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Abstract The exploration of multi-dimensional brain activity with high temporal and spatial resolution is of great significance in the diagnosis of neurological disease and the study of brain science. Although the integration of electroencephalogram (EEG) with magnetic resonance imaging (MRI) and computed tomography (CT) provides a potential solution to achieve a brain-functional image with high spatiotemporal resolution, the critical issues of interface stability and magnetic compatibility remain challenging. Therefore, in this research, we proposed a conductive hydrogel EEG electrode with an asymmetrical bilayer structure, which shows the potential to overcome the challenges. Benefiting from the bilayer structure with different moduli, the hydrogel electrode exhibits high biological and mechanical compatibility with the heterogeneous brain-electrode interface. As a result, the impedance can be reduced compared with conventional metal electrodes. In addition, the hydrogel-based ionic conductive electrodes, which are free from metal conductors, are compatible with MRI and CT. Therefore, they can obtain high spatiotemporal resolution multi-dimensional brain information in clinical settings. The research outcome provides a new approach for establishing a platform for early diagnosis of brain diseases and the study of brain science.

Abstract The orbitofrontal cortex (OFC) and hippocampus (HC) both contribute to the cognitive maps that support flexible behavior. Previously, we used the dopamine neurons to measure the functional role of OFC. We recorded midbrain dopamine neurons as rats performed an odor-based choice task, in which expected rewards were manipulated across blocks. We found that ipsilateral OFC lesions degraded dopaminergic prediction errors, consistent with reduced resolution of the task states. Here we have repeated this experiment in male rats with ipsilateral HC lesions. The results show HC also shapes the task states, however unlike OFC, which provides information local to the trial, the HC is necessary for estimating upper-level hidden states that distinguish blocks. The results contrast the roles of the OFC and HC in cognitive mapping and suggest that the dopamine neurons access rich information from distributed regions regarding the environment’s structure, potentially enabling this teaching signal to support complex behaviors.

BackgroundPrevious studies have demonstrated widespread brain neurodegeneration in Alzheimer’s disease (AD). However, the neurobiological and pathogenic substrates underlying this structural atrophy across the AD spectrum remain largely understood.MethodsIn this study, we obtained structural MRI data from ADNI datasets, including 83 participants with early-stage cognitive impairments (EMCI), 83 with late-stage mild cognitive impairments (LMCI), 83 with AD, and 83 with normal controls (NC). Our goal was to explore structural atrophy across the full clinical AD spectrum and investigate the genetic mechanism using gene expression data from the Allen Human Brain Atlas.ResultsAs a result, we identified significant volume atrophy in the left thalamus, left cerebellum, and bilateral middle frontal gyrus across the AD spectrum. These structural changes were positively associated with the expression levels of genes such as ABCA7, SORCS1, SORL1, PILRA, PFDN1, PLXNA4, TRIP4, and CD2AP, while they were negatively associated with the expression levels of genes such as CD33, PLCG2, APOE, and ECHDC3 across the clinical AD spectrum. Further gene enrichment analyses revealed that the positively associated genes were mainly involved in the positive regulation of cellular protein localization and the negative regulation of cellular component organization, whereas the negatively associated genes were mainly involved in the positive regulation of iron transport.ConclusionOverall, these results provide a deeper understanding of the biological mechanisms underlying structural changes in prodromal and clinical AD.

Regenerative fuel cells can operate alternately as an electrolyzer and as a fuel cell, frequently involving water as a reactant or product. Modifying the electrode surface to manipulate water can prevent electrode flooding and enhance the electrode's mass transfer efficiency by facilitating better contact with gaseous reactants. However, conventional electrodes face difficulties in allowing water droplets to penetrate in a single direction leaving electrodes. In this work to address this issue, a wettability gradient electrode is designed and fabricated for efficient water manipulation in regenerative fuel cells. The findings demonstrate that the water removal strategy in the electrolyzer mode yields the highest ammonia yield and Faradaic efficiency of 3.39 × 10-10 mol s-1 cm-2 and 0.49 %, respectively. Furthermore, in the fuel cell mode, the discharging process sustains for approximately 20.5 h, which is six times longer than the conventional strategy. The ability to sustain the discharging process for extended periods is particularly advantageous in regenerative fuel cells, as it enables the cells to operate for longer periods without the need for regeneration.

