Search Results (91,096)

Clay–cement slurry, as a widely used anti-seepage material, is prone to calcium leaching and deterioration when exposed to environmental water. The influence of microstructural and mineralogical evolution on the transport properties of clay–cement samples under leaching conditions remains to be investigated. In this paper, accelerated calcium leaching tests were conducted on clay–cement pastes. A variety of techniques, including XRD, SEM, and NMR, were used to characterize the microstructural and mineralogical changes in the leached samples. The effect of accelerated leaching on transport behavior was studied by measuring changes in the water permeability and calculating diffusivity. XRD and SEM analyses show that after 28 days, the characteristic peaks of portlandite and ettringite almost disappear, while C-S-H gel undergoes decalcification and decomposition, leading to an increase in pore number and a notable rise in pore size (up to 1.90 μm). NMR results indicate that total porosity and peak pore size increase significantly, with the proportion of gel pores decreasing and that of small capillary pores (10–50 nm) rising from 10% to 22.1%. Moreover, the surface layer porosity (0–5 mm) increases from 31.33% to 50.65%, while the middle and lower layers show less degradation, indicating a progressive deterioration pattern. Regarding transport properties, the hydraulic conductivity increases from 4.7 × 10⁻¹⁰ cm/s to 2.14 × 10⁻⁸ cm/s (a two-order-of-magnitude increase), and the diffusion coefficient rises from 1.6 × 10⁻¹¹ m²/s to 8.6 × 10⁻¹¹ m²/s (a 5.3-fold increase). Both the diffusion coefficient and its increase factor gradually decrease from the surface to the interior, consistent with the evolution of porosity. Full article

The rapid growth of digital transformation (Dx) initiatives across sectors has created an urgent need for structured, scalable, and accurate management of project knowledge. Without effective organization, valuable insights from Dx projects remain fragmented, limiting their reuse and hindering informed decision-making. This research addresses the gap by designing, developing, and validating Dx-Onto, a domain-specific core ontology implemented in OWL and purpose-built for representing and managing knowledge about Dx projects. Dx-Onto models entities, relationships, and attributes from diverse project documentation into a unified knowledge graph, enabling semantic search, cross-project analysis, and context-aware retrieval. To assess performance, a two-pronged evaluation strategy was adopted: (1) scalability experiments using synthetic datasets measured query execution times across volumes ranging from 10 to 1000 projects, and (2) a comparative benchmark against the Core Ontology of Organisational Transformation (COOT) was conducted using a heterogeneous real-world corpus of Thai digital transformation documents. The results confirm Dx-Onto’s capacity to scale and demonstrate a higher domain fit (85.2% vs. 77.3%) and superior analytical utility—including transformation-phase and strategic-dimension diagnostics that are structurally impossible under a general-purpose baseline. By positioning Dx-Onto as the core semantic layer for a future Hybrid LLM-Ontology framework, this work lays the groundwork for intelligent, scalable, and reliable knowledge management solutions in the digital transformation domain. Full article

Rural communities face persistent economic challenges, and universities increasingly serve as catalysts for regional entrepreneurial development. This study evaluates the RISE29 program, a consulting-based internship initiative placing interdisciplinary undergraduate teams as paid interns with small businesses in economically distressed counties of Eastern North Carolina, examining what students across disciplines learned. Drawing on Cope’s entrepreneurial learning framework as a sensitizing theoretical lens, the study asks: (1) What themes characterize the entrepreneurial learning experiences described by RISE29 interns across cohorts and disciplines? (2) How do reflective depth, student agency, and program satisfaction vary across cohort periods, disciplinary affiliations, and program structures? A multi-layered qualitative analysis of 158 written reflection papers submitted across ten cohorts (Spring 2019–Summer 2022) was integrated with deductive qualitative analysis guided by Cope’s entrepreneurial learning framework, constructivist grounded theory coding, reflexive thematic analysis, reflective depth coding, sentiment and tone analysis, and cross-group comparison. Seven themes emerged: communication as the central axis of learning; collaborative identity development; leadership identity formation; encounter with Eastern North Carolina’s rural communities; professional identity and career clarity; real-world learning and classroom transfer; and program satisfaction with constructive critique. All four dimensions of Cope’s framework were present across disciplines, and non-Business students, in several cases, demonstrated a high degree of analytical depth warranting further investigation. Reflective depth increased in cohorts using an expanded prompt, though overlapping structural changes across the study period preclude single-factor attribution. Place-based, consulting-driven experiential programs generate substantive entrepreneurial learning across disciplinary lines, though findings reflect students’ perceived learning rather than verified competency acquisition. These results support investment in client vetting, structured reflection, cross-disciplinary teaming, and in-person community engagement. Full article

