Search Results (8,336)

Extremely high temperatures can severely impact urban livability and public health safety. However, risk assessments for high temperatures in cold-region cities remain inadequate. This study focuses on Dalian, a coastal city in northeastern China. Utilizing multi-source data, we established a population density prediction model based on the random forest algorithm and a heat vulnerability index (HVI) framework following the “Exposure-Sensitivity-Adaptability” paradigm constructed using an indicator system method, thereby building a high-temperature risk assessment system suited for more refined research. The results indicate the following: (1) Strong positive correlations exist between nighttime light brightness (NL), Road Density (RD), the proportion of flat area (SLP), the land surface temperature (LST), and the population distribution density, with correlation coefficients reaching 0.963, 0.963, 0.956, and 0.954, respectively. (2) Significant disparities exist in the spatial distribution of different criterion layers within the study area. Areas characterized by high exposure, high sensitivity, and low adaptability account for 13.04%, 8.05%, and 21.44% of the total area, respectively, with exposure being the primary contributing factor to high-temperature risk. (3) Areas classified as high-risk or extremely high-risk for high temperatures constitute 31.57% of the study area. The spatial distribution exhibits a distinct pattern, decreasing gradually from east to west and from the coast inland. This study provides a valuable tool for decision-makers to propose targeted adaptation strategies and measures based on the assessment results, thereby better addressing the challenges posed by climate change-induced high-temperature risks and promoting sustainable urban development. Full article

Tumor-associated macrophages (TAMs) are one of the most important components of the tumor microenvironment and play a critical role in promoting tumor invasion and metastasis. These cells have become a new therapeutic target for inhibiting tumor progression. Lysine/leucine-rich antimicrobial peptides have well-documented anticancer activity in vitro, but their immune regulatory activity in human macrophages is not clear. The present study investigated the regulatory effects of lysine/leucine-rich peptides on the polarization of M2-like macrophages and the metastasis of breast cancer cells mediated by M2-like TAMs in the tumor microenvironment (TME). Our results revealed remarkable inhibition of the polarization of M2-like macrophages following treatment with lysine/leucine-rich antimicrobial peptides, which was accompanied by a significant reduction in the expression of the M2-like macrophage-specific factors interleukin-10 (IL-10) and transforming growth factor–β (TGF-β1) and the M2 macrophage-specific marker CD206. The lysine/leucine-rich antimicrobial peptides downregulated the expression of PPARγ and Krüppel-like factor 4 (KLF4) and the phosphorylation of STAT6 in the STAT6 signaling pathway, which resulted in a decrease in IL-10 and TGF-β1. Moreover, we found that lysine/leucine-rich antimicrobial peptide-treated macrophages reduced the migration of cancer cells by inhibiting the phosphorylation of the mTOR, smad2 and ERK proteins during tumor metastasis. These findings highlight the potential of lysine/leucine-rich antimicrobial peptides as therapeutic agents that target M2-like macrophages to inhibit cancer cell metastasis. Full article

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature. Full article

To enhance the speed regulation accuracy and robustness of permanent magnet synchronous motor (PMSM) drives under complex operating conditions, this paper proposes a dual-loop active disturbance rejection control strategy optimized by an opposition-based learning hybrid optimization algorithm (DLADRC-OBLHOA). First, the vector control system and ADRC model of the PMSM are established. Then, a nonlinear function, ifal, is introduced to improve the performance of the speed-loop ADRC. Meanwhile, an active disturbance rejection controller is also introduced into the current loop to suppress current disturbances. To address the challenge of tuning multiple ADRC parameters, an opposition-based learning hybrid optimization algorithm (OBLHOA) is developed. This algorithm integrates chaotic mapping for population initialization and employs opposition-based learning to enhance global search capability. The proposed OBLHOA is utilized to optimize the speed-loop ADRC parameters, thereby achieving high-precision speed control of the PMSM system. Its optimization performance is validated on 12 benchmark functions from the IEEE CEC2022 test suite, demonstrating superior convergence speed and solution accuracy compared to conventional heuristic algorithms. The proposed strategy achieves superior speed regulation accuracy and reliability under complex operating conditions when deployed on high-performance processors, but its effectiveness may diminish on resource-limited hardware. Moreover, simulation results show that the DLADRC-OBLHOA control strategy outperforms PI control, traditional ADRC, and ADRC-$ifal$ in terms of tracking accuracy and disturbance rejection capability. Full article

