Search Results (55)

Accurate estimation of forest carbon storage is fundamental to climate change mitigation. However, the highly heterogeneous site conditions, together with the biological limitations of conventional linear and polynomial models, have posed considerable challenges for estimating carbon accumulation in voluntary carbon reduction projects. Based on data from 1190 permanent sample plots across Taiwan, this study developed a site-class-based carbon storage prediction model for coniferous and broad-leaved plantations in Taiwan by using the nonlinear Chapman–Richards growth function. The estimated model parameters indicated distinct carbon accumulation patterns for the two forest types. Coniferous plantations exhibited a higher model-estimated asymptotic carbon storage (1232.506 Mg CO₂ ha⁻¹), whereas broad-leaved plantations accumulated carbon more rapidly during the early stages of stand development. Because a single base model may produce substantial prediction errors when applied over large spatial scales, the stands were classified into high-, medium-, and low-site classes by using a reference age of 30 years. Site classification reduced the out-of-sample root mean square error by 56.8% for coniferous and 50.8% for broad-leaved plantations under nested cross-validation. The resulting site class models may provide useful quantitative tools for establishing baseline and project scenarios in voluntary forest carbon reduction projects. Full article

Reliability prediction of aerospace dormant components requires fusing natural-storage observations at the operating temperature with accelerated-storage testing data at elevated temperatures. Existing scalar-weight fusion methods apply a global weight that cannot reflect the time-varying trustworthiness of the accelerated branch as Arrhenius extrapolation distance grows. Physics-based fusion propagates accelerated-test scatter through least squares but leaves the dominant error source—the degradation-model form itself—unaccounted for, and no method in either class verifies the coverage of its intervals. This paper proposes an empirically calibrated multi-fidelity fusion that selects a mechanism-specific natural-branch degradation model by the corrected Akaike information criterion and augments the accelerated-branch variance with an additive model-form term fitted from natural-storage residuals. This term turns the fusion weight into a continuous, time-varying diagnostic that detects Arrhenius misspecification from training data alone and falls back safely to the natural-only estimate. Prediction intervals are calibrated by split-conformal prediction on a disjoint simulated population, giving finite-sample, distribution-free coverage, and the remaining-storage-life interval follows from the band’s first-passage time. On a 1000-run varying-truth simulation, the calibrated band attains 95.5% trajectory coverage at the narrowest band width among six methods; on the torsion-bar case, the fusion reaches a held-out RMSE of 0.045 N$·$m and a remaining-life interval of 10.4–12.6 years. The model-form variance ratio provides a single-number regime diagnostic across all cases. Full article

Lightweight alloys in aerospace precision structures undergo slow but cumulative creep deformation during long-term storage, wherein strain accumulation over years can compromise dimensional stability and operational reliability. However, continuum damage mechanics (CDM) constitutive models, while physically grounded, require extensive parameter calibration and exhibit degraded accuracy during the primary creep stage. Meanwhile, purely data-driven approaches are impractical for the sparse datasets typical of accelerated creep testing, wherein as few as 14 data points may be available per condition. Although physics-informed neural networks (PINNs) have shown promise in computational mechanics, existing PINN-based creep studies predict only scalar life quantities rather than the full strain–time curve $\epsilon \left(t\right)$, and none embed damage evolution equations as differential constraints. This study proposes a damage-coupled PINN framework (termed DC-PINN) that predicts the complete creep strain evolution $\epsilon \left(t\right)$ by embedding CDM damage evolution ordinary differential equations (ODEs) as hierarchical differential constraints within the learning process. The framework couples the predicted strain rate $\mathrm{d}\epsilon /\mathrm{d}t$ with the damage state $D\left(t\right)$ through material-specific constitutive ODEs, supplemented by monotonicity enforcement and boundary conditions. Alloy-specific formulations are developed for 2A12-T4 aluminum (Arrhenius kinetics, no damage) and ZM6 magnesium (Sandström dislocation model with Ostwald-ripening-driven grain coarsening damage). Validated on 13 experimental conditions spanning both alloys (50–100 °C, 20–60 MPa, 14–100 points per condition), DC-PINN achieves $R^2>0.99$ for 2A12-T4 and $R^2>0.97$ for ZM6 across all tested conditions. Ablation studies show that the total physics-driven $R^2$ improvement is 5.8 times larger for the data-sparse ZM6 (14–34 points) than for the data-rich 2A12-T4 (∼100 points), with the CDM damage coupling alone accounting for 22% of the improvement in ZM6. To the best of our knowledge, this represents the first integration of CDM damage evolution ODEs as differential constraints within PINNs for creep strain modeling, providing a physically consistent and data-efficient tool for the storage life assessment of aerospace structures. Full article

