Search Results (176,174)

Background: Photothermal therapy (PTT), a highly efficient and controllable method with minimal drug resistance, transforms near-infrared (NIR) radiation into heat. This process exerts antibacterial effects, aids in tissue repair, and promotes healing. Methods: Our study presented a novel kind of composite wound dressing that incorporated adhesive conductive hydrogel combined with piezoelectric film for NIR-responsive applications. The inherent adhesiveness of the hydrogel ensured robust anchoring of the piezoelectric film to both hydrogel matrix and wound site. Its conductivity enabled synergistic endogenous electrical stimulation with the piezoelectric film, while also serving as therapeutic layer to augment hemostasis, analgesia, and antibacterial activity. Results: The hydrogel’s capacity for moisture retention and exudate absorption sustained optimal wound environment, thereby supporting debridement and recovery. Furthermore, the piezoelectric film possessed excellent photothermal properties and transferred heat to the hydrogel through heat conduction to enhance antibacterial activity and promote wound healing. The in vitro and ins vivo experiments confirmed that the composite dressing exhibited strong promotion effect on wound healing under NIR irradiation. Conclusions: In summary, our research provided a new strategy for developing advanced piezoelectric biomaterials with great clinical potential for wound healing. Full article

Marine macroalgae, microalgae, and associated microorganisms are increasingly recognised as valuable sources of bioactive compounds with applications across biotechnology and health. The environmental and ecological conditions they inhabit shape their metabolite diversity, leading to the production of high-value compounds such as sulphated polysaccharides, lipids, pigments, phenolics, and peptides. These compounds exhibit conserved biological activities that underpin potent antioxidant, anti-inflammatory, cytotoxic, and pro-regenerative effects with strong potential for translation. Although external factors drive rich metabolite diversity, continual variation can also lead to translational constraints including heavy-metal accumulation, inconsistency in extract composition, and regulatory complexity. This review examines the environmental drivers of metabolite diversity and the functional potential of bioactives derived from marine algae. We focus on their translational application within four areas of growing interest: nutraceuticals, cosmetics, regenerative medicine, and oncology, where emerging evidence suggests their promise as next-generation bioactive ingredients and therapeutic leads. In addition, insights from Irish and Welsh Small and Medium Enterprises (SMEs) are collated to identify key bottlenecks in commercialisation and the requirements for effective marine biodiscovery pipelines. We consider the importance of controlled cultivation, standardised analytics, preclinical testing platforms, and collaborative innovation ecosystems and highlight the need for coordinated scientific, technical, and regulatory advances to unlock the full translational potential of marine-derived compounds. Full article

Heart failure with reduced ejection fraction (HFrEF) is increasingly recognized as a systemic metabolic disorder. The aim of this study was to characterize amino acid-related metabolic differences between heart failure with moderately reduced ejection fraction (HFmrEF) (LVEF 40–49%) and HFrEF (LVEF < 40%) and to derive a biologically interpretable composite metabolomic index capable of discriminating between these two stages of systolic dysfunction. We conducted a cross-sectional metabolomic analysis of 42 patients stratified by left ventricular ejection fraction (LVEF < 40% vs. 40–49%). The reference group comprised patients with mildly reduced ejection fraction (LVEF 40–49%), without inclusion of individuals with preserved or normal cardiac function. Targeted amino acid profiling was performed using liquid chromatography-tandem mass spectrometry (LC–MS/MS). Metabolites were standardized and analyzed individually and in combination. A composite index (Heart Failure Amino Acid-Derived Systolic Index: HASI-40), integrating markers of proteolysis and metabolic resilience, was derived to distinguish patients with HFrEF from those with HFmrEF. Discrimination was assessed using receiver operator curve (ROC) analysis with internal validation and multivariable adjustment. Patients with LVEF < 40% exhibited a coordinated metabolic phenotype characterized by reduced methionine, sarcosine, serine, and taurine. While individual metabolites did not retain significance after multiple-testing correction, the composite HASI-40 index remained strongly associated with HFrEF (OR 5.56, 95% CI: 1.70–18.14; p = 0.004), although the wide confidence interval indicates limited precision due to sample size. The index demonstrated good discrimination with an area under the curve (AUC) of 0.862, which improved when combined with age (AUC 0.932). The index represents a standardized composite measure and does not define a diagnostic cutoff for individual patients. These findings suggest that HFmrEF and HFrEF exhibit partially distinct metabolic phenotypes despite overlapping clinical characteristics. These findings suggest that HASI-40 captures metabolic differences between patients with HFmrEF (LVEF 40–49%) and those with HFrEF (LVEF < 40%), reflecting progression toward more advanced systolic dysfunction. However, due to the absence of a control group with preserved ejection fraction, small sample size, and lack of external validation, the index should be considered exploratory and hypothesis-generating rather than clinically applicable. Full article

