Search

Next point-of-interest (POI) recommendation aims to predict a user's next destination based on sequential check-in history and a set of POI candidates. Graph neural networks (GNNs) have demonstrated a remarkable capability in this endeavor by exploiting the extensive global collaborative signals present among POIs. However, most of the existing graph-based approaches construct graph structures based on pre-defined heuristics, failing to consider inherent hierarchical structures of POI features such as geographical locations and visiting peaks, or suffering from noisy and incomplete structures in graphs. To address the aforementioned issues, this paper presents a novel Bi-level Graph Structure Learning (${\sf BiGSL}$BiGSL) for next POI recommendation. ${\sf BiGSL}$BiGSL first learns a hierarchical graph structure to capture the fine-to-coarse connectivity between POIs and prototypes, and then uses a pairwise learning module to dynamically infer relationships between POI pairs and prototype pairs. Based on the learned bi-level graphs, our model then employs a multi-relational graph network that considers both POI- and prototype-level neighbors, resulting in improved POI representations. Our bi-level structure learning scheme is more robust to data noise and incompleteness, and improves the exploration ability for recommendation by alleviating sparsity issues. Experimental results on three real-world datasets demonstrate the superiority of our model over existing state-of-the-art methods, with a significant improvement in recommendation accuracy and exploration performance.

• Providing a paradigm to achieve excellent thermal stability of nanoprecipitates. • Elucidating evolution behaviors of hierarchical nanostructure in HEA. • Revealing mutual diffusion barrier mechanism between BCC and L1 2 phases in HEA. High-density coherent nanoprecipitates have been widely introduced into the design of new structural materials to achieve a superior strength-ductility balance. However, the thermal instability of nanostructures limits their fabrication and application. In this study, we investigated the temporal evolution of nanoprecipitates in coherent nanoprecipitation-strengthened Al 0.5 Cr 0.9 FeNi 2.5 V 0.2 high-entropy alloy during isothermal aging. When annealed at 600 °C for more than 100 h, we found that its nanoprecipitates were invariably stable, with no obvious changes occurring in terms of morphology and distribution. The excellent stability was mainly attributed to the restricted state of interface migration and diffusion owing to the hierarchical nanostructure. The Cr-enriched nano-lamellar BCC phase divided the Cr-depleted FCC(L1 2) matrix, forming barriers to long-range diffusion and resulting in a kinetically slow coarsening rate. As the nano-lamellar BCC phase spheroidized as the aging temperature increased to 700 °C, the diffusion barriers were destroyed. Remarkable coarsening occurred after that, which further verified the significant effect of the nano-lamellar BCC phase on the microstructural stability. These results provide a paradigm for designing alloys stabilized via hierarchical nanostructure, achieving good strength-ductility synergy while excellent thermal stability. [Display omitted] [ABSTRACT FROM AUTHOR]

In the chemical looping gasification (CLG) process, support material is used to improve the mechanical strength and thermal stability of the oxygen carrier (OC). The presence of a support material can affect the properties and performance of the OC, which influences the synthesis gas generation pathways. In this work, the density functional theory calculations were applied to investigate the reaction mechanisms and syngas formation during CLG, while Fe2O3is used with support Al2O3or TiO2as oxygen material. Adsorption and reaction pathways of C atom and main constituents in syngas (CO, H2, and H2O) on the surface of the composite carrier were studied. The reaction pathway and energy transformation regarding oxidation of C atom and CO on the composite carrier’s surface were calculated and evaluated. The results indicate that CO desorption and COO* formation on the Fe2O3/Al2O3surface were the rate-limiting steps that affect the conversion of C and CO under the studied conditions. In addition, the coadsorption of H2O and CO on composite carrier’s surfaces and possible reaction pathways of them were assessed to compare performance of the two studied composite carriers. The results indicate that formation of the carboxyl intermediate is the dominant pathway during coadsorption of H2O and CO and further conversion on the Fe2O3/Al2O3surface. On the other hand, formation of the formate as an intermediate on surface composite carrier Fe2O3/TiO2was easier and more direct because of its highest energy barrier, which is only 2.01 eV. Therefore, Fe2O3/Al2O3can enhance particle adsorption and capture, while Fe2O3/TiO2was more suitable for improving water–gas shift reaction performance. These results are essential to the design and optimization of cost-effective composite materials for enhancing performance and sustainability in chemical looping processes.

The burgeoning domain of the metaverse has sparked significant interest from a diverse array of industries, including healthcare services. However, the metaverse and its associated applications present various challenges to existing networks. First, to meet the increasing demands of the metaverse, there is a need for enhanced bandwidth, reduced latency, and improved packet loss control. Furthermore, the transmission mechanism should exhibit flexibility to automatically adapt to the diverse hybrid needs of different healthcare services. In this article, a multipath transmission-based paradigm tailored for the metaverse-based healthcare services is developed. Significantly, we devise an orchestration framework to reconcile edge-side subflow management with diverse healthcare applications. Using machine learning techniques, the framework can produce near-optimal subflow adjustment strategies for client nodes and miscellaneous services. Comprehensive experiments are performed on applications with diverse requirements to validate the adaptability of the framework to the application needs. The experimental results demonstrate that the proposed method enables the network to autonomously adapt to changing network conditions and service requirements. This includes applications' preferences for high throughput, low delay, and high stability. Moreover, the test results show that the proposed approach can notably decrease the occurrences of network quality falling below the minimum requirement. Given its adaptability and impact on network quality, this work paves the way for future metaverse-based healthcare services.

