Search Results (779)

Topological data analysis (TDA) has evolved into a flexible and robust paradigm for obtaining qualitative, geometry-inspired insights from high-dimensional, noisy, and complex data. Grounded in algebraic topology, geometry, statistics, and machine learning (ML), TDA provides multiscale descriptions through persistent homology, Mapper (a graph-based method that summarizes the shape of high-dimensional data), and related topological signatures that are often inaccessible to standard linear and metric methods. In recent years, and especially during 2024–2025, TDA has expanded rapidly across science, engineering, biomedical research, and socio-economic studies, while also being integrated with modern learning paradigms such as deep learning (DL) and graph learning. This survey summarizes recent developments in TDA using a carefully selected set of articles, with emphasis on 2024–2025. We first present the mathematical and computational foundations of TDA, covering simplicial complexes, filtrations, persistent homology, the Mapper algorithm, and computational advances such as data simplification, stability, and efficiency. We then review applications in time series and dynamical systems, biomedical imaging and precision medicine, engineering and physical sciences, finance and risk analysis, DL and interpretability, and security and critical infrastructure systems. Throughout, we highlight how TDA can extract informative features, function as a model component, and provide a conceptual lens for studying complex systems. However, the survey also emphasizes recurrent failure patterns: TDA performance is highly sensitive to filtration, embedding, and vectorization choices; aggressive simplification can dilute or remove informative topological signals; and integration into standard ML workflows still lacks uniform validation and reporting protocols. We conclude by outlining key challenges—including scalability, statistical foundations, interpretability, and compatibility with rapidly evolving artificial intelligence (AI) paradigms—and by identifying directions for future research. The survey also provides a unifying design perspective for TDA systems, highlighting methodological trade-offs and emerging research directions for integrating topology with modern ML. Full article

The energy performance of a building reflects its typical energy use and is influenced by factors such as the building envelope (insulation and windows), system efficiency (particularly for heating, cooling, and domestic hot water), and the integration of renewable energy sources. Improving energy performance helps save energy, boost energy independence and security, lower energy costs, and reduce the need for grid investments. Standardizing energy performance assessments enables benchmarking and comparison of building efficiency, encouraging informed decision-making. In this context, the paper presents a knowledge discovery-driven intelligent decision-making system, designed, developed, and tested to identify the best strategies for prioritizing buildings in the envelope process. The system combines data mining techniques with statistical analysis to precisely rank and thoroughly evaluate low-energy-performance buildings and to develop scenario-based strategies for enveloping the buildings to achieve high energy efficiency (associated with nearly zero-energy buildings) under real-world conditions. Testing of the proposed intelligent decision-making system was conducted using a real building database of approximately 3900 records, uploaded from the Romanian central administration website. Under the highest-performance scenario of the envelope-priority strategy, which includes nearly zero-energy building standards, energy savings exceeded 50% across all categories: 51.70% for healthcare, 53.40% for residential, 60.11% for administrative and office buildings, and 69.92% for educational institutions. Overall, the average savings across all building types were 59.81% (644.86 GWh/year). Full article

In user–server authentication environments, persistent server-side verification tables, such as password verifiers, shared authentication records, or per-user secret tables, may become a critical point of failure once leaked. To address this problem in the post-quantum setting, this paper proposes an ML-DSA-specific verification-table-free authenticated key agreement (AKA) scheme based on the NIST-standardized Module-Lattice-Based Digital Signature Algorithm (ML-DSA). The main contribution is a protocol-level use of the signer-recoverable masking vector in ML-DSA as an on-demand reconstruction mechanism for user-related authentication material. This enables the server to reconstruct the required user-related authentication material from its own signature and long-term secret key. This architecture reduces the exposure associated with centralized verification-table leakage, but it should be understood as a storage-relocation tradeoff rather than a storage-free design, because each user must retain the issued signature and the corresponding hash-derived authentication value. By combining the recovered value with identity information through a quantum-resistant one-way hash function, the server can authenticate the user and establish a session key. Its security is analyzed within a Canetti–Krawczyk-style adversarial model and further discussed in the random-oracle setting through a sequence-of-games argument. The analysis supports session-key indistinguishability under the stated freshness and exposure assumptions, while explicitly excluding full forward secrecy under compromise of the server’s long-term ML-DSA secret key. In addition, an operation-level comparison is provided to clarify computational, storage, and communication tradeoffs relative to representative post-quantum AKA schemes. Since the present work does not include implementation-level benchmarking, the performance discussion should be interpreted as analytical rather than empirical validation. The proposed scheme is therefore most suitable for account-login-oriented applications in which reducing centralized verification-table leakage is a primary design objective and where user-side credential storage can be securely managed. Full article

