Search Results (99)

Facial recognition systems are increasingly used for authentication across domains such as finance, e-commerce, and public services, but their growing adoption raises significant concerns about spoofing attacks enabled by printed photos, replayed videos, or AI-generated deepfakes. To address this gap, we introduce a multi-layered Face Recognition-as-a-Service (FRaaS) platform that integrates passive liveness detection with active challenge–response mechanisms, thereby defending against both low-effort and sophisticated presentation attacks. The platform is designed as a scalable cloud-based solution, complemented by an open-source SDK for seamless third-party integration, and guided by ethical AI principles of fairness, transparency, and privacy. A comprehensive evaluation validates the system’s logic and implementation: (i) Frontend audits using Lighthouse consistently scored above 96% in performance, accessibility, and best practices; (ii) SDK testing achieved over 91% code coverage with reliable OAuth flow and error resilience; (iii) Passive liveness layer employed the DeepPixBiS model, which achieves an Average Classification Error Rate (ACER) of 0.4 on the OULU–NPU benchmark, outperforming prior state-of-the-art methods; and (iv) Load simulations confirmed high throughput (276 req/s), low latency (95th percentile at 1.51 ms), and zero error rates. Together, these results demonstrate that the proposed platform is robust, scalable, and trustworthy for security-critical applications. Full article

Soil is fundamental to life on Earth through the provision of many ecosystem services. The current model of economic development exerts significant pressure on this resource, leading to degradation processes that are accelerated by the effects of climate change. This situation hinders the achievement of the UN Sustainable Development Goals, and some parts of the world have started a process to reverse this trend, among them the European Union, which has chosen the living labs approach as a strategic solution. The growing interest in this subject within the EU has led to the establishment of a new framework to design and test sustainable policies to improve soil health and management at the continental scale. This review presents State-of-the-Art information on the use of the living labs approach to improve soil health. It also introduces the SOILL Support Structure for Soil Health Living Labs (SHLLs) and Lighthouses and the significant role of the SOILL-Startup project to help establish a network of 100 such structures across the EU. Following the PRISMA methodology, the review describes the main features of SHLLs (definition, types of stakeholders, field and scale of application), as well as their current geographical distribution. The work provides information that can be used by the scientific community, policy makers, and soil stakeholders who prioritise soil health, regardless of the context in which they operate. Full article

Traditional navigation relies on visual alignment with leading lights, a task typically monitored by bridge officers over extended periods. This process can lead to fatigue-related human factor errors, increasing the risk of maritime accidents and environmental damage. To address this issue, this study explores the use of convolutional neural networks (CNNs), evaluating different training strategies and hyperparameter configurations to assist officers in identifying deviations from proper visual leading. Using video data captured from a navigation simulator, we trained a lightweight CNN capable of advising bridge personnel with an accuracy of 86% during night-time operations. Notably, the model demonstrated robustness against visual interference from other light sources, such as lighthouses or coastal lights. The primary source of classification error was linked to images with low bow deviation, largely influenced by human mislabeling during dataset preparation. Future work will focus on refining the classification scheme to enhance model performance. We (1) propose a lightweight CNN based on SqueezeNet for night-time ship navigation, (2) expand the traditional binary risk classification into six operational categories, and (3) demonstrate improved performance over human judgment in visually ambiguous conditions. Full article

We introduce Quantum Mechanics for Proteins (QMProt), a dataset developed to support quantum computing applications in protein research. QMProt contains precise quantum-mechanical and physicochemical data, enabling the accurate characterization of biomolecules and supporting advanced computational methods like molecular fragmentation and reassembly. The dataset includes 45 molecules covering all 20 essential human amino acids and their core structural elements: amino terminal groups, carboxyl terminal groups, alpha carbons, and unique side chains. QMProt primarily features organic molecules with up to 15 non-hydrogen atoms (C, N, O, S), offering comprehensive molecular Hamiltonians, ground state energies, and detailed physicochemical properties to enhance reproducibility and advance quantum simulations in molecular biology, biochemistry, and drug discovery. Full article

We present a scalable and resource-aware framework for the quantum simulation of large proteins, grounded in systematic molecular fragmentation, analytical Toffoli gate modeling, and empirical validation. The ground-state energy of a target biomolecule is reconstructed from capped amino acid fragments, with fixed corrections to account for artificial boundaries. Analytical cost estimates—derived from reduced Hamiltonians—are benchmarked against empirical Toffoli counts using PennyLane’s resource estimation module. Our model maintains predictive accuracy across biologically relevant systems of up to 1852 electrons, capturing consistent patterns across diverse fragments. This framework enables early-stage feasibility assessments for achieving quantum advantage in biochemical simulation pipelines. Full article

