Search Results (1,687)

The electrification of floating production, storage, and offloading (FPSO) units has emerged as a strategic solution to meet the growing demand for increased oil production while reducing carbon emissions associated with onboard gas turbine generation. Power-from-shore (PFS) systems represent a promising approach to achieving this goal, with transmission technologies based on high-voltage direct current (HVDC) and high-voltage alternating current (HVAC) solutions. Although HVDC is more suitable for long-distance and high-power applications, HVAC systems offer advantages in terms of robustness, simplicity, and operational maturity. Nevertheless, the reactive power compensation requirements arising from the high capacitance of submarine cables remain a major technical challenge. This study investigates and compares several reactive power compensation topologies applied to three distinct PFS systems. The proposed methodology enables a comprehensive evaluation of both onshore and subsea reactor placement strategies under technically and technologically feasible conditions. The results demonstrate that long-distance transmission of 75 MW over 250 km was achieved exclusively through subsea compensation configurations, which maintained efficiencies above 90% and voltage and current profiles within operational limits. Conversely, onshore-only compensation proved to be the most efficient solution for shorter transmission distances. The results demonstrate that the full electrification of an FPSO is technically feasible, with voltage and current profiles remaining within acceptable operational limits. The findings also indicate that mid-cable reactor placement (at 50%) is not the most effective configuration, with superior results observed for placements at 20–80% and 40–70% of the cable length. Overall, the outcomes confirm that subsea reactor placement enables higher power transfer over longer distances, significantly extending the technical boundaries traditionally separating HVDC and HVAC solutions. These results emphasize the need for continued technological development to make subsea shunt reactor installation a viable and reliable option for future FPSO electrification projects. Full article

Standard fringe-based structured-light processing estimates wrapped phase from phase-shifted sinusoidal images and commonly relies on phase unwrapping to obtain a globally consistent phase representation. In practical measurements, this approach may become unstable on reflective objects and under low or non-uniform illumination, where the recorded fringe signal is distorted and the recovered phase becomes unreliable. To address these limitations, we propose a correspondence extraction method based on subpixel peak localization performed directly on phase-domain images. The wrapped phase is transformed into absolute value phase profiles, $\Phi =|{\varphi }_w|$, whose local structure follows the projected fringe pattern and is less affected by object-dependent intensity variations. The proposed method reformulates correspondence extraction as a local signal-based estimation problem in the phase-domain, thereby reducing reliance on global phase-consistency constraints at the correspondence stage. A practical advantage observed in the evaluated examples is that the method remained usable in some regions where the phase became locally flat because of low modulation, saturation, or reflective surface effects. In such regions, conventional processing relies on sufficiently reliable phase gradients and subsequent unwrapping, whereas the proposed method uses local peak geometry in the transformed phase representation. In the implementation used here, Gray-code information is employed only for pixel-wise phase extension and reference indexing, not as a spatial phase-unwrapping mechanism. The method does not require machine learning models or training data and can be integrated as a correspondence analysis stage in practical structured-light systems. Full article

Existing BIM (Building Information Modeling) validation mechanisms, namely geometric clash detection and attribute completeness checking of individual objects (MVD, IDS), do not cover a significant category of informational incompleteness: situations in which the properties of interdependent entities become fully defined only as a result of their mutual presence in the model. This article introduces the new concept of ontosaturation as a new mechanism of formal ontology that formalizes this phenomenon. Ontosaturation describes the relationship between existentially independent entities whose certain properties remain undetermined (unsaturated) in isolation and acquire values only after the attributes of related objects are taken into account. The article proposes a formal definition of ontosaturation and the supporting concepts needed to apply it in practice. These include the saturant (an entity that completes the properties of another), the saturation cluster (a group of mutually saturating entities), and the saturation index, a metric enabling a quantitative assessment of the relational completeness of a BIM model at the level of a single entity (s(e)) and the entire model (S(M)). The concept of a saturation profile was also introduced, complementary to the Level of Information Need (LOIN) in accordance with the ISO 19650 series of standards, defining minimum saturation thresholds for successive phases of the project lifecycle. The mechanism was demonstrated using the example of an installation penetration through a fire separation wall, modeled in Autodesk Revit 2025, showing that collision detection and attribute validation fail to detect four unsaturated properties critical to fire safety and structural integrity, which ontosaturation identifies. The proposed approach constitutes a third layer of BIM model validation, alongside the geometric and attribute layers, addressing the relational completeness of information between interdependent objects. Full article

