Search Results (558)

This paper presents a comprehensive review of existing research on the integration of blockchain technologies with the trust service ecosystem governed by the Electronic Identification, Authentication and Trust Services (eIDAS) Regulation of the European Union (EU). While Public Key Infrastructure (PKI) and electronic signature (ES) systems deployed under eIDAS provide strong cryptographic guarantees, standardized protocols, and cross-border legal recognition, their operational model remains largely centralized, concentrating trust in supervised authorities and service providers. This centralization raises concerns related to transparency, auditability, and resilience that blockchain, with its decentralized consensus and immutable distributed ledgers, has been increasingly explored to address. This review covers the most relevant application domains in which blockchain has been proposed as a complementary layer for Trust Service Providers (TSPs): certificate lifecycle management, remote signature services, signature preservation, signature validation, timestamping, content provenance and authenticity, and the European digital identity (EUDI) Wallet ecosystem. For each domain, this paper analyzes how blockchain can strengthen auditability and distributed trust while preserving the interoperability, legal assurance, and standards compliance required by eIDAS and ETSI (European Telecommunications Standards Institute) frameworks. A quantitative comparison of latency, throughput, and operational costs between blockchain-augmented and traditional architectures is provided, together with a technology maturity classification for each application domain. Finally, the paper identifies current limitations, including scalability, regulatory alignment, privacy constraints, and the absence of production-scale pilot data, and outlines open research challenges for the adoption of blockchain in regulated digital trust services. Full article

This study examines institutional processes in digital justice through a mixed conceptual approach that integrates bibliometric analysis and technology-adoption modeling, incorporating artificial intelligence (AI) as a projected component rather than an implemented system. A corpus of approximately 200 Scopus-indexed documents (2003–2024) was analyzed, identifying five dominant thematic clusters: advanced technologies, institutional justice, digital government, judicial information management, and digital criminal justice. The results reveal persistent gaps in the literature, particularly in rural and underserved communities, where connectivity barriers and the limited application of adoption models hinder inclusive digital transformation. As an institutional contribution, the study presents the conceptual design of the digital solution “Travel Permits—Accessible Justice”, developed under a Service-Oriented Architecture (SOA) and projected for future integration with AI-supported components to automate judicial authorizations through biometric validation, electronic signatures, and digital delivery. To evaluate its potential acceptance, the Technology Acceptance Model (TAM) is analytically adapted and extended to the community-based judicial context, framing institutional learning processes as a prospective form of learning analytics focused on user interaction, perceived usefulness, perceived ease of use, and behavioral intention. Taken together, the integration of bibliometric evidence with an extended TAM, along with the projected incorporation of AI-supported institutional learning processes, offers a coherent foundation for future studies on inclusive digital innovation in justice environments. Full article

Composite materials are extensively used in the aeronautical and aerospace industries for their high strength-to-weight ratios but are vulnerable to barely visible impact damage (BVID), which can severely compromise structural integrity. Digital shearography (DS) provides a non-contact, full-field solution for subsurface inspection; however, low signal-to-noise ratios in raw phase maps often hinder precise damage identification. This study explores a post-processing methodology utilizing a band-pass filtering algorithm and temporal summation to isolate damage-related spatial frequencies. An in-house digital shearography system was used to inspect a carbon-fiber-reinforced polymer (CFRP) plate subjected to 13.5 J and 26.2 J impacts. Twelve phase maps, acquired during the thermal cooling stage, were processed using a multi-pass filters to systematically analyze different frequency ranges. Results demonstrate that summing multiple filtered phase maps significantly enhances the contrast of damage signatures compared to single phase maps or traditional unwrapping techniques. Furthermore, quantitative assessment using image quality metrics, such as the generalized contrast-to-noise ratio (gCNR), confirmed that optimal frequency selection is essential for an accurate damage delineation. This approach provides a robust framework for improving the reliability and sensitivity of non-destructive testing in composite structures. Full article

Post-quantum cryptography (PQC) artifacts are one to three orders of magnitude larger than their classical counterparts and must be segmented via ISO-TP across a shared CAN-FD bus while coexisting with periodic safety-critical traffic. No prior work has quantitatively mapped the transport-level feasibility of these artifacts under realistic multi-electronic control unit (ECU) contention. This paper presents a validated discrete-event simulator and evaluates 29 parameter sets from nine algorithm families—spanning the KpqC final portfolio, NIST FIPS 203–205 standards, and the draft FIPS 206—across 534 scenarios classified as feasible, borderline, or infeasible. Results show that key encapsulation mechanism (KEM) feasibility is scenario-dependent: domain scale and startup coordination dominate over algorithm choice, with 4-ECU staggered deployments feasible for all Level-1 candidates, while 16-ECU simultaneous startup is universally infeasible. For digital signatures, FN-DSA achieves the best transport feasibility due to its compact signature, while HQC is uniformly infeasible and SLH-DSA is nearly uniformly infeasible, quantifying the CAN-FD bandwidth premium of algorithmic diversity. System-side traffic shaping—staggered startup and reserved bus windows—outperforms algorithm substitution as a mitigation strategy. To the best of our knowledge, these findings constitute the first design-space map of PQC artifact transport on CAN-FD and provide actionable deployment guidelines for post-quantum transition. Full article

