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The linearity assumption is widely used when acquiring the hydrodynamic coefficients of a floating structure. However, the linear damping is frequently underestimated, especially for the natural frequency. To investigate the sloping seafloor effects on the damping terms of a single module of a semi-submersible Very Large Floating Structure (VLFS), this paper revisits the conventional formulation and further proposes the direct integration method for obtaining the linear and quadratic damping coefficients from free-decay tests. Numerical free-decay simulations of the single module over variable bathymetry are carried out by the CFD numerical tank. Corresponding model tests are also implemented to verify and validate against the numerical solutions. The effects of the sloping seafloor, as well as the water depth, on the hydrodynamic coefficients are investigated based on the validated CFD modeling. Both numerical and experimental results indicate that the acquisition of the linear and quadratic damping coefficients is sensitive to the data-processing and identification approaches. For the case studied in present paper, the identification errors introduced by the conventional method are 1.5 % while they are 0.5 % using the direct integration method. The quadratic damping coefficient for heave mode decreases about 10.4 % when the sloping angle increases from 0 to 6 deg.

In this paper, a new step-down resonant modular multilevel DC/DC converter is proposed for medium-voltage applications. The proposed converter is derived from the combination of the modular multilevel converter (MMC) and resonant converter with a passive auxiliary circuit. It can achieve zero-voltage-switching (ZVS) over a wide input voltage range for all switches compared with the conventional DC/DC MMC. Specifically, the asymmetric triangular compensating current provided by the passive auxiliary circuit is first introduced to flows into the bridge arms. Then, the current bias caused by the MMC structure is eliminated in the lower side switches of the submodules to achieve ZVS. Moreover, asymmetrical pulse-width modulation is applied to double the equivalent switching frequency and obtain a high step-down ratio. In this way, the resonant current is reduced, and the conversion efficiency is improved. Besides, the simulation and experimental results of the prototype are presented to demonstrate the viability of the proposed converter. The results convincingly verify the ability of the proposed converter to offer ZVS for all the switches over a wide input voltage range.

Bike-sharing systems have made notable contributions to cities by providing green and sustainable mobility service to users. Over the years, many studies have been conducted to understand or anticipate the usage of these systems, with the hope to inform their future developments. One important task is to accurately predict usage patterns of the systems. Although many deep learning algorithms have been developed in recent years to support travel demand forecast, they have mainly been used to predict traffic volume or speed on roadways. Few studies have applied them to bike-sharing systems. Moreover, these studies usually focus on one single dataset or study area. The effectiveness and robustness of the prediction algorithms are not systematically evaluated. In this study, we propose a Spatial-Temporal Memory Network (STMN) to predict short-term usage of bicycles in bike-sharing systems. The framework employs Convolutional Long Short-Term Memory models and a feature engineering technique to capture the spatial-temporal dependencies in historical data for the prediction task. Four testing sites are used to evaluate the model. These four sites include two station-based systems (Chicago and New York) and two dockless bike-sharing systems (Singapore and New Taipei City). By assessing STMN with several baseline models, we find that STMN achieves the best overall performance in all the four cities. The model also achieves superior performance in urban areas with varying levels of bicycle usage and during peak periods when demand is high. The findings suggest the reliability of STMN in predicting bicycle usage for different types of bike-sharing systems.

A multi-port converter with bipolar outputs to integrate renewable energy and energy storage into the bipolar dc bus is the desirable solution due to its high reliability and efficiency. A novel symmetric bipolar outputs full-bridge four-port converter based on phase-shift modulated buck-boost voltage balancer is proposed in this paper. The interleaved bidirectional buck/boost circuit with pulse width modulation is used as the primary side to achieve the integration of renewable energy and energy storage, while the phase-shift modulated buck-boost voltage balancer is derived as a secondary side circuit to generate a symmetrical and common ground bipolar output voltage. Single-stage power conversion, symmetrical bipolar output voltage, approximately decoupled control loop, and soft switching can be realized in the proposed four-port converter. Detailed analysis of the proposed converter is presented, including operation principle, steady-state performance, soft switching performance, and control method. Finally, experimental verifications are carried out to illustrate the feasibility and effectiveness of the proposed topology.

Evolutionary multitasking (EMT) is an emerging research direction in the field of evolutionary computation. EMT solves multiple optimization tasks simultaneously using evolutionary algorithms with the aim to improve the solution for each task via intertask knowledge transfer. The effectiveness of intertask knowledge transfer is the key to the success of EMT. The multifactorial evolutionary algorithm (MFEA) represents one of the most widely used implementation paradigms of EMT. However, it tends to suffer from noneffective or even negative knowledge transfer. To address this issue and improve the performance of MFEA, we incorporate a prior-knowledge-based multiobjectivization via decomposition (MVD) into MFEA to construct strongly related meme helper-tasks. In the proposed method, MVD creates a related multiobjective optimization problem for each component task based on the corresponding problem structure or decision variable grouping to enhance positive intertask knowledge transfer. MVD can reduce the number of local optima and increase population diversity. Comparative experiments on the widely used test problems demonstrate that the constructed meme helper-tasks can utilize the prior knowledge of the target problems to improve the performance of MFEA.

