Search Results (775)

User experience (UX) evaluation is central to software quality, yet it remains difficult to integrate into software engineering workflows in a systematic, explainable, and early-stage manner. This paper presents TraceUX, a framework for operationalizing UX heuristics and design guidance into a rule-based static evaluation pipeline that combines machine-interpretable formalization, executability-aware assessment, context-sensitive scoring, and actionable reporting. The framework is instantiated using Apple Human Interface Guidelines, Swift abstract syntax trees, and mobile games, and implemented in a proof-of-concept tool named TraceHIG. Evaluation was conducted in four layers: analysis of the full rule repository, controlled synthetic validation with injected violations, baseline assessment of 12 public Swift game projects, and a case study on one project. The full repository contained 206 rules; after excluding non-iOS yet platform-specific rules, 193 rules were retained for the downstream experiments. In controlled validation, 216 injected violations yielded 99.2% precision, 61.6% recall, and an F1-score of 0.760. In baseline analysis, overall project scores ranged from 41.6 to 88.0, reflecting rule-conformance spread under the instantiated rule base rather than direct measures of UX quality. The case study demonstrated that profile-aware scoring can yield materially different UX assessments for the same codebase under different game configurations, highlighting the importance of app profiling in static UX evaluation. These findings show that a meaningful subset of UX knowledge can be operationalized into explainable, context-aware static analysis that provides structured and actionable decision support while complementing, rather than replacing, manual and empirical UX evaluation. Full article

Against the backdrop of a new wave of scientific and technological revolution and industrial transformation, artificial intelligence has emerged as a pivotal technology for fostering new quality productive forces and advancing high-quality development, and is profoundly reshaping firms’ production organization and governance structures. Using data on Chinese A-share listed companies from 2016 to 2024, this study empirically examines the impact of AI on corporate internal control quality and its underlying mechanisms. The results indicate that AI significantly improves corporate internal control quality, mainly by enhancing firms’ human capital and reducing agency costs. Further heterogeneity analysis shows that the positive effect of AI on internal control quality is more pronounced among manufacturing firms, firms with higher levels of digital infrastructure, and firms with greater information transparency. From the perspective of internal corporate governance, this study extends the literature on the economic consequences of AI and provides empirical evidence on how AI, as embedded in a complex socio-technical system, empowers high-quality corporate development through institutional governance mechanisms. The findings also offer useful implications for governments seeking to refine AI-related policies and for firms aiming to promote the coordinated upgrading of intelligent transformation and internal control systems. Full article

Digital transformation has increased interest in the use of blockchain in Accounting Systems Auditing because of its potential to strengthen data integrity, decentralized validation, traceability, and accountability. However, the available evidence remains fragmented across technical, theoretical, methodological, and bibliometric dimensions. This study systematically analyzes the literature on blockchain and its influence on Accounting Systems Auditing, focusing on effectiveness criteria, auditing indicators, journal quartiles, definitions, theoretical foundations, thematic patterns, and keyword co-occurrence. A systematic literature review was conducted following the Kitchenham approach and the PRISMA guideline, covering studies published between 2019 and August 2025. From 5031 initial records, 63 studies were retained after applying exclusion criteria and quality assessment. The findings show that blockchain effectiveness is mainly evaluated through data integrity and decentralization, while scientific production is concentrated in Q1 and Q2 journals. The literature remains strongly oriented toward technical and operational foundations, with limited integration of broader theoretical frameworks. Thematic analysis identifies tokenization and governance as motor themes, while blockchain, auditing, and accounting constitute the most influential semantic nodes. This review contributes by integrating conceptual, theoretical, methodological, metric, and bibliometric dimensions into a single framework, offering a clearer understanding of how blockchain may transform audit evidence, internal control, assurance quality, fraud risk, accountability, and auditor judgment. The field shows high scientific visibility but still requires stronger theoretical integration, evaluative standardization, and empirical validation in real-world auditing contexts. Full article

