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ABSTRACTThis study investigates empirically if land characteristics (especially forests and mountains) are of importance for the local ability to withstand the shock of the Covid-19 on domestic tourism demand during the summer of 2020. A second step of the analysis focuses on the recovery stage in the subsequent summer. Three tourism indicators are considered: arrivals, overnight stays and length of stay. Official data on land use characteristics of 2029 villages in the Federal state of Bavaria (South Germany) are employed for the analysis. Estimations using the Spatial Durbin model combined with the Heckman selection model reveal that there is a significant relationship between the proportion of forest within as well as surrounding the village and the demand for domestic tourism compared with the 2019 baseline. There is also a significant relationship with the altitude of the surrounding areas. The importance of mountains and forests is present in both the initial year of the pandemic and in the recovery year of 2021, although the magnitude is lower in the summer of 2021. Direct and spatial effects can also be found for lakes and rivers on overnight stays and length of stay.

The recent interest in the development of wideband optical networks brings up challenges for in-service optical time-domain reflectometer (OTDR) operation due to the stimulated Raman scattering (SRS) effect. Depending on its wavelength, OTDR probe pulses can be pumped or depleted by wideband traffic signal during the propagation along a fiber span. Consequently, the measured traces are distorted and unable to provide accurate fiber loss profiles. In order to correct distorted traces in a real-time online manner, we propose a dither-based method that doesn't require any prior information on channel loading condition or fiber parameters. The proposed method prompts a low frequency sinusoidal power dither on the traffic light, and a large number of OTDR traces are collected in the positive and negative half cycles, respectively. Averaging traces from the two half cycles generates two differential traces, and SRS gain can be calculated and used for correction. The induced dither causes power variation of traffic signal, but this impact can be minimized substantially by employing a second dither at the end of fiber span properly. The proposed method is verified experimentally in a $ 700$ km multiple spans fiber link. In the presence of SRS effect, the mean of absolute errors between the distorted traces and the actual loss trace are about $ 0.72$ dB and $ 0.3$ dB when OTDR pulses and traffic co-propagate and counter-propagate, respectively. By applying the proposed method, these errors are reduced to around $ 0.07$ dB and $ 0.05$ dB correspondingly. Meanwhile, performance of data transmission in terms of bit error rate is monitored during the process, and no penalty is observed.

Marine turbines have been extensively utilized to harness tidal stream energy from free-flowing tides and currents. However, the assessment of the influences of these marine structures on the surrounding environment is still in its early stage. In this study, a numerical model that couples hydrodynamics and sediment transport is developed to simulate the scour processes around a monopile-supported horizontal axial tidal stream turbine under steady currents. The flow characteristics are calculated by solving the 3-D Navier-Stokes equations with the k -ωshear stress transport (SST) turbulence model for closure. The simulation of sediment bed elevation is achieved by solving the Exner equation. The turbine rotor is parameterized using the actuator line method. The developed model is validated against wake velocity and scour depth measurement obtained from previous literature, showing a good agreement. Subsequently, the effects of tip clearance on the flow characteristics around the turbine model on a rigid flatbed are examined. Finally, the scour processes of the turbine model are presented, along with the vortex system within the scour hole. The numerical model proposed in this study has the potential to contribute to the understanding of the scour mechanism of the tidal stream turbines.

Repairable superhydrophobic surfaces have promising application potential in many fields. However, so far, it is still a challenge to develop a superhydrophobic surface with repairability for multiple types of damage through a simple method. In this paper, a repairable superhydrophobic coating was obtained on various substrates by blade-coating mixtures of polydimethylsiloxane (PDMS), polyvinylidene fluoride (PVDF), and multiwalled carbon nanotubes (MWCNTs) modified with dopamine (PDA) and octadecylamine (ODA). The obtained coating has a good liquid-repellent property with a water contact angle above 150° and a water sliding angle of ~6° and possesses an excellent absorbance (~97%) in the wavelength range of 250–2500 nm. Due to its high absorbance, the coating displays an outstanding photothermal effect with a temperature rise of ~65 °C under irradiation by 1.0 kW/m2of simulated sunlight. Furthermore, after being degraded by multiple stimuli, including plasma treatment, acid/alkali/oil immersion, sand impact, and the icing–thawing cycle, the coating can recover superhydrophobicity via sunlight irradiation, demonstrating the good photothermal-induced repairability of the coating. It can be expected that the good water-repellent property, photothermal effect, and repairability give this coating a promising prospect in practical applications.

