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Image super-resolution (SR) algorithms based on deep learning yield good visual performances on visible images. Due to the blurred edges and low contrast of infrared (IR) images, methods transferred directly from visible images to IR images have a poor performance and ignore the demands of downstream detection tasks. Therefore, an Inception Dilated Super-Resolution (IDSR) network with multiple branches is proposed. A dilated convolutional branch captures high-frequency information to reconstruct edge details, while a non-local operation branch captures long-range dependencies between any two positions to maintain the global structure. Furthermore, deformable convolution is utilized to fuse features extracted from different branches, enabling adaptation to targets of various shapes. To enhance the detection performance of low-resolution (LR) images, we crop the images into patches based on target labels before feeding them to the network. This allows the network to focus on learning the reconstruction of the target areas only, reducing the interference of background areas in the target areas' reconstruction. Additionally, a feature-driven module is cascaded at the end of the IDSR network to guide the high-resolution (HR) image reconstruction with feature prior information from a detection backbone. This method has been tested on the FLIR Thermal Dataset and the M3FD Dataset and compared with five mainstream SR algorithms. The final results demonstrate that our method effectively maintains image texture details. More importantly, our method achieves 80.55% mAP, outperforming other methods on FLIR Dataset detection accuracy, and with 74.7% mAP outperforms other methods on M3FD Dataset detection accuracy. [ABSTRACT FROM AUTHOR]

As global economic integration and rapid technological advancements transform international business, international engineering contracting has become essential for achieving sustainable development goals (SDGs). This paper investigates the significant impact of China's strategic initiatives, notably the "Going Global" strategy and the Belt and Road Initiative (BRI), on the operational practices of Chinese enterprises involved in overseas investments. Central to this transformation is the Engineering, Procurement, and Construction (EPC) model, which emphasizes the integration of supply chain management and stakeholder collaboration to enhance performance in international EPC projects and underscores the crucial role of these elements in promoting sustainability. Incorporating insights from social network data analysis, this study reveals that contractors collaborating with various stakeholders—such as owners/consulting engineers, domestic and foreign customs departments, and group headquarters/design parties—exhibit a high degree of similarity in personnel profiles. This suggests that the internal organizational structure and personnel allocation of contractors could be optimized to enhance operational efficiency, aligning with the collaborative patterns identified. This study addresses a critical research gap by exploring how effective supply chain management and collaborative stakeholder engagement within multinational EPC projects contribute to sustainable outcomes. Employing advanced social network analysis software, the research examines the complex interactions among stakeholders and their influence on procurement dynamics. Findings indicate that strong relational networks and strategic collaborations significantly enhance procurement efficiency and project success, underscoring the importance of supply chain integration. Ultimately, integrating supply chain management principles into the EPC model not only offers innovative perspectives for advancing sustainability in international projects but also provides actionable insights for improving project outcomes within the BRI framework. This research underscores the pivotal role of supply chain organization and stakeholder cooperation in achieving sustainability objectives, thereby enriching the discourse on sustainable enterprise operation and supply chain management in the context of global initiatives. [ABSTRACT FROM AUTHOR]