The requirement for credible and reliable positioning of a multi-sensor integration system is an essential foundation for comprehensive PNT (Positioning, Navigation, and Timing) services. However, the credibility of positioning results based on multi-sensor integration is difficult to evaluate and even there is no effective mode at present. To try to solve such a problem, a credible positioning model based on the tight integration of BDS Real Time Kinematic (RTK) and Inertial Navigation System (INS) is presented in this paper. In such a model, a multi-information cross-validation algorithm is introduced to ensure the credibility of RTK/INS tight integration. Meanwhile, a backward quality checking model is generated based on the result of the calculated credible positioning error level, which can optimize the positioning results of RTK/INS integration furtherly. After the mathematical model descriptions, a simulated test and a real experiment are adopted to evaluate this presented method. Results illustrated that: 1) envelope level of the credible factor for the real error can reach the centimeter level and the average probability of unenveloped error is within 2%; 2) after applying the quality control scheme based on credible positioning feedback information, the positioning results of RTK/INS integration are improved by 67.6%, 78.9%, and 77.3% on average.

Accurate visual perception and comprehensive scene understanding are critical for the safety and reliability of autonomous vehicles (AVs). Nevertheless, the efficacy of visual perception systems can be impaired by the intricacy of road scenes, and the existing scene understanding approach may be insufficient. Consequently, this study proposes an enhanced scene understanding model to achieve precise awareness of driving situations. Recognizing the limitations posed by the oversimplification of samples in current urban scene datasets, we selected critical frames from 336000 video frames, sourced from real-world driving environments, to assemble a more complex road scene (CRS) dataset. We integrated Residual Neural Network and pyramid scene parsing network architectures and refined them through class mapping and targeted network fine-tuning. Based on the segmentation outputs and the XGBoost algorithm, we identified the driving scenarios for the ego vehicle, enabling instantaneous driving situation analysis. The predictive model also evaluated the trajectory of interactive vehicles and estimated their kinematic states. Furthermore, we have conducted a thorough evaluation of scenario complexity, integrating the features described above. The findings indicate that our model achieves a segmentation accuracy of 78.8% in CRSs, with a twofold improvement in training efficiency. We also confirmed the effectiveness of the scene understanding approach through real-world road testing in China. This research provides insight into situation awareness within CRSs, thereby enhancing the visual perception capabilities of AVs. The implications of these results are substantial for their application in autonomous driving tests and advancing decision-making and control algorithms.

ABSTRACTVentricular remodeling (VR) after acute ST-elevation myocardial infarction (STEMI) is an important predictor for medium- and long-term prognosis. This study focuses on the relevant indexes of VR in patients with AMI, in which, the intervention effects of sacubitril/valsartan and enalapril were compared, guiding the clinical treatment. 58 patients with acute STEMI treated with PCI were divided into research group and control group. UCG was performed at 1 week, 1 month and 3 months after MI, and the patients’ indexes were collected to compare VR and adverse reactions in the two groups. The test results showed that there was no statistical difference in the baseline data of patients in the two groups, which were comparable. In the blood biochemical index examination, no statistical difference was found in cTnI and NT-proBNP between the two groups. At 1 week after operation, the levels of cTnI and NT-proBNP in research group were lower than those in the control group. In ECG examination, there was no statistical significance in the levels of LVEF, LVEDD and LVESD at admission between the two groups. After 1 week, the results of LVEF, LVEDD, LVESD in the research group were higher than those in the control group. The results of this study show that sacubitril/valsartan can be used in patients with AMI instead of enalapril. Sacubitril/valsartan improves cardiac function in patients with emergency percutaneous coronary intervention (PCI) for AMI, inhibits ventricular remodeling, and has a low incidence of adverse cardiac events and adverse drug reactions.

Evidence-aware fake news detection aims to conduct reasoning between news and evidences, which are retrieved based on news content, to find uniformity or inconsistency. However, we find evidence-aware detection models suffer from biases, i.e., spurious correlations between news/evidence contents and true/fake news labels, and are hard to be generalized to Out-Of-Distribution (OOD) situations. To deal with this, we propose a novel Dual Adversarial Learning (DAL) approach. We incorporate news-aspect and evidence-aspect debiasing discriminators, whose targets are both true/fake news labels, in DAL. Then, DAL reversely optimizes news-aspect and evidence-aspect debiasing discriminators to mitigate the impact of news and evidence content biases. At the same time, DAL also optimizes the main fake news predictor, so that the news-evidence interaction module can be learned. This process allows us to teach evidence-aware fake news detection models to better conduct news-evidence reasoning, and minimize the impact of content biases. To be noted, our proposed DAL approach is a plug-and-play module that works well with existing backbones. We conduct comprehensive experiments under two OOD settings, and plug DAL in four evidence-aware fake news detection backbones. Results demonstrate that, DAL significantly and stably outperforms the original backbones and some competitive debiasing methods.

The prevalence and perniciousness of fake news have been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). Most previous methods first employ sequential models to embed the semantic information and then capture the claim-evidence interaction based on different attention mechanisms. Despite their effectiveness, they still suffer from three weaknesses. First, due to the inherent drawbacks of sequential models, they fail to integrate the relevant information that is scattered far apart in evidences for veracity checking. Second, they underestimate much redundant information contained in evidences that may be useless or even harmful. Third, insufficient data utilization limits the separability and reliability of representations captured by the model, which are sensitive to local evidence. To solve these problems, we propose a unified Graph-based sEmantic structure mining framework with ConTRAstive Learning, namely GETRAL in short. Specifically, different from the existing work that treats claims and evidences as sequences, we first model them as graph-structured data and capture the long-distance semantic dependency among dispersed relevant snippets via neighborhood propagation. After obtaining contextual semantic information, our model reduces information redundancy by performing graph structure learning. Then the fine-grained semantic representations are fed into the downstream claim-evidence interaction module for predictions. Finally, the supervised contrastive learning accompanied with adversarial augmented instances is applied to make full use of data and strengthen the representation learning. Comprehensive experiments have demonstrated the superiority of GETRAL over the state-of-the-arts and validated the efficacy of semantic mining with graph structure and contrastive learning.

