Search Results (4,130)

An internal geomembrane (GMB)–clay seepage control system is an important form of seepage control structure for rockfill dams. In order to investigate the stress and deformation characteristics of GMB in GMB–clay core-wall rockfill dams (GMCWRD) under different construction and operation conditions, the stress and deformation fields of GMCWRDs were calculated by numerical simulation under a variety of working conditions. The stress and deformation characteristics of the dam and GMB during the impoundment period were investigated, and the influences of the spreading thickness of the clay core-wall and the location of the GMB defects and hydraulic head on the stress and deformation of the GMB were analyzed. The results show that the maximum tensile strain of the GMB upstream of the clay core-wall during the impoundment period occurs at the anchorage of the GMB and the concrete cut-off, with a maximum tensile strain of 2.70%. With the increase in the spreading thickness of the clay core-wall, the maximum tensile stress and strain of the GMB fluctuated. Under the dam construction and foundation conditions in this paper, when the spreading thickness of the clay core-wall was 2 m, the tensile stress and strain of GMB were at the lowest level. As the defect location of the GMB decreases, the phreatic line of the dam gradually increases, and the seepage discharge of the dam and the tensile strain of the GMB gradually increase, with the maximum tensile strain of 3.98%. The maximum deformation of the GMB in each case is much smaller than the maximum elastic deformation range of the selected PVC GMB, and the conclusion of the study provides a certain scientific basis for the design and construction of the seepage control of the core rockfill dam. Full article

Introduction: Pelvic exenteration (PEx) is a radical procedure used in the treatment of locally advanced (LARC) and locally recurrent rectal cancer (LRRC). With recent advancements in perioperative treatment regimens, there has been renewed interest in this procedure as it offers the opportunity for complete tumour resection in a select cohort. This has resulted in large heterogeneity in outcome reporting, making comparing and conducting a meta-analysis of published results challenging. Standardising outcome reporting will ensure meaningful data reporting and allow the cross-centre comparison of data. Aims: To conduct a systematic review of the current literature, to identify the various outcomes reported for PEx in rectal cancer, and to develop a standard outcome reporting set. Methods: A systematic review was carried out following the PRISMA guidelines. Relevant domains were identified first. Data elements (DEs) were extracted verbatim prior to standardisation and mapping to relevant domains. Results: There has been a noticeable trend of increased literature on PEx in the last decade. Forty-nine papers were identified. A total of 1549 DEs were extracted verbatim. These were standardised to 119 unique DEs mapped to ten distinct domains capturing the patient care journey. There was large variation in the frequency of reporting, with some key outcomes reported in a limited number of studies. Conclusions: There is considerable heterogeneity at present in data reporting for PEx in LARC and LRRC. Standardisation of outcomes is the first step in guiding the development of a core information set to overcome heterogeneity and guide future research development. Full article

Traditional information distillation networks using single-scale convolution and simple feature fusion often result in insufficient information extraction and ineffective restoration of high-frequency details. To address this problem, we propose a lightweight image super-resolution reconstruction network based on multi-order information optimization. The core of this network lies in the enhancement and refinement of high-frequency information. Our method operates through two main stages to fully exploit the high-frequency features in images while eliminating redundant information, thereby enhancing the network’s detail restoration capability. In the high-frequency information enhancement stage, we design a self-calibration high-frequency information enhancement block. This block generates calibration weights through self-calibration branches to modulate the response strength of each pixel. It then selectively enhances critical high-frequency information. Additionally, we combine an auxiliary branch and a chunked space optimization strategy to extract local details and adaptively reinforce high-frequency features. In the high-frequency information refinement stage, we propose a multi-scale high-frequency information refinement block. First, multi-scale information is captured through multiplicity sampling to enrich the feature hierarchy. Second, the high-frequency information is further refined using a multi-branch structure incorporating wavelet convolution and band convolution, enabling the extraction of diverse detailed features. Experimental results demonstrate that our network achieves an optimal balance between complexity and performance, outperforming popular lightweight networks in both quantitative metrics and visual quality. Full article