Natural polymers are complex organic molecules that occur in the natural environment and have not been subjected to artificial synthesis. They are frequently encountered in various creatures, including mammals, plants, and microbes. The aforementioned polymers are commonly derived from renewable sources, possess a notable level of compatibility with living organisms, and have a limited adverse effect on the environment. As a result, they hold considerable significance in the development of sustainable and environmentally friendly goods. In recent times, there has been notable advancement in the investigation of the potential uses of natural polymers in the field of biomedicine, specifically in relation to natural biomaterials that exhibit antibacterial and antioxidant characteristics. This review provides a comprehensive overview of prevalent natural polymers utilized in the biomedical domain throughout the preceding two decades. In this paper, we present a comprehensive examination of the components and typical methods for the preparation of biomaterials based on natural polymers. Furthermore, we summarize the application of natural polymer materials in each stage of skin wound repair. Finally, we present key findings and insights into the limitations of current natural polymers and elucidate the prospects for their future development in this field.

Unmanned Aerial Vehicles (UAVs) have recently attracted significant attention due to their potential applications in reconnaissance and search. This paper aims to investigate the issue of multi-UAV cooperative reconnaissance and search (MCRS) to ensure ample coverage of the mission area and precise localization of static targets. The MCRS problem is modeled as a multi-objective optimization problem, taking into account the credibility of search results. To achieve this, we design a belief probability map based on the Dempster-Shafer (DS) evidence theory, comprising an uncertainty map and two target maps. This representation enables a clear depiction of both the presence of the target and the uncertainty within the map. Subsequently, we reformulate this multi-objective optimization problem within the framework of Decentralized Partially Observable Markov Decision Process (Dec-POMDP). To address this reformulation, a new deep reinforcement learning approach called Double Critic Deep Deterministic Policy Gradient (DCDDPG) is proposed. Specifically, we introduce both a centralized critic and a local critic for each UAV agent to estimate the action-value function. This approach helps balance the bias in the action-value function estimation and the variance in the policy updates, thereby improving the coordination effect. Extensive simulation results demonstrate that DCDDPG outperforms existing techniques in terms of search efficiency and coverage.

Sleep deprivation (SD) is a global public health burden, and has a detrimental role in the nervous system. Retina is an important part of the central nervous system; however, whether SD affects retinal structures and functions remains largely unknown. Herein, chronic SD mouse model indicated that loss of sleep for 4 months could result in reductions in the visual functions, but without obvious morphologic changes of the retina. Ultrastructural analysis by transmission electron microscope revealed the deterioration of mitochondria, which was accompanied with the decrease of multiple mitochondrial proteins in the retina. Mechanistically, oxidative stress was provoked by chronic SD, which could be ameliorated after rest, and thus restore retinal homeostasis. Moreover, the supplementation of two antioxidants, α-lipoic acid and N-acetyl-l-cysteine, could reduce retinal reactive oxygen species, repair damaged mitochondria, and, as a result, improve the retinal functions. Overall, this work demonstrated the essential roles of sleep in maintaining the integrity and health of the retina. More importantly, it points towards supplementation of antioxidants as an effective intervention strategy for people experiencing sleep shortages.

Background/aimsTo improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data.MethodTwo datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea.ResultsThe detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients’ smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%).ConclusionOur fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands.

Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively.

In modern optical networks, reconfigurable optical add-drop multiplexers consisting of wavelength selective switches are widely adopted, and they are the major cause of polarization dependent loss (PDL). Link PDL evolves with time due to the time-varying polarization state of propagating light in the optical fiber, and this randomness needs to be considered in the design and operation of optical networks. Therefore, characterizing link PDL using statistical methods is important. It is well known that link PDL in ultra long-haul systems containing a large number of PDL elements is Maxwellian-distributed. However, it is not appropriate when links include a reduced number of PDL elements, or there exist a few dominant PDL elements. In this letter, statistics of link PDL is studied, and a generalized probability density function (PDF) is derived for links including a variety of number of elements. Simulations and experiments are performed, and results show that the generalized PDF fits in a wide scope of realistic scenarios. On the other hand, the conventional Maxwellian distribution exhibits significant discrepancy with the actual one in the studied cases.