Benefiting from the pay-as-you-go business model, cloud computing has significantly promoted service computing techniques in real-world industrial applications. Software applications based on cloud computing are becoming more and more popular. By integrating existing component cloud services through the internet, composite cloud systems can be built to meet sophisticated application logic. Stable execution of such systems is desirable in the long term so that the service-level agreements (SLAs), as well as users’ quality of experience (QoE), can be fulfilled. To achieve this goal, it is critical to identify and fault-tolerate system components at high risks of failing. This is extremely challenging due to the dynamic and uncertainty of the cloud environment that hosts the component cloud services. Nevertheless, existing approaches pay little attention to the modeling and analysis of system components’ reliability time series. To address the above issues, we first present a reliability evaluation method for component cloud services based on the reliability model and their failure probability under continuous client-side invocation tests. Then, we propose a perturbation-aware reliability sensitivity measurement approach (named PARS) for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services’ historical reliability time series based on the Markov chain rule. Then, it calculates the reliability sensitivity of component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. To guarantee the execution quality of the composite cloud system, we further propose a proactive adaptation approach named PA-PARS that enables 1-out-of-2 N-version Programming fault-tolerance for composite cloud systems based on PARS. PA-PARS takes the reliability sensitivity of component cloud services estimated by PARS as input to assure the reliability of the cloud system. It consists of four parts: 1) risky system component identification; 2) adaptation trigger; 3) candidate component cloud service selection; and 4) NVP-based system construction as the proactive adaptation for the composite cloud system. The results of experiments conducted on two widely-used datasets demonstrate the effectiveness and efficiency of the proposed approaches in ensuring the reliability of composite cloud systems.

Deploying small cell base stations (SBS) under the coverage area of a macro base station (MBS), and caching popular contents at the SBSs in advance, are effective means to provide high-speed and low-latency services in next generation mobile communication networks. In this paper, we investigate the problem of content caching (CC) and user association (UA) for edge computing. A joint CC and UA optimization problem is formulated to minimize the content download latency. We prove that the joint CC and UA optimization problem is NP-hard. Then, we propose a CC and UA algorithm (JCC-UA) to reduce the content download latency. JCC-UA includes a smart content caching policy (SCCP) and dynamic user association (DUA). SCCP utilizes the exponential smoothing method to predict content popularity and cache contents according to prediction results. DUA includes a rapid association (RA) method and a delayed association (DA) method. Simulation results demonstrate that the proposed JCC-UA algorithm can effectively reduce the latency of user content downloading and improve the hit rates of contents cached at the BSs as compared to several baseline schemes.

Background and Objectives Despite advances in wound treatments, chronic diabetic wounds remain a significant medical challenge. Exosomes from mesenchymal stem cells (MSCs) and small molecule activators of nuclear factor erythroid 2-related factor 2 (Nrf2) have emerged as potential therapies for nonhealing diabetic wounds. This study aimed to evaluate the effects of exosomes from bone marrow-derived MSCs (BMSCs) alone, or in combination with a small molecule activator of Nrf2 on diabetic wound healing. Methods and Results BMSCs and endothelial progenitor cells (EPCs) were isolated from the femur and tibia bone marrow of Sprague-Dawley (SD) rats and culture-expanded. Exosomes were harvested from the BMSC culture supernatants through ultracentrifugation. The effects of the exosomes and Nrf2 knockdown, alone or in combination, on EPC tube formation were evaluated. Streptozotocin-induced diabetic rats bearing a fresh full-thickness round wound were treated with the exosomes alone, or in combination with a lentiviral shRNA targeting Nrf2 (Lenti-sh-Nrf2) or tert-butylhydroquinone (tBHQ), a small molecule activator of Nrf2. Two weeks later, wound closure, re-epithelization, collagen deposition, neovascularization, and local inflammation were evaluated. BMSC exosomes promoted while Nrf2 knockdown inhibited EPC tube formation. BMSC exosomes accelerated wound closure, re-epithelization, collagen deposition, and neovascularization, and reduced wound inflammation in diabetic rats. These regenerative and anti-inflammatory effects of the exosomes were inhibited by Lenti-sh-Nrf2 but enhanced by tBHQ administration. Conclusions BMSC exosomes in combination with a small molecule Nrf2 activator hold promise as a new therapeutic option for chronic diabetic wounds.

In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition method inspired by neuroscience with respect to the brain response to different emotions. The proposed method, denoted by R2G-STNN, consists of spatial and temporal neural network models with regional to global hierarchical feature learning process to learn discriminative spatial-temporal EEG features. To learn the spatial features, a bidirectional long short term memory (BiLSTM) network is adopted to capture the intrinsic spatial relationships of EEG electrodes within brain region and between brain regions, respectively. Considering that different brain regions play different roles in the EEG emotion recognition, a region-attention layer into the R2G-STNN model is also introduced to learn a set of weights to strengthen or weaken the contributions of brain regions. Based on the spatial feature sequences, BiLSTM is adopted to learn both regional and global spatial-temporal features and the features are fitted into a classifier layer for learning emotion-discriminative features, in which a domain discriminator working corporately with the classifier is used to decrease the domain shift between training and testing data. Finally, to evaluate the proposed method, we conduct both subject-dependent and subject-independent EEG emotion recognition experiments on SEED database, and the experimental results show that the proposed method achieves state-of-the-art performance.

Extracorporeal photopheresis (ECP), a personalized cellular immunotherapy, constitutes a promising treatment for steroid-refractory/-resistant graft-versus-host disease (SR-GvHD), with encouraging clinical response rates. To further investigate its mechanism of action, ECP's effects on T helper (Th) cells as well as on expression of immune checkpoint (PD-1 and Tim-3) and apoptotic (Fas receptor [FasR]) molecules were investigated in 27 patients with SR-GvHD. Our data show that GvHD patients had significantly higher levels of Th2, Th17, Th22 and granulocyte-macrophage colony-stimulating factor (GM-CSF)-positive Th (ThG) cells and clearly lower levels of T follicular helper (Tfh) cells, including Th1- and Th2-like cells, compared with healthy donors. ECP therapy for GvHD was effective through the modulation of different Th subsets: increases of Th22 (1.52-fold) and Tfh cells (1.48-fold) in acute GvHD (aGvHD) and increases of Th2-like Tfh cells (1.74-fold) in chronic GvHD (cGvHD) patients were associated with clinical response. Expression of FasR was further upregulated in CD4+CD8+ T cells. Additionally, Tim-3-expressing effector T cells associated with the severity of GvHD were reduced. Taken together, these data show that ECP therapy exerts immunomodulatory effects by promoting a balanced immune reconstitution and inducing immune tolerance. Therefore it represents an attractive option for the treatment of GvHD.

