Abstract

This study presents the first application-ready demonstration of radio-frequency (RF) switches based on memristors fabricated through a combination of electrochemical exfoliation and liquid-liquid interfacial assembly (EC-LL). This 2D layer fabrication method yields uniform, low-defect bilayer MoS2 nanosheet networks without relying on high-temperature processes or hazardous gases typical of chemical vapor deposition (CVD), offering a low-cost and environmentally friendly route towards CMOS-compatible integration. Remarkably, the resulting devices exhibit robust unipolar resistive switching which simplifies biasing requirements and reduces power consumption. Reproducibility with retention of 104 sec, and endurance of 100 cycles is reported. RF measurements confirm reliable operation at millimeter wave (mmWave) frequencies across 10–110 GHz, demonstrating low insertion loss (0.42–0.9 dB), isolation >18 dB, and an intrinsic cut-off frequency of ~5.4 THz. Integration into Reconfigurable Intelligent Surface Unit Cells (RIS-UCs) further showcases the technology’s utility in next-generation mmWave communication systems, including 5G/6G and satellite applications. Simulations of a 24×24-element RIS panel confirm high gain (>21.6 dBi) and efficient beam steering (-60º, 60º degrees) over the 26.8–29.1 GHz band, while the ultra-low switching energy (~330 pJ per unit cell) enables zero static power consumption—critical for scalable and sustainable 6G infrastructure. This work establishes a new benchmark by delivering the first solution-processed, application-suitable 2D material in solid-state RF switches combining non-volatility, high-frequency operation, and CMOS integration potential. It marks a significant step toward reconfigurable, energy-efficient wireless communication platforms.

Abstract
Heritage preservation requires innovative sensing technologies to analyze their chemical composition while minimizing damage. This study introduces a Laser-induced Breakdown Spectroscopy (LIBS) system featuring a fiber laser source and optical fiber-based collection system for the analysis of heritage ceramics. Comparative experiments with a conventional Nd:YAG laser LIBS system highlight the advantages and trade-offs of the fiber laser system in terms of ablation capability, spectral mapping, and depth profiling. Results were validated against X-ray Fluorescence (XRF). Experiments demonstrate minimal surface alteration and high-quality spectral data for elements such as Pb, Fe, Zn, Sb, Mn, Ti Na, Ba and Ca. The compact design and good results position this system as a transformative tool for heritage conservation.

Abstract
Location privacy is a major concern in the current digital society, due to the sensitive information that can be inferred from location data. This has led smartphones' Operating Systems (OSs) to strongly tighten access to location information in the last few years. The same tightening has, however, not yet happened when it comes to our second most carried around device: the laptop. In this work, we demonstrate the privacy risks resulting from the fact that major laptop OSs still expose WiFi data to installed software, thus enabling to infer location information from WiFi Access Points (APs). Using data collected in a real-world experiment, we show that laptops are often carried along with smartphones and that a large fraction of our mobility profile can be inferred from WiFi APs accessed on laptops, thus concluding on the need to protect the access to WiFi data on laptops.

Abstract
Introduction: Urban centers face increasing congestion and pollution due to population growth driven by jobs, education, and entertainment. Promoting active modes like walking and cycling offers healthier and less polluting alternatives. Understanding perceptions of comfort (green areas, commercial areas, crowd density, noise, thermal sensation, air quality, allergenics), safety and security (street illumination, traffic volume, surveillance, visual appearance, and speed limits) are crucial for encouraging active modes adoption. This study categorizes user groups based on these indicators, supporting policymakers in the development of targeted strategies. Methods: We developed a questionnaire to support our empirical study and collected 653 responses. We have analyzed the data using clustering methods such as Affinity Propagation, BIRCH, Bisecting K-means, HAC, K-means, Mini-Batch K-means, and Spectral clustering. The best performing method (K-means) was used to identify the user groups while a random forest model evaluated the relative importance of indicators for each group. Results: The study identified five user groups based on urban mobility indicators for safety and security, comfort, and distance and time. Conclusions: These groups, distinguished by sociodemographic features, include: Street Aesthetes (young men valuing visual appeal), Safety Seekers (employed men prioritizing speed limits), Working Guardians (employed men focused on surveillance and green spaces), Urban Explorers (young women valuing air quality and low traffic), and Comfort Connoisseurs (employed women prioritizing noise reduction and aesthetics).

Abstract
In order to facilitate the adoption of deep learning in areas where decisions are of critical importance, understanding the model's internal workings is paramount. Nevertheless, since most models are considered black boxes, this task is usually not trivial, especially when the user does not have access to the network's intermediate outputs. In this paper, we propose IBISA, a model-agnostic attribution method that reaches stateof-the-art performance by optimizing sampling masks using the Information Bottleneck Principle. Our method improves on the previously known RISE and IBA techniques by placing the bottleneck right after the image input without complex formulations to estimate the mutual information. The method also requires only twenty forward passes and ten backward passes through the network, which is significantly faster than RISE, which needs at least 4000 forward passes. We evaluated IBISA using a VGG-16 and a ResNET-50 model, showing that our method produces explanations comparable or superior to IBA, RISE, and Grad-CAM but much efficiently.

Abstract
Road safety is a global issue, with road-related accidents being one of the biggest leading causes of death. Motorcyclists are especially susceptible to injuries and death when there is an accident, due to the inherent characteristics of motorcycles. Accident prevention is paramount. To improve motorcycle safety, this paper discusses and proposes a preliminary architecture of a system composed of various sensors, to assist and warn the rider of potentially dangerous situations such as front and back collision warnings, pedestrian collision warnings, and road monitoring.