific LEC phenotypes involved in molecular pathways regulating lymphocyte trafficking and LEC-T cell interactions. Functionally, loss of LEC autophagy prevents recruitment of tumor-infiltrating T and natural killer cells and abrogates response to immunotherapy. Thus, an LEC-autophagy program boosts immune-checkpoint responses by guiding systemic T cell dynamics.

The analysis of Earth’s surface is strongly associated with the creation of three dimensional representations. In light of this, researchers involved in any realm of research as, geological, hydrological, ecological planning, city modelling, civil infrastructure monitoring, disaster management and emergency response, require 3D information of high fidelity and accuracy. For many decades, aerial photos or satellite data and photogrammetry provided the necessary information. In recent years, high-resolution imagery acquired by Unmanned Aerial Vehicles (UAV) has become a cost-efficient and quite accurate solution. In this framework, an infrastructure-monitoring project, named called “PROION”, focuses among others on the generation of very fine and highly accurate 3D infrastructure (city) model. The specific study evaluates a high-resolution nadir camera and an oblique camera for the creation of a 3D representation of the Patras University Campus. During the project, two identical flights over a part of the campus were conducted. The flights were performed with a vertical take-off and landing (Vtol) fixed wind UAV equipped with PPK receiver on-board. Based on the conducted flights, many data sets have been evaluated regarding the accuracy and fidelity. It was proved that both nadir and oblique cameras produced very accurate 3D representations of the University campus buildings. The RMSE error of the nadir imagery is almost two times higher than the respective error of the oblique imagery reaching 30cm.

Optimizing the design of the top compression ring holds immense importance in reducing friction across both traditional Internal Combustion (IC) engines and hybrid power systems. This study investigates the impact of alternative fuels, specifically hydrogen and CNG, on the behavior of top piston rings within internal combustion (IC) engines. The goal of this approach is to understand the complex interplay between blow-by, fuel type, material behavior, and their effects on ring friction, energy losses, and resulting ring strength. Two types of IC engines were analyzed, taking into account flow conditions derived from in-cylinder pressures and piston geometry. Following ISO 6622-2:2013 guidelines, thick top compression rings made from varying materials (steel, cast iron, and silicon nitride) were investigated and compared. Through a quasi-static ring model within Computational Fluid Dynamics (CFD), critical tribological parameters such as the minimum film and ring friction were simulated, revealing that lighter hydrogen-powered engines with higher combustion pressures could potentially experience approximately 34.7% greater power losses compared to their heavier CNG counterparts. By delving into the interaction among the fuel delivery system, gas blow-by, and material properties, this study unveils valuable insights into the tribological and structural behavior of the top piston ring conjunction. Notably, the silicon nitride material demonstrates promising strength improvements, while the adoption of Direct Injection (DI) is associated with approximately 10.1% higher energy losses compared to PFI. Such findings carry significant implications for enhancing engine efficiency and promoting sustainable energy utilization.

Rockfalls are natural geological phenomena characterized by the abrupt detachment and freefall descent of rock fragments from steep slopes. These events exhibit considerable variability in scale, velocity, and trajectory, influenced by the geological composition of the slope, the topography, and other environmental conditions. By employing advanced modeling techniques and terrain analysis, researchers aim to predict and control rockfall hazards to prevent casualties and protect properties in areas at risk. In this study, two rockfall events in the villages of Myloi and Platiana of Ilia prefecture were examined. The research was conducted by means of HY-STONE software, which performs 3D numerical modeling of the motion of non-interacting blocks. To perform this modeling, input files require the processing of base maps and datasets in a GIS environment. Stochastic modeling and 3D descriptions of slope topography, based on Digital Elevation Models (DEMs) without spatial resolution limitations, ensure multiscale analysis capabilities. Considering this capability, seven freely available DEMs, derived from various sources, were applied in HY-STONE with the scope of performing a large number of multiparametric analyses and selecting the most appropriate and efficient DEM for the software requirements. All the necessary data for the multiparametric analyses were generated within a GIS environment, utilizing either the same restitution coefficients and rolling friction coefficient or varying ones. The results indicate that finer-resolution DEMs capture detailed terrain features, enabling the precise identification of rockfall source areas and an accurate depiction of the kinetic energy distribution. Further, the results show that a correct application of the model to different DEMs requires a specific parametrization to account for the different roughness of the models.