Due to their unique structural configuration, Very Large Floating Structures (VLFS) exhibit significant hydroelastic responses during their motion in the water. These responses, which are a result of the interaction between the structure and the waves, can lead to undesirable vibrations and deformations, potentially compromising the stability and performance of the VLFS. Reducing the hydroelastic response in VLFS has become a critical research focus for scholars worldwide. In the field of marine engineering, various methods are employed to address this issue, with the use of porous structures being one of the most effective solutions. These porous structures help to dissipate the energy of propagating waves, thereby reducing the magnitude of hydroelastic responses. This paper introduces a multi-layer porous plate structure designed to mitigate the hydroelastic response of horizontally moored VLFS. The proposed structure consists of multiple layers of porous plates strategically arranged to optimize the dissipation of wave energy. To evaluate the performance of this structure, a series of physical model tests were conducted, focusing on the hydrodynamic behavior of the VLFS with the multi-layer porous plate structure. The experimental results indicate that within a specific wavelength range, the properly configured multi-layer porous plate structure can significantly reduce the hydroelastic response of the VLFS. This reduction is especially noticeable in the attenuation of wave-induced forces, leading to a decrease in the structural vibrations and enhancing the stability of the floating system. The findings demonstrate that this innovative design can provide a reliable method for improving the performance of VLFS in challenging marine environments. Full article

To address the issues of capacity resource waste and increased carbon emissions caused by the asymmetry between import and export container transportation tasks in port collection and dispatching, a green and cooperative task-and-route optimization method for container trucks with heterogeneous carriers based on task sharing is proposed from the perspective of system optimization. Based on the concept of a sharing economy, a sharing and cooperation mechanism with dual elasticity in capacity and information is designed, which integrates the container trucks’ resources and dissymmetric transportation tasks of heterogeneous carriers to expand the revenue potential for all participants. Based on task sharing and matching, a green and cooperative task-and-route optimization model for container trucks with heterogeneous carriers based on task sharing is formulated in order to optimize container trucks’ resources and transportation tasks comprehensively and reduce the system’s carbon emissions. A column generation algorithm embedded with a ring-increasing strategy is designed to solve the problem to improve computational efficiency. Through algorithm testing and a case analysis, the effectiveness of the model and algorithm is validated. The optimization results show that the overall carbon emissions are reduced by more than 28%, the number of used trucks decreases by 28%, and the profits of participants are increased by 24–65% compared with independent operations. Finally, several management insights are obtained regarding the number of shared trucks, the external market demand, task demand variability, the mixed fleet composition, subsidies, and bonus adjustments. Full article

Soil aggregates are crucial for fertility and organic carbon (C) sequestration, with straw decomposition by soil microbes playing a key role in this process. However, the mechanisms of how fertilization and microbes control straw decomposition and the subsequent formation of straw-derived C in soil aggregates are still unclear. Therefore, topsoil samples (0~20 cm) were collected from three fertilization treatments in a long-term (29-year) Mollisol field experiment: (i) no fertilization control, CK; (ii) inorganic fertilizer, IF; and (iii) inorganic fertilizer plus manure, IFM. Thereafter, an in situ micro-plot incubation experiment was conducted without/with ¹³C-labeled straw (abbreviated as CKS, IFS, and IFMS, respectively). Soil aggregates were separated into macro- (>0.25 mm) and microaggregates (<0.25 mm). The aggregate-based changes in straw-derived C content, microbial community composition, co-occurrence network, keystone taxa, and functional characteristics were measured on the 1st, 60th, and 150th day after straw addition. The results showed that straw-derived C content increased averagely by 7 (CKS), 13 (IFS), and 20 times (IFMS) from day 1 to day 150 in the macroaggregates. The straw-derived C content in the microaggregates was the highest in the IFS (0.70%) and IFMS (0.67%) treatments on day 60. After straw addition, the relative abundance of Humicola within the soil macroaggregates significantly decreased from 2.9% (CK) to 1.4% (CKS), and that of Penicillium within the soil microaggregates decreased from 7.5% (IF) to 4.0% (IFS) on day 150. Network analysis revealed greater microbial complexity in microaggregates than in macroaggregates, with fungal keystone taxa responding more strongly to straw than bacterial keystone taxa. The SEM model identified bacterial composition and fertilization as key drivers of straw-derived C formation in macro- and microaggregates, respectively. These findings highlight the distinct roles of bacteria and fungi in various sizes of aggregate and the importance of customized soil management for improving soil fertility and C storage. Full article