As a key engine of corporate innovation, digital transformation permeates business management. Can digital transformation improve corporate financial flexibility by leveraging the external supply chain? Our sample comprises Chinese listed companies over the period from 2015 to 2024, employing Python 3.10 crawling to measure the degree of digital transformation and utilizing the entropy weight method to construct supply chain resilience. Using a moderated mediation model, this analysis examines how corporate innovation mediates the relationship between digital transformation and financial flexibility, and how supply chain resilience exerts a phased moderating effect along this pathway. The findings reveal the following: (1) Digital transformation has a positive effect on financial flexibility, where corporate innovation plays a mediating role. (2) The promoting effect of digital transformation on financial flexibility exhibits significant heterogeneity, varying with firm-specific micro-level characteristics and internal control quality. (3) Supply chain resilience plays a significant moderating role throughout the entire mediation path. It positively moderates the chain of “digital transformation → corporate innovation → financial flexibility”. This study provides empirical evidence on the mechanisms of digital transformation’s impact on corporate financial flexibility and offers a theoretical view for evaluating the outcomes of digital transformation from a financial perspective. Full article

Background/Objectives: Artificial intelligence is increasingly applied to dental imaging, yet favorable internal performance does not necessarily indicate clinical transferability. This systematic review evaluated whether imaging-based dental artificial intelligence models have progressed beyond internal algorithmic development toward external validation, generalizability, reproducibility, privacy-preserving learning, and clinical implementation readiness. Methods: Searches were conducted in PubMed/MEDLINE, Scopus, and Embase up to May 2026. Eligible studies were primary empirical investigations based on human dental or oral imaging data that assessed at least one translational-validation dimension beyond internal development, including external testing, multicenter or multi-device validation, cross-dataset reproducibility, or privacy-preserving learning. Evidence was synthesized using a structured narrative synthesis reported according to the Synthesis Without Meta-analysis framework. Results: Fifteen studies published between 2023 and 2026 were included. They addressed caries detection, periodontal bone loss, gingival inflammation, root morphology, palatal radicular grooves, radiographic quality control, tooth-width estimation, and dental-structure segmentation. Translational-readiness domains included external validation, generalizability, reproducibility, privacy-preserving learning, transparency, and workflow relevance. Validation varied across cohorts, repositories, centers, devices, cross-dataset benchmarks, and federated-learning settings. Reproducibility, annotation harmonization, uncertainty reporting, explainability, workflow evaluation, and code or model availability were inconsistent. Quantitative pooling was not performed because tasks, modalities, units of analysis, reference standards, validation designs, and metrics were highly heterogeneous. Conclusions: Within this selected subset of externally tested studies, translational progress is emerging but remains uneven. Implementation readiness requires stronger reproducibility, clinically meaningful validation, workflow evaluation, and attention to regulatory, organizational, and human-factor barriers. Full article

Material discontinuities and defects can profoundly impact the structural integrity and overall product quality. In a multitude of industries, ranging from aerospace and automotive to the nuclear sector and manufacturing, even surface discontinuities can pose significant risks to component reliability. This paper presents a planar microwave sensor for non-destructive testing (NDT) to quantify the electromagnetic response to controlled crack-like discontinuities in polylactic acid (PLA) materials. The sensor comprises a host coplanar waveguide (CPW) positioned at the base of an RO3210 substrate and a multiple split-ring resonator (MSRR) on the surface, creating a compact device measuring 30 mm × 50 mm × 1.27 mm. When a discontinuity-free PLA sample-under-test (SUT) is placed above the sensor, the transmission coefficient exhibits a resonance at 1.780 GHz. As the width of the groove-based discontinuity increases, a systematic blue shift in the resonant frequency is observed. The relationship between resonant frequency shift and discontinuity width is established through empirical calibration for both surface and subsurface configurations. The results demonstrate the feasibility of the proposed sensor for calibrated detection and sensitivity-based discrimination of millimeter-scale crack-like discontinuities in PLA within the tested dimensional range. Full article