In this article, I investigate the ontological status of the minor working-class character Mrs. McNab, the cleaner in “Time Passes", the middle section of Virginia Woolf’s tripartite novel To the Lighthouse. Woolf regarded this section as the connecting block between the two outer blocks, “The Window” and “The Lighthouse”, in which she aimed to depict an empty house, devoid of human presence, and to highlight the passage of time. This section has often been analysed by literary-stylistic criticism as if written from a non-anthropocentric worldview. However, the presence of a lower-class cleaner and the absence of the upper middle-class characters who predominate in the other two blocks has also raised much debate in the literary arena. Literary critics agree that this character is given a narrative voice, but how this voice functions, and whether this character is granted narrative agency in terms of the class issues and social relations in the period of transition between Victorian England and the early twentieth-century, is an issue which still remains open. Drawing upon cognitive stylistics, I suggest reading this character both as a category-based and person-based character, and as a narrative device. First, I carry out the analysis of the repetitive she-clusters and their semantic prosodies; then, through samples of the section “Time Passes", I analyse how viewpoint blending between narrator/author and character concur to grant narrative agency to Mrs. McNab and to what extent such agency may be limited by our perception of her through the social schemata of a servant, or whether such a perception may undergo a process of schema refreshment. Last, I suggest that this character may also be viewed as a narrative agent by means of which the reader can activate mental processes of TIME and SPACE blending between the three different blocks of the novel. This blending process allows for the completion of the narrative design of the novel: the journey to the lighthouse. Full article

Measurements of concentrations of nitrogen oxides (NO_x) were collected over a period of thirteen years (2011–2023). The data were collected at Giordan Lighthouse atmospheric monitoring and research station on the Island of Gozo, forming part of the Maltese Archipelago, in the Central Mediterranean. Trend analysis shows that NO_x concentrations are decreasing at an annual rate of 0.15 ppbv per year. This is in comparison to what has been recorded across Europe due to stricter regulations and improvements in vehicle technologies. Significant reductions were observed during the COVID-19 pandemic due to the implementation of lockdowns and restrictions on mobility. NO_x concentrations also exhibit an amplitude seasonal variability of 35% with a maximum in October and a minimum in December, while the nitric oxide (NO) component of NO_x presents a broad maximum in May/June and a minimum in February. NO_x concentrations also show a strong diurnal variability with a maximum in the morning and a minimum in the afternoon. The diurnal amplitude of NO_x and NO is 44% and 148%, respectively. With respect to wind direction, the NO_x concentrations exhibit a maximum when the prevailing wind is from the South-East sector and a minimum when the wind is blowing from the West sector. Full article

As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe Lighthouse project called ENFIELD: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) zero-knowledge proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) privacy-preserving machine learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization. Full article

This study investigates the dynamic response of large-diameter monopile foundations subjected to ice loads, emphasizing sustainable design in cold-region offshore wind energy development. Through a combined ice–structure–soil model test and subsequent development of a three-dimensional ice–OWT–soil system model using Abaqus software 2022, this research addresses the sustainability of infrastructure exposed to harsh environmental conditions. The dynamic ice loads are simulated using the coupled CEM–FEM approach, while the Mohr–Coulomb model calculates soil–structure interactions. The calibration and verification processes include comparisons of simulated ice forces, ice-crushing processes, and pile deflections with experimental results. This study comprehensively assesses the effects of ice velocity and thickness on ice actions, as well as the monopile’s top displacement, shear force, and bending moment. The findings indicate that ice thickness significantly influences the dynamic response more than ice velocity, guiding the design toward more sustainable and resilient offshore wind infrastructures. Additionally, a semi-empirical calculation method incorporating the aspect ratio effect is proposed, enhancing the predictive accuracy and sustainability of large-diameter monopile foundations, as validated against field monitoring data from the Norströmsgrund lighthouse. Compared to traditional ice pressure calculation methods, the proposed approach focuses on the influence of the aspect ratio of large-diameter monopile foundations, enabling a more realistic and objective assessment of ice load calculations for OWTs in cold regions. The results demonstrate the efficacy of the proposed approach and offer a new perspective for the design of OWT structures under ice loads. Full article

The underrepresentation of women in science fields limits their potential in solving current global challenges. As a lighthouse to close this gender gap, role models are crucial for girls to build their science identity, even in their early years. Therefore, we describe a combined mentoring intervention taking place during scientific summer camps for girls. In these camps, young girls lived for a week undertaking an intensive program conducting cutting-edge research and in close contact with female mentors and other girls within a community of practice. They were mentored in small groups, but there were also chances for participating in talks, entrepreneurial workshops, and social activities all together. On the last day, the girls presented their research results, sharing their perceptions about the camp and their concerns regarding the role of women in science in an open-door final conference. By means of a mixed-method assessment conceptualization, the aim of this study is to provide evidence supporting the impact of non-formal education settings to effectively enhance girls’ science potential by using inspiring female scientists acting as mentors and role models. Furthermore, the study intends to shed light on what were regarded to be the key factors of the camp design that had such an impact on the participating girls. In particular, the findings demonstrate that the participating girls improved their attitudes towards STEM after the camp, highlighting how role models, in several roles, were key to empowering them in science through the building of a gender-responsive and inclusive community. Finally, the paper also discusses the key elements of the intervention based on the lessons learned and its transferability to different educational contexts in order to expand the beneficial effects of a gender-sensitive science education to build an inclusive future. Full article