We theoretically investigate high-order harmonic generation from ethylbenzene (C₈H₁₀), toluene (C₇H₈), and benzene (C₆H₆) molecules driven by a circularly polarized laser field using time-dependent density functional theory. By comparing the harmonic spectra of these structurally related molecules, we find that ethylbenzene, which features a larger molecular size due to the ethyl group, exhibits a higher harmonic cutoff and stronger harmonic intensity than toluene and benzene. Time-resolved electron density distributions, together with the probability current density analysis, indicate that under long-wavelength conditions (e.g., 1200 nm), the ethyl group in ethylbenzene and the methyl group in toluene significantly enhance the probability of ionized electrons from neighboring nuclei colliding with nearby nuclei, thereby leading to stronger harmonic emission, with ethylbenzene > toluene > benzene. In contrast, under short-wavelength conditions (e.g., 200 nm), the harmonic intensities of the three molecules show little difference, and the effects of the ethyl and methyl groups on the harmonic yield can be neglected. The influence of laser intensity and wavelength on high-order harmonic generation is further analyzed, confirming the robustness of the structural enhancement effect. Additionally, we study the harmonic ellipticity of ethylbenzene under different carrier-envelope phases, and find that while circularly polarized harmonics can be obtained, their spectral continuity is insufficient for synthesizing isolated circularly polarized attosecond pulses. This limitation is attributed to the broken ring symmetry caused by the ethyl substitution. Our findings offer insight into the relationship between molecular structure and harmonic response in strong-field physics, and provide a pathway for designing efficient circularly polarized attosecond pulse sources. Full article

F_TIDE has been proven to be effective in obtaining the time-varying harmonic parameters of nonstationary tidal signals, and the results near the two endpoints of the analyzed time series are more accurate than those obtained by S_TIDE, which provides good conditions for the prediction of future sea levels. In this paper, F_TIDE is used for the short-term prediction of nonstationary tides in Nome (Alaska) and South Beach (Oregon). The significance of each standard parameter of F_TIDE is quantified by calculating its signal-to-noise ratio to determine the appropriate parameters that can be used for prediction. F_TIDE performs well in forecasting the sea level for three weeks at the Nome gauge and one week at the South Beach gauge. F_TIDE causes 30.1% and 42.0% decreases in the mean absolute errors between the forecasts and the observations compared to T_TIDE. F_TIDE is applied to the original signal at the Nome gauge, and the results show a strong correlation between the variation in M₂ amplitude and the variation in the mean sea level. A potential mechanism is speculated in that changes in tides are affected by the changes in water depth on different time scales, which the sea level pressure, wind, sea ice, and other marine motions may contribute to. Full article