Low-light images suffer from low contrast and unclear details, which not only reduces the available information for humans but limits the application of computer vision algorithms. Among the existing enhancement techniques, Retinex-based and learning-based methods are under the spotlight today. In this paper, we bridge the gap between the two methods. First, we propose a novel "generative" strategy for Retinex decomposition, by which the decomposition is cast as a generative problem. Second, based on the strategy, a unified deep framework is proposed to estimate the latent components and perform low-light image enhancement. Third, our method can weaken the coupling relationship between the two components while performing Retinex decomposition. Finally, the RetinexDIP performs Retinex decomposition without any external images, and the estimated illumination can be easily adjusted and is used to perform enhancement. The proposed method is compared with ten state-of-the-art algorithms on seven public datasets, and the experimental results demonstrate the superiority of our method. Code is available at:https://github.com/zhaozunjin/RetinexDIP.

Aims This study aimed to explore the therapeutic effects of Lactiplantibacillus plantarum HG20 (HG20) on collagen-induced arthritis (CIA) rats and its mechanism. Methods and Results CIA rats were established by injecting bovine type II collagen for 7 days, and treated by intragastric administration HG20 for 21 days. The foot palm temperature and arthritis score were measured once a week. The pathological changes in the knee joint were observed by hematoxylin and eosin staining. The levels of cytokines were detected by enzyme linked immunosorbent assay, and the effects of HG20 on inflammatory and apoptosis pathway of spleen cells were detected by western blot analysis. The results indicated that HG20 reduced the joint swelling degree and foot palm temperature, inhibited the development of joint histopathology, decreased the levels of pro-inflammatory cytokines, down-regulate the expression of pro-inflammatory cytokines by nuclear factor kappa-B pathway, and inhibited the apoptosis of spleen cells by inhibiting phosphatidylinositol 3-kinase/protein kinase B pathway and regulating apoptosis pathways. Conclusions HG20 had an adjuvant therapeutic effect on arthritis in CIA rats, and its mechanism might be related to the inflammatory and apoptosis pathway. Significance and Impact of Study These results revealed that HG20 could be used as a functional probiotic in the field of food and medical, and which played a potential role in the prevention and treatment of arthritis.

Utilizing an intersectional framework (Crenshaw, 1989) and socioecological theory (Bronfenbrenner, 1979), we investigated the gendered racialized messages Asian American women receive growing up, otherwise known as gendered racial socialization, from their family, peers, and mass media. Consensual Qualitative Research was used to analyze interview data from 12 second-generation Asian American women. The results demonstrated that (a) family socialization messages included gendered racial discrimination, body image and physical appearance, marital/dating attitudes, role responsibilities and expectations, and academic/work expectations, (b) peer socialization included oppressive messages (e.g., physical objectification, denial of identity, lack of presence) and affirming messages (e.g., positive self-concept messages), and (c) mass media socialization included oppressive messages (e.g., lack of representation, stereotypical depictions), and affirming messages (e.g., messages about empowerment). These messages impacted Asian American women's views on their body image and physical appearance, self-esteem, career/work, mental health, and critical consciousness. Implications and findings of the need to dismantle interlocking oppressive forces are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Cutaneous infection by Balamuthia mandrillaris is a rare condition that is sometimes complicated by life-threatening CNS involvement. It often evades timely diagnosis due to its rarity and non-specific clinical manifestations. Patients can be either immunocompetent or immunocompromised. It is probably transmitted via inhalation or inoculation through broken skin, and then spreads to the brain and other organs through haematogenous spread. It is important for clinicians to be aware of this disease because rapid diagnosis and subsequent therapy has, in some cases, been associated with survival. In this Grand Round, we report the case of a 7-year-old boy who presented with large, chronic plaques on his face. Several biopsies showed non-specific granulomatous inflammation. The patient deteriorated rapidly and died within 1 month of displaying abnormal symptoms in the CNS. Immunohistochemical staining of skin tissue identified B mandrillaris as the infectious agent. The diagnosis was confirmed with PCR, which detected B mandrillaris DNA in formalin-fixed skin tissue sections. B mandrillaris infection should be considered in the differential diagnosis of patients with chronic granulomatous lesions. We also reviewed the epidemiology, B mandrillaris in nature and in the laboratory, clinical manifestations, histopathology, diagnosis, and treatment of infection.

This paper presented a high-efficiency push-pull resonant converter solution for auxiliary power supply in 70 kV isolated applications. This power supply consists of a two-stage structure with a resonant push-pull inverter and a buck regulator. A series resonance is introduced in the inverter with its operation and parameter design to achieve high-efficiency voltage isolation. A buck converter with feedforward control is presented with the small-signal model and controller design to regulate the output voltage. Besides, the transformer is another challenge as a crucial part of the voltage isolation. Specifically, the optimization design of toroidal core and sector windings is presented, including the analysis of isolation, loss analysis, and leakage inductance. The paper presents comprehensive converter and transformer models, describing the proposed power supply’s design process. Furthermore, a case study is demonstrated under 70 kV isolation with 36 V-84 V input and 48 V output. A prototype of a 200 W, 1.4 L isolated power supply has been built to demonstrate the design model’s validity and process. Results convincingly indicate that the proposed converter has the highest efficiency of 93.1% and can maintain the 70 kV isolation voltage with good regulation performance.