This paper presents the findings of a 12-year study on radon conducted from January 2011 to December 2022 at the Giordan Lighthouse station on the island of Gozo, Malta. Located in the Central Mediterranean, Gozo’s strategic position enables effective monitoring of air mass movements between Africa and Europe (from south to north) and between Europe and Central Asia (from west to east). Our research involves an analysis of seasonal and diurnal variations in radon levels, alongside analysis of relevant meteorological variables, clustering of air mass back trajectories, and assessment of local and remote radon production. The findings provide critical insights into the dynamics of atmospheric radon, which are significant not only for the Maltese islands, but also for enhancing our understanding of transcontinental radon transport in the Central Mediterranean, a region that has remained largely unexplored. Full article

The Kashima Lighthouse, one of the lighthouses that suffered damage in the 2011 Great East Japan Earthquake, required a careful investigation to estimate its long-term behavior and seismic vulnerability. This study, therefore, undertook a meticulous process of the dynamic testing, dynamic identification, and long-term structural health monitoring of the Kashima Lighthouse. The results of dynamic tests reveal that the fundamental frequencies of the Kashima Lighthouse are estimated to be around 2.60 Hz and 2.63 Hz in the east–west and north–south directions, respectively. The natural modes and damping factors are identified using an SSIM (Stochastic Subspace Identification Method). This paper also discusses the result of long-term structural health monitoring, where machine learning techniques were applied for data processing, highlighting the rigor and thoroughness of this research. Full article

The KREIS-Haus, an inhabited living lab in Switzerland, serves as a demonstrator of the implementation of sustainable and circular building practices. Addressing the environmental impacts associated with construction, operation, and deconstruction, this study presents an innovative systematic design concept that synthesizes principles of the circular economy, Cradle-to-Cradle design, and ecological engineering. The design process was applied to the KREIS-Haus as a lighthouse project, combining theoretical frameworks with real-word application to derive actionable insights. The novelty of the KREIS-Haus lies in the holistic integration of circular and sustainable concepts within a compact footprint, realized in a real-life, publicly accessible living lab. Its design maximizes resource efficiency by incorporating locally sourced materials, modular construction techniques, and flexible interior features, which allow for easy disassembly and reuse. At the heart of its circular design is the multifunctional conservatory, which provides heat and sound insulation, generates solar power, and expands the living space. Additionally, it supports plant cultivation and enables the reuse of treated wastewater and nutrients, as part of the off-grid water and nutrient management system to reduce reliance on external resources. The principles of solar architecture further minimize the building’s energy demands. Key insights from the design and construction process highlight the challenges of navigating conflicting goals, the importance of partner alignment, and considerations for scaling these concepts to larger developments. While technical challenges may arise, addressing systemic barriers will be essential for advancing sustainable and circular building practices on a broader scale. Full article

Industry 4.0 presents an opportunity to gain a competitive advantage through productivity, flexibility, and speed. It also empowers the manufacturing sector to drive the sustainability revolution to achieve net zero carbon by reducing emissions in operations. In this paper, the aim is to demonstrate a practical implementation of a smart manufacturing application using a systematic approach based on conceptual six-gear smart factory roadmap with connectivity, integration and analytics stages to build a smart production management ecosystem using off-the-shelf technologies applied in precision manufacturing. Business benefits from the smart manufacturing application implementation are realized in terms of operational performance, economic benefits, and environmental sustainability over a period of three years (before and after smart manufacturing). The productivity improves as a result of the 47% improvement made to the machines’ utilization and the 53% reduction in the total downtime waste. Economic benefits are realized in terms of a cost saving of GBP 420 K that could cost the business and the returns of the financial investment made, which is recovered within a year. An environmental sustainability impact is realized by a reduction in the total greenhouse gas (GHG) emissions by 43%, mostly due to the reduction in the Scope 2 emissions in operations by 50%, which is significantly impacted by the reduction of energy consumption and better power consumption management. The significance of this work is the bridging of the gap between theory and practice by rapidly applying the six-gear smart factory roadmap to start, scale, and sustain the implementation of smart manufacturing applications in the manufacturing industry. This roadmap can serve as a strategic framework tool for smart manufacturing implementations. The technical architecture can serve as a guide for the practical implementation of smart manufacturing applications to reduce the complexity of development. This work also bridges the gap in academia and in industry by showcasing a real-world actual business benefits realized from smart manufacturing, as well as showcasing the practical implementations, limitations, and opportunities of smart manufacturing applications in the precision manufacturing industry, all of which reduce the internal barriers and challenges facing smart manufacturing and industry 4.0 adoption. The value realized in gaining a competitive advantage and driving environmental sustainability from smart manufacturing in this study can serve as a case study for academics and for industry business leaders, digital champions, and digital lighthouses to support value creation and to drive and accelerate smart manufacturing applications, digital transformation initiatives, and industry 4.0 adoption across the value chain. Full article