The Effects of the Use of Exoskeletons for Manual Handling on Cognitive Abilities: A Mixed Reality Approach

Recently, the “Industry 5.0” paradigm has been proposed, thus underlining the need for a “human-centric” approach within the industries and a shift in focus from welfare to the wellbeing of the operator by exploiting enabling technologies, such as exoskeletons. However, the impact of these technologies on cognitive load and motor control – and their interaction - during the execution of working tasks, has not yet been thoroughly analyzed. The present study aims to develop and preliminarily present an integrated tool for the evaluation of the effects induced using exoskeletons on cognitive load. A single participant performed a free-hand cognitive task implemented in a mixed reality (MR) environment and under three different conditions: (1) static condition, (2) while performing a whole-body motor task (i.e., lifting), and (3) while performing the same motor task but with the assistance of a hybrid upper-body exoskeleton. Accuracy data and reaction times were collected for the cognitive task, while whole-body kinematics and kinetics were acquired to assess the motor performance by using wearable inertial measurement unit-based and surface EMG systems. The obtained results highlighted differences in cognitive effort for the realized motor task when performed with or without the exoskeleton; in fact, accuracy decreased and reaction times increased when performing the motor task while using the exoskeleton. This preliminary study resulted promising and allowed to obtain useful hints for gathering multifactorial, quantitative, and reliable information concerning the motor-cognitive interactions while using exoskeletons within specific working environments

Design of a Cost Effective Spatial Image Registration System for Augmented Reality in Vehicular Applications

The paper describes the design and validation of a spatial image registration algorithm for a vehicular Head-Mounted Augmented Reality (AR) system. AR can considerably improve the driving experience by increasing the driver's situational awareness. AR can only work if stable and realistic holograms are generated. The process of generating the holograms so that they appear in a specific position in the world is also known as image registration. Since AR devices employ see-through Head-Mounted Displays, realistic image registration requires high-accuracy head tracking. Solutions exist in static environments where state-of-the-art simultaneous localization and mapping algorithms suffice. Vehicles are more challenging. In aerospace, costly optical-inertial tracking systems are regularly employed. This paper focuses instead on low-cost ground vehicles and proposes a solution that does not require aerospace-grade Inertial Measurement Units and is easily integrable on cars. The proposed solution, tested on a racing circuit, is based on passive markers and on the stereoscopic detection of the road plane on which the AR features are anchored

Tensor-Product Vertex Patch Smoothers for Biharmonic Problems

We discuss vertex patch smoothers as overlapping domain decomposition methods for fourth order elliptic partial differential equations. We show that they are numerically very efficient and yield high convergence rates. Furthermore, we discuss low rank tensor approximations for their efficient implementation. Our experiments demonstrate that the inexact local solver yields a method which converges fast and uniformly with respect to mesh refinement and polynomial degree. The multiplicative smoother shows superior performance in terms of solution efficiency, requiring fewer iterations in both two- and three-dimensional cases. Additionally, the solver infrastructure supports a mixed-precision approach, executing the multigrid preconditioner in single precision while performing the outer iteration in double precision, thereby increasing throughput by up to 70 %

Enabling nature-based solutions for climate adaptation in cities of the Global South: planning dimensions and cross-cutting pathways for implementation

Despite the increasing recognition of the critical role that nature-based solutions (NBS) play in urban resilience, decision-makers in many cities across the Global South continue to prioritize grey infrastructure and engineered solutions. This prevailing approach may offer short-term economic advantages but ultimately falls short in ensuring the long-term sustainability and resilience of communities facing the challenges of a changing climate. This article aims to identify the main enabling factors that foster the application and implementation of NBS in cities, through a detailed analysis of urban NBS case studies. For this reason, the research focused on grey literature, providing insights into realworld implementation and identified 52 case studies through a thorough review of web databases and relevant publications on NBS case studies, supplemented by a web-based questionnaire distributed to identify additional cases. A qualitative methodology was employed to analyse the data collected for each case, covering various phases of each project, including planning, delivery, and stewardship. The findings indicate that enabling the implementation of urban NBS in the Global South requires attention to four key dimensions: good governance, financial feasibility and economic sustainability, social acceptance, and environmental sustainability. Additionally, the results highlight the importance of cross-cutting pathways emerging from these dimensions, such as adopting an integrated, context-specific, and data-driven approach in planning and implementation, enabling mechanisms for participatory approaches and multi-stakeholder engagement, planning for the delivery of multiple benefits by NBS, and prioritizing NBS in urban land acquisition and management policies