Silicon nitride (SiN) plays a critical role in silicon photonics because of its lower refractive index, low waveguide loss, broad operating bandwidth and compatibility with complementary metal oxide semiconductor (CMOS) fabrication process. Here, we propose a polarization-independent sub-wavelength grating (SWG) edge coupler with high efficiency based on SiN-Si dual-stage structure with a length of only 315.8 μm. Such a structure can be fabricated on 8-inch silicon photonics pilot lines and doesn't require special fabrication processes for making it suspended. We simulate the minimum TE/TM light coupling loss from a standard SMF-28 fiber to be 0.61 dB/0.95 dB with polarization dependent loss (PDL) less than 0.4 dB in the whole band between 1500 nm and 1600 nm.

Time-sensitive networking (TSN) is a kind of network communication technology applied in fields such as industrial internet and intelligent transportation, capable of meeting the application requirements for precise time synchronization and low-latency deterministic forwarding. In TSN, cyclic queuing and forwarding (CQF) is a traffic shaping mechanism that has been extensively discussed in the recent literature, which allows the delay of time-triggered (TT) flow to be definite and easily calculable. In this paper, two algorithms are designed to tackle the start time planning issue with the CQF mechanism, namely the flow–path–offset joint scheduling (FPOJS) algorithm and congestion-aware scheduling algorithm, to improve the scheduling success ratio of TT flows. The FPOJS algorithm, which adopts a novel scheduling object—a combination of flow, path, and offset—implements scheduling in descending order of a well-designed priority that considers the resource capacity and resource requirements of ports. The congestion-aware scheduling algorithm identifies and optimizes congested ports during scheduling and substantially improves the scheduling success ratio by dynamically configuring port resources. The experimental results demonstrate that the FPOJS algorithm achieves a 39% improvement in the scheduling success ratio over the naive algorithm, 13% over the Tabu-ITP algorithm, and 10% over the MSS algorithm. Moreover, the algorithm exhibits a higher scheduling success ratio under large-scale TSN.

Background: Phthalates are widely used plasticizers, which were identified as risk factors in the development of many human diseases. However, the effects of phthalates in the periodontitis are unknown. We aimed to investigated the relationship of periodontitis and phthalate exposure as well as the underlying mechanisms. Materials and methods: Univariate and multivariate logistic regressions were employed to evaluate the association between phthalate metabolites and periodontitis. The generalized additive model and piecewise logistic regression were conducted to investigate the dose-response relationship. Cell and animal models were used to explore the role and mechanism of DEHP in the development of periodontitis. Transcriptome sequencing, bioinformatics analysis, western blot, immunofluorescence and mice model of periodontitis were also employed. Results: MEHP (OR 1.14, 95% CI 1.05–1.24), MCPP (OR 1.08, 95% CI 1.00–1.17), MEHHP (OR 1.18, 95% CI 1.08–1.29), MEOHP (OR 1.18, 95% CI 1.07–1.29), MiBP (OR 1.15, 95% CI 1.04–1.28), and MECPP (OR 1.20, 95% CI 1.09–1.32) were independent risk factors. And MEHHP, the metabolite of DEHP, showed the relative most important effects on periodontitis with the highest weight (0.34) among all risk factors assessed. And the increase of inflammation and the activation of NFκB pathway in the periodontitis model mice and cells were observed. Conclusion: Exposure to multiple phthalates was positively associated with periodontitis in US adults between 30 and 80 years old. And DEHP aggravated inflammation in periodontitis by activating NFκB pathway.

The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.

Background: Pure magnesium-based ortho-implants have a number of advantages. However, vital parameters like degradation rate and biocompatibility still call for significant improvement. Methods: In this study, poly (1,3-trimethylene carbonate) (PTMC) and polydopamine (PDA) bilayer and micro arc oxidation composite coatings were prepared successively on magnesium surface by immersion method and microarc oxidation. Its corrosion resistance and biocompatibility were evaluated by in vitro corrosion tests, cellular compatibility experiments, and in vivo animal experiments. Results: In vitro experiments demonstrated that the composite coating provides excellent corrosion protection and biocompatibility. Animal studies demonstrated that the composite coating slowed the degradation of the implant and was not toxic to animal viscera. Conclusion: In conclusion, the inorganic-organic composite coating proposed in this study provided good corrosion resistance and enhanced biocompatibility for pure magnesium implants. The translational potential of this article: The translational potential of this article is to develop an anti-corrosion composite coating on a pure magnesium surface and to verify the viability of its use in animal models. It is hoped to open up a new approach to the design of new degradable orthopedic magnesium-based implants.