In this paper, a low loss and high polarization-maintaining single-mode hollow-core anti-resonant fiber (PM-HC-ARF) is designed. The elliptical core in the PM-HC-ARF is formed by strategically enlarging selected cladding air holes along the y-axis. Additionally, the variations in the wall thickness in both the x and y directions generate the distinct surface modes. By simultaneously employing an elliptical core and asymmetric core-wall thickness, we enhance the phase birefringence. Theoretical analysis results show that the proposed PM-HC-ARF achieves a transmission loss of 0.00082 dB/m at wavelength 1450 nm, along with a birefringence of 1.38 × 10⁻⁴; it demonstrates CL levels an order of magnitude below state-of-the-art polarization-maintaining HC-ARFs. Moreover, within the S+C+L+U communication bands, it achieves a bandwidth exceeding 380 nm (1420–1800 nm) while maintaining a birefringence of greater than 1.45 × 10⁻⁴. In particular, this PM-HC-ARF demonstrates a maximum higher-order mode extinction ratio of over 32,070; the single-mode transmission characteristics are excellent, along with exceptional bending resistance characteristics. When the bending radius exceeds 3 cm, the impacts on the loss and birefringence are negligible; this also demonstrates that the fiber structure shows good robustness when subjected to harsh environment interference. The proposed PM-HC-ARF is believed to have important applications in fiber optic gyroscopes, optical amplifiers, and hydrophones. Full article

Accurate genomic prediction of complex phenotypes is crucial for accelerating genetic progress in swine breeding. However, conventional methods like Genomic Best Linear Unbiased Prediction (GBLUP) face limitations in capturing complex non-additive effects that contribute significantly to phenotypic variation, restricting the potential accuracy of phenotype prediction. To address this challenge, we introduce a novel framework based on a self-supervised, pre-trained encoder-only Transformer model. Its core novelty lies in tokenizing SNP sequences into non-overlapping 6-mers (sequences of 6 SNPs), enabling the model to directly learn local haplotype patterns instead of treating SNPs as independent markers. The model first undergoes self-supervised pre-training on the unlabeled version of the same SNP dataset used for subsequent fine-tuning, learning intrinsic genomic representations through a masked 6-mer prediction task. Subsequently, the pre-trained model is fine-tuned on labeled data to predict phenotypic values for specific economic traits. Experimental validation demonstrates that our proposed model consistently outperforms baseline methods, including GBLUP and a Transformer of the same architecture trained from scratch (without pre-training), in prediction accuracy across key economic traits. This outperformance suggests the model’s capacity to capture non-linear genetic signals missed by linear models. This research contributes not only a new, more accurate methodology for genomic phenotype prediction but also validates the potential of self-supervised learning to decipher complex genomic patterns for direct application in breeding programs. Ultimately, this approach offers a powerful new tool to enhance the rate of genetic gain in swine production by enabling more precise selection based on predicted phenotypes. Full article

