Search Results (401)

Soil salinization severely limits plant growth and productivity. Mulberry (Morus alba L.), an economically and ecologically important tree, is widely cultivated, yet its salt-tolerance mechanisms at the seedling stage remain insufficiently understood. This study investigated the physiological and biochemical responses of two-year-old mulberry (‘Tailai Sang’) seedlings subjected to six NaCl treatments (0, 50, 100, 150, 200, and 300 mmol L⁻¹) for 28 days. Results showed that growth parameters and photosynthetic gas exchange exhibited dose-dependent declines. The reduction in net photosynthetic rate (Pn) was attributed to both stomatal limitations (decreased stomatal conductance) and non-stomatal limitations, as evidenced by a significant decrease in the maximum quantum efficiency of photosystem II (Fv/Fm) under high salinity. To cope with osmotic stress, seedlings accumulated compatible solutes, including soluble sugars, proteins, and proline. Critically, mulberry seedlings demonstrated effective ion homeostasis by sequestering Na⁺ in the roots to maintain a high K⁺/Na⁺ ratio in leaves, a mechanism that was compromised above 150 mmol L⁻¹. Concurrently, indicators of oxidative stress—malondialdehyde (MDA) and H₂O₂—rose significantly with salinity, inducing the activities of antioxidant enzymes (SOD, CAT, APX, and GR), which peaked at 150 mmol L⁻¹ before declining under extreme stress. A biomass-based LC₅₀ of 179 mmol L⁻¹ NaCl was determined. These findings elucidate that mulberry salt tolerance is a coordinated process involving three key mechanisms: osmotic adjustment, selective ion distribution, and a robust antioxidant defense system. This study establishes an indicative tolerance threshold under controlled conditions and provides a physiological basis for further field-based evaluations of ‘Tailai Sang’ mulberry for cultivation on saline soils. Full article

A novel macrodesign for a dental implant characterized by a non-monotonic variation in core diameter and thread shape has been described to produce lower stress levels during insertion as compared to conventional tapered implants. Two finite element models resembling the lower left molar region with preformed osteotomies were created based on a cone beam computed tomography (CBCT) scan. Insertion of both the novel and the conventional, tapered implant type were simulated using Standard for the Exchange of Product model data (STEP) files of both implant types. Von Mises equivalent stress, strain development, and amount of redistributed bone were recorded. The conventional implant demonstrated a continuous increase in strain values and reaction moment throughout the insertion process, with a brief decrease observed during the final stages. Stress levels in the cortical bone gradually increased, followed by a reduction when the implant was finally positioned subcrestally. The novel implant achieved the maximum magnitude of reaction moment and cortical bone strain values when the implant’s maximum core diameter passed the cortical bone layer at around 60% of the insertion process. Following a notable decrease, both the reaction moment and stress started to rise again as the implant penetrated further. The novel implant removed more bones in the trabecular region while the conventional implant predominantly interacted with cortical bone. Overall, the novel design seems to be less traumatic to alveolar bone during the insertion process and hence may lead to reduced levels of initial peri-implant bone loss. Full article

