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In recent years, Aksu apple has become popular with consumers because of its unique texture and taste. At present, maintaining quality during storage is the key problem with the apples in the Aksu region. 1-Methylcyclopropene (1-MCP) can delay fruit senescence, so is widely used in fruit preservation. However, many factors affect the preservation effect of 1-MCP. The effects of 1-MCP concentration (0 µL·L−1, 1 µL·L−1, 3 µL·L−1, 5 µL·L−1, and 8 µL·L−1) and postharvest application time (0, 1 and 2 d after harvest) on the quality of stored apple were studied. It was found that 1 µL·L−1 1-MCP was more beneficial in improving the quality of stored apples, reduced the respiration intensity and decay rate, increased the fruit firmness and total soluble solid content, and reduced the relative content of ester volatile aroma components. In addition, 1-MCP treatment applied at different postharvest times also affected the sensory quality and flavor of apples. The effect of 1-MCP treatment immediately after harvest was better. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) stabilization with molecular glues plays a crucial role in drug discovery, albeit with significant challenges. In this study, we propose a dual-site approach, targeting the PPI region and its dynamic surroundings. We conduct molecular dynamics simulations to identify critical sites on the PPI that stabilize the cyclin-dependent kinase 12 - DNA damage-binding protein 1 (CDK12-DDB1) complex, resulting in further cyclin K degradation. This exploration leads to the creation of LL-K12-18, a dual-site molecular glue, which enhances the glue properties to augment degradation kinetics and efficiency. Notably, LL-K12-18 demonstrates strong inhibition of gene transcription and anti-proliferative effects in tumor cells, showing significant potency improvements in MDA-MB-231 (88-fold) and MDA-MB-468 cells (307-fold) when compared to its precursor compound SR-4835. These findings underscore the potential of dual-site approaches in disrupting CDK12 function and offer a structural insight-based framework for the design of cyclin K molecular glues.Protein-protein interactions (PPIs) stabilization with molecular glues plays a crucial role in drug discovery. Here, the authors propose a dual-site approach for the design of clyclin K molecular glues, targeting the PPI region and its dynamic surroundings, and report a dual-site molecular glue for the cyclin-dependent kinase 12 - DNA damage-binding protein 1 complex, resulting in further cyclin K degradation. [ABSTRACT FROM AUTHOR]

Using elaboration of likelihood model, this study examines how syntactic structure (structural and lexical simplicity) and nonverbal cues (smiling and professional attire) interact to influence crowdfunding performance. By studying 209 crowdfunding projects, we show that both structural simplicity and lexical simplicity are positively related to crowdfunding performance. Further, professional attire strengthens the simplicity and performance relationship. However, smiling shows mixed effects. [ABSTRACT FROM AUTHOR]

An affective brain-computer interface (aBCI) can detect affective states based on brain signals and might assist people in improving their emotion regulation abilities. However, individual differences in emotional brain patterns make cross-subject emotion identification extremely challenging. Traditional supervised single-subject classification schemes require considerable calibration samples from new individuals to train subject-dependent models. Individuals are easily fatigued with long-term EEG collection processes, which may affect performance in subsequent online experiments. In this study, we propose a real-time aBCI system using domain-fusion-based multisource style transfer mapping (DF-MS-STM) to detect positive, neutral, and negative emotional states without the need for additional training sessions. Sixteen subjects participated in our online experiments to test the performance of our aBCI system and an average online prediction accuracy of 72.17±12.25% was obtained for three-class emotion recognition tasks in the last three experimental sessions. Our proposed algorithm significantly outperformed numerous baseline methods in terms of cross-subject emotion classification. In addition, we identified distinct brain patterns in response to different emotional stimuli based on the results of event-related spectral perturbation (ERSP) analyses. These neural patterns might provide new insights for emotional brain mechanistic studies and related aBCIs. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts' waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing's historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of "1 h rainfall intensity water logging index" and "the waterlogging risk index in historical and cultural districts" were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts. [ABSTRACT FROM AUTHOR]