Inverter-dominated microgrids are highly vulnerable to stealthy cyber–physical drift attacks, low-amplitude, slowly varying perturbations that bypass conventional statistical filters to induce voltage degradation and delayed collapse. This paper introduces a resilient, delay-aware supervisory control architecture that acts as an online safety shield at the actuator interface. By jointly modeling nonlinear power-flow interactions and directional communication topologies, we construct physics-informed Control Barrier Functions (CBFs), embedding structural electrical invariants derived from the nodal admittance matrix $Y_{bus}$. The supervisor directly incorporates heterogeneous, time-varying network delays into its safety constraints and utilizes a threat-adaptive modulation loop driven by spatio-temporal residuals to dynamically scale intervention aggressiveness. Using a Lyapunov–Krasovskii functional, we prove that the closed-loop tracking error is Input-to-State Stable (ISS) under bounded drift and worst-case latencies. High-fidelity simulations on an IEEE 14-bus test feeder demonstrate that the supervisor consistently enforces non-negative safety margins and reduces time-integrated voltage violations. Under coordinated sub-threshold attacks designed to exploit network jitter, the architecture bounds trajectories to physically consistent manifolds and prevents voltage collapse, establishing a scalable cross-layer safety framework for resilient distribution systems. Full article

Maritime operations rely on the Automatic Identification System (AIS), an open broadcast protocol whose unauthenticated, self-reported messages are easily abused. This survey provides an AIS-first, security-focused synthesis of AIS cybersecurity research. It makes three main contributions. First, it explains AIS protocol mechanics and uses them to derive the main security weaknesses that arise from open VHF broadcast, self-reported data, lack of built-in authentication and replay protection, and GNSS dependence. Second, it organizes AIS threats and mitigations into a unified taxonomy that links attack vectors, technical effects, operational impacts, and security measures across the prevent–detect–respond–recover lifecycle. Third, it assesses practical defenses, including authentication proposals, endpoint and network hardening, behavior-aware analytics, cross-sensor validation, and governance measures. The survey shows that existing work is strongest on anomaly detection and AIS data analytics, whereas standards-compatible authentication, scalable key management, backward-compatible deployment, trust-aware display integration, and operational validation remain open challenges. Consequently, AIS security is likely to require layered, staged, and standards-compatible mitigations rather than a single technical fix. By bringing together cybersecurity, maritime operations, and data-science perspectives, the survey provides practical guidance for securing AIS-based systems and highlights open problems for future standardization and implementation. Full article

Public sector organizations are under increasing pressure to modernize service delivery while preserving transparency, interoperability, accountability, and citizen trust. Blockchain technology offers relevant capabilities for these objectives, particularly through shared ledgers, cryptographic verification, and programmable rules. However, its adoption in public sector contexts remains constrained by the lack of architectural guidance tailored to inter-organizational services. This study proposes BRA-PS, a Blockchain Reference Architecture for Public Sector Citizen-Centric Applications, developed from a real-world digitalization project in Quebec, Canada. The architecture organizes components into six layers (presentation, business, communication, smart contract, blockchain, and data) with cross-cutting concerns addressing governance, access control, security, and monitoring. A key design principle is the public–private workflow separation, which enables inter-organizational collaboration while preserving each organization’s operational autonomy and data confidentiality. We validated the architecture through a case study involving a vehicle registration process between two public agencies, supported by a proof-of-concept implementation using Hyperledger Fabric. An Architecture Trade-off Analysis Method (ATAM) evaluation, conducted with a panel of five domain experts, identified six architectural risks, including InterPlanetary File System (IPFS) confidentiality exposure and smart contract inflexibility, six non-risks, six sensitivity points, and six trade-offs across three key quality attributes: autonomy, collaboration, and functional suitability. The results show that BRA-PS can support implementation decisions, clarify stakeholder responsibilities, and expose relevant architectural trade-offs. The recommendations derived from the evaluation provide practical guidance for the adoption of blockchain in citizen-centric public sector services. Full article