Germplasm resources embody the genetic diversity of plants and form the foundation for breeding and the ongoing improvement of elite cultivars. The establishment of germplasm banks, along with their systematic evaluation, constitutes a critical step toward the conservation, sustainable use, and innovative utilization of these resources. Liriodendron, a rare and endangered tree genus with species distributed in both East Asia and North America, holds considerable ecological, ornamental, and economic significance. However, a standardized evaluation system for Liriodendron germplasm remains unavailable. In this study, 297 Liriodendron germplasm accessions were comprehensively evaluated using 34 phenotypic traits and whole-genome resequencing data. Substantial variation was observed in most phenotypic traits, with significant correlations identified among several characteristics. Cluster analysis based on phenotypic data grouped the accessions into three distinct clusters, each exhibiting unique distribution patterns. This classification was further supported by principal component analysis (PCA), which effectively captured the underlying variation among accessions. These phenotypic groupings demonstrated high consistency with subsequent population structure analysis based on SNP markers (K = 3). Notably, several key traits exhibited significant divergence (p < 0.05) among distinct genetic clusters, thereby validating the coordinated association between phenotypic variation and molecular markers. Genetic diversity and population structure were assessed using 4204 high-quality single-nucleotide polymorphism (SNP) markers obtained through stringent filtering. The results indicated that the Liriodendron sino-americanum displayed the highest genetic diversity, with an expected heterozygosity (He) of 0.18 and a polymorphic information content (PIC) of 0.14. In addition, both hierarchical clustering and PCA revealed clear population differentiation among the accessions. Association analysis between three phenotypic traits (DBH, annual height increment, and branch number) and SNPs identified 25 highly significant SNP loci (p < 0.01). Of particular interest, the branch number-associated locus SNP_17_69375264 (p = 1.03 × 10⁻⁵) demonstrated the strongest association, highlighting distinct genetic regulation patterns among different growth traits. A minimal set of 13 core SNP markers was subsequently used to construct unique DNA fingerprints for all 297 accessions. In conclusion, this study systematically characterized phenotypic traits in Liriodendron, identified high-quality and core SNPs, and established correlations between key phenotypic and molecular markers. These achievements enabled differential analysis and genetic diversity assessment of Liriodendron germplasm, along with the construction of DNA fingerprint profiles. The results provide crucial theoretical basis and technical support for germplasm conservation, accurate identification, and utilization of Liriodendron resources, while offering significant practical value for variety selection, reproduction and commercial applications of this species. Full article

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions. Full article

The energy management strategy (EMS) is a core technology for improving the fuel economy of hybrid electric vehicles (HEVs). However, the coexistence of both discrete and continuous control variables, along with complex physical constraints in HEV powertrains, presents significant challenges for the design of efficient EMSs based on deep reinforcement learning (DRL). To further enhance fuel efficiency and coordinated powertrain control under complex driving conditions, this study proposes a hierarchical DRL-based EMS. The proposed strategy adopts a layered control architecture: the upper layer utilizes the soft actor–critic (SAC) algorithm for continuous control of engine torque, while the lower layer employs a deep Q-network (DQN) for discrete gear selection optimization. Through offline training and online simulation, experimental results demonstrate that the proposed strategy achieves fuel economy performance comparable to dynamic programming (DP), with only a 3.06% difference in fuel consumption. Moreover, it significantly improves computational efficiency, thereby enhancing the feasibility of real-time deployment. This study validates the optimization potential and real-time applicability of hierarchical reinforcement learning for hybrid control in HEV energy management. Furthermore, its adaptability is demonstrated through sustained and stable performance under long-duration, complex urban bus driving conditions. Full article

Background: Loneliness and social isolation among older people are currently widespread and recognized as the foremost public health problems globally and locally. Hong Kong, which exhibits a rapid aging trend and an expanding elderly population, is inevitably facing these issues. This study explored the lived experiences of loneliness among older adults in Hong Kong. Methods: Qualitative interviews were conducted among older adults in the community aged 60 or above who were cared for by migrant domestic workers and presented varying levels of loneliness. Purposive sampling was used to select subjects for face-to-face, semi-structured individual interviews, with consent for audio recording, which led to the inclusion of 19 older adults, among whom five were male, nine lived with a spouse, and three lived with their children. Interpretative phenomenological analysis was adopted. Results: We identified a core theme, “Should I stay at home alone?”, and the following four interrelated themes: (1) experience of inadequate social support and networks, (2) altered family dynamics and support, (3) deterioration in physical functions and mobility limitations, and (4) experience of negative and complex emotions. Conclusions: Based on our investigation into the lived experience of loneliness among older adults locally, we recommend that the government, non-governmental organizations, and healthcare institutions establish appropriate strategies and integrated services to address the social, physical, familial, and emotional issues in this population to foster healthy aging, improve their quality of life, and encourage support from families and communities. Full article