AbstractDC Beads and CalliSpheres are commonly used microspheres in clinical transcatheter arterial chemoembolization, but these microspheres cannot be visualized by themselves. Therefore, in our previous study, we developed multimodal imaging nano-assembled microspheres (NAMs), which are visualized under CT/MR and the location of embolic microspheres can be determined during postoperative review, facilitating the evaluation of embolic areas and guiding subsequent treatment. Moreover, the NAMs can be carried with positively and negatively charged drugs, increasing the choice of drugs. Systematic comparative analysis of the pharmacokinetics of NAMs with commercially available DC Bead and CalliSpheres microspheres is important for evaluating the clinical application of NAMs. In our study, we compared the similarities and differences between NAMs and two drug-eluting beads (DEBs) in respect to drug loading capacity, drug release profiles, diameter variation and morphological characteristics. The results indicate that NAMs had good drug delivery and release characteristics as well as DC Bead and CalliSpheres in vitro experimental stage. Therefore, NAMs have a good application prospect in transcatheter arterial chemoembolization treatment of hepatocellular carcinoma.

DC power grid has received increasing attentions recently, and with it comes the demand for highly reliable and stable metering solutions. At present, in the DC distribution projects that have been implemented, some DC metering devices have been put into use. However, from the national and industrial level, there is no clear standard for the design of the metering scheme for the medium and low-voltage DC distribution network, and there is a situation of imperfect metering device configuration. Therefore, based on the analysis of DC metering devices, algorithms and location selection, this paper takes the Hangzhou Jiangdong flexible DC distribution network as an example to analyze the error distribution caused by metering algorithm selection and device configuration. In addition, the influence of harmonics on DC measurement is analyzed to provide the suggestions for DC measurement solutions. The analysis has been verified by MATLAB/Simulink, which provides references for DC metering standards.

Objectives To investigate the potential mechanisms of 125 I seed implantation therapeutic treatment on inactivating the VEGFR2/PI3K/AKT pathway in cholangiocarcinoma. Methods The human cholangiocarcinoma cell lines HCCC-9810 and HuCCT1 were purchased for in vitro studies. The BALB/c nude mice were obtained for in vivo studies. The proliferation of cells was detected by CCK-8, colony formation, and BrdU staining. The migration and invasion of cells were determined by wound healing assay and Transwell assay, respectively. Hematoxylin and eosin staining was utilized for histological evaluation. Protein expression was determined by western blotting and immunohistochemistry. Results Compared with the control group, .6 mCi group and .8 mCi group inhibited cholangiocarcinoma cells proliferation, invasion, migration, and promoted apoptosis, the protein expression of p-VEGFR2, VEGFR2, PI3K, p-AKT/AKT, cyclin B1, cyclin A, CDK1, and Bcl-2 was decreased. Similar results were obtained from in vitro experiments. However, when VEGF is overexpressed, the inhibitory effect of .8 mCi was partially significantly reversed on cholangiocarcinoma cells. The in vivo studies further confirmed the inhibitory effects of .6 mCi group and .8 mCi group on cholangiocarcinoma. Conclusion 125 I seed irradiation could inhibit cholangiocarcinoma cells proliferation, migration, and invasion and promote apoptosis through inactivation of the VEGFR2/PI3K/AKT signaling pathway.

The source of the evidence accumulation signal widely observed in the brain during decision making is unknown. Here, the authors used a two-stage decision task to show that the accumulation signal in the posterior parietal cortex arises locally.