As the evolutionary ancestor of Cas12 nuclease, the transposon (IS200/IS605)-encoded TnpB proteins act as compact RNA-guided DNA endonucleases. To explore their evolutionary diversity and potential as genome editors, we screened TnpBs from 64 annotated IS605 members and identified 25 active in Escherichia coli, of which three are active in human cells. Further characterization of these 25 TnpBs enables prediction of the transposon-associated motif (TAM) and the right-end element RNA (reRNA) directly from genomic sequences. We established a framework for annotating TnpB systems in prokaryotic genomes and applied it to identify 14 additional candidates. Among these, ISAam1 (369 amino acids (aa)) and ISYmu1 (382 aa) TnpBs demonstrated robust editing activity across dozens of genomic loci in human cells. Both RNA-guided genome editors demonstrated similar editing efficiency as SaCas9 (1,053 aa) while being substantially smaller. The enormous diversity of TnpBs holds potential for the discovery of additional valuable genome editors. Compact genome editors were discovered by mining a transposon library. [ABSTRACT FROM AUTHOR]

The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation models, enhancing our understanding of physical and biochemical processes in nature. However, the limitations in swath width and orbit altitude prevent a single sensor from providing LST data with both high spatial and high temporal resolution. To tackle this challenge, the unmixing-based spatiotemporal fusion model (STFM) offers a promising solution by integrating data from multiple sensors. In these models, the surface reflectance is decomposed from coarse pixels to fine pixels using the linear unmixing function combined with fractional coverage. However, when downsizing LST through STFM, the linear mixing hypothesis fails to adequately represent the nonlinear energy mixing process of LST. Additionally, the original weighting function is sensitive to noise, leading to unreliable predictions of the final LST due to small errors in the unmixing function. To overcome these issues, we selected the U-STFM as the baseline model and introduced an updated version called the nonlinear U-STFM. This new model incorporates two deep learning components: the Dynamic Net (DyNet) and the Chang Ratio Net (RatioNet). The utilization of these components enables easy training with a small dataset while maintaining a high generalization capability over time. The MODIS Terra daytime LST products were employed to downscale from 1000 m to 30 m, in comparison with the Landsat7 LST products. Our results demonstrate that the new model surpasses STARFM, ESTARFM, and the original U-STFM in terms of prediction accuracy and anti-noise capability. To further enhance other STFMs, these two deep-learning components can replace the linear unmixing and weighting functions with minor modifications. As a deep learning-based model, it can be pretrained and deployed for online prediction. [ABSTRACT FROM AUTHOR]

Phenotypic transformation of vascular smooth muscle cells (VSMCs) plays a crucial role in abdominal aortic aneurysm (AAA) formation. CARMN, a highly conserved, VSMC-enriched long noncoding RNA (lncRNA), is integral in orchestrating various vascular pathologies by modulating the phenotypic dynamics of VSMCs. The influence of CARMN on AAA formation, particularly its mechanisms, remains enigmatic. Our research, employing single-cell and bulk RNA sequencing, has uncovered a significant suppression of CARMN in AAA specimens, which correlates strongly with the contractile function of VSMCs. This reduced expression of CARMN was consistent in both 7- and 14-day porcine pancreatic elastase (PPE)-induced mouse models of AAA and in human clinical cases. Functional analyses disclosed that the diminution of CARMN exacerbated PPE-precipitated AAA formation, whereas its augmentation conferred protection against such formation. Mechanistically, we found CARMN's capacity to bind with SRF, thereby amplifying its role in driving the transcription of VSMC marker genes. In addition, our findings indicate an enhancement in CAMRN transcription, facilitated by the binding of NRF2 to its promoter region. Our study indicated that CARMN plays a protective role in preventing AAA formation and restrains the phenotypic transformation of VSMC through its interaction with SRF. Additionally, we observed that the expression of CARMN is augmented by NRF2 binding to its promoter region. These findings suggest the potential of CARMN as a viable therapeutic target in the treatment of AAA. [ABSTRACT FROM AUTHOR]

Much attention has been paid to compound a ZIF-8 antibacterial agent with textiles for recycling. However, antibacterial inactivation may be caused by dissolution of the ZIF-8 antibacterial agent in postfinishing technology. In this work, a nondissolution-bonded T-ZIF-8-PEG-TPU photocatalyst was prepared by covalent immobilization, and then, bonded antibacterial fibers T-ZIF-8-PEG-TPU were fabricated by electrospinning technology. The microstructure of the fibers was regulated by optimizing the spinning solution concentration, PEG content, and conductive salt content. The optimum process parameters are 22 wt % spinning solution, 3.3 wt % PEG, and 0.5 wt % ZnCl2 conductive salt. The intrinsic relationship between the fiber microstructure and photocatalytic antibacterial property, moisture permeability, and hydrophobicity was investigated. The antibacterial rate of the fiber membrane against Staphylococcus aureus reaches 100% after 30 min of visible light irradiation, and the antibacterial rate of Escherichia coli is 99.6% after visible light irradiation for 120 min. The fiber membrane has a standard grade I moisture permeability rate and a certain waterproof effect. It has potential applications in the field of health protection such as medical dressings and medical protective products. [ABSTRACT FROM AUTHOR]