A novel dual-band circularly polarized (CP) planar endfire antenna with the front-to-back ratio (FBR) enhancement is proposed. First, a half-mode substrate integrated waveguide cavity serving as a magnetic dipole (M-dipole) is devised for the dual-band vertical polarizations, and four slots are etched on the M-dipole to adjust the frequency ratio reasonably. Two independent electric dipoles (E-dipoles) are introduced for the dual-band horizontal polarizations. Then, the M-dipole and the E-dipoles are connected by the parallel double-side compensate lines to adjust their amplitudes and phase differences, thus dual-band CP waves are radiated. A two-stage reflector is also loaded behind the M-dipole, which improves FBRs of the antenna in two bands. Finally, a 2.4/5 GHz prototype antenna is designed, fabricated and measured to validate our design idea. A reasonable agreement between simulated and measured results is obtained. Measured FBRs of the propose antenna at 2.42 GHz and 5.02 GHz are 15 dB and 14.2 dB, respectively.

The social Internet of Things (IoT) (SIoT) helps to enable an autonomous interaction between the two architectures that have already been established: social networks and the IoT. SIoT also integrates the concepts of social networking and IoT into collaborative edge computing (CEC), the so-called CEC-based SIoT architecture. In closer proximity, IoT devices self-organize into a CEC-based SIoT computing cluster and provide social device-to-device (S-D2D) services, such as computation offloading, service discovery, and content delivery. In the CEC-based SIoT, however, cooperation based on social connections leads to a problem called social and spatial physical trade-off. This problem is also referred to as the mismatch problem, which arises because the spatial neighbors in the social layer cannot always be related. The spatial distance thus calls for additional multi-hop transmissions. This work presents a novel solution called 3-D-social identifier structure (3-D-SIS) model. The 3-D-SIS model is based on 3-D social space (3-D-SS) and considers social ties and physical connections (i.e., intra-neighbor) of the SIoT devices and utilizes a 3-D structure to evaluate that relationship. Moreover, it minimizes the end-to-end delay and communication cost to address the mismatch problem. To validate the performance of the (3-D-SIS) model, we use the real traces of social networks (INFOCOM06). The results show that the 3-D-SIS selects the best neighbor in S-D2D communication and improves performance in terms of end-to-end delay and throughput.

Vehicular Edge Computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing energy consumption of systems and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., Deep Reinforcement Learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage of the system. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train a learning policy by following the expert's demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50% improvement compared with the benchmark.

The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment.

In this paper, we propose a simultaneous localization and mapping (SLAM) framework that combines the methods of the unscented article filter (UPF) and particle smothers (PS). The UPF is used to estimate the position and orientation of the mobile robot and particle smothers are used to update the locations of landmarks. We show that UPF is capable of estimating the pose of the robot more consistently and accurately than generic particle filter and unscented Kalman filter (UKF) do, especially when the dynamic models of the robot are highly nonlinear or noises are non-Gaussian. Then, the proposed SLAM framework is applied to high frequency (HF) band radio-frequency identification (RFID) system. Since detection model of the HF band RFID system is highly non-Gaussian, the traditional SLAM frameworks with the Gaussian assumption are not suitable and the proposed UPF-PS framework presents the superior performance. The proposed SLAM framework is evaluated through simulated experiments with a HF band RFID system and a range and bearing sensor. The comparisons between the proposed SLAM framework and four other SLAM frameworks, i.e., FastSLAM, UFastSLAM, and P-SLAM 1.0 and 2.0 are also given. Finally, the proposed SLAM framework is verified in a real experimental environment. The experimental results validate the effectiveness and superiority of the proposed SLAM framework.

We propose a dual-polarized leaky-wave antenna (LWA) with orthomode based on a metallic square waveguide, for broadband endfire radiation applications. Several double-sided slots acting as radiation elements are notched on the waveguide to produce an endfire pattern. To overcome the restriction associated with the endfire radiation condition of a linear array, a metallic core periodically loaded with pins is used to control the dispersion of the proposed double-sided-slot waveguide. By optimizing the height distribution of the loading pins along the longitudinal direction using a cosine function, stable endfire radiation can be achieved over a broad operating band. Moreover, dual-polarized radiation can be automatically implemented because of the rotational symmetry of the proposed LWA. Furthermore, two rectangular waveguides connected with a square waveguide are used as the orthomode transducer (OMT) of the proposed LWA. The numerical and experimental results verify that dual-polarized endfire radiation is achieved in the frequency range of 7.6-9.1 GHz with an isolation of higher than 45 dB. The realized gain achieved is in the range of 13-15 dBi with a cross-polarization level of below -30 dB.

Three-port converters with high voltage gain are desirable solutions for integrating renewable energy and energy storage devices into high voltage dc bus. A high gain three-port converter with soft switching is proposed in this paper, which can realize zero voltage switching for all switches and zero current switching for all diodes in various operating modes. Single coupled inductor is used to achieve high voltage gain and to reduce the voltage stress of switches so that switches with low on -resistance can be selected to reduce the conduction loss. In addition, the advantages of fewer components, higher voltage gain, and very low switch voltage stresses make the proposed converter more suitable for application in renewable energy systems than similar solutions. Various operating modes, performance analysis, design considerations, efficiency analysis, and control method of the proposed converter are discussed. A laboratory prototype with 30 V renewable energy source, 48 V energy storage device and 400 V output is designed to verify the performance of the proposed three-port converter.