Overvoltage may occur in low-voltage direct current (LVDC) distribution system during fault recovery, and cause permanent damages to the vulnerable power electronics devices. To suppress the overvoltage, the parameter matching principle of the inductance type superconducting fault current limiter (SFCL) is proposed. First, by deriving the equivalent circuit of LVDC system during fault recovery, mechanism and related factors of the overvoltage were studied. It is found the inconsistent drop rate of voltages of converters during the fault is the main reason for fault recovery overvoltage. Then, the method was proposed by designing the inductance in accordance with the capacitance of each converter with keeping voltage drop rate of the converter is the same and the fault current is within the breaking ability of DC circuit breaker. Finally, the method is validated by simulation in PSCAD/EMTDC, resulting in the overvoltage is reduced by 50%.

Identify risk factors for development of preeclampsia refractory to aspirin prophylaxis in women at high-risk of preeclampsia.

Deep vein thrombosis (DVT) is a medical condition characterized by forming a blood clot, or thrombus, in one of the deep veins, typically in the legs. It is a type of venous thromboembolism, which refers to the formation of blood clots in the veins. It is caused by Virchow’s triad (stasis, hypercoagulation, and endothelial injury).

Due to the increasing risk of obesity and cardiovascular diseases caused by high-fat diets, low-fat foods have become a priority demand for consumers’ health. However, the smoothness perception and scientific assessment methods of the existing low-fat foods should be improved. In this study, three food emulsions were prepared, and their lubrication characteristics, sensory evaluation of smoothness, and electroencephalogram (EEG) signals were assessed to preliminarily investigate the effects of food emulsion components on their above characteristics. The results showed that fat substitute (FSU) and fat could significantly reduce coefficient of friction (CoF) of the food emulsions, with average CoF reduced by 28% and 63% compared to the original food emulsions. In addition, fat-enriched food emulsions continued to exhibit excellent lubrication characteristics after adding artificial saliva, with an average CoF reduced by 31.1% compared to that of the food emulsions without artificial saliva. Both FSU and fat improved the smoothness of food emulsions, and the lubricating properties of fat were more pronounced, with fat-enriched food emulsion which could provide a substantial improvement in smoothness compared to the fat-free food emulsion. Comparison of subjects’ EEG signals revealed that food emulsion with lower CoF and higher smoothness triggered higher P3 amplitudes and longer latencies. These findings provide better insights into the scientific evaluation of food texture and the development of low-fat foods.

The effects of Ta on the tensile behavior and deformation mechanisms of a Ni-based single crystal superalloy were investigated in this study from room temperature to elevated temperature. The findings demonstrated that the higher content of Ta could improve the tensile properties of the alloy at different temperatures. Due to the different deformation mechanisms at various temperatures, the influence of Ta on tensile deformation varied. At room temperature, the higher content of Ta enhanced the solid solution strengthening, which would enhance the tensile strength of 6.5Ta alloy. After standard heat treatment of 6.5Ta alloy, precipitation of the secondary γʹphase would hinder the movement of dislocations. When the temperature was elevated to 760 °C, the higher content of Ta not only promoted the interaction of stacking faults to form Lomer–Cottrell (L-C) locks that impeded dislocation motion, but also reduced the occurrence of dislocation pile-up groups, thus enhancing the yield strength. At 1120 °C, due to the narrower γchannels and higher APB energy in γʹphase of the alloy with higher Ta addition, the processes of bypassing and shearing of dislocations were hindered, respectively. Meanwhile, the denser and more regular dislocation networks were formed in 6.5Ta alloy; and thus, the tensile strength of 6.5Ta alloy was enhanced. This study systematically investigated the effect of Ta on the tensile behavior at three different temperatures, which provided an important theoretical basis for the design of nickel-based single crystal superalloys in the future.

Zeolites are typically synthesized in the presence of strong alkaline or fluoride species, which is not atom-economic for zeolite synthesis due to the high solubility of strong alkaline and fluoride species to silica. One of the solutions for this issue is to reduce solubility of silica in the zeolite synthesis, but it is challenging. Herein, we show that nucleation and growth of zeolites can occur under near neutral conditions, giving an atom-economical synthesis of zeolites with almost full silica utilization due to very low silica solubility. Compared to conventional hydrothermal synthesis, this work both enhances the zeolite yield and reduces waste emissions, even water zero emission. Particularly, structural defects (terminal silanols) in zeolites are obviously lowered, thus giving high thermal and hydrothermal stabilities and good performance in the Beckmann rearrangement.

The design and analysis of computer-aided design (CAD) sketches play a crucial role in industrial product design, primarily involving CAD primitives and their interprimitive constraints. To address challenges related to error accumulation in autoregressive models and the complexities associated with self-supervised model design for this task, we propose a two-stage network framework. This framework consists of a primitive network and a constraint network, transforming the sketch analysis task into a set prediction problem to enhance the effective handling of primitives and constraints. By decoupling target types from parameters, the model gains increased flexibility and optimization while reducing complexity. In addition, the constraint network incorporates a pointer module to explicitly indicate the relationship between constraint parameters and primitive indices, enhancing interpretability and performance. Qualitative and quantitative analyzes on two publicly available datasets demonstrate the superiority of this method.