Water temperature stands as a crucial environmental element, exerting an impact on the survival and growth of organisms in aquaculture. Heat stress poses a significant threat to the survival and aquaculture of the Hong Kong oyster Magallana hongkongensis (also known as Crassostrea hongkongensis), yet the underlying physiological and molecular mechanisms remain poorly understood. This study investigated the effects of elevated temperatures (35 °C and 37 °C) on survival, DNA damage, antioxidant enzyme activities, and gene expression related to apoptosis, inflammation, and heat shock proteins (HSPs) in M. hongkongensis. The median lethal temperature (LT50) of M. hongkongensis was determined to be 37.09 °C, with significant mortality observed at 35 °C compared with the control (29 °C). Antioxidant enzyme activities (SOD, CAT, and GPx) and T-AOC were up-regulated initially but exhibited divergent patterns under prolonged stress, indicating a temperature-dependent threshold for oxidative defense. Comet assay results also showed that heat stress induced severe DNA damage in hemocytes. Moreover, heat stress significantly up-regulated mRNA expression of apoptosis-related genes (Caspase-2, Caspase-8, Bax, and P53), inflammatory genes (TNF, p38-MAPK, and AP-1), and HSP family members (Hsp70, Hsp90, Hsp27, and Hsp68). The expression peaks of these genes were generally earlier and more pronounced at 37 °C, reflecting intensified cellular damage and protective responses. Collectively, this study demonstrates that M. hongkongensis employs integrated antioxidant, apoptotic, inflammatory, and HSP-mediated mechanisms to counteract heat stress, but temperatures exceeding 35 °C disrupt these defenses, leading to survival impairment. These findings provide critical insights into the heat adaptation strategies of M. hongkongensis and serve as a scientific foundation for developing sustainable aquaculture practices to mitigate summer heat stress. Full article

Understanding how endangered carnivores partition spatiotemporal distribution in human-dominated landscapes is pivotal for mitigating biodiversity loss in climate-sensitive boreal ecosystems. Here, we used kernel density data derived from a 16-month camera-trap survey (140 UVL7 cameras), cold single-season (November–April) occupancy models, and MaxEnt 3.4.4 to identify the effects of biotic interactions, anthropogenic disturbance, and environmental factors on the spatiotemporal distribution of the wolverine (Gulo gulo) in Beijicun National Nature Reserve, Heilongjiang Province, China. We found that wolverines exhibited crepuscular activity patterns using night-time relative abundance index (NRAI) = 50.29% with bimodal peaks (05:00–07:00, 13:00–15:00), with dawn activity predominant during the warm season (05:00–06:00) and a bimodal activity pattern in the cold season (08:00–09:00, 14:00–15:00). Temporal overlap with prey (overlap coefficient Δ = 0.84) and competitors (Δ = 0.70) was high, but overlap with human-dominated temporal patterns was low (Δ = 0.58). Wolverines avoided human settlements and major roads, preferred moving along forest trails and gentle slopes, and avoided high-altitude deciduous forests. Populations were mainly concentrated in southern Hedong and Qianshao Forest Farms, which are characterized by high habitat integrity, high prey densities, and minimal anthropogenic disturbance. These findings suggest that wolverines may influence boreal trophic networks, especially in areas with intact prey communities, competitors, and spatial refugia from human disturbances. We recommend that habitat protection and management within the natural reserve be prioritized and that sustainable management practices for prey species be implemented to ensure the long-term survival of wolverines. Full article

Municipal wastewater treatment relies primarily on biological methods, yet effective disposal of residual sludge remains a major challenge. Converting sludge into biochar via oxygen-limited pyrolysis presents a novel approach for waste resource recovery. This study prepared sludge-based biochar (SBC) through one-step pyrolysis of sewage sludge and applied it to activate peroxymonosulfate (PMS) for degrading diverse contaminants. Characterization (SEM, XPS, FTIR) revealed abundant pore structures and diverse surface functional groups on SBC. Using Acid Orange 7 (AO7) as the target pollutant, SBC effectively degraded AO7 across pH 3.0–9.0 and catalyst dosages (0.2–2.0 g·L⁻¹), achieving a maximum observed rate constant (k_obs) of 0.3108 min^–1. Salinity and common anions showed negligible inhibition on AO7 degradation. SBC maintained 95% degradation efficiency after four reuse cycles and effectively degraded sulfamethoxazole, sulfamethazine, and rhodamine B besides AO7. Mechanistic studies (chemical quenching and ESR) identified singlet oxygen (¹O₂) and superoxide radicals (O₂^•− ) as the dominant reactive oxygen species for AO7 degradation. XPS indicated a 39% reduction in surface carbonyl group content after cycling, contributing to activity decline. LC-MS identified five intermediates, suggesting a potential degradation pathway driven by SBC/PMS system. ECOSAR model predictions indicated significantly reduced biotoxicity of the degradation products compared to AO7. This work provides a strategy for preparing sludge-derived catalysts for PMS activation and pollutant degradation, enabling effective solid waste resource utilization. Full article