The incentive mechanism functions as a core safeguard to ensure the efficient execution of consortium-based Public–Private Partnership (PPP) projects and the realization of value-added outcomes. The heterogeneity of consortium members, their reciprocal preferences, and the collaborative dynamics of the team collectively contribute to the formation of project alliances characterized by resource synergy, complementary advantages, and risk sharing. However, these same factors also contribute to the multi-layered structure of principal–agent relationships and the inherent complexity of incentive pathways and mechanisms in consortium-based PPP settings. Drawing upon the team collaboration effect and reciprocal preferences among consortium members, this study incorporated the member heterogeneity and developed three incentive models for such projects, such as the Dual-Performance (DP) mode, the Total-Performance (TP) mode, and the Individual-Performance (IP) mode. This study examined the conditions under which these incentive modes were established, the relationship between incentive intensity and optimal effort levels of consortium members, and the influence of reciprocal preferences on incentive effectiveness. Further, the selection criteria and appropriate application scenarios for each of the three incentive models were analyzed according to a comparative analysis, thereby putting forward effective suggestions for improving the effort levels of private investors in consortium-based PPP projects. The study results indicate that team synergy effects play an imperative role in improving the optimal effort levels under all three modes, whereas reciprocity preferences exhibit a negative relationship with effort in the DP and TP modes. When reciprocity remains within a moderate range, the DP mode achieves highest aggregate effort levels, whereas the IP mode induces positive incentive effects only under extreme reciprocity conditions. Thus, the application of dual incentive coefficients can enhance operational adaptability and allocative efficiency and governments should establish a multidimensional collaborative incentive for consortium-based PPP projects to strengthen effectiveness and project quality. This comprehensive evaluation provides crucial insights for policymakers, emphasizing the strategic selection of incentive mechanisms to enhance the sustainability and effectiveness of consortium-based PPP Projects. Full article

Efficient clustering of high-spatial-dimensional satellite image datasets remains a critical challenge, particularly due to the computational demands of spectral distance calculations, random centroid initialization, and sensitivity to outliers in conventional K-Means algorithms. This study presents a comprehensive comparative analysis of eight parallelized variants of the K-Means algorithm, designed to enhance clustering efficiency and reduce computational burden for large-scale satellite image analysis. The proposed parallelized implementations incorporate optimized centroid initialization for better starting point selection using a dynamic K-Means sharp method to detect the outlier to improve cluster robustness, and a Nearest-Neighbor Iteration Calculation Reduction method to minimize redundant computations. These enhancements were applied to a test set of 114 global land cover data cubes, each comprising high-dimensional satellite images of size 3712 × 3712 × 16 and executed on multi-core CPU architecture to leverage extensive parallel processing capabilities. Performance was evaluated across three criteria: convergence speed (iterations), computational efficiency (execution time), and clustering accuracy (RMSE). The Parallelized Enhanced K-Means (PEKM) method achieved the fastest convergence at 234 iterations and the lowest execution time of 4230 h, while maintaining consistent RMSE values (0.0136) across all algorithm variants. These results demonstrate that targeted algorithmic optimizations, combined with effective parallelization strategies, can improve the practicality of K-Means clustering for high-dimensional-satellites image analysis. This work underscores the potential of improving K-Means clustering frameworks beyond hardware acceleration alone, offering scalable solutions good for large-scale unsupervised image classification tasks. Full article

To address the issues of poor thermal stability, inadequate salt tolerance, and environmental risks in conventional gel systems for the development of high-temperature, high-salinity heterogeneous reservoirs, a triple-synergy gel system comprising anionic polyacrylamide (APAM), polyethyleneimine (PEI), and phenolic resin (SMP) was developed in this study. The optimal synthesis parameters—APAM of 180 mg/L, PEI:SMP = 3:1, salinity of 150,000 ppm, and temperature of 110 °C—were determined via response surface methodology, and a time–viscosity model was established. Compared with existing binary systems, the proposed gel exhibited a mass retention rate of 93.48% at 110 °C, a uniform porous structure (pore size of 2–8 μm), and structural stability under high salinity (150,000 ppm). Nuclear magnetic resonance displacement tests showed that the utilization efficiency of crude oil in 0.1–1 μm micropores increased to 21.32%. Parallel dual-core flooding experiments further confirmed the selective plugging capability in heterogeneous systems with a permeability contrast of 10:1: The high-permeability layer (500 mD) achieved a plugging rate of 98.7%, while the recovery factor of the low-permeability layer increased by 13.6%. This gel system provides a green and efficient profile control solution for deep, high-temperature, high-salinity reservoirs. Full article