Objective Discuss the application methods and effects of closed femoro - femoral partial bypass (C-FPB) in thoracoabdominal aortic replacement (TAAAR) surgery. Methods A retrospective analysis of the clinical data of 70 cases of TAAAR assisted by C-FPB from April 2021 to May 2023, some of which combined with abdominal organ perfusion. The main evaluation indicators were in-hospital mortality, postoperative stroke, postop¬erative spinal cord injury, and the incidence of hemodialysis. Based on an open-type conventional Cardiopulmonary bypass (CPB) circuit, the venous tubing was split with a 10 x 10 x 10 mm "Y" connector directly connected to the inlet of the centrifugal pump, and the tubing after the roller pump was connected to the outlet of the centrifugal pump, while the front of the membrane oxygenator with another 10 x 10 x 10 mm "Y" connector to establish a closed femoro-femoral bypass. During bypass, the lower body is perfused by the centrifugal pump, while the upper body is perfused by the roller pump. Results No system failures happen in all cases. The average bypass time was (101.0 ± 22.2) minutes, the average time for intercostal artery reconstruction was (18.6 ± 5.4) minutes, with an average of (4.7 ± 1.8) pairs. The average length of stay in the ICU was (5.1 ± 1.5) days, and the average length of hospital stay was (34.4 ± 12.5) days. 2 cases (2.9%) experienced postoperative stroke, 1 case (1.4%) resulted in in-hospital mortality, 2 cases (2.9%) experienced postoperative paraplegia, 7 cases (10.0%) underwent postoperative hemodialysis, and 3 cases (4.3%) experienced prolonged mechanical ventilation. Conclusion The Closed-type femoro-femoral partial bypass takes the whole surgical process into account, reduces the management difficulty for perfusionist, and provides a unique advantage for distal perfusion during thoracoabdominal aortic replacement surgery. [ABSTRACT FROM AUTHOR]

This study investigated the effects of borehole diameter and borehole interval on the stress relief performance of coal seams during mining. A discrete element numerical model was established based on the Voronoi and block models, and the stress relief performance was investigated in terms of axial stress, vertical displacement, and plastic zone development using the control variates method. The results showed that a decrease in the distance from the workface was accompanied by an increase in the effects of mining, the stress concentration, and the vertical displacement of the roof. Coal mining had positive effects on fracture propagation in front of the workface. As the borehole interval increased, the density of boreholes in the coal seams decreased, reducing the stress concentration and fracture propagation in front of the workface. The stress relief performance of the boreholes in the coal seams was improved at a borehole interval of 8 m. However, as the borehole diameter increased, the stress concentration near the boreholes decreased, reducing the effects of the workface mining on stress relief through the boreholes. Meanwhile, increasing the borehole diameter decreased the vertical displacement of the roof but exacerbated fracture propagation in front of the workface, which improved the stress relief performance of the boreholes in the coal seams. This study provides a reference for arranging boreholes in coal seams during mining. [ABSTRACT FROM AUTHOR]

Reverse-time migration (RTM) is widely regarded as one of the most accurate migration methods available today. A crucial step in RTM involves extending seismic wavefields forward and backward. Compared to the conventional central finite-difference (CFD) scheme, the combined compact difference (CCD) scheme offers several advantages, including a shorter difference operator and the suppression of numerical dispersion under coarse grids. These attributes conserve memory and enhance effectiveness while maintaining the same level of differential precision. In this article, we begin with the five-point eighth-order CCD scheme and utilize the least squares method and Lagrange multiplier method to optimize the difference coefficients. This optimization is guided by the concept of dispersion-relation-preserving (DRP). The result is the acquisition of an optimized combined compact difference (OCCD) scheme, further enhancing the ability to suppress numerical dispersion. We thoroughly compare and analyze dispersion relationships and stability conditions. In addition, we examine several crucial steps in the RTM of the second-order acoustic wave equation. These steps include absorption boundary conditions, boundary storage strategy, and Poynting vector imaging conditions. Finally, we apply both the CCD and OCCD schemes in the RTM of the layered model, graben model, and SEG/EAGE salt model. We compare these results with those obtained from CFD's RTM. Numerical findings demonstrate that, in contrast to the CFD scheme, the CCD scheme effectively suppresses numerical dispersion and enhances imaging accuracy. Moreover, the optimized OCCD scheme further improves the ability to suppress numerical dispersion and can obtain better imaging results, which is an effective RTM method suitable for coarse grid conditions. [ABSTRACT FROM AUTHOR]

