Search Results (1,282)

Anthropic’s April 2026 Claude Mythos Preview release established a new operational threat category: frontier AI systems whose extended-context reasoning, recursive self-correction, native system-tool integration, and agentic scaffolding render dominant AI safety paradigms—RLHF, output filtering, contractual access vetting, human-in-the-loop supervision—insufficient as sole controls. This paper develops a defense-in-depth reference architecture against that category, structured around four named contributions: a five-indicator operational definition of the Mythos-class (capability conjoined with scaffold, access pattern, autonomy depth, and persistence); the Mythos-Class Posture Rubric (MCPR), a three-tier detection framework spanning evaluation, deployment, and runtime with explicit routing to mitigation layers; a four-layer mitigation stack comprising the Vetted-Access Operational Pattern (VAOP), Authority-Bound Output Release (ABOR) cryptographically grounded in FIPS 203/204/205 post-quantum primitives, and the Compute-Plane Isolation Profile (CPIP); and an integrated architecture that crosswalks to the NIST AI Risk Management Framework, NIST Cybersecurity Framework 2.0, and CISA Zero Trust Maturity Model 2.0. The architecture is applied to three deployment surfaces—post-quantum cryptography migration, federal AI supply-chain assurance, and critical-infrastructure operational technology defense—demonstrating that the four contributions generalize across heterogeneous operational contexts. The contribution is a reference design rather than a deployed system; limitations, falsifiability criteria, and a research agenda for empirical refinement are developed. Full article

Field-service operations for utility companies require routing technicians across multiple depots while guaranteeing same-day response to critical infrastructure customers, a constraint that standard multi-depot routing methods cannot structurally enforce. We introduce the MDVRP with Priority Requests (MDVRP-PR), formalised as a lexicographic optimisation problem that guarantees service to priority customers before maximising coverage and minimising route duration. A three-stage pipeline is proposed: hybrid DBSCAN-Hierarchical clustering for topology-aware depot assignment, an Enhanced Max-Min Ant System (MMAS) with priority-driven construction, lexicographic solution selection, and repair, and a Boundary Relocate post-optimisation stage with global cross-depot recovery. The approach is evaluated on a real-world applied case study from Algérie Télécom (Guelma, Algeria), comprising a single four-depot field-service instance scaled to three sizes (55, 90, and 150 customers) and assessed over 2135 controlled runs. On this case study, the proposed clustering method outperforms the MDVRP-adapted Sweep baseline by $22.9$ percentage points on the largest instance (n = 150; Friedman p < 0.001). The priority mechanisms sustain $100\%$ feasibility across all configurations, compared to complete collapse without them ($0/10$ seeds at $\ge 40\%$ priority), at a route-time overhead below $5\%$. Relative to the company’s current manual practice, the framework improves customer coverage by $16.1$ percentage points within 28 s, confirming its practical utility for daily deployment in this capacity-constrained, priority-sensitive routing context. Full article

Recyclers worldwide face a common bottleneck: incoming mixed plastic bales are chemically opaque, yet the choice between mechanical recycling, chemical recycling, and energy recovery hinges on contaminant levels that cannot be judged by visual inspection alone. This study develops and validates a tiered analytical decision-support framework that translates standard laboratory measurements into explicit, actionable go/no-go routing criteria for any mixed polyolefin waste stream. The framework is organized into three successive analytical tiers of increasing specificity: Tier 1 uses FTIR and DSC for rapid polymer identification and thermal subclass confirmation; Tier 2 applies TGA/DTG for thermal stability assessment and filler quantification; and Tier 3 deploys ICP-OES, WD-XRF, CIC, and TG–MS for targeted heavy metal, halogen, and evolved gas profiling, triggered only when Tier 1/2 flags are raised. This staged logic minimizes unnecessary testing while ensuring that contaminant-relevant information is captured where it matters. The framework is demonstrated on nine blind mixed plastic waste streams (P1–P9) supplied by an industrial recycling facility without prior disclosure of polymer identity, filler content, or additive history—conditions that replicate the uncertainty encountered at any sorting plant globally. Application of the tiered protocol identified dominant polymers (HDPE, LDPE, PP), quantified inorganic fillers (CaCO₃ up to ~38 wt%), and detected hazardous contaminants, including chlorine (up to ~1900 ppm), lead, chromium, and titanium, enabling each stream to be assigned to a specific recycling route with defined contaminant thresholds. Because the method relies exclusively on commercially available, vendor-independent instrumentation and follows a reproducible, rule-based decision logic, it is directly transferable to recycling facilities in any geographic context without site-specific calibration. The proposed framework thus provides a practical, scalable decision-support tool for feedstock-level quality control under emerging regulations such as the UNEP Global Plastics Treaty. Full article