Conceptual Site Models (CSMs) are essential tools for characterizing contaminated sites, integrating hydrobiogeochemical information to support remediation planning. Historical datasets are often underutilized, while additional investigations can be costly, limiting our understanding of contaminant dynamics. This study aims to develop a sustainable and cost-effective framework for constructing an enhanced CSM of the Mantua Lakes through the integration of historical (2008) and recent (2024–2025) sediment and water quality datasets, resulting in more than 2000 data points. Objectives included the reconstruction of a 3D geological model (55 boreholes), the estimation of contaminant masses in sediments, and the evaluation of temporal trends in contaminant distribution and natural attenuation. Sediment cores (collected at 25 cm intervals) and surface water samples were analyzed for arsenic, cadmium, chromium, mercury, and heavy petroleum hydrocarbons. A harmonized set of 39 georeferenced points enabled a multi-temporal comparison. Voronoi polygons and volumetric calculations were used to estimate contaminant mass within sediment layers. Bathymetric and stratigraphic data, consisting of 458 depth points and 64 isobaths, were integrated into a 3D geodatabase and extended into a 4D framework to capture temporal evolution. Sediments exhibited overall reductions in contaminants, particularly cadmium and hydrocarbons, while arsenic and chromium showed localized variations. Water column concentrations mirrored sediment trends, indicating significant bioattenuation. Integrating historical and recent data strengthens CSMs, provides quantitative mass estimates, and offers a comprehensive framework for understanding contaminant dynamics, natural attenuation processes, and sustainable site management. Full article

The transition from education to employment remains a major challenge, particularly for individuals who may require accessibility support during competency assessment and occupational guidance. However, many current approaches remain fragmented because they evaluate soft skills, accessibility conditions, and occupational requirements as separate dimensions. This study presents an accessibility-aware computational proof of concept for employability analytics using workplace simulation logic, derived competency indicators, semantic modeling, clustering, person–job fit estimation, and heuristic multi-objective optimization. The framework integrates open secondary employability data, O*NET-derived occupational descriptors, and simulated accessibility scenarios within a reproducible analytical pipeline. The results show differentiated computational employability profiles, with mean person–job fit values of 0.85, 0.74, and 0.63 for high, medium, and low profiles, respectively. The derived competency indicators showed high internal consistency ($\alpha =0.905$), although they are interpreted as exploratory proxy dimensions rather than as an exploratory psychometric scale. Principal component analysis indicated a dominant general employability factor, with the first component explaining 75.3% of the variance. The optimization layer produced interpretable heuristic convergence patterns and modeled scenario assignments under predefined validity, accessibility, alignment, and diagnostic criteria. Person–job fit was interpreted under sensitivity scenarios involving alternative competency weights, scalarization parameters, and accessibility assumptions. The study does not include observed participants with disabilities, measured accessibility support use, field simulator interaction records, or longitudinal employment outcomes. Therefore, the term accessibility-aware refers to the computational framework’s design orientation. At the same time, the empirical evidence should be interpreted as a secondary-data-based proof of concept rather than as validation of an inclusive simulator for future users with accessibility needs. The main numerical indicators were: high-profile mean fit = 0.85, medium-profile mean fit = 0.74, low-profile mean fit = 0.63, Cronbach’s alpha = 0.905, first principal component variance = 75.3%, and heuristic iterations = 900. Full article