BeiDou navigation satellite system with global coverage (BDS-3) has been fully operational since July 2020, currently providing the positioning, navigation and timing service together with regional BDS-2. In addition to the legacy signals of B1I and B3I, the BDS-3 also transmits several new signals such as BIC, B2a and B2b, which brings new opportunities for rapid ambiguity resolution (AR) of BDS precise point positioning (PPP). In this contribution, a multi-frequency (MF) rapid PPP-AR method with regional network augmentation was proposed. Firstly, BDS five-frequency observations were introduced into uncombined double-differenced models to retrieve regional augmentation corrections at the server. Thereafter, a cascade PPP-AR strategy using extra-wide-lane, wide-lane and narrow-lane ambiguity was employed at the user. Once ambiguities were fixed to integers, the phase correction accuracy could reach about 3 cm on average overall BDS frequencies in the network with inter-station distances of 100–200 km. Subsequently, the statistical results of seven-day simulated kinematic experiments showed that over 99% of epochs on average realized PPP-AR. Correspondingly, the positioning accuracy of the MF fixed solution reached 1.8, 1.9, 4.7 cm in the east, north and up components, respectively, improving by 5–15% compared with the dual-frequency scheme. Moreover, several vehicle-borne experiments under different urban scenarios were also conducted. Under an open-sky or a relatively open highway scene, the BDS-MF scheme similarly exhibited good performance, and over 98% of epochs achieved rapid PPP-AR with a positioning accuracy better than 3 cm. More encouragingly, for this BDS-challenged experiment with an average satellite number of 8.6, although only 72.06% of epochs were available due to serious signal blockages caused by overpass, tunnels or tall buildings, the horizontal positioning accuracy could still remain 7 cm on average. [ABSTRACT FROM AUTHOR]

Purpose: Hippocampal-avoidance whole-brain radiotherapy (HA-WBRT) planning can present challenges. This study examines the influence of head tilt angles on the dosimetric characteristics of target and organs at risk (OARs), aiming to identify the optimal tilt angle that yields optimal dosimetric outcomes using tomotherapy (TOMO). Methods: Eight patients diagnosed with brain metastases underwent CT scans at five tilt angles: [0°, 10°), [10°, 20°), [20°, 30°), [30°, 40°), and [40°, 45°]. Treatment plans were generated using TOMO and volumetric modulated arc therapy (VMAT). Dosimetric parameters including conformity index (CI), homogeneity index (HI), D2cc, D98%, and Dmean of PTV, as well as Dmax, and Dmean of OARs were analyzed. Furthermore, a comparison was made between the dosimetric parameters of TOMO and VMAT plans. Finally, delivery efficiency of TOMO plans were assessed. Results: For the PTV, [40°, 45°] tilt angle demonstrated significantly better conformity, homogeneity, lower D2cc, and lower Dmean for the PTV. Regarding the OARs, the [40°, 45°] head tilt angle demonstrated significantly lower Dmax and Dmean in hippocampus, eyes, optic chiasm, and optic nerves. The [40°, 45°] tilt angle also showed significantly lower Dmax for brainstem and cochleas, as well as a lower Dmean for lens. In the [40°,45°] tilt angle for HA-WBRT, TOMO showed superior performance over VMAT for the PTV. TOMO achieved lower Dmax for brainstem, cochleas, optic nerves, and optic chiasm, as well as a lower Dmean for hippocampus. Furthermore, a significant correlation was found between delivery time and the PTV projection length in the sagittal plane. Conclusion: The TOMO plan utilizing a tilt angle range of [40°, 45°] demonstrated superior PTV conformity and uniformity, along with enhanced OARs sparing. Furthermore, it exhibited a dosimetric advantage over VMAT for PTV and most OARs at the same angle range. [ABSTRACT FROM AUTHOR]

Restorative environments have a positive impact on improving the physical and mental health of the elderly. In China, the proportion of elderly residents in aging urban communities is significantly higher than in newly constructed ones, making it essential to understand the restorative potential of public open spaces (POSs) in these settings to promote the development of aging-in-place models. To investigate this issue, we employed the Fuzzy Delphi Method (FDM) and the Analytic Hierarchy Process (AHP) to construct an evaluation system for the restorative potential of public open spaces. Subsequently, we assessed the restorative effects of POSs in the Dabeizhuang community using 320 residents' questionnaires and the fuzzy comprehension evaluation method. The results indicate that the dimension of safety is the most critical factor in creating restorative environments. In contrast, the dimension of comfort is the primary reason affecting the current evaluation of the community's restorative environment. By establishing a restorative environment evaluation system, this research will facilitate the creation of more restorative environments in urban areas, thereby promoting active and healthy aging among elderly residents. [ABSTRACT FROM AUTHOR]