With rapid advancements in artificial intelligence (AI) technologies, AI-powered machines are increasingly capable of collaborating with humans to enhance decision-making in various human–machine collaboration scenarios, e.g., medical diagnosis, criminal justice, and autonomous driving. As a result, human–machine computing (HMC) has emerged as a promising computing paradigm that integrates the expertise of humans with the reliable data processing capabilities of machines. Using HMC to facilitate the processing of domain-specific tasks has a lot of potential, but is limited in system-level scalability, i.e., there is no one common easy-to-use interface. In this article, we present human-machine operating system (hmOS), an open extensible platform for researchers to experiment with HMC for investigating system-centric human–machine collaboration problems. hmOS supports flexible human–machine collaboration on the strength of the quality-aware task decomposition and allocation. To achieve that, the underlying system architecture and runtime environment are first developed to build a foundational abstraction for the kernel of hmOS. Second, hmOS facilitates flexible human–machine collaboration through a suitability-based task allocation mechanism, quality estimation guided by fuzzy rules, and iterative feedback on result tuning. We implement the newly proposed hmOS in a prototype featuring interactive interfaces. Finally, we conduct extensive and realistic experiments to validate the effectiveness of our platform across diverse tasks, showcasing the broad feasibility of hmOS.

In the front-end of two-stage voltage regulator modules in 48 V-based data centers, a dc transformer (DCX) is required to convert the 48 V bus to an intermediate voltage with a fixed buck ratio. In this article, we present a novel switched-capacitor and series-resonant hybrid DCX specifically for this application. The proposed DCX combines a 2:1 resonant switched-capacitor cell with a series-resonant converter, enabling a conversion ratio of 4n:1 with an n:1 transformer. It demonstrates excellent soft-charging and soft-switching performances, effectively mitigating the switching loss. Furthermore, zero-voltage switching does not rely on the transformer's magnetizing current. Therefore, the magnetizing inductance can be optimized to improve the efficiency and to facilitate a simplified magnetic design. Theoretical analysis, design considerations, and topology comparison are provided to showcase the advantages of the proposed topology. A 1 MHz, 300 W-rated, 48 V to 12 V converter prototype is designed and tested. Experimental results well validate the concept with a 97.23% peak efficiency.

For a more secure, anti-interference characterization of high-speed wide-bandgap (WBG) power devices, there is a growing need for galvanically isolated measurement methods with higher bandwidth and wider dynamic range. Among these techniques, the differential sensing method has gained attention due to its simplicity, cost-effectiveness, wide dynamic range, and floating measurement capability. Nevertheless, the limited bandwidth of the differential method remains a significant obstacle for characterizing ultrafast switching transients of WBG devices. This article highlights the significant bandwidth limitations encountered in the front-end high-voltage attenuation of the differential method, i.e., the parasitic effects of capacitive networks and the overlooked high-frequency transmission line effects. To address these limitations, the concept of transmission line voltage divider (TL-VD) is introduced, enabling the extension of the operating frequency range of the front-end high-voltage attenuation into the GHz level. To achieve this, four underlying matching principles regarding resistance, capacitance, inductance, and wave impedance are established, which are further simplified to three decoupled, mutually independent, and easily implementable matching objectives of resistance, wave impedance, and transmission line length. These contributions collectively result in the successful development of a ±2.0-kV differential voltage sensing system (DVSS) with an ultrahigh measurement bandwidth of 1.3 GHz and a common-mode rejection ratio of 48 dB at 100 MHz. The experimental comparisons with state-of-the-art commercial galvanically isolated products in both the frequency and time domains confirm the superior performance of the developed TL-VD-based DVSS.

Fluidization technology has been used in CO2capture processes, the successful design and operation of the heat exchangers involved in this process require much information on the bed-to-wall heat transfer of the sorbent particles in fluidized states. In this study, the bed-to-wall heat transfer coefficient (h) of a solid amine sorbent was measured by a heat transfer probe in a large-scale circulating fluidized bed cold model unit, where full spectrum of fluidization regimes can be realized. The corresponding hydrodynamic signals were also studied by pressure sensors and optical fiber probes to further explain the newly discovered phenomenon. The results show that in a dense bed, due to the counterbalanced effect of time fraction of packet and packet renewal frequency, hof the Geldart B particle reaches a peak within the bubbling fluidized regime, and the radial distribution of hare opposite in bubbling and turbulent fluidized regimes. In a fast fluidization regime, gas convection becomes the dominant factor affecting hwhen the solids holdup is low enough. Correlations were provided or recommended to guide the design of heat exchangers in the fluidized bed CO2capture processes.

Climate warming has caused a widespread increase in extreme fire weather, making forest fires longer-lived and larger1–3. The average forest fire size in Canada, the USA and Australia has doubled or even tripled in recent decades4,5. In return, forest fires feed back to climate by modulating land–atmospheric carbon, nitrogen, aerosol, energy and water fluxes6–8. However, the surface climate impacts of increasingly large fires and their implications for land management remain to be established. Here we use satellite observations to show that in temperate and boreal forests in the Northern Hemisphere, fire size persistently amplified decade-long postfire land surface warming in summer per unit burnt area. Both warming and its amplification with fire size were found to diminish with an increasing abundance of broadleaf trees, consistent with their lower fire vulnerability compared with coniferous species9,10. Fire-size-enhanced warming may affect the success and composition of postfire stand regeneration11,12as well as permafrost degradation13, presenting previously overlooked, additional feedback effects to future climate and fire dynamics. Given the projected increase in fire size in northern forests14,15, climate-smart forestry should aim to mitigate the climate risks of large fires, possibly by increasing the share of broadleaf trees, where appropriate, and avoiding active pyrophytes.