Purpose: Based on a theoretical framework of ambidexterity in technology sourcing beyond organizational and technological boundaries, this study aims to explore how start-ups balance technology sourcing in organizational ambidexterity (TSOA) to produce high venture performance. Design/methodology/approach: A questionnaire survey is distributed among start-ups in the science and technology park of a university in eastern China, producing a total of 45 valid responses. The fuzzy-set approach to qualitative comparative analysis is used in this study. Findings: The findings show that start-ups achieve high venture performance through external technology sourcing (external exploration and exploitation) in the initial stage. In the growth stage, start-ups adopt external and internal technology sourcing (internal exploration and exploitation) to produce high venture performance. The technology sourcing strategy in ambidextrous activity for start-ups is punctuated equilibrium and evolving from the external ambidexterity to internally and externally coordinate ambidexterity at entrepreneurial stages. Originality/value: This study creatively adopts configuration-based thinking to investigate how to balance TSOA for high venture performance, extending the literature on technology sourcing and contributing to the balance theory of exploration and exploitation. [ABSTRACT FROM AUTHOR]

With the increased concern over environment protection, cellulose acetate (CA) has drawn great interests as an alternative for packaging material due to its biodegradability and abundant resources; whereas, the poor antistatic property and thermal conductivity restrict its application in packaging. In this work, we proposed a simple but effective strategy to produce high performance graphene nanoplatelet (GNP)/CA composite films via the consecutive homogenization and solvent casting processes. Relying on the spontaneous absorption of CA during homogenization, the GNP/CA produced shows an excellent dispersibility in the N,N-Dimethylformamide (DMF) solution and many fewer structural defects compared with GNPs alone. As a result, the composite films obtained exhibit simultaneously and significantly enhanced antistatic, heat dissipative and mechanical properties compared with CA. Specifically, the GNP/CA composite with the optimal formula has promising overall performances (namely, surface resistivity of 3.33 × 107 Ω/sq, in-plane thermal conductivity of 5.359 W (m · K) , out-of-plane thermal conductivity of 0.785 W (m · K) , and tensile strength of 37.1 MPa). Featured by its promising overall properties, simple production processes and biodegradability, the as-prepared GNP/CA composite film shows a great potential for application in packaging. [ABSTRACT FROM AUTHOR]

Forest fires are one of the most severe natural disasters facing global ecosystems, as they have a significant impact on ecological security and social development. As remote sensing technology has developed, burned areas can now be quickly extracted to support fire monitoring and post-disaster recovery. This study focused on monitoring forest fires that occurred in Chongqing, China, in August 2022. The burned area was identified using various satellite images, including Sentinel-2, Landsat8, Environmental Mitigation II A (HJ2A), and Gaofen-6 (GF-6). The burned area was extracted using visual interpretation, differenced Normalized Difference Vegetation Index (dNDVI), and differenced Normalized Burnup Ratio (dNBR). The results showed that: (1) The results of the three monitoring methods were very consistent, with a coefficient of determination R2 > 0.96. (2) A threshold method based on the dNBR-extracted burned area was used to analyze fire severity, with moderate-severity fires making up the majority (58.05%) of the fires. (3) Different topographic factors had some influence on the severity of the forest fires. High elevation, steep slopes and the northwestern aspect had the largest percentage of burned area. [ABSTRACT FROM AUTHOR]

Through the more available acoustic information or the polarization information provided, vector sensor arrays outperform the scalar sensor arrays in accuracy of localization. However, the cost of a vector sensor array is higher than that of a scalar sensor array. To reduce the cost of a two-dimensional (2-D) vector sensor array, a hybrid T-shaped sensor array consisting of two orthogonal uniform linear arrays (ULAs) is proposed, where one ULA is composed of acoustic vector sensors and the other is composed of scalar sensors. By utilizing the cross-correlation tensor between the received signals from the two ULAs, two virtual uniform rectangular arrays (URAs) of acoustic vector sensors are obtained, and they can be combined into a larger URA. It is shown that a larger acoustic vector sensor URA with M2+1 degrees of freedom (DOFs) can be obtained from the specially designed T-shaped array with M acoustic vector sensors and 2M scalar sensors. Furthermore, by means of the proposed tensor model for the larger URA, the inter-sensor spacing can be allowed to exceed greatly a half-wavelength. Accordingly, the proposed method can achieve both a high DOF and a large array aperture. Simulation results show that the proposed method has a better performance in 2-D direction-of-arrival estimation than some existing methods under the same array cost. [ABSTRACT FROM AUTHOR]