Ground-penetrating radar (GPR) was utilized for subsurface soil investigation in the Yellow River Delta, aiming to provide a scientific basis for the remediation performance of saline soils. The study particularly focuses on the red clay layer, a typical and characteristic soil horizon in this region. GPR antennas with central frequencies of 400 MHz and 900 MHz were adopted to investigate shallow soils within 1 m of the ground surface across three experimental plots (pits, undisturbed soils, and tilled soils) and 18 scattered measurement sites, followed by systematic analysis and interpretation of the acquired GPR profiles. During data acquisition, reasonable survey lines were deployed across the patchy bare areas of cultivated lands covering the experimental plots and measurement points to collect raw GPR data. Meanwhile, subsurface soil data were collected via test pits and borehole sampling along the survey lines. Raw GPR data were further preprocessed and postprocessed to characterize soil horizons and interpret subsurface stratigraphic structures. Finally, the correlations between the relative dielectric permittivity, reflection coefficient, and reflected wave amplitude of each soil layer were systematically analyzed. The results demonstrate that the 400 MHz antenna enables effective identification of soil layers within 1 m depth, while the 900 MHz antenna provides high-resolution detection for soil layers above 0.5 m. The red clay layer presents a distinct strong-amplitude reflection on GPR profiles, and the average relative dielectric permittivity of soils across the study area reaches 30.57. GPR profiles reveal that soil horizons with an absolute reflection coefficient greater than 0.01 yield detectable continuous reflection signals and allow uninterrupted stratigraphic interpretation. An empirical formula was established to calculate soil relative dielectric permittivity from soil moisture content, with a correlation coefficient of 0.9173. However, this formula ignores the influences of soil salinity and other trace soil elements. This study realizes rapid and accurate characterization of the depth and thickness of shallow soil layers, providing technical support for soil remediation of saline–alkali land in the Yellow River Delta. The findings also provide a valuable reference for evaluating the remediation effects, optimizing arable land utilization, preventing and mitigating soil salinization risks, and promoting the sustainable economic development of the study area. Full article

The Enterobacteriaceae family is a significant source of foodborne pathogens and represents a severe threat to human and animal health. These bacteria can penetrate the dairy supply chain through direct contact with cattle and the livestock environment and can survive production processes. Escherichia coli (E. coli), one of the most diffuse bacteria in raw and processed milk, exposes consumers to the risk of contaminated milk. As a result of this exposition, several milk-borne illness outbreaks have been reported worldwide, underscoring the urgent need for effective detection and prevention measures. Conventional analysis methods are effective but have significant limitations, including the requirement of pre-treatment and pre-enrichment steps. Thus, the need for advanced detection techniques that can accurately identify these pathogens without pre-treatment steps is critical. In this work, a proof-of-concept biosensor based on a spoon-shaped optical biochip was developed to detect E. coli via surface plasmon resonance (SPR) phenomena and was combined with a polyclonal antibody layer against E. coli as a molecular recognition element (MRE). The proposed label-free biosensing strategy, achieved by exploiting simple SPR spoon-shaped biochips, exhibits a remarkable detection limit (6.8 colony-forming units, CFU/mL) and high specificity towards other interfering bacteria belonging to the Enterobacteriaceae family. In addition, tests on commercial milk samples were carried out, achieving recovery values of 95% and 102% for whole milk and infant milk, respectively. The proposed spoon-shaped biosensor enables label-free biosensing without the need for microfluidic systems. It provides a rapid response (10 min), paving the way for its use as a point-of-care test (POCT) in real-world settings. Full article

As frequent and highly destructive geologic hazards, debris flows necessitate effective monitoring alongside rapid and accurate detection to support disaster prevention and mitigate losses of life and property. Current detection technologies, however, are often limited by high false alarm rates, insufficient accuracy, considerable model complexity that complicates deployment, and the elevated costs of contact-based detection. To overcome these limitations, this paper introduces a lightweight real-time debris flow detection model based on the Roboflow Detection Transformer (RF-DETR). First, we constructed a dataset of realistic debris flow scenarios including debris flow disaster events worldwide. Based on this, the lightweight vision transformer model EfficientFormerV2 is adopted as the backbone. By employing a dimension-consistent architecture, the model avoids the frequent switching between 4D and 3D features found in traditional Vision Transformers (ViTs), thereby reducing a significant number of inefficient operations. Additionally, we optimized the multiscale projection layer by removing downsampling and feature aggregation operations, which reduces redundant computations and improves feature extraction efficiency. Furthermore, the introduction of the Efficient Intersection over Union (EIoU) loss function achieves faster convergence and improved detection accuracy for debris flows. Ablation studies performed on our debris flow dataset demonstrate that the improved model reduces parameters by 54.2% and computational load by 36.4% while ensuring acceptable losses in detection accuracy and latency. This significant reduction in model size and complexity achieves effective lightweighting, fulfilling the requirements for deployment on edge devices and enabling real-time debris flow detection. Full article

