Search Results (338)

Integrating full-coverage 2D seismic profiles, local 3D seismic volumes, and borehole data, this study characterizes the transfer structures within the Hangjinqi area of the northern Ordos Basin and elucidates their control on natural gas accumulation. We propose a novel model for hydrocarbon migration and aggregation within transfer structures under an intraplate strike-slip framework. Our findings reveal the following: (1) The transfer structures in the study area manifest as accommodation zones formed by the soft-linkage of overlapping segments of the Hangjinqi fault zone (HFZ)—comprising the Porjianghaizi fault (PF), Wulanjilinmiao fault (WF), and Sanyanjingfault (SF)—since the Hercynian period. Specifically, these consist of two diagonally distributed relay ramps. The relay ramp between the PF and WF hosts a complex reticulate tectonic pattern, characterized by NE- and NWW-trending en-echelon sub-basement fault belts. (2) Transfer structures govern natural gas accumulation through three primary mechanisms: reservoir distribution, reservoir reformation, and fluid migration. ① During the Late Carboniferous to Late Triassic (C2-T3), under N-S compression, the relay ramps controlled the strike and distribution of sedimentary channels. Simultaneously, stress concentration at these ramps under sinistral transpression induced NWW-trending en-echelon faults, which significantly enhanced reservoir petrophysical properties. ② During the Middle-Late Jurassic (J2-3) primary charging period, a shift to a NE-high/SW-low paleogeographic setting triggered a strike-slip reversal of the HFZ. Natural gas migrated northward primarily through bedding-parallel pathways at the relay ramps. ③ By the Early Cretaceous (K1) secondary charging period, weakened shale smear effects along segments of the PF provided lateral pathways, allowing simultaneous northward migration via both strata and fault surfaces. This prolonged differential activity ultimately dictated the heterogeneous distribution of natural gas across the Hangjinqi area. Full article

Continuous stream-oriented data-plane processing in 5G/6G edge nodes increases the energy and latency cost of CPU-centered execution. This paper studies this boundary on an Intel Cyclone V SoC FPGA and proposes Hardware Accountability: a partitioning discipline in which Linux performs supervisory control while high-rate payload processing remains in programmable logic. The evaluation uses the Strumok stream cipher, adopted as the Ukrainian national standard DSTU 8845:2019, as a secure fronthaul/payload workload with XOR- and shift-dominated logic. On the evaluated USB 2.0/Cortex-A9/Linux path, the software-driven stream approaches saturation near 20 MSPS. In contrast, the RTL core reaches 9.6 Gbps at 150 MHz and occupies less than 6% of the available logic. Quartus Prime vectorless power analysis estimates 24.00 mW dynamic power for the RTL computational core, corresponding to approximately 2.5 pJ/bit. Control-plane measurements show P99 orchestration jitter below 1 ms under Spatial Isolation, conservative full context reloads near 1290 per second, and more than 7200 shadow-register context/state update operations per second. A design-space exploration then projects an 83.2 Gbps multi-core data path when external DDR traffic is avoided through internal stream aggregation and elastic buffering. Full article

Rubber aggregate concrete (RAC) offers excellent frost resistance and impact toughness, but the incorporation of rubber particles substantially reduces its compressive strength, limiting structural applications. Existing improvement strategies have mainly relied on a single modification route, whereas the dynamic compressive response of RAC containing both ceramic balls and steel fibers remains insufficiently clarified. Here, a ceramic ball–steel fiber rubber aggregate concrete (CBSFRC) was investigated using quasi-static compression, a Φ120 mm split Hopkinson pressure bar system, and high-speed photography. Three steel-fiber volume fractions (1.0%, 1.5%, and 2.0%) were tested. The CBSFRC waveform displayed a characteristic ‘low-first, high-second’ double-peak pattern, in contrast to the ‘high-first, low-second’ pattern of the reference rubber aggregate concrete (CRC). At the common interpolated strain rates of 40, 45, and 50 s⁻¹, the dynamic compressive strengths of CBSFRC were 35.9–53.6% higher than those of CRC; no extrapolation was used. The largest quasi-static strength increase among the tested mixtures was 51.7%. The observed CBSFRC ultimate strains and strain energy densities ranged from 11.1–17.5 × 10⁻³ to 8.1–14.1 × 10⁵ J/m³, respectively. Matched-rate analysis showed that the largest DIF increment was 18.8% for SF-1CBRC at 40 s⁻¹, whereas the largest strain-energy-density increment was 70.2% for SF-1.5CBRC at 50 s⁻¹. These results describe the tested range and do not establish a universal optimum steel-fiber content. Full article