Porcine circovirus type 3 (PCV3) is commonly associated with clinical symptoms such as porcine dermatitis and nephropathy syndrome (PDNS)-like lesions, respiratory signs, and reproductive disorders. This study aimed to investigate the epidemiology of PCV3 in a boar stud. The objectives were to detect PCV3 in semen, as well as matched serum, oral fluid, and preputial fluid samples from adult boars using quantitative polymerase chain reaction (qPCR), analyze PCV3-IgG antibody data, and genetically characterize a positive sample. A total of 112 samples from 28 boars were collected from a large-scale pig farm in Guangxi, China. The qPCR results showed that the PCV3 DNA was not detected in semen, with a positive rate of 0% (0/28), while it was detected in serum (3.57%—1/28), oral fluid (64.28%—18/28), and preputial fluid (46.4%—13/28). The seropositivity rate of PCV3-IgG in serum was 82.14% (23/28) according to the indirect enzyme-linked immunosorbent serologic assay (ELISA) results. Phylogenetic analysis revealed that one of the PCV3 isolates belonged to the PCV3c clades. This is the first report of PCV3 detection in preputial fluid from boars. The results suggest that PCV3 is transmitted among boars on pig farms and exhibits epidemic characteristics.

Visfatin, a multifunctional adipocytokine, is particularly important in the regulation of apoptosis and inflammation through an unidentified mechanism. Clarifying the control mechanisms of visfatin on inflammation and apoptosis in RAW264.7 cells and mice immunological organs was the goal of the current investigation. In order to create a pathophysiological model, the RAW264.7 cells were stimulated with 200 ng/mL visfatin and 20 μg/mL lipopolysaccharide (LPS), either separately or combined. The effects of exogenous visfatin on inflammation and apoptosis in RAW264.7 cells were investigated by flow cytometry assay, RNA-seq analysis and fluorescence quantitative PCR. According to the findings, exogenous visfatin exhibits dual effects on inflammation by modulating the expression of IL-1α, TNFRSF1B, and LIF as well as taking part in various signaling pathways, including the MAPK and Rap1 signaling pathways. By controlling the expression levels of Bcl2l1, Bcl2a1a, and Fas and primarily participating in the PI3K/AKT signaling pathway and Hippo signaling pathway, exogenous visfatin can inhibit apoptosis in RAW264.7 cells. The visfatin inhibitor FK866 was used to further confirm the effects of visfatin on inflammation and apoptosis in mice immune organs. Subsequently, mice spleen and thymus were collected. It is interesting to note that in LPS-treated mice, suppression of endogenous visfatin might worsen the immune system’s inflammatory response and even result in rapid mortality. Additionally, endogenous visfatin promotes the apoptosis in mice immune organs by regulating the expression levels of Bcl2l1, Fas, Caspase 3, Bcl2a1a, and Bax. Together, these results imply that visfatin is a multifaceted molecule that regulates inflammation and apoptosis in RAW264.7 cells and mice immunological organs by taking part in a variety of biological processes and regulating the amounts of associated cytokines expression. Our findings offer additional understandings of how visfatin affects apoptosis and inflammation.

The solar sail spacecraft utilizing a hybrid approach of solar sail and solar electric propulsion in the heliocentric displaced orbit is affected by external disturbances, internal unmodeled dynamics, initial injection errors, and input saturation. To solve the station-keeping control problem under such complex conditions, an adaptive control strategy is proposed. First, the dynamical equations of the spacecraft utilizing hybrid low-thrust propulsion in the cylindrical coordinate system are derived. Second, the combined disturbance acceleration introduced by external disturbances and internal unmodeled dynamics is constructed, and a radial basis function neural network estimator is designed to estimate it online in real time. Third, an adaptive high-performance station-keeping controller based on an improved integral sliding surface and multivariate super-twisting sliding mode approaching law is designed. Then, stability analysis is conducted using Lyapunov theory, adaptive laws are designed, and the introduced virtual control accelerations are converted into actual control variables. Finally, simulations are conducted under different simulation conditions based on the disturbance sources. The results show that although the use of hybrid low-thrust propulsion breaks the symmetry of the solar sail in configuration, the proposed control strategy can effectively achieve the station-keeping and disturbance estimation of the spacecraft with only a small amount of propellant consumed and position tracking errors up to decimeters.