Emotion regulation plays a vital role in human beings daily lives by helping them deal with social problems and protects mental and physical health. However, objective evaluation of the efficacy of emotion regulation and assessment of the improvement in emotion regulation ability at the individual level remain challenging. In this study, we leveraged neurofeedback training to design a real-time EEG-based brain-computer interface (BCI) system for users to effectively regulate their emotions. Twenty healthy subjects performed 10 BCI-based neurofeedback training sessions to regulate their emotion towards a specific emotional state (positive, negative, or neutral), while their EEG signals were analyzed in real time via machine learning to predict their emotional states. The prediction results were presented as feedback on the screen to inform the subjects of their immediate emotional state, based on which the subjects could update their strategies for emotion regulation. The experimental results indicated that the subjects improved their ability to regulate these emotions through our BCI neurofeedback training. Further EEG-based spectrum analysis revealed how each emotional state was related to specific EEG patterns, which were progressively enhanced through long-term training. These results together suggested that long-term EEG-based neurofeedback training could be a promising tool for helping people with emotional or mental disorders. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. Method: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images. Results: Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM. Conclusion: The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME. [ABSTRACT FROM AUTHOR]

In recent years, the retrieval and validation of remotely-sensed leaf area index (LAI) products over complex land surfaces have received much attention due to the high-precision land surface model simulations and applications in global climate change. However, most of these related researches mainly focus on coarse resolution products. This is because few products have been specifically designed for solving the problems derived from complex land surfaces in mountain areas until now. MuSyQ LAI is a new product derived from Gaofen-1 (GF-1) satellite data. This product is characterized with a temporal resolution of 10 days and a spatial resolution of 16 m. As is well known, high-resolution products have less uncertainties because of the homogeneities of sub-pixel. Therefore, to evaluate the precision and uncertainty of MuSyQ LAI, an up-scaling strategy was employed here to validate MuSyQ LAI for three mountain regions in Southwest China. The validation strategy can be divided into three parts. First, a regression model was built by in situ LAI measured by LAI-2200 and the normalized difference vegetation index (NDVI) from unmanned aerial vehicle (UAV) images to obtain a 0.5 m resolution LAI map. Second, an up-scaled LAI map with a spatial resolution consistent with MuSyQ LAI was calculated by the pixel-averaging method from the UAV-based LAI map. Third, the MuSyQ LAI was validated by the up-scaled UAV-based LAI in pixel scale. Simultaneously, the sources of uncertainty were analyzed and compared from the view of data source, retrieval model, and scale effects. The results suggested that MuSyQ LAI in the study areas are significantly underestimated by 53.69% due to the complex terrain and heterogeneous land cover. There are three main reasons for the underestimation. The differences between GF-1 reflectance and UAV-based reflectance employed to estimate LAI are the largest factors for the validation results, even accounting for 61.47% of the total bias. Subsequently, the scale effects led to about 28.44% bias. Last but not least, the models employed to retrieve LAI contributed merely 10.09% uncertainties to the total bias. In conclusion, the accuracy of MuSyQ LAI still has a large space to be improved from the view of reflectance over complex terrain. This study is quite important for applications of MuSyQ LAI products and also provides a reference for the improvement and application of other high-resolution remotely sensed LAI products. [ABSTRACT FROM AUTHOR]

Conductive polymer composites based strain sensors have been widely investigated in recent years. However, the integration of a wide strain detection range and high sensitivity is still challenging. In this work, a double-percolation structured fiber strain sensor based on a carbon nanotube (CNT)/styrene butadiene styrene (SBS) conductive phase and a thermoplastic polyurethane (TPU) insulating phase (CNT/SBS@TPU) was fabricated using a simple melt mixing and fused filament fabrication strategy. The effect of SBS: TPU ratios on the electrical and sensing properties of samples was investigated, at 1 wt% CNT loading. It was shown that the SBS: TPU ratios significantly affected the performance of sensors. The highest conductivity of 1.08 × 10–3 S/m was obtained for SBS/5-TPU/5 (SBS: TPU = 5: 5). Due to the squeezing and interruptive effects of the insulating phase, SBS/5-TPU/5 showed a high sensitivity of 2,645,866, wide detection range (0.2%-92% strain), high linearity (R2 = 0.95 at 0–10% strain) and excellent stability (2000 cycles). Additionally, the SBS/5-TPU/5 sensor could effectively monitor varying frequencies within 0.05–1 Hz and showed prospective applications in human motion monitoring and tactile recognition. This work provides a route for manufacturing high-performance fiber strain sensors used for wearable intelligent devices and human-computer interaction. [ABSTRACT FROM AUTHOR]