Crowd Professionalism and Top Management Team Replacement

Crowdfunding literature has mainly focused on the financial or innovation performance of crowd-backed startups, while the crowd impact on startups' organizational design remains underexplored. Drawing from the literature on collective intelligence of the crowd, we hypothesize that the professionalism of the crowd attracted during crowdfunding campaigns influences organizational changes within startups. Using a unique dataset of equity-crowdfunded Italian startups, we employ social network analysis to characterize the professionalism of the crowd and investigate its correlation with organizational changes, focusing on top management team (TMT) dynamics. Our empirical analysis reveals that startups attracting a more professional crowd are more likely to change TMT members but less likely to replace the CEO. These findings contribute to understanding the nuanced effects of equity crowdfunding on startup organizational dynamics and challenge the notion that crowdfunding lacks post-campaign treatment effects

Integration of geospatial foundation models in unsupervised change detection workflows for landslide identification

This study investigates the integration of Geospatial Foundation Models (GFMs) into an unsupervised change detection workflow for landslide identification. As climate change increases landslide frequency, rapid and automated detection systems are essential for a timely response. However, the high cost of annotating satellite datasets has driven interest toward unsupervised methods that could operate effectively with limited data. This work addresses two key gaps: (1) the absence of a dedicated dataset for unsupervised landslide change detection, and (2) limited investigation of GFMs as feature extractors in unsupervised frameworks. To address these, we introduce the Global Landslide Dataset for Change Detection (GLaD4CD), comprising 174 Sentinel-2 bi-temporal image pairs of global landslide events, and propose LandslideMetric-CD, an unsupervised model based on Metric-CD by Bandara and Patel [2023. “Deep Metric Learning for Unsupervised Remote Sensing Change Detection.” arXiv:2303.09536 [cs]], adapted to incorporate the SSL4EO DINO GFM. While domain-guided approaches like band-specific thresholding achieve higher F1 scores (48.41% for Band 04), LandslideMetric-CD (F1 = 31.68%) outperforms fully automatic differential thresholding using the full spectral range (F1 = 19.33%) and RGB-based deep learning methods (F1 = 19.66% for Change Detection based on image Reconstruction Loss (CDRL) [Noh et al. 2024. “Unsupervised Change Detection Based on Image Reconstruction Loss.” Remote Sensing Letters 15 (9): 919–929]). These findings underscore the importance of spectral band selection and demonstrate the potential of GFMs for automated, expert-independent landslide detection

Quantitative Phase Analysis in Carbon Steel EAF Slag for the Determination of Phase-Controlled Leaching Mechanism

The production of Electric Arc Furnace (EAF) slag is 150-180 kg per ton of steel, making its recycling essential for the steelmaking process’s sustainability. Despite favourable mechanical properties as its good abrasion resistance, environmental challenges, such as the leaching of harmful elements (e.g., Cr, V, Ba and Mo), limit its direct reuse in industries like cement production, road construction and water filtration. The mineralogical composition of the slag is the key to address its leaching behaviour, as the hydration of specific phases (e.g., larnite and brownmillerite) can release these elements. Current regulations demand minimal environmental impact and require precise quantification of slag phases to identify and mitigate the leaching mechanisms. The slag is generally and rapidly characterized by means of X-Ray diffraction (XRD) and Scanning Electron Microscopy (SEM), however the quantitative analysis obtained by the Rietveld method is not always reliable because of the presence of preferential orientations common to several phases and the high sensitivity to the imposed background. Therefore, it is necessary to implement a methodology to refine the X-Ray analysis. Selective dissolution of larnite and brownmillerite, dangerous for Ba and Cr leaching, can be used for this purpose. In this work, different quantitative techniques were compared to tune-up the Rietveld analysis. These methods facilitated the accurate determination of critical phases like larnite and brownmillerite. SEM and XRD analyses have intrinsic limitation in quantifying phase contributions. In particular the identification of brownmillerite with XRD is overestimated respect the chemical prediction due to its strong crystallographic orientation al low 2θ angle. Selective chemical dissolution is instead the most reliable technique to quantify the phases, since a previous salicylic acid methanol (SAM) dissolution dissolves all the calcium silicates and a following potash sucrose (KOSH) treatment quantify all the calcium aluminates. The difficulties encountered doing only KOSH were associated to the formation of a viscous gel due to the hydration of silicates. If the dissolution of the brownmillerite was not complete, a second KOSH dissolution is required