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Author(s): Yuan Li [1]; Yue Peng [2]; Xiaoyu Zhang [3]; Baihan Lyu [4]; Xiaoxiao Zhang [5]; Ying Ji [2]; Yan Liu [1]; Linsong Yang [3]; Zhongmin Peng (corresponding author) [6]; [...], Aims: To find out a better propaganda and education plan for the popularization of the ground-glass opacities-related (GGO-related) lung cancer screening. Methods and Material: The control group took a lung cancer screening knowledge test directly before receiving the health education. By contrast, the experimental group took the same knowledge test after receiving health education. This study designed unimodal and multimodal materials about GGO-related lung cancer. The text and graph were considered unimodal information, while the video was multimodal information. According to the different information forms they were exposed to, the experimental group was further divided into text, graphic, and video groups. An eye-tracking system was performed to record eye-tracking data synchronously. Results: Compared with the control group, the knowledge test scores of each experimental group were remarkably improved. Furthermore, the graphic group had a significantly higher correct rate on the No. 7 question, while the video group had the lowest. In terms of saccades, the video group had significantly higher speed and amplitude of saccades than the other two groups. In terms of fixation, the interval duration, total duration of whole fixations, and a number of whole fixations of the graphic group were significantly lower than those of the other two groups, while the video group had the highest values for these variables. Conclusions: It was on the unimodal information, such as text and graphics, that people can spend less time and cost to achieve effective acquisition of GGO-related lung cancer screening knowledge. Keywords: Eye-tracking, health communication, information modality, lung cancer screening

In grid-connected inverter systems, frequency-domain passivity theory is increasingly employed to analyze grid–inverter interactions and guide inverter control designs. However, due to difficulties in meeting sufficient passivity-based stability conditions at low frequencies, passivity theory often falls short of achieving stable system specifications. This article introduces an extended frequency-domain passivity theory. By incorporating a weighting matrix, an extended stability condition is derived. Compared to conventional passivity-based stability conditions, the proposed theory significantly reduces conservativeness and is more suited for analyzing grid–inverter interactions and guiding inverter control design. Theoretical analyses, numerical examples, and experimental results are provided to validate the effectiveness of the proposed methods.

There has much debate over the timing of the overlap and transition between the closure of the Palaeo-Asian Ocean and the initiation of subduction of the Palaeo-Pacific Ocean. This geological process is recorded by the Jilin-Heilongjiang high-pressure metamorphic belt located at the easternmost end of the Central Asian Orogenic Belt. To address this issue, we investigated the Heilongjiang Complex exposed in the Yilan and Huanan regions using geological and geochemical analyses combined with laser ablation inductively coupled plasma mass spectrometry zircon U–Pb and Ar–Ar isotopic dating. Our geochemical studies indicate that the protoliths of the Yilan blueschist consist of subalkaline basalt formed in an ocean island environment. The zircon U–Pb analyses yielded zircon crystallization ages of 247 ± 4, 259 ± 3, 252 ± 5 and 266 ± 3 Ma. The Ar–Ar analyses yielded phengite ages of 185 ± 2 and 177 ± 2 Ma for a mica schist and a muscovite–albite schist in the Huanan area, respectively. Our findings indicate that the opening of the Jilin-Heilongjiang Ocean occurred during the late Permian to early Triassic (266–247 Ma) and its subduction occurred during the early Triassic to early Jurassic (247–185 Ma). These results provide new constraints on the timing of the overlap and transition between the closure of the Palaeo-Asian Ocean and the initiation of subduction of the Palaeo-Pacific Ocean. [ABSTRACT FROM AUTHOR]

Vehicular Edge Computing (VEC) is an emerging computing paradigm that serves as a specific application of Mobile Edge Computing (MEC) in intelligent transportation systems. As a core technology of VEC, task offloading improves computing efficiency and service quality by offloading computing tasks from vehicular devices to edge nodes. However, the high mobility of vehicles, heterogeneity of resources, and real-time requirements present significant challenges for task offloading. To address the issue of reducing overall system latency and increasing the offloading success rate in multi-task offloading, we propose a task offloading scheme combining Deep Reinforcement Learning (DRL) and Kuhn-Munkres (KM) Matching algorithm, named the PDQKM offloading scheme. Firstly, to mitigate the delay caused by frequent Roadside Unit (RSU) handovers, we propose a method to detect whether a vehicle is within the coverage area of the RSU. This method filters out unreasonable offloading decisions, avoiding the overhead associated with frequent RSU handovers. Secondly, the combination of DRL and the KM matching algorithm leverages the strengths of both approaches. DRL provides initial offloading strategies in highly dynamic and high-dimensional decision environment. Although DRL may get stuck in local optima, it can quickly adapt to environmental changes. The KM matching algorithm, a classic solution for perfect task-resource matching, performs global optimization on the initial strategies provided by DRL. This integration overcomes the limitations that a single algorithm might have. Finally, to effectively coordinate and manage the heterogeneous resources of RSU, we utilize an improved KM matching algorithm to update computational resources in real time, enhancing matching efficiency. Experimental results demonstrate that PDQKM outperforms comparable offloading schemes in terms of overall system latency and offloading success rate optimization.

This study reconstructs ancient China's transport system, exploring its intersection with societal networks. We begin by procuring population data indicative of years 2, 742, 1102, and 1522. The transportation matrix, inclusive of roads, rivers, and canals, is subsequently reconstituted. In continuation, we ascertain the transport accessibility for heavy goods. Our accessibility model reveals that history lends itself to a more coherent explanation through quantitative research. We then deploy a gravity model to examine the correlation between transport systems and social networks. The model delineates how transport, particularly travel duration, influenced societal interconnections. The implemented gravity model further highlights the steady deterrent of travel time, suggesting that transport infrastructure might sustainably govern social networks on a historic spectrum. Finally, our research draws on the analysis of five key capitals throughout history, along with other connections inadequately modeled by the gravity approach, to discuss the self-perpetuation effects of social networks. We discern that cities with elevated political stature or a sizable population/economic foundation tend to exhibit a more robust self-reinforcing influence on their social networks.