The growing interconnectivity of digital systems has led to the massive collection and centralization of sensitive data, raising serious concerns about confidentiality and compliance with privacy regulations. Biometric authentication systems, such as offline signature verification, are particularly vulnerable. Federated learning (FL) provides a promising framework by enabling model training without exposing raw client data. However, keeping data strictly localized inherently creates severe data scarcity, which is a significant barrier to building robust deep learning (DL) models. This work investigates the feasibility of a privacy-preserving writer-dependent (WD) offline signature verification (OSV) system within an FL framework. To make local training viable under these constraints, we integrate complementary techniques into the federated pipeline: data augmentation is utilized to increase local sample diversity, while transfer learning provides robust pre-trained feature representations, drastically reducing the volume of data required for effective local fine-tuning. The proposed WD-OSV system was trained and evaluated on the popular CEDAR signature dataset, for which an average area under the curve of 0.8893, along with an average binary accuracy (ACC) of 80.12%, are reported as preliminary results. Full article

Niobium is a strategic critical mineral that supports emerging energy and high-end manufacturing. The geophysical parameters of carbonatite-alkaline rock-type niobium deposits constitute essential baseline data for regional geophysical exploration and prospecting target delineation. To clarify the geophysical response characteristics and exploration the significance of the Dagele niobium deposit in the Eastern Kunlun Region (western China). This study focuses on drill hole ZK3202. Samples from ore bodies, mineralized zones, and wall rocks of different lithologies were continuously measured. Combined with 1001.8 m of full-hole core digital logging data, statistical methods, including box plots, histograms, multi-parameter cross-plots, and correlation coefficient analysis, were applied to quantitatively investigate the physical property responses of lithologies such as calcite-biotite rock (ore body), calcite-bearing pyroxenite (mineralized zone) and amphibolite in the vertical profile. Lithological identification thresholds were established to divide the drill-hole into lithological and mineralized ore layers. The results show that the ore-bearing lithofacies exhibit a distinctive geophysical signature characterized by high density, strong magnetism, medium-low resistivity, high polarizability, and slightly elevated natural radioactivity, which clearly distinguishes them from surrounding from wall rocks. Based on five key parameters—density, magnetic susceptibility, resistivity, polarizability, and natural gamma—a lithological identification model for amphibolite and mineralized altered rock assemblages was established. This study also summarizes the multi-parameter coupling mechanism of ore-bearing lithofacies, which can effectively delineate favorable niobium-bearing horizons. This work fills a gap in the geophysical property characterization of carbonatite-alkaline complex-type niobium deposits in the Eastern Kunlun region and provides data support and regional reference for integrated gravity-magnetic-electrical-radioactive geophysical exploration, prospecting target delineation, and the exploration of similar niobium deposits in western China. Full article

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality globally. While multi-modal artificial intelligence (AI) models offer significant predictive potential, their translation into routine clinical practice is delayed by the “black box” nature of complex algorithms and the fragmentation of heterogeneous data. We present LANTERN-XGB, a hierarchical machine learning workflow designed to bridge this gap by generating interpretable “digital human avatars” for precision oncology. The methodology employs a multi-stage scalable tree boosting system (XGBoost) architecture utilizing shapley additive explanations (SHAP) for rigorous hierarchical feature selection, missing value management, and patient-specific decision support. The workflow was developed and benchmarked using a retrospective cohort of 437 patients with clinical N0 NSCLC, followed by validation on a prospective dataset (n = 100) and an independent external dataset (n = 100). The pipeline integrates diverse data modalities to predict occult lymph node metastasis (OLM). LANTERN-XGB identified a robust consensus signature driven by non-linear interactions among CT textural fragmentation, PET metabolic heterogeneity, tumor density distribution, and systemic clinical modulators. Exploratory transcriptomic pathway analysis (GSVA) revealed that high-risk predictions strongly correlate with systemic molecular dysregulation, such as the enrichment of immune-inflammatory signaling and metabolic stress pathways. The model achieved robust discrimination in external validation (AUC ≈ 0.77), performing comparably to state-of-the-art nomogram benchmarks. Crucially, the LANTERN-XGB framework demonstrated superior utility in handling diagnostic ambiguity; local force plots allowed for the correct reclassification of “borderline” prediction by visualizing feature interactions that standard linear models fail to capture. LANTERN-XGB provides a validated, open-source framework that successfully balances predictive power with clinical transparency. By empowering clinicians to visualize and verify the logic behind AI predictions, this workflow offers a pragmatic path for integrating reliable multi-modal avatars into daily medical decision-making. Full article