In this work, we firstly used a new two-dimensional (2D) semiconductor material Bi2O2Se for optical-fiber SPR sensor sensitization, proposed and validated a U-shaped optical-fiber SPR sensor with Au film- Bi2O2Se Sandwich structure. The finite element simulation results illustrate that the electric field strength on the surface is 2.08 times higher than that of the pure Au film, which excites a stronger SPR phenomenon thus leading to a high sensitivity of the sensor. The effect of the Radius on the sensor performance was investigated, and the optimal structural parameters were derived to fabricate the sensing probes. The RI sensitivity of the presented sensor is 6827.41 nm/RIU, 1.98 times superior to that of the pure Au film U-shaped optical SPR sensor. The sensor is insensitive to temperature fluctuations and has good repetitive and environmental stability. The proposed sensor has good potential for future applications in biomass detection, which also indicates that Bi2O2Se has good prospects for future applications in optoelectronic devices.

The convergence of Software-Defined Networking (SDN) and Internet of Vehicular (IoV) integrated with fog computing, known as software-defined vehicle network-based fog computing (SDV-F), has recently been established to take advantage of both paradigms and efficiently control the wireless networks. SDV-F tackles numerous problems, such as scalability, load-balancing, energy consumption, and security. It lags, however, in providing a promising approach to enable ultra-reliable and delay-sensitive applications with high vehicle mobility over SDV-F. We propose ARTNet, an AI-based Vehicle-to-Everything (V2X) framework for resource distribution and optimized communication using the SDV-F architecture. ARTNet offers ultra-reliable and low-latency communications, particularly in highly dynamic environments, which is still a challenge in IoV. ARTNet is composed of intelligent agents/controllers, to make decisions intelligently about i) maximizing resource utilization at the fog layer, and ii) minimizing the average end-to-end delay of time-critical IoV applications. Moreover, ARTNet is designed to assign a task to fog nodes based on their states. Our experimental results show that considering a dynamic IoV environment, ARTNet can efficiently distribute the fog layer tasks while minimizing the delay.

Pancreatic cancer (PC) is a highly fatal disease correlated with an inferior prognosis. The tumor protein p53 (TP53) is one of the frequent mutant genes in PC and has been implicated in prognosis. We collected somatic mutation data, RNA sequencing data, and clinical information of PC samples in the Cancer Genome Atlas (TCGA) database. TP53 mutation was an independent prognostic predictor of PC patients. According to TP53 status, Gene set enrichment analysis (GSEA) suggested that TP53 mutations were related to the immunophenotype of pancreatic cancer. We identified 102 differentially expressed immune genes (DEIGs) based on TP53 mutation status and developed a TP53-associated immune prognostic model (TIPM), including Epiregulin (EREG) and Prolactin receptor (PRLR). TIPM identified the high-risk group with poor outcomes and more significant response potential to cisplatin, gemcitabine, and paclitaxel therapies. And we verified the TIPM in the International Cancer Genome Consortium (ICGC) cohort (PACA-AU) and Gene Expression Omnibus (GEO) cohort (GSE78229 and GSE28735). Finally, we developed a nomogram that reliably predicts overall survival in PC patients on the bias of TIPM and other clinicopathological factors. Our study indicates that the TIPM derived from TP53 mutation patterns might be an underlying prognostic therapeutic target. But more comprehensive researches with a large sample size is necessary to confirm the potential.

An active-passive integration technique for operation near a wavelength of 1030 nm has been developed on a gallium arsenide (GaAs) photonic integrated circuit platform. The technique leverages quantum wells (QWs) that are slightly offset vertically from the center of the waveguide, and selectively removed prior to upper cladding regrowth to form active and passive regions. The active region consists of indium gallium arsenide (InGaAs) QWs, gallium arsenide phosphide (GaAsP) barriers, GaAs separate confinement heterostructure layers, and aluminum gallium arsenide (AlGaAs) cladding. Fabry Perot lasers with various widths were fabricated and characterized, exhibiting high injection efficiency of 98.8%, internal active loss of 3.44 cm−1, and internal passive loss of 4.05 cm−1 for 3 μm wide waveguides. The 3 μm, 4 μm, and 5 μm wide lasers demonstrated greater than 50 mW output power at 100 mA continuous wave (CW) current and threshold current as low as 9 mA. 20 μm wide broad area lasers demonstrated 240 mW output power, 35.2 mA threshold current under CW operation, and low threshold current density of 94 A/cm2 for 2 mm long lasers. Additionally, these devices exhibit transparency current density of 85 A/cm2 and good thermal characteristics with T 0 = 205 K, and Tη = 577K.

Photocatalytic water-splitting with Z-scheme semiconductor heterojunctions is a promising way to achieve renewable solar fuels. Nevertheless, developing earth-abundant direct Z-scheme photocatalytic systems for efficient H2 production is still under-developed. In this work, porous heterostructures of S-deficient CoS/CdS hexagonal nanoplates (HNPs) were fabricated for the first time through the self-template approach combined with a solvothermal process. As indicated by the scanning Kelvin probe microscopy and ∙OH radicals measurements, such CoS/CdS heterojunction follows the Z-scheme charge transfer pathway. Noticeably, these unique CoS/CdS HNPs demonstrated excellent photocatalytic activity and stability for H2 evolution reaction (HER) under visible-light irradiation (λ > 400 nm). Specifically, the optimal HER rate is as high as 39.29 mmol∙g-1∙h-1 (corresponding to the apparent quantum yield of 14.5% at 400 nm), approximately 152, 112, and 51 times that of CoS, CdS, and 3 wt% Pt-loaded CdS, respectively. The exceptional HER capability of CoS/CdS HNPs could be associated with their outstanding visible-light harvesting, efficient Z-scheme charge separation, and abundant H2 evolution active sites. The findings indicated here could impel the rational design of high-performance Z-scheme photocatalysts for energy and environmental applications.