Grid-level population projection is essential for spatial planning under demographic decline, particularly for ensuring that population allocation accounts for grid extinction risk. This study develops a two-stage machine learning framework to predict residential grid transitions across South Korea’s 1 km grid system and assess how spatial policies shape depopulation outcomes through 2050. Stage 1 employs Random Forest classification to predict grid state transitions (macro-averaged F1 score = 0.694), while Stage 2 applies LightGBM regression for population prediction (coefficient of determination = 0.950). The extinction probability map from Stage 1 is incorporated into scenario simulations to adjust population allocation based on predicted residential viability. Feature importance analysis reveals that baseline population, household count, and demographic composition are key determinants of grid-level residential transitions. Five spatial development scenarios simulated through 2050 reveal substantial policy sensitivity. Cumulative extinction rates range from 3.1% under extreme dispersion to 24.5% under extreme concentration, representing a 25 percentage point divergence attributable to spatial allocation policy. Provincial heterogeneity is pronounced, with rural provinces facing extinction rates up to 39.9% while metropolitan areas remain largely unaffected. Comparing scenario outcomes enables pre-identification of policy-sensitive grids (19.5%) where allocation choices determine residential survival. These grids are predominantly located in areas with high forest cover and greater spatial isolation compared to stable grids, but differ in demographic profiles. Aging-Vulnerable grids (14.0%) exhibit high aging ratios with limited economic base, while Moderate-Vulnerability grids (5.5%) show younger demographics with relatively higher economic activity. These differential characteristics provide a spatially explicit basis for differentiated policy responses. Beyond depopulation planning, the spatial outputs of this framework can inform related planning domains such as land use transition planning, carbon management, and infrastructure prioritization under demographic decline. Full article

To reduce the conservatism of local trajectory planning in dynamic road scenarios caused by redundant projection of predicted trajectories, this paper proposes a hierarchical hybrid trajectory-planning framework with a time-to-arrival (TTA)-driven dynamic risk-filtering mechanism. In the Frenet coordinate system, road boundaries, ego states, and static and dynamic obstacles are represented uniformly to construct an S–L fused risk field and an S–T spatiotemporal interaction graph, enabling the filtering of temporally irrelevant conflict regions based on TTA relationships. At the path-planning layer, risk-guided adaptive sampling is integrated with dynamic programming and quadratic programming to improve search efficiency and trajectory quality. At the speed-planning layer, spatiotemporal coordination is achieved through non-uniform discretization, safe-corridor extraction, and speed-profile optimization. Simulation results show that the proposed method generates safe, smooth, continuous, and executable local trajectories in scenarios involving static-obstacle avoidance, adjacent-vehicle cut-ins, non-motorized road-user crossings, and mixed multi-obstacle interactions, while reducing unnecessary deceleration and detours. Ablation results further indicate that adaptive sampling reduces the number of DP search nodes by approximately 50% and the average planning time by about 30%, while maintaining a nearly unchanged minimum safety distance. These findings demonstrate that the proposed framework effectively suppresses redundant conflict regions and improves planning efficiency, solution feasibility, and motion continuity without compromising safety. Full article