Epoxy resin (EP) exhibits pronounced intrinsic brittleness arising from the highly crosslinked network formed after curing, thereby restricting its application in load-bearing structures. Although TiO₂ nanoparticles possess the potential for impact-strength improvement, they are highly prone to aggregation, which compromises stress-transfer efficiency within the composite. To overcome this challenge, a ball-milling strategy is adopted to anchor TiO₂ nanoparticles onto three-dimensional graphene oxide (3DGO), leading to the successful fabrication of a 3DGO@TiO₂ hybrid filler. At an ultralow loading of 0.03 wt%, the 3DGO@TiO₂ epoxy resin composite shows a 221.5% increase in impact strength to 19.55 kJ/m² and 33.53% and 32.34% increases in tensile and flexural strength to 64.32 MPa and 96.17 MPa, respectively, relative to neat EP. Morphological analyses indicate that the 3DGO spatial confinement reduces TiO₂ aggregate characteristic length by 55.1% from 1123 nm to 504 nm. Molecular dynamics simulations show that the hybrid filler decreases fractional free volume to 17.6%, induces denser matrix packing, and increases the calculated physical interfacial energy to 1023 kcal/mol, which is 2.2 times that of the pure TiO₂ epoxy resin system. This work confirms that 3DGO simultaneously optimizes nanofiller dispersion and physical confinement, offering a novel strategy for high-performance epoxy composites at ultralow loadings. Full article

The valorisation of electric arc furnace (EAF) slag in construction materials can reduce the consumption of virgin raw materials while creating opportunities for CO₂ mineralisation. However, the combined influence of EAF slag aggregate, aggregate particle-size range and biochar addition on the mechanical, pore-structure and thermal behaviour of carbonated mortars remains insufficiently understood. This study investigated mortars produced with EAF slag as binder and, in selected formulations, as aggregate, with and without biochar. Three aggregate particle-size ranges were assessed: 0.5–1 mm, 1–2 mm, and 2–4 mm. After static compaction and accelerated carbonation, the mortars were characterised in terms of compressive strength, degree of carbonation, pore structure, thermal conductivity, heat-storage capacity and dynamic thermal response. The 100% EAF slag mixtures achieved the highest carbonation degrees, reaching 32.62% for the 1–2 mm formulation containing biochar. Among the tested mixtures, formulation 3C, consisting of 100% EAF slag without biochar and with a particle-size range of 1–2 mm, showed the most balanced overall performance, with a compressive strength of 17.91 MPa, a carbonation degree of 32.13%, a porosity of 24.01%, a thermal conductivity of 0.804 W m⁻¹ K⁻¹, a specific heat capacity of 884 J kg⁻¹ K⁻¹ and a thermal lag of 1.22 h. Although the thermal lag was limited for the 4 cm-thick specimens, the results indicate potential for carbonated EAF-slag mortars to contribute as functional layers in solar-exposed multilayer building components requiring mechanical integrity and short-term heat storage. Full article

Small businesses face many of the same cyber threats as larger organisations but often lack equivalent budgets, specialist personnel, and formal security operations capability. This paper proposes Agile Resilience in Security for Enterprises (ARISE), a lightweight cyber-resilience framework and maturity model designed for resource-constrained small businesses. ARISE adapts prevention, detection, response, and recovery into an agile lifecycle so that organisations can improve cybersecurity maturity progressively rather than through a single large compliance project. The study combines framework benchmarking, a practical interpretive case used to position ARISE within reflexive thematic analysis (RTA) and maturity-model benchmarking, and quantitative machine-learning experiments using the UNSW-NB15 intrusion-detection dataset. The experimental component evaluates selected Weka classifiers, including J48, JRip, Random Tree, Decision Table, and Naive Bayes, to examine whether low-cost tools can support network intrusion detection in small-business contexts. Results show very high aggregate performance for tree- and rule-based classifiers, while class-level outcomes highlight that overall accuracy can conceal weak detection of minority attack categories. The paper therefore positions ARISE as a practical, iterative, and standards-informed framework that complements, rather than replaces, enterprise-grade security operations. Its value lies in giving small businesses an accessible starting point for identifying risk, selecting controls, improving cyber awareness, detecting suspicious activity, responding consistently, and learning from incidents. Full article