Background: Coronavirus disease 2019 (COVID-19) pandemic continues to escalate intensively worldwide. Massive studies on general populations with SARS-CoV-2 infection have revealed that pre-existing comorbidities were a major risk factor for the poor prognosis of COVID-19. Notably, 49–75% of COVID-19 patients had no comorbidities, but this cohort would also progress to severe COVID-19 or even death. However, risk factors contributing to disease progression and death in patients without chronic comorbidities are largely unknown; thus, specific clinical interventions for those patients are challenging. Methods: A multicenter, retrospective study based on 4806 COVID-19 patients without chronic comorbidities was performed to identify potential risk factors contributing to COVID-19 progression and death using LASSO and a stepwise logistic regression model. Results: Among 4806 patients without pre-existing comorbidities, the proportions with severe progression and mortality were 34.29% and 2.10%, respectively. The median age was 47.00 years [interquartile range, 36.00–56.00], and 2162 (44.99%) were men. Among 51 clinical parameters on admission, age ≥ 47, oxygen saturation < 95%, increased lactate dehydrogenase, neutrophil count, direct bilirubin, creatine phosphokinase, blood urea nitrogen levels, dyspnea, increased blood glucose and prothrombin time levels were associated with COVID-19 mortality in the entire cohort. Of the 3647 patients diagnosed with non-severe COVID-19 on admission, 489(13.41%) progressed to severe disease. The risk factors associated with COVID-19 progression from non-severe to severe illness were increased procalcitonin levels, SpO2 < 95%, age ≥ 47, increased LDH, activated partial thromboplastin time levels, decreased high-density lipoprotein cholesterol levels, dyspnea and increased D-dimer levels. Conclusions: COVID-19 patients without pre-existing chronic comorbidities have specific traits and disease patterns. COVID-19 accompanied by severe bacterial infections, as indicated by increased procalcitonin levels, was highly associated with disease progression from non-severe to severe. Aging, impaired respiratory function, coagulation dysfunction, tissue injury, and lipid metabolism dysregulation were also associated with disease progression. Once factors for multi-organ damage were elevated and glucose increased at admission, these findings indicated a higher risk for mortality. This study provides information that helps to predict COVID-19 prognosis specifically in patients without chronic comorbidities.

A coupling enhanced surface plasmon resonance (SPR) sensor based on coreless fiber is proposed. Compared with the common fiber, the coreless fiber is easier to perceive the changes of the external medium environment. As one-dimensional material with superior performance, the Au nano biconical particles are used to improve the sensor performance. Based on the finite element method (FEM), the electric field enhancement effect of the coupling between the SPR of the gold film and the Localized surface plasmon resonance (LSPR) of the Au nano biconical particles on the surface of the coreless fiber is analyzed, and the experimental results show that the Au nano biconical particles can improve the sensitivity. The sensitivity of the improved Au nano biconical particle sensor is 3514 nm/RIU, which is 64% higher than that of the traditional gold film SPR sensor, and the quality factor is increased by 24%. The new sensor based on Au nano biconical particles and gold film has a good application prospect in biosensor and chemical measurement.

In this work, a covalent organic framework (COF), which is constructed by the building blocks of [5,10,15,20-tetrakis(4-aminophenyl)porphinato]copper(II) (CuTAPP) and p-benzaldehyde, is employed to integrate with TiO2 for the purpose of establishing a Z-scheme hybrid. Within the system, isonicotinic acid performs the role of a bridge that connects the two components through a coordination bond. Further photocatalytic application reveals the hybrid framework is able to catalyze CO2 conversion under simulated solar light, resulting in CO production rate of 50.5 μmol g−1 h−1, about 9.9 and 24.5 times that of COF and pristine TiO2, respectively. The ameliorated catalytic performance owes much to the porphyrin block acting as photosensitizer that augments the light absorbance, and the establishment of Z-scheme system between the inorganic and organic components that enhances the separation of the carriers. In addition, the chemical bridge also ensures a steady usage and stable charge delivery in the catalysis. Our study sheds light on the development of versatile approaches to covalently incorporate COFs with inorganic semiconductors.

Recent image-generation methods have demonstrated that realistic images can be produced from captions. Despite the promising results achieved, existing caption-based generation methods confront a dilemma. On the one hand, the image generator should be provided with sufficient details for realistic hallucination, meaning that longer sentences with rich content are preferred, but on the other hand, the generator is meanwhile fragile to long sentences due to their complex semantics and syntax like long-range dependencies and the combinatorial explosion of object visual features. Toward alleviating this dilemma, a novel approach is proposed in this article to hallucinate images from attribute pairs, which can be extracted from natural language processing (NLP) toolsets in the presence of complex semantics and syntax. Attribute pairs, therefore, enable our image generator to tackle long sentences handily and alleviate the combinatorial explosion, and at the same time, allow us to enlarge the training dataset and to produce hallucinations from randomly combined attribute pairs at ease. Experiments on widely used datasets demonstrate that the proposed approach yields results superior to the state of the art.