Cross-border blockchain investigations frequently face data isolation challenges where multiple jurisdictions may conduct parallel inquiries into the same suspicious entities, leading to operational conflicts and redundant efforts. This paper presents a purpose-built t-out-of-n watchlist-anchored private set intersection (PSI) protocol, adapting established threshold secret-sharing techniques for secure jurisdictional discovery, enabling agencies to identify overlapping investigative targets without prematurely disclosing sensitive case details. The methodology is built upon Shamir’s Secret Sharing (SSS) and polynomial interpolation over the $2^{127}-1$ Mersenne prime field. A deterministic dual-hash field mapping ensures statistical uniformity over the prime field. Experimental validation using the Elliptic++ dataset confirmed the system’s high efficiency. The protocol maintains linear communication complexity of $O(n·|{\mathcal{S}}_0\left|\right)$, where complexity scales with the watchlist size rather than the full participant dataset and remains stable under varying consensus requirements, where increasing the threshold t results in a marginal increase in total latency. Under the semi-honest adversarial model, the false-positive rate is cryptographically negligible at $2^{-127}$. The protocol achieves a hybrid security model wherein share privacy is information-theoretic under SSS, while field mapping and share authentication rely on standard computational assumptions. By integrating native source traceability, this framework provides a practical technological foundation for initiating formal Mutual Legal Assistance Treaty (MLAT) requests based on confidential matches identified across independent investigative workflows. Full article

Post-Quantum Cryptography (PQC) migration to National Institute of Standards and Technology (NIST) Federal Information Processing Standards (FIPS) 203, 204, and 205 under the National Security Agency (NSA) Commercial National Security Algorithm Suite (CNSA) 2.0 is a multi-year, multi-domain transformation across cloud, enterprise, embedded, operational technology (OT), tactical, and national-security systems. Anthropic’s Claude Mythos Preview (April 2026) introduces artificial intelligence (AI)-accelerated cybersecurity capabilities that intersect this migration directly, performing autonomous reasoning against previously unknown vulnerabilities in production software—a qualitative departure from signature-based and static and dynamic application security testing (SAST/DAST) tooling. Drawing on federal guidance from NIST, NSA, the Office of Management and Budget (OMB), and the Cybersecurity and Infrastructure Security Agency (CISA), and on independent analyses from the Centre for Emerging Technology and Security (CETaS) and the UK AI Security Institute, we present a lifecycle and architecture analysis of how Mythos-class models alter PQC migration timelines, risk surfaces, lifecycle dependencies, and architectural constraints. Modeling Mythos as both accelerator and destabilizer, we derive an analytic projection of a compressed two-to-four-year migration window for highest-exposure systems, against traditional baselines of five-to-ten years for small organizations and twelve-to-fifteen-plus years for large enterprises. The compression collapses human-labor bottlenecks in discovery, planning, and code modification, not cryptography itself. We propose a lifecycle-aligned migration model, an updated cost model, and governance requirements for frontier-model access. The binding constraint shifts domain-conditionally: defender capacity at adversary tempo governs software-analytical phases, while non-compressible external cadence governs embedded and regulated domains. Full article