Four sweet cherry cultivars (FuChen, Redlight, Huangmi, and Samituo) grown in northern China were used to produce sweet cherry wines with two alcohol levels. Physicochemical properties, antioxidant capacity, and volatile aroma compounds of the wines were systematically investigated. The results showed that wine from the Redlight cultivar with an alcohol content of 11.22 ± 0.17% contained the highest phenolic content and also exhibited the strongest antioxidant capacity as measured by DPPH and ABTS•⁺ assays. Meanwhile, wine from the FuChen cultivar with an alcohol content of 11.45 ± 0.03% had the highest anthocyanin content and showed the strongest FRAP antioxidant activity. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on electronic nose data clearly distinguished the eight sweet cherry wine samples from different cultivars. A total of 58 volatile compounds were identified by headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS). Both principal component analysis (PCA) and OPLS-DA revealed clear differences among the sweet cherry wines based on their volatile composition. Using variable importance in projection (VIP) scores > 1 and relative odor activity values (ROAVs), the key aroma compounds contributing to the characteristic aroma profiles of the eight sweet cherry wines were identified as ethyl butanoate, isoamyl acetate, isoamyl hexanoate, methyl decanoate, ethyl decanoate, ethyl benzoate, methyl salicylate, citronellol, and eugenol. These findings provide important guidance for the selection of raw materials to improve the production of sweet cherry wines with targeted alcohol levels. Full article

Asthma is a chronic respiratory disease characterized by airway hyperresponsiveness, obstruction, and persistent inflammation, arising from complex interactions among genetic, epigenetic, immune, and environmental factors. To elucidate the stage-specific molecular mechanisms underlying asthma progression, we constructed candidate genome-wide genetic and epigenetic networks (GWGENs) of human cells through large-scale biological database mining. Using a system order detection scheme, false-positive interactions were pruned to identify real GWGENs corresponding to three clinical stages of asthma: quiet, exacerbation, and follow-up. Core GWGENs were subsequently extracted from each real network using the principal network projection (PNP) method to highlight dominant regulatory structures and pathogenic pathways. Based on the inferred core networks, key stage-specific biomarkers were identified and further explored as potential drug targets. Drug–target relationships were investigated by integrating gene expression perturbation profiles from the Connectivity Map (cMap), comprising microarray data for 14,207 genes across 1327 compounds. This network-guided analysis enabled the qualitative design of multi-molecule drug combinations tailored to each disease stage. Our results suggest that asthma onset is associated with reduced innate immunity, increased disease susceptibility, and impaired endothelial barrier recovery influenced by microenvironmental factors such as cigarette smoke and lipopolysaccharides, together with genetic and epigenetic alterations. During the exacerbation stage, enhanced differentiation of T cells toward the T helper 2 lineage contributes to airway inflammation and tissue injury. In the follow-up stage, T helper 1–mediated responses are linked to mucus hypersecretion, airway obstruction, and sustained inflammation. Collectively, these findings demonstrate that a systems-level, network-based framework can uncover stage-specific pathogenic mechanisms of asthma and provide hypothesis-generating insights for network-informed drug repurposing strategies. Full article

Background and Objectives: Gastric cancer remains a leading cause of cancer-related mortality worldwide and develops through complex interactions between environmental factors, microbial dysbiosis, and host molecular pathways. Although Helicobacter pylori infection is a well-established risk factor, emerging evidence suggests that broader alterations in the gastric microbiome may also contribute to carcinogenesis. However, the associations between gastric cancer-associated microbial taxa and host gene expression profiles remain insufficiently characterized. This study aimed to identify host gene signatures associated with gastric cancer-related microbial taxa through a descriptive analysis integrating microbiome-derived taxa with transcriptome data. Materials and Methods: Microbial taxa associated with gastric cancer were systematically retrieved from the Disbiome database. Taxon set enrichment analysis (TSEA) was performed using the MicrobiomeAnalyst platform to identify host genes associated with gastric cancer-associated taxa. Importantly, TSEA relies on healthy reference data from the Human Microbiome Project and does not establish gastric cancer-specific interactions or causal relationships. Gene expression levels were subsequently evaluated using The Cancer Genome Atlas (TCGA) PanCancer stomach adenocarcinoma (STAD) dataset by comparing tumor and matched normal gastric tissues. Gene interaction network and transcription factor (TF) enrichment analyses were conducted to explore predicted regulatory relationships. Results: Among 64 microbial taxa associated with gastric cancer, 43 were reported as elevated. After removing overlapping taxa across studies, 37 elevated and 21 reduced taxa were retained for analysis. TSEA identified 11 host genes associated with gastric cancer-related microbial taxa. Transcriptomic analysis demonstrated significant downregulation of DPP6 and DLG2, while KDM4D, USP34, and VDR were significantly upregulated in gastric cancer tissues compared with normal controls. Network and TF enrichment analyses revealed predicted co-expression and co-localization patterns among these genes, suggesting their potential involvement in immune-related processes, epigenetic regulation, and cellular organization. Conclusions: This descriptive study identifies distinct host gene expression signatures associated with gastric cancer-associated microbial dysbiosis. This study is purely associative and hypothesis-generating; no causal or mechanistic inferences are made. TSEA used healthy reference data and therefore does not reflect gastric cancer-specific host–microbe interactions. The findings provide a basis for future hypothesis-driven research but require validation in independent cohorts. Full article