There is a growing demand in the marketplace for wirelessly powered consumer applications. Some of these low-power applications require wireless power transfer with bi-directional power flow. For such consumer applications, this paper proposes a new bi-directional wireless power transfer (BD-WPT) topology that facilitates wireless power transfers in both directions using fewer components. The paper describes the operating principles of the topology in detail and presents a mathematical model to predict and investigate its performance. Besides, the the control method is proposed for this topology including the power transfer control and synchronization. Simulations and theoretical results are presented in comparison to the measured results of a 60W prototype BD-WPT to demonstrate the viability of the proposed converter topology. Results are convincing and indicate that the proposed converter is suitable for low power applications being low cost, reliable, and efficient.

Sea surface height (SSH) prediction is theoretically and practically significant for global and regional ocean-related research. Numerous studies have been conducted to acquire accurate prediction results. However, most investigations on SSH ignore the importance of data at each time step on the prediction, which limits the accuracy of the final prediction. Therefore, a deep learning model combined Long Short-Term Memory (LSTM) network and Attention mechanism is proposed in this letter. This model integrates attention mechanism in both of time and space dimensions into LSTM. For time dimension, it assigns reasonable weight for data at each time step. For space dimension, it groups the data points close to each other, let model concentrate on points in the same group and eliminates the impact from other points. Daily absolute dynamic topography (ADT) in the South China Sea from January 2010 to December 2017 is adopted to conduct experiments. The proposed model demonstrates reliable results, the root mean square error is 0.38 cm, the mean absolute error is 0.0031, and the correlation coefficient reaches up to 0.999. The results show that the deep learning method based on attention mechanism is reliable for SSH prediction with high performance.

The operational stability of organic (opto)electronic devices largely depends on the intrinsic stability of organic materials on service. For organic light-emitting diode (OLED) materials, a key pa...

Performance-based salary distribution is one of the important contents of modern hospital management system. In general, the distribution of performance salary in public hospitals of China can be divided into two stages: one is from hospitals to departments, the other is from departments to individuals. It is of great significance to improve the performance-based salary distribution system in clinical departments of public hospital, which is beneficial to ensure the public nature, motivate hospital staff to work hard, and raise public healthcare service quality. Therefore, this paper focuses on the issue of performance-based salary distribution in clinical departments of public hospital, adopts super-efficiency DEA model to evaluate the performance of each clinical department, introduces a new utility function for processing the original values of DEA efficient DMUs in order to encourage more clinical departments to pursue higher performance, and finally verifies the comprehensive model by empirical analysis. The result of empirical analysis shows that the performance of DMU7 is highest with an efficiency value of 1.53, followed by DMU3, DMU8, and DMU1. The efficiency value of DMU9 is lowest in all clinical departments.

This letter presents a C-band high-efficiency fully integrated high-power amplifier (HPA) monolithic microwave integrated circuit (MMIC) in 0.25 μm gallium nitride (GaN) high electron mobility transistor (HEMT) technology. The proposed GaN HPA MMIC consists of two amplifying stages, and its output stage includes 8 transistors in parallel. In order to realize the high efficiency, a low-loss harmonic control network (HCN) using a parallel LC resonate block is applied to obtain the optimum load impedance to each transistor employed in the output stage of the HPA at both f0 and 2f0. Thus, the designed GaN HPA can operate in pulse conditions of 100 μs pulse width and 10% of duty cycle over the frequency from 5.6 GHz to 6.3 GHz. The proposed high efficiency GaN HPA MMIC provides an average output power of 45.0 dBm-45.5 dBm (31.6 W-35.5 W) with a power added efficiency (PAE) of 59%-61.8%, and a gain of 26 dB-28.5 dB under a drain voltage of 28 V. The area of the proposed GaN HPA MMIC is 4.2 mm × 4 mm including pads.

In this article, a 1×12 millimeter-wave (mmW) linearly tapered slot antenna (LTSA) array, fed by high-order waveguide modes, is introduced. Due to the antiphase characteristic between adjacent channels of a power divider based on the TE2n,0 mode waveguide, the cross-polarized E-field components can cancel each other out in an end-fire direction, even in the case of a relatively thick substrate. Moreover, the electric separations between the adjacent channels employed in a traditional substrate integrated waveguide (SIW) power divider can be removed without affecting the field distributions. This facilitates in improving robustness to avoid fabrication errors. Additionally, a general condition of generating a single high-order mode with symmetrical protection is outlined. By combining two antiphase TE30 mode waveguides, an equivalent TE60 mode waveguide with six identical output channels is realized. To feed a 12-element array, two equivalent TE60 mode waveguides are employed as an equivalent TE12,0 mode-feeding network. Instead of the complex 1-to-n-way power divider and relatively thin substrate featured in previous works, the high-order mode-fed LTSA array introduced in this study is able to operate over 26.5b–30 GHz with substrate height of 2 mm (0.19λ0), yielding a cross-polarization level of less than -15 dB.

Inertial measurement units (IMUs)-based sensing systems have received extensive attention for wearable gait measurement due to their small size, low price, and high accuracy. However, reconstructing walking dynamics usually requires a large number of sensor units attached to major body segments, which is generally not suitable for daily-life scenarios. Previous studies showed that two shank-mounted IMUs can detect gait events and estimate many important gait parameters. However, it is unclear whether this simple two-IMU system can be used to reconstruct walking dynamics. In this paper, we propose a two-stage method for predicting walking dynamics from measurements of two shank-mounted IMUs. Displacements of ankle joints and shank angles are first estimated from the IMU outputs. Then a model-based whole-step optimization approach is used to solve the gait dynamics by tracking the estimated shank motion. The proposed method is validated with both normal and asymmetric walking data, achieving a root mean square error of 5.3, 6.1, and 6.8 degrees in the hip, knee, and ankle joint angle estimation and 3.9% and 12.2% body weight in the fore-aft and vertical ground reaction force estimation. Comparing with the reported results in literature, the number of IMUs is significantly reduced with similar accuracy. This implies that gait dynamic information may be estimated from very limited measurements and the inherent gait characteristics can be used to reconstruct gait dynamics from the motion of a small number of segments.

