Aristotle University of Thessaloniki

A novel compact extraction system has been developed at CERN to optimize high-current linacs for medical and industrial use. Replacing the traditional LEBT with a 2 cm-long electrode-based design, it enables efficient proton extraction and acceleration to 45 keV. Tested at the Linac4 test stand, both experimental and simulation results confirm its ability to match to the RFQ acceptance with improved beam quality and reduced footprint, offering a viable solution for next-generation compact linacs.

As the world moves toward a low–carbon future, hydrogen (H 2 ) is playing a key role in sustainable energy strategies, driving research into the development of efficient H 2 separation systems. In this study, a scalable low–temperature (250 °C) chemical vapor deposition (CVD) method using tetraethoxysilane (TEOS) and oxygen was employed to deposit a H 2 –selective silica layer on a commercial titania membrane. In contrast to conventional single-stage CVD routes, a novel addition of intermediate ozone (O₃) treatments between deposition steps promoted the formation of a stable, purely inorganic structure by decomposing residual organics and activating the surface for the subsequent deposition. The resulting SiO 2 /TiO 2 membrane demonstrated a favorable balance between permeance and selectivity, achieving a H 2 permeance of 1.9 × 10 – 7 mol m −2 s −1 Pa −1 and a H 2 /CO 2 permselectivity of 80.4 at 250 °C. Additionally, a theoretical mass transport model was applied at each modification stage to assess pore structure evolution, identify the dominant transport mechanisms, and advance the understanding of structure–permeation relationship. • A hydrogen – selective silica layer was deposited on a commercial titania membrane. • The membrane demonstrated a favorable balance between H 2 permeance and selectivity. • Chemical vapor deposition was assisted by ozone post-treatments, performed in stages. • Mass transport model assessed pore-size distributions monitoring structural evolution. • Model validated and predicted transport mechanism contributions for six pure gases.

The central Ionian Islands area, including Kefalonia and Lefkada Islands constitutes an area with the highest seismic hazard in Greece. Lefkada Island has experienced two strong (M > 6.0) earthquakes in 2003 (M W 6.2) associated with a fault segment in the northwestern part of the Island, and in 2015 (M W 6.5), associated with the adjacent southern fault segment, both aligned along the western coastline. The close temporal proximity along with the ongoing high seismic activity motivated our investigation. Microseismic data were relocated over a period of more than 4 years, following the 2015 mainshock resulting in a comprehensive seismic catalog and the determination of 110 focal mechanisms for the stronger events. A density-based clustering algorithm (DBSCAN) was applied to the relocated hypocenters and focal mechanisms, enabling improved association of seismicity patterns with the kinematic properties of the fault zone. The effects of the 2015 mainshock on regional seismicity were assessed through coseismic static stress changes, calculated using a variable slip model derived from a joint inversion of regional and teleseismic seismic data, combined with geodetic displacement estimates. Stress inversion of the calculated focal mechanisms identified faults prone to instability, which were used as receiver faults in the stress change analysis. The results reveal that post-2015 seismicity was strongly influenced by the mainshock, with clusters aligning along the primary fault zone as well as secondary structures within a rather narrow faulting zone. These findings enhance understanding of the seismic behavior of the Kefalonia Transform Fault Zone and its secondary faults, providing critical insights for seismic hazard assessment in this tectonically active region. • Relocated microseismicity reveals active fault structures in Lefkada Island. • Clustering of earthquakes and focal mechanisms aids structural interpretation. • Joint inversion yields a variable slip model for the 2015 Mw6.5 mainshock. • Static stress changes explain post-seismic clusters along fault structures.