Background: Frailty impacts the prognosis of respiratory diseases but lacks standardized evaluation criteria. This pilot study aimed to develop a frailty assessment method using unsupervised clustering of various physical function tests. Methods: Clinical data, handgrip strength (HS), lower limb strength (LLS), the 6 min walk test (6 min WT), the 5 m walk test (5 m WT), body composition, such as skeletal muscle mass index (SMI), whole-body phase angle (WBPhA), and pulmonary function variables were measured. Frailty status was evaluated in three groups (frail, pre-frail, and robust) using the J-CHS and Kihon Checklist. Unsupervised hierarchical clustering was performed, followed by dimensionality reduction using Principal Component Analysis. Results: Ninety-eight patients and healthy volunteers (70 males, 28 females; mean age, 57.5 years) were divided into four clusters, ranging from robust to pronounced frailty. On the 2-principal component plane, data points formed clusters across the four regions. The biplot showed variables aggregating in two directions: one including %FEV₁, FEV₁%, 6 min WT, and 5 m WT speed (exercise tolerance), and the other including HS, LLS, SMI, and WBPhA (physical elements). Tracking 39 participants (mean, 636 days later) showed cluster shifts that were broadly reproducible, although the small follow-up sample warrants cautious interpretation. Conclusions: As an exploratory, hypothesis-generating pilot study with a small single-center sample, this novel frailty model may offer a more granular assessment to help guide management; however, external validation in larger cohorts is required before clinical application. Full article

Melanin pigments protect human tissues from ultraviolet and visible radiation, yet their phototoxic potential increases with oxidative degradation. This computational study investigates how the oxidation state influences the first excited states of eu- and pheomelanin using molecular models of varying complexity (monomers to tetramers, both covalently and non-covalently bonded). First, vertical and adiabatic electronic transitions were computed, and supramolecular interactions were characterized with the ETS-NOCV method. In eumelanin, oxidation drastically lowers the first triplet-state (T₁) energies (from above 230 kJ/mol) to levels comparable to retinal carotenoids (≤66 kJ/mol), emphasizing its role in triplet quenching rather than singlet oxygen generation. Pheomelanin showed greater heterogeneity in the values of the first triplet state, staying mostly above the eumelanin T₁ energies. However, selected pheomelanin structures also exhibited relatively low triplet energies, particularly oxidized benzothiazole (BZox) and trichochromes, and although their T₁ energetics remained higher than those calculated for oxidized eumelanin, they were still sufficiently low to suggest a potential ability to quench singlet oxygen. Furthermore, supramolecular analysis reveals that eumelanin aggregates are moderately stabilized by both π-π stacking and hydrogen bonding, whereas pheomelanin aggregates are dominated by dense hydrogen-bond networks. Full article

Rural policy is pivotal to achieving the UN 2030 Agenda amid rapid global urbanization. This study integrates bibliometric analysis, stage-based comparative policy analysis, and quantitative SDG alignment modeling across six economies (USA, Germany, Japan, China, India, South Africa) spanning 79 rural policy documents from 1913 to 2025. Each document was scored against all 17 SDGs using a three-point ordinal scale, with AI-assisted coding validated through independent human review (inter-coder reliability: Cohen’s κ = 0.763, indicating substantial agreement prior to reconciliation). Bibliometric results document a post-2015 shift from sectoral silos to integrated sustainability frameworks. Generalized Linear Models (GLMs) identify a pattern of “aggregate convergence with structural divergence”: the year of policy enactment is the sole significant predictor of overall SDG alignment (p < 0.01), while income stage and development status show no independent effect on total scores, indicating that global discourse diffusion drives the universal rise in SDG coverage. However, per-SDG regressions demonstrate that income stage and the developed–developing divide significantly shape which specific SDGs receive attention: “late-emergence” goals scale with income, while “development-imperative” goals are systematically prioritized in developing countries. Three distinct evolutionary trajectories are proposed as interpretive constructs derived from comparative analysis: a U-shaped remedial path in developed economies, a J-shaped leapfrogging path in developing economies, and China’s unique Compressed Checkmark trajectory. A Research–Policy–Development nexus model suggests that economic stages act as a “filter” channeling governance capacity toward goals aligned with prevailing social needs. The findings suggest that developing countries may benefit from a “late-comer discursive advantage” in policy-text alignment; however, policy-text alignment does not imply implementation capacity, and realizing SDGs depends fundamentally on developmental resources to bridge vision and reality. Full article