Breaking the trade-off between activity and stability of supported metal catalysts has been a long-standing challenge in catalysis, especially for metal nanoparticles (NPs) with high hydrogenation activity but poor stability. Herein, we report a porous poly(divinylbenzene) polymer-supported Pd NP catalyst (Pd/PDVB) with both high activity and excellent stability for the solvent-free hydrogenation of nitrobenzene, even at ambient temperature (25 °C) and H2pressure (0.1 MPa). Pd/PDVB gave a turnover frequency as high as 22,632 h–1at 70 °C and 0.4 MPa, exceeding 5556 h–1of the classical Pd/C catalyst under equivalent conditions. Mechanistic studies reveal that the polymer support benefits the desorption of the aniline product from the Pd surface, which is crucial for rapid hydrogenation under solvent-free conditions. In addition, the polymer support in Pd/PDVB efficiently hindered Pd leaching, resulting in good stability.

All‐perovskite tandem solar cells are regarded as the next generation of devices capable of enhancing the solar energy utilization rate. Unlike single‐junction perovskite solar cells (PSCs), the efficacy of tandem cells is contingent upon the performance of both the top and bottom cells. In this study, we employed a simultaneous co‐modification strategy to incorporate phenylethylammonium iodide (PEAI) at both the top and bottom interfaces of the perovskite film, aiming to boost the top cell's performance. Both experimental and theoretical findings indicate that PEAI not only elevates the perovskite film quality through chemical interactions but also mitigates nonradiative recombination within the device. Consequently, the efficiency of the wide‐bandgap (1.77 eV) PSCs based on nickel oxide (NiOx) attained a level of 16.5%. Simultaneously, the all‐perovskite tandem solar cells achieved an efficiency of 26.81% and demonstrated superior stability. Phenylethylammonium iodide was used to collaboratively passivate the upper and lower interfaces of the perovskite film. Quantitative calculations revealed the reasons for the improvement inpower conversion efficiency.Density functional theory calculations were also used to prove the underlying reasons for this improvement.

In the present work, a wall-structured nickel-aluminum bronze (NAB) alloy was fabricated via electron beam directed energy deposition (EB-DED) additive manufacturing with a single-pass multi-layer deposition strategy. A comprehensive comparative analysis of microstructure, mechanical properties, and corrosion resistance was conducted against a conventionally cast NAB alloy. The inherent rapid solidification and cyclic thermal processing of the EB-DED technique profoundly influenced the microstructural evolution of the NAB alloy. The as-deposited NAB alloy exhibited a fine-grained microstructure, devoid of the martensitic β′ phase, accompanied by the spheroidization of partial κIIIprecipitates, and a homogeneous distribution of alloying elements. These distinctive microstructural attributes synergistically enhanced the strength, hardness, and toughness of the NAB alloy, conferring superior mechanical properties compared to its cast counterpart. Furthermore, the as-deposited alloy demonstrated remarkable corrosion resistance in a 3.5 wt% NaCl solution, significantly outperforming the cast alloy. The underlying mechanisms governing the structure-property relationships were elucidated. This comprehensive investigation provided insights into the unique characteristics and potential applications of EB-DED fabricated NAB alloys, positioning them as promising candidates for high-performance components subjected to severe mechanical and corrosive service conditions.

In general, the Ti–6.5Al–2Zr–1Mo–1V (TA15) components fabricated via laser powder bed fusion (L-PBF) exhibit high strength and low ductility. Herein, we report a novel approach to enhance the comprehensive mechanical properties of L-PBF TA15 alloy by adjusting the laser power. Samples processed using the optimal laser power exhibit a grid structure of alternating wide and narrow prior-β grains (PBGs), the inside of which is composed of a fully martensitic microstructure. The paper discusses in detail the alterations in the microstructure of samples processed at both low and high laser powers, clarifying the relationship between microstructure and mechanical properties. Thinner martensite contributes to higher strength, while a homogenous microstructure improves ductility. These findings provide valuable insights for controlling microstructure and achieving strength-ductility synergy in L-PBF additive manufacturing of titanium alloys.

1 wt% polymer acceptor was incorporated into polymer donor, or inversely, to form diluted donor/acceptor heterojunctions, which enhanced structural order and exciton diffusion, achieving 19.4% of efficiency for all-polymer solar cells.

Enhancing the capabilities of unmanned space exploration, such as satellite monitoring and space science missions, requires efficient and reliable nuclear power systems. A viable solution is found in the 1–10 kWe power level of space nuclear reactor power systems, offering advantages such as a manageable research and development process, and relatively low investment requirements. This paper introduces a conceptual design for a 5 kWe space nuclear reactor power system, outlining its components and characteristics. The study includes a thorough analysis of potential challenges, encompassing heat pipe failure accidents, re-entry scenarios, and weight estimation considerations. The results demonstrate that the proposed space nuclear reactor power system effectively meets the safety requirements. The total mass of the power system is estimated at approximately 1.5 tons, with a specific mass of around 300 kg/kWe. This research contributes valuable insights for the design of space nuclear reactor power systems operating within a similar power range.

Pulmonary hypertension (PH) is a progressive fatal disease with no cure. Canagliflozin (CANA), a novel medication for diabetes, has been found to have remarkable cardiovascular benefits. However, few studies have addressed the effect and pharmacological mechanism of CANA in the treatment of PH. Therefore, our study aimed to investigate the effect and pharmacological mechanism of CANA in treating PH. First, CANA suppressed increased pulmonary artery pressure, right ventricular hypertrophy, and vascular remodeling in both mouse and rat PH models. Network pharmacology, transcriptomics, and biological results suggested that CANA could ameliorate PH by suppressing excessive oxidative stress and pulmonary artery smooth muscle cell proliferation partially through the activation of PPARγ. Further studies demonstrated that CANA inhibited phosphorylation of PPARγ at Ser225 (a novel serine phosphorylation site in PPARγ), thereby promoting the nuclear translocation of PPARγ and increasing its ability to resist oxidative stress and proliferation. Taken together, our study not only highlighted the potential pharmacological effect of CANA on PH but also revealed that CANA-induced inhibition of PPARγ Ser225 phosphorylation increases its capacity to counteract oxidative stress and inhibits proliferation. These findings may stimulate further research and encourage future clinical trials exploring the therapeutic potential of CANA in PH treatment.

