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Abstract As electro-hydrostatic actuator (EHA) technology advances towards lightweight and integration, the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies. However, owing to the constraints imposed by traditional manufacturing processes, conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy dissipation. Notably, the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive process. This study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve block. The layout environment for the valve block passages was established, and path optimization was achieved using the ant colony algorithm, complemented by smoothing using B-spline curves. Three-dimensional modeling was performed using SolidWorks software, revealing a 10.03% reduction in volume for the optimized passages compared with the original passages. Computational fluid dynamics (CFD) simulations were performed using Fluent software, demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations, exhibited superior flow characteristics, and concurrently reduced pressure losses by 34.09%–36.36%. The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design, offering a viable solution for channel design in additive manufacturing.

Global energy and environmental issues are becoming increasingly problematic, and the vibration and noise problem of 110 kV transformers, which are the most widely distributed, have attracted widespread attention from both inside and outside the industry. DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers. To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias, a multi-field coupling model of a 110 kV transformer was established using the finite element method. The electromagnetic, vibration, and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC, harmonic, and DC power sources. It was found that a DC bias can cause significant distortions in the magnetic flux density, force, and displacement distributions of the core and winding. The contributions of the DC bias effect to the core and winding are different at Kdc = 0.85. At this point, the core approached saturation, and the increase in the core force and displacement slowed. However, the saturation of the core increased the leakage flux, and the stress and displacement of the winding increased faster. The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics. When the DC bias coefficient was 1.25, the noise sound pressure level reached 73.6 dB.

The activation and utilization of traditional villages was difficult due to the complex geographical characteristics. To explore the spatial differentiation of traditional villages spatial accessibility characteristics of and influencing factors. Taking 696 traditional villages in the Central Plains Urban Agglomeration of China as the research object, their spatial accessibility time costs were calculated via the path planner of Amap online map API and visualized based on ArcGIS10.8. Subsequently, we summarized three distinct activation methods, derived from a geographical perspective, which are day trips in the core area, weekend tours in the transition zone, and summer resort tourism in the fringe area. Finally, the spatial differentiation of each influencing factor was analyzed quantitatively via GWR. The results showed that (1) the spatial distribution of traditional villages was clustered, and there was a high-value core-cluster area in the north and two subhigh-value areas in the central and southern parts. (2) The spatial accessibility levels exhibited a “core-edge” pattern, decreasing from the core to the edge area. (3) Factors that exhibited spatial heterogeneity. Influence from the largest to the smallest: slope, density of river, density of road, GDP, density of population, elevation.

Abstract Coherent phonons have aroused considerable attention in condensed matter physics owing to their extraordinary capacity of reflecting and controlling the physical properties of matter. However, the investigation on the interaction between coherent phonons and other microscopic particles on the ultrafast timescale within topological systems continues to be an active and unresolved area. Here, we show the energy transfer of coherent optical phonons (COP) in Dirac semimetal PtTe2 thin films using ultrafast optical pump-probe spectroscopy. Specifically, the helicity-dependent light-driven anisotropic COP signals disclose their direct connection with the light-excited anisotropic spin-polarized electrons via an angular momentum transfer. Furthermore, we observe the notable decreases in the COP oscillation frequency and the decay rate with increasing temperatures due to the anharmonic phonon-phonon scattering and electron-phonon scattering in the COP dissipation process, respectively. Our work paves the way for uncovering the coherent phonons in Dirac semimetals for the potential applications in optoelectronics and opto-spintronics.

Abstract Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of ‘ground truth’, we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes.

Abstract Background Osteoarthritis (OA) is an aging-related degenerative joint disorder marked by joint discomfort and rigidity. Senescent chondrocytes release pro-inflammatory cytokines and extracellular matrix-degrading proteins, creating an inflammatory microenvironment that hinders chondrogenesis and accelerates matrix degradation. Targeting of senescent chondrocytes may be a promising approach for the treatment of OA. Herein, we describe the engineering of an injectable peptide-hydrogel conjugating a stem cell–homing peptide PFSSTKT for carrying plasmid DNA-laden nanoparticles and Tanshinon IIA (pPNP + TIIA@PFS) that was designed to attenuate OA progression by improving the senescent microenvironment and fostering cartilage regeneration. Results Specifically, pPNP + TIIA@PFS elevates the concentration of the anti-aging protein Klotho and blocks the transmission of senescence signals to adjacent healthy chondrocytes, significantly mitigating chondrocyte senescence and enhancing cartilage integrity. Additionally, pPNP + TIIA@PFS recruit bone mesenchymal stem cells and directs their subsequent differentiation into chondrocytes, achieving satisfactory chondrogenesis. In surgically induced OA model rats, the application of pPNP + TIIA@PFS results in reduced osteophyte formation and attenuation of articular cartilage degeneration. Conclusions Overall, this study introduces a novel approach for the alleviation of OA progression, offering a foundation for potential clinical translation in OA therapy.

Summary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Netrin-1 (NTN1) is involved in psychological alterations caused by central nerve system diseases. The primary objective of this research was to investigate whether a deficiency of neuron-derived NTN1 in the remote brain regions affects SCI outcomes. To examine the roles and mechanisms of neuron-derived NTN1 during SCI, Western blots, Nissl staining, immunochemical technique, RNA-sequence, and related behavioral tests were conducted in the study. Our study revealed that mice lacking NTN1 exhibited normal morphological structure of the spinal cords, hippocampus, and neurological function. While neuron-derived NTN1deletion mechanistically disrupted neuronal regeneration and aggregates neuronal apoptosis and ferroptosis in the intermediate phase following SCI. Additionally, neuroinflammation was significantly enhanced in the early phase, which could be related to activation of the NF-κB signaling pathway. Overall, our findings indicate that the deletion of neuron-derived NTN1 leads to the activation of the NF-κB pathway, contributing to the promotion of neuronal apoptosis and ferroptosis, and the pathological progression of SCI.