Cutaneous adnexal carcinomas (CACs) comprise a diverse group of malignant tumors that show morphological differentiation toward one of the four main adnexal structures in normal skin: hair follicles, sebaceous glands, sweat-apocrine glands, and sweat-eccrine glands. These tumors can arise sporadically or may be associated with rare genetic syndromes. A total of 276 CACs cases underwent hybrid capture-based comprehensive genomic profiling (CGP) to assess all classes of genomic alterations (GA). Sequencing data were used to determine microsatellite instability (MSI) status, tumor mutational burden (TMB), genomic loss of heterozygosity (gLOH), genomic ancestry, and COSMIC mutational signatures. PD-L1 expression was evaluated by immunohistochemistry (TPS; Dako 22C3). Statistical analyses were performed using Fisher’s exact test, with false discovery rate correction via the Benjamini–Hochberg method. Sequencing was performed on primary cutaneous tumors in 131 cases (47.4%) and on local recurrence or metastatic site biopsies in 145 cases (52.5%). Across all groups, there was a male predominance (64–81%) and similar mean ages (59–63 years), with apocrine (APO) tumors occurring in older patients than eccrine (ECC) tumors (72 vs. 62 years; p = 0.001). Histologically, 173 tumors (62.7%) were sweat gland-derived (SWT), 55 (19.9%) sebaceous gland-derived (SEB), 14 (5.1%) hair follicle-derived (HRF), and 34 (12.3%) unclassified (UNK). Among SWT tumors, 150 (86.7%) were eccrine and 23 (13.3%) apocrine. SWT tumors included digital papillary adenocarcinomas (DPA, 6.9%), mucinous carcinomas (MC, 6.3%), porocarcinomas (POR, 11.0%), spiradenocarcinomas (SPR, 8.1%), syringoadenocarcinomas (SRNG, 5.8%), and 77 (44.5%) unclassified cases. The number of GA per tumor was highest in SEB compared with SWT tumors (7.9 vs. 4.9; p = 0.005) and lowest in DPA (2.1 vs. 5.0 in non-DPA; p = 0.03). No differences in ancestry distribution were observed. Compared with SWT tumors, SEB tumors exhibited higher frequencies of RB1 (38.2% vs. 8.1%; p < 0.0001) and TP53 alterations (76.4% vs. 43.4%; p = 0.0002), suggesting potential neuroendocrine differentiation. MC tumors showed significantly higher PTCH1 alterations than non-MC tumors (36.4% vs. 1.8%; p = 0.044). MSI-high status was most frequent in SEB tumors compared with all other groups (15.7% vs. 1.2%; p = 0.005), and gLOH > 16% was also more common in SEB than SWT tumors (19.6% vs. 7.2%; p = 0.081). The MMR signature occurred more frequently in SEB than SWT tumors (32.0% vs. 2.1%; p = 0.005). Mean TMB was elevated across most CACs types, ranging from 10.4 mutations/Mb in HRF to 38.8 mutations/Mb in MC, with the exceptions of APO (2.7 mut/Mb; p = 0.001) and DPA (1.4 mut/Mb; p = 0.003). PD-L1 expression was generally low and did not differ significantly between SWT and SEB tumors (37.0% vs. 33.3%; NS). Given the limited data on CAC treatment, this study provides a catalog of commonly observed GA. SEB tumors exhibited the highest frequency of genomic alterations. Prospective clinical trials are needed to determine the prognostic and predictive value of CAC-specific biomarkers for immune checkpoint inhibitor (ICI) response, which is essential for integrating novel therapies into the evolving treatment landscape. Full article