Recent meta-learning models often learn priors from observed tasks using a network optimized via stochastic gradient descent (SGD), which usually takes more training steps to convergence. In this paper, we propose an accelerated Bayesian meta-learning structure with a stochastic inference network (ABML-SIN). The proposed model aims to solve the training procedure of Bayesian meta-learning to improve the training speed and efficiency. Current approaches of meta-learning hardly converge within a few descent steps, owing to the small number of training samples. Therefore, we introduce an accelerated gradient descent learning network based on teacher–student architecture to learn the meta-latent variable θt for task t. With this amortized fast inference network, the meta-learner is able to learn the task-specific latent θt within a few training steps; thus, it improves the learning speed of the meta-learner. To refine the latent variables generated from the transductive amortization network of the meta-learner, SIN—followed by a conventional SGD-optimized network—is introduced as the student–teacher network to online-update the parameters. SIN extracts the local latent variables and accelerates the convergence of the meta-learning network. Our experiments on simulation data demonstrate that the proposed method provides generalization and scalability on unseen samples, and produces competitive/superior uncertainty estimations on few-shot learning tasks on two widely adopted 2D datasets with fewer training epochs compared to the state-of-the-art meta-learning approaches. Furthermore, the parameters generated by SIN act as perturbations on latent weights, enhancing the probability of accelerating the training efficiency of the meta-learner. Extensive qualitative experiments show that our method performs well across different meta-learning tasks in both simulated and real-world circumstances.

Currently, clinically available drug-loaded embolic microspheres have some shortcomings, such as being invisible with standard medical imaging modalities and only being able to carry positively charged drugs. The visualization of drug-loaded microspheres is very important for real-time monitoring of embolic position to improve the therapeutic effect. Meanwhile, the visualization of microspheres can enable postoperative reexamination, which is helpful for evaluating the embolization area and guiding the subsequent treatment. In addition, microspheres capable of loading different charged drugs can increase the choice of chemotherapeutic drugs and provide more possibilities for treatment. Therefore, it is of great importance to explore drug-loaded microspheres capable of multimodal imaging and loading drugs with different charges for transarterial chemoembolization (TACE) treatment of liver tumors. In our study, we designed a kind of nano-assembled microspheres (NAMs) that can realize computer X-ray tomography (CT)/magnetic resonance imaging (MRI)/Raman multimodal imaging, be loaded with positively and negatively charged drugs and test their imaging ability, drug loading and biological safety. The microspheres have strong attenuation performance for CT, high T2 relaxation for MRI and good sensitivity for surface enhanced Raman spectroscopy (SERS). At the same time, our microspheres can also load the positively charged drug, doxorubicin (DOX), and negatively charged drug Cisplatin. One gram of NAMs can hold 168 mg DOX or 126 mg Cisplatin, which has good drug loading and sustained-release capacity. Cell experiments also showed that the nano-assembled microspheres had good biocompatibility. Therefore, as multimodal developed drug loaded microspheres, nano assembled microspheres have great potential in TACE treatment of liver cancer.

Wing touching induces a defensive response in D. melanogaster. Here, the authors show that female flies change the defensive response during courtship and after mating. This switch is mediated by functional reconfiguration of a neural circuit in the ventral nerve cord.

The orbitofrontal cortex (OFC) encodes expected outcomes and plays a key role in outcome-guided behavior. The authors show here that the top-down projection from the OFC to the visual cortex drives visual associative learning by modulating the response gain of V1 neurons to non-relevant stimuli.

ObjectiveTo assess the efficacy and safety of transarterial Chemoembolization (TACE) combined with lenvatinib plus sintilimab in unresectable hepatocellular carcinoma (HCC).Patients and MethodsThe data of patients with unresectable HCC administered a combination therapy with TACE and lenvatinib plus sintilimab were retrospectively assessed. Patients received lenvatinib orally once daily 2 weeks before TACE, followed by sintilimab administration at 200 mg intravenously on day 1 of a 21-day therapeutic cycle after TACE. The primary endpoints were objective response rate (ORR) and duration of response (DOR) by the modified RECIST criteria.ResultsMedian duration of follow-up was 12.5 months (95%CI 9.1 to 14.8 months). ORR was 46.7% (28/60). Median DOR in confirmed responders was 10.0 months (95%CI 9.0-11.0 months). Median progression-free survival (PFS) was 13.3 months (95%CI 11.9-14.7 months). Median overall survival (OS) was 23.6 months (95%CI 22.2-25.0 months).ConclusionsTACE combined with lenvatinib plus sintilimab is a promising therapeutic regimen in unresectable hepatocellular carcinoma.