Background: The nursing profession is recognized as a high-risk occupation, with the emotional toll on healthcare workers reaching a critical point. A complex interplay of anger and cynicism, often stemming from systemic pressures and chronic moral injury, seems to increasingly affect nurses’ professional and personal lives. This psychological strain does not end when the shift ends; rather, it often manifests as insomnia and nightmare distress, creating a vicious cycle of exhaustion and emotional instability. This article explores how anger, cynical distrust, nightmare distress and insomnia are interrelated and jeopardize the well-being of nursing staff and what these “invisible” symptoms reveal about the current state of healthcare by confirming their prevalence rates. Methods: This cross-sectional study was conducted online in October 2025 and included 441 hospital nurses who completed the Dimensions of Anger Reactions-5 (DAR-5), the 8-item Cynical Distrust scale (CDS-8), the Nightmare Distress Questionnaire (NDQ) and the Athens Insomnia Scale (AIS). Results: The prevalence rates of anger, nightmare distress and insomnia were 41.5%, 6.6%, and 62.1%, respectively. Based on the CDS-8 scores, a notable proportion (20.9%) of nurses fell within the highest quartile of CDS-8 scores (CDS-8 > 29), indicating relatively elevated cynical distrust within this sample; this threshold is sample-derived and does not correspond to a validated clinical cut-off. Hierarchical multiple regression analysis indicated that the DAR-5 explained 22.1% of the variance in AIS, while an additional 10.2% was explained by NDQ and another 1.5% by the CDS-8. Both cynical distrust and nightmare distress displayed a chain mediation pattern in the association between anger and insomnia; however, given the cross-sectional design, the temporal order of these variables cannot be confirmed. Conclusions: Anger exhibited significant direct and indirect associations with insomnia, with cynical distrust and nightmare distress acting as serial mediators in this cross-sectional model. Findings from this cross-sectional study tentatively suggest that future intervention efforts targeting insomnia in nurses might benefit from addressing anger alongside nightmare distress and cynical attitudes; however, experimental studies are needed to confirm whether such interventions would be effective. Full article

Sustainable power-system operation requires carbon-reduction strategies that are emission-effective, physically deliverable, economically feasible, and compatible with user-side decarbonization claims. As Scope 2 carbon accounting increasingly emphasizes temporal, spatial, and physical consistency, dispatch models need to link user-level carbon claims with network-constrained power delivery. This paper proposes a User-Centric Carbon Cost-Constrained Low-Carbon Dispatch (CCC-LCD) framework that integrates carbon emission flow (CEF), nodal carbon intensity (NCI), network-constrained optimal dispatch, and endogenous demand response. A PTDF-based DC-OPF model represents active-power deliverability, while dual virtual flow variables determine carbon-flow directions endogenously. The model minimizes the target user’s physically traced Scope 2 emissions under a cost-tolerance budget and flexible-load constraints. Case studies on a modified IEEE 14-bus system show that nodal decarbonization is topology-dependent: high-load and high-NCI nodes obtain larger reductions from source-side generation substitution, whereas renewable-adjacent nodes exhibit limited marginal gains. The CEF-DR strategy outperforms single-mechanism cases, indicating the value of coordinating physical carbon-flow constraints with flexible demand. From a sustainability perspective, the proposed framework supports verifiable low-carbon electricity consumption, improves the economic feasibility of user-side decarbonization, and provides a practical dispatch tool for sustainable energy transition and corporate Scope 2 emission reduction. Full article