HIV-1 integrase strand transfer inhibitors (INSTIs) are pivotal to antiretroviral therapy. However, the emergence of drug-resistant mutations necessitates the development of new agents. Here, we present ACC017 as a novel INSTI candidate. ACC017 demonstrated potent activity against the laboratory-adapted HIV-1_IIIB strain (EC₅₀ = 0.59 nM; SI > 34,525) and maintained efficacy against a panel of drug-resistant strains (EC₅₀ range from 0.34 to 9.12 nM) and clinical isolated strains (EC₅₀ range from 0.11 to 1.78 nM). Mechanism of action studies confirmed its ability to inhibit the integrase enzyme (IC₅₀ = 9.19 nM) and effectively block viral genome integration. Notably, in vitro resistance selection primarily yielded D232N and R263K mutations, without the emergence of G140S/A/C/R or Q148H/R/K. This promising profile, combined with synergistic interactions with other antiretroviral drugs, positions ACC017 as a potential therapeutic option. Full article

In order to study the problem of obvious wall thinning in the wellbore caused by proppant backflow and sand production under throttling conditions in tight gas wells. Based on the gas-phase control equation, particle motion equation, and erosion model, the wellbore erosion model is established. The distribution law of pressure, temperature, and velocity trace fields under throttling conditions is analyzed, and the influences of different throttling pressures, particle diameters, and particle mass flows on wellbore erosion are analyzed. The flow field at the nozzle changes drastically, and there is an obvious pressure drop, temperature drop, and velocity rise. When the surrounding gas is completely mixed, the physical quantity gradually stabilizes. The erosion shape of the wellbore outlet wall has a point-like distribution. The closer to the throttle valve outlet, the more intense the erosion point distribution is. Increasing the inlet pressure and particle mass flow rate will increase the maximum erosion rate, and increasing the particle diameter will reduce the maximum erosion rate. The particle mass flow rate has the greatest impact on the maximum erosion rate, followed by the particle diameter. The erosion trend was predicted using multiple regression model fitting of the linear interaction term. The research results can provide a reference for the application of downhole throttling technology and wellbore integrity in tight gas exploitation. Full article

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with unmet therapeutic needs. This study investigates the therapeutic potential of Periplaneta americana L. extract (PAE) and its molecular mechanisms, integrating network pharmacology and experimental validation. Liquid chromatography–mass spectrometry identified 1355 compounds in PAE. Network pharmacology analysis revealed that inosine, vidarabine, and adenosine 5′-monophosphate (AMP) were core components and the core components synergistically regulated key targets and acted on inflammation-related pathways, thereby establishing a multi-target anti-inflammatory regulatory network. In vivo experiments demonstrated that these compounds significantly alleviated colitis symptoms in dextran sulfate sodium-induced mice, as evidenced by reduced disease activity index scores, preserved colonic mucosal architecture, and decreased inflammatory infiltration. Mechanistically, core compounds down-regulated granulocyte-macrophage colony-stimulating factor (GM-CSF), inducible nitric oxide synthase (iNOS)/NOS2, monocyte chemoattractant protein 1 (MCP-1), and transforming growth factor beta 1 (TGF-β1), while they up-regulated interleukin-10 (IL-10) and epidermal growth factor (EGF). Additionally, they activated epidermal growth factor receptor (EGFR)-mediated pathways. Molecular docking analysis revealed that adenosine analogs preferentially bound to A1/A2a receptors, triggering signaling cascades essential for epithelial repair and inflammation resolution. This study established the multi-component, multi-pathway mechanism of PAE in UC, highlighting its dual role in suppressing inflammation and promoting mucosal healing. By bridging traditional herbal use with modern molecular insights, these findings provided a translational foundation for developing PAE-based therapies for UC. Full article