BackgroundMetagenomic next-generation sequencing (mNGS) has been widely reported to identify pathogens in infectious diseases (IDs). In this work, we intended to investigate the diagnostic value and clinical acceptance of paired-samples mNGS as compared to the culture method.MethodsA total of 361 patients with suspected infection were retrospectively included. With reference to the clinical diagnosis, we compared the diagnostic performance and clinical acceptance in pathogen detection between mNGS and culture tests. Moreover, the pathogen concordance of paired blood and respiratory tract (RT) samples in mNGS assay was investigated.ResultsAmong 511 samples, 62.04% were shown to be pathogen positive by mNGS, and that for clinical diagnosis was 51.86% (265/511). When compared to culture assay (n = 428), mNGS had a significantly higher positivity rate (51.87% vs. 33.18%). With reference to the clinical diagnosis, the sensitivity of mNGS outperformed that of culture (89.08% vs. 56.72%). Importantly, mNGS exhibited a clinically accepted rate significantly superior to that of culture. In addition, the mNGS result from 53 paired blood and RT samples showed that most pairs were pathogen positive by both blood and RT, with pathogens largely being partially matched.ConclusionThrough this large-scale study, we further illustrated that mNGS had a clinically accepted rate and sensitivity superior to those of the traditional culture method in diagnosing infections. Moreover, blood and paired RT samples mostly shared partial-matched positive pathogens, especially for pathogens with abundant read numbers in RT, indicating that both blood and RT mNGS can aid the identification of pathogens for respiratory system infection.

Objectives: Metagenomic next-generation sequencing (mNGS) is a powerful tool for pathogen detection. The accuracy depends on both wet lab and dry lab procedures. The objective of our study was to assess the influence of read length and dataset size on pathogen detection. Methods: In this study, 43 clinical BALF samples, which tested positive via clinical mNGS and were consistent with the diagnosis, were subjected to re-sequencing on the Illumina NovaSeq 6000 platform. The raw re-sequencing data, consisting of 100 million (M) paired-end 150 bp (PE150) reads, were divided into simulated datasets with eight different data sizes (5 M, 10 M, 15 M, 20 M, 30 M, 50 M, 75 M, 100 M) and five different read lengths (single-end 50 bp (SE50), SE75, SE100, PE100, and PE150). Both Kraken2 and IDseq bioinformatics pipelines were employed to analyze the previously diagnosed pathogens in the simulated data. Detection of pathogens was based on read counts ranging from 1 to 10 and RPM values ranging from 0.2 to 2. Results: Our results revealed that increasing dataset sizes and read lengths can enhance the performance of mNGS in pathogen detection. However, a larger data sizes for mNGS require higher economic costs and longer turnaround time for data analysis. Our findings indicate 20 M reads being sufficient for SE75 mode to achieve high recall rates. Additionally, high nucleic acid loads in samples can lead to increased stability in pathogen detection efficiency, reducing the impact of sequencing strategies. The choice of bioinformatics pipelines had a significant impact on recall rates achieved in pathogen detection. Conclusions: Increasing dataset sizes and read lengths can enhance the performance of mNGS in pathogen detection but increase the economic and time costs of sequencing and data analysis. Currently, the 20 M reads in SE75 mode may be the best sequencing option.

Focused on the problem that the stream computing platform was suffering from the high energy consumption and low efficiency due to the lack of consideration for energy efficiency in designing process, an energy-efficient optimization strategy based on elastic data migration in big data streaming platform (EEDM-BDSP) was proposed.Firstly, models of the load prediction and the resource judgment were set up, and the load prediction algorithm was designed, which predicted the load tendency and determine node resource occupancy, so as to find nodes of resource overload and redundancy.Secondly, models of the resource constraint and the optimal data migration were set up, and the optimal data migration algorithm was proposed, which data migration for the purpose of improving node resource utilization.Finally, model of the energy consumption was set up to calculate the energy consumption saved by the cluster after data migration.The experimental results show that the EEDM-BDSP changes node resources in the cluster can responded on time, the resource utilization and the energy-efficient are improved.

Abstract Background Adenomatous polyps (APs) with inflammation are risk factors for colorectal cancer. However, the role of inflammation-related gut microbiota in promoting the progression of APs is unknown. Methods Sequencing of the 16S rRNA gene was conducted to identify characteristic bacteria in AP tissues and normal mucosa. Then, the roles of inflammation-related bacteria were clarified by Spearman correlation analysis. Furthermore, colorectal HT-29 cells, normal colon NCM460 cells, and azoxymethane-treated mice were used to investigate the effects of the characteristic bacteria on progression of APs. Results The expression levels of inflammation-related markers (diamine oxidase, d-lactate, C-reactive protein, tumor necrosis factor-α, interleukin-6 and interleukin-1β) were increased, whereas the expression levels of anti-inflammatory factors (interleukin-4 and interleukin-10) were significantly decreased in AP patients as compared to healthy controls. Solobacterium moorei (S. moorei) was enriched in AP tissues and fecal samples, and significantly positively correlated with serum inflammation-related markers. In vitro, S. moorei preferentially attached to HT-29 cells and stimulated cell proliferation and production of pro-inflammatory factors. In vivo, the incidence of intestinal dysplasia was significantly increased in the S. moorei group. Gavage of mice with S. moorei upregulated production of pro-inflammatory factors, suppressed proliferation of CD4+ and CD8+cells, and disrupted the integrity of the intestinal barrier, thereby accelerating progression of APs. Conclusions S. moorei accelerated the progression of AP in mice via activation of the NF-κB signaling pathway, chronic low-grade inflammation, and intestinal barrier disruption. Targeted reduction of S. moorei presents a potential strategy to prevent the progression of APs.

On the production side, blockchain is being widely applied to agricultural product supply chains (APSCs), which can effectively improve circulation efficiency and reduce transportation losses. On the sales side, many companies are utilizing key opinion leaders (KOLs) to promote and sell agricultural products. The combination of the two enhances the farm-to-fork transparency of agricultural products and stimulates consumer purchases. Based on these, we construct four theoretical models to study blockchain investment and livestreaming mode strategies in the APSC. The results show that investing in blockchain always stimulates consumer purchases of agricultural products, while KOL livestreaming increases consumer purchases only when the increase-traffic power is greater than a certain threshold. There is a win-win situation where the fresh product supplier (FPS) and the e-retailer can benefit from investing in blockchain and introducing KOL livestreaming, respectively. Investing in blockchain is always beneficial for the KOL, and there is free-riding behavior in the APSC. Interestingly, the enhanced increase-traffic power within a certain interval may become a negative driving force, seriously harming the FPS. In addition, we find that investing in blockchain and introducing KOL livestreaming does not always benefit consumer surplus and social welfare, which depend on the KOL’s increase-traffic power, commission rate, and unit cost.

Cu interconnect chemical mechanical polishing (CMP) technology has been continuously evolving, leading to increasingly stringent post-CMP cleaning requirements. To address the environmental pollution caused by traditional post-CMP cleaning solutions, we have explored the use of photocatalytic processes to remove citric acid, which is a commonly used complexing agent for CMP. In this study, CeO2 abrasives, characterized by a hardness of 5.5, are extensively employed in CMP. Importantly, CeO2 also exhibits a suitable band structure with a band gap of 2.27 eV, enabling it to photocatalytically remove citric acid, a commonly used complexing agent in Cu CMP. Additionally, the integration of H2O2, an essential oxidant in Cu CMP, enhances the photocatalytic degradation efficiency. The research indicates that the removal rate of single-phase CeO2 was 1.78 mmol/g/h and the degradation efficiency increased by 40% with the addition of H2O2, attributed to the hydroxyl radicals generated from a Fenton-like reaction between H2O2 and CeO2. These findings highlight the potential of photocatalytic processes to improve organic contaminant removal in post-CMP cleaning, offering a more sustainable alternative to conventional practices.