This study investigates how different rural landscape types shape visual attention and physiological responses, with the aim of informing more targeted rural landscape renewal. Four typical rural landscape types in the suburbs of Hefei, China, were examined: Flat Farmland (FF), Hilly Forest (HF), Developed Plain (DP), and Water-network Lowland (WNL). All four study villages are project villages in the suburban area of Hefei where rural revitalization is currently being advanced. This study therefore treats them as empirical cases within the context of rural revitalization in China, using them to examine perceptual differences among rural landscape types and their implications for rural landscape renewal. A two-stage research design was adopted to balance field realism and laboratory control. In the first stage, 40 representative scene images were selected by combining field video records with fluctuations in on-site skin conductance response (SCR). In the second stage, laboratory experiments were conducted while participants viewed the selected images, during which eye-tracking, skin conductance, and heart rate data were recorded simultaneously. These measures were used to characterize visual attention allocation and autonomic physiological responses across different rural landscape types, rather than to directly measure landscape preference. For Area of Interest (AOI) analysis, each image was coded into six landscape element categories: vegetation, buildings, roads, sky, vernacular buildings, and water bodies. The results revealed significant typological differences in overall visual search patterns and autonomic responses. Gaze hotspots were concentrated on identifiable targets and boundary regions in the foreground and midground, whereas the sky attracted relatively limited attention. FF primarily emphasized vernacular buildings and farmland boundaries, HF emphasized settlement interfaces and spatial transition nodes, DP emphasized road junctions and facilities along routes, and WNL emphasized water bodies and water–land interface zones. These findings suggest that a two-stage multimodal design can provide supporting evidence for understanding type-specific perceptual responses and can support more targeted strategies for rural landscape renewal. Full article

The geographic and economic contexts play a major role in decision-making when it comes to municipal solid waste management. In the present study, simulations are carried out using the Waste and Resource Assessment Tool for the Environment (WRATE) software academic version 3.0.1, based on the Ecoinvent database (version 2) to assess the greenhouse gas emissions released by 1 ton of municipal solid waste with a typical composition characterizing upper middle-income countries, with an organic fraction of approximately 50% by weight. The variation over time (2000 to 2022) with no intended transformation in the management strategy is first analyzed, then several transformations are applied by varying the waste management routes (open dumping, landfilling, recycling and composting) as well as the energy recovery integration. The results are then discussed based on the waste categories and the performed operations (landfilling, recycling, transportation, treatment and recovery). The results revealed that the most promising scenario includes limited open dumping that does not exceed 10%, landfilling with at least 20% energy recovery, and major fractions addressed to composting and recycling. Overall, this scenario returns a negative carbon footprint with a value of approximately−0.35 tons of CO₂-Eq/ton of MSW. Results are mostly applicable to countries with similar waste composition and infrastructure levels; preconditions include source segregation, compost markets, and landfill gas infrastructure. Full article