This study investigates the environmental significance of ciprofloxacin as an emerging contaminant and the need for effective degradation methods. The chemical coprecipitation method was used in this study to prepare the Zn-Cu-Ni composite silicate, serving as a heterogeneous ozonation catalyst. The catalytic activity was then evaluated by degrading ciprofloxacin (CIP). Scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, nitrogen adsorption–desorption, and Fourier transform infrared analysis (FTIR) were used to characterize the Zn-Cu-Ni composite silicate. The catalyst had a high surface area (308.137 m²/g), no regular morphology, and a particle size of 7.6 µm and contained Si-O-Si, Ni-O-Si, and Zn-O-Si. The results showed that the CIP degradation and mineralization rates (pH 7.0, CIP 3.0 mg/L, Ozone 1.5 mg/L) were significantly enhanced in the presence of the Zn-Cu-Ni composite silicate. The CIP and total organic carbon (TOC) removal rates were increased by 51.09% and 18.72%, respectively, under optimal conditions, compared with ozonation alone. The adsorption of Zn-Cu-Ni composite silicate, ozone oxidation, and ·OH oxidation synergistically promoted the efficient removal of CIP. This study provides valuable catalytic ozone technology for degradation of antibiotics in wastewater to reduce environmental pollution with potential practical applications. Full article

Green tea catechins (GTCs) are a group of bioactive polyphenolic compounds found in fresh tea leaves (Camellia sinensis (L.) O. Kuntze). They have garnered significant attention due to their diverse health benefits and potential therapeutic applications, including as antioxidant and sunscreen agents. Human skin serves as the primary barrier against various external aggressors, including pathogens, pollutants, and harmful ultraviolet radiation (UVR). Skin aging is a complex biological process influenced by intrinsic factors such as genetics and hormonal changes, as well as extrinsic factors like environmental stressors, among which UVR plays a pivotal role in accelerating skin aging and contributing to various dermatological conditions. Research has demonstrated that GTCs possess potent antioxidant properties that help neutralize free radicals generated by oxidative stress. This action not only mitigates cellular damage but also supports the repair mechanisms inherent in human skin. Furthermore, GTCs exhibit anti-carcinogenic effects by inhibiting pathways involved in tumor promotion and progression. GTCs have been shown to exert anti-inflammatory effects through modulation of inflammatory signaling pathways. Chronic inflammation is known to contribute significantly to both premature aging and various dermatological diseases such as psoriasis or eczema. By regulating these pathways effectively, GTCs may alleviate symptoms associated with inflammatory conditions. GTCs can enhance wound healing processes by stimulating angiogenesis. They also facilitate DNA repair mechanisms within dermal fibroblasts exposed to damaging agents. The photoprotective properties attributed to GTCs further underscore their relevance in skincare formulations aimed at preventing sun-induced damage. Their ability to screen UV light helps shield underlying tissues from harmful rays. This review paper aims to comprehensively examine the beneficial effects of GTCs on skin health through an analysis encompassing in vivo and in vitro studies alongside insights into molecular mechanisms underpinning these effects. Such knowledge could pave the way for the development of innovative strategies focused on harnessing natural compounds like GTCs for improved skincare solutions tailored to combat environmental stresses faced by the human epidermis. Full article