With the advancement in quantum computing technology, the number theory-based hard problems underlying traditional searchable encryption algorithms are now vulnerable to efficient quantum attacks. To address this challenge, this paper proposes Dilithium-PAEKS (Dilithium-Public Authenticated Encryption with Keyword Search), a searchable encryption scheme based on the post-quantum cryptographic algorithm CRYSTALS-Dilithium. By transforming the verification relationship of digital signatures into a matching relationship between trapdoors and ciphertexts, the scheme not only meets the functional requirements of searchable encryption but also demonstrates quantum resistance. The implementation enhances algorithm efficiency through keyword-based signatures and dynamic matching testing mechanisms. The security of the scheme is defined by the MLWE and MSIS hard problems, with proofs of keyword ciphertext indistinguishability and trapdoor indistinguishability under the random oracle model. Additionally, the scheme provides strong resistance against both outside and insider keyword guessing attacks through sender–receiver binding mechanisms and trapdoor indistinguishability properties. Experimental results show that, compared to the post-quantum schemes CP-Absel and LB-FSSE, the proposed scheme demonstrates superior overall computational efficiency while maintaining stronger quantum resistance than the traditional scheme SM9-PAEKS. Full article

Background/Objectives: Breast cancer remains the leading cause of cancer-related death among women worldwide. While tools such as Adjuvant Online, PREDICT, OncotypeDx and Mammoprint identify patients at higher risk of relapse who should therefore be offered chemotherapy, there are currently no tools to accurately predict response to chemotherapy, with varied response rates (regardless of subtypes, etc.) of 8–70% reported. Accurately stratifying patients based on their likelihood of benefiting from SoC chemotherapy is therefore critical to guide personalised treatment decisions. Methods: A retrospective cohort of 293 breast cancer patients treated with SoC adjuvant anthracycline-based regimen was analysed. Five biomarkers (TOP2A, PTEN, EGFR, IGF1R, and phospho-mTOR), selected for their prognostic and therapeutic relevance, were assessed using immunohistochemistry (IHC) combined with digital pathology. Results: Biomarker expression was quantified using the digital pathology platform, QuPath, with each marker, when stratified based on high/low expression, demonstrating a significant association with relapse-free survival following SoC chemotherapy in specific subtypes of breast cancer. A composite five-biomarker signature was then generated by integrating the individual biomarker scores to improve prognostic precision. Patients with a five-biomarker signature score greater than zero exhibited a significantly higher likelihood of favourable outcomes following anthracycline-based chemotherapy compared with those with a score of zero or below. Conclusions: This study establishes a novel IHC-based five-biomarker signature capable of predicting patient outcome in the context of SoC chemotherapy. As the signature relies exclusively on IHC, it is simple, cost-effective and readily integratable into routine diagnostic workflows. In addition to its prognostic value, several biomarkers within the panel are potentially actionable, offering opportunities to guide targeted therapies in patients predicted to have poor response to conventional chemotherapy. Full article

The fast-paced progress in quantum computing introduces significant new challenges for digital forensics by undermining classical cryptographic mechanisms that protect digital evidence. Algorithms such as Shor’s and Grover’s threaten the long-term reliability of traditional hash functions, digital signatures, and encryption schemes, thereby compromising the integrity, authenticity, and confidentiality of evidence. This paper investigates how quantum entanglement can be leveraged to enhance the security of digital forensic evidence in the post-quantum era. A hybrid quantum–classical forensic framework is proposed, integrating three entanglement-based components: an entanglement-assisted quantum hashing mechanism for integrity assurance, a CHSH nonlocality-based protocol for authenticity verification, and a BBM92 quantum key distribution scheme for confidentiality and secure chain-of-custody management. All components are implemented using IBM Qiskit and evaluated with the AerSimulator under realistic Noisy Intermediate-Scale Quantum conditions. Experimental results measured using Hamming distance, CHSH S-values, and Quantum Bit Error Rate demonstrate improved tamper detection, reliable authenticity validation, and strong overall confidentiality. Full article

Background/Objectives: Prostate cancer testing relies on prostate-specific antigen testing and digital rectal examination, which have limited specificity and face cultural or geographic barriers to access. We developed a non-invasive urine-based liquid biopsy assay using engineered hydrogel arrays and machine learning to detect disease-specific biochemical profiles. Methods: We collected voided urine samples from 283 participants at 26 U.S. urology practices prior to prostate biopsy. Random forest classifiers trained on 184 biopsy-confirmed cancer cases and 75 controls analyzed colorimetric signatures. Results: Across all Gleason grades (6–10), the assay achieved 97.8% sensitivity and 53.3% specificity. Performance varied by grade: high-grade cancers showed 97.3% specificity, while low-to-intermediate grades demonstrated 94.0% sensitivity. Conclusions: This accessible, culturally-appropriate platform could expand prostate cancer detection in diverse populations while reducing unnecessary invasive biopsies. Full article