Insects represent powerful experimental systems for investigating host–microorganism interactions, providing valuable insights into bacterial pathogenicity, immune regulation, symbiosis, and antimicrobial discovery. This review examines the complex relationships between insects and bacteria, focusing on the mechanisms that control infection, immune activation, and microbial adaptation. Particular attention is given to the routes of pathogen entry and to the conserved innate immune pathways that coordinate host defenses, including the Toll, Imd, Duox, and Jak/Stat signaling cascades. The review illustrates how bacterial pathogens exploit toxins, immune evasion strategies, and metabolic adaptation to overcome host defenses, while insects rely on tightly regulated cellular and humoral responses, antimicrobial peptides, melanization, and microbiota-mediated homeostasis. Interactions between pathogenic and commensal bacteria in the insect gut are discussed in the context of immune tolerance, dysbiosis, and ecological adaptation. The dual role of bacterial virulence factors in both pathogenesis and symbiosis is highlighted through examples involving entomopathogenic bacteria such as Photorhabdus spp., Xenorhabdus spp., and Bacillus thuringiensis. In addition, the review summarizes the use of insect models, including Drosophila melanogaster, Galleria mellonella, Bombyx mori, and Apis mellifera, in experimental infections aimed at studying virulence mechanisms, host immune responses, and antimicrobial efficacy. Finally, multi-omic approaches, including transcriptomics, metabolomics, epigenomics, and single-cell technologies are discussed as transformative tools for dissecting host–microbe interactions at molecular and systems levels. Overall, insect–bacteria interactions emerge as dynamic and evolutionarily shaped systems in which immunity, metabolism, microbiota composition, and environmental factors are closely interconnected, offering important perspectives for both basic research and the development of sustainable biocontrol and antimicrobial strategies. Full article

With the 2030 deadline for the United Nations Sustainable Development Goals (SDGs) approaching, there is a growing global urgency to identify innovative, scalable, and inclusive AI-based or AI-enabled solutions capable of accelerating progress across sectors. Yet the benefits of AI remain unevenly distributed, particularly in low-resource settings where limited infrastructure, cost barriers, and unequal access to skills constrain adoption. This paper explores how Tiny Machine Learning (TinyML)—a low-power, low-cost edge AI paradigm—offers a concrete technological pathway aligned with the principles of Frugal AI, providing accessible, energy-efficient, and context-adapted tools for sustainable development. We evaluate how participatory citizen science, when combined with TinyML, enables communities to co-create AI applications that address locally defined challenges in environmental monitoring, agriculture, and public health. Drawing on early outcomes from workshops, collaborative projects, and innovation competitions, the paper examines how TinyML-enabled participatory approaches cultivate technical skills, stimulate grassroots entrepreneurship, and generate prototypes suited to low-resource environments. Using a qualitative multiple-case study of 50 participatory TinyML initiatives across 22 countries, we analyse how frugal edge-AI practices support skills formation, prototype development, and early entrepreneurial engagement. The analysis identifies the pedagogical, technical, and institutional frameworks that support successful participatory AI initiatives, emphasizing open educational resources, cross-sector partnerships, and community-driven problem formulation. We introduce the Frugal Edge AI Lean Canvas to help innovators identify novelty, ethical implications, and measurable impact. TinyML-based participatory innovation offers a promising route for accelerating SDG progress by expanding who can create, deploy, and benefit from AI. Full article

Low-speed automated driving (LSAD) shuttles operate in complex urban environments where abrupt braking can affect safety, service quality, and operational interpretability. This study proposes a telemetry-based workflow for the detection and characterization of hard braking (HB) events in autonomous shuttle operations. The workflow includes preprocessing of autonomous in-service telemetry data, deterministic HB detection under irregular sampling, evidence-based attribution using diagnostic and obstacle-related signals, and driving-context characterization through K-means clustering, applied to a 62-day dataset from an autonomous on-demand shuttle operating on a fixed 2.8 km urban loop in Turin. After preprocessing, 71% of the 16,670,518 observations are retained. The analysis identified 734 HB events, of which 89% are linked to specific contextual conditions, highlighting environmental and operational influences on safety-critical situations. Driving-context analysis relies on 316,280 observations collected at 1 Hz and yields a nine-cluster solution. When projected back onto the route through waypoint-level modal regimes, HB events are found to be over-represented in high-speed segments. These results show that HB events can be interpreted not only as a threshold exceedance, but as an operational indicator linked to route-level driving regimes. The proposed framework supports data-driven safety assessment and operational decision-making in autonomous shuttle systems by researchers and practitioners. Full article