Hypertension is a common comorbid condition in patients with diabetes. The pathogenesis of hypertension in diabetes has not been fully clarified. Primary tubular hyperreabsorption may contribute, which may be counteracted by glomerular hyperfiltration in the early diabetic kidney. In this study, we hypothesize that in early diabetes, the macula densa neuronal nitric oxide synthase (NOS1)-derived nitric oxide (NO) production is enhanced, which blunts tubuloglomerular feedback (TGF) response, promotes glomerular hyperfiltration, and maintains normal blood pressure; conversely, insufficient NO generation by the macula densa induces hypertension by lowering glomerular filtration rate and thus inhibiting natriuresis. To test this hypothesis, we examined the changes of macula densa NOS1 expression and phosphorylation as well as NO production, TGF response, glomerular filtration rate, sodium excretion, and blood pressure in a murine model of leptin receptor-deficient (db/db) diabetes with or without macula densa-specific NOS1 deletion. We found that db/db mice presented reduced fractional renal sodium excretion and only a small increase in blood pressure, associated with upregulated expression and activity of macula densa NOS1, inhibited TGF response, and glomerular hyperfiltration. Genetic knockout of macula densa NOS1 restored the TGF response and attenuated glomerular hyperfiltration in db/db mice but also further reduced fractional renal sodium excretion and substantially increased blood pressure. In conclusion, the present study demonstrates that in the early stage of leptin receptor-deficient diabetes, the upregulation of macula densa NOS1 inhibits TGF and increases glomerular filtration rate, which counteracts renal sodium retention and limits the rise in blood pressure.

In this letter, two novel broadband circularly polarized slot-microstrip antennas (SMPA) with circular and square structures are elaborated. The circular SMPA is composed of four crown patches as parasitic elements, four crown slots etched on a circular ground plane, and a circular loop sequential phase feeding structure. However, the square SMPA consists of four parasitic rectangular patches, four square slots with cut corners, and a square-loop feeding structure. In the circular SMPA, the parasitic crown patches and crown slots are used to excite multiple circularly polarized (CP) resonant modes, which are fed by a circular loop sequential phase feeding structure through capacitive coupling. Combined with these CP modes, the CP bandwidth of the presented antenna could be improved. Eventually, the proposed circular SMPA has a broad 3-dB axial ratio bandwidth (ARBW) of 20.6% (5.206.40, 5.8 GHz) and a wide 10-dB impedance bandwidth (IBW) of 25.8% (4.926.38, 5.65 GHz), which could occupy the WLAN (5.7255.85 GHz), ITS (5.8 GHz), and WIFI (5.85-5.925 GHz) bands.

With the increased number of synthetic aperture radar (SAR) systems that have been launched in recent years, many high-quality images have been produced for precise earth observation. The number of available images requires new feature extraction techniques to capture enough discriminant information from high-resolution SAR images. To effectively characterize different land covers, a multiscale local Fisher pattern (MLFP) is proposed to extract both statistical and spatial properties in this work. First, the Fisher vector (FV) of each pixel is calculated after the statistical distribution of high-resolution SAR intensities is modeled via a Gamma mixture model. Then, the generative and discriminant information implied in the FV is exploited after the extraction of the geometrical information between neighboring pixels within different scales. Two experiments are conducted to demonstrate the performance of the proposed MLFP, which are high-resolution SAR image land-cover classification and SAR scene classification. Compared with some existing approaches, the proposed MLFP shows superior performance after the statistical and spatial structure information from different land covers is exploited.

Massive development of China's high-speed rail (HSR) network has resulted in unprecedented improvement in spatial accessibility. This paper uses the Yangtze River delta (YRD), one of the most populous and developed mega-regions in China, as a case study to examine the spatial impacts of the HSR network development. It uses official train timetables to employ a weighted measurement, including rail travel time and train service frequency, to assess the changing network accessibility and connectivity at the multi-scale of provincial, regional and urban between April 2007 (pre-HSR) and October 2016 (16 HSR lines). The study finds that the introduction of the HSR network has enhanced, rather than comprehensively restructured, already-existing uneven patterns of accessibility and connectivity. Developed cities have benefited more from increases in connectivity, while the less-developed peripheral cities have experienced greater accessibility gains, which are, in part, the results of the YRD's persistent pattern of economic geographies. The research advances understanding of the impacts of HSR development in the mega-regions.

As a more energy-efficient WPT technology, directional WPT is applied to supply energy for wireless rechargeable sensor networks (WRSNs). Conventional methods that ignore anisotropic energy receiving property of rechargeable sensors cause a waste of energy. To address this issue, in this paper, we focus on minimizing the charging delay with a directional charging scheme. At first, we introduce linear constraints to improve an energy transfer model, which is verified to be practical by experiments. Then, we concern a Minimal chArging Delay with Single charger (S-MAD) problem to promote efficiency, followed by an Optimal Direction Charge with Single charger (S-ODC) solution. Through discretizing charging power and angle, we bound the performance gap of the solution to the optimal one with a 1/1-e² approximation ratio, where e is the error threshold of discretization. After that, we extend the original S-MAD problem into the large scale WRSN with multiple chargers (i.e., M-MAD) and solve it by proposing M-ODC (i.e., Optimal Direction Charge with Multiple chargers (M-ODC)). Theoretical analyses are presented to exploit the feature of the proposed schemes. Finally, we demonstrate that our methods outperform the baseline methods by an average of 34.2% through simulations and test-bed experiments.