Surface soil moisture (SSM) is essential for understanding the interactions between the atmosphere and Earth’s surface. The rapid development of remote sensing technology in recent decades has provided feasible alternatives for SSM retrieval. The triangle model is one such method that uses the relationship between land surface temperature (LST) and vegetation index (VI) on a triangular space to estimate SSM. However, the traditional LST-VI triangle models inherently suffer from two major drawbacks. First, the subjective requirements for a sufficient number of pixels are characterized by a wide range of vegetation and SSM under uniform atmospheric conditions. Second, this is the need for date-to-date calibration. To overcome these limitations, the present study proposed a novel scheme of the feature space, the single pixel in time-series (SPTS) triangle model. The basic assumption of this feature space is that a given satellite pixel for cropland or grassland will undergo distinct vegetation cover and SSM status due to natural growth and soil moisture dynamics over a relatively long period. Unique triangles for 44 sites in two networks of the International Soil Moisture Network (ISMN)—the TxSon (US) dominated by grassland and REMEDHUS (Spain) dominated by cropland—were constructed based on Landsat data over a period of ~10 years (2013–2023). Compared to the traditional triangle model, the proposed model reveals enhanced skills for SSM retrieval, with a decrease in root-mean-square error (RMSE) by 13.5% (~0.050 m3/m3) over the study sites.

Particle erosion is the main reason for the failure of pipelines in the coal chemical, oil, and gas industries. The elbow is the main component that changes the direction of flow in a pipe and is usually installed in series in the pipeline. In this paper, the paint removal experiment and computational fluid dynamics (CFD) simulation were performed to study the particle erosion behavior of two elbows in series. The parametric optimization of the pipeline structure was also carried out. The paint removal pattern of the pipeline was conducted by the paint removal experiment. Based on the Euler-Lagrange method, CFD numerical simulations were performed to calculate the erosion pattern of pipelines under different structures. The results showed that the paint removal regions were located at 45°-50° and 60°-70° on the outer arch surface of the two elbows. The area of paint removal from the first elbow was greater than that from the second elbow. There was a good agreement in the erosion profile and the distribution of the maximum erosion region between CFD calculation and paint removal experiment. The accuracy of the numerical calculation was also verified by the qualitative comparison between the online thickness measurement results of the pipeline provided by the gasification plant and the numerical calculation results. The optimization of the serial pipeline structure was carried out by the orthogonal experiment method based on the CFD numerical calculation model, and the sensitivity of each structural parameter to the erosion of the pipeline was also analyzed. The optimized pipeline structure based on the orthogonal experimental method has a lower erosion rate, and the maximum erosion rate at the first elbow was reduced by about 26 %.

This study investigated the long-term outcomes of different thrombus removal methods in patients with acute left iliofemoral vein thrombosis (IFDVT) accompanied by left iliac vein compression (LIVC).

We report an efficient, metal-free method for late-stage rapid [18F]trifluoromethyl radiolabeling of terminal alkenes at room temperature. Utilizing 3,3-difluoroallyl sulfonium salts as precursors, the process achieves high radiochemical yields (up to 94 ± 2%) in just 30 s, with excellent functional group tolerance. This method offers a simplified and efficient pathway to produce [18F]trifluoromethylated terminal alkene compounds, enabling their application in PET imaging and expanding the chemical space for drug discovery.

Per- and polyfluoroalkyl substances (PFAS), specifically perfluorooctanesulfonate (PFOS) and its alternative, 2-[(6-chloro-1,1,2,2,3,3,4,4,5,5,6,6-dodecafluorohexyl)oxy]-1,1,2,2-tetrafluoroethanesulfonic acid (6:2 Cl-PFESA), are associated with environmental health concerns and potential cancer progression. However, their impact on multidrug resistance (MDR) in pancreatic cancer (PC) chemotherapy remains unclear. Here, we employed drug-sensitivity assays, including IC50calculations, in vitroand in vivomodels with various chemotherapeutics, and paclitaxel (PTX) as a representative agent, combined with transcriptomic/proteomic sequencing and clinical prognostic analysis, to identify MDR-related genes and validate their relevance, with the objective of establishing the correlation between PFOS/6:2 Cl-PFESA exposure and MDR in PC at molecular, cellular, and animal model levels. Our findings demonstrate that PFOS/6:2 Cl-PFESA exposure increases the drug IC50in three different PC cell lines for various chemotherapeutic agents. Compared with PFOS, 6:2 Cl-PFESA demonstrated a more pro-MDR effect on PC cells in vitro. In vivoexperiments further revealed that PFOS/6:2 Cl-PFESA exposures significantly reduced the efficacy of PTX in PC, with inhibition rates dropping from 78.3% to 23.8%/6.1%, respectively (p< 0.05). This effect was driven by the aberrant activation of the PI3K–ABCB1 pathway, with 6:2 Cl-PFESA demonstrating a stronger capacity to promote this signal pathway’s expression and function compared with PFOS. These data suggest that exposure to PFAS may elevate the risk of MDR and subsequent disease progression. Although marketed as a safer alternative to PFOS, the notable impact of 6:2 Cl-PFESA on MDR highlights the necessity for a comprehensive assessment of its potential carcinogenic risks.

Defects in chemical vapor deposition (CVD)-grown monolayer MoS2are unavoidable and provide a powerful approach to creating single-photon emitters and quantum information systems through localizing excitons. However, insight into the A–trion and B/C exciton localization in monolayer MoS2remains elusive. Here, we investigate defect-mediated A–trion and B/C exciton localization and relaxation in CVD-grown monolayer MoS2samples via transient absorption spectroscopy. The localization rate of A–trions is five times faster than B excitons, which is attributed to the distinctions in the Bohr radius, diffusion rate, and multiphonon emission. Furthermore, we obtain unambiguous experimental evidence for the direct excitation of localized C excitons. Varying gap energy at the band-nesting region revealed by first-principles calculations explains the anomalous dependence of localized C exciton energy on delay time. We also find that the rapid dissociation of localized C excitons features a short characteristic time of ∼0.14 ps, while the measured relaxation time is much longer. Our results provide a comprehensive picture of the defect-mediated excitonic relaxation and localization dynamics in monolayer MoS2.