The solution-treated second-generation single crystal (SX) superalloys underwent hot isostatic pressing (HIP) at 1316 °C and 105 MPa for varying durations. This study investigates the effect of HIP on micropores and stress-rupture behavior under conditions of 980 °C and 250 MPa. The results illustrate that the application of HIP treatment significantly reduces the porosity and volume of micropores, thereby increasing the rupture life of the alloys. With the disappearance of micropores, the crack initiation site shifts from the micropores to the γ/γʹ interface, resulting in reduced crack dimensions and decelerated propagation. Consequently, the smaller size of cracks in HIPed specimens slow down fracture. Nonetheless, the crack propagation pattern remains consistent: cracks expand perpendicular to the stress axis, connecting with cracks at other heights via secondary cracks along the <112> direction.

Unsupervised domain adaptation (UDA) of time series aims to teach models to identify consistent patterns across various temporal scenarios, disregarding domain-specific differences, which can maintain their predictive accuracy and effectively adapt to new domains. However, existing UDA methods struggle to adequately extract and align both global and local features in time series data. To address this issue, we propose the Local-Global Representation Alignment framework (LogoRA), which employs a two-branch encoder–comprising a multi-scale convolutional branch and a patching transformer branch. The encoder enables the extraction of both local and global representations from time series. A fusion module is then introduced to integrate these representations, enhancing domain-invariant feature alignment from multi-scale perspectives. To achieve effective alignment, LogoRA employs strategies like invariant feature learning on the source domain, utilizing triplet loss for fine alignment and dynamic time warping-based feature alignment. Additionally, it reduces source-target domain gaps through adversarial training and per-class prototype alignment. Our evaluations on four time-series datasets demonstrate that LogoRA outperforms strong baselines by up to 12.52%, showcasing its superiority in time series UDA tasks.

With the rapid expansion of multimodal content in online social media, automatic detection of multimodal fake news has received much attention. Multimodal joint training commonly used in existing methods is expected to benefit from thoroughly leveraging cross-modal features, yet these methods still suffer from insufficient learning of uni-modal features. Due to the heterogeneity of multimodal networks, optimizing a single objective will inevitably make the models prone to rely on specific modality while leaving other modalities under-optimized. On the other hand, simply expecting each modality to play a significant role in identifying all the rumors is also not appropriate as the multimodal fake news often involves tampering in only one modality. Therefore, how to find the genuine tampering on the per-sample basis becomes the key point to unlock the full power of each modality in a good collaborative manner. To address these issues, we propose a Uni-modal Event-agnostic Knowledge Distillation framework (UEKD), which aims to transfer knowledge contained in the fine-grained prediction from uni-modal teachers to the multimodal student model through modality-specific distillation. Specifically, we find that the uni-modal teachers simply trained on the whole training set are easy to memorize the event-specific noise information to make a correct but biased prediction, failing to reflect the genuine degree of tampering in each modality. To tackle this problem, we propose to train and validate the teacher models on different domains in training dataset through a cross-validation manner, as the predictions from the out-of-domain teachers can be regarded as event-agnostic knowledge without spurious connections with event-specific information. Finally, to balance the convergence speeds across modalities, we dynamically monitor the involvement of each modality during training, through which we could identify the more under-optimized modalities and re-weight the distillation loss accordingly. Our method could be served as a plug-and-play module for existing multimodal fake news detection backbones. Extensive experiments on three public datasets and four state-of-the-art fake news detection backbones show that our proposed method can improve the performance by a large margin.

Automated robotic systems require reliable docking mechanisms to extend their functional capabilities. In this article, we present a simplified piezoelectric docking mechanism (PDM) inspired by the bolt–nut connector to overcome the limitations of existing docking mechanisms regarding electromagnetic compatibility, size, and complexity. The piezoelectric nut (PN) is a driving component of the PDM that is capable of driving screws by exciting traveling waves. The key dimension of the PN is optimized to increase the output performance by creating dynamical model. Experimental results show that the maximum clamping force of the PDM prototype in the rigid connection state is 3.1 N, and the prototype can work in an environment of 100 °C, 1.8-T magnetic field, and 10−4-Pa pressure. Finally, two simple metal arms are machined to verify the feasibility of potential application of the PDM. This study provides a new idea to develop the rigidly connected docking mechanisms.

Virtual machines (VMs) heavily rely on virtual CPUs (vCPUs) scheduling to achieve efficient I/O performance. The vCPU scheduling interference can cause inconsistent scheduling latency and degraded I/O performance, potentially compromising the services provided by affected VMs. Existing solutions have limitations, such as inefficiency in diagnosing interference issues or imposing undesired side effects on cloud systems. To address these challenges, we present Otter, a holistic technique for optimizing I/O performance in the presence of vCPU scheduling interference. Otter employs innovative methods to enhance interference diagnosis efficiency. First, we propose lightweight methods to measure the dynamic changes in scheduling latencies for co-running vCPUs, ensuring both flexibility and accuracy. Second, we propose fine-grained quantification methods to timely determine the interference, with low false positive and false negative rates. Third, we identify interference patterns that aid in analyzing the root causes of interference and preventing similar issues from recurring. Otter has been operational for one year in the production cloud at the National Supercomputing Center (Wuxi). It diagnoses and helps fix more than 470 vCPU scheduling interference-related issues, resulting in a 19.6% improvement in cloud service I/O performance with negligible overhead in production.