Introduction: Two-stage revision with an antibiotic-loaded, temporary static cement spacer is a common treatment for periprosthetic joint infection (PJI) of the knee. However, limited data exists on in vivo antibiotic elution kinetics after spacer implantation. This pilot study uses the technique of microdialysis (MD) to collect intra-articular knee samples. The aim was to evaluate MD as an intra-articular sampling method to detect spacer-eluted antibiotics within 72 h after surgery and to determine whether they show specific elution kinetics. Methods: Ten patients (six male, four female; age median 71.5 years) undergoing two-stage revision for knee PJI were included. A MD catheter was inserted into the joint during explantation of the infected inlying implant and implantation of a custom-made static spacer coated with COPAL cement (0.5 g gentamicin (G) and 2 g vancomycin (V)). Over 72 h postoperatively, samples were collected and analyzed for spacer-eluted antibiotics, intravenously administered antibiotics (e.g., cefazolin and cefuroxime), metabolic markers (glucose and lactate), and Interleukin-6 (IL-6). Local and systemic levels were compared. Results: All catheters were positioned successfully and well tolerated for 72 h. Antibiotic concentrations in MD samples peaked within the first 24 h (G: median 9.55 µg/mL [95% CI: 0.4–17.36]; V: 37.57 µg/mL [95% CI: 3.26–81.6]) and decreased significantly over 72 h (for both p < 0.05, G: 4.27 µg/mL [95% CI: 2.26–7.2]; V: 9.69 µg/mL [95% CI: 3.86–24]). MD concentrations consistently exceeded blood levels (p < 0.05), while intravenously administered antibiotics showed higher blood concentrations. Glucose in MD samples decreased from 17.71 mg/dL to 0.89 mg/dL (p < 0.05). IL-6 and lactate concentrations showed no difference between MD and blood samples. Conclusions: Monitoring antibiotics eluted by a static spacer with intra-articular MD for 72 h is feasible. Gentamicin and vancomycin levels remained above the minimal inhibitory concentration. Differentiating infection from surgical response using metabolic and immunological markers remains challenging. Prolonged in vivo studies with MD are required to evaluate extended antibiotic release in two-stage exchanges. Full article

This work proposes a systematic study of different computational schemes for fluorine Nuclear Magnetic Resonance (¹⁹F NMR) chemical shifts, with special emphasis placed on the basis set issue. This study encompasses two stages of calculation, namely, the development of the computational schemes for the geometry optimization of fluorine compounds and the NMR chemical shift calculations. In both stages, the performance of different density functional theory functionals is considered against the method of coupled-cluster singles and doubles (CCSD), with the latter representing a theoretical reference in this work. This exchange-correlation functional study is accompanied with a basis set study in both stages of calculation. Basis sets of different families, sizes, and valence-splitting levels are considered. Various locally dense basis sets (LDBSs) are proposed for the calculation of ¹⁹F NMR chemical shifts, and their performance is assessed by comparison of the calculated chemical shifts with both theoretical and experimental reference data. Overall, the pcS-3/pcS-2 LDBS scheme is recommended as the most balanced locally dense basis set scheme for fluorine chemical shift calculations. Full article

Dendrobium, a prominent genus in the Orchidaceae family, has generated significant research attention due to its demonstrated biological potential, particularly its notable anti-inflammatory and antioxidant activities. In this study, two fractions of fermented Dendrobium officinale polysaccharides (FDOPs) were successfully isolated through a multi-stage purification strategy including gradient ethanol precipitation, gel column chromatography, and ion exchange chromatography with Lactobacillus reuteri CCFM863. Structural characterization revealed that both Dendrobium officinale polysaccharide fractions consisted of (1→4)-β-D-Manp, (1→4)-β-D-Glcp, and (1→4)-α-D-Glcp residues. The anti-inflammatory efficacy and keratinocyte-protective potential of FDOPs (FDOP-1A and FDOP-2A) were investigated by using lipopolysaccharide (LPS)-induced RAW264.7 and HaCaT cells models, which showed significant inhibitions on the inflammatory factors of monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-alpha (TNF-α), nitric oxide (NO), and interleukin-1 beta (IL-1β); recovered levels of filaggrin (FLG), aquaporin 3 (AQP3), transient receptor potential vanilloid 4 (TRPV4), cathelicidin antimicrobial peptide (CAMP)/LL-37, and adiponectin (ADIPOQ); and the reduced protein expression of the TLR4/IκB-α/NF-κB/NLRP3 pathway. Notably, the FDOPs exhibited a remarkable reactive oxygen species (ROS) scavenging capacity, demonstrating superior antioxidant activity. Therefore, FDOPs show dual anti-inflammatory and antioxidant properties, making them suitable as active ingredients for modulating epidermal inflammation and promoting skin barrier repair. Full article