This article proposes a new auxiliary power supply solution for medium-voltage converters using a unidirectional-balancing-cell-based approach. The proposed approach reduces the number of active devices and required driving circuitry by half compared with existing voltage-balancing circuit designs. In addition, it significantly reduces the size of the resonant tank by decoupling the $LC$ resonant frequency from the switching frequency. The proposed solution is scalable and adaptable to different voltage levels. Each balancing cell operates independently in a self-powered manner, enhancing reliability and simplicity. The balancing cells are connected in an interleaved manner, enabling a step-down ratio of $(n+1):1$ for $n$ cells. A 6 to $1 \,\mathrm{kV}$ prototype is presented to verify the proposed concept. To meet the voltage requirement for the auxiliary power in a medium-voltage system, a two-stage high step-down converter was developed. The converter is capable of converting 6 to $48 \,\mathrm{V}$ and is constructed using the proposed converter as the first stage and commercial power supplies as the second stage.

Medium voltage SiC mosfets have recently garnered considerable attention in the medium-voltage high-power areas like high-frequency solid-state transformers and multilevel converters. While direct series connection of these mosfets is an option for higher voltage levels, it requires complex voltage balancing approaches for device voltage balancing during fast switching transients. Alternatively, converter-level solutions such as the three-level (3L) neutral-point-potential converter can be used. This article proposes a new modulation strategy, combining 3L, and Quasi-two-level modulations, to minimize the rating and volume of clamping diodes by tightly controlling the thermal stress on the diodes. This approach achieves better efficiency, higher power density, and a simpler converter bus-structure to stack two SiC mosfets effectively in series. We present a real-time clamping diode loss estimation to improve the effectiveness of the proposed modulation strategy. To verify the proposed converter-level approach and modulation strategy, we test a 20 kV rated phase-leg with two 10 kV SiC mosfets and 3.3 kV SiC diodes.

Hole transport materials (HTMs) with appropriate energy levels and comprehensive passivation effects help to obtain highly efficient and stable perovskite solar cells (PSCs). Electron-deficient character-induced HTMs can generate varying energy level alignments near the HTM/perovskite interface. Herein, we report the synthesis and investigation of two new dipolar HTMs, WWC103and WWC105, based on 2-(1,1-dicyanomethylene)rhodamine and 4-cynophenylacetonitrile acceptors, enabling high-efficiency mixed-cation mixed-halide perovskite solar cells. Apart from having different acceptors, these HTMs are built on a heterocyclic frame, which can provide passivation effects and improve the morphology of the perovskite layer. As a result, these dopant-free HTM-based solar cells show a high open-circuit voltage and good power conversion efficiency. Among both, the solar cell based on the HTM with 2-(1,1-dicyanomethylene)rhodamine exhibits a high open-circuit voltage of 1.09 V with a champion power conversion efficiency of over 20.51%. The improved performance of WWC103over WWC105(19.74%) is attributed to the new acceptor, which, in addition to providing good energy-level alignments and hole mobility, also holds the ability to passivate the defects. The findings suggest a new acceptor unit for constructing dopant-free HTMs for efficient PSCs.

Angelman syndrome (AS) is a rare neurodevelopmental disorder caused by loss of function mutations in maternally expressed UBE3A. No gene-specific treatment is available for patients so far. Although intact and transcriptionally active, paternally inherited UBE3Ais silenced by elongation of antisense long noncoding RNA UBE3A-ATSin neurons. Here, we demonstrated that RNA targeting of paternal Ube3a-ATSwith a high-fidelity CRISPR-Cas13 (hfCas13x.1) system could restore Ube3aexpression to similar levels as that of maternal Ube3ain the cultured mouse neurons. Furthermore, injection into lateral ventricles with neuron-specific hSyn1promoter-driven hfCas13x.1 packaged in adeno-associated virus (AAV-PHP.eb) could restore paternal Ube3aexpression in cortex and hippocampus of neonatal AS mice for up to 4 months after treatment. Behavioral tests showed that expression of paternal Ube3asignificantly alleviated AS-related symptoms, including obesity and motor function. Our results suggested that hfCas13x.1-mediated suppression of the Ube3a-ATSlncRNA potentially serves as a promising targeted intervention for AS.