The International IgA Nephropathy (IgAN) Network developed and validated two prognostic prediction models for IgAN, one incorporating a race parameter. These models could anticipate the risk of a 50% reduction in estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease (ESRD) subsequent to an IgAN diagnosis via renal biopsy. This investigation aimed to validate the International IgA Nephropathy Prediction Tool (IIgANPT) within a contemporary Chinese cohort. Within this study,185 patients diagnosed with IgAN via renal biopsy at the Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, between January 2012 and December 2021, were encompassed. Each patient's risk of progression was assessed utilizing the IIgANPT formula. The primary outcome, a 50% decline in eGFR or progression to ESRD, was examined. Two predictive models, one inclusive and the other exclusive of a race parameter, underwent evaluation via receiver-operating characteristic (ROC) curves, subgroup survival analyses, calibration plots, and decision curve analyses. The median follow-up duration within our cohort spanned 5.1 years, during which 18 patients encountered the primary outcome. The subgroup survival curves exhibited distinct separations, and the comparison of clinical and histological characteristics among the risk subgroups revealed significant differences. Both models demonstrated outstanding discrimination, evidenced by the areas under the ROC curve at five years: 0.882 and 0.878. Whether incorporating the race parameter or not, both prediction models exhibited acceptable calibration. Decision curve analysis affirmed the favorable clinical utility of both models. Both prognostic risk evaluation models for IgAN exhibited remarkable discrimination, sound calibration, and acceptable clinical utility. [ABSTRACT FROM AUTHOR]

In order to understand the community structure of plankton and environmental factors in the Chongqing section of the National Nature Reserve for Rare and Endemic Fishes (referred to as the "Reserve") along the upper Yangtze River, this study investigated phytoplankton and zooplankton in the water body from 2021 to 2022. The results revealed a diverse phytoplankton community with 243 species from 105 genera and 8 phyla, dominated by Bacillariophyta and Chlorophyta. Phytoplankton showed average densities of 1.7 × 106 cells/L and biomass of 2.6221 mg/L, following a seasonal pattern of summer > spring > winter. Zooplankton analysis identified 141 species from 77 genera and 4 phyla, with rotifers most abundant, followed by protozoa. Zooplankton displayed average densities of 0.17 × 104 ind./L and biomass of 0.3226 mg/L, also following a seasonal pattern of summer > spring > winter. Total phosphorus (TP) emerged as the primary environmental factor influencing plankton community structure, positively correlating with phytoplankton density and zooplankton biomass. Plankton biodiversity indices classified water quality in the Chongqing section of the "Reserve" as oligo-/mesotrophic. Overall, plankton diversity in this section is notably rich, with similar species composition between mainstems and tributaries but seasonal variations in community structure. While mainstem water quality generally meets standards, some tributaries exhibit varying degrees of pollution, underscoring the need for improved ecological management and protection measures. This is crucial for maintaining the sustainability of the ecosystem. [ABSTRACT FROM AUTHOR]

The photovoltaic poverty alleviation project, part of the "Ten Major Precise Poverty Alleviation Projects" implemented by the Poverty Alleviation Office of the State Council, significantly contributes to eradicating poverty and rural revitalization. A difference-in-differences model was utilized in this study to assess this project's impact on rural households. This analysis used tracking data from households both with photovoltaic equipment installed and without in "S Town," Jiangsu Province, from 2017 to 2021. The results indicate that photovoltaic installations lead to an increase in per capita disposable income, hence reducing poverty. However, further analysis suggests that better health and work capacity in disadvantaged households correlate with lesser benefits from the photovoltaic project in terms of income. The policy implications of these findings include a necessity for developing innovative models in the photovoltaic sector to enhance conditions for farmers, reducing their dependence on government subsidies. [ABSTRACT FROM AUTHOR]