Packet loss-resilient and security are two major challenges faced by real-time audio transmission over IP networks. Due to the capability of recovering the signal from a small set of samplings and the randomness in the acquisition process, compressive sensing (CS) has a vast prospect in dealing with these problems. In this paper, we propose a secure and packet loss-resistant real-time audio transmission framework (CS-SPT) based on the principle of CS. Inspired by the interleaving technique, an ultralow complexity scrambling matrix was adopted in the proposed CS-SPT to improve its packet loss-resilient capability by increasing the information redundancy. Moreover, the energy of ciphertext is homogenized using a diffusion operation. Experimental results show that compared with existing methods, the proposed CS-SPT not only improves the packet loss-resilient ability significantly but also can resist several major attacks, such as COAs and KPAs. [ABSTRACT FROM AUTHOR]

How to effectively regulate the spread of Internet public opinion from multisubject is the focus that governments around the world need to face. From the cross perspective of multilayer network and multisubject association, this paper studies the diffusion mechanism of Internet public opinion, constructs the multilayer network of Internet public opinion and its diffusion model, and then systematically analyzes the evolution characteristics of Internet public opinion diffusion. The main conclusions of the study are as follows: it is easier to trigger the spread of Internet public opinion by establishing associations among netizens in a preferred way. When the probability of random connection in different layers increases synchronously, the diffusion intensity of Internet public opinion shows a gradual downward trend. The intensity of government intervention has the greatest impact on the diffusion intensity of Internet public opinion. When the factors of netizens, Internet platform, and government interact, it can effectively reduce the intensity and scope of Internet public opinion diffusion. Once the media participate, it will improve the complexity of Internet public opinion diffusion. The government's intervention in Internet public opinion is a "double-edged sword," which needs to set the intervention intensity according to different subjects and their factor characteristics. This study can provide a theoretical reference for formulating strategies to adjust Internet public opinion diffusion from the cross perspective of multilayer network and multisubject association. [ABSTRACT FROM AUTHOR]

Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients. [ABSTRACT FROM AUTHOR]

In recent years, neuroimaging studies have remarkably demonstrated the presence of cognitive motor dissociation in patients with disorders of consciousness (DoC). These findings accelerated the development of brain–computer interfaces (BCIs) as clinical tools for behaviorally unresponsive patients. This article reviews the recent progress of BCIs in patients with DoC and discusses the open challenges. In view of the practical application of BCIs in patients with DoC, four aspects of the relevant literature are introduced: consciousness detection, auxiliary diagnosis, prognosis, and rehabilitation. For each aspect, the paradigm design, brain signal processing methods, and experimental results of representative BCI systems are analyzed. Furthermore, this article provides guidance for BCI design for patients with DoC and discusses practical challenges for future research. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of optimizing the power allocation at each time slot for energy-harvesting sensors in an IoT system, where each sensor transmits its observation via a coherent multiple access channel, and thus the observation vector received at the fusion center (FC) becomes a compressed version of the original observations. Our goal is to minimize the number of transmissions required by the high-fidelity reconstruction of the original observations in the FC. In this scenario, the power allocation, coupling with the channel effect, constitute an effective measurement matrix in compressive sensing. Because its performance decides the requirement on the number of transmissions, the goal can be achieved through constructing the measurement matrix as good as possible, or equivalently, solving the power optimization allocation problem, subject to the available energy constraints at each sensor. However, the sensors can only obtain unreliable and intermittent available energy, so that the traditional performance metric, i.e., mutual coherence (MC), of the measurement matrix cannot be directly used to guide the optimization, because an “equal-norm columns” assumption is implicitly required, but not satisfied in our scenario due to the available energy constraints. Moreover, this optimization problem is also non-convex. To overcome these obstacles, we first carry out a distortion analysis based on the generalized MC, which abandons the “equal-norm columns” assumption. The theoretical results indicate that the measurement matrix construction can be formulated as an optimization problem that not only minimizes the MC, but also minimizes the maximum and maximizes the minimum of the column norms of the effective matrix. We further transform this problem into a sequence of surrogate convex problems and iteratively find the solution. Numerical results show that the proposed framework improves the tradeoffs between reconstruction accuracy and the number of transmissions over various power allocation strategies. In some cases, where other strategies achieve a probability of exact recovery of below 0.7, the proposed framework can achieve a more than 0.9 probability. [ABSTRACT FROM AUTHOR]