In recent years, the remediation of heavy metal-contaminated soils has attracted great attention worldwide. Previous research on the removal of toxic heavy metals from wastewater effluents through adsorption by typical solid wastes (e.g., fly ash and coal gangue) has mainly focused on the control of wastewater pollutants. In this study, a coal gangue (CG) by-product from Hancheng City was used as a raw material to prepare polymeric aluminum chloride-loaded coal gangue-based porous carbon (PAC-CGPC) by hydrothermal synthesis. This material was subsequently employed to assess its performance in mitigating Pb²⁺ in soils. In addition, the effects of the pore structure of the prepared material on the adsorption rates, adsorption mechanisms, and plant root uptakes of soil Pb²⁺ were investigated in this study. The raw CG and prepared PAC-CGPC materials exhibited specific surface areas of 1.8997 and 152.7892 m²/g, respectively. The results of adsorption kinetics and isotherms indicate that the adsorption of Pb²⁺ based on PAC-CGPC mainly follows a pseudo-second-order kinetic model, suggesting that chemisorption may be the dominant process. In addition, the adsorption isotherm results showed that the Freundlich model explained better the adsorption process of Pb²⁺, suggests that the adsorption sites of lead ions on APC-CGPC are not uniformly distributed and tend to be enriched in APC, and also shows the ion exchange between aluminum and lead ions. The thermodynamic model fitting results demonstrated the occurrence of spontaneous and exothermic PAC-CGPC-based adsorption of Pb²⁺, involving ion exchange and surface complexation. The effects of the PAC-CGPC addition on soybean plants were further explored through pot experiments. The results revealed substantial decreases in the Pb²⁺ contents in the soybean organs (roots, stems, and leaves) following the addition of the PAC-CGPC material at a dose of 3% compared with the control and raw CG groups. Furthermore, the addition of the PAC-CGPC material at a dose of 3% effectively reduced the bioavailable Pb²⁺ content in the soil by 82.11 and enhanced soybean growth by 15.3%. These findings demonstrated the inhibition effect of the PAC-CGPC material on the translocation of Pb²⁺ in the soybean seedlings. The modified CG adsorbent has highly pore structure and good hydrophilicity, making it prone to migration in unsaturated soils and, consequently, enhancing Pb²⁺ immobilization. This research provides theoretical support for the development of CG-based materials capable of immobilizing soil pollutants. Full article

A predictive model utilizing near-infrared spectroscopy was developed to estimate the loss on drying, total contents of crocin I and crocin II, and picrocrocin content of saffron. Initially, the LD values were determined using a moisture-ash analyzer, while HPLC was employed for measuring the total contents of crocin I, crocin II, and picrocrocin. The near-infrared spectra of 928 saffron samples were collected and preprocessed using first derivative, standard normal variable transformation, detrended correction, multivariate scattering correction, Savitzky–Golay smoothing, and mean centering methods. Leveraging the partial least squares method, regression models were constructed, with parameters optimized through a selective combination of the above six preprocessing methods. Subsequently, prediction models for loss on drying, total contents of crocin I and crocin II, and picrocrocin content were established, and the prediction accuracy of the models was verified. The correlation coefficients and root mean square error of loss on drying, total contents of crocin I and crocin II, and picrocrocin content demonstrated high accuracy, with R² values of 0.8627, 0.8851, and 0.8592 and root mean square error values of 0.0260, 0.0682, and 0.0465. This near-infrared prediction model established in the present study offers a precise and efficient means of assessing loss on drying, total contents of crocin I and crocin II, and picrocrocin content in saffron and is useful for the development of a rapid quality evaluation system. Full article

With the rapid development of anion exchange membrane technology and the availability of high-performance non-noble metal cathode catalysts in alkaline media, the commercialization of anion exchange membrane fuel cells has become feasible. Currently, anode materials for alkaline anion-exchange membrane fuel cells still rely on platinum-based catalysts, posing a challenge to the development of efficient low-Pt or Pt-free catalysts. Low-cost ruthenium-based anodes are being considered as alternatives to platinum. However, they still suffer from stability issues and strong oxophilicity. Here, we employ a metal–organic framework compound as a template to construct three-dimensional porous ruthenium–tungsten–zinc nanocages via solvothermal and high-temperature pyrolysis methods. The experimental results demonstrate that this porous ruthenium–tungsten–zinc nanocage with an electrochemical surface area of 116 m² g⁻¹ exhibits excellent catalytic activity for hydrogen oxidation reaction in alkali, with a kinetic density 1.82 times and a mass activity 8.18 times higher than that of commercial Pt/C, and a good catalytic stability, showing no obvious degradation of the current density after continuous operation for 10,000 s. These findings suggest that the developed catalyst holds promise for use in alkaline anion-exchange membrane fuel cells. Full article