The powder metallurgy route (PM route) for producing aluminum closed-cell foams has recently attracted significant scientific and industrial interest. The process involves mixing a blowing agent powder (e.g., TiH₂) with aluminum powder, then compacting the mixture to produce a high-density bulk foamable precursor (BFP). The BFP is then heated above the melting point of aluminum, where the hydrogen released from TiH₂ particles forms bubbles in the molten aluminum, which become closed pores (cells) upon solidification. Despite metal powder agglomeration being an important factor in powder metallurgy research that can significantly influence processing, it has surprisingly received little to no attention in the powder-based foaming of metals. To the best of our knowledge, this paper is the first to address aluminum powder agglomeration within the context of powder-based metallic foams. Results show that significant aluminum powder agglomeration not only leads to an inhomogeneous distribution of the TiH₂ particles within the BFP, but also to the formation of locally higher than nominal concentrations of TiH₂ particle-rich regions, which greatly influence foaming characteristics. The work, for the first time, highlights the need to seriously consider metal-powder agglomeration (even partial agglomeration) in future foaming research via the PM route, and its effect on foaming characteristics. Full article

As a novel urban leisure activity, Citywalk is reshaping the spatial organization of urban tourism resources and spatial experience patterns. This phenomenon provides a crucial entry point for understanding new tourist–destination relationships from the perspective of spatial behavior. This paper takes Harbin, an Internet-Famous City (IFC), as a case study and integrates multi-source data, including pedestrian trajectories, social media texts, and urban infrastructure. A cross-modal analytical framework for Citywalk networks is constructed to examine the structural evolution of Citywalk networks and the relationship between digital-space and physical-space in the context of IFCs. The results indicate that: (1) During its rise as an IFC, Harbin’s Citywalk network transformed from a single-core agglomeration structure to a multi-nodal radial structure, exhibiting a pattern of core reinforcement and outward expansion. (2) Online visibility was associated with the emergence of new nodes and network expansion, but a structural misalignment was observed between digital-space association and physical-space linkage. (3) Emotional differentiation among newly visible nodes further reflected the uneven development of the Citywalk network, while concentrated digital attention was accompanied by persistent structural imbalance. This study highlights the digital–physical misalignment in urban tourism networks, suggests the important role of social media in shaping tourists’ route imagination and emotional evaluation, and provides references for the spatial optimization and sustainable management of urban tourism resources in the new development stage. Full article

Avian influenza (AI) remains a serious threat to poultry and public health worldwide due to its zoonotic nature and pandemic potential. This paper develops and analyzes a coupled system of nonlinear ordinary differential equations and an SEIR-SEIR model that describes the transmission dynamics of avian influenza in both human and bird populations. The model incorporates multiple transmission routes (bird-to-bird, bird-to-human, human-to-human), exposed/latent compartments in both hosts, disease-induced mortality, and demographic processes. From a mathematical perspective, we present a rigorous analysis of this eight-dimensional dynamical system. We prove positivity and boundedness of solutions in ${\mathbb{R}}_{+}^8$, characterize the equilibrium points, and derive the basic reproduction numbers $R_0^b$ and $R_0^h$ using the next-generation matrix method. Local asymptotic stability of the disease-free equilibrium is established via the Routh–Hurwitz criterion. A composite Lyapunov function is constructed to prove global asymptotic stability when both reproduction numbers are less than unity—a result that exploits the cascade structure of the system and provides a template for analyzing similar multi-host models. Sensitivity analysis using normalized forward sensitivity indices identifies critical parameters. In addition, we use neural network models to validate both models and provide error analysis. These results emphasize the crucial role of controlling cross-species transmission and improving recovery efforts, which have significant implications for the design of effective intervention and surveillance programs in the context of the One Health framework. Full article

Metal additive manufacturing (AM) has progressed from prototyping toward industrial deployment, yet adoption remains uneven because many initiatives are still driven by isolated process demonstrations rather than system-level manufacturing strategy. This framework review proposes a gated decision workflow for integrating metal AM into industrial systems by coupling process-family selection and route definition, Design for Additive Manufacturing (DfAM) and sustainability considerations. The paper consolidates a comparative matrix of six metal AM process families for early down-selection, introduces a minimal evidence checklist linking each decision gate to required artifacts, and contextualizes the workflow through representative part archetypes. The framework is further supported by practical guidance on process-specific DfAM constraints, including support strategy, residual stress, and surface integrity in powder bed fusion; shrinkage-driven design in sinter-based routes; and machining allowances in repair and hybrid manufacturing. Rather than positioning metal AM as a universal substitute for conventional manufacturing, this work defines it as a complementary, strategy-dependent enabler whose sustainability benefits depend on system-level integration and application context. Full article