Snapshot compressive imaging (SCI), including coded aperture compressive temporal imaging (CACTI), reconstructs high-speed video frames from compressed low-frame-rate measurements. Most deep SCI reconstruction networks are designed around a measurement-wise formulation: each compressed exposure is reconstructed independently, and the resulting frame segments are concatenated to form a continuous video. This protocol is effective for within-measurement reconstruction, but it leaves cross-measurement temporal continuity largely unmodeled. Boundary artifacts such as flickering, texture drift, or motion jumps can therefore appear between adjacent reconstructed segments, even when frame-wise reconstruction metrics remain competitive. This work identifies and empirically analyzes the underexplored problem of cross-measurement temporal continuity in continuous SCI, and it provides TCA-EfficientSCI as a lightweight, causal, and reproducible baseline. The Temporal Context Adapter uses the last m reconstructed frames from the previous measurement as causal temporal context and injects this history through a gated residual feature pathway. A boundary consistency loss regularizes the predicted temporal variation across measurement boundaries without forcing adjacent frames to be identical. In a controlled three-seed comparison, Full TCA with boundary loss reduces mean Boundary Difference Error (BDE) by 2.23% relative to the matched-epoch EfficientSCI control while maintaining similar PSNR and SSIM. Correct-history inference gives BDE 0.01615, while zero and shuffled history give 0.01725 and 0.01810, respectively. The adapter adds 1,019,905 parameters, or 11.56% relative to the EfficientSCI baseline parameters, and it changes $256\times 256$ mean latency from 54.35 ms to 68.58 ms per measurement in the profiling protocol. Rather than claiming broad reconstruction-quality improvement, this study highlights cross-measurement continuity as an important evaluation and design dimension for continuous SCI deployment. Full article

Sustainable air-quality governance requires not only local emission reduction but also a shift from single-pollutant control to coordinated PM_2.5–O₃ control, and from city-bounded management to regional governance under spatial spillovers. Based on balanced annual city-level panel data for the “2 + 26” urban agglomeration in the Beijing–Tianjin–Hebei region and surrounding areas from 2013 to 2020, this paper uses the dynamic Spatial Durbin Model (SDM) to analyze the spatial spillover effect of PM_2.5 and O₃ pollution and the effect of regional governance policies. The results show that both PM_2.5 and O₃ exhibit significant spatial autocorrelation and cross-city dependence, indicating that isolated local control measures are insufficient for sustainable air pollution prevention and that city-bounded governance cannot fully address regionally connected pollution risks. Economic output and secondary-industry employment remain important structural factors of pollution. The policy-text analysis shows that measures centered on coal-related control and industrial governance were more directly aligned with PM_2.5 reduction, whereas O₃-related governance lagged, suggesting that single-pollutant-oriented control may generate a sustainability trade-off when PM_2.5 reduction is not accompanied by coordinated O₃ control. These findings highlight two sustainability challenges in China’s regional air-quality governance: first, single-pollutant control can improve particulate pollution but may not ensure sustainable air-quality improvement when O₃ and its precursors are insufficiently addressed; second, isolated city-level governance may be insufficient when pollution outcomes exhibit significant spatial dependence across administrative boundaries. The study provides empirical evidence for sustainable air-quality governance by emphasizing differentiated PM_2.5 and O₃ responses, coordinated PM_2.5–O₃ control, regional governance beyond individual city boundaries, and the integration of spatial spillover assessment into regional environmental policy design. Full article

Generative artificial intelligence (GenAI) can accelerate Building Information Modeling (BIM) and digital-twin workflows, but probabilistic outputs and manipulated inputs create data-integrity risks that conventional construction-software liability rules do not fully explain. This study develops a reproducible doctrinal and comparative-law method that maps the GenAI value chain onto the Architecture, Engineering, and Construction (AEC) lifecycle. Its principal advantage over single-actor product or professional-negligence analyses is a qualitative proportional-liability assessment framework that evaluates technical control, foreseeability, verification capacity, causal contribution, and evidence preservation across developers, deployers, and professional users. The analysis identifies the following two recurrent pathways of harm: autonomous hallucination entering safety-relevant design information and adversarial or erroneous data entering digital-twin feedback loops. It also specifies syntax, semantic, cybersecurity, and human-verification controls linked to AEC information-management and quality standards. The framework is not an empirically calibrated formula and does not determine legally binding percentages. Instead, it provides a transparent decision aid for applying existing tort doctrines and regulatory duties to distributed GenAI-enabled construction workflows. Full article