Probabilistic risk assessment of complex offshore oil and gas systems is often challenged by scarce statistical data and multiple uncertainties. Traditional point-value probability and standard Bayesian networks cannot fully represent and propagate these uncertainties, which may mislead high-risk security decision-making. To address this issue, this paper proposes a new hybrid risk assessment framework that combines interval-valued T-spherical fuzzy sets (IVTSFS) with credal networks (CN). First, IVTSFS is used to quantify the subjective risk perception of multiple experts, effectively capturing hesitancy, fuzziness, and group disagreement. An improved probability mapping mechanism is introduced to align linguistic evaluations with objective failure frequency spaces, thereby avoiding systemic transformation biases. Subsequently, the interval conditional probability table is constructed using the imprecise leakage noise-OR model, which alleviates the problem of parameter dimension explosion in complex causal structure and explicitly retains the parameter uncertainty. The 2U algorithm is then applied to perform accurate interval inference in CN. The feasibility and comparative advantages of the method are illustrated in the actual case of the single-point mooring system. The results clearly output the upper and lower bounds of the system failure risk, and identify the key vulnerable nodes through diagnostic reasoning and sensitivity analysis. This study has theoretical contributions in fuzzy decision-making and uncertainty modeling. By unifying advanced fuzzy cognitive quantification and imprecise probability propagation, it provides a structured uncertainty representation tool for expert-informed risk screening under data scarcity. Full article

Introduction: Remote education is increasingly used in ostomy care, but its components, timing, governance, and evaluation remain inconsistently defined. This study aimed to develop practice-oriented recommendations for implementing remote patient education for people living with an ostomy. Methods: An Italian expert consensus using a modified Delphi method and reported according to the ACCORD guidelines was conducted. An expert panel (n = 11), recruited nationally, included stomatherapists (n = 6) and people living with an ostomy (n = 5). Round 1 comprised a remotely conducted focus group to generate and refine statements informed by a targeted literature search. Rounds 2 and 3 were anonymous online surveys in which panelists rated statements on a four-point Likert scale and could provide comments or propose additional items. Consensus was predefined as ≥75% agreement. Results: Response rates were 100% across the three rounds (October–November 2025). The panel achieved consensus on 8 definitions and 14 statements, organized into six domains: (1) model of care and eligibility; (2) privacy and data protection; (3) program structure, outcomes, and evaluation; (4) educational content and teaching strategies; (5) timing, intensity, follow-up, and caregiver involvement; and (6) dignity, relational quality, and professional and organizational requirements. Recommendations supported a hybrid-by-default model with eligibility criteria, privacy-by-design using secure platforms and traceable documentation, structured programs with tailored multimodal content, staged pathways lasting 2–6 months after an initial in-person foundation, dignity-preserving options during remote encounters, professional training in communication and digital empathy, and integration into clinical planning and records. Conclusions: This consensus provides the first ostomy-specific, implementation-focused recommendations for standardizing remote patient education in Italy, with an emphasis on equity, privacy, dignity, evaluation, and workforce competencies. Full article

The counterfeiting of official documents and banknotes represents a critical threat to global security and requires robust and low-cost protection techniques. This work presents an innovative information security system that uses photoluminescent inks for chromatic multiplexing of QR codes. Unlike conventional cryptographic methods, the proposed approach employs physical-layer information hiding through the superposition of two QR codes encoded in magenta and cyan colors on a white background. The controlled interaction between these codes generates an additional logical state that enables a third representation of information through pixel-level operations. The resulting chromatic QR code remains visually imperceptible under ambient illumination and can be reliably recovered through chromatic demultiplexing and thresholding process. Additionally, its visibility can be enhanced under ultraviolet (UV) excitation due to photoluminescent behavior and spectral response variations. The experimental results demonstrate that both encoded data layers can be extracted independently with high fidelity using standard CMOS sensors, while preserving structural integrity and decodability. The proposed scheme increases information density within a single optical tag while improving resistance against unauthorized replication and visual forgery. Full article

Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era. Full article

Egg production in Gallus gallus domesticus represents a complex, economically critical trait shaped by multiple interrelated phenotypes, including age at first egg, total egg number, egg weight, and clutch characteristics. These traits are governed by polygenic inheritance and modulated by environmental factors, making the dissection of their genetic architecture essential for improving breeding efficiency, particularly under the emerging “long-life layers” production model. This systematic review aimed to integrate current knowledge on the genetic and molecular basis of egg production traits through analysis of genome-wide association studies and related genomic approaches. A structured literature search identified 27 eligible studies, which were evaluated following PRISMA guidelines. Data extraction and meta-analysis were conducted using standardized genome annotations and computational pipelines. The synthesis of available evidence demonstrates moderate to high heritability for key reproductive traits and highlights consistent genomic signals across multiple chromosomes. Importantly, the findings reveal a shift toward a systems-level understanding of egg production, involving conserved biological pathways related to neuroendocrine regulation, folliculogenesis, and energy metabolism. The integration of diverse genomic approaches enables the development of more precise, breed-specific selection strategies. Overall, these advances support a transition from traditional selection toward molecularly informed breeding frameworks, with significant implications for productivity, sustainability, and global food security. Full article