Toxic metal contamination in aquatic systems commonly occurs as multicomponent mixtures, making competitive adsorption assessment essential for realistic adsorbent evaluation. This study investigated corn stalk-derived hydrochar functionalized with Mg-Fe layered double hydroxide (Mg–Fe-LDH@HC) for simultaneous Cd(II) and Zn(II) adsorption in aqueous bicomponent systems. The material was evaluated through pH and dosage optimization, kinetic assays, bicomponent equilibrium modeling, thermodynamic assessment, mixture-design experiments, regeneration tests, and applicability assays with interfering ions and real water matrices. Under the selected conditions, pH 6.75, 4 g L⁻¹ Mg–Fe-LDH@HC, 1 mM equimolar Cd(II)/Zn(II), 298.15 K, and 180 min, near-complete removal of both metals was achieved. Kinetic analysis showed rapid initial uptake followed by a slower approach to equilibrium. Bangham, Elovich, and Weber-Morris analyses supported a multistage adsorption process involving external surface uptake, diffusion-related resistance, and heterogeneous surface interactions, although intraparticle diffusion was not the sole rate-controlling step. Bicomponent equilibrium was better described by heterogeneous models, particularly the double-layer model and Extended Sips, indicating non-equivalent adsorption domains. Thermodynamic parameters showed favorable and mildly endothermic adsorption with limited temperature dependence. Mixture-design experiments demonstrated that metal proportion influenced adsorption more strongly than temperature, with increasing Cd(II) fractions reducing Zn(II) retention. Overall, Mg–Fe-LDH@HC showed promising performance for Cd(II)/Zn(II) removal under competitive conditions, although the adsorption pathway should be interpreted as an evidence-supported combined process rather than individually confirmed mechanisms. Full article

Background: Digital marketing increasingly functions as a complex adaptive system characterised by non-stationary environments, strategic interaction, and multi-agent competition. Programmatic advertising exemplifies this complexity, where decisions must be made in real time under uncertainty. Under such conditions, traditional static optimisation methods often fail to deliver robust performance. This review synthesises bio-inspired computational approaches, reinforcement learning (RL), multi-agent reinforcement learning (MARL), and agentic artificial intelligence (AI) to develop an integrated theoretical perspective on adaptive optimisation in digital marketing. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of peer-reviewed research across six databases: Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and arXiv, supplemented by manual reference checking. Each computational paradigm is explicitly grounded in foundational biological literature, including work on evolution, foraging, swarm intelligence, and immune cognition. Reinforcement learning supports adaptive decision-making through mechanisms closely aligned with operant conditioning and foraging behaviour. Multi-agent reinforcement learning extends these principles to interactive marketing ecosystems via decentralised coordination and swarm-based learning. Agentic AI further advances adaptive capability by introducing goal-directed reasoning, memory, and higher-level decision orchestration. Contributions: The review identifies persistent fragmentation across marketing sub-domains and a lack of formal mathematical grounding for widely used bio-inspired analogies. To address these gaps, the study proposes a multi-layer bio-inspired framework and outlines a structured research agenda to guide the development of autonomous digital marketing systems. Full article