Indonesia is among the most landslide-prone countries in the world, with thousands of fatalities and widespread infrastructure damage recorded over recent decades. Despite this high hazard level, regional-scale landslide monitoring remains constrained by the limitations of conventional bitemporal satellite imagery, which is susceptible to cloud contamination, dependent on precise acquisition timing, and unable to capture the full temporal dynamics of landslide occurrence and recovery. While the LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) algorithm has been widely applied for detecting vegetation disturbances such as forest loss and land-use change, its potential for landslide detection in tropical environments has not been sufficiently explored. This study aims to evaluate the applicability of LandTrendr applied to long-term Landsat time series imagery for automated regional-scale landslide detection and mapping in Indonesia. The method integrates temporal segmentation of the Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery spanning 2000–2022 with slope information from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) to identify the characteristic drop-recovery spectral signature associated with landslide events. The algorithm was applied and evaluated in two geologically distinct study areas: Lombok, West Nusa Tenggara, and Pasaman, West Sumatra. Detection accuracies of 25.9% by location and 20.3% by area were achieved in Lombok and 76.3% by location and 85.3% by area in Pasaman. The lower accuracy in Lombok is primarily attributed to the predominance of small landslides below the sensor’s spatial resolution and rapid vegetation recovery. The proposed approach demonstrates the unique capability of LandTrendr to model the entire life cycle of a mass movement event, from pre-event stability through abrupt disturbance to ecological recovery within a single unified framework, providing a scalable and cost-effective tool for long-term landslide monitoring applicable to other tropical, landslide-prone regions. Full article

Future 6G-enabled smart hospital infrastructures will support latency-critical medical operations such as robotic surgery, autonomous monitoring, and real-time clinical decision systems, which require communication mechanisms that ensure both ultra-low latency and long-term cryptographic security. Existing security solutions either rely on classical cryptographic protocols that are vulnerable to quantum attacks or deploy isolated post-quantum primitives without providing a unified framework for secure real-time medical command transmission. This research presents a latency-aware, multi-layered post-quantum security architecture for 6G-enabled smart hospital environments. The proposed framework establishes an end-to-end secure command transmission pipeline that integrates hardware-rooted device authentication, post-quantum key establishment, hybrid payload protection, dynamic access enforcement, and tamper-evident auditing within a coherent system design. In contrast to existing approaches that focus on individual security mechanisms, the architecture introduces a structured integration of Kyber-based key encapsulation and Dilithium digital signatures with hybrid AES-based encryption and legacy-compatible key transport, while Physical Unclonable Function authentication provides hardware-bound device identity verification. Zero Trust access control, metadata-driven anomaly detection, and blockchain-style audit logging provide continuous verification and traceability, while threshold cryptography distributes cryptographic authority to eliminate single points of compromise. The proposed architecture is evaluated using a discrete-event simulation framework representing adversarial conditions in realistic 6G medical communication scenarios, including replay attacks, payload manipulation, and key corruption attempts. Experimental results demonstrate improved security and operational efficiency, achieving a 48% reduction in detection latency, a 68% reduction in false-positive anomaly detection rate, and a 39% improvement in end-to-end round-trip latency compared to conventional RSA-AES-based architectures. These results demonstrate that the proposed framework provides a practical and scalable approach for achieving post-quantum secure and low-latency command transmission in next-generation 6G smart hospital systems. Full article

Modern computing platforms increasingly rely on random number generators (RNGs) for modeling probabilistic processes in simulation, probabilistic computing, and system validation. They are also essential for cryptographic operations such as key generation, authenticated encryption, and digital signatures. Deterministic Random Bit Generators (DRBGs), as specified in the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90A, provides a standardized method for expanding entropy into cryptographically strong pseudorandom sequences. This work presents the design and Field Programmable Gate Array (FPGA) implementation of a hash-based DRBG using Ascon-Hash256, a lightweight, quantum-resistant hash function from the NIST-standardized Ascon cryptographic suite. It implements hash-based derivation, instantiation, generation, and reseeding of the generator via iterative hash invocations and state updates. Leveraging Ascon’s sponge-based structure, the design achieves efficient entropy absorption and diffusion while maintaining an area-efficient FPGA architecture, making it well suited for resource-constrained platforms. The diffusion properties of the proposed DRBG are evaluated through avalanche and reproducibility analyses, confirming strong sensitivity to input variations and secure, repeatable operation. Moreover, Monte Carlo and stochastic-diffusion evaluation of the generated bitstreams demonstrates correct convergence and statistically consistent behavior. These results confirm that the proposed hash-based DRBG provides reproducible, hardware-efficient, and cryptographically secure random numbers suitable for next-generation neuromorphic, probabilistic computing systems, and Internet of Things (IoT) devices. Full article