The effects of nanostructuring on the mechanical and dry-sliding wear behaviors of a FeCoNi medium- entropy alloy (MEA) were systematically investigated through nano-indentation and ball-on-disc wear tests. The results show that reducing the grain size down into the nano-meter regime, on the one hand, significantly elevates the hardness of the FeCoNi alloy, and on the other hand, facilitates the formation of a surface oxide layer. As a result, the wear rate of the nanocrystalline (NC) FeCoNi alloy is one order of magnitude lower than its coarse-grained counterpart. The NC FeCoNi alloy also exhibits obviously enhanced wear resistance compared with conventional NC Ni and Ni-based alloys in terms of both lower wear rate and friction coefficient. Such enhancement in tribological properties mainly stems from the improved strain hardening ability, owing to the inevitable concentration heterogeneity in MEA that imposes extra resistance to dislocation motion.

This article addresses the urgent need for a comprehensive understanding of the global media coverage of Coronavirus Disease 2019 (COVID-19) by conducting a massive-scale analysis using the Global Database of Events, Language, and Tone. With a dataset encompassing 53,967,878 news items from 4,708 online news sources across 67 countries, the study spans the entire trajectory of the pandemic from the first reported case on December 31, 2019, to September 11, 2020. The analysis aims to surpass the limitations of previous research by offering a worldwide perspective on COVID-19 media coverage over a significant timeframe. Additionally, the paper explores the intricate relationships between media portrayals and the epidemiological situations in primary countries. By examining the media's impact on public perceptions, the study contributes valuable insights to the ongoing discourse on COVID-19, addressing the limitations of prior research and emphasizing the importance of a nuanced and expansive approach to understanding the role of media in the context of global health crises.

Forecasting spatiotemporal social events has significant benefits for society to provide the proper amounts and types of resources to manage catastrophes and any accompanying societal risks. Nevertheless, forecasting event subtypes are far more complex than merely extending binary prediction to cover multiple subtypes because of spatial heterogeneity, experiencing a partial set of event subtypes, subtle discrepancy among different event subtypes, nature of the event subtype, spatial correlation of event subtypes. We present Deep multi-task learning for spatio-temporal incomplete qualitative event forecasting (DETECTIVE) framework to effectively forecast the subtypes of future events by addressing all these issues. This formulates spatial locations into tasks to handle spatial heterogeneity in event subtypes and learns a joint deep representation of subtypes across tasks. This has the adaptability to be used for different types of problem formulation required by the nature of the events. Furthermore, based on the “first law of geography”, spatially-closed tasks share similar event subtypes or scale patterns so that adjacent tasks can share knowledge effectively. To optimize the non-convex and strongly coupled problem of the proposed model, we also propose algorithms based on the Alternating Direction Method of Multipliers (ADMM). Extensive experiments on real-world datasets demonstrate the model’s usefulness and efficiency.

Vehicular Edge Computing (VEC) is a transportation-specific version of Mobile Edge Computing (MEC) designed for vehicular scenarios. Task offloading allows vehicles to send computational tasks to nearby Roadside Units (RSUs) in order to reduce the computation cost for the overall system. However, the state-of-the-art solutions have not fully addressed the challenge of large-scale task result feedback with low delay, due to the extremely flexible network structure and complex traffic data. In this paper, we explore the joint task offloading and resource allocation problem with result feedback cost in the VEC. In particular, this study develops a VEC computing offloading scheme, namely, a Lagrange multipliers-based adaptive computing offloading with prediction model, considering multiple RSUs and vehicles within their coverage areas. First, the VEC network architecture employs GAN to establish a prediction model, utilizing the powerful predictive capabilities of GAN to forecast the maximum distance of future trajectories, thereby reducing the decision space for task offloading. Subsequently, we propose a real-time adaptive model and adjust the parameters in different scenarios to accommodate the dynamic characteristic of the VEC network. Finally, we apply Lagrange Multiplier-based Non-Uniform Genetic Algorithm (LM-NUGA) to make task offloading decision. Effectively, this algorithm provides reliable and efficient computing services. The results from simulation indicate that our proposed scheme efficiently reduces the computation cost for the whole VEC system. This paves the way for a new generation of disruptive and reliable offloading schemes.

This work investigates how multiple unmanned aerial vehicles (UAVs) assist the large-scale IoT devices (its count $\geq$≥ 100) in the edge computing system in accomplishing their tasks. The UAVs serve the latter as edge servers, and fly to footholds to collect task data from the latter, execute tasks locally and return results to the latter. The goal of this work is to minimize overall energy consumption by jointly optimizing the association between each UAV and ground-based IoT devices, deployments of UAVs, and their flight trajectories. To achieve this, this work proposes a joint optimization approach (JOA). It has three parts: 1) an improved k-means method is designed to handle the association between each UAV and ground-based IoT devices, where the number of clusters is equal to that of UAVs, which means that each UAV is responsible for the IoT devices within a cluster; 2) for the deployments of UAVs, an improved fireworks algorithm (IFWA) with variable-length encoding strategy and population size update strategy is proposed to optimize the number and locations of footholds of each UAV, where each member of the population symbolizes a UAV foothold, and each firework and its offspring are considered as the deployment of UAV. Also, the population size update strategy is employed to dynamically change the number of footholds; and 3) regarding UAV flight trajectory, a pre-computed greedy algorithm based on the footholds of UAVs obtained by IFWA is proposed to minimize the total UAV distance. The proposed approach is verified on ten large-scale instances, and the results demonstrate its effectiveness in achieving minimal energy consumption when compared to other state-of-the-art methods.