As the safety requirements of elevator systems continue to rise, the detection of loose bolts and the high-precision segmentation of anti-loosening lines have become critical challenges in elevator landing door inspection. Traditional manual inspection and conventional visual detection often fail to meet the requirements of high precision and robustness under real-world conditions such as oil contamination and low illumination. This paper proposes two improved algorithms for detecting loose bolts and segmenting anti-loosening lines in elevator landing doors. For small-bolt detection, we introduce the DS-EMA model, an enhanced YOLOv8 variant that integrates depthwise-separable convolutions and an Efficient Multi-scale Attention (EMA) module. The DS-EMA model achieves a 2.8 percentage point improvement in mAP over the YOLOv8n baseline on our self-collected dataset, while reducing parameters from 3.0 M to 2.8 M and maintaining real-time throughput at 126 FPS. For anti-loosening-line segmentation, we develop an improved DeepLabv3+ by adopting a MobileViT backbone, incorporating a Global Attention Mechanism (GAM) and optimizing the ASPP dilation rate. The revised model increases the mean IoU to 85.8% (a gain of 5.4 percentage points) while reducing parameters from 57.6 M to 38.5 M. Comparative experiments against mainstream lightweight models, including YOLOv5n, YOLOv6n, YOLOv7-tiny, and DeepLabv3, demonstrate that the proposed methods achieve superior accuracy while balancing efficiency and model complexity. Moreover, compared with recent lightweight variants such as YOLOv9-tiny and YOLOv11n, DS-EMA achieves comparable mAP while delivering notably higher recall, which is crucial for safety inspection. Overall, the enhanced YOLOv8 and DeepLabv3+ provide robust and efficient solutions for elevator landing door safety inspection, delivering clear practical application value. Full article

Assessing fish biodiversity is essential for freshwater ecosystem conservation. This study compares environmental DNA (eDNA) metabarcoding and traditional morphological surveys to investigate fish communities in the Gaya River, China. A total of 42 fish species were identified, with 13 detected only by eDNA, 7 exclusively by morphology, and 11 by both methods. A comparative analysis of species composition, functional diversity, and phylogenetic diversity revealed significant differences between the two approaches. Notably, eDNA data indicated higher phylogenetic diversity (PD), while morphological surveys captured greater functional evenness (FEve). Multivariate analyses indicated that total phosphorus (TP), total suspended solids (TSS), electrical conductivity (EC), temperature (T), and pH significantly influenced fish community composition, while dissolved oxygen (DO) was a key driver of species richness (SR), functional richness (FRic), and PD. These findings highlight the methodological differences and complementary strengths of eDNA and morphological approaches in biodiversity assessments. By providing comparative insights into fish diversity patterns, this study underscores the importance of using multi-method approaches to improve freshwater biodiversity monitoring and conservation strategies. Full article

The supply chain network is important for manufacturing enterprises to obtain innovation resources and information; hence, their network location significantly influences innovation performance. The existing research has focused on the factors and the macro environment of the innovation performance but lacks the exploration of how supply chain network location affects innovation performance from a supply chain network perspective. Based on a sample of A-share listed manufacturing enterprises from 2013 to 2023, this research constructs a supply chain network characterized by supplier–customer relationships to explore the impact of supply chain network location on innovation performance and the underlying mechanisms. The findings show that (1) the supply chain network location has a significant positive impact on the innovation performance; (2) operational risk plays a mediating role in the path between supply chain network location and innovation performance; and (3) digital transformation and information asymmetry play a moderating role in the relationship between supply chain network location and innovation performance. This research enriches the theoretical research on supply chain network location and innovation performance, providing insights for manufacturing enterprises to optimize innovation strategies and adjust their supply chain network locations. Full article