This study investigates the nanomechanical response of aluminum substrates coated with crystalline or amorphous equiatomic FeNiCrCo compositionally complex alloy (CCA) layers using molecular dynamics nanoindentation. We evaluated the influence of coating microstructure and pre-relaxation via Monte Carlo/molecular dynamics (MC/MD) on deformation behavior at shallow (35 Å) and deep (65 Å) indentation depths. The relaxation process is critical for equilibrating internal stresses and homogenizing the initial stress field in amorphous phases, while preventing chaotic defect multiplication in crystalline lattices, yet it simultaneously promotes Fe and Cr surface segregation consistent with the equilibrium chemical short-range ordering of the alloy. The results reveal distinct deformation mechanisms: crystalline coatings exhibit higher peak indentation forces of about 300 ± 16 eV/Å characterized by discrete force fluctuations indicative of localized plastic events, while amorphous coatings show lower peak loads (~170–220 ± 12 eV/Å), corresponding to a reduction in load-bearing capacity of roughly 25%–40%, and smooth, continuous deformation governed by shear transformation zones. Notably, in amorphous systems, pressure-induced local crystallization occurs under load, with ordered FCC/HCP regions persisting after unloading, indicating partial irreversibility of the phase transition. Upon deep indentation into the substrate, the amorphous system exhibits a sharp increase in stiffness due to substrate compaction, whereas the crystalline system maintains high load-bearing capacity with reduced defect density in the relaxed state compared to the non-relaxed counterpart. Relaxation significantly reduces force-curve fluctuations in both systems, enhancing the stability of the mechanical response. Compared with uncoated aluminum, which exhibits extensive twin propagation and deep defect penetration, the FeNiCrCo-coated systems approximately halve the defect penetration depth and reduce the defective-atom volume fraction in the substrate by about a factor of two, thereby more effectively confining plastic deformation and preserving substrate integrity under the simulated conditions. These findings demonstrate that the synergy between coating crystallinity and rigorous relaxation protocols governs stress distribution patterns—localized hotspots in amorphous phases versus extended networks in crystalline ones—providing key insights for designing advanced protective coating–substrate systems with optimized mechanical performance. Full article

This study describes a comparative assessment of the structural properties, mechanical properties and gamma-ray shielding effectiveness of monzogranite to determine whether or not they can be used for sustainable shielding construction materials. The results of the petrographic, X-ray fluorescence (XRF), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS) analyses reveal that the monzogranite is composed essentially of quartz, K-feldspar, plagioclase and biotite. The SiO₂ contents of all the monzogranite studied also indicated that they are highly crystalline (70.77% to 73.34% SiO₂ by weight) and chemically stable (therefore, monzogranite); other properties such as density (2.70 to 3.06 g/cm³), porosity (19 to 23%) and water absorption (12 to 15%) demonstrated the structural compactness and durability of the samples studied. Additionally, the mechanical properties of all of the samples were extremely high, and included: (a) the unconfined compressive strength ranged from 89.28 to 240.20 MPa; (b) the engineering modulus ranged from 40.6 to 66.5 GPa; (c) the Brazilian tensile strength ranged from 7.4 to 15.2 MPa; and (d) the flexural strength ranged from 9.3 to 16.4 MPa. The shielding effectiveness against gamma rays was rated over a wide range of photon energies (0.015–15 MeV) via Phy-X/PSD and experimentally using NaI (Tl) spectroscopy at specific gamma photon energies 0.662 MeV, 1.173 MeV and 1.332 MeV. The experimental measurements of gamma-ray attenuation were validated with Phy-X/PSD calculations, with the average variation being 5.8% and no single variation over 10%, and therefore, reliability has been successfully demonstrated. The linear attenuation coefficients (LACs) were measured from 24.674 cm⁻¹ at 0.015 MeV to 0.065 cm⁻¹ at 15 MeV, which illustrates the dependence of gamma-ray interactions’ mechanisms on the energy of the incoming radiation. The half value layer (HVL) went from 0.028 cm to 10.621 cm and the mean free path (MFP) increased from 0.041 cm to 15.323 cm. The best measured performance properties were attributed to specimen MB3, as it had the highest radiation protective efficiency (88.58% at 0.15 MeV) and the lowest radiation transmission (72.16% at 0.09 MeV) in comparison to all of the experimental conditions considered. The high attenuation properties of MB3 were attributed to its high density and high levels of iron oxide, Fe₂O₃. The present work demonstrates that monzogranite, specifically sample MB3, provides excellent mechanical strength, as well as effective shielding from gamma radiation. Therefore, monzogranite, and particularly MB3, is a creative alternative for sustainable construction, as it provides materials that will be used for radiation shielding in nuclear, medical and industrial applications. Full article