Multimodal emotion recognition in conversational videos (ERC) develops rapidly in recent years. To fully extract the relative context from video clips, most studies build their models on the entire dialogues which make them lack of real-time ERC ability. Different from related researches, a novel multimodal emotion recognition model for conversational videos based on reinforcement learning and domain knowledge (ERLDK) is proposed in this paper. In ERLDK, the reinforcement learning algorithm is introduced to conduct real-time ERC with the occurrence of conversations. The collection of history utterances is composed as an emotion-pair which represents the multimodal context of the following utterance to be recognized. Dueling deep-Q-network (DDQN) based on gated recurrent unit (GRU) layers is designed to learn the correct action from the alternative emotion categories. Domain knowledge is extracted from public dataset based on the former information of emotion-pairs. The extracted domain knowledge is used to revise the results from the RL module and is transformed into other dataset to examine the rationality. The experimental results on datasets show that ERLDK achieves the state-of-the-art results on weighted average and most of the specific emotion categories. [ABSTRACT FROM AUTHOR]

Grant-free non-orthogonal multiple access (NOMA) has recently received wide attention for reducing signaling overhead and transmission latency in massive machine-type communications (mMTC). In grant-free NOMA systems, user activity and data (UAD) has to be detected, which is challenging in practice. As an emerging technique, compressive sensing (CS) shows great promise in solving this problem by exploiting the inherent sparsity nature of user activity. This paper proposes to use the weighted $\ell _{2, 1}$ minimization (WL21M) to jointly detect UAD in realistic dynamic scenarios. At first, the average recoverability of the WL21M is analyzed. This analysis reveals the fact that the WL21M can improve the detection performance by means of an appropriate weighting and the incorporation of intrinsic temporal correlation. Motivated by the analysis, a collaborative hierarchical match pursuit (C-HiMP) algorithm is proposed for dynamic UAD detection. In the C-HiMP, a sequence of WL21M problems are solved in the subspaces spanned by all of the components in the hierarchical estimated support sets, where the weights are collaboratively updated by the solutions in previous time slots so that an attractive self-correction capacity is obtained. Simulation results demonstrate that the proposed C-HiMP can obtain significant performance improvements, in terms of detection accuracy and computational complexity, compared with several state-of-the-art CS-based detection algorithms. [ABSTRACT FROM AUTHOR]

Covert attention aids us in monitoring the environment and optimizing performance in visual tasks. Past behavioral studies have shown that covert attention can enhance spatial resolution. However, electroencephalography (EEG) activity related to neural processing between central and peripheral vision has not been systematically investigated. Here, we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from 0.75° to 13.90°, as well as tasks involving overt attention and no attention. EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked potentials (SSVEPs) for attention evaluation. We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance. Moreover, more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention. Furthermore, offline classification of overt attention, covert attention, and no attention yielded an average accuracy of 91.42%. This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces (BCIs) that allow people to communicate with choices simply by shifting their attention to them. [ABSTRACT FROM AUTHOR]

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l 2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification. [ABSTRACT FROM AUTHOR]

Recognizing human emotions based on electroencephalogram (EEG) signals has received a great deal of attentions. Most of the existing studies focused on offline analysis, and real-time emotion recognition using a brain computer interface (BCI) approach remains to be further investigated. In this paper, we proposed an EEG-based BCI system for emotion recognition. Specifically, two classes of video clips that represented positive and negative emotions were presented to the subjects one by one, while the EEG data were collected and processed simultaneously, and instant feedback was provided after each clip. Ten healthy subjects participated in the experiment and achieved a high average online accuracy of 91.5 $\pm$ ± 6.34 percent. The experimental results demonstrated that the subjects emotions had been sufficiently evoked and efficiently recognized by our system. Clinically, patients with disorder of consciousness (DOC), such as coma, vegetative state, minimally conscious state and emergence minimally conscious state, suffer from motor impairment and generally cannot provide adequate emotion expressions. Consequently, doctors have difficulty in detecting the emotional states of these patients. Therefore, we applied our emotion recognition BCI system to patients with DOC. Eight DOC patients participated in our experiment, and three of them achieved significant online accuracy. The experimental results show that the proposed BCI system could be a promising tool to detect the emotional states of patients with DOC. [ABSTRACT FROM AUTHOR]