The functional performance and structural integrity of natural rock materials under fluctuating environmental stressors are pivotal for their advanced applications. As a non-ionizing and radiation-free technology, terahertz (THz) spectroscopy offers a safe and promising alternative for non-destructive testing (NDT), uniquely capable of being deployed in open and unshielded environments. However, limited penetration depth, exacerbated by both the dense geological matrix and the extreme sensitivity of THz waves to moisture states, has long hindered its widespread application in rock characterization. This study establishes a quantitative Terahertz Time-Domain Spectroscopy (THz-TDS) framework to characterize four lithologies under drying–wetting cycles. Exponential signal attenuation across thicknesses was quantified based on the Beer–Lambert law, with attenuation coefficients ranging from 0.15 to 0.74 per millimeter. Planar transmission imaging successfully visualizes lithologic and moisture-dependent heterogeneity: limestone exhibits a dense, homogeneous structure with stable amplitude distribution; sandstone and purple sandstone show parallel statistical trends, reflecting uniform pore networks; and granite demonstrates the most pronounced imaging contrast under varying moisture states, driven by complex grain-boundary scattering. The findings reveal that THz transmission is dictated by the synergistic effects of mineral compositions and pore structures: scattering at grain boundaries and fractures leads to significant energy dissipation, whereas clay-rich lithologies exhibit the highest sensitivity to moisture variations due to water adsorption and interfacial polarization effects. As an exploration of THz technology in the non-destructive evaluation of rock materials, these findings establish an analytical framework for the quantitative assessment of microstructure evolution. Full article

Snow cover plays an important role in ecological stability and seasonal water regulation in the western Greater Khingan Mountains of Inner Mongolia, a cold-region transitional zone where climate warming may intensify environmental vulnerability and sustainability challenges. Using long-term remote sensing, meteorological, and topographic datasets, this study examined the spatiotemporal changes in snow cover and assessed the relative influences of climatic and geographic factors. The results showed pronounced spatial heterogeneity, with greater snow depth and longer snow cover duration occurring in the northeastern, high-altitude, gentle-slope, and north-facing areas. Snow depth showed a slight but marginally significant declining trend during 1982–2024 at a rate of 0.026 cm a⁻¹, while snow cover days decreased by 0.39 d a⁻¹ during 1982–2020. Snow cover onset exhibited a slight but significant delay, whereas snowmelt timing showed strong interannual variability. Compared with precipitation, temperature showed stronger and more persistent associations with snow cover variations, and climatic factors explained a larger proportion of snow-depth variability than geographic factors. Overall, the results suggest that regional warming has played a leading role in recent snow cover decline. These findings improve understanding of climate-sensitive snow dynamics and provide useful evidence for ecological conservation, seasonal water-resource adaptation, and sustainable regional management in cold-region landscapes of northern China. Full article

The construction of green mines is a core strategy for promoting ecological civilization in China’s mining sector, yet its long-term ecological effects require quantitative assessment. Using a cement-grade limestone mine operated by Linyi Zhonglian Cement Co., Ltd. in Shandong Province as an illustrative case, we employed Landsat 8 OLI/TIRS imagery acquired in 2015, 2020, and 2025 to develop a five-indicator framework for assessing ecological environment quality. The selected indicators comprised greenness (NDVI), wetness, dryness (NDBSI), land surface temperature (LST), and dust concentration (MECDI). These five indicators were subsequently integrated via principal component analysis to generate the Mine Ecological Quality Index (Mine-EQI). Using this index, we applied the Theil–Sen median slope estimator alongside zonal statistics to examine ecological change trajectories across the full study area and three functional zones—the industrial square, haul roads, and active mining area—over the 2015–2025 period. The ecological outcomes attributable to the green mine policy were then quantified. The results show that (1) the mean Mine-EQI of the study area decreased from 0.3713 in 2015 to 0.3460 in 2025, exhibiting a slight overall decline. However, the rate of decline decreased from −6.1% during 2015–2020 to −0.7% during 2020–2025, yielding a Temporal Change Intensity index (TCI) of +88.5%, indicating that the ecological degradation trend has been effectively curbed. (2) Significant spatial heterogeneity was observed. The industrial square showed substantial improvement (Theil–Sen slope = +0.0726), while the haul roads (slope = −0.0705) and mining area (slope = −0.0408) continued to exhibit degradation trends. The improved areas (9.7% of the study area) were spatially coincident with green mine engineering projects. (3) The dust indicator (MECDI) decreased by 24.7% during 2020–2025, and the vegetation index (NDVI) increased by 19.5% over the decade, representing the dominant contributors to ecological improvement. This study reveals that China’s green mine policy has yielded remarkable ecological improvements in relatively stable functional zones such as industrial squares. In contrast, ecological restoration within persistently disturbed areas, including haul roads and mining pits, demands long-term sustained investment and governance. By integrating remote sensing techniques with policy analysis, this research establishes a replicable framework for evaluating progress toward sustainable mining practices. The findings directly support the monitoring of SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), providing a quantitative pathway to balance mineral resource extraction with ecological protection—a core sustainability challenge for resource-dependent regions. Full article

