Search Results (132,877)

Blockchain technology (BCT) is often discussed as digital support for timber traceability in the context of the EU Deforestation-Free Products Regulation (EUDR), which requires operators and traders to demonstrate legality, deforestation-free production, geolocation at the forest parcel level, and verifiable supply chain documentation from 30 December 2026, with an extended timeline for micro, small, and medium-sized enterprises (SMEs) until 30 June 2027. This study assesses where BCT can realistically add value in timber supply chains under operational forestry conditions and identifies the necessary technical, organizational, and legal prerequisites. A combination of a targeted literature review and empirical input from experts in the forestry and timber industry, including guided expert web-conferencing interviews (n = 41), an online survey (n = 69 completed responses), and a transdisciplinary workshop (n = 18) was utilized to obtain a comprehensive overview of industry perspectives. Qualitative data from interviews and workshop sessions were analyzed using structured qualitative content analysis, while survey data were evaluated using descriptive statistics. The expected benefits are associated with the introduction of tamper-proof timber harvesting practices and cross-organizational verification mechanisms. To address the discrepancy between biological uncertainty and digital rigidity, the study proposes a dynamic allocation model adapted from the energy sector that distinguishes between fixed and variable wood capacities to automate logistical planning via smart contracts. However, respondents emphasize that practical obstacles, such as limited digital maturity in forestry, fragmented data infrastructures across the supply chain, and unresolved issues of data sovereignty hinder the implementation of BCT. BCT alone is unable to resolve the problem of weak physical-digital identity continuity, a phenomenon widely known as the oracle problem; however, coupling the ledger with physical or biological anchors (e.g., photo-optical, automated inkjet marking identification) can re-establish this physical–digital continuity and thereby resolve the oracle problem. The results demonstrate that blockchain acts most plausibly as a supporting component within hybrid traceability architectures that prioritize event-based authentication, off-chain data processing where appropriate, and integration with existing certification systems. The study highlights the necessity of defining distinct organizational roles and responsibilities while integrating user-centric digital solutions tailored specifically to small and medium-sized enterprises (SMEs). Full article

Accurate prediction of pile base resistance is essential for the safe and economical design of deep foundations, particularly in soft soils where load-transfer mechanisms are highly nonlinear and uncertain. This study develops a comparative, probabilistic, and interpretable machine learning framework for predicting pile unit base resistance using five input variables: applied load, settlement, effective pile length, axial stiffness, and SPT value. A Gaussian Process Regression model with an automatic relevance determination (ARD) Exponential kernel achieved the best performance, with RMSE = 262.11 kPa, R² = 0.943 on an independent test set, and 95% prediction intervals with 96.46% coverage. Beyond record-level evaluation, a leave-one-pile-out validation (the first grouped validation applied to this database) showed harder generalization to entirely unseen piles, driven mainly by a per-pile level offset rather than shape mismatch (within-pile correlation = 0.975). A sequential next-stage scheme, calibrating this level from a pile’s early loading stages, then predicted its remaining segments with consistently strong agreement (Willmott’s d = 0.76–0.83), supporting practical extension of partial load tests. Interpretability was assessed using ARD, SHAP, permutation/ablation importance, and partial dependence/accumulated local effects analysis, identifying settlement as the dominant predictor. The framework combines accuracy, calibrated uncertainty, interpretability, and validated segment-level extrapolation for reliability-oriented pile assessment. Full article