Permeability anisotropy, which is widely present in natural soil deposits, plays an important role in controlling groundwater flow patterns and ground deformation during deep excavation dewatering. However, isotropic assumptions are still commonly adopted in engineering practice, making it difficult to accurately capture realistic subsurface hydraulic conditions. In this study, a deep foundation pit of a metro station in Jinan, China, is taken as a case study. A three-dimensional excavation–dewatering model incorporating permeability anisotropy is established using PLAXIS 3D to systematically investigate the influence of the permeability ratio (Kx/Kz) ranging from 0.1 to 10 on the seepage field evolution, dewatering influence radius, ground surface settlement, and consolidation time history. The results indicate that increasing permeability anisotropy promotes a fundamental transition of the seepage regime from vertically concentrated recharge to laterally dominated radial flow. Correspondingly, the dewatering influence radius exhibits a pronounced non-monotonic response to Kx/Kz, decreasing significantly with increasing permeability ratio and reaching a minimum at approximately Kx/Kz ≈ 5, followed by a slight rebound. Meanwhile, surface settlement profiles evolve from a localized concentration pattern to a widely distributed form as permeability anisotropy increases, accompanied by a remarkable outward expansion of the settlement influence zone. Both the magnitude and spatial distribution of settlement show high sensitivity to variations in permeability anisotropy. Based on these findings, a three-stage conceptual seepage structure model accounting for permeability anisotropy is proposed, characterized by vertically dominated flow, a transitional competition regime, and horizontally dominated flow. The staged evolution of seepage structures is shown to govern the non-monotonic variation in the dewatering influence radius and the spatial–temporal response of ground settlement. The results indicate a dual-scale influence mechanism of permeability anisotropy on dewatering-induced hydro-mechanical behavior, providing a theoretical basis for refined dewatering design and environmental impact assessment in deep excavation projects. Full article

The purpose of this project was to define gene expression changes associated with the acquisition and loss of resilience to diabetic retinopathy (RDR) in individual retinal cell types. A non-immune form of type 1 diabetes mellitus (DM) was induced by injecting male C57Bl6J mice with streptozotocin. Single-cell RNA sequencing was performed on retinas from mice that experienced DM for 5 or 15 days, along with retinas from age-matched, non-DM mice. The resulting data sets were analyzed to identify DM-associated differentially expressed genes and pathway enrichments after each duration of DM. We observed that acquisition of RDR, previously shown to arise after 5 days of DM was linked to altered expression of genes in a subset of retinal cells, mainly Müller cells. Pathway analysis indicated enhancement of numerous modes of protection, including reinforced neurovascular and structural homeostasis through phagocytosis, integrin signaling, and interferon-mediated defense. After 15 days of DM, when we previously showed that RDR is waning this pro-protection surge in gene expression subsided. We conclude that a duration of DM that is too short to cause diabetic retinopathy (DR) nonetheless evoked a profound change in the gene expression profile within a subset of retinal cell types. The nature and timing of this molecular shift indicated that it was not the preamble to DM-related damage that eventually develops. Rather, DM engaged numerous defense programs within Müller cells. The temporal alignment between RDR and activation of Müller cell-based defense provides a molecular foundation for the retina’s transient ability to remain healthy in the face of DM. Full article