South Korea has been advancing the National Digital Twin Land initiative; however, many existing urban digital twin projects have relied on non-standard, visualization-oriented datasets, thereby encountering persistent difficulties in securing interoperability and reusability. In particular, the lack of a standardized methodology capable of systematically fusing fragmented public administrative data with 3D geospatial information remains a major barrier to the practical use of digital twins in administrative operations. To address this gap, this study proposes a standardized urban digital twin data construction methodology that complies with the international standard while effectively accommodating Korea’s building-related public datasets. Specifically, the OGC CityGML Building module is adopted as the reference model, and an extension is implemented to design a data model that extends and integrates heterogeneous sources—such as building height records, building register attributes, and road-name address data—within a unified standard schema. Furthermore, using Busanjin-gu, Busan Metropolitan City, as a case area, we develop high-precision LoD 1~4 building objects from aerial surveying outputs and empirically validate an end-to-end workflow by loading and visualizing the resulting dataset on a national public platform. By constructing operational digital twin data that tightly couples physical geometry with administrative semantics and verifying its feasibility in an actual platform environment, this study establishes a practical, standards-based foundation for deploying and operating geospatial digital twins in smart city and related urban governance applications. Full article

Building Information Modeling (BIM) implementation is increasingly adopted in the architecture, engineering, and construction (AEC) industries. However, its integration into the academic curricula in developing countries remains limited. Therefore, this study aims to investigate the barriers to integrating BIM into the curricula of civil engineering in Jordanian higher education institutions (HEIs). A quantitative approach was used, including Exploratory Factor Analysis (EFA) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The data was collected from 102 respondents, including industry professionals and academics. Six key barrier constructs were identified: support, standards, delivery, resources, knowledge, and infrastructure and security. Altogether, they explain 66.896% of the BIM integration barriers. The results of the structural model indicate that institutional and governmental support is the most critical barrier (β = 0.486), followed by the lack of standards (β = 0.206) and curriculum-delivery constraints (β = 0.166). Other barriers, including infrastructure and security-related factors, knowledge gaps, and resource limitations, were found to have statistically significant effects on BIM integration. The findings revealed that the barriers to integrating BIM into civil engineering curricula in Jordanian HEIs are institutional and systemic rather than purely technical or resource-based. This study contributes to the BIM education literature by developing one of the first empirically validated PLS-SEM models to investigate barriers to integrating BIM curriculum in Jordan and in developing countries. This research is distinct from previous descriptive studies by prioritizing the institutional, technical, and curricular barriers to the integration of BIM into civil engineering education. Practically, the research provides a specific roadmap for Jordan to integrate BIM into curricula through improving the collaboration between HEIs and the Jordan Engineering Association, strengthening the accreditation standards, enhancing the support of the government for digital construction education, and endorsing the partnerships between HEIs and the industry to align the graduates with the needs for digital transformation of the construction sector in Jordan. Full article

In Brazil, approximately 16.4 million people (8.1% of the population) live in informal settlements (favelas), with Rio de Janeiro among the most heavily affected. This situation results from rapid rural–urban migration and unplanned urbanization, leading to persistent land tenure conflicts, exemplified by the decades-long struggle in the Santa Luzia favela. This study demonstrates how participatory geospatial methodologies can support land regularization while preventing displacement. Unlike conventional participatory mapping studies that often prioritize community empowerment over technical precision or, conversely, state-led cadastres that prioritize accuracy over local participation, this study integrates two complementary frameworks: counter-cartographies (to redress power asymmetries) and fit-for-purpose land administration (to ensure minimal technical standards for tenure security). Through a university–community collaboration, a low-cost cadastral survey of Santa Luzia was conducted using remotely piloted aircraft photogrammetry to generate high-resolution orthoimagery (2 cm ground sample distance), GIS vectorization integrated with resident interviews and local knowledge, and spatial analysis compliant with local technical standards. The findings demonstrate three specific innovations: (1) methodological: volunteer students and community residents co-produced cartography achieving 2 cm precision, meeting legal requirements for land regularization without expensive professional surveys; (2) participatory: unlike purely community-led mapping that may lack legal enforceability or top-down systems that exclude local knowledge, this model embeds participatory data collection within Brazil’s Social Interest Regularization (REURB-S) framework, ensuring both grassroots legitimacy and state recognition; and (3) policy-making: the project operationalizes counter-cartographies not as symbolic resistance but as a legally compliant pathway to tenure security, offering a transferable model for democratizing land administration in informal settlements while challenging exclusionary urban planning. Full article