Campylobacter jejuni (C. jejuni) is a common pathogen that often causes diarrhea, loss of appetite, and even enteritis in domestic cats, affecting their growth and development, especially in kittens under 6 months of age. Oral passive immunization with chicken yolk antibody Y has been proved effective for the treatment of gastrointestinal pathogen infections due to its high specificity. In this study, C. jejuni was isolated from diarrheal cat feces, and the specific egg yolk antibody Y against C. jejuni was demonstrated to effectively inhibit its proliferation in vitro experiments. To evaluate the effect of anti-C. jejuni IgY, the mouse C. jejuni infection model was established and it was found that IgY could alleviate C. jejuni-induced clinical symptoms. Consistent with these results, the reduction of pro-inflammatory factors and intestinal colonization by C. jejuni in the IgY-treated groups, especially in the high dose group. We then evaluated the protective effect of IgY on young Ragdoll cats infected with C. jejuni. This specific antibody reduced the rate of feline diarrhea, protected the growth of young cats, inhibited systemic inflammatory hyperactivation, and increased fecal short-chain fatty acid concentrations. Notably, IgY may have a protective role by changing intestinal amino acid metabolism and affecting C. jejuni chemotaxis. Collectively, specific IgY is a promising therapeutic strategy for C. jejuni-induced cat diarrhea.

Aiming at the insufficient anti-impact performance of existing components of the energy-absorbing anti-impact support, a foam aluminum filled multicellular square tube structure is proposed. According to the axial energy absorption theory of thin-walled structures filled with foam materials, the formula of average crushing load is obtained. The axial impact simulation of square tube, multicellular square tube and foam aluminum filled multicellular square tube are completed by using ABAQUS/Explicit. On this basis, the impact resistance of ordinary hydraulic column and energy absorbing hydraulic column is analyzed. The results showed that compared with the square tube and multicellular square tube, the foam aluminum filled multicellular square tube has an ideal axisymmetric progressive deformation. The initial load peak value, load bearing-mean and energy absorption are greatly improved, the effective deformation distance is reduced, the load fluctuation is reduced, and the load carrying efficiency is improved. With the increase of foam aluminum filling ratio, the effective deformation distance and energy absorption of the component are reduced. As the porosity of aluminum foam decreases, the force variance decreases, and energy absorption and the load carrying efficiency increase. Aluminum foam-filled multicellular square tube with 60% porosity at 25% filling rate is an ideal energy absorption component. Ordinary column surges after impact load, bending deformation is serious, column relies on its own deformation to absorb energy, supporting effect is poor. After the energy-absorbing column is impacted, the energy absorption component begins to deform and absorb energy. Greatly reduce the load of the column, absorb most of the outside impact energy, avoid the column bending deformation, improve the impact resistance of the support.

Focused on the problem that the planned routes cannot meet the personalized demand of different users in route planning of personalized self-driving tour, a keyword-aware optimal route planning method based on different user interests was proposed.Firstly, the road network information preprocessing model was set up and the road network information query graph was built by the road network information preprocessing algorithm.Secondly, the inverted index algorithm was proposed to prune the road network information query graph according to the personalized requirements from users, which improved the execution efficiency of keyword-aware optimal route planning method and reduced the memory cost of large-scale data processing effectively.Finally, the keyword-aware optimal route planning algorithm was proposed to realize personalized recommendation according to user interest by bidirectional parallel extension.The experimental results show that the method not only realizes the route planning to meet the individual needs of users but also improves the execution efficiency of the method through pruning and bidirectional parallel extension.

As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizing satellite remote sensing for lock extraction can significantly reduce manual workload and costs, assist in the daily dynamic maintenance of lock hubs, and provide more comprehensive data support for the construction and management of water transport infrastructure. In this context, this paper proposes a new method for ship lock object extraction. Leveraging fuzzy theory and prior knowledge of locks, the extraction of lock objects is achieved from Gaofen-1 (GF-1) high-resolution remote sensing images. The experimental results demonstrate that the proposed algorithm can effectively extract small lock objects in remote sensing images, achieving an average extraction accuracy of 80.9% in the study area.

Studying urban land use and its impact on carbon emissions is crucial for achieving China’s dual carbon goals. This research utilized the Shared Socio-economic Pathways (SSPs) scenarios 126, 245, and 585 from the Sixth International Coupled Model Intercomparison Project (CMIP6), along with a coupled System Dynamics (SD) and Patch-generating Land Use Simulation (PLUS) model and a carbon emission coefficient method to simulate and predict Xi’an’s land use carbon emissions from 2020 to 2040. The results indicate the following: (1) Cultivated and forest lands are the predominant land use types in Xi’an, with cultivated and grassland areas projected to decline under all three SSP scenarios by 2040. The most significant expansion of construction land, primarily at the expense of farmland, is projected under the SSP585 scenario, with an increase of 515.92 km2 by 2040. (2) Land use carbon emissions increased from 414.15 × 104 t in 2000 to 2376.10 × 104 t in 2020, with construction land being the primary source of emissions and forest land serving as the main carbon sink. However, the carbon sink capacity remained low at only 21.38 × 104 t in 2020. (3) Carbon emissions are expected to continue increasing under all scenarios through 2030 and 2040, though at a decreasing rate. The SSP126 scenario predicts the lowest emissions, reaching 9186.00 × 104 t by 2040, while SSP585 predicts the highest at 14,935.00 × 104 t. The findings of this study provide theoretical support for future low-carbon and high-quality urban development strategies.

The performance of the supercritical CO2 (sCO2) Brayton cycle deteriorates due to the mismatch between critical temperature and cold source temperature. An innovative solution based on ternary CO2 mixtures with adjustable compositions is proposed to expand the variation range of the critical temperature. An in-house MATLAB code is developed to investigate the design-point performance of the proposed system. The results indicate that the effect of improving cycle efficiency by using the modified floating critical point method becomes increasingly significant as the target critical temperature deviates from that of CO2. The CO2–H2S–Kr is screened out as the best ternary mixture, with the cycle efficiency being increased by over 8 % compared to the sCO2 cycle at very high (45 °C) and low (−15 °C) temperatures. The long-term performance is also evaluated based on the hourly historical weather data of two typical climate regions. The annual performance depends on both the average value and the distribution characteristics of the local ambient temperature. A relative improvement in annual average efficiency of 7%–9.56 % can be obtained depending on the regions. The present work is helpful to make the supercritical Brayton cycle give full play to its efficiency advantage in a wide cold source temperature range.