In this study, the microstructural changes and coarsening behavior of Ag₃Sn in Sn-6.4Sb-3.9Ag-0.25Ni-0.003Ge (mass%) during high-temperature aging were investigated. Additionally, low-cycle fatigue tests were conducted to compare the fatigue behavior of Sn-6.4Sb-3.9Ag-0.25Ni-0.003Ge with that of Sn-3.0Ag-0.5Cu. At room temperature, SbSn phases are dispersed in the β-Sn matrix. As the temperature rises, Sb atoms dissolve in the β-Sn phase; thus, the SbSn phases disappear, and some of the atoms aggregate. The activation energy was 45 kJ/mol for the coarsening of Ag₃Sn in Sn-6.4Sb-3.9Ag-0.25Ni-0.003Ge due to aging. Ag₃Sn coarsening was estimated to be controlled by the lattice diffusion of Ag atoms in the β-Sn phase. Furthermore, it was confirmed that the solid solution of Sb atoms in the β-Sn phase reduces the solubility limit of Ag atoms in the β-Sn phase, which delays the coarsening of Ag₃Sn. Regarding fatigue properties, while both alloys exhibited comparable low-cycle fatigue behavior at room temperature, the fatigue ductility exponent’s increase was confirmed to be suppressed for the Sn-6.4Sb-3.9Ag-0.25Ni-0.003Ge alloy at 175 °C. This trend suggests that the delayed coarsening of Ag₃Sn maintains the cyclic strain-hardening exponent, thereby influencing high-temperature fatigue behavior. Full article

Polydiacetylene (PDA) nanovesicles are widely recognized as versatile chromatic sensing platforms, exhibiting a visible blue-to-red colorimetric transition in response to external stimuli such as temperature, pH, and molecular recognition events. In contrast to the conventional goal of amplifying this chromatic response, this work presents a supramolecular approach to inhibit the α-cyclodextrin (α-CD)-induced colorimetric transition in PDA systems. α-CD is known to interact with PDA vesicles through host–guest inclusion at the vesicle interface, triggering the characteristic chromatic change. Here, we show that the incorporation of an EO–PO–EO triblock copolymer (L64) into PDA suspensions enables controlled modulation of the α-CD-induced chromatic response, leading to a progressive attenuation of the blue-to-red transition as the L64 concentration increases. Isothermal titration calorimetry reveals a stronger affinity of α-CD for L64 (K = 11,300) than for PDA vesicles (K = 4000), with both interactions being spontaneous (ΔG° ≈ −21 kJ mol⁻¹) and predominantly entropy-driven. Copolymer aggregation and phase separation occur without compromising the PDA vesicles, indicating that the observed chromatic modulation arises from supramolecular competition. This study introduces a strategy to regulate PDA affinity chromism using biocompatible triblock copolymers, offering a tunable and robust pathway for the design of responsive and safe chromatic sensing platforms. Full article

Digital transformation has become an essential component of sustainable rural development, yet substantial inequalities persist in how communities access, adopt, and benefit from digital technologies. Understanding these disparities requires not only information about technological resources but also knowledge of the relational structures through which information, support, and opportunities circulate. This article presents a reproducible synthetic socio-digital network dataset designed to support the analysis of digital gaps in community-based tourism (CBT) environments. Rather than containing original respondent-level observations, the repository was computationally reconstructed from aggregate statistics derived from field studies conducted in three rural communities in the province of Guayas, Ecuador: Bucay (5 de Septiembre), Manglares Churute, and Ruta de los Chirijos. All node-level records, survey variables, and support relationships included in the repository were synthetically generated to preserve aggregate community characteristics while protecting participant confidentiality and preventing individual re-identification. The repository contains synthetic actor metadata, reconstructed socio-digital variables, directed support networks, graph representations in interoperable formats, and precomputed Social Network Analysis (SNA) indicators. The dataset includes 90 synthetic actors, more than one thousand generated support interactions distributed across multiple socio-digital dimensions, machine-readable metadata, and reusable scripts for preprocessing, validation, graph construction, and metric computation. The represented dimensions include financial assistance, training support, information exchange, technological support, social media promotion, institutional collaboration, trust, and emotional closeness. To facilitate reuse, all resources are distributed in standardized formats compatible with NetworkX, Gephi, Neo4j, and graph-learning frameworks. The repository follows FAIR principles and includes documentation intended to support transparency, reproducibility, and methodological benchmarking. Potential applications include social network analysis, graph mining, graph neural networks, digital inequality research, computational social science, community resilience studies, and educational activities. By providing an openly documented synthetic dataset and reproducible computational workflow, the repository contributes to the study of socio-digital systems, privacy-preserving data sharing, and community-level digital transformation processes. Full article