Diabetes mellitus (DM) is a chronic metabolic disease characterized by hyperglycemia, and the diaphragm—the primary respiratory muscle—is adversely affected by this diabetic process. The aim of this study is to investigate the effects of zinc sulfate (ZnSO₄) treatment on diaphragm muscle contractile dynamics, calcium homeostasis, apoptosis, and fibrosis in an 18-month-old female Type 2 diabetic rat model. Thirty-two 18-month-old female Wistar rats were randomly divided into four groups: Control (CON), CON + ZnSO₄, Diabetes Mellitus (DM), and DM + ZnSO₄. The DM model was induced by a high-fat diet and administration of 30 mg/kg streptozotocin (STZ); after the disease was confirmed, ZnSO₄ was administered intraperitoneally at a daily dose of 10 mg/kg to the treatment groups. The mechanical functions of the diaphragm muscle were evaluated using a post-rest potentiation protocol in an isolated organ bath; qPCR analyses (Caspase-3, TGF-β1, SERCA 2a) were performed to investigate cellular apoptosis, fibrosis, and calcium regulation. Compared with the CON group, the DM group exhibited a severe ~90% reduction in diaphragmatic contraction force (CF) and a ~97% decline in maximal contraction/relaxation velocities (±dF/dt_max) (p < 0.0001), which strongly correlated with a 30% suppression of SERCA2a gene expression (p < 0.01). Concomitantly, apoptotic Caspase-3 (~2.6-fold) and profibrotic TGF-β1 (~3.1-fold) mRNA levels were significantly elevated (p < 0.0001). In the DM + ZnSO₄ group, daily zinc treatment (10 mg/kg/day, i.p. for 6 weeks, initiated 4 weeks after diabetes confirmation) did not reverse the elevated Caspase-3 and TGF-β1 expressions (p > 0.05). However, SERCA2a expression was fully preserved back to control levels (p < 0.05 vs. DM), leading to a substantial ~3-fold improvement in CF and velocities (p < 0.05 to p < 0.0001 vs. DM). On the other hand, the healthy CON + ZnSO₄ group exhibited a physiological slowing of contractility (~53% decrease in CF), without histological damage, likely due to a competitive antagonism between excess divalent zinc (Zn²⁺) and calcium (Ca²⁺) on myofilaments. Although zinc cannot reverse the structural apoptotic and fibrotic remodeling in the aged diabetic diaphragm, it successfully rescues functional contractility by preserving SERCA2a transcriptional expression. Full article

Dry eye disease (DED) exposes the ocular surface to persistent hyperosmolar, oxidative, metabolic, and inflammatory stress that may increase susceptibility to ferroptosis. Whether autophagy promotes or limits ferroptotic injury in DED remains unresolved. This review critically evaluates evidence from corneal and conjunctival epithelia, lacrimal glands, and meibomian glands, using non-ocular models only to interpret mechanisms not yet resolved in ocular tissues. We propose a threshold-balance framework in which autophagy has context-dependent effects. Ferritinophagy, lipophagy, and impaired autophagic flux can expand the labile iron pool, mobilize peroxidizable lipids, and promote lipid peroxidation. Conversely, intact autophagic–lysosomal flux and mitophagy can remove damaged cargo, limit mitochondrial oxidative stress, and preserve membrane antioxidant defenses. The balance between these processes may determine whether stressed ocular-surface cells adapt or progress toward ferroptotic injury, with consequences for epithelial barrier integrity, tear-film homeostasis, glandular secretion, and inflammation. Studies combining autophagy-related pathway perturbation with ferroptosis-related outcomes are concentrated in ocular-surface epithelial models, whereas this relationship has been less directly examined in lacrimal and meibomian tissues. Establishing causality will require flux-aware measurements, targeted pathway perturbation, tissue-resolved models, and functional validation in human samples. Such studies should clarify when and where autophagy–ferroptosis crosstalk is therapeutically actionable in DED. Full article

Complex nonlinear physical systems can exhibit dynamic responses that provide useful resources for information processing. In this study, an electrical circuit-based multiple-electron model was developed and implemented in a random network to investigate its dynamic electrical properties and information-processing capability. The model represents discrete electron transfer and charge accumulation using tunnel junctions and charge-storage nodes and was constructed as a two-dimensional random network inspired by carbon nanotube/polyoxometalate (CNT/POM) networks. The network exhibited time-varying current responses under a constant voltage and nonlinear and hysteretic current–voltage characteristics. The hysteresis became more pronounced as the number of charge-storage nodes increased. The information-processing capability of the network was further investigated using delayed XOR and sine waveform generation tasks. The delayed XOR task was achieved using the integrated squared current response, whereas a target sine waveform was reconstructed from multiple network responses under a constant voltage input using a linear readout, yielding a coefficient of determination of 0.816. These results demonstrate that the proposed multiple-electron network exhibits nonlinear and history-dependent electrical dynamics and can support information-processing tasks. Full article