From a novel historical angle, this paper examines a fundamental question of why bribery is so prevalent throughout Vietnamese history, why it has become a culturally acceptable norm, and what historical factors attributed to its prevalence at all levels, individual, firm, and country. We examine the moderating effect of Vietnam's historical eras on the relationship between the ethical judgement of bribery and the likelihood of bribing. Moreover, the concept of strategic firm competition is introduced to the ongoing debate on whether large firms are more likely to bribe and whether there is a link between market openness and bribery. [ABSTRACT FROM AUTHOR]

Fusion of multimodal features is a momentous problem for video emotion recognition. As the development of deep learning, directly fusing feature matrixes of each mode through neural networks at feature level becomes mainstream method. However, unlike unimodal issues, for multimodal analysis, finding the correlations between different modal is as important as discovering effective unimodal features. To make up the deficiency in unearthing the intrinsic relationships between multimodal, a novel modularized multimodal emotion recognition model based on deep canonical correlation analysis (MERDCCA) is proposed in this letter. In MERDCCA, four utterances are gathered as a new group and each utterance contains text, audio and visual information as multimodal input. Gated recurrent unit layers are used to extract the unimodal features. Deep canonical correlation analysis based on encoder-decoder network is designed to extract cross-modal correlations by maximizing the relevance between multimodal. The experiments on two public datasets show that MERDCCA achieves the better results. [ABSTRACT FROM AUTHOR]

Motion capture is a cross-cutting application field developed in recent years, which comprises electronics, communications, control, computer graphics, ergonomics, navigation, and other disciplines. The accurate application of basketball technical movements in the basketball game is very important. Therefore, it is of great significance to capture and standardize athletes' movements and improve their training. Unfortunately, there are numerous issues in traditional classroom teaching that largely helps to train the athletes. To solve the issues of traditional basketball classroom teaching, a virtual simulation system for students' sports training is designed in this paper. Firstly, the information of basketball dribbling movement is captured and simulated in three dimensions. Secondly, we compare it with the standard database to judge the irregularities of athletes' movements, and carry out digital processing on athletes' movements and skill improvements statistics in combination with system functions. Thirdly, we set up a gradual training cycle. Finally, the Kinect-based capture technology is adopted to obtain the activity information of different joints of the human body. Through processing the motion data, relevant motion analysis data are fed to the established motion model, to realize the comparative analysis of motion pictures. In our experiments, we observed better training of the physical education. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient. Therefore, this article mainly studies image recommendation algorithms based on deep neural networks in social networks. First, according to the time stamp information of the dataset, the interaction records of each user are sorted by the closest time. Then, some feature vectors are created via traditional feature algorithms like LBP, BGC3, RTU, or CNN extraction. For image recommendation, two LSTM neural networks are established, which accept these feature vectors as input, respectively. The compressed output of the two sub-ESTM neural networks is used as the input of another LSTM neural network. The multilayer regression algorithm is adopted to randomly sample some network nodes to obtain the cognitive information of the nodes sampled in the entire network, predict the relationship between all nodes in the network based on the cognitive information, and perform low sampling to achieve relationship prediction. The experiments show that proposed LSTM model together with CNN feature vectors can outperform other algorithms. [ABSTRACT FROM AUTHOR]

Stereoelectroencephalography (SEEG) signals can be obtained by implanting deep intracranial electrodes. SEEG depth electrodes can record brain activity from the shallow cortical layer and deep brain structures, which is not achievable through other recording techniques. Moreover, SEEG has the advantage of a high signal-to-noise ratio (SNR). Therefore, it provides a potential way to establish a highly efficient brain-computer interface (BCI) and aid in understanding human brain activity. In this study, we implemented a P300-based BCI using SEEG signals. A single-character oddball paradigm was applied to elicit P300. To predict target characters, we fed the feature vectors extracted from the signals collected by five SEEG contacts into a Bayesian linear discriminant analysis (BLDA) classifier. Thirteen epileptic patients implanted with SEEG electrodes participated in the experiment and achieved an average online spelling accuracy of 93.85%. Moreover, through single-contact decoding analysis and simulated online analysis, we found that the SEEG-based BCI system achieved a high performance even when using a single signal channel. Furthermore, contacts with high decoding accuracies were mainly distributed in the visual ventral pathway, especially the fusiform gyrus (FG) and lingual gyrus (LG), which played an important role in building P300-based SEEG BCIs. These results might provide new insights into P300 mechanistic studies and the corresponding BCIs. [ABSTRACT FROM AUTHOR]