Intelligent Transportation Systems (ITS) aim to enhance traffic management by improving connectivity and data sharing among vehicles and road infrastructure. In a Mixed Connected and Connectionless Vehicles (MCCV) scenario consisting of connected vehicles equipped with On-Board Units (OBUs) and non-connected vehicles lacking OBUs, communication disparities create challenges in critical lane-changing overtaking decisions. These discrepancies hinder the adaptation of fully connected scenarios to dynamic interactions among these different types of vehicles. Considering the diversity in decision-making ways and capabilities of non-connected vehicles in MCCV scenarios, ensuring the coordinated execution of safe and efficient lane-changing overtaking maneuvers by multiple connected vehicles is crucial for enhancing traffic efficiency. Therefore, we propose a collaborative strategy to facilitate safer and more efficient lane-changing overtaking maneuvers for connected vehicles in the MCCV scenario. First, we design a multi-criteria priority detection, and a dynamic event-triggered mechanism based on confidence intervals to foster efficient collaboration among connected vehicles, optimizing decision-making and reducing conflicts. Second, to accommodate diverse driving styles of autonomous and human-driven vehicles, we introduce an Improved Dynamic Precise Fuzzy C-Means (IDP-FCM) algorithm to dynamically identify and adapt to different driving styles, thereby improving safety. Finally, tackling the challenge of multiple connected vehicles performing lane-changing overtaking involving hybrid action space, our proposed Multi-agent Contrastive Parameterized Dueling Deep Q-Network (MCPDDQN) algorithm incorporates contrastive learning to improve strategy stability in complex driving scenarios. Experimental results demonstrate the effectiveness of our strategy in improving road safety and traffic efficiency of the MCCV scenario.

The prediction of user trajectories is a fundamental component to support urban traffic management and various advanced transportation applications, such as traffic optimization and location-based services. Trajectory data typically contains multiple behavioral patterns and contexts, including different travel purposes, modes of transportation, time intervals, and geographic regions. These complex factors collectively influence the prediction of user trajectories. However, trajectory prediction models face challenges in effectively distinguishing between these various patterns. In this paper, we propose a novel stack Transformer-based multimodal long-term trajectory prediction (SMTTP) scheme for heterogeneous user behavior patterns. First, a learnable trajectory similarity measure method is proposed to estimate the relative distance between multi-attribute variable-length trajectories. Then, to address the instability of trajectory clustering caused by random initialization, a cluster head initialization algorithm based on high confidence nodes is developed to improve clustering stability and reduce convergence time. In addition, a Transformer-based trajectory prediction model with multi-dimensional feature fusion is proposed to achieve accurate and efficient long-term trajectory prediction. Experimental results on the real telecom dataset in Shanghai, China show that the proposed SMTTP scheme can achieve improved performance in trajectory prediction in terms of prediction error, and also has high accuracy and stability in unsupervised trajectory clustering.

Vehicular crowd sensing (VCS), emerging as a new paradigm within mobile crowd sensing, leverages vehicles as the participator, which can obtain broader sensing coverage and higher sensing flexibility. Previous works ignored the strong impact of environmental factors on workers’ travel costs, as well as improper gains from speculative behavior (i.e., workers detour or delay to get more compensation), resulting in unfair income of workers. Moreover, these works focused solely on sensing tasks within specific domains, lacking generalization ability. Therefore, our work is dedicated to providing a fair and universal VCS platform, which is called Environment-cost-based Heterogeneous Task Allocation (EHTA) framework. Our work differs from previous works in the following aspects: 1) We introduce the Environment Cost (EC) based on the investigation of traffic conditions to accurately quantify workers’ efforts, and propose a straightforward yet efficacious detection methods to identify speculative behavior of malicious workers, both of which could guarantee the fairness in workers’ income. 2) We design a spatial-temporal fair incentive mechanism based on monetary reward to ensure the fair execution of tasks in both space and time dimensions. 3) We summarize the characteristics of three kinds of sensing tasks and propose a universal task allocation algorithm to assign multiple types of tasks simultaneously. The effectiveness of our framework was validated by simulations, which are conducted on a data set comprising 13,000 taxi trajectories from Shanghai in April 2015. We compared our framework against four baseline algorithms, and the results shows that EHTA framework outperforms in terms of task expenditure, task utility and fairness.

• The progress on synchrotron radiation characterization of laser additive manufacturing is systematically reviewed. • The mechanisms involved laser-matter interaction, molten pool evolution, solidification structure and defects formation are summarized. • Future development direction of real-time characterization technique and its application on laser additive manufacturing are proposed. Laser additive manufacturing (LAM) has been widely used in high-end manufacturing fields such as aerospace, nuclear power, and shipbuilding. However, it is a grand challenge for direct and continuous observation of complex laser-matter interaction, melt flow, and defect formation during LAM due to extremely large temperature gradient, fast cooling rate, and small time (millisecond) and space (micron) scales. The emergence of synchrotron radiation provides a feasible approach for in situ observation of the LAM process. This paper outlines the current development in real-time characterization of LAM by synchrotron radiation, including laser-matter interaction, molten pool evolution, solidification structure evolution, and defects formation and elimination. Furthermore, the future development direction and application-oriented research are also discussed. [Display omitted] [ABSTRACT FROM AUTHOR]

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In this paper, we design a new method for inferring a polymer based on the framework. For this, we introduce a new way of representing a polymer as a form of monomer and define new descriptors that feature the structure of polymers. We also use linear regression as a building block of constructing a prediction function in the framework. The results of our computational experiments reveal a set of chemical properties on polymers to which a prediction function constructed with linear regression performs well. We also observe that the proposed method can infer polymers with up to 50 non-hydrogen atoms in a monomer form.

Few-shot named entity recognition (NER) exploits limited annotated instances to identify named mentions. Effectively transferring the internal or external resources thus becomes the key to few-shot NER. While the existing prompt tuning methods have shown remarkable few-shot performances, they still fail to make full use of knowledge. In this work, we investigate the integration of rich knowledge to prompt tuning for stronger few-shot NER. We propose incorporating the deep prompt tuning framework with threefold knowledge (namely TKDP), including the internal 1) context knowledge and the external 2) label knowledge & 3) sememe knowledge. TKDP encodes the three feature sources and incorporates them into soft prompt embeddings, which are further injected into an existing pre-trained language model to facilitate predictions. On five benchmark datasets, the performance of our knowledge-enriched model was boosted by at most 11.53% F1 over the raw deep prompt method, and it significantly outperforms 9 strong-performing baseline systems in 5-/10-/20-shot settings, showing great potential in few-shot NER. Our TKDP framework can be broadly adapted to other few-shot tasks without much effort.