Among the existing techniques to mitigate the problem of contamination in the indoor environment, photocatalytic technology is considered to be the most promising solution in terms of effectiveness and cost. To that end, in the frame of the LIFEVISIONS project, a novel photocatalytic powder (photo-powder) was mixed in paints’ matrix, producing a photocatalytic building material (photo-paint) able to improve indoor air quality (IAQ), upon its application, without downgrading paint physical properties. As a result, of IAQ improvement, less energy will be needed from ventilation systems, addressing not only health issues related to air quality but also energy reduction targets. Many powder formulae were synthesized using different synthetic pathways, concentration of dopants, and TiO2 particles’ size. They were tested in a photocatalytic reactor (lab-scale tests), according to EN 16980-1:2021, under visible light and the results showed that the most promising photocatalytic performance degrades 85.4% and 32.4% of nitrogen oxide (NO) and toluene, respectively. This one was used for the production of two different kinds of paints, organic (with organic binder) and inorganic (with potassium silicate binder), in an industrial scale. Both were tested in the Demo Houses’ prototype demonstrator (real-scale tests) with an ultimate scope to estimate their effectiveness to degrade air pollutants under real-world conditions. In addition, the reduced energy consumption as a result of less ventilation needs was calculated in Demo Houses. More specifically, the energy reduction based on simulation results on Demo Houses was more than 7%. Although lab-scale tests showed better photocatalytic performance than the real scale, the efficiency of the paints under a more complicated environment was very promising.

Intense long-duration rainfall or extreme precipitation in a few hours can provoke many simultaneous shallow landslides. In the past, the term multi-occurrence regional landslides (MORLEs) was proposed to describe such phenomena. In the current study, unmanned aerial vehicles in combination with a global navigation satellite system sensor and geographical information systems seem to be the ideal solution for the rapid assessment of many landslides occurring in Aitoloakarnania Prefecture, Western Greece. Fourteen landslides were accurately mapped within a few working days, and precise orthophotos and reports were created and submitted to the local authorities. The analysis of meteorological data proved that there is a peak in precipitation height that triggers the MORLEs in the specific area. Specifically, the value of the daily precipitation was defined at 80 mm.

During the past few decades, remote sensing has been established as an innovative, effective and cost-efficient option for the provision of high-quality information concerning infrastructure to governments or decision makers in order to update their plans and/or take actions towards the mitigation of the infrastructure risk. Meanwhile, climate change has emerged as a serious global challenge and hence there is an urgent need to develop reliable and cost-efficient infrastructure monitoring solutions. In this framework, the current study conducts a comprehensive review concerning the use of different remote-sensing sensors for the monitoring of multiple types of infrastructure including roads and railways, dams, bridges, archaeological sites and buildings. The aim of this contribution is to identify the best practices and processing methodologies for the comprehensive monitoring of critical national infrastructure falling under the research project named “PROION”. In light of this, the review summarizes the wide variety of approaches that have been utilized for the monitoring of infrastructure and are based on the collection of remote-sensing data, acquired using the global navigation satellite system (GNSS), synthetic aperture radar (SAR), light detection and ranging (LiDAR) and unmanned aerial vehicles (UAV) sensors. Moreover, great emphasis is given to the contribution of the state-of-the-art soft computing methods throughout infrastructure monitoring aiming to increase the automation of the procedure. The statistical analysis of the reviewing publications revealed that SARs and LiDARs are the prevalent remote-sensing sensors used in infrastructure monitoring concepts, while regarding the type of infrastructure, research is orientated onto transportation networks (road and railway) and bridges. Added to this, deep learning-, fuzzy logic- and expert-based approaches have gained ground in the field of infrastructure monitoring over the past few years.