UV-C light is a promising non-thermal technology for microbial inactivation on food surfaces; however, its efficacy may be compromised by the spatial structure of microbial colonies. The present work investigated the influence of Listeria monocytogenes colony size on UV-C treatment effectiveness using agar-based model systems. Petri dishes were inoculated at defined concentrations and incubated to generate colonies of varying sizes, which were subsequently exposed to a UV-C dose of 0.12 J/cm². Colony growth was monitored over 48 h using an image-based analysis workflow implemented in MATLAB, combined with individual colony tracking. A neural network model was developed to predict the probability of growth cessation based on colony diameter, and quantitative PCR combined with bead-beating was used to estimate cell counts per colony. UV-C treatment applied immediately after inoculation achieved high inactivation efficacy, consistent with minimal cell aggregation. As colony size increased, treatment effectiveness declined markedly. Bootstrap analysis of the neural-network predictions identified a minimum mean growth cessation probability at a colony diameter of approximately 0.862 mm. At this diameter, the predicted probability was 56.8%, with a pointwise 95% bootstrap interval of 50.3–62.8%, corresponding to approximately ${10}^{6.14}$ (viable + non-viable) cells per colony. These findings demonstrate that colony spatial structure substantially limits UV-C efficacy and underscore the importance of early-stage intervention in food surface UV-C decontamination protocols. Full article

Active control of multi-mode light–matter interactions is crucial for advancing quantum photonic technologies. Although triple-mode plasmon–exciton systems involving two distinct excitonic transitions offer a pathway to multi-level polaritonic states, achieving reversible electrical tuning at room temperature remains challenging. Here, we numerically investigate an electrically tunable triple-mode strong-coupling system comprising a J-aggregate-coated Au@Ag nanorod coupled with monolayer WS₂. The simulated spectra show a UPB–LPB energy separation of approximately 239 meV near the zero-detuning condition. A modest gate voltage (2.0 V to 3.8 V) selectively modulates the middle and lower polariton branches over ∼46 meV, while the upper branch remains largely unaffected. This selective control is elucidated via a triple-mode coupled-oscillator model and Hopfield coefficient analysis, linking the polariton response to the excitonic composition. These results establish a framework for electrically reconfigurable multi-level polaritonic devices, offering potential for ultracompact optical modulators, high-sensitivity multiplexed sensors, and programmable quantum photonic circuits. Full article

This paper documents a fundamental sectoral divergence in capital–employment relationships using UK quarterly data (2014Q1–2024Q4, N = 44). While manufacturing automation studies consistently find negative employment effects, we show that information-intensive service sectors (SIC J: Information and Communication; K: Financial and Insurance; M: Professional/Scientific/Technical) exhibit robust positive co-movement between capital formation and employment. Structural vector autoregression analysis reveals persistent positive employment responses following capital shocks, with effects peaking at 5–6 quarters and remaining significant through 10 quarters. This pattern holds across eight alternative specifications with varying lag structure, variable ordering, and subsample periods. Granger causality tests reveal bidirectional temporal relationships (capital → employment: F = 3.932, p = 0.028; employment → capital: F = 5.659, p = 0.007), indicating joint determination from anticipated demand growth rather than unidirectional technology-driven dynamics. This finding—while complicating causal interpretation—strengthens the contribution by providing honest empirical characterization of coordination mechanisms in information-intensive sectors. Our capital formation proxy measures all investment in AI-intensive sectors (buildings, equipment, conventional IT, emerging AI systems) rather than AI expenditure specifically, creating measurement ambiguity we acknowledge transparently. The sectoral focus (J+K+M sectors with 22–34% AI adoption rates exceeding the 15% economy-wide average) provides indicative evidence that patterns relate to advanced technology deployment, but measurement breadth prevents definitive AI-specific conclusions. The contribution lies not in establishing AI-specific causality—which aggregate time-series methods cannot achieve—but in documenting robust sectoral heterogeneity using methodology comparable to manufacturing displacement studies. The positive association in information-intensive services contrasts sharply with manufacturing’s negative relationship, suggesting technology–employment dynamics vary fundamentally across sectors with different task structures. Three limitations constrain interpretation: (i) recursive identification cannot definitively rule out common demand shocks, (ii) the 44-quarter sample provides limited statistical power for precise magnitude estimation, and (iii) external validity to other countries, time periods, or service sectors remains uncertain. The findings motivate sector-specific rather than economy-wide technology policy approaches, recognizing that extrapolating manufacturing evidence to service-dominated economies may systematically mischaracterize employment dynamics. Full article