The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is analogous to the statistical inference that has been extensively studied in the natural language processing field, where recent developments of recurrent neural networks have found many successes. We wonder whether these neural networks, the gated recurrent unit (GRU) networks in particular, reflect how the brain solves the contingency problem. Therefore, we build a GRU network framework inspired by the statistical learning approach of NLP and test it with four exemplar behavior tasks previously used in empirical studies. The network models are trained to predict future events based on past events, both comprising sensory, action, and reward events. We show the networks can successfully reproduce animal and human behavior. The networks generalize the training, perform Bayesian inference in novel conditions, and adapt their choices when event contingencies vary. Importantly, units in the network encode task variables and exhibit activity patterns that match previous neurophysiology findings. Our results suggest that the neural network approach based on statistical sequence learning may reflect the brain's computational principle underlying flexible and adaptive behaviors and serve as a useful approach to understand the brain.

Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes. However, understanding the progression risk and differentiating long- and short-term survivors cannot be achieved by analyzing data from a single modality due to the heterogeneity of disease. Using a scientifically developed and tested deep-learning approach that leverages aggregate information collected from multiple repositories with multiple modalities (e.g., mRNA, DNA Methylation, miRNA) could lead to a more accurate and robust prediction of disease progression. Here, we propose an autoencoder based multimodal data fusion system, in which a fusion encoder flexibly integrates collective information available through multiple studies with partially coupled data. Our results on a fully controlled simulation-based study have shown that inferring the missing data through the proposed data fusion pipeline allows a predictor that is superior to other baseline predictors with missing modalities. Results have further shown that short- and long-term survivors of glioblastoma multiforme, acute myeloid leukemia, and pancreatic adenocarcinoma can be successfully differentiated with an AUC of 0.94, 0.75, and 0.96, respectively.

Abstract Background Adjustable single-incision mini-sling (SIMS) is a new category of SIMS for stress urinary incontinence (SUI). The aim of this study was to compare the efficacy and safety of adjustable single-incision mini-sling with other slings. Methods Literature search in databases such as Pubmed, and Conchrane Library was performed up to December, 2015. The outcomes including cure rate, operation time, postoperative pain score and complications were reanalyzed. The pooled relative risk (RR) and mean difference (MD) with their 95% confidence interval (95% CI) were calculated by RevMan v5.0. Results Eight studies with 1093 SUI female patients were included. There was no significant difference between adjustable SIMS and other slings (transobturator slings and MiniArc) in patients subjective cure rate and objective cure rate. In addition, adjustable SIMS was associated with a significantly shorter operative time and lower postoperative pain score when comparing adjustable SIMS with transobturator tape (MD = − 1.35; 95%CI: -2.24 to − 0.46, P = 0.003). For the complications, there was also no significant difference between adjustable SIMS and transobturator slings. Conclusions Adjustable SIMS had equally efficacy for SUI compared with transobturator slings and MiniArc. However, the significantly shorter operative time and lower postoperative pain score than transobturator tape supported the clinical application of adjustable SIMS.

Detecting the foreground region of interest (ROI) for video sequences is an important issue both for video codecs and monitoring systems. In this paper, we propose a flow-process-based method to detect foreground ROI using four steps: global motion compensation, motion block extraction, multi-layer segmentation, and model updating. The former two procedures extract the foreground motion blocks and form a motion mask, and the latter two procedures remove the pixels belonging to the background inside the motion mask and update the color distributions of the background model. In addition, a block-based to pixel-based detection scheme is proposed to allow detection flexibility. Another benefit of the proposed method is that it can be embedded in video codecs for real-time ROI detection and encoding. Experimental results demonstrate that our method achieves improved performance in terms of both detection accuracy and time consumption.