A solar steam evaporator provides a sustainable and efficient alternative water purification solution to address the global freshwater shortage. Previous efforts have made significant advances in maximizing its water evaporation rate, but no single evaporator has all the properties necessary for practical point-of-use application, including a high efficiency for generation of drinkable water, an excellent portability critical for on-site water purification, good washability for mitigating evaporator fouling, and good reusability. We report a strategy to produce a high-performance photothermal material for point-of-use water purification. By simultaneously incorporating graphene and gold particles grown from recycled electronic waste in a mechanically strong sponge, we achieved highly efficient water purification under realistic conditions. In addition to a high evaporation rate (3.55 kg/m2/h under one-sun irradiation) attributed to a control of atomic structure of graphene and the size-dependent surface plasmon resonance of gold nanoparticles, it is portable which can be folded, vacuum compacted, dried and rehydrated without compromising performance. It also allows repeated washing to remove contaminant fouling so that it can be reused. The evaporator transforms various types of contaminated water into drinkable clean water, and can be mounted at any angle to optimize the incident solar irradiation. Furthermore, the assembled steam evaporator device could gain purified water meeting the World Health Organization drinking water standards with a high evaporation rate of 9.36 kg/m2/h under outdoor sunlight.

The main purpose of this article is to elaborate on the limitations of using frequency-domain passivity theories in analyzing grid-inverter interactions within the low-frequency range. It primarily covers three levels of limitations: 1) the limitations and selection criteria of two kinds of passivity index, 2) potential conflicts between different passivity index tuning methods, and 3) the relationship between the frequency range of negative passivity index and system stability robustness. The findings suggest that caution should be exercised when applying passivity theory, particularly in the low-frequency range.

Sustainable electricity-to-chemical conversion via the utilization of artificial catalysts inspired by redox biological systems holds great significance for catalyzing synthesis. Herein, we develop a biomimetic electrosynthesis strategy mediated by a nicotinamide adenine dinucleotide (NADH) mimic-containing coordination capsule for efficiently producing α-hydroxy/amino esters. The coordination saturated metal centers worked as an electron relay to consecutively accept single electrons while donating two electrons to the NAD+mimics simultaneously. The protonation of the intermediate generated active NADH mimics for biomimetic hydrogenation of the substrates via the conventional enzymatic manifold with or without the presence of natural enzymes. The pocket of the capsule encapsulated the substrate and enforced the close proximity between the substrate and the NADH mimics, forming a preorganized intermediate to shift the redox potential by 0.4 V anodically. The cobalt capsule gave methyl mandelate over a range of applied potentials, with an improved yield of 92% when operated at −1.2 V compared to that of Hantzsch ester or natural NADH. Kinetic experiments revealed a Michaelis–Menten mechanism with a Kmof 7.5 mM and a Kcatof 1.1 × 10–2s–1. This extended strategy in tandem with an enzyme exhibited a TON of 650 molE–1with an initial TOF of 185 molE–1·h–1, outperforming relevant Rh-mediated enzymatic electrosynthesis systems and providing an attractive avenue toward advanced artificial electrosynthesis.

Incorporating heterometals into titanium–oxygen clusters represents an effective approach to adjusting the band gap and absorption properties. Herein, a family of heterometallic Ti–Cu clusters was synthesized under solvothermal conditions. The unique structural feature of these clusters is the formation of ring clusters with myo-inositol ligands serving as structure-directing ligands. The myo-inositol ligand, as a typical polyol ligand, demonstrates flexible and distinctive coordination modes capable of chelating up to eight transition metal ions simultaneously. Compounds 4and 5show significant absorption in the visible region, indicating that the incorporation of Cu ions can efficiently tune the band gap of titanium–oxygen clusters.

The simultaneous enhancement of lipophagy and mitochondrial biogenesis has emerged as a promising strategy for lipid lowering. The transcription factor EB (TFEB) exhibits a dual role, whereby it facilitates the degradation of lipid droplets (LDs) through the process of lipophagy while simultaneously stimulating mitochondrial biogenesis to support the utilization of lipophagy products. The purpose of this study was to explore the effect of astragaloside I (AS I) on hyperlipidemia and elucidate its underlying mechanism. AS I improved serum total cholesterol and triglyceride levels and reduced hepatic steatosis and lipid accumulation in db/db mice. AS I enhanced the fluorescence colocalization of LDs and autophagosomes and promoted the proteins and genes related to the autolysosome. Moreover, AS I increased the expression of mitochondrial biogenesis-related proteins and genes, indicating that AS I promoted lipophagy and mitochondrial biogenesis. Mechanistically, AS I inhibits the protein level of p-TFEB (ser211) expression and promotes TFEB nuclear translocation. The activation of TFEB by AS I was impeded upon the introduction of the mammalian target of rapamycin (mTOR) agonist MHY1485. The inhibition of p-mTOR by AS I and the activation of TFEB were no longer observed after administration of the Akt agonist SC-79, which indicated that AS I activated TFEB to promote lipophagy-dependent on the Akt/mTOR pathway and may be a potentially effective pharmaceutical and food additive for the treatment of hyperlipidemia.

Detection and analysis of the sulfur hexafluoride (SF6) decomposition product is an effective way to monitor the operating status of gas-insulated switchgear (GIS) equipment. To overcome the large size, high cost, and difficult maintenance of the traditional detection methods like gas chromatograph, and photoacoustic spectroscopy, a sensor array composed of microelectromechanicalical system (MEMS) metal oxide semiconductor (MOS) gas sensors was proposed to detect its response to different concentrations of carbon monoxide (CO) and sulfur dioxide (SO2) in SF6 background in this work. The Kendall coefficient was employed to evaluate the selectivity of MEMS sensors to characteristic gases. A number of algorithms were used and compared in qualitative and quantitative detection, and results indicated all the algorithms had classification accuracy of over 90%. Among them, the multilayer perception (MLP) model was the best one for concentration prediction, with which ${R} ^{{2}}$ of 0.90 and 0.74 can be realized for CO and SO2, and RMSE are 0.91 and 0.79 for them, respectively. After comparing prediction results of the MEMS sensor array tested both in air and SF6 backgrounds, the MEMS sensor array was verified to be more suitable for quantitative detection of CO and SO2 in the SF6 background, and its qualitative recognition and quantitative measurement accuracy could be improved by 29.86% and 38.64%, respectively. Due to the small size and high prediction accuracy, the MEMS sensor array has the potential for large-scale deployment in online monitoring of GIS operations.