A recent high-throughput screening of the NatureBank marine extract library (7616 samples) identified an extract from the Australian marine sponge Agelas axifera with in vitro activity against an economically important parasitic nematode, Haemonchus contortus (barber’s pole worm). The bioassay-guided fractionation of the CH₂Cl₂/MeOH extract from A. axifera led to the purification of a new diterpene alkaloid, agelasine Z (1), together with two known compounds agelasine B (2) and oxoagelasine B (3). Brominated compounds (–)-mukanadin C (4) and 4-bromopyrrole-2-carboxylic acid (5) were also isolated from neighbouring UV-active fractions. All compounds, together with agelasine D (6) from NatureBank’s pure compound library, were tested for in vitro anthelmintic activity against exsheathed third-stage (xL3s) and fourth-stage larvae (L4s) of H. contortus and young adult Caenorhabditis elegans. Compounds 1, 2 and 6 induced an abnormal “skinny” phenotype, while compounds 2 and 6 also reduced the motility of H. contortus L4s by 50.5% and 51.8% at 100 µM, respectively. The minimal activity of agelasines against C. elegans young adults suggests a possible species-specific mechanism warranting further investigation. For the first time, the unexpected lability of agelasine H-8′ was explored using kinetic studies, revealing rapid deuterium exchange in MeOH-d₄ at room temperature. Full article

The rapid development of online media has significantly facilitated the public’s information consumption, knowledge acquisition, and opinion exchange. However, it has also led to more violent conflicts in online discussions. Therefore, controversy detection becomes important for computational and social sciences. Previous research on detection methods has primarily focused on larger datasets and more complex computational models but has rarely examined the underlying mechanisms of conflict, particularly the psychological motivations behind them. In this paper, we propose a lightweight and language-independent method for controversy detection by introducing two novel psychological features: ascending gradient (AG) and tier ascending gradient (TAG). These features capture psychological signals in user interactions—specifically, the patterns where controversial comments generate disproportionate replies or replies outperform parent comments in likes. We develop these features based on the theory of the backfire effect in ideological conflict and demonstrate their consistent effectiveness across models and platforms. Compared with structural, interaction, and text-based features, AG and TAG show higher importance scores and better generalizability. Extensive experiments on Chinese and English platforms (Reddit, Toutiao, and Sina) confirm the robustness of our features across languages and algorithms. Moreover, the features exhibit strong performance even when applied to early-stage data or limited “one-page” scenarios, supporting their utility for early controversy detection. Our work highlights a new psychological perspective on conflict behavior in online discussions and bridges behavioral patterns and computational modeling. Full article

Neural Architecture Search (NAS) has made significant advancements in autonomously constructing high-performance network architectures, capturing extensive attention. However, a key challenge of existing NAS approaches is the intensive performance evaluation, leading to significant time and computational resource consumption. In this paper, we propose an efficient Evolutionary Neural Architecture Search (ENAS) method to address this issue. Specifically, in order to accelerate the convergence speed of the algorithm and shorten the search time, thereby avoiding blind searching in the early stages of the algorithm, we drew on the principles of biometrics to redesign the interaction between individuals in the evolutionary algorithm. By making full use of the information carried by individuals, we promoted information exchange and optimization between individuals and their neighbors, thereby improving local search capabilities while maintaining global search capabilities. Furthermore, to accelerate the evaluation process and minimize computational resource consumption, a multi-metric training-free evaluator is introduced to assess network performance, bypassing the resource-intensive training phase, and the adopted multi-metric combination method further solves the ranking offset problem. To evaluate the performance of the proposed method, we conduct experiments on two widely adopted benchmarks, NAS-Bench-101 and NAS-Bench-201. Comparative analysis with state-of-the-art algorithms shows that our proposed method identifies network architectures with comparable or better performance while requiring significantly less time. Full article