In recent years, deep neural networks have achieved great success in many fields, such as computer vision and natural language processing. Traditional image recommendation algorithms use text-based recommendation methods. The process of displaying images requires a lot of time and labor, and the time-consuming labor is inefficient. Therefore, this article mainly studies image recommendation algorithms based on deep neural networks in social networks. First, according to the time stamp information of the dataset, the interaction records of each user are sorted by the closest time. Then, some feature vectors are created via traditional feature algorithms like LBP, BGC3, RTU, or CNN extraction. For image recommendation, two LSTM neural networks are established, which accept these feature vectors as input, respectively. The compressed output of the two sub-ESTM neural networks is used as the input of another LSTM neural network. The multilayer regression algorithm is adopted to randomly sample some network nodes to obtain the cognitive information of the nodes sampled in the entire network, predict the relationship between all nodes in the network based on the cognitive information, and perform low sampling to achieve relationship prediction. The experiments show that proposed LSTM model together with CNN feature vectors can outperform other algorithms. [ABSTRACT FROM AUTHOR]

A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent model and eliminates subject-specific calibration for new users. However, building subject-independent BCIs remains difficult because electroencephalography (EEG) is highly noisy and varies by subject. In this study, we propose an invariant pattern learning method based on a convolutional neural network (CNN) and big EEG data for subject-independent P300 BCIs. The CNN was trained using EEG data from a large number of subjects, allowing it to extract subject-independent features and make predictions for new users. We collected EEG data from 200 subjects in a P300-based spelling task using two different types of amplifiers. The offline analysis showed that almost all subjects obtained significant cross-subject and cross-amplifier effects, with an average accuracy of more than 80%. Furthermore, more than half of the subjects achieved accuracies above 85%. These results indicated that our method was effective for building a subject-independent P300 BCI, with which more than 50% of users could achieve high accuracies without subject-specific calibration. [ABSTRACT FROM AUTHOR]

Event-related potential (ERP) is bioelectrical activity that occurs in the brain in response to specific events or stimuli, reflecting the electrophysiological changes in the brain during cognitive processes. ERP is important in cognitive neuroscience and has been applied to brain-computer interfaces (BCIs). However, because ERP signals collected on the scalp are weak, mixed with spontaneous electroencephalogram (EEG) signals, and their temporal and spatial features are complex, accurate ERP detection is challenging. Compared to traditional neural networks, the capsule network (CapsNet) replaces scalar-output neurons with vector-output capsules, allowing the various input information to be well preserved in the capsules. In this study, we expect to utilize CapsNet to extract the discriminative spatial-temporal features of ERP and encode them in capsules to reduce the loss of valuable information, thereby improving the ERP detection performance for BCI. Therefore, we propose ERP-CapsNet to perform ERP detection in a BCI speller application. The experimental results on BCI Competition datasets and the Akimpech dataset show that ERP-CapsNet achieves better classification performances than do the state-of-the-art techniques. We also use a decoder to investigate the attributes of ERPs encoded in capsules. The results show that ERP-CapsNet relies on the P300 and P100 components to detect ERP. Therefore, ERP-CapsNet not only acts as an outstanding method for ERP detection, but also provides useful insights into the ERP detection mechanism. [ABSTRACT FROM AUTHOR]

Global warming has been reported to cause reductions in crop yields. However, it was suggested that warming temperature might benefit crop productivity in some cool areas at high latitude. In this study, we conducted a 17‐year field experiment (2002–2018) on spring wheat in Inner Mongolia. Temperature changes during each growth stage of spring wheat were investigated. Responses of spring wheat yield to temperature changes during the specific growing stages were evaluated. Average annual maximum temperature (Tmax) and minimum temperature (Tmin) significantly increased over the past 17 years. However, Tmax did not show obvious increase trend during spring wheat growing seasons (p = 0.0672). Furthermore, Tmax also had no distinct change before or after anthesis. Tmin significantly increased during the whole growing season, as well as in pre‐ and post‐anthesis stages. Correlation analysis indicated that Tmax in the entire growing season and post‐anthesis did not affect spring wheat yield, but high Tmax during pre‐anthesis can improve grain yield. The Tmin during the life cycle and pre‐anthesis both had positive relationship with grain yield. Moreover, elevated temperature from seedling to stem elongation can benefit tiller formation and thus increasing spike number, which contributed to the significant yield