Contrary to conventional journal bearings, which operate using oil-based substances, water-lubricated bearings (WLBs) utilize water and, thus, constitute a more environmentally responsible solution. The shipping industry, among others, as already been introduced to this technology with a lot of commercial ships using water-lubricated stern tube systems; in other cases, hydropower plants manage to keep up with the strict environmental regulations by implementing the use of WLBs in water turbines. However, there are a lot of challenges when it comes to transitioning from conventional bearings to water-based ones. Such challenges are caused by the low viscosity of water and lead to phenomena of high complexity. Such phenomena are related but not limited to cavitation and turbulent flow due to the interaction between the lubricating water and bearing surface. In this study, a numerical method will be used to simulate the fluid film and bearing geometries in order to perform a thermo-elastohydrodynamic (TEHD) analysis. The dynamic characteristics of the bearing will be calculated and the results will be discussed. The novelty of the study is evident in but not limited to the determination of the elastic deformation of a WLB during operation, as well as the effect of surface roughness, cavitation, and thermal effects on bearing characteristics.

Superlubricity, the tribological regime where the coefficient of friction between two sliding surfaces almost vanishes, is currently being investigated as a viable route towards the energy efficiency envisioned by major long-term strategies for a sustainable future. This current study provides new insights towards the development of self-lubricating systems by material and topological design, systems which tend to exhibit near-superlubric tribological performance, by reporting the synergistic effect of selective surface patterning and presence of carbon micro/nano-fillers on the frictional coefficients of additively manufactured structures. Geometric and biomimetic surface patterns were prepared by fused deposition modelling (FDM), using printing filaments of a polymeric matrix infused with graphene nanoplatelets (GNPs) and carbon fibers (Cf). The calorimetric, spectroscopic, mechanical and optical microscopy characterization of the starting materials and as-printed structures provided fundamental insights for their tribological characterization under a ball-on-disk configuration. In geometrically patterned PLA-based structures, a graphene presence reduced the friction coefficient by ca. 8%, whereas PETG exhibited the lowest coefficients, in the vicinity of 0.1, indicating a high supelubric potential. Biomimetic patterns exhibited an inferior frictional response due to their topologically and tribologically anisotropy of the surfaces. Overall, a graphene presence in the starting materials demonstrated great potential for friction reduction, while PETG showed a tribological performance not only superior to PLA, but also compatible with superlubric performance. Methodological and technical challenges are discussed in the text.

In this article, the problem of an online autonomous aerial inspection, specifically for discontinuous and distributed objects is presented. The proposed approach imposes view culling and photogrammetric constraints based on a geometrically modeled three-dimensional view pyramid, a view cone to filter surfaces by desired observation angle, a framework-integrated passive collision-avoidance scheme with the object under inspection, and a dynamically enveloping bounding-box region to map the visited surfaces. Furthermore, the proposed inspect-explore framework is validated for the case of an unknown environment with no prior knowledge of the object model under inspection. The overall inspection scheme is based on the novel First-Look approach, enabling the UAV to progressively adapt its inspection path to match the profile of the structure autonomously. The implemented exploration strategy imposes a tiered policy enabling the UAV to search, identify and navigate towards the structure for inspection. The presented work utilizes a unified architecture of the aforementioned inspect-explore framework to improve situational awareness in a previously unknown environment by enabling the UAV to explore its surrounding space and identify structures to execute closer inspection tasks. Extended simulations to evaluate the efficacy of the proposed inspect-explore framework are presented with multiple structure scenarios.

Offloading processes responsible for a robot’s control operation to external computational resources has been in the spotlight for many years. The vision of having access to a full cloud cluster for any autonomous robot has fueled many scientific fields. Such implementations rely strongly on a robust communication link between the robot and the cloud and have been tested over numerous network architectures. However, various limitations have been highlighted upon the realization of such platforms. For small-scale local deployments, technologies such as Wi-Fi, Zigbee, and blacktooth are inexpensive and easy to use but suffer from low transmit power and outdoor coverage limitations. In this study, the offloading time-critical control operations for an unmanned aerial vehicle (UAV) using cellular network technologies were evaluated and demonstrated experimentally, focusing on the 5G technology. The control process was hosted on an edge server that served as a ground control station (GCS). The server performs all the computations required for the autonomous operation of the UAV and sends the action commands back to the UAV over the 5G interface. This research focuses on analyzing the low-latency needs of a closed-loop control system that is put to the test on a real 5G network. Furthermore, practical limitations, integration challenges, the intended cellular architecture, and the corresponding Key Performance Indicators (KPIs) that correlate to the real-life behavior of the UAV are rigorously studied.