Blue honeysuckle (Lonicera caerulea L.) is subject to environmental stressors, leading to variability in both severe fruit drop during development and fruit quality. Zinc, an essential micronutrient, is critical to sustainable fruit tree production by enhancing yield and nutritional quality. Different forms of zinc fertilizers, particularly nano-zinc versus conventional ionic zinc, exhibit marked differences in absorption efficiency and agronomic performance, thereby determining their practical efficacy. In this two-year study, we evaluated the effects of foliar-applied zinc forms, ZnO nanoparticles (30, 50, and 90 nm) and ionic zinc (ZnCl₂ and ZnSO₄), applied at the young fruit, veraison, and maturity stages on yield and fruit quality. Results showed that ZnO nanoparticles were more effective than ionic zinc at 80 mg/L. In particular, among the ZnO NP treatments, 90 nm ZnO NPs exhibited the best overall effect, significantly improving fruit quality. The 30 nm ZnO NPs treatment performed best in terms of single fruit weight, yield per plant, and fruit firmness. This study highlights the potential of nano-zinc to enhance productivity and quality in blue honeysuckle, providing a theoretical basis for selecting optimal zinc fertilizer types and particle sizes in specialty berry production, with implications for sustainable, high-quality fruit cultivation. Full article

Layered saline soils containing weakly permeable interlayers exhibit restricted infiltration, surface salt accumulation, and limited deep salt discharge. This study investigated how weakly permeable interlayer thickness, hydraulic-parameter scenario, hole diameter, hole spacing, and irrigation salinity affect soil water redistribution, salt leaching, and profile desalination under vertical-hole treatment. Pilot-scale soil-box experiments were used for model calibration and validation, and HYDRUS-3D simulations were then used for controlled-condition scenario analysis and preliminary layout screening. The weakly permeable interlayer reduced hydraulic connectivity, increased water retention above the interface, and intensified surface salt enrichment, with stronger effects at greater thickness. Vertical holes improved hydraulic continuity and promoted downward percolation and salt leaching, but their effectiveness depended on layout. At a spacing of 30 cm, increasing hole diameter from 5 to 10 cm increased the mean desalination rate from 7.07% to 13.44% in the surface layer and from 4.06% to 18.61% in the deep layer. Irrigation salinity had little effect on water content but increased soil salt accumulation. Under the assumed conceptual cost–performance framework, the 10 cm diameter and 30 cm spacing combination showed the highest composite performance within the tested parameter range. These findings provide a mechanistic basis and preliminary layout-screening reference for vertical-hole treatment in layered saline soils with weakly permeable interlayers. Full article

At present, there is still a lack of effective treatments to slow the progression of Parkinson’s disease. Naturally derived active substances, valued for their safety and multi-target potential, have become an important direction in anti-PD drug development, with marine organisms representing a valuable source of bioactive peptides. This study aimed to isolate and identify anti-PD peptides from Rapana venosa protein hydrolysates. Through bioactivity-guided screening combined with an MPTP-induced zebrafish PD model, three novel active peptides—KSTELLI, FLVKLPMFM, and SDSLSEILIS—were successfully identified. The study showed that these peptides significantly alleviated dopaminergic neuron loss, improved the cerebral vascular system, restored motor and sensory function, and alleviated oxidative stress. Molecular docking confirmed their stable binding to key PD targets (DDC, α-synuclein, and MAO-B). Further transcriptomic and gene expression analyses revealed that their neuroprotective effects involve the regulation of pathways related to metabolism, oxidative stress, inflammation, and apoptosis, with the three peptides exhibiting distinct mechanistic emphases. The research demonstrates that these marine-derived peptides exert neuroprotective effects through a synergistic multi-target mechanism, laying a foundation for the development of novel lead compounds against Parkinson’s disease. Full article