The quality fluctuation of video is significant in human visual system, and thus, many rate control schemes are widely developed in the area of video communication. In recent years, researchers show more interests in region of interest (ROI)-based encoding, and it is widely applied in the latest video codecs, such as HEVC and VP9. This paper presents a new rate control scheme for ROI mode coding based on discrete fourier transform coefficient model and radial basis function neuron network. A new R-D model is proposed by classifying blocks into different depth, ROI groups, and so on. Then, rate and distortion are described based on the Laplacian distribution model using mathematical ways. A machine learning approach is induced to enhance the accuracy of the distortion estimation. By utilizing the new R-D model, a new rate control scheme is designed for ROI mode coding from the group of picture layer to coding unit layer. By comparisons with other rate control approaches, the proposed one has a better result in terms of visual quality, R-D performance, bitrate accuracy, and so on. Hence, it outperforms the conventional schemes especially for sequences with obvious ROI details.

We consider the problem of delivering region of interest (ROI)-coded mobile video streams using limited radio resources. Under the conditions of limited bandwidth and time-varying channel status, the goal is to optimize the transmission latency, while ensuring the quality of the ROI parts. Multi-homing support enables the terminals to establish multiple connections for transmission performance improvement. In this paper, we propose a novel framework for ROI-based video transmission in heterogeneous wireless networks with multi-homed terminals. The framework contains the modules of ROI detector and frame splitter, where macroblocks are categorized based on ROI detection and encapsulated into transforming units. It also includes a channel monitor that keeps track of the status of each communication path and sends feedback signals to the streaming controller for packet-scheduling control; a deep learning method is proposed for channel status prediction. To address the delivery problem, we propose a scheduling approach based on the formulated network model and the rate-distortion model. The scheduling method makes a tradeoff between the transmission delay and the distortion. It also guarantees that packets with ROI content are delivered on paths with sufficient bandwidths and low loss rates. Through comparisons with other scheduling methods, we find that the proposed scheme outperforms the other scheduling methods in terms of improving the quality (peak signal-to-noise ratio), balancing the end-to-end delay, and maintaining the playback fluency.

Residual stress distribution induced by multistep fabrication processes is of great significance in reliable design of critical engineering components, such as powder metallurgy Ni-based superalloy turbine discs. By using neutron diffraction and 2D-detector X-ray diffraction, the through-thickness residual stress distribution was determined in fabricated turbine discs via isothermal forging, solution treatment and aging treatment. The experimental results demonstrated that residual stress exhibited typical thermal stress distribution while plastic deformation-induced surface stress was relieved surprisingly after solution treatment. After aging treatment, the reduction of matrix supersaturation after long-time diffusion contributed to increase of compressive radial stress in the rim of disc. By introducing temperature-dependent supplementary phase transformation latent heat into the thermo-mechanical model, overall residual stress distribution after solution treatment was simulated by the finite element method. Numerical simulated stress distribution is in agreement with the experimental results, with the deviation

Reinforcement learning has been widely used in explaining animal behavior. In reinforcement learning, the agent learns the value of the states in the task, collectively constituting the task state space, and uses the knowledge to choose actions and acquire desired outcomes. It has been proposed that the orbitofrontal cortex (OFC) encodes the task state space during reinforcement learning. However, it is not well understood how the OFC acquires and stores task state information. Here, we propose a neural network model based on reservoir computing. Reservoir networks exhibit heterogeneous and dynamic activity patterns that are suitable to encode task states. The information can be extracted by a linear readout trained with reinforcement learning. We demonstrate how the network acquires and stores task structures. The network exhibits reinforcement learning behavior and its aspects resemble experimental findings of the OFC. Our study provides a theoretical explanation of how the OFC may contribute to reinforcement learning and a new approach to understanding the neural mechanism underlying reinforcement learning.

As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency.

Detecting the Region of Interest (ROI) for video clips is a significant and useful technique both in video codecs and surveillance/monitor systems. In this paper, a new model-based detection method is designed which suits video compression codecs by proposing two models: an “inter” and “intra” model. The “inter” model exploits the motion information represented as blocks by global motion compensation approaches while the “intra” model extracts the objects details through objects filtering and image segmentation procedures. Finally, the detection results are formed through a new clustering with fine-tune approach from the “intra” model assisted with the “inter” model. Experimental results show that the proposed method fits well for real-time video codecs and it achieves a good performance both on detection precision and on computing time. In addition, the proposed method is versatile for a wide range of surveillance videos with different characteristics.