The Arunachali mithun (Bos frontalis) is a semi-domesticated bovine of profound cultural and economic significance to the indigenous Arunachali tribal communities of Northeastern India, yet it remains among the least genomically characterised large ruminants, leaving its conservation status without an empirical genetic foundation. We performed whole-genome re-sequencing (~10× coverage) of 11 individuals and analysed 4,943,593 high-quality biallelic single nucleotide polymorphisms (SNPs) after stringent quality control. Genome-wide mean observed heterozygosity (H_o = 0.2854), expected heterozygosity (H_e = 0.3347), and nucleotide diversity (π = 7.16 × 10⁻⁴) revealed moderate genetic diversity, substantially lower than that of related commercial bovine species. A consistent heterozygosity deficit (H_o − H_e = −0.0493) and the convergence of four independent inbreeding coefficients around 0.143–0.147 indicated moderate inbreeding of predominantly reflecting an ancient origin, corroborated by runs of homozygosity (ROH) analysis in which 93.2% of 24,937 detected segments fell in the short length class (100–250 kb). Linkage disequilibrium decayed from r² ≈ 0.57 at <100 kb to a plateau of r² ≈ 0.33 beyond 4–5 Mb, consistent with a small effective population size (N_e) declining from approximately 101,850 (~2228 generations ago) to approximately 160 (~5 generations ago), with ab Ne of approximately 3865 at ~100 generations ago and 423 at ~10 generations ago. These findings establish a whole-genome-based genetic diversity baseline for the Arunachali mithun and provide actionable genomic evidence for conservation and managed breeding interventions. Full article

Background: Rejection remains a major barrier to long-term kidney allograft survival, and pre-transplant risk stratification remains incomplete. This study evaluated whether pre-transplant serum Fourier-transform infrared (FTIR) spectra, analyzed using machine learning methods, could identify kidney transplant recipients at increased risk of subsequent biopsy-proven rejection. Methods: In this retrospective single-center study, 80 pre-transplant serum samples collected on the day of transplantation were initially evaluated; after spectral quality control, 79 samples were retained for analysis. FTIR spectra were acquired in transmission mode and analyzed in the 600–1900 cm⁻¹ and 2800–3400 cm⁻¹ regions. Multiple preprocessing strategies were assessed, including Rubber Band baseline correction, vector normalization, and first- and second-derivative transformation, with and without normalization. Naïve Bayes classifiers with Leave-One-Out Cross-Validation and Fast Correlation-Based Filter feature selection were applied. Results: Exploratory analysis showed broad overlap between groups, indicating a subtle multivariate spectral signal. In the initial exploratory workflow, classifier performance depended strongly on preprocessing and feature selection. Because non-nested feature selection may produce optimistic estimates, the main supervised analysis was repeated using FCBF nested within each LOOCV training fold. The best-performing nested model was obtained using second derivative transformation followed by normalization in the combined 600–1900 and 2800–3400 cm⁻¹ regions, achieving an AUC of 0.837, accuracy of 0.747, sensitivity of 0.675, specificity of 0.821, balanced accuracy of 0.748, and F1-score of 0.730. Permutation testing with 1000 label-randomized repetitions supported performance above chance expectation, with no permuted model reaching the observed AUC (empirical p = 0.000999). Conclusions: Pre-transplant serum FTIR spectroscopy combined with leakage-aware nested machine learning analysis identified an internally validated spectral signal associated with subsequent biopsy-proven rejection. These findings support FTIR as a promising complementary and hypothesis-generating approach for pre-transplant biochemical risk phenotyping, requiring external multicenter validation before clinical application. Full article