• Optimal reduced-order vehicle-bridge interaction model. • Reliable Structural Health Monitoring (SHM) and damage detection. • VBI computational framework with significantly reduced computational cost. • Enriched model with realistic rail irregularities. • Experimental validation over multiple operating speeds, loads, and damage scenarios. Bridge structures subjected to moving vehicle loads exhibit complex dynamic behavior influenced by structural flexibility and rail surface irregularities. The correct approach and optimal modeling of vehicle-bridge interaction effects are crucial for the accurate computational determination of dynamic bridge vibrations, for reliable Structural Health Monitoring (SHM) and damage detection. The purpose of this work is twofold. First, a novel computational framework is introduced to generate an optimal reduced-order Vehicle-Bridge Interaction (VBI) model that captures the essential dynamics of the system with significantly reduced computational cost. This process begins with the development of a high-fidelity finite element (FE) model of the bridge which is updated using experimental modal data. This model is then reduced using Component Mode Synthesis (CMS) to enable efficient simulation while preserving the essential dynamic behavior and is combined with a simplified vehicle model enriched with realistic rail irregularities, producing an accurate and computationally efficient VBI system. Second, the study introduces a classification framework based on a lightweight Convolutional Neural Network (CNN) trained on numerically generated data from the validated VBI model. The CNN operates on frequency-domain features derived from bridge acceleration responses, which reflect damage-induced changes in stiffness and energy distribution of the structure. Using these features and an ensemble of independently trained CNNs, the framework classifies the structural health condition of the bridge.

Apathy is a neuropsychiatric symptom that is present in various disorders, including dementia and Mild Cognitive Impairment (MCI). Patients with MCI who exhibit symptoms of apathy are at a higher risk of progressing to dementia compared to those with depressive symptoms. The aim of the present study was to investigate the psychometric properties of the clinician-rated version of the Apathy Evaluation Scale (AES-C) in a Greek sample of MCI patients and healthy older adults. The translation and adaptation of the scale were conducted using the forward-backward method. The final sample consisted of 100 participants, 14 men (n = 14) and 86 women (n = 86), with a mean age of 72 years. Participants were administered the translated and adapted version of the AES-C, as well as the Greek version of the Beck Depression Inventory. In terms of reliability, Cronbach's alpha was found to be high (α = 0.91), indicating excellent internal consistency. Confirmatory Factor Analysis (CFA) revealed a one-factor solution with a very good model fit (RMSEA = 0.018, CFI = 0.985, TLI = 0.983, SRMR = 0.076). The AES-C can serve as an important addition to neuropsychological assessment for detecting apathy symptoms in patients with MCI, thereby contributing to the early prognosis of dementia.

Abstract Objective We assessed impact of PCSK9 inhibitors (PCSK9i) on carotid intima-media thickness (cIMT) and their interplay with lipid profile parameters. Methods Systematic review conducted in accordance with PRISMA guidelines. Primary endpoint was cIMT reduction (millimetres). Meta-regression examined influence of demographic, imaging, and biochemical parameters on cIMT. Heterogeneity exploration was conducted via leave-one-out and Baujat plot analyses. Results Four randomized controlled trials comparing PCSK9i plus statin versus placebo plus statin, and two single-arm studies were included, enrolling 607 patients and 2175 carotid segments. Pooled analysis demonstrated significant reduction in cIMT; mean difference (MD) −0.03 (95%c.i.: −0.05 to −0.01) (I2 = 95.4%, P < 0.01). Following exploration and mitigation of heterogeneity, this reduction persisted; MD −0.01 (95%c.i.: −0.02 to −0.01) (I2 = 0%, P = 0.59). Initially, comparison between PCSK9i and placebo yielded non-significant cIMT reduction favouring PCSK9i; MD −0.04 (95%c.i.: −0.10 to 0.03) (I² = 98.6%, P < 0.01). Following heterogeneity adjustment, effect estimate reached statistical significance, demonstrating consistent cIMT reduction with PCSK9i; MD −0.01 (95%c.i.: −0.02 to −0.01) (I² = 52%, P = 0.12). Meta-regression displayed associations between baseline cIMT (β = −0.45, P < 0.01), HDL (β = 1.92, P < 0.01) and cIMT. Pooled between-group cIMT differences showed moderate correlations with the respective differences in total (r = 0.69) and LDL cholesterol (r = 0.59). Conclusions Evidence supports potential association between PCSK9i treatment and cIMT reduction, with observed correlations involving total and LDL cholesterol indicating lipid-mediated vascular response. These findings warrant cautious interpretation given substantial heterogeneity, partly driven by baseline differences in cIMT and lipid profiles. Future studies should ensure baseline comparability in vascular and biochemical parameters to strengthen causal inferences.