Intelligent transportation systems (ITS) utilize advanced technologies to enhance traffic safety and efficiency, contributing significantly to modern transportation. The integration of Vehicle-to-Everything (V2X) further elevates road safety and fosters the progress of ITS through enabling direct vehicle communication and interaction with infrastructure. However, the penetration rate of V2X vehicles is advancing gradually. Consequently, there will be mixed scenarios on the road, involving both on-board units (OBUs)-equipped and non-equipped vehicles. This results in disparities in communication capabilities, highlighting the need to ensure the efficient and safe operation of vehicles in such mixed scenarios. This paper addresses this challenge by presenting a feasibility analysis and prediction method for lane-changing overtaking maneuvers in mixed scenarios, specifically for vehicles equipped with OBUs. This method assists vehicles in completing overtaking maneuvers by offering a non-binary lane-changing overtaking feasibility index along with corresponding speed guidance. First, vehicle sensors are used to sense the state of surrounding vehicles, addressing any missing sensor data due to occlusions. Moreover, the future driving behavior of the vehicle is taken into account to more accurately predict the future state of the vehicle. Then, a deep reinforcement learning algorithm is deployed to process the hybrid action space to train a lane-changing overtaking model, which also takes into account the influence of the flow of each lane in front of the vehicle, and finally predicts the feasibility of the vehicle performing lane-changing overtaking. Experimental results demonstrate that our method can accurately predict the vehicle’s future state and effectively assist the vehicle in completing lane-changing overtaking maneuvers. This research provides strong support for the integration of ITS and V2X technologies.

There is an increasing demand on the grid-forming (GFM) capability of grid-connected voltage-source converters under large grid disturbances, even when the current-limiting control (CLC) is triggered. This letter demonstrates that the GFM capability can be maximized by matching the angle of virtual impedance emulated by the CLC and that of the passive impedance composed by filter impedance and grid impedance. This finding offers insight for optimizing the CLC with the enhanced GFM capability. The experimental results are given to verify the theoretical findings.

The degree of processing is rarely considered an independent factor in the health effects of fruit juices and beverages (FJBs) consumption. In fact, the consumption of ultra-processed foods has been shown to pose health risks. In this study, we first integrated 4 systems used to classify the degree of food processing and then classified FJBs into three major categories, low (minimal), moderate and high. Second, we compared the differences in attitudes towards FJBs in dietary guidelines. Third, we integrated the results of existing epidemiological surveys, randomized controlled trials, and animal experiments to explore the health risks associated with consuming FJBs. Deepening the processing of FJBs has been found to lead to an increased risk of diseases. Dietary pattern, nutrients, addition agents and consumer preferences may be influential factors. Finally, we investigated whether there were any changes in the health benefits of 100 % fruit juices produced by different processing methods. In conclusion, minimally/moderately processed 100 % fruit juices provide more health benefits than highly processed fruit beverages. The results support the need to consider the extent of FJBs processing in future studies to adjust official nutritional recommendations for beverage consumption.

A novel framework for designing the molecular structure of chemical compounds with a desired chemical property has recently been proposed. The framework infers a desired chemical graph by solving a mixed integer linear program (MILP) that simulates the computation process of two functions: a feature function defined by a two-layered model on chemical graphs and a prediction function constructed by a machine learning method. To improve the learning performance of prediction functions in the framework, we design a method that splits a given data set $\mathcal {C}$C into two subsets $\mathcal {C}^{(i)},i=1,2$C(i),i=1,2 by a hyperplane in a chemical space so that most compounds in the first (resp., second) subset have observed values lower (resp., higher) than a threshold $\theta$θ. We construct a prediction function $\psi$ψ to the data set $\mathcal {C}$C by combining prediction functions $\psi _{i},i=1,2$ψi,i=1,2 each of which is constructed on $\mathcal {C}^{(i)}$C(i) independently. The results of our computational experiments suggest that the proposed method improved the learning performance for several chemical properties to which a good prediction function has been difficult to construct.

Mesenchymal stem cells (MSCs) have emerged as an indispensable source for stem cell research and preclinical studies due to their capacity for in vitro proliferation and their potential to differentiate into mesodermal lineages, particularly into osteoblasts. This capability has propelled their application in the fields of bone regeneration and osteochondral repair. Traditional methodologies for assessing the differentiation status of MSCs necessitate invasive procedures such as cell lysis or fixation. In this study, we introduce a nondestructive technique that utilizes an integrated label-free approach to evaluate the osteogenic maturation of MSC spheroid aggregates. This method employs scanning electrochemical microscopy (SECM) with a flexible probe in conjunction with a top-removable microfluidic device designed for easy SECM access. By tracking the production rate of p-aminophenol (PAP) in the generation/collection mode and assessing morphological changes via the negative feedback mode using [Ru(NH3)6]Cl3(Ruhex), we can discern variations in the alkaline phosphatase (ALP) activity indicative of osteogenic differentiation. This innovative strategy enables the direct evaluation of osteogenic differentiation in MSC spheroids cultured within microwell arrays without necessitating any labeling procedures. The utilization of a flexible microelectrode as the probe that scans in contact mode (with probe–substrate distances potentially as minimal as 0 μm) affords enhanced resolution compared to the traditional stiff-probe technique. Furthermore, this method is compatible with subsequent molecular biology assays, including gene expression analysis and immunofluorescence, thereby confirming the electrochemical findings and establishing the validity of this integrative approach.

