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Lake water temperature (LWT) is an important indicator of physical processes within a lake, but traditional process‐based and data‐driven models are limited in their ability to estimate long‐term changes in LWT because of simplified physical laws, insufficient onsite measurements and high computational demands. To overcome these limitations, this study proposes a hybrid multi‐parameter scientific knowledge‐guided neural network (MP‐KgNN) for solving 1‐D lake temperature governing equation trained using both simulations of the WRF‐Lake model and onsite LWT measurements based on a novel training framework called physics‐informed deep learning (PIDL) framework and simulates the thermodynamics in a large deep reservoir located in eastern China from 1960 to 2021. The results revealed that the MP‐KgNN can estimate the dynamic changes in LWT with satisfactory accuracy (mean absolute error [MAE] = 1.14 K, root mean square error [RMSE] = 1.49 K). Moreover, it outperformed the pre‐trained MP‐KgNN trained with only the WRF‐Lake model (MAE = 2.43 K, RMSE = 2.77 K), which indicates its successful prediction of the thermal structure of the lake. The prediction derived by MP‐KgNN showed an increasing trend (0.04 K decade−1) of LWT in the Lake Qiandaohu. Specifically, the LWT was experienced to increase at a rate of 0.10 K decade−1 near the lake surface. These changes resulted in an extension and deepening of lake thermal stratification, as indicated by a 0.58 m increase in metalimnion thickness and a 20.46 kJ increase in Schmidt stability. The proposed MP‐KgNN is expected to become a powerful tool for estimating long‐term variations in the thermodynamics of lake ecosystems.

Distributed denial of service (DDoS) is a type of cyberattack in which multiple compromised systems flood the bandwidth or resources of a single system, making the flooded system inaccessible to legitimate users. Since large-scale botnets based on the Internet of Things (IoT) have been hotbeds for launching DDoS attacks, it is crucial to defend against DDoS-capable IoT botnets effectively. In consideration of resource constraints and frequent state changes for IoT devices, they should be equipped with repair measures that are cost-effective and adaptive to mitigate the impact of DDoS attacks. From the mitigation perspective, we refer to the collection of repair costs at all times as a repair strategy. This paper is then devoted to studying the problem of developing a cost-effective and adaptive repair strategy (ARS). First, we establish an IoT botware propagation model that fully captures the state evolution of an IoT network under attack and defense interventions. On this basis, we model the ARS problem as a data-driven optimal control problem, aiming to realize both learning and prediction of propagation parameters based on network traffic data observed at multiple discrete time slots and control of IoT botware propagation to a desired infection level. By leveraging optimal control theory, we propose an iterative algorithm to solve the problem, numerically obtaining the learned time-varying parameters and a repair strategy. Finally, the performance of the learned parameters and the resulting strategy are examined through computer experiments.

Clinical work and research on diabetic wound repair remain challenging globally. Although various conventional wound dressings have been continuously developed, the efficacy is unsatisfactory. The effect of drug delivery is limited by the depth of penetration. The sustained release of biomolecules from biological wound dressings is a promising treatment approach to wound healing. An assortment of cell-derived exosomes (exos) have been proved to be instrumental in tissue regeneration, and researchers are dedicated to developing biomolecules carriers with unique properties. Herein, we reported a methacrylate gelatin (GelMA) microneedles (MNs) patch to achieve transdermal and controlled release of exos and tazarotene. Our MNs patch comprising GelMA/PEGDA hydrogel has distinctive biological features that maintain the biological activity of exos and drugs in vitro. Additionally, its unique physical structure prevents it from being tightly attached to the skin of the wound, it promotes cell migration, angiogenesis by slowly releasing exos and tazarotene in the deep layer of the skin. The full-thickness cutaneous wound on a diabetic mouse model was carried out to demonstrate the therapeutic effects of GelMA/PEGDA@T + exos MNs patch. As a result, our GelMA/PEGDA@T + exos MNs patch presents a potentially valuable method for repairing diabetic wound in clinical applications. Graphic Abstract

This paper reports on a sensor based on multi-element complementary split-ring resonator for the measurement of liquid materials. The resonator consists of three split rings for improved measurement sensitivity. A hole is fabricated at the centre of the rings to accommodate a hollow glass tube, through which the liquid sample can be injected. Electromagnetic simulations demonstrate that both the resonant frequency and quality factor of the sensor vary considerably with the dielectric constant and loss tangent of the liquid sample. The volume ratio between the liquid sample and glass tube is 0.36, yielding great sensitivity in the measured results for high loss liquids. Compared to the design based on rectangular split rings, the proposed ring structure offers 37% larger frequency shifts and 9.1% greater resonant dips. The relationship between dielectric constant, loss tangent, measured quality factor and resonant frequency is derived. Experimental verification is conducted using ethanol solution with different concentrations. The measurement accuracy is calculated to be within 2.8%, and this validates the proposed approach.

False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies.