Time series forecasting is critical for decision-making in numerous domains, yet achieving high accuracy across both short-term and long-term horizons remains challenging. In this paper, we propose a general hybrid forecasting framework that integrates a traditional statistical model (ARIMA) with modern deep learning models (such as LSTM and Transformer). The core of our approach is a novel multi-scale prediction mechanism that combines the strengths of both model types to better capture short-range patterns and long-range dependencies. We design a dual-stage forecasting process, where a classical time series component first models transparent linear trends and seasonal patterns, and a deep neural network then learns complex nonlinear residuals and long-term contexts. The two outputs are fused through an adaptive mechanism to produce the final prediction. We evaluate the proposed framework on eight public datasets (electricity, exchange rate, weather, traffic, illness, ETTh1/2, and ETTm1/2) covering diverse domains and scales. The experimental results show that our hybrid method consistently outperforms stand-alone models (ARIMA, LSTM, and Transformer) and recent, specialized forecasters (Informer and Autoformer) in both short-horizon and long-horizon forecasts. An ablation study further demonstrates the contribution of each module in the framework. The proposed approach not only achieves state-of-the-art accuracy across varied time series but also offers improved interpretability and robustness, suggesting a promising direction for combining statistical and deep learning techniques in time series forecasting. Full article

As participants in sustainable development, corporations face the important and controversial issue of whether they can promote corporate sustainability through environmental, social, and governance (ESG) practices. To address this issue, we examine the relationship between ESG performance and corporate sustainability, measured by green total factor productivity (GTFP). Using a panel dataset of 17,559 firm-year observations from non-financial firms listed on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2019, we employ fixed-effects regression models and two-stage least squares (2SLS) with instrumental variables to empirically test the impact of ESG ratings on GTFP, identify the underlying mechanisms, and examine potential heterogeneity across firms. The results show that higher ESG ratings are significantly associated with increased GTFP. Mediation analysis further reveals that this positive relationship operates through reduced financing constraints and enhanced green innovation. Notably, the mediating role of financing constraints is more pronounced for firms with greater reliance on external capital. Heterogeneity analysis indicates that ESG ratings exert stronger effects in eastern regions, pollution-intensive sectors, and state-owned enterprises. These findings provide empirical support for the role of ESG performance as an effective mechanism to advance corporate sustainability through ethics-driven financial access and innovation capability. Full article

Drought stress severely limits agricultural productivity, with atmospheric and soil water deficits often occurring simultaneously in field conditions. While plant responses to individual drought factors are well-documented, recovery mechanisms following combined atmospheric–soil drought remain poorly understood, hindering drought resistance strategies and irrigation optimization. We set up two VPD treatments (low and high vapor pressure deficit) and two soil moisture treatments (CK: control soil moisture with sufficient irrigation, 85–95% field capacity; drought: soil moisture with deficit irrigation, 50–60% field capacity) in the pot experiment. We investigated wheat’s hydraulic transport (leaf hydraulic conductance, K_leaf) and gas exchange (stomatal conductance, g_s; photosynthetic rate, A_n) responses to combined drought stress from atmospheric and soil conditions at the heading stage, as well as rewatering 55 days after treatment initiation. The results revealed that: (1) high VPD and soil drought significantly reduced leaf hydraulic conductance (K_leaf), with a high VPD decreasing K_leaf by 31.6% and soil drought reducing K_leaf by 33.2%; The high VPD decreased stomatal conductance (g_s) by 43.6% but the photosynthetic rate (A_n) by only 12.3%; (2) After rewatering, g_s and A_n of atmospheric and soil drought recovered relatively rapidly, while K_leaf did not; (3) Atmospheric and soil drought stress led to adaptive changes in wheat’s stomatal regulation strategies, with an increasing severity of drought stress characterized by a shift from non-conservative to conservative water regulation behavior. These findings elucidate wheat’s hydraulic–stomatal coordination mechanisms under drought stress and their differential recovery patterns, providing theoretical foundation for improved irrigation management practices. Full article

This study examines how leading-edge inclination angles affect a two-stage centrifugal compressor’s aerodynamic performance using numerical and experimental methods. Five impellers with varied inclination configurations were designed for both stages. The results show that negative inclination improves the pressure ratio and efficiency under near-choke conditions, with greater enhancements in the low-pressure stage. Positive inclination significantly boosts the pressure ratio and efficiency under near-stall conditions, particularly in the low-pressure stage. Negative inclinations optimize blade loading and choke flow capacity, while effectively reducing incidence angle deviations induced by interstage pipeline distortion and decreasing outlet pressure fluctuation amplitude in the high-pressure stage. Positive inclinations delay flow separation, suppress tip leakage vortices, and extend the stall margin. Full article