At present, biomass foamlike materials are a hot research topic, but they need to be improved urgently due to their defects such as large size shrinkage rate, poor mechanical strength, and easy hydrolysis. In this study, the novel konjac glucomannan (KGM) composite aerogels modified with hydrophilic isocyanate and expandable graphite were prepared by a facile vacuum freeze-drying method. Compared with the unmodified KGM aerogel, the volume shrinkage of the KGM composite aerogel (KPU-EG) decreased from 36.36 ± 2.47% to 8.64 ± 1.46%. Additionally, the compressive strength increased by 450%, and the secondary repeated compressive strength increased by 1476%. After soaking in water for 28 days, mass retention after hydrolysis of the KPU-EG aerogel increased from 51.26 ± 2.33% to more than 85%. The UL-94 vertical combustion test showed that the KPU-EG aerogel can achieve a V-0 rating, and the limiting oxygen index (LOI) value of the modified aerogel can reach up to 67.3 ± 1.5%. To sum up, the cross-linking modification of hydrophilic isocyanate can significantly improve the mechanical properties, flame retardancy, and hydrolysis resistance of KGM aerogels. We believe that this work can provide excellent hydrolytic resistance and mechanical properties and has broad application prospects in practical packaging, heat insulation, sewage treatment, and other aspects.

Wound healing has always been a focus of clinical study due to its universality, difficult treatment, large number of patients, and heavy medical burden. A great deal of effort has been devoted to generating various wound dressings with special features and functions to satisfy specific demands. Here, we present novel bio-printed textiles based on fish skin decellularized extracellular matrix (dECM) for wound healing. Thanks to the desirable biocompatibility of the fish-derived dECM, the bio-printed textiles exhibit excellent performance in terms of cell adherence and proliferation. Since the dECM-based hydrogels are generated using a bio-printing method, the bio-printed textiles exhibit an adjustable porous structure with good air permeability throughout the whole textile. Moreover, the high specific surface areas of the porous structure on the hydrogel skeleton make it possible to load a variety of active molecules to improve the wound healing effect. According to an in vivostudy, we demonstrate that the prepared textiles loaded with the active drug molecules curcumin (Cur) and basic fibroblast growth factor (bFGF) can significantly speed up the chronic wound healing process. These remarkable properties indicate the potential value of fish-skin-dECM textiles in wound healing and biomedical engineering.

This study evaluated adebrelimab (a programmed death-ligand 1 antibody) plus nab-paclitaxel and carboplatin as perioperative treatment for resectable NSCLC.

ABSTRACTPeriosteum is expected for bone repairing due to excellent regenerative potential. PDCs are the main source of cells for promoting bone repair. However, PDCs from different sites have been confirmed to be site specific due to their distinct embryonic origin and the methods of bone formation. Hippo-YAP pathway is proved to play a critical role in fate decision of mesenchymal stem cells. The effect of Hippo-YAP on PDCs has not been reported so far. Hence, we aim to explore the differences of PDCs from mandible and femur along with their possible responses to YAP signaling. mPDCs and fPDCs were obtained and tested through flow cytometry for identification. Follow-up results illustrated mPDCs was cubic shape and with better proliferation while fPDCs preferred slender cell shape with worse cell viability compared with mPDCs. mPDCs was superior to fPDCs in ALP activity, related mRNA expression and calcium deposits in late stage. Interestingly, downregulation of YAP promoted the ALP activity, related mRNA expression and calcium deposits of fPDCs while hindered that of mPDCs in vitro. Moreover, implant animal model in mandible and femur were constructed for evaluation in vivo. Histological results were similar to the results in vitro. We speculate this may result from their different embryonic origin and the way of bone formation. Taken together, results available suggested that mPDCs may serve as more optimal seed cells for tissue engineering compared with fPDCs; however, considering their different response to YAP signaling, to ensure sufficient YAP expression in mPDCs and appropriate declining YAP expression in fPDCs may establish better osteogenesis.

For multilevel flying-capacitor (FC) converter in dc–ac applications, variable switching frequency (VSF) control is an attractive solution to regulate the FC voltage ripple compared with the constant switching frequency (CSF) control. However, it is shown that the existing VSF control presents a higher switching loss in the three-phase operation when the identical switching frequency is applied to three phase-legs to reduce the complexity of VSF control implementation. To reduce the device switching losses in three-phase operation, this letter proposes a new strategy for the switching frequency selection, which allows each phase-leg to select its switching frequency individually. The control complexity of the proposed method is also reduced and is very similar to CSF control. The experiment result is also shown to verify the proposed VSF control strategy.