The collusive false data injection attack (CFDIA) is a false data injection attack (FIDA), in which false data are injected in a coordinated manner into some adjacent pairs of captured nodes of an attacked wireless sensor network (WSN). As a result, the defense of WSN against a CFDIA is much more difficult than defense against ordinary FDIA. This paper is devoted to identifying the compromised sensors of a well-behaved WSN under a CFDIA. By establishing a model for predicting the reading of a sensor and employing the principal component analysis (PCA) technique, we establish a criterion for judging whether an adjacent pair of sensors are consistent in terms of their readings. Inspired by the system-level fault diagnosis, we introduce a set of watchdogs into a WSN as comparators between adjacent pairs of sensors of the WSN, and we propose an algorithm for diagnosing the WSN based on the collection of the consistency outcomes. Simulation results show that the proposed diagnosis scheme achieves a higher probability of correct diagnosis.

The alpine region is important in riverine and watershed ecosystems as a contributor of freshwater, providing and stimulating specific habitats for biodiversity. In parallel, recent climate change, human activities and other perturbations may disturb hydrological processes and eco-functions, creating the need for next-generation observational and modeling approaches to advance a predictive understanding of such processes in the alpine region. However, several formidable challenges, including the cold and harsh climate, high altitude and complex topography, inhibit complete and consistent data collection where and when it is needed, which hinders the development of remote-sensing technologies and alpine hydrological models. The current study presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen-ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin (HRB) in China. Meteorological and hydrological data were monitored from an observation network connecting a group of automatic meteorological stations (AMSs). In addition, to capture snow accumulation and ablation processes, snow cover properties were collected from a snow observation superstation using state-of-the-art techniques and instruments. High-resolution soil physics datasets were also obtained to capture the freeze–thaw processes from a frozen-ground observation superstation. The updated datasets were released to scientists with multidisciplinary backgrounds (i.e., cryospheric science, hydrology and meteorology), and they are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote-sensing products and hydrological models for a broader community. The datasets are available from the Cold and Arid Regions Science Data Center at Lanzhou (https://doi.org/10.3972/hiwater.001.2019.db, Li, 2019).

This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key factor determining the viral prevalence. Specifically, the value range of the maximum eigenvalue is partitioned into three subintervals: viruses tend to extinction very quickly or approach extinction or persist depending on into which subinterval the maximum eigenvalue of the propagation network falls. Consequently, computer virus can be contained by adjusting the propagation network so that its maximum eigenvalue falls into the desired subinterval.

The recently discovered kagome metals AV 3 Sb 5 (A = Cs, Rb, K) exhibit a variety of intriguing phenomena, such as a charge density wave (CDW) with time-reversal symmetry breaking and possible unconventional superconductivity. Here, we report a rare non-monotonic evolution of the CDW temperature ( T CDW ) with the reduction of flake thickness approaching the atomic limit, and the superconducting transition temperature ( T c ) features an inverse variation with T CDW . T CDW initially decreases to a minimum value of 72 K at 27 layers and then increases abruptly, reaching a record-high value of 120 K at 5 layers. Raman scattering measurements reveal a weakened electron-phonon coupling with the reduction of sample thickness, suggesting that a crossover from electron-phonon coupling to dominantly electronic interactions could account for the non-monotonic thickness dependence of T CDW . Our work demonstrates the novel effects of dimension reduction and carrier doping on quantum states in thin flakes and provides crucial insights into the complex mechanism of the CDW order in the family of AV 3 Sb 5 kagome metals. The kagome superconductor CsV 3 Sb 5 exhibits a charge density wave (CDW) as well as superconductivity (SC). Here, the authors find that the CDW transition temperature decreases with decreasing sample thickness to 72 K at 27 atomic layers, but then unexpectedly increases to 120 K at 5 layers, an opposite trend to SC.

Autoreactive CD4 + T helper cells are critical players that orchestrate the immune response both in multiple sclerosis (MS) and in other neuroinflammatory autoimmune diseases. Ubiquitination is a posttranslational protein modification involved in regulating a variety of cellular processes, including CD4 + T cell differentiation and function. However, only a limited number of E3 ubiquitin ligases have been characterized in terms of their biological functions, particularly in CD4 + T cell differentiation and function. In this study, we found that the RING finger protein 213 (RNF213) specifically promoted regulatory T (Treg) cell differentiation in CD4 + T cells and attenuated autoimmune disease development in an FOXO1-dependent manner. Mechanistically, RNF213 interacts with Forkhead Box Protein O1 (FOXO1) and promotes nuclear translocation of FOXO1 by K63-linked ubiquitination. Notably, RNF213 expression in CD4 + T cells was induced by IFN-β and exerts a crucial role in the therapeutic efficacy of IFN-β for MS. Together, our study findings collectively emphasize the pivotal role of RNF213 in modulating adaptive immune responses. RNF213 holds potential as a promising therapeutic target for addressing disorders associated with Treg cells. Multiple sclerosis and some other neuroinflammatory diseases are associated with aberrant CD4 + T cell differentiation and regulatory T cell function. Here authors show that the E3 ubiquitin ligase RNF213 is central to both physiological and pathologic CD4 + T cell differentiation, and finetunes IFN-β responses in multiple sclerosis.