Abstract Medical devices are commonly implanted underneath the skin, but how to real‐time noninvasively monitor their migration, integrity, and biodegradation in human body is still a formidable challenge. Here, the study demonstrates that benzyl violet 4B (BV‐4B), a main component in the FDA‐approved surgical suture, is found to produce fluorescence signal in the first near‐infrared window (NIR‐I, 700–900 nm) in polar solutions, whereas BV‐4B self‐assembles into highly crystalline aggregates upon a formation of ultrasmall nanodots and can emit strong fluorescence in the second near‐infrared window (NIR‐II, 1000–1700 nm) with a dramatic bathochromic shift in the absorption spectrum of ≈200 nm. Intriguingly, BV‐4B‐involved suture knots underneath the skin can be facilely monitored during the whole degradation process in vivo, and the rupture of the customized BV‐4B‐coated silicone catheter is noninvasively diagnosed by NIR‐II imaging. Furthermore, BV‐4B suspended in embolization glue achieves hybrid fluorescence‐guided surgery (hybrid FGS) for arteriovenous malformation. As a proof‐of‐concept study, the solid‐state BV‐4B is successfully used for NIR‐II imaging of surgical sutures in operations of patients. Overall, as a clinically translatable solid‐state dye, BV‐4B can be applied for in vivo monitoring the fate of medical devices by NIR‐II imaging.

Wildfires have long been a danger to the atmosphere and ecological environment. With the advancement of deep learning technology and sensor equipment, wildfire identification methods based on optical (RGB) or thermal infrared (TIR) images have made significant progress. However, single-modal identification technologies often experience serious performance degradation in challenging scenarios. Therefore, a multi-modal framework that combines both RGB and TIR is necessary for accurate wildfire identification. Despite this, current RGB-T based identification methods focus solely on developing fusion strategies to learn shared representations of the two modalities, while ignoring the effect of modality-specific features. To address this issue, we propose a novel wildfire identification framework that can adaptively learn modality-specific and shared features. Specifically, our model utilizes two parallel encoders to extract multi-scale RGB and TIR features, and integrates these modality-specific features into the fusion feature layer. Each modality-specific decoder and shared decoder corresponds to three independent label supervisions that oversee feature adaptive learning. To validate the effectiveness of our approach, we propose a new RGB-T paired wildfire semantic segmentation dataset that includes flames of different scales captured under varying lighting conditions from multiple UAV-based and ground-based cameras. Finally, we evaluate our proposed method alongside benchmarking single-modal methods and state-of-the-art multi-modal methods on two tasks: a common test setting under various lighting conditions, and a generalization test across three subsets. The experimental results demonstrate the superiority of our proposed method, with the average ＋6.41% (IoU) and ＋3.39% (F1-score) improvement over the second-best RGB-T semantic segmentation method.

Abstract In recent years, the survival and development of traditional villages in China have been serious challenges. Rural tourism is regarded as an important way to solve rural problems, and the combination of rural culture and tourism has become a new power point for rural development. Therefore, it is necessary to explore the spatial distribution structure between traditional villages and rural tourism. In this paper, rural tourism was represented by the rural tourism characteristic village (RTCV), and Henan Province, China, was taken as a study area to analyze the distribution pattern and spatial correlation of rural tourism and traditional village (TV) and discuss the relationship between the spatial correlation and regional natural environment and socioeconomic factors. The results show that the coupling of the spatial correlation between RTCVs and TVs in Henan was clear. They could be divided into 5 regions based on geographical factors. In addition, the research summarized 4 typical spatial structures between TVs and RTCVs in Henan based on the regional symbiosis theory, and the spatial pattern formation mechanism of TVs and RTCVs was discussed based on three driving mechanisms. The spatial structure of the two can provide reference value for other developing countries and regions to achieve sustainable rural development.

Abstract Background Epicardial adipose tissue (EAT) accumulation is associated with multiple cardiometabolic risk factors and prognosis of heart failure with preserved ejection fraction (HFpEF). The correlation between EAT density and cardiometabolic risk and the effect of EAT density on clinical outcome in HFpEF remain unclear. We evaluated the relationship between EAT density and cardiometabolic risk factors, also the prognostic value of EAT density in patients with HFpEF. Methods We included 154 HFpEF patients who underwent noncontrast cardiac computed tomography (CT) and all patients received follow-up. EAT density and volume were quantified semi-automatically. The associations of EAT density and volume with cardiometabolic risk factors, metabolic syndrome and the prognostic impact of EAT density were analyzed. Results Lower EAT density was associated with adverse changes in cardiometabolic risk factors. Each 1 HU increase in fat density, BMI was 0.14 kg/m2 lower (95% CI 0.08–0.21), waist circumference was 0.34 cm lower (95% CI 0.12–0.55), non-HDL-cholesterol was 0.02 mmol/L lower (95% CI 0–0.04), triglyceride was 0.03 mmol/L lower (95% CI 0.01–0.04), fasting plasma glucose was 0.05 mmol/L lower (95% CI 0.02–0.08), TyG index was 0.03 lower (95% CI 0.02–0.04), Log2(TG/HDL-C) was 0.03 lower (95% CI 0.02–0.05), METS-IR was 0.36 lower (95% CI 0.23–0.49), MetS Z-score was 0.04 lower (95% CI 0.02–0.06), and Log2(CACS + 1) was 0.09 lower (95% CI 0.02–0.15). After adjusting for BMI and EAT volume, the associations of non-HDL-cholesterol, triglyceride, fasting plasma glucose, insulin resistance indexes, MetS Z-score, and CACS with fat density remained significant. The area under the curve (AUC) for the presence and severity of metabolic syndrome was greater in EAT density than volume (AUC: 0.731 vs 0.694, 0.735 vs 0.662, respectively). Over a median follow-up of 16 months, the cumulative incidence of heart failure readmission and composite endpoint increased with lower level of EAT density (both p

Proinflammatory macrophages are involved in rheumatoid arthritis (RA). Here the authors use an efferocytosis-mimetic self-deliverable nanoimitator to mitigate RA by targeted reprogramming of synovial inflammatory macrophages, reducing proinflammatory cytokines and reinstating articular immune homeostasis.