Educational robots represent a unique form of teacher presence. Exploring how the communication features of robot instructors affect student learning experience could contribute to the advancement of educational robots. This study examined the impact of speech rate, voice type, and emotional tone of robots on students' cognitive load, attitudes toward robot-assisted teaching, and learning performance. We recruited 477 Chinese primary school students assigned to either the speech rate, voice type, or emotional tone experiment. The results indicate that speech rate significantly influenced students' cognitive load, with the medium speed condition resulting in higher germane load compared to both fast and slow speed conditions. Moreover, students had a lower preference for adult male voices over adult female, boy, or girl voices. However, voice type did not significantly impact attitudes toward robot-assisted teaching or learning outcomes. Emotional tone did not affect students' cognitive load, attitudes, or learning performance. These findings provide valuable insights for instructors and designers when configuring the communication features of educational robots in classroom environments. Additionally, students generally prioritized the intelligence of the robot over its communication features, and they did not perceive the teaching content as difficult in all experiments. This study has methodological and practical significance. [ABSTRACT FROM AUTHOR]

The silicon carbide (SiC) crystal growth is a multiple‐phase aggregation process of Si and C atoms. With the development of the clean energy industry, the 4H‐SiC has gained increasing attention as it is an ideal material for new energy automobiles and optoelectronic devices. The aggregation process is normally complex and dynamic due to its distinctive formation energy, and it is hard to study and trace back in a non‐destructive and comprehensive way. Here, this work developed a non‐destructive and deep learning‐enhanced characterization method of 4H‐SiC material, which was based on micro‐CT scanning, the verification of various optical measurements, and the convolutional neural network (ResNet‐50 architecture). Harmful defects at the micro‐level, polytypes, micropipes, and carbon inclusions could be identified and orientated with more than 96% high performance on both accuracy and precision. The three‐dimensional visual reconstruction with quantitative analyses provided a vivid tracing back of the SiC aggregation process. This work demonstrated a useful tool to understand and optimize the SiC growth technology and further enhance productivity. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to investigate the association between autoinducer‐2 (AI‐2) of oral microbial flora and the alveolar bone destruction in periodontitis to determine if AI‐2 may have the potential that monitor periodontitis and predict bone loss. Background: Plaque biofilm was the initiating factor of periodontitis and the essential factor of periodontal tissue destruction. The formation of biofilms depended on the complex regulation of the quorum sensing (QS) system, in which bacteria could sense changes in surrounding bacterial density by secreting the autoinducer (AI) to regulate the corresponding physiological function. Most oral bacteria also communicated with each other to form biofilms administrating the QS system, which implied that the QS system of periodontal pathogens was related to periodontitis, but the specific relationship was unknown. Method: We collected the gingival crevicular fluid (GCF) samples and measured the concentration of AI‐2 in samples using the Vibrio harveyi BB180 bioluminescent‐reporter system. To explore the interaction between AI‐2 and bone metabolism, we utilized AI‐2 purified from Fusobacterium nucleatum to investigate the impact of F. nucleatum AI‐2 on osteoclast differentiation. Moreover, we constructed murine periodontitis models and multi‐species biofilm models to study the association between AI‐2 and periodontal disease progression. Results: The AI‐2 concentration in GCF samples increased along with periodontal disease progression (p <.0001). F. nucleatum AI‐2 promoted osteoclast differentiation in a dose‐dependent manner. In the periodontitis mice model, the CEJ‐ABC distance in the F. nucleatum AI‐2 treatment group was higher than that in the simple ligation group (p <.01), and the maxilla of the mice in the group exhibited significantly lower BMD and BV/TV values (p <.05). Conclusions: We demonstrated that the AI‐2 concentration varied with the alveolar bone destruction in periodontitis, and it may have the potential for screening periodontitis. F. nucleatum AI‐2 promoted osteoclast differentiation in a dose‐dependent manner and aggravated bone loss. [ABSTRACT FROM AUTHOR]