Internal diameter atmospheric plasma spraying (ID-APS) commonly employs an inherently inclined nozzle configuration to overcome geometric interference in confined cylindrical components. This non-orthogonal deposition condition breaks the symmetry of the plasma jet and produces asymmetric thickness distributions, making uniform coating formation difficult to control using conventional models developed for planar or external spraying. In this study, a kinematic-based mathematical model was developed from experimentally measured single-path deposition data obtained under representative internal spraying conditions. A skew-normal formulation was introduced to describe the asymmetric cross-sectional profile, and a superposition framework was established to relate kinematics and geometric constraints to coating quality metrics, including mean thickness, profile uniformity, flatness, and lateral distance. The effects of kinematic parameters and workpiece geometric characteristics were systematically analyzed, and the resulting model was implemented on an internal cylindrical surface to predict spatial thickness evolution. Experimental validation was conducted at both macroscopic and microscopic scales through surface reconstruction and cross-sectional microscopy, confirming that the proposed approach can capture the main features of coating buildup and provide reliable estimates of thickness uniformity. The developed framework offers a practical tool for process design and quality control in ID-APS, reducing dependence on empirical parameter tuning and enabling more consistent thickness control on internal surfaces. Full article

Forging is a key manufacturing route for high-performance structural components, but its process design, quality prediction, and adaptive control still rely heavily on empirical rules, offline simulations, and fragmented production data. This review examines intelligent forging from the perspective of mechanism–data–knowledge fusion, with emphasis on forging-specific process chains, real alloy systems, model validation, and industrial maturity. To improve methodological traceability, a structured literature search was conducted using Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, and Google Scholar, covering studies published from 1996 to 2026. The screened literature was organized around process perception, mechanism-based modeling, data-driven learning, hybrid modeling, knowledge representation, digital twins, online prediction, and adaptive regulation. Representative cases are discussed for closed-die forging, open-die/large forging, multistage forging, radial forging, and forging of aluminum alloys, titanium alloys, steels, and Ni-based superalloys. Particular attention is given to how specific models are validated, including independent experiments, finite-element benchmarks, industrial datasets, new geometries, sensor noise, and cross-material or cross-equipment transfer. The review further distinguishes consolidated technologies, such as FEM-based process simulation and die/preform optimization, from methods still under validation, including hybrid digital twins, sensor-updated models, and adaptive control. Large-model-assisted forging is considered a prospective direction mainly for information retrieval, case recovery, diagnostic support, and engineer-supervised recommendation rather than unsupervised real-time control. This review provides a more process-specific and critically assessed reference for developing explainable, validated, and deployable intelligent forging systems. Full article

Sustainable innovation constitutes the cornerstone of firms’ long-term competitive edge, yet the underlying mechanisms via which patient capital facilitates corporate sustainable innovation remain understudied. Based on a sample of Chinese A-share listed firms spanning 2013 to 2024, this study operationalizes patient capital through two proxies: relational debt and stable institutional ownership. We systematically investigate the impact of patient capital on sustainable innovation, alongside the mediating pathway of internal control quality and the moderating role of climate policy uncertainty. The empirical outcomes indicate that both forms of patient capital exert a significant positive effect on sustainable innovation, with internal control quality serving as a partial mediator in this relationship. Additionally, climate policy uncertainty reinforces the promotional influence of patient capital on sustainable innovation. We further stratify heterogeneity analyses into two dimensions: firm-inherent heterogeneity and external environmental heterogeneity. From the perspective of endogenous firm attributes, the innovation-stimulating effect of patient capital differs markedly across enterprises with distinct ownership types, life-cycle stages, and total asset sizes. Externally, the observed positive impact varies considerably conditional on industrial factor intensity and the regional marketization degree of the firm’s location. These findings expand the existing literature concerning long-term capital and sustainable innovation, and yield actionable implications for corporate management, institutional investors, and policymakers. Full article

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM_2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM_2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks. Full article