The development of current sensors for wide-bandgap (WBG) applications consistently emphasizes high bandwidth, minimal invasiveness, and integration. Despite the meticulous design that enables current shunts to achieve excellent performance with high bandwidth and low parasitics, their inherent bulkiness remains a significant challenge for their integration into high-integrated applications that depend on the high switching speeds of WBG power devices. This study thoroughly investigates and uncovers the tradeoff faced by traditional current shunts in balancing miniaturization and high bandwidth. Building upon this understanding, the miniaturized current shunt (MiniShunt) concept is introduced to overcome inherent limitations and achieve both high performance and compact size. To realize this concept, a physical implementation that involves the high-density stacking of multiple coupled transmission lines is presented. Additionally, a thermal network model specifically for the proposed MiniShunt configuration is developed and a comprehensive thermal analysis methodology for the current shunts is established. By applying this methodology to finite element analysis, the thermal safe operating area for the MiniShunt can be determined, further providing valuable insights into its maximum static power dissipation and maximum energy loss. These findings contribute to the development of a highly compact 9 × 9-mm, 100-mΩ current shunt with an ultrahigh bandwidth of 3 GHz, near-zero parasitic inductance, and a maximum energy loss of 2.0 J at a reference temperature rise limit of 20 °C. Extensive experiments conducted in both the frequency and time domains serve to further validate the advantages of the MiniShunt in terms of its miniaturization, integration, ultrafast response, and low invasiveness for future high-integrated power electronics applications.

Enhanced insulation and superior thermal characteristics are crucial in advanced packaging for medium voltage (MV) silicon carbide (SiC) power modules. Nonetheless, the reduction in thermal resistance typically compromises insulation level due to the decreased number of insulation layers. In this paper, the forced fluorinated liquid cooling is proposed, whereby fluorinated liquid (3M FC-40) is directed into the power module, substituting silicone gel and diminishing thermal resistance. Through the utilization of forced fluorinated liquid cooling, a reduction of 21°C in junction temperature at full load is demonstrated in the experiment, compared to traditional water cooled power modules. Furthermore, there is a 35.6% reduction observed in the maximum temperature rise ΔTj. As dissipation power is transferred from the topside of the chip to the fluorinated liquid, the possibility of unlimited stacking of direct bonded copper (DBC) is enabled to lower the electrical field, thereby facilitating insulation at the chip bottom side without any worries regarding heightened thermal resistance. Forced fluorinated liquid cooling resulted in a 39°C reduction in the double-stacked DBC power module. Moreover, partial discharge (PD) detection under square wave revealed that the DBC immersed in the fluorinated liquid remains free from PD at 10 kV, a significantly higher threshold than observed with silicone gel. A higher partial discharge inception voltage (PDIV) is anticipated for double-stacked DBC when immersed in 3M FC-40. Thus, the forced fluorinated liquid cooling can be considered a promising solution for the next generation of MV SiC power modules.

Byline: Jun. Wang, Jing. Liang, Mingxin. He, Qi. Xie, Qingming. Wu, Guanxin. Shen, Baoli. Zhu, Jun. Yu, Li. Yu, Xiaohua. Tan, Lanlan. Wei, Jun. Ren, Youyong. Lv, Lijuan. Deng, Qian. [...]

The efficiency and stability of organic solar cells (OSCs) is often restricted by the metastable photoactive and charge transport layers. Here, we report the acquiring of stable photovoltaics via vacuum-assisted thermal annealing (VTA), which not only enhances the molecular packing of donor and acceptor but also restrains the over-growth of photovoltaic molecules and leads to a slender fibrillar network, resulting in enhanced charge transport and suppressed carrier recombination. In situellipsometry measurements reveal that VTA can remove the trapped solvents and reduce the free volume of the photoactive layer, leading to slower structural relaxation during operation and therefore superior morphological and operational stability. As a result, the VTA-treated D18:L8-BO and PM6:L8-BO OSCs exhibit superior PCEs of 19.7% and 19.2%, respectively, with an ITO/PEDOT:PSS/active layer/PDINN/Ag structure, and a PCE of 18.0% with a T80lifetime of 45,200 h for the ITO/MoO3/PM6:L8-BO/C60/BCP/Ag-structured device, corresponding to an unprecedented lifetime of 30 years.

Graphical abstract:

The medium voltage (MV) silicon carbide (SiC) power modules hold significant promise as a route for the solid-state converters to achieve higher power density, higher switching frequency, and lower loss. However, the emergence of partial discharge (PD) in the MV SiC power modules has posed a formidable challenge to enhancing its nominal voltage. In this article, a comprehensive review and comparative analysis of characterization, mitigation, and detection of PD under the square wave excitation is proposed for MV SiC power modules. First, the mechanism, detriments, and characterization of the PD issue and electrical tree in the MV power module are insightfully demonstrated. Meanwhile, the challenges from the PD for the MV SiC power module are emphasized. Besides, the geometric modification of direct bonded copper (DBC) structures to mitigate the PD issue is reviewed in detail. The limitations of the customized DBC are analyzed, considering the cost, reliability, and thermal resistance. Additionally, advanced dielectric materials are introduced and categorized. It is indicated that the effectiveness of the new dielectric under the square wave excitation stress still needs further investigation. Meanwhile, the optical, electromagnetic, electrical, and ultrasound detection methods under square wave excitation are further reviewed to assess the PD behavior in the MV SiC power module. Finally, suggested future researches for MV SiC power module packaging are proposed, aiming to inspire new ideas to further promote the insulation level of the MV SiC power module.