This study estimates China’s methane (CH₄) emissions from 43 specific emission sources in 2020 and projects future trends through 2050 under two scenarios: Current Legislation (CLE) and Maximum Technically Feasible Reduction (MFR). The analysis utilises the Greenhouse gas and Air pollution Interactions and Synergies (GAINS) model methane framework, incorporating updated province-level activity data to capture the pronounced regional heterogeneity inherent in emission profiles and mitigation capacities. The results reveal a national CH₄ budget of 1114 MtCO₂e in 2020, with the energy sector (59%) and agriculture (28%) emerging as the primary contributors. A substantial technical mitigation potential is identified; by 2050, emissions could be curtailed by up to 48% relative to the CLE scenario, representing a 46% reduction from 2020 levels. The energy and waste sectors emerge as the primary contributors to this potential. Specifically, coal mining CH₄ abatement constitutes 58% of the energy sector’s total reduction potential, while enhanced solid waste management accounts for 97% of the mitigation within the waste sector. Key measures include ventilation air methane (VAM) oxidation and pre-mining degasification, as well as anaerobic digestion and recovery and utilization for energy use. Owing to regional disparities in hydrothermal conditions (representing the combined influence of temperature and moisture), demographic status, economic development, the most effective mitigation strategies vary across provinces. For example, pre-mining degasification and VAM oxidation are most impactful in major coal-producing regions such as Shanxi, Inner Mongolia, and Shaanxi. In contrast, anaerobic digestion, recovery and utilization, and waste incineration play a dominant role in more economically developed and densely populated provinces such as Jiangsu, Shandong and Zhejiang. By delineating region-specific technological priorities, this study quantifies the maximum technical mitigation potential for China and offers guidance for other nations facing similar mitigation challenges. Full article

High renewable penetration reduces effective inertia and increases frequency variability in microgrids, thereby limiting the performance of purely reactive frequency regulation. This paper presents a two-timescale frequency-support strategy based on bidirectional electric vehicles. The main novelty lies in introducing a learning-assisted correction layer between forecast-based aggregate regulation and final EV-level dispatch. Rather than replacing the predictive controller with an end-to-end data-driven policy, this layer uses measured fleet-state information to correct the supervisory aggregate request online before a final feasibility-preserving dispatch stage converts it into executable vehicle-level commands under concurrent power, energy, plug-in, and departure constraints. A supervisory predictive layer determines the aggregate support action from forecasted photovoltaic and load disturbances, whereas a lower real-time dispatch layer redistributes that action across the available fleet. Feasibility is enforced through an explicit projection stage prior to actuation. The method is assessed in simulation using measured campus operating profiles of irradiance, temperature, demand, frequency, and electric-vehicle availability. Across four representative operating days, the proposed strategy reduced the mean cumulative frequency deviation by 30.3% relative to droop control and by 24.7% relative to predictive-only operation, while reducing the mean time outside the admissible frequency band by 22.2% and 20.0%, respectively. Zero post-projection constraint violations were observed in all evaluated cases. These gains were obtained at the expense of higher actuation usage, thereby making the regulation–usage trade-off explicit. Full article

Hybrid-electric propulsion and alternative energy carriers are being considered to mitigate the climate impact of short-range regional aviation. Within this framework, the HERA (Hybrid Electric Regional Architecture) project investigates advanced propulsion architectures for a next-generation 72 passenger regional platform. This work presents a cradle-to-grave Life Cycle Assessment of two HERA reference configurations and compares them with a conventional 70 passenger turboprop representative of current service aircraft. The analysis focuses on lithium–sulphur batteries, proton exchange membrane fuel cells, liquid hydrogen storage tanks, and electric motors. The assessment is implemented through a parametric LCA tool supported by a detailed Life Cycle Inventory based on Ecoinvent v3.8 and evaluated using ReCiPe 2016 midpoint indicators. The system boundary includes raw material extraction, manufacturing and assembly, operation under defined mission profiles, maintenance with component replacement, and End-of-Life (EoL) treatment. Results show that the operational phase remains the main driver of climate change impacts, exceeding 95% of total CO₂ equivalent emissions across configurations. The battery-based hybrid reduces fuel consumption but increases manufacturing and maintenance burdens. The fuel cell configuration shows a more balanced life cycle profile, with platinum identified as a critical hotspot. Full article