To improve the efficiency of comparative process-window screening in selective laser melting (SLM), this study developed a finite element simulation-driven machine learning framework for 316L stainless steel. A simulation dataset covering laser power (LP), scanning speed (SS), heat-source diameter (HSD), and substrate preheating temperature (SPH) was generated using ANSYS and used to train nine regression models. In the present work, the primary machine learning target was defined as the simulated average thermal stress, σ_avg, which is used as a simulation-derived comparative thermal stress indicator for ranking process conditions within the investigated parameter window rather than as a direct prediction of the final residual-stress field. Among the evaluated models, the Backpropagation Neural Network (BPNN) showed the best predictive performance and was selected as the representative surrogate model because of its strong predictive accuracy, stable behavior, and direct applicability to the present structured tabular dataset. Shapley additive explanations (SHAP) and partial dependence plots (PDPs) indicated that LP is the dominant variable governing the σ_avg-based response, followed by SPH, whereas SS and HSD mainly affect the response through secondary or coupled effects. Within the investigated parameter window, conditions near 180–200 W corresponded to a relatively lower predicted σ_avg level. Experimental observations provided limited but meaningful trend-level support for the simulation-guided screening results: metallographic examination showed improved forming quality near 200 W, while XRD-derived macroscopic stress estimates exhibited a similar variation trend to the simulated σ_avg values under the tested LP–SS conditions. These results suggest that the proposed framework can serve as an efficient surrogate-based tool for comparative parameter screening in SLM-fabricated 316L stainless steel within the assumptions and parameter range of the present model. Full article

Considerable debate remains regarding the mechanisms responsible for the reduction in organic matter (OM) pores in post-mature shales. To address this issue, complementary techniques including scanning electron microscopy (SEM), total organic carbon (TOC) analysis, and helium porosity measurement were employed to characterize the microstructure and porosity of post-mature shales from the Qiongzhusi Formation in the southwestern Upper Yangtze region, China. The results show that OM pores in these shales are poorly developed and exhibit highly irregular morphologies. Notably, the degree of OM pore development is negatively correlated with TOC. Interestingly, in samples with TOC < 2.5 wt.%, well-preserved spongy migrated OM is still observable under SEM. The average porosity of Qiongzhusi mudstones is 1.8%; siltstone samples with TOC < 2 wt.% yield an average porosity of 3.5%, whereas samples with TOC > 4 wt.% have an average porosity of only 1.9%. These findings do not support the hypothesis that graphitization causes the significant destruction of OM pores in post-mature shales. Instead, we propose that compaction has been the dominant factor controlling OM pore destruction. Accordingly, we introduce a “depth window” for the development of high-quality shale gas reservoirs: Beyond a certain maximum paleoburial depth, compaction leads to extensive OM pore destruction and a marked decline in reservoir quality. This study advances our understanding of pore evolution in post-mature shales and provides practical guidance for shale gas exploration. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

Quantifying the detection efficiency of buoy-based sonar and optimizing deployment strategies in complex marine environments remain significant challenges. This study proposes a transceiver depth optimization method based on the Bellhop ray model to enhance underwater remote sensing data quality. For the first time, we validated the applicability of acoustic reciprocity in deep-sea environments exceeding 5000 m, characterized by non-uniform sound speed profiles, horizontal inhomogeneity, and steep seamount terrain, with a maximum relative error of <1.2%. This extends the applicable boundaries of the acoustic reciprocity theorem from idealized simple waveguides to complex, realistic deep-sea environments. Building on this validation, we developed a novel, equivalent, superposition modeling framework for bidirectional transmission loss (TL), which converts the computationally intractable TL from target to receiver into the calculable TL from receiver to target, thus significantly reducing computational complexity. Systematic simulations uncovered a depth-layered dependency mechanism: shallow sources (23.14~69.42 m) and deep sources (≥347.10 m) show robustness to large depth differences exceeding 500 m, whereas mid-layer sources (161.98~231.40 m) exhibit a distinct critical threshold effect. Static simulations identify a performance degradation cliff with an onset at an approximate depth difference of 185 m, leading to a 50% reduction in detection range and fragmented near-field detection coverage. To accommodate environmental temporal variability (e.g., internal waves), a conservative safety margin was incorporated, establishing a robust engineering threshold of 150 m. Accordingly, we define 160~350 m as the optimal detection depth window and propose a layered deployment protocol that fills a critical industry gap in quantitative deployment design for deep-sea acoustic detection. Specifically, transceiver depth differences should be strictly constrained to <150 m for mid-layer operations, while more-flexible depth configurations are permissible for shallow and deep sources. These findings furnish quantitative engineering criteria for the design of reliable underwater remote sensing networks, while balancing long-range detection stability and near-field coverage integrity. Full article