Physical exercise effectively alleviates chronic pain associated with complex regional pain syndrome type-I. However, the mechanism of exercise-induced analgesia has not been clarified. Recent studies have shown that the specialized pro-resolving lipid mediator resolvin E1 promotes relief of pathologic pain by binding to chemerin receptor 23 in the nervous system. However, whether the resolvin E1-chemerin receptor 23 axis is involved in exercise-induced analgesia in complex regional pain syndrome type-I has not been demonstrated. In the present study, a mouse model of chronic post-ischemia pain was established to mimic complex regional pain syndrome type-I and subjected to an intervention involving swimming at different intensities. Chronic pain was reduced only in mice that engaged in high-intensity swimming. The resolvin E1-chemerin receptor 23 axis was clearly downregulated in the spinal cord of mice with chronic pain, while high-intensity swimming restored expression of resolvin E1 and chemerin receptor 23. Finally, shRNA-mediated silencing of chemerin receptor 23 in the spinal cord reversed the analgesic effect of high-intensity swimming exercise on chronic post-ischemic pain and the anti-inflammatory polarization of microglia in the dorsal horn of the spinal cord. These findings suggest that high-intensity swimming can decrease chronic pain via the endogenous resolvin E1-chemerin receptor 23 axis in the spinal cord.

Radio frequency (RF) repeaters have been deployed in 2G, 3G and 4G to improve the coverage of cellular system.While it is treated as a cost effective solution to extend the network coverage, conventional RF repeaters simply perform amplify-and-forward operation based on the set-up without the capability of doing flexible adjustments based on control and scheduling indication from network.An overview on the new study item in 5G-Advanced was given, i.e.network-controlled repeater.As an enhancement over conventional RF repeaters, network-contrdled repeater will enable the repeater to receive and process repeater control information from the network and perform its amplify-and-forward operation in a more efficient manner including the directive forwarding of the signal, dynamic on-/off of transmissions and receptions, and simplified network integration.

The emergence of modern prosthetics controlled by bio-signals has been facilitated by AI and microchip technology innovations. AI algorithms are trained using sEMG produced by muscles during contractions. The data acquisition procedure may result in discomfort and fatigue, particularly for amputees. Furthermore, prosthetic companies restrict sEMG signal exchange, limiting data-driven research and reproducibility. GANs present a viable solution to the aforementioned concerns. GANs can generate high-quality sEMG, which can be utilised for data augmentation, decrease the training time required by prosthetic users, enhance classification accuracy and ensure research reproducibility. This research proposes the utilisation of a one-dimensional deep convolutional GAN (1DDCGAN) to generate the sEMG of hand gestures. This approach involves the incorporation of dynamic time wrapping, fast Fourier transform and wavelets as discriminator inputs. Two datasets were utilised to validate the methodology, where five windows and increments were utilised to extract features to evaluate the synthesised sEMG quality. In addition to the traditional classification and augmentation metrics, two novel metrics—the Mantel test and the classifier two-sample test—were used for evaluation. The 1DDCGAN preserved the inter-feature correlations and generated high-quality signals, which resembled the original data. Additionally, the classification accuracy improved by an average of 1.21–5%.

Polydopamine (PDA) as a melanin-like biomimetic material with excellent biocompatibility, full spectrum light absorption capacity and antioxidation property has been extensively applied in the biomedical field. Based on the high reactivity of dopamine (DA), exploiting new strategies to fabricate novel PDA-based nano-biomaterials with controllable size and improved performance is valuable and desirable. Herein, we reported a facile way to synthesize pyrrole-doped polydopamine-pyrrole nanoparticles (PDA-nPY NPs) with tunable size and enhanced near-infrared (NIR) absorption capacity through self-oxidative polymerization of DA with PY in an alkaline ethanol/H2O/NH4OH solution. The PDA-nPY NPs maintain excellent biocompatibility and surface reactivity as PDA. By regulating the volume of added PY, PDA-150PY NPs with a smaller size (

Total hip arthroplasty (THA) is the most common and radical treatment for end-stage hip disease. Prosthesis loosening and periprosthetic infection are the most serious complications after THA, which may lead to prosthetic dysfunction. The early prevention of infection and the promotion of fast osseointegration between the prosthesis and host bone are essential to the success of THA. However, Ti6Al4V does not have biological function, such as promoting osseointegration and antibacterial properties, which limits its widespread use. Therefore, it is necessary to design a functional hip prosthesis to simultaneously reduce the incidence of loosening and periprosthetic infection. In this study, we developed a novel functionalized coating via micro-arc oxidation modification combined with vancomycin to treat a prosthetic surface for the early postoperative period to resist bacterial infection and promote osseointegration. The functionalized coating simultaneously prevents periprosthetic infections and promotes osseointegration at the prosthesis–host bone interface. We successfully constructed a prosthetic coating with the dual function of promoting osseointegration and preventing infection, which provides a practical and effective method for the treatment of periprosthetic infection and prosthetic loosening after THA.

Blockchain is currently used in a wide range of industries to improve the efficiency of the circulation of goods and effectively reduce counterfeiting in supply chains. In order to improve consumer trust in their purchases and reduce returns, the paper develops four models of consumers return based on blockchain technology from a consumer utility perspective. We conducted a Stackelberg game to analyze the impact of return modes and blockchain technology on optimal decisions and consumers, where consumers can return goods through the original channel, all through the online channel, and all through the offline channel. The major results of our study show that when blockchain technology is not used, the costs of return hassles in one channel can have an impact on other channels, and the adoption of online returns is advantageous to both consumers and the retailer. When blockchain technology is used, the manufacturer offers the retailer a lower wholesale price as a subsidy for the unit validation fee, which is always advantageous to the retailer. In most situations, implementing blockchain technology can boost consumer surplus. Only if the fixed cost of blockchain technology is low would the manufacturer adopt it.