Ordinal regression (OR) aims to solve multiclass classification problems with ordinal classes. Support vector OR (SVOR) is a typical OR algorithm and has been extensively used in OR problems. In this article, based on the characteristics of OR problems, we propose a novel pinball loss function and present an SVOR method with pinball loss (pin-SVOR). Pin-SVOR is fundamentally different from traditional SVOR with hinge loss. Traditional SVOR employs the hinge loss function, and the classifier is determined by only a few data points near the class boundary, called support vectors, which may lead to a noise sensitive and re-sampling unstable classifier. Distinctively, pin-SVOR employs the pinball loss function. It attaches an extra penalty to correctly classified data that lies inside the class, such that all the training data is involved in deciding the classifier. The data near the middle of each class has a small penalty, and that near the class boundary has a large penalty. Thus, the training data tend to lie near the middle of each class instead of on the class boundary, which leads to scatter minimization in the middle of each class and noise insensitivity. The experimental results show that pin-SVOR has better classification performance than state-of-the-art OR methods.

Vehicular Edge Computing (VEC) is the transportation version of Mobile Edge Computing (MEC). In VEC, task offloading enables vehicles to offload computing tasks to nearby Roadside Units (RSUs), thereby reducing the computation cost. Recent trends in task offloading cause a proliferation of studies in academia. However, the existing offloading schemes still face many challenges, such as high-dynamic network topology, massive and complex data, dynamic scenes with high-speed vehicles and low-latency requirements. Digital Twin (DT)-based VEC is emerging as a promising solution. It monitors the state of the VEC network in real time through mappings and interactions between the physical and virtual entities. Consequently, the task offloading scheme can make more reasonable offloading decisions at the physical layer and further improve the efficiency of VEC. Above all, we propose a VEC computing offloading scheme, namely, Adaptive Swarm Intelligent Offloading Scheme Based on Digital-Twin-Assisted PRediction In VEC (STRIVE). The VEC network architecture is established to combines DT with an improved Generative Adversarial Network (GAN). The powerful prediction ability of GAN is used to assist in constructing DT in the pre-processing phase, reducing the size of the decision space. To adapt to the dynamic nature of VEC, we establish an adaptive model to adjust the real-time parameter under various scenarios. Then, we deploy an improveD genetIc simulatEd annealing-baSEd particLe swarm optimization (DIESEL) algorithm to task offloading decision-making, which can provide reliable computing services for vehicles at a lower cost. The simulation results demonstrate that the proposed scheme can effectively reduce computing delay and energy consumption compared with its counterparts.

It is envisioned that the future electric grid will be underpinned by a vast number of smart inverters linking renewables at the grid edge. These inverters' dynamics are typically characterized as impedances, which are crucial for ensuring grid stability and resiliency. However, the physical implementation of these inverters may vary widely and may be kept confidential. Existing analytical impedance models require a complete and precise understanding of system parameters. They can hardly capture the complete electrical behavior when the inverters are performing complex functions. Online impedance measurements for many inverters across multiple operating points are impractical. To address these issues, we present the InvNet, a machine learning framework capable of characterizing inverter impedance patterns across a wide operation range, even with limited impedance data. Leveraging transfer learning, the InvNet can extrapolate from physics-based models to real-world ones and from one inverter to another with the same control framework but different control parameters with very limited data. This framework demonstrates machine learning as a powerful tool for modeling and analyzing black-box characteristics of grid-tied inverter systems that cannot be accurately described by traditional analytical methods, such as inverters under model-predictive control. Comprehensive evaluations were conducted to verify the effectiveness of the InvNet.

A-type granites generally have much lower water, higher temperature, and incompatible element concentrations than I-type granitoids. Yet it remains unclear why I-A-type granitic complexes occur in convergent plate margins. Here we conduct geochemical analyses on apatite and mafic minerals from the late Cretaceous I-A-type granitic complex in Fuzhou area, SE China, aiming to decipher differentiation, fluid metasomatism, and degassing that primarily control the compositional diversity of felsic magmas. Apatites in both rock types are F-rich and show large H2O and δD variations, i.e., 341–3892 ppm H2O and –325 to +336‰ δD in I-type granitoids; 67–1366 ppm H2O and –251 to +1439‰ δD in A-type granites. H2O in apatite is negatively correlated with La/Sm and Sr/Y in the I-type granitoids, whereas it is positively correlated with Ce and total rare earth element (REE) concentrations in the A-type granites. Once H2O increases up to hundreds of ppm, both rock types show a rapid decrease of H2O/Ce, an increase of F/Cl, and extensive H isotope fractionation.

The selective oxidation of alcohols into aldehydes is a basic and significant procedure, with great potential for scientific research and industrial applications. However, as an important factor in the C(sp3)–H activation process, high selectivity is generally difficult to achieve due to the fact that the more easily activated properties of aldehydes are compared to alcohols. Herein, by the ingenious decoration of eosin Y into a Zr-based metal–organic framework (MOF–808), EY@MOF–808 was prepared as a selectivity regulator for the aerobic oxidation of the benzyl alcohols into corresponding aldehydes, possessing applicability for the benzylic alcohols with various substituents. By anchoring eosin Y on Zr6O4(OH)4clusters of MOF–808 and maintaining open metal nodes with selective binding effects, the benzyl alcohol substrates were selectively coordinated to the unsaturated metal clusters adjacent to eosin Y, which ensured that the excited eosin Y rapidly activated substrates to generate carbon radicals by the hydrogen atom transfer (HAT) process. The rapid electron transfer (ET) simultaneously produced reactive oxygen species (O2•–) and then a combination of both to further promote the generation of benzaldehydes. The weak interaction of benzaldehydes with the skeleton allowed it to dissociate rapidly, thus preventing overoxidation. Under the catalysis of EY@MOF–808, the selectivity of various benzaldehydes was more than 99%. In contrast, eosin Y gave only benzoic acid products under the same conditions, which demonstrated the superiority of regulatory selectivity of EY@MOF–808. Taking advantage of the heterogeneity of the MOF, EY@MOF–808 was recycled four times without a decrease in its selectivity and avoided the quenching effect of eosin Y. The organic functional units postdecorated MOF-based photocatalyst strategy exhibits a promising new perspective approach to sustainably regulating the selectivity of inert oxidation.

