Soft tissues are commonly fiber-reinforced hydrogel composite structures, distinguishable from hard tissues by their low mineral and high water content. In this work, we proposed the development of 3D printed hydrogel constructs of the biopolymers chitosan (CHI) and cellulose nanofibers (CNFs), both without any chemical modification, which processing did not incorporate any chemical crosslinking. The unique mechanical properties of native cellulose nanofibers offer new strategies for the design of environmentally friendly high mechanical performance composites. In the here proposed 3D printed bioinspired CNF-filled CHI hydrogel biomaterials, the chitosan serves as a biocompatible matrix promoting cell growth with balanced hydrophilic properties, while the CNFs provide mechanical reinforcement to the CHI-based hydrogel. By means of extrusion-based printing (EBB), the design and development of 3D functional hydrogel scaffolds was achieved by using low concentrations of chitosan (2.0–3.0% (w/v)) and cellulose nanofibers (0.2–0.4% (w/v)). CHI/CNF printed hydrogels with good mechanical performance (Young’s modulus 3.0 MPa, stress at break 1.5 MPa, and strain at break 75%), anisotropic microstructure and suitable biological response, were achieved. The CHI/CNF composition and processing parameters were optimized in terms of 3D printability, resolution, and quality of the constructs (microstructure and mechanical properties), resulting in good cell viability. This work allows expanding the library of the so far used biopolymer compositions for 3D printing of mechanically performant hydrogel constructs, purely based in the natural polymers chitosan and cellulose, offering new perspectives in the engineering of mechanically demanding hydrogel tissues like intervertebral disc (IVD), cartilage, meniscus, among others.
High mountain environments have shown substantial geomorphological changes forced by rising temperatures in recent decades. As such, paraglacial transition zones in catchments with rapidly retreating glaciers and abundant sediments are key elements in high alpine river systems and promise to be revealing, yet challenging, areas of investigation for the quantification of current and future sediment transport. In this study, we explore the potential of semi-automatic image analysis to detect the extent of the inundation area and corresponding inundation frequency in a proglacial outwash plain (Jamtal valley, Austria) from terrestrial time-lapse imagery. We cumulated all available records of the inundated area from 2018–2020 and analyzed the spatial and temporal patterns of flood flows. The approach presented here allows semi-automated monitoring of fundamental hydrological/hydraulic processes in an environment of scarce data. Runoff events and their intensity were quantified and attributed to either pronounced ablation, heavy precipitation, or a combination of both. We detected an increasing degree of channel concentration within the observation period. The maximum inundation from one event alone took up 35% of the analyzed area. About 10% of the observed area presented inundation in 60–70% of the analyzed images. In contrast, 60–70% of the observed area was inundated in less than 10% of the analyzed period. Despite some limitations in terms of image classification, prevailing weather conditions and illumination, the derived inundation frequency maps provide novel insights into the evolution of the proglacial channel network.
Across both the public and private sector, cybersecurity decisions could be informed by estimates of the likelihood of different types of exploitation and the corresponding harms. Law enforcement should focus on investigating and disrupting those cybercrimes that are relatively more frequent, all else being equal. Similarly, firms should account for the likelihood of different forms of cyber incident when tailoring risk management policies. This paper reviews the quantitative evidence available for both cybercrime victimisation and cyber risk likelihood, providing a bridge between the academic fields of criminology and cybersecurity. We extract estimates from 48 studies conducted by a mix of academics, statistical institutes, and cybersecurity vendors using a range of data sources including victim surveys, casecontrol studies, and the insurance market. The victimisation estimates are categorised into: cyber attack; malware; ransomware; fraudulent email; online banking fraud; online sales fraud; unauthorised access; Denial of Service; and identity theft. For each category, we display all estimates in the years 2017-2021. Our review shows: (i) firms face higher victimisation rates than individuals, which increases in the number of employees; (ii) global surveys reveal a consistent relative ranking of countries in ransomware victimisation; (iii) although trends could be identified within studies that collect longitudinal data, these trends tended to contradict each other when compared across studies; and (iv) broad categories with unclear consequences (e.g. malware and fraudulent emails) displayed higher variance and average values than categories associated with specific outcomes (e.g. identity theft or online banking fraud). We discuss the outlook for cybercrime and cyber risk research.
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