ObjectiveTo explore the effect of inspiratory muscle training (IMT) on patients with heart failure and further explore the impact of IMT on patients with heart failure with preserved ejection fraction.MethodsPubMed, EMBASE, Cochrane Library, CNKI, Wanfang and VIP databases were systematically searched. Randomized controlled trials of inspiratory muscle training in patients with heart failure were included. Revman 5.3 software was used to calculate the weighted mean difference (MD) of the combined effect size. The effects of IMT on the maximum oxygen uptake (peakVO2), maximum inspiratory pressure (PImax), ventilation efficiency (VE/VCO2), six-minute walking distance (6MWD), forced expiratory volume (FEV1), forced vital capacity (FVC) and quality of life in patients with heart failure were compared and analyzed.ResultsAfter systematic retrieval and screening, 17 studies were included in this study, and the quality of the included studies was good. The results showed that IMT could increase peakVO2 (MD 2.53; 95% CI 1. 54, 3. 51; P < 0.0001) and PImax (MD 17.25; 95% CI 13. 75, 20. 75; P < 0.00001); improve the VE/VCO2 (MD −4.22; 95% CI −6.78, −1.66; P = 0.001) and significantly improve the quality of life in patients with heart failure (MD −13.34; 95% CI −20.42, −6.26; P = 0.0002). However, the effect of IMT on 6MWD in patients with heart failure was not statistically significant (MD 74.45; 95% CI −12.88,161.79; P = 0.09), and the effect on lung function (FEV1 and FVC) was also not statistically significant (P = 0.08; P = 0.86). IMT had a more significant positive effect on peakVO2 (MD 2.98; 95% CI 1.63, 4.34; P < 0.0001) and quality of life (MD −14.52; 95% CI −18.53, −10.52; P < 0.00001) in patients with heart failure with preserved ejection fraction. Descriptive analysis suggested that IMT may positively affect dyspnoea in patients with heart failure. In addition, the choice of evaluation scale may affect the evaluation results of quality of life and dyspnoea.ConclusionIMT has a significant positive effect on respiratory status in patients with heart failure, but different dyspnoea and quality of life evaluation scales can affect the final evaluation results.

Culm color variation is an interesting phenomenon that contributes to the breeding of new varieties of ornamental plants during domestication. De-domesticated variation is considered ideal for identifying and interpreting the molecular mechanisms of plant mutations. However, the variation in culm color of bamboo remains unknown. In the present study, yellow and green culms generated from the same rhizome of Phyllostachys vivax cv. Aureocaulis (P. vivax) were used to elucidate the molecular mechanism of culm color formation. Phenotypic and physiological data showed that environmental suitability was higher in green culms than in yellow culms. High-throughput sequencing analysis showed 295 differentially expressed genes (DEGs) and 22 differentially expressed miRNAs (DEMs) in two different colored bamboo culms. There were 103 DEM-DEG interaction pairs, of which a representative “miRNA-mRNA” regulatory module involved in photosynthesis and pigment metabolism was formed by 14 DEM-DEG pairs. The interaction of the three key pairs was validated by qPCR and dual-luciferase assays. This study provides new insights into the molecular mechanism of miRNAs involved in P. vivax culm color formation, which provides evidence for plant de-domestication and is helpful for revealing the evolutionary mechanism of bamboo.

The integrated design of the motor and axial piston pump eliminates the coupling structure, resulting in a compact and lightweight motor-pump structure. The challenge of motors overheating has always been a major concern. To address this issue, the hydraulic oil throughout the motor pump is utilized for cooling the high-speed motor, effectively improving the power density and heat dissipation capability of the hydraulic power unit. This integrated design approach has successfully resolved the significant issue of overheating motors, leading to enhanced performance of the hydraulic power unit. To address this concern, the entire motor pump’s oil is utilized to cool the high-speed motor. Consequently, the thermodynamic prediction of high-speed motor pumps has become increasingly important. In this study, the impact of motor heat generation on hydrodynamics is analyzed, and the heat transfer of the motor pump is investigated using the control volume method. Furthermore, thermodynamic models of hysteresis loss, eddy current loss, alternating current loss, churning loss, and throttling loss are established for the oil-immersed motor pump. The change in oil viscosity is also considered. The instantaneous temperature change rule of the oil within the oil-immersed motor pump is derived. Additionally, the influence of various working conditions such as pressure and speed on the temperature of the motor pump’s key node is examined. The experimental results indicate the accuracy of the thermodynamic calculation, and the significant effect of motor loss on the leakage temperature.

The most significant technical challenges of current aerial image object-detection tasks are the extremely low accuracy for detecting small objects that are densely distributed within a scene and the lack of semantic information. Moreover, existing detectors with large parameter scales are unsuitable for aerial image object-detection scenarios oriented toward low-end GPUs. To address this technical challenge, we propose efficient-lightweight You Only Look Once (EL-YOLO), an innovative model that overcomes the limitations of existing detectors and low-end GPU orientation. EL-YOLO surpasses the baseline models in three key areas. Firstly, we design and scrutinize three model architectures to intensify the model’s focus on small objects and identify the most effective network structure. Secondly, we design efficient spatial pyramid pooling (ESPP) to augment the representation of small-object features in aerial images. Lastly, we introduce the alpha-complete intersection over union (α-CIoU) loss function to tackle the imbalance between positive and negative samples in aerial images. Our proposed EL-YOLO method demonstrates a strong generalization and robustness for the small-object detection problem in aerial images. The experimental results show that, with the model parameters maintained below 10 M while the input image size was unified at 640 × 640 pixels, the APS of the EL-YOLOv5 reached 10.8% and 10.7% and enhanced the APs by 1.9% and 2.2% compared to YOLOv5 on two challenging aerial image datasets, DIOR and VisDrone, respectively.

Wide-range application scenarios, such as industrial, medical, rescue, etc., are in various demand for human spatial positioning technology. However, the existing MEMS-based sensor positioning methods have many problems, such as large accuracy errors, poor real-time performance and a single scene. We focused on improving the accuracy of IMU-based both feet localization and path tracing, and analyzed three traditional methods. In this paper, a planar spatial human positioning method based on high-resolution pressure insoles and IMU sensors was improved, and a real-time position compensation method for walking modes was proposed. To validate the improved method, we added two high-resolution pressure insoles to our self-developed motion capture system with a wireless sensor network (WSN) system consisting of 12 IMUs. By multi-sensor data fusion, we implemented dynamic recognition and automatic matching of compensation values for five walking modes, with real-time spatial-position calculation of the touchdown foot, enhancing the 3D accuracy of its practical positioning. Finally, we compared the proposed algorithm with three old methods by statistical analysis of multiple sets of experimental data. The experimental results show that this method has higher positioning accuracy in real-time indoor positioning and path-tracking tasks. The methodology can have more extensive and effective applications in the future.

Abstract The purpose of relation extraction is to identify the semantic relations between entities in sentences that contain two entities. Recently, many variants of the convolution neural network (CNN) have been introduced to relation extraction for the extracting of features—the quality of the neural network model directly affects the final quality of relation extraction. However, the traditional convolution network uses a fixed convolution kernel, so it is difficult to choose the size of the convolution kernel dynamically, which results in networks with weak representation ability. To address this, a novel CNN is designed with selective kernel networks and multigranularity. In the process of feature extraction, the model can adaptively select the size of the convolution kernel, that is, give more weight to the appropriate convolution kernel. It is then combined with multigranularity convolution to obtain more abundant semantic information. Finally, a new pooling method is designed to obtain more comprehensive information and improve model performance. Experimental results indicate that this method is effective without excessively deep network layers, and it also outperforms several competitive baseline methods.