In this work the use of an experimental setup for studying the network security level of power electronic devices is proposed. A methodology which represents the individual stages of the studying process of the network security level of power electronic devices is also developed. To prove the applicability of the developed experimental setup, two power distribution units were studied. The experimental setup is simple and easy to implement. It uses well known tools for network security analysis and traffic monitoring. From the experimental studies carried out, it was found that the two power distribution units have similar network vulnerabilities and the exchange of the information is unsecure. From the penetration tests carried-out, expected results were found, but unexpected ones were also found. The experimental results prove the applicability of the developed experimental setup for studying the network security level of power electronic devices. Full article

Weather derivative markets, particularly Chicago Mercantile Exchange (CME) Heating Degree Day (HDD) and Cooling Degree Day (CDD) futures, face challenges from complex temperature dynamics and spatially heterogeneous co-extremes that standard Gaussian models overlook. Using daily data from 13 major U.S. cities (2014–2024), we first construct a two-stage baseline model to extract standardized residuals isolating stochastic temperature deviations. We then estimate the Extreme Value Index (EVI) of HDD/CDD residuals, finding that the nonlinear degree-day transformation amplifies univariate tail risk, notably for warm-winter HDD events in northern cities. To assess multivariate extremes, we compute Tail Dependence Coefficient (TDC), revealing pronounced, geographically clustered tail dependence among HDD residuals and weaker dependence for CDD. Finally, we compare Gaussian, Student’s t, and Regular Vine Copula (R-Vine) copulas via joint VaR–ES backtesting. The R-Vine copula reproduces HDD portfolio tail risk, whereas elliptical copulas misestimate portfolio losses. These findings highlight the necessity of flexible dependence models, particularly R-Vine, to set margins, allocate capital, and hedge effectively in weather derivative markets. Full article

Sugar beet is a nitrogen (N)-sensitive crop, and its N regulation and utilization are critical for enhancing productivity. Sugar beet seedlings at the two-true-leaf-pair stage were hydroponically grown in an artificial climate chamber. Leaves and roots from three seedlings per treatment were sampled at 10, 20, 25, and 30 days after exposure to N treatments (N5: 5 mmol/L, N10: 10 mmol/L, N15: 15 mmol/L, and N20: 20 mmol/L) to assess the effects of N supply level on growth, photosynthesis, and carbon and nitrogen metabolism. The results revealed a time-dependent dynamics in beet biomass accumulation, with N20 inducing chlorosis and necrosis symptoms by 10 days post-treatment (DPT), resulting in the lowest biomass. While N15 significantly promoted root biomass by 30 DPT, showing a 23.70% (root dry weight, RDW) increase over N20; chlorophyll content and gas exchange parameters-net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr) exhibited significant N dependence, with N15 maintaining high chlorophyll level (0.78 mg/g) and photosynthetic rate (220.33 μmol/(m²·s). Nitrogen assimilation, as indicated by glutamine synthetase and glutamate synthetase activity (GS and GOGAT), was stronger under N15, promoting amino acid synthesis and root growth, whereas N20 inhibited enzyme activity. Carbon metabolism analysis revealed that N15-driven sucrose synthesis significantly increased root sucrose content, sucrose phosphate synthase and sucrose synthase activity (SPS and SS), optimizing source–sink allocation. Correlation analysis showed a positive relationship between leaf and root biomass (r = 0.91), and root sucrose content was positively correlated with GOGAT activity (r = 0.90), emphasizing the synergistic regulation of C/N metabolism. On the contrary, N20 led to disrupted C/N metabolic homeostasis, inhibited enzyme activity, and C/N distribution. These results indicated that the photosynthetic output, enzyme efficiency, and sucrose distribution were coordinated by nitrogen optimization, and the growth of sugar beet seedlings was optimized. Full article