Geopolitical fragmentation, intensifying geoeconomic competition, and the securitisation of economic relations are reshaping the international environment and changing the conditions under which the European Union (EU) conducts its external economic policy. As economic interdependence has become a source of both prosperity and vulnerability, the EU has begun to redefine the relationship between openness, resilience, and economic security. This article examines how these developments are reshaping the EU’s geoeconomic model and its role within an evolving global order. The study develops an analytical framework that links geopolitical pressures, external dependencies, and institutional responses to the Union’s geoeconomic adaptation. The research employs a concept-driven qualitative design combining qualitative content analysis, analytically focused comparison, and process-oriented interpretation of key EU strategic documents and the relevant academic literature. The study shows that the EU is developing a hybrid geoeconomic model that combines openness with resilience, market integration with economic security, and international cooperation with a stronger capacity to manage critical vulnerabilities. This process reflects a longer-term adjustment of the Union’s external economic strategy and its international role. The article advances current debates on geoeconomics, European integration, and global governance by explaining how geopolitical fragmentation is reshaping the EU’s external action in a more contested international environment. Full article

Background/Objectives: Osteoarthritis (OA) is a whole-joint disease, and cellular senescence is one of several processes associated with cartilage degeneration. This study aimed to identify senescence-associated differentially expressed genes in established OA and to examine checkpoint-related candidates without assuming a causal checkpoint-imbalance mechanism. Methods: Five Gene Expression Omnibus (GEO) datasets spanning articular-cartilage tissue and primary cartilage-derived chondrocytes (GSE57218, GSE117999, GSE114007, GSE246425, and GSE169077) were integrated as a training cohort; the meniscus dataset GSE98918 was reserved as an independent cross-tissue validation cohort. OA-associated differentially expressed genes (DEGs) were intersected with CELLAGE genes. Enrichment, protein–protein interaction, transcription-factor, competing endogenous RNA, drug-enrichment, and molecular-docking analyses were performed. CDK6 and WEE1 expression was evaluated in IL-1β-treated human C28/I2 chondrocytes by RT-qPCR and representative Western blotting. Results: Forty-one senescence-associated DEGs were identified, and seven network-central genes (CDKN1A, CDK6, WEE1, NFKB2, ID1, RBL2, and IGFBP7) were prioritized. CDKN1A, CDK6, and WEE1 were reduced in OA-associated meniscal samples in GSE98918; within-dataset ROC analyses yielded AUCs of 0.931, 0.882, and 0.792, respectively. In IL-1β-treated C28/I2 cells, WEE1 mRNA decreased whereas CDK6 mRNA increased; representative immunoblots showed concordant qualitative trends. Berberine- and folic acid-related docking findings were computational only. Conclusions: Checkpoint-related gene expression is associated with senescence-linked transcriptomic changes in established OA. The discordant CDK6 results between clinical tissue datasets and an acute inflammatory cell model do not establish stage-dependent regulation. The present data also do not demonstrate p53-mediated CDKN1A activation, checkpoint failure, cell-cycle arrest, or cellular senescence; these hypotheses require dedicated functional experiments. Full article

Wallpaper has been used for centuries to enhance the appearance of internal walls in traditional buildings. While there is evidence that wallpaper can influence the moisture performance of wall assemblies, particularly by delaying drying, limited experimental work has characterised the hygric properties of wallpaper and its role in this process. This paper investigates the vapour permeability and drying behaviour of lime plaster, wallpaper paste, and five wallpaper types to assess their impact on moisture dynamics within wall assemblies. Hygrothermal modelling using the measured material parameters is undertaken, enabling detailed analysis of moisture transport and drying behaviour across the full wall thickness. The findings indicate that the bio-based wallpaper paste and many traditional and non-woven wallpapers do not significantly affect vapour permeability or drying. However, the vinyl-coated wallpaper showed a measurable impact on moisture behaviour, reducing both vapour permeability and drying. Hygrothermal modelling shows that wallpapers with low vapour permeability (high s_d value) can increase relative humidity within wall assemblies. This effect is more pronounced in walls with higher baseline moisture content, whether due to substrate properties or environmental exposure, making such walls more vulnerable to potential fabric decay. Full article