The cessation of marine hydrate exploitation in the Nankai Trough resulted from severe sand production challenges. Currently, the mechanisms underlying sand production during hydrate reservoir exploitation lack comprehensive understanding. To address this knowledge gap, we developed a COMSOL-based computer model to predict gas and sand production for hydrate exploitation, which efficiently integrated a sand production model with a convectional thermo-hydro-mechanical coupling model. In the sand production model, the detachment of skeletal particles from sediments is regulated by both fluid erosion and shear deformation. Specifically, when hydraulic gradient exceeds a certain critical value and meanwhile shear deformation also occurs, skeletal particles will detach and migrate with the flowing fluids. Based on the developed computer model, we numerically simulated gas and sand production in the 2013 Nankai field test, and conducted a systematic analysis about sand production mechanism. Our numerical simulations showed: (1) For weakly cemented sand-dominated sediments, a moderate hydraulic gradient (>5) could potentially trigger particle detachment. Within the 6-day test duration, cumulative gas production volume reached 1.17 × 105m3with 26.9 m3of sand generated, exhibiting a striking alignment with measured data, with a mere 3% deviation; (2) Due to weak mechanical strength, the overburden experienced more obvious deformation than other areas, which, together with its high hydraulic gradient, led to the heightened particle detachment. Thus, the overburden provided the main source of sand particles for sand production; (3) Both small depressurization amplitude and low permeability tend to moderate shear deformation and hydraulic gradient, and accordingly constitute unfavorable conditions for sand production. The negative correlation of permeability with sand production well explains why severe sand production occurred in the Nankai Trough rather than the Shenhu area where sediments have extremely low permeability.

Membrane tension is an important physical parameter of describing cellular homeostasis, and it is widely used in the study of cellular processes involving membrane deformation and reorganization, such as cell migration, cell spreading, and cell division. Despite the importance of membrane tension, direct measurement remains difficult. In this work, we developed a ratiometric fluorescent probe sensitive to membrane tension by adjusting the carbon chain structure based on polarity-sensitive fluorophores. The probe is sensitive to changes in membrane tension after cells were subjected to physical or chemical stimuli, such as osmotic shock, lipid peroxidation, and mechanical stress. When the polarity of the plasma membrane increases (the green/red ratio decreases) and the membrane tension increases, the relative magnitude of the membrane tension can be quantitatively calculated by fluorescence ratio imaging. Thus, the probe proved to be an efficient and sensitive membrane tension probe.

Two-dimensional metal halide perovskites (TMHPs) are distinguished by their component-dependent material properties, and outstanding photophysical properties, positioning them as promising candidates for optoelectronic devices. Although numerous A-site cations have been integrated into TMHP scaffolds, only a few incorporate complex three-dimensional organic frameworks. Here we report the synthesis of five new TMHP single crystals incorporated with spirocyclic (SC) intercalating cations via a two-step synthetic approach. This method involves the formation of a thiazolidine-derived framework via condensation of zwitterionic ligands and cyclic ketones. Strong hydrogen bonding interactions between the ammonium termini of the SC ligands and the inorganic layers are observed in all the new TMHPs, drawing the SC cations closer to the [PbI6]4–octahedra and stretching the heterocyclic scaffolds. Computational analyses reveal that the sulfur-containing SC ligands may significantly enhance the charge carrier mobility. These results demonstrate the potential of three-dimensional intercalating cation synthesis for the development of new perovskite-derived structures.

In this work, a numerical simulation model of an industrial-scale magnesite flash calciner (MFC) was established based on computational fluid dynamics (CFD) method. The discrete phase model (DPM) was employed, and the results of kinetic analysis for magnesite decomposition were taken into consideration. Then, the influence of swirling gas inlet design on particle motion and decomposition in MFC was analyzed, thereby providing suggestions for production. The results indicate that incorporating a swirling design can attenuate the particle deposition at the feeding port and increase particle residence time in furnace. However, the conversion degree of magnesite is decreased. This phenomenon can be attributed to the particle accumulation near the wall, resulting in a localized lower gas temperature and subsequently leading to a gas-solid heat transfer degradation. By elevating the gas temperature and reducing its flow rate, the enhancement of magnesite decomposition and reduction in energy consumption are achieved. The gas-solid water equivalent ratio, determined by heat of reaction and sensible heat of flue gas, needs to be higher than 1.8 to maintain a mass fraction of MgO above 0.9.

Extreme precipitation is exacerbating the burden of infectious diarrhea in the context of climate change, it is necessary to identify the critical and easy-to-intervene intermediate factors for public health strategies. Water quality may be the most important mediator, while relevant empirical evidence is limited. This study aimed to examine the role of water quality in the process of infectious diarrhea caused by extreme precipitation. Weekly infectious diarrhea cases, meteorological factors and water quality data in Yangtze River Basin in China between October 29, 2007 to February 19, 2017 were obtained. Two-stage statistical models were used to estimate city-specific extreme precipitation, water quality and infectious diarrhea relationships that were pooled to derive regional estimates. A causal mediation analysis was used to assess the mediation effect of water quality. In Yangtze River Basin, extreme precipitation events had a significant impact on infectious diarrhea (Incidence Rate Ratios [IRR]: 1.027, 95% Confidence Interval [CI]: 1.013∼1.041). After extreme precipitation events, the dissolved oxygen (DO) in surface water decreased (-0.123 mg/L, 95%CI: -0.159 mg/L∼-0.086 mg/L), while the un-ionized ammonia (NH(3)-N) increased (0.004 mg/L, 95%CI: 0.001 mg/L∼0.006 mg/L). The combined overall effect of DO and NH(3)-N on infectious diarrhea showed that both low and high concentrations were associated with an increased risk of infectious diarrhea. The causal mediation analysis showed that the mediation proportion of the two water quality indexes (DO and NH(3)-N) is 70.54% (P< 0.001). To reduce the health effects of extreme precipitation, in contrast to current population-oriented health strategies, those that take into account more direct and easy-to-intervene water quality indicators should be encouraged by future policies.