The skin serves as the primary barrier between the human body and external environment, and is therefore susceptible to damage from various factors. In response to this challenge, animal tissue-derived biomaterials have emerged as promising candidates for wound healing due to their abundant sources, low side-effect profiles, exceptional bioactivity, biocompatibility, and unique extracellular matrix (ECM) mimicry. The evolution of modern engineering technology and therapies has allowed these animal tissue-derived biomaterials to be transformed into various forms and modified to possess the necessary properties for wound repair. This review provides an overview of the wound healing process and the factors that influence it. We then describe the extraction methods, important properties, and recent practical applications of various animal tissue-derived biomaterials. Our focus then shifts to the critical properties of these biomaterials in skin wound healing and their latest research developments. Finally, we critically examine the limitations and future prospects of biomaterials generated from animal tissues in this field.

This letter proposes an estimator based on the neural network (NN) to jointly estimate the linear and nonlinear signal-to-noise ratios. The proposed NN-based estimator utilizes new input features based on the entropy extracted from the received signal. Moreover, the computational complexity of the proposed estimator is analyzed. The dataset utilized for training and testing is constructed from dual-polarization 16-ary quadrature amplitude modulation format over different system configurations of the standard single-mode fiber, such as launch power, transmission distances, and the number of wavelength division multiplexed channels. Numerical results reveal the superiority of the proposed NN-based estimator in terms of accuracy and computational complexity compared to the existing NN-based estimators in the literature.

With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.

Unfortunately, the published version of this article contains several typographical errors due to editorial oversights. These errors, which appear throughout the document, may cause confusion regarding the specifications of the developed converter. However, the overall design procedure and discussions of the converter remain unchanged. This article proposes a 6 kV to 48 V high step-down (HSD) converter with a two-stage design structure, as demonstrated in the test results. The first stage consists of five proposed balancing-circuit cells, each capable of converting 2 kV to 1 kV. With two different configurations based on power flow directions, the final stack of five cells produces 6 kV to 1 kV while a load is connected to the middle capacitor. The second stage uses four commercial power supplies in an input-parallel-output-series configuration to step down the voltage from 1 kV to 48 V. However, in the published article, the input voltage of 6 kV is mistakenly written as 6 V in the following sections. For clarity, the correct paragraphs are provided below.

This work systemically investigates the wettability and pore connectivity and their potential influence on gas recovery from three overmature marine shales (Niutitang, Longmaxi, and Donggangling Formations) in southern China with a range of total organic carbon (TOC) contents, mineralogical compositions, and pore geometry characteristics. To achieve these objectives, TOC, X-ray diffraction (XRD), field emission-scanning electron microscopy (FE-SEM), contact angle, and spontaneous fluid imbibition were performed. The contact angle and imbibition were tested using both hydrophilic (deionized water, DIW) and hydrophobic (2:1 in volume of n-decane:toluene, 2DT) fluids. Experimental results show that in both the Niutitang and Longmaxi Shales, the dominant minerals are quartz and carbonate. In contrast, the Donggangling Shale has contents of higher clay minerals and lower TOC. The integrated results from SEM images, measured contact angles, and imbibition behavior indicate that the Donggangling Shale is mixed-wet, while the Niutitang and Longmaxi Shales are both strongly oil-wet and intermediately water-wet. In the clay-rich Donggangling Shale, during spontaneous imbibition, intense water–shale interaction occurs when water contacts the shale, resulting in the generation of abundant and macroscopic induced fractures. These induced fractures not only promote the migration of water into the deeper reservoir matrix and the decrease of water concentration in the main fracturing channels but also effectively improve the seepage of shale matrix, eventually making the shale gas more easily flow into the wellbore. Therefore, shutting-in the well for a period of time after hydraulic fracturing to maximize the utilization of the induced fracture networks formed by water–shale interaction could be beneficial to shale gas recovery. For Niutitang and Longmaxi Shales with high contents of brittle minerals, after the generation of extensive fracture networks by hydraulic fracturing, rapid flow-back should be considered to reduce the retention of fracturing fluid in hydraulic fractures; the residual fracturing fluid could then be used to displace the trapped gas from the shale matrix into the hydraulic fractures.