The electromagnetic spectrum grows increasingly crowded with the rapid expansion of mobile, satellite and Internet of Things communications, making intelligent spectrum sensing and efficient management an urgent priority. Automatic modulation classification (AMC) serves as the core of cognitive radio and intelligent communication. Existing deep models often suffer from a large number of parameters and low storage efficiency. To overcome these limitations, we propose MDSCNet, a multi-scale depth-wise separable complex network. Built upon complex depth-wise separable convolution, the network makes full use of the phase information in in-phase and quadrature signals while naturally preserving the symmetric relationship between the in-phase and quadrature components (IQ). The asymmetric multi-scale structure combined with the embedded lightweight attention module jointly forms the overall feature extraction process. The overall parameter count is kept extremely low, at only 47.739 k. Experiments on the RML2016.10a and RML2016.10b datasets show that MDSCNet delivers recognition performance under low signal-to-noise ratios (SNR), reaching 63.42% and 66.71% respectively. More importantly, it outperforms mainstream methods in both parameter count and storage efficiency. Full article

Small-animal phobia (SAP) is characterized by exaggerated fear responses to biologically relevant threat cues, yet the extent to which personality traits influences neural threat processing remains unclear. This study examined whether Cluster C personality traits (avoidant, dependent, and obsessive–compulsive) modulate neural responses to phobic stimuli in SAP. Functional magnetic resonance imaging (fMRI) data were collected from 39 individuals with SAP and non-phobic controls while viewing phobia-relevant and neutral stimuli. Whole-brain analyses were conducted to identify group differences in brain activation, and additional models assessed the influence of Cluster C personality traits on neural responses. Compared with controls, individuals with SAP showed greater activation in regions involved in threat processing and emotional salience, including the anterior cingulate cortex, parahippocampal gyrus, pallidum, fusiform gyrus, and thalamus. Higher avoidant traits were associated with increased activation in visual regions implicated in threat detection, whereas dependent and obsessive–compulsive traits were linked to greater recruitment of prefrontal areas involved in cognitive control and emotion regulation. These findings suggest that neural responses to phobic stimuli are influenced not only by the presence of SAP but also by personality-related vulnerability, highlighting the role of Cluster C traits in shaping perceptual and regulatory aspects of threat processing. Full article

Prediction of 2 m temperature during extreme cold events remains challenging because it is obtained diagnostically from near-surface atmospheric states and depends on various physical parameterization processes, thereby introducing multiple sources of forecast uncertainty. Our previous study has demonstrated that the Combined Nonlinear Forcing Singular Vectors (C-NFSVs) based ensemble forecasts can effectively characterize forecast uncertainty and recognize the dominant sources. However, the specific dynamical and physical parameterization processes associated with model-related forecast uncertainty remain unclear. In this study, we investigate the sensitivity of model-related 2 m temperature forecast uncertainty within the Weather Research and Forecasting (WRF) model to different dynamical and physical parameterization processes during extreme cold events over southern China. Based on the good reliability of C-NFSVs-based ensemble forecasts, the sensitivities of different temperature tendency terms to model perturbations were diagnosed by comparing the ensemble spreads from experiments using full C-NFSVs and those using only the initial component of C-NFSVs. The results indicate that, for the extreme cold events examined over southern China, the vertical advection and Planetary boundary layer (PBL) parameterization terms in the WRF model exhibit the strongest sensitivities to model perturbations, suggesting their close association with model-related 2 m temperature forecast uncertainty. This sensitivity may be associated with the roles of these processes in regulating the vertical redistribution of heat and the evolution of lower-tropospheric thermal structures. These findings provide new insights into the processes associated with model-related forecast uncertainty of 2 m temperature. Furthermore, they highlight the need for further investigations of vertical advection and PBL-related processes in the WRF model. Such investigations are expected to improve the understanding of their roles in forecast uncertainty and evaluate their potential implications for improving 2 m temperature forecasts during extreme cold events. Full article

Advanced analysis has been shown to improve material efficiency in statically indeterminate steel-framed structures compared with member-based linear elastic design methods. However, limited research has investigated its applicability to geometrically nonlinear steel structures where residual stresses are induced by the bending process. In this study, the material optimization potential of advanced analysis has been quantified for two arch-based structures by comparing the volume of steel required to satisfy the criteria of both the system and member-based analysis methods in accordance with AS 4100:2020. The two structures were analyzed using the finite element analysis software Strand7 (R3.1.6) and subjected to combined gravity and wind loading in alignment with the serviceability and ultimate limit states specified in AS 1170.0:2002. System behavior was analyzed through the Arc-length plastic zone method. The results indicate that in one of the arch-based structures, advanced analysis can improve material utilization by 8.1%. Provided that future research both validates the use of the reduced stiffness method for treatment of initial geometric imperfections and verifies system reliability factors for structures with curved geometries, advanced analysis presents a practical design method for this structure. Comparison of the two case studies found that advanced analysis has the potential to improve material efficiency only when linear elastic failure is governed by ultimate limit state criteria. It is therefore evident that the material optimization findings of this research cannot be generalized to all arch-based structures, as they are contingent upon the geometry of the model analyzed, the loading scenarios considered, and the deflection limits adopted. Full article