The standard for effective communication between Internet of Things (IoT) devices has been demonstrated by the increasing demand for IoT technologies in Industry 5.0, along with the growing use of actuators, sensors, and automated processes in these settings. De-vice-to-device interactions controlled by communication protocols that specify data sharing are essential to effective operation. By establishing a single standard that permits plug-and-play integration and improves flexibility across various IoT devices, the IEEE 1451 standard represents an approach. This standard ensures interoperability and enables smooth communication with devices from various companies, regardless of their features. By addressing major obstacles to system integration, the IEEE 1451 standard enables IoT technologies to reach their full potential. By integrating information technology (IT) through automation and industrial control systems (ICSs), the Industrial IoT (IIoT) is transforming many industries, especially essential sectors such as energy, chemicals, oil and gas, and water plants. Although drinking water is an essential resource for life and an aspect of technological progress, little is known about the potential for cyberattacks, including the disastrous consequences they could have for water treatment plants. This re-view identifies and documents several adversarial cyberattacks targeting the water distribution and purification sector. Understanding the range of risk factors in this sector is our primary objective. This study presents a technical assessment from an IIoT perspective that addresses attack scenarios, real-world instances of cyberattacks in the water industry, a range of security challenges, and security measures. The contribution is an informative, up-to-date resource that benefits both prospective scholars and industrial practitioners. By integrating key findings to build a secure and reliable digital future, this work will advance a comprehensive understanding of the cybersecurity environment in water plants in Industry 5.0 and smart cities. Full article

Weak-bus identification is a key task for online security assessment, preventive control, maintenance verification, and resilience-oriented dispatch of interconnected power grids. In large-scale grids, conventional full-graph graph neural networks preserve the complete network topology but may become inefficient when many operating scenarios must be screened repeatedly. Direct graph partitioning improves computational tractability, but it may cut tie-line channels and weaken the boundary evidence that determines cross-area risk propagation. To address this trade-off, this paper proposes a boundary-compensated partition-based parallel graph neural network for weak-bus identification. The method first constructs a scenario-aware weighted power-grid graph and divides it into electrically coherent subgraphs under coupling-strength and partition-size constraints. Local graph encoders are then executed in parallel to learn intra-partition vulnerability representations. A boundary compensation module further restores cross-partition information by weighting tie-line neighbors according to electrical coupling, branch loading, and cross-area association. Standardized partition scores are finally fused into a whole-grid weak-bus ranking, and a composite learning objective jointly considers node-score regression, boundary consistency, and pairwise ranking stability. The method is evaluated on the IEEE 57-bus benchmark with mechanism-based node and branch vulnerability labels. Compared with the original full-graph GNN, the proposed method reduces the mean square error from 0.0359 to 0.0147, improves the Spearman rank coefficient from 0.248 to 0.446, and increases Hit@10 from 30% to 70%. Topological interpretation further shows that the identified weak buses are concentrated around high-risk branches such as 8-12, 12-14, 0-14, and 7-8, indicating that the proposed framework captures local aggregation, boundary transmission, and corridor-driven vulnerability propagation. The IEEE 57-bus benchmark is used as a focused validation case because it provides aligned node- and branch-level vulnerability evidence for evaluating weak-bus ranking behavior. Because the available aligned vulnerability evidence is concentrated in this medium-scale benchmark, the results should be interpreted as a focused validation of the proposed ranking mechanism rather than as a complete large-system scalability study. Full article