Capillary-force-induced welding can effectively reduce the contact resistance between two silver nanowires (AgNWs) by merging the NW–NW junctions. Herein, we report a model for quantifying the capillary force between two nano-objects. The model can be used to calculate the capillary force generated between AgNWs and carbon nanotubes (CNTs) during water evaporation. The results indicate that the radius of one-dimensional nano-objects is crucial for capillary-force-induced welding. AgNWs with larger radii can generate a greater capillary force (FAgNW-AgNW) at NW–NW junctions. In addition, for AgNW/CNT hybrid films, the use of CNTs with a radius close to that of AgNWs can result in a larger capillary force (FAgNW-CNT) at NW–CNT junctions. The reliability of the model is verified by measuring the change in sheet resistance before and after capillary-force-induced welding of a series of AgNW and AgNW/CNT conductive films with varying radii.

ABSTRACTThe power distribution network is an important link between the end of power grid and the users. Precise predictions on the risk probability of the distribution network in severe weather could provide the electric company with a reference to daily operation and maintenance arrangements. The company could also prepare professional mechanists and necessary supplies in advance and restoring power supply in a short time. In this paper, a failure risk prediction of power distribution network method based on particle swarm optimisation and extreme gradient boosting tree algorithm is proposed. The local weather data is fed into the model, outputting the failure severity and probability of the area in the same period. The case study shows that our proposed method relieves the low accuracy problem by introducing the particle swarm optimisation algorithm to search the optimal values of critical parameters. On the testing dataset, the accuracy of our method reaches 96.19%, showing that our model can efficiently evaluate the risk level of the distribution network working conditions. Moreover, the algorithm can extract the association rules between the weather features and the failure risk levels, offering the data support for the failure risk prevention of the distribution network under severe weather.

IL‐10‐producing regulatory B (Breg) cells are well recognized for maintaining immune tolerance. The impaired Breg cell function with decreased IL‐10‐producing capacity has been found in autoimmune diseases, such as rheumatoid arthritis, lupus, and primary Sjogren's syndrome (pSS). However, seldom therapeutic agents targeting Breg cells are available to treat those autoimmune diseases. Here, we showed that acteoside (AC), a caffeoyl phenylethanoid glycoside from a medicinal herb Radix Rehmanniae, could promote IL‐10 production from both human and murine B cells via critically regulating the TLR4/PI3K axis. Moreover, TLR4 was found increased in Breg cells from mice with experimental SS (ESS), a mouse model that recapitulates human pSS. Thus, B cells from the ESS mice were susceptible to AC treatment, showing higher IL‐10‐producing capacity than those from naïve controls. In addition, AC treatment also promoted the production of IL‐10 from TLR4+CXCR4+plasma cells of ESS mice. Notably, we found that AC was able to enter lymphoid organs upon oral administration. AC treatment effectively increased IL‐10+B cells in ESS mice and ameliorated disease pathology accompanied by reduced T effector cells, including Th17 and T follicular helper cells in the ESS mice. In conclusion, AC could promote Breg cell function and attenuate ESS pathology in vivo, which may be a promising drug candidate for treating pSS and other autoimmune diseases. Acteoside promotes IL‐10‐producing capacity of B cells in a TLR4/PI3K dependent manner, and thus restores regulatory B cell function during autoimmune progression.

ABSTRACTThis study was done to observe the safety and effect of Xiaoyaosan (XYS) on the stimulated depressive disorder (DD) related dry eye disease (DED) in mice. Normal control (NC) group, Vehicle group, and drug treatment groups, including Sertraline Hydrochloride (SH), XYS low-dose (XYS-LD), medium-dose (XYS-MD), and high-dose (XYS-HD), were established. The drug quality of XYS was assessed by high-performance liquid chromatography (HPLC). XYS toxicity in kidney and liver was assessed with Hematoxylin & Eosin (H&E) staining. Serum enzyme-linked immunosorbent assay (ELISA) and body weights were used to evaluate the depression status of mice. Tear production, corneal sensitivity, Oregon Green Dye (OGD) staining, and corneal confocal microscopy were used to assess ocular surface changes. H&E staining was also used to assess pathological cornea and lacrimal gland changes. HPLC results showed that XYS complied with Chinese drug quality standards. The drug treatment groups observed no drug toxicity reactions in the liver and kidney. SH and XYS groups improved DD-related serological indices as compared with Vehicle. Body weight was enhanced in mice with XYS groups compared to Vehicle and SH. Mice with XYS treatments showed increased tear production and corneal sensitivity, decreased corneal OGD staining scores, improved morphology, and decreased inflammatory cell infiltration in the cornea and lacrimal gland. In conclusion, XYS had no drug toxicity, improved serological indices, and ocular surface pathological changes in DD-related DED mice. XYS is safe and may have a therapeutic effect on DD-related DED.