University admissions services must answer large volumes of applicant questions that differ substantially in complexity, ranging from repetitive FAQ-type requests to multi-step questions involving programs, entrance exams, admission rules, passing scores, and temporal comparisons. Ungrounded large language model responses are risky in this domain because answers must be factually correct, source-based, and consistent with official institutional data. This paper presents a three-tier hybrid architecture for an admissions dialogue assistant that combines deterministic FAQ matching, hybrid retrieval-augmented generation, and graph-grounded retrieval for complex queries. The first tier, Hash-FAQ, returns verified answers for frequent intents using normalized keys, hash-based lookup, near-duplicate fingerprinting, and semantic similarity checks. The second tier applies hybrid RAG based on BM25 retrieval, vector search, rank fusion, and optional cross-encoder reranking. The third tier uses GraphRAG to extract a constrained k-hop subgraph from a Neo4j knowledge graph built from relational admissions data and document-derived facts. All tiers are synchronized through a versioned indexing pipeline with shadow collections and atomic switching across lexical, vector, FAQ, relational, and graph stores. The system was evaluated using real admissions-campaign traffic and a labeled subset of applicant queries. Tier 1 resolved 68.7% of requests with low latency, while the GraphRAG branch improved factual accuracy with attribution on multi-step queries from 0.55 to 0.91 compared with the non-graph baseline. The main contribution of the study is a production-oriented, cost-aware retrieval-and-generation architecture that links tiered routing, synchronized knowledge publication, source attribution, and operational evaluation for applicant-facing institutional dialogue systems. Full article

Sustainability in aviation is crucial to address the environmental issues and climate impacts associated with the aviation industry. Moreover, efficient operations are critical affected by increasing air traffic volumes. In this context, analyzing aircraft fuel consumption and efficiency is necessary. In particular, fuel consumption per unit may vary depending on route characteristics, which should be considered when analyzing and assessing aircraft fuel consumption. In this study, aircraft fuel consumption was estimated using trajectory data, and route-dependent differences in fuel consumption were subsequently analyzed. Fuel consumption per unit distance and unit time were calculated for each analyzed route, and the significance of differences between routes was tested using a one-way analysis of variance. A post hoc test was conducted to identify specific differences between routes, along with an effect size analysis. The results indicated that differences in fuel consumption per distance and fuel consumption per time between different routes were both statistically significant. In the detailed analysis, eta-squared was calculated to assess the proportion of total variance in fuel consumption explained by differences among routes. Furthermore, post hoc tests were conducted to identify which route pairs exhibited statistically significant differences in fuel consumption. The results of this study confirm that aircraft fuel consumption characteristics can vary depending on the route. Therefore, route-dependent differences must be considered, and both macroscopic and microscopic assessments at the route level must be performed when assessing aircraft fuel consumption. Full article

Urban tourism research has long recognised that understanding cities depends not only on accumulated knowledge but also on the ability to read space, interpret urban form and connect physical settings with cultural meaning. Although these ideas are well established in tourism geography, fewer studies have examined how such skills can be developed through structured learning activities in higher education. This article addresses this gap by analysing the Urban Hunting Game (UHG) as a place-based learning approach designed to strengthen students’ spatial awareness and analytical capacity to interpret urban environments through fieldwork and digital mapping. The UHG was implemented in two European cities, Porto and Kaunas, through distinct pedagogical structures shaped by local conditions. In Porto, students followed a collaborative process using uMap to co-create a single itinerary. In Kaunas, international student groups independently designed thematic routes using MyMaps. This differentiated methodological approach proved advantageous, as it showed how different levels of autonomy and digital engagement influence spatial decisions, interpretive strategies and the narratives that the students construct. Based on student-generated maps and observational notes, the findings show that the UHG enhances spatial literacy, encourages attention to detail and supports the translation of field observation into coherent tourism experiences. This study contributes to tourism geography by illustrating how map-centred, place-based learning methodologies can be adapted to diverse urban contexts and by highlighting their potential to develop interpretive and analytical competences relevant to urban tourism studies. Full article