With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time. Unfortunately, the domain shift between multi-source remote sensing data from various sensors and regions poses a significant challenge. Segmentation models face difficulty in adapting to incremental domains due to catastrophic forgetting, which can be addressed via incremental learning methods. However, current incremental learning methods mainly focus on class-incremental learning, wherein classes belong to the same remote sensing domain, and neglect investigations into incremental domains in remote sensing. To solve this problem, we propose a domain-incremental learning method for semantic segmentation in multi-source remote sensing data. Specifically, our model aims to incrementally learn a new domain while preserving its performance on previous domains without accessing previous domain data. To achieve this, our model has a unique parameter learning structure that reparametrizes domain-agnostic and domain-specific parameters. We use different optimization strategies to adapt to domain shift in incremental domain learning. Additionally, we adopt multi-level knowledge distillation loss to mitigate the impact of label space shift among domains. The experiments demonstrate that our method achieves excellent performance in domain-incremental settings, outperforming existing methods with only a few parameters.

Abstract Background To report the prevalence of ocular abnormalities and investigate visual acuity in a large cohort of retinitis pigmentosa (RP) patients in Western China. Methods The medical records and ophthalmic examination reports of 2127 eyes of 1065 RP patients at one eye hospital were retrospectively reviewed to determined the prevalence of ocular abnormalities and the relationship between best corrected visual acuity (BCVA) and macular abnormalities. Results Nyctalopia (58.2%) and blurred vision (27.1%) were the leading reasons for RP patients to request an ophthalmic examination. BCVA measurements in the better eyes at first clinical presentation showed that 304 patients (28.5%) were categorised as blind and 220 patients (20.7%) as low vision. The most common ocular abnormalities were macular abnormalities (59.7%) and cataracts (43.1%). The macular abnormalities included epiretinal membranes (51.1%), cystoid macular edema (18.4%), vitreomacular traction syndrome (2.4%), macular holes (2.3%) and choroidal neovascular membranes (0.05%). Glaucoma was found in 35 eyes (1.6%). The proportions of epiretinal membranes (p = 0.001) and macular holes (p = 0.008) increased significantly with age. Cystoid macular edema was significantly associated with poorer visual acuity in RP patients with clear lens (p = 0.002). Conclusion Cataracts and macular abnormalities are common in RP patients. Of the macular abnormalities, cystoid macular edema may have a negative effect on BCVA in RP patients with clear lens. Therefore, optical coherence tomography screening in RP patients is highly recommended for early detection and treatment of maculopathy.

As one of the most popular high-performance data processing technology, existing task and resource scheduling strategies for stream computing platforms are suffering from the problem of triggering hysteresis, which seriously affects the cluster performance. To address this problem, the idea of performance prediction based on timeline series data is proposed. Firstly, the performance variation rule of the stream processing platform is analyzed, which provides a basis for proposing the performance prediction model. Secondly, the basic topology paradigm, periodic load prediction model, and real-time throughput prediction model are proposed as the theoretical foundation for performance prediction. Thirdly, the performance prediction algorithm is proposed to predict the variation trend of the processing load and throughput of the cluster. The processing load is predicted periodically while the throughput of the cluster is predicted in a real-time manner. Finally, the performance evaluation algorithm is proposed to evaluate the cluster performance based on the prediction results and trigger the corresponding scheduling strategies in advance. The experimental results showed that the prediction accuracy of the proposed algorithm meets the requirements in practical applications. Meanwhile, the proposed method improves the performance of stream computing performance by triggering the scheduling strategies in advance.

Background: Congenital muscular torticollis is a condition typically managed by a combination of nonsurgical and surgical means. Methods of preoperative evaluation and surgical treatment options remain areas of continued study. Methods: Novel MRI-based 3D visualization software is utilized to evaluate the sternocleidomastoid bilaterally for preoperative planning and stair-step surgical treatment of an eight-year-old male with congenital muscular torticollis. Results: Presurgical measurements demonstrated head tilt of 8°, with spine radiographs revealing leftward listhesis of cervical spine, with Cobb angle 23°. Utilizing a 3DSlicer 4.10, total SCM discordance was measured to be 2cm. A 3cm stair-step plasty was performed. At 9 month follow up, head tilt was noted to be normalized on postoperative frontal imaging. Conclusion: 3D visualization techniques can improve preoperative evaluation and patient education. Continued reports and studies regarding evaluation and treatment of muscular torticollis surgical candidates can help delineate factors and techniques for improved patient outcomes.

In order to solve the problem that the load of big data stream computing platform fluctuates drastically while the cluster was suffering from the performance bottleneck due to the shortage of computing resources,the load prediction based elastic resource scheduling strategy in Flink (LPERS-Flink) was proposed.Firstly,the load prediction model was set up as the foundation to propose the load prediction algorithm and predict the variation tendency of the processing load.Secondly,the resource judgment model was set up to identify the performance bottleneck and resource redundancy of the cluster while the resource scheduling algorithm was proposed to draw up the resource rescheduling plan.Finally,the online load migration algorithm was proposed to execute the resource rescheduling plan and migrate processing load among nodes efficiently.The experimental results show that the strategy provides better performance promotion in the application with drastically fluctuating processing load.The scale and resource configuration of the cluster responded to the variation of processing load in time and the communication overhead of the load migration was reduced effectively.

PurposePrevious studies on cyberloafing focus on individual and organization factors, ignoring the situation of employes as the event observers. Drawing on affective events theory (AET), the present study proposed a theoretical model for the relationships among peer abusive supervision, negative affectivity, cyberloafing, and hostile attribute bias, which aims to bridge the above research gap.MethodologyMultiwave data of 355 employes from 8 service-oriented companies in Southwest China supported our model. Time-lag method and critical incident techniques were introduced during the data collection stage. Ordinary least squares (OLS) regression and bootstrapping method were employed for hypothesis test.FindingsThe empirical results indicated that peer abusive supervision was positively related to third party’s cyberloafing, and the third party’s negative affectivity plays a mediating role among the above relationships. In addition, the third party’s hostile attribution bias moderated the mediating role of third party’s negative affectivity. Specifically, the effect of peer abusive supervision on third party’s negative affectivity and the mediating effect of this negative affectivity were stronger when the third party’s hostile attribution bias was higher.OriginalityDrawing on AET, the current study constructed a process model of third party’s cyberloafing reactions to peer abusive supervision, which helps explain the affective mechanism and the boundary conditions of the above “events-affectivity-behavior” path. Our model is a positive response to previous scholars’ calls for research of abusive supervision from multiple perspectives. Meanwhile, the current study explored the antecedent variable of cyberloafing from the perspective of event observers, which provides a theoretical basis for follow-up-related research. Thirdly, this study further expanded the theoretical boundaries of AET.