For high-precision position and angle control in robots, it is essential to compensate for the hysteretic behaviour caused by friction when the motion direction is reversed. An accurate friction model which is suitable for control system analysis and implementation is highly desirable. A differential model is proposed in the current paper for the modelling of hysteresis effects caused by friction phenomena. The model is constructed by employing a phenomenological phase-transition theory to mimic the friction mechanism. The bristle friction mechanism is adapted. The switching between static and dynamic friction is regarded as a reversible phase-transition phenomenon, which could be characterized by the local minima of a non-convex potential energy function. The Stribeck effect and the hysteretic relation between friction and velocity are modelled by a nonlinear ordinary differential equation. The comparison of the numerical simulation results and existing experimental friction data are presented. It is illustrated that the friction hysteresis loops are well captured, the capability of the proposed model is verified.

In the realm of modern information technology, data compression technology occupies a pivotal position. With advancements in quantum information technology, the need to compress large-scale qubits ensembles has become urgent, aiming to reduce the demand on quantum storage resources. However, existing quantum state compression schemes generally face a limitation: the particles before and after compression must reside in the same dimensional space. In specific scenarios, compressing qubits into particles of higher dimensions not only enhances the efficiency of quantum state compression but also further reduces the usage of quantum storage resources. Here we experimentally demonstrated a quantum state compression between particles of different dimensions, successfully compressing two qubits into a single qutrit. The average fidelity of the resulting qutrit with the ideal quantum state is 0.8835. Our study may have potential applications in future quantum information, such as increasing quantum communication bandwidth and reducing storage resource consumption in quantum computing.

Because of the low atomization and/or ionization efficiencies of many biological macromolecules, the application of mass spectrometry to the direct quantitative detection of low-abundance proteins and nucleic acids remains a significant challenge. Herein, we report mass spectrum tags (MS-tags) based upon gold nanoparticle (AuNP)-templated phosphatidylcholine phospholipid (DSPC) liposomes, which exhibit high and reliable signals via electrospray ionization (ESI). Using these MS-tags, we constructed a liposome signal amplification-based mass spectrometric (LSAMS) “digital” counting assay to enable ultrasensitive detection of target nucleic acids. The LSAMS system consists of liposomes modified with a gold nanoparticle core and surface-anchored photocleavable DNA. In the presence of target nucleic acids, the modified liposome and a magnetic bead simultaneously hybridize with the target nucleic acid. After magnetic separation and photolysis, the MS-tag is released and can be analyzed by ESI-MS. At very low target concentrations, one liposome particle corresponds to one target molecule; thus, the concentration of the target can be estimated by counting the number of liposomes. With this assay, hepatitis C (HCV) virus RNA was successfully analyzed in clinical samples.

Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover undiscovered cell types. Most methods for clustering scRNA-seq data use an unsupervised learning strategy. Since the clustering step is separated from the cell annotation and labeling step, it is not uncommon for a totally exotic clustering with poor biological interpretability to be generated—a result generally undesired by biologists. To solve this problem, we proposed an active learning (AL) framework for clustering scRNA-seq data. The AL model employed a learning algorithm that can actively query biologists for labels, and this manual labeling is expected to be applied to only a subset of cells. To develop an optimal active learning approach, we explored several key parameters of the AL model in the experiments with four real scRNA-seq datasets. We demonstrate that the proposed AL model outperformed state-of-the-art unsupervised clustering methods with less than 1000 labeled cells. Therefore, we conclude that AL model is a promising tool for clustering scRNA-seq data that allows us to achieve a superior performance effectively and efficiently.

Service function chains (SFC) mapping takes the responsibility for managing virtual network functions (VNFs). In SFC mapping, existing solutions duplicate VNFs with redundant instances to provide high availability in response to failures. However, as a compromise, these solutions result in high resource consumption due to device maintenance. In this paper, we propose a novel method named dynamic backup sharing (DBS) that allows SFCs to dynamically share backups to reduce resource consumption. DBS formulates the problem of sharing backups among different VNFs as an integer linear programming (ILP). Thereafter, we design a novel online algorithm based on dynamic programming to solve the problem. The experimental results indicate that DBS outperforms state-of-the-art works by reducing resource consumption and improving the number of accepted requests.

Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, which limits the fulfillment of their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatially constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process in two steps: (1) the spatial information is encoded by using a graphical neural network model, and (2) cell-to-cell constraints are built based on the spatial expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottleneck layer of autoencoder by Kullback–Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model that can use information from both spatial coordinates and marker genes to guide cell/spot clustering. Extensive experiments on both simulated and real data sets show that DSSC boosts clustering performance significantly compared with the state-of-the-art methods. It has robust performance across different data sets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data.

Magneto-Rheological (MR) fluid is a controllable material upon the applied magnetic field, and various MR dampers with different structures are designed to take advantage of this unique property. In the current paper, the squeeze mode MR damper is analyzed. The two-dimensional MR fluid squeeze flow in the damper is simulated using the Navier-Stokes’ equations. The shear stress of MR fluid is characterized by a non-convex constitutive relation, which is capable of capturing the solid-like to liquid-like switching. The two-dimensional velocity field and pressure distribution of MR fluid are obtained, from which the damping force of the MR damper is obtained. The unique hysteresis characteristic of the force versus velocity relation of the MR damper is captured. Further, the dependence on the loading rate and the field strength of the hysteresis characteristic is studied in the current paper.

The searching for new nonvolatile memories with the ability of working in harsh environments such as high temperature or humidity will be significant for industrial automation. Herein, a thermochromic polyoxometalate-based metal–organic framework (POMOF) constructed from Keggin-type polyoxometalates and metalloviologen has been fabricated as a nonvolatile memory device, which can exhibit stable nonvolatile memory behavior both at room and high temperature (150 °C) with stable cycle performance. Furthermore, its extreme working temperature can be monitored by color change from yellow to black, which stems from the reversible thermochromism of the POMOF. Importantly, the crystal structures at room and high temperature (150 °C) have been determined, which illustrates that the Keggin-type POM (α-GeW12O40)4–anions are anchored in the metalloviologen cationic [Co2(bpdo)4(H2O)6]n4n+cavities through numerous C–H···OPOMhydrogen bonds. The high temperature could not destroy its framework but remove its three lattice water molecules; in addition, more condensed structures with shorter Ge/W/Co–O lengths, stronger hydrogen bonds, and more distorted terminal bpdo ligands can be observed. Consequently, better cycling stability with a more uniform high-resistance state/low-resistance state can be achieved. Finally, the charge transport mechanism in this POMOF-based device during the resistive switching process has been discussed. In all, the combination of electron-poor metalloviologen with electron reservoir POM can give rise to the enhanced nonvolatile memory and better thermal stability accompanied with observable chromism.

The emerging personal healthcare has significantly propelled the development of advanced wearable electronics with novel functions of providing diagnostic information and point-of-care therapies for specific diseases. However, it is still challenging to simultaneously achieve high sensitivity for health biomonitoring and multifunction integration for point-of-care therapies in a one single flexible, lightweight yet robust fiber-based device. Here, a knittable and sewable spandex yarn with conductive nacre-mimetic composite coating has been developed through an alternant dip-coating method employing MXene nanosheets as the “brick” and polydopamine (PDA)/Ni2+as the “mortar”. The resultant spandex yarn coating with MXene/PDA/Ni2+(MPNi@Spandex) can be assembled as a strain sensor with high sensitivity (up to 5.7 × 104for the gauge factor), wide sensing range (∼61.2%), and low detection limit (0.11%) to monitor the biological activities of the human body. Furthermore, MPNi@Spandex displays great potential to give on-demand thermotherapy by virtue of the fast response to near-infrared irradiation, controllable surface temperature, and applicability even under sewing conditions. In addition, MPNi@Spandex knitted textiles demonstrate a strong antibacterial effect due to the sharp edges, anionic, and hydrophilic nature of MXene nanosheets. Remarkably, near-infrared irradiation further improves the bacteria-killing efficiency of an MPNi@Spandex knitted textile to more than 99.9%. This work paves the way for the design of multifunctional wearable electronics with an all-in-one theranostic platform for personal healthcare.