Detrimental non-local effect (NLE) causes fatal measurement errors and limits the allowed optical power in long-range Brillouin optical time-domain analysis (BOTDA) sensing system. NLE occurs in both the single-sideband BOTDA (SSB-BOTDA) and dual-sideband BOTDA (DSB-BOTDA), namely the 1st-order NLE and 2nd-order NLE, respectively. Conventional methods to overcome NLE usually require modification of system setup which greatly increases the system complexity and cost. Here we have proposed a BOTDA system powered by deep learning method, i.e., deep neural network (DNN), to overcome both the 1st-order NLE and 2nd-order NLE without any system modification. Analytical expressions of Brillouin gain spectrum (BGS) profile under the 1st-order NLE and 2nd-order NLE are derived, respectively, which are used to generate training dataset for DNN training. A single DNN model has been successfully demonstrated to be capable of accurately extracting the Brillouin frequency shift (BFS) from the normal BGS and distorted BGS, no matter whether NLE exists or not in the system. Through both simulation and experiment, the performance of the proposed DNN is verified and compared with that of conventional Lorentzian curve fitting (LCF) method. Under strong 1st-order NLE in the SSB-BOTDA, the use of our DNN reduces the temperature root mean square error (RMSE) by 6.5 times, and the maximum allowed probe power is increased by 16.5 dB. Under strong 2nd-order NLE in the DSB-BOTDA with 50 km sensing distance, the DNN reduces the temperature error by 6.1 times, and the maximum allowed probe power per sideband is increased by 11 dB. The DNN model does not need any prior knowledge on whether the data has NLE or not, thus a single DNN model is enough to accurately extract BFS under both conditions without and with NLE, which makes the scheme practical and can greatly improve the system tolerance to NLE with low cost.

The aim of this study is to fabricate nanofibrous membranes with excellent filtration and mechanical properties for application in the field of flue gas purification. Carboxylated Multiwalled carbon nanotubes (MWCNTs-COOH) were used to enhance the performance of PMIA nanofibrous membranes. The results showed that the average particle size D50of MWCNTs-COOH in the PMIA electrospining solution was significantly reduced from 644 nm to 361 nm, the stability and dispersibility of MWCNTs-COOH were significantly enhanced. The resultant membranes exhibit a tree-like structure with a bimodal distribution of diameters and interconnected bonding points, where the trunk nanofibers exhibit an average diameter of 134.80 nm, while the branch nanofibers demonstrate an average diameter of 27.19 nm. The MWCNTs-COOH/PMIA nanofibrous membranes exhibited a filtration efficiency of 99.98 % for the most penetrating particle size (MPPS), while maintaining a tensile strength of 46.736 MPa. Notably, they retained significant strength even under harsh conditions, e.g. 56.46 % at 250 °C, 58.26 % with 30 wt% H2SO4, and 82.71 % with 10 wt% NaOH. The filtration efficiency remained at approximately 88.97 % even at 250 °C. These findings expand PMIA's application to harsh environments and offer insights for designing high-performance filters for high-temperature settings.

The systolic accelerator is one of the premier architectural choices for DNN acceleration. However, the conventional systolic architecture suffers from low PE utilization due to the mismatch between the fixed array and diverse DNN workloads. Recent studies have proposed flexible systolic array architectures to adapt to DNN models. However, these designs support only coarse-grained reshaping or significantly increase hardware overhead. In this study, we propose ReDas, a flexible and lightweight systolic array that supports dynamic fine-grained reshaping and multiple dataflows. First, ReDas integrates lightweight and reconfigurable roundabout data paths, which achieve fine-grained reshaping using only short connections between adjacent PEs. Second, we redesign the PE microarchitecture and integrate a set of multi-mode data buffers around the array. The PE structure enables additional data bypassing and flexible data switching. Simultaneously, the multi-mode buffers facilitate fine-grained reallocation of on-chip memory resources, adapting to various dataflow requirements. ReDas can dynamically reconfigure to up to 129 different logical shapes and 3 dataflows for a $128\times 128$128×128 array. Finally, we propose an efficient mapper to generate appropriate configurations for each layer of DNN workloads. Compared to the conventional systolic array, ReDas can achieve about 4.6$\times$× speedup and 8.3$\times$× energy-delay product (EDP) reduction.

Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as life detection task in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the sensing task completion rate. we propose a MARL-based heterogeneous multi-agent route planning algorithm called MANF-RL-RP. The algorithm has made targeted designs in terms of global-local dual information processing and model structure for heterogeneous multi-agent, making it effectively considers the collaboration among heterogeneous agents and the long-term impact of current decisions. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant performance improvement. Compared to MANF-DNN-RP and Greedy-SC-RP, the task completion rate based on MANF-RL-RP increased by an average of 8.82% and 56.8%, respectively.

ABSTRACTIn this paper, studies of competing risks model are considered when the observations are left-truncated and right-censored data. When the failure times of the competing risks are distributed by a generalized inverted exponential model with same scale but different shape parameters with partially observed failure causes, statistical inference for the unknown model parameters is discussed from classical and Bayesian approaches, respectively. Maximum likelihood estimators of the unknown parameters, along with associated existence and uniqueness, are established, and the asymptotic likelihood theory is also used to construct the confidence interval via the observed Fisher information matrix. Moreover, Bayesian estimates and the corresponding highest posterior density credible intervals are also obtained based a flexible Gamma-Beta prior, and a Gibbs sampling technique is constructed to compute associated estimates. Further, under a general practical assumption with order-restriction parameter case, classical and Bayesian estimations are also established under order restriction situations, respectively. Extensive Monte-Carlo simulations are carried out to investigate the performances of our results and two real-life examples are analyzed to show the applicability of the proposed methods.