This study studies a supply chain consisting of one supplier and two retailers and analyzes the optimal decisions of ordering quantity in four types of no fairness concern, horizontal fairness concern, vertical fairness concern, and dual-fairness concern and the impacts of fairness concern on supply chain. The results show that (1) vertical fairness concern can decrease the ordering quantity of the retailer with vertical fairness concern and the wholesale price and improve the ordering quantity of the retailer without fairness concern; (2) horizontal fairness concern can increase the ordering quantity of the retailer with horizontal fairness concern and improve the ordering quantity of the retailer without fairness concern, but does not influence the wholesale price; and (3) dual-fairness concern can decrease the ordering quantity of the retailer with dual-fairness concern and the wholesale price and improve the ordering quantity of the retailer without fairness concern as a whole. The numerical analysis also proves the findings.

Storm is suffering the problems of high energy consumption but low efficiency.Aiming at this problem,the resource constraint model,the optimal principle of data reorganization in executors and node voltage reduction principle were proposed based on the analysis of the architecture and topology of Storm,and further the energy-efficient strategy for data migration and merging was put forward in Storm(DMM-Storm),which was composed of resource constraint algorithm,data migration and merging algorithm as well as node voltage reduction algorithm.The resource constraint algorithm estimates whether work nodes are appropriate for data migration according to the resource constraint model.The data migration and merging algorithm designs an optimal method to migrate data according to the the optimal principle of data reorganization in executors.The node voltage reduction algorithm reduces voltage of work nodes according to node voltage reduction principle.The experimental results show that the DMM-Storm can reduce energy consumption efficiently without affecting the performance of cluster compared with the existing researches.

Based on the Wentze–Kramers–Brillouin approximation, we derive formulae to calculate the position of convergence zones in a range-dependent environment with sound speed profiles varying in linear and ellipsoidal Gaussian eddy cases. Simulation results are provided for the linear and ellipsoidal Gaussian eddy cases. Experiment data are used for calculations considering linearly varying sound speed, and the findings suitably agree with the simulation results. According to the evaluated environment, the influence of the range-dependent sound speed profile on the position of the upper and lower convergence zones for different source depths is analyzed through simulations. The corresponding results show that the influence of the sound speed profile on the position of the upper convergence zone is greater for a shallower source. In contrast, the position of the lower convergence zone for large-depth reception is less affected.

The stress’s accumulation will critically affect optical device stability, leading to components’ deviation and film peeling. Stress control techniques based on strain cancelation cannot precisely match film stress, and it is difficult to reduce the total stress of multilayer film to ultra-low levels. Existing software for film system design is mainly based on 2D design concepts such as wavelength-spectrum and does not consider 3D design concepts such as wavelength-spectrum-stress, but the stress parameter plays an essential role in film performance, and it is necessary to use it as an optimization index for film design. In this paper, we introduce mechanical synchronization design into film system design. The multilayer film’s optimal structure is precisely matched by the genetic algorithm, and an ultra-low stress and wide-spectrum optical film element is developed under the dual requirements of optical performance and mechanical performance. This technique incorporates computer-aided design and automatically seeks an optimal combination of layered film nanostructures through self-compiled optical and mechanical compatible design software. While ensuring invariant optical properties, it can effectively reduce the total stress of multilayer film. This can provide a new idea for film stress control.

In order to solve the problem that the load of big data stream computing platform is increasing with fluctuation while the cluster was not able to rescale efficiently,the Flow-network based auto rescale strategy for Flink was proposed.Firstly,the flow-network model was set up and the capacity of each edge that was calculated by self-learning algorithm.Secondly,the bottleneck of the cluster was acquired by maximum-flow algorithm and the resource rescheduling plan was drawn up.Finally,the resource rescheduling plan was executed and the stateful data was migrated efficiently by the data migration algorithm based on the strategy of data partitioning by bulk and bucket.The experimental results show that the strategy can effectively provide performance promotion in the application with complex stateful data.It improved the throughput of the cluster and reduced the time overhead of the data migration on the premise of satisfying the latency constrain of the application,which means that the strategy promotes the scalability of the cluster efficiently.

To elucidate the transcriptomic changes of long noncoding RNAs (lncRNAs) in high-fat diet (HFD)-fed mice, we defined their hepatic transcriptome by RNA sequencing. Aberrant expression of 37 representative lncRNAs and 254 protein-coding RNAs was observed in the livers of HFD-fed mice with insulin resistance compared with the livers from control mice. Of these, 24 lncRNAs and 179 protein-coding RNAs were upregulated, whereas 13 lncRNAs and 75 protein-coding RNAs were downregulated. Functional analyses showed that the aberrantly expressed protein-coding RNAs were enriched in various lipid metabolic processes and in the insulin signaling pathway. Genomic juxtaposition and coexpression patterns identified six pairs of aberrantly expressed lncRNAs and protein-coding genes, consisting of five lncRNAs and five protein-coding genes. Four of these protein-coding genes are targeted genes upregulated by PPARα. As expected, the corresponding lncRNAs were significantly elevated in AML12 cells treated with palmitic acid or the PPARα agonist, WY14643. In Hepa1-6 cells, knockdown of NONMMUG027912 increased the cellular cholesterol level, the expression of cholesterol biosynthesis genes and proteins, and the HMG-CoA reductase activity. This genome-wide profiling of lncRNAs in HFD-fed mice reveals one lncRNA, NONMMUG027912, which is potentially regulated by PPARα and is implicated in the process of cholesterol biosynthesis.

Focusing on the sediment in Suyahu Reservoir (a typical plain reservoir in Henan Province, China), concentrations of eutrophication-causing substances (TOC, TN and TP) and heavy metal contamination (Zn, Pb, Cd, Cu, Cr, Ni, As and Hg) were measured. Spatial distribution of these contaminants and the resulting potential ecological risk were investigated in detail. The results indicated that the eutrophication-causing substances were concentrated in the reservoir sediment, with high concentrations in the south and low concentrations in the north due to hydrodynamics. The reservoir sediments were polluted and had an eutrophication problem due to the high average organic index. Heavy metals were deposited at the bottom near the centre of the reservoir. The comprehensive potential ecological risk index was Moderate, whereas Cd and Hg contributed 78.8% to the total risk-index values, and were the main factors of heavy mental pollution. Therefore, it was necessary to prevent and treat polluted sediments to prevent secondary pollution.