Background/Objectives: Wound infections cause significant morbidity, yet current diagnostics rely on time-consuming microbial culture. Volatile organic compounds (VOCs) from bacterial metabolism offer potential for early diagnosis. This study aimed to validate the volatile metabolites profiled by gas chromatography–ion mobility spectrometry (GC-IMS) combined with machine learning for rapid identification of wound infections and certain bacterial infections. Methods: Headspace of clinical wound samples were analyzed using GC-IMS. Volatile metabolite profiles were compared between infected and non-infected groups and between Escherichia coli (E. coli)-positive and negative samples. Partial least squares discriminant analysis (PLS-DA) and Mann–Whitney U test were used for preliminary screening with variable importance in projection (VIP) > 1 and p-value < 0.05. Three machine learning algorithms, namely support vector machine (SVM), logistic regression (LR), and random forest (RF), were trained on the selected features for classification, using 5-fold cross-validation with 10 repeated runs. Model performance was assessed using key evaluation metrics, including accuracy, sensitivity, specificity, the area under the curve (AUC) and feature importance ranking to identify the most relevant biomarkers. Results: A total of 19 volatile metabolites associated with clinical wound samples were identified. The RF model achieved 90.15% sensitivity and 0.91 AUC for bacterial infection detection. For E. coli identification, LR reached 85.35% sensitivity and 0.89 AUC. Potential volatile metabolic biomarkers including elevated 3-methyl-1-butanol, 2-methyl-1-butanol, and ethyl hexanoate for identifying bacterial infection were selected through the cross-validation results of the three algorithms. Conclusions: Untargeted metabolomics by GC-IMS effectively captures infection-specific volatile metabolic signatures in complex wound samples. Integration with machine learning enables rapid, high-accuracy diagnosis of bacterial infections and E. coli identification at point of care. This approach addresses clinical metabolomics translational challenges by providing a portable and cost-effective method, potentially reducing antibiotic misuse through more timely and targeted therapy. Full article

Global aging is accelerating, with the proportion of the population aged 60 and above projected to reach 22% by 2050. In cold-region communities, the visual landscape environment is closely associated with the health of older adults, particularly showing associations with blood pressure (BP) and emotion states. However, associations between these factors across different landscape spaces and potential gender differences remain underexplored. This study utilized eye-tracking experiments to collect visual attention data from older adults in three types of cold-region community spaces: inter-building spaces, walkways and squares. The ground, buildings, trees, lawn, and the sky were identified as the primary Areas of Interest (AOIs). The Profile of Mood States (POMS) scale was used to assess emotion during walking experiments, revealing suggestive gender–environment interaction characteristics. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) were measured, and a Mann–Whitney U test indicated that DBP in community squares exhibited significant environmental dependency (U = 114.5, p = 0.004, r = 0.44). Hierarchical Linear Models (HLMs) revealed that, after controlling for individual differences, the number of fixation points on ground was independently associated (i.e., independent of measured individual characteristics) with elevated SBP ($\gamma =0.31, p=0.011$), while fixation on trees was associated with reduced SBP ($\gamma =-0.24, p=0.018$). Furthermore, gender moderation effects were observed: the association between ground fixation and SBP was stronger in females ($\gamma =0.18, p=0.022$), whereas the association between sports facilities and DBP was stronger in males ($\gamma =0.29, p=0.009$). Based on these findings, evidence-based design strategies are proposed, including the optimization of ground safety, gender-differentiated planting configurations, and targeted layouts for sports facilities. These results provide empirical support for age-friendly community design in cold regions. Full article