Oncolytic virus (OV) immunotherapy, particularly with oncolytic herpes simplex virus (oHSV), has become a promising new strategy in cancer treatment. This field has achieved significant clinical milestones, highlighted by the FDA approval of Talimogene laherparepvec (T-VEC) for melanoma in 2015 and the approval of Teserpaturev/G47Δ for malignant glioma in Japan in 2021. This review synthesizes the key preclinical and clinical advancements in oHSV therapy over the last decade, critically analyzing the core challenges in target selection, genetic modification, administration routes, and targeted delivery. Key findings indicate that arming oHSV with immunomodulatory transgenes, such as cytokines and antibodies, and combining it with immune checkpoint inhibitors are critical strategies for enhancing therapeutic efficacy. Future research will focus on precision engineering using CRISPR/Cas9, the development of novel delivery vehicles like nanoparticles and mesenchymal stem cells (MSCs), and biomarker-guided personalized medicine, aiming to provide safer and more effective solutions for refractory cancers. This review synthesizes oHSV advances and analyzes novel delivery and gene-editing strategies. Full article

This study proposes an image processing framework based on Bionic principles to optimize 3D visual perception in virtual reality (VR) systems. By simulating the physiological mechanisms of the human visual system, the framework significantly enhances depth perception and visual fidelity in VR content. The research focuses on three core algorithms: Gabor texture feature extraction algorithm based on directional selectivity of neurons in the V1 region of the visual cortex, which enhances edge detection capability through fourth-order Gaussian kernel; improved Retinex model based on adaptive mechanism of retinal illumination, achieving brightness balance under complex illumination through horizontal–vertical dual-channel decomposition; the RGB adaptive adjustment algorithm, based on the three color response characteristics of cone cells, integrates color temperature compensation with depth cue optimization, enhances color naturalness and stereoscopic depth. Build a modular processing system on the Unity platform, integrate the above algorithms to form a collaborative optimization process, and ensure per-frame processing time meets VR real-time constraints. The experiment uses RMSE, AbsRel, and SSIM metrics, combined with subjective evaluation to verify the effectiveness of the algorithm. The results show that compared with traditional methods (SSAO, SSR, SH), our algorithm demonstrates significant advantages in simple scenes and marginal superiority in composite metrics for complex scenes. Collaborative processing of three algorithms can significantly improve depth map noise and enhance the user’s subjective experience. The research results provide a solution that combines biological rationality and engineering practicality for visual optimization in fields such as implantable metaverse, VR healthcare, and education. Full article

This study investigated how specific sustainability topics disclosed in standalone sustainability reports influence firm value and whether third-party assurance moderates this relationship. Drawing on signaling, agency, stakeholder, and legitimacy theories, we applied latent Dirichlet allocation (LDA) to extract latent topics from U.S. corporate sustainability reports. We analyzed their impact on Tobin’s Q using panel regressions and supplement our findings with discrete Bayesian networks (DBNs) and Shapley additive explanations (SHAP) to capture non-linear patterns. We identified six core topics: environmental impact, sustainable consumption, daily necessities, socio-economic impact, healthcare, and operations. The results revealed that topics of healthcare and daily necessities have immediate and sustained positive effects on firm value, while environmental and socio-economic impact topics demonstrate lagged effects, primarily two years after disclosure. The presence of assurance, however, produces mixed outcomes: it enhances credibility in some cases, but reduces firm value in others, especially when applied to environmental and socio-economic disclosures. This suggests a dual signaling effect of assurance, potentially increasing investor scrutiny when gaps in performance are highlighted. Our findings underscore the importance of topic selection, consistency in reporting, and strategic application of assurance in ESG communications to maintain stakeholder trust and market value. Full article