Reliable integration of Building Information Modeling (BIM) and Building Energy Modeling (BEM) is essential to sustainable design and renovation because energy-efficiency decisions depend on consistent analytical models. However, native gbXML exports often contain geometric and semantic inconsistencies that can distort energy assessments. This study evaluates native export limitations and the effect of middleware-based transformation on BIM-to-BEM interoperability. A residential building was modelled in Autodesk Revit and Graphisoft Archicad. gbXML exports and the BIMTWIN workflow were assessed using geometry, analytical integrity, and heat-loss indicators; only geometric results were compared with the Energy Performance Certificate. Although all workflows generated gbXML files, their analytical quality differed substantially. The middleware-transformed model deviated by only +1.53% and +2.25% from the reference external and internal volumes, respectively. Native Revit export omitted intermediate floor constructions and produced a total heat loss of 95,785 W, 25.2% higher than the 76,533 W obtained from the transformed model. Native Archicad export produced fragmented geometry unsuitable for reliable heat-loss calculation. The results show that BIM-to-BEM interoperability should be treated as a controlled validation and transformation process. By reducing analytical errors and manual reconstruction, this approach supports more reliable assessment of energy-efficiency measures and better-informed decisions in sustainable building design and renovation. Full article

Infrared small target detection (IRSTD) is a challenging task in intelligent infrared sensing and electronic imaging systems, because dim targets often occupy only a few pixels and are easily disturbed by clutter, noise, and low-contrast background structures. A practical detector should preserve pixel-level target cues while suppressing target-like false responses. This paper proposes a Pyramid Target Perception Network (PTPN) for single-frame pixel-level IRSTD. The network integrates three complementary components: an Efficient Context Modeling (ECM) encoder employing 7 × 7 depthwise separable convolution for lightweight contextual feature extraction, a multi-scale target cross-attention (MTCA) module for hierarchical feature interaction, and a small-target feature pyramid network (STFPN) for target-preserving multi-scale aggregation. In addition, a physics-constrained loss (PCL) is introduced during training to regularize predictions according to infrared imaging characteristics, including point spread consistency, target-region relative intensity consistency, and signal-to-noise-ratio-aware separability. Experiments on IRSTD-1k, NUAA-SIRST, and NUDT-SIRST demonstrate that PTPN achieves $IoU$ scores of 71.87%, 79.56%, and 86.47%, respectively, with 4.55M parameters, 4.96G FLOPs at an input resolution of 256 × 256, and an inference speed of 45.0 FPS. Although PTPN achieves competitive overall performance, it does not attain the highest $IoU$ on NUDT-SIRST, indicating that pixel-level target-region estimation under complex scenes remains an area for further improvement. Overall, PTPN provides an effective balance between target localization, false-alarm suppression, and computational efficiency, supporting its potential application in AI-driven infrared image processing and intelligent electronic sensing systems. Full article

This paper proposes a three-layer adaptive Kalman filter-based multi-source time fusion method for constructing a high-precision, continuous, and robust chip-level time reference. A digitally temperature-compensated TCXO is used as the short-term local time reference, and the Allan variance is introduced to characterize oscillator frequency stability and model the process-noise covariance. For medium-term correction, BeiDou observations are fused with oscillator prediction through an adaptive Kalman filter. A reliability score based on C/N₀, DOP, pseudo-range residuals, and other quality indicators is used to dynamically adjust the observation-noise covariance and Kalman gain. When BeiDou signals become unreliable or unavailable, eLoran is introduced as a backup timing source to maintain continuous output. In addition, Kalman filter residuals are fed back to the TCXO temperature-compensation module, forming a closed-loop correction mechanism to suppress residual frequency drift and accumulated timing errors. Experimental results show that the proposed method significantly reduces timing errors and improves continuity, stability, and recovery capability under BeiDou degradation and outage conditions. Full article