We evaluated the drying characteristics and structure, as well as the physicochemical and flavor properties, of G. elata treated by hot-air drying (HAD), vacuum drying (VD), freeze drying (FD), microwave drying (MD), and microwave vacuum drying (MVD). We found that MD and MVD showed the shortest drying times, while FD and MVD were able to better retain the active ingredients and color of the samples. However, the different drying methods did not change the internal structure of G. elata, and its main components did not fundamentally change. In addition, E-nose and HS-SPME-GC-MS effectively differentiated the volatile components, and 36 compounds were detected by HS-SPME-GC-MS. Of these samples, alcohols and aldehydes were the main substances identified. In particular, MVD samples possessed the most species of organic volatiles, but the FD method effectively eliminated pungent odors from the G. elata. Overall, MVD shows the most obvious advantages, improving drying rate while maintaining the original shape, color, and active components in G. elata. Ultimately, MVD is the preferred method to obtain high-quality dried G. elata, and our drying-method characterizations can be used to investigate similar structural and chemical changes to similar herbs in the future. Full article

Studying how objects are positioned is vital for improving technologies like robots, cameras, and virtual reality. In our earlier papers, we introduced a bio-inspired artificial visual system for orientation detection, demonstrating its superiority over traditional systems with higher recognition rates, greater biological resemblance, and increased resistance to noise. In this paper, we propose a hardware-based orientation detection system (ODS). The ODS is implemented by a multiple dendritic neuron model (DNM), and a neuronal pruning scheme for the DNM is proposed. After performing the neuronal pruning, only the synapses in the direct and inverse connections states are retained. The former can be realized by a comparator, and the latter can be replaced by a combination of a comparator and a logic NOT gate. For the dendritic function, the connection of synapses on dendrites can be realized with logic AND gates. Then, the output of the neuron is equivalent to a logic OR gate. Compared with other machine learning methods, this logic circuit circumvents floating-point arithmetic and therefore requires very little computing resources to perform complex classification. Furthermore, the ODS can be designed based on experience, so no learning process is required. The superiority of ODS is verified by experiments on binary, grayscale, and color image datasets. The ability to process data rapidly owing to advantages such as parallel computation and simple hardware implementation allows the ODS to be desirable in the era of big data. It is worth mentioning that the experimental results are corroborated with anatomical, physiological, and neuroscientific studies, which may provide us with a new insight for understanding the complex functions in the human brain. Full article

China is experiencing unprecedented urbanization and aging. Previous studies mostly ignored the internal mechanism of the effect of urban green space on the mental health of older adults. Consequently, the relationship between social participation in urban green spaces and mental health remains uncertain. Therefore, this study explored the impact of urban green spaces, social participation, and other factors on the mental health of older adults and investigated the mechanisms of these effects. This study used linear regression models and conducted a moderating effect analysis using data from the 2018 China Labor Dynamics Survey, comprising 3501 older adults in 146 cities in China. Furthermore, we analyzed differences between solitary and non-solitary older adults. The results indicated that urban green space, road density, physical health, history of hospitalization, subjective well-being, and economic satisfaction significantly affected mental health. Social participation played a significant positive moderating role in the connection between green spaces and mental health among older adults. For solitary older adults, social participation weakened the positive impact of green spaces on mental health; for non-solitary older adults, social participation enhanced the positive impact of green spaces on mental health. These findings could contribute to the future construction of aging-friendly cities in China and help optimize urban construction and strategies for building healthy environments. Full article

Drones have been increasingly used in firefighting to improve the response speed and reduce the dangers to human firefighters. However, few studies simultaneously consider fire spread prediction, drone scheduling, and the configuration of supporting staff and supplies. This paper presents a mathematical model that estimates wildfire spread and economic losses simultaneously. The model can also help us to determine the minimum number of firefighting drones in preparation for wildfire in a given wild area. Next, given a limited number of firefighting drones, we propose a method for scheduling the drones in response to wildfire occurrence to minimize the expected loss using metaheuristic optimization. We demonstrate the performance advantages of water wave optimization over a set of other metaheuristic optimization algorithms on 72 test instances simulated on selected suburb areas of Hangzhou, China. Based on the optimization results, we can pre-define a comprehensive plan of scheduling firefighting drone and configuring support staff in response to a set of scenarios of wildfire occurrences, significantly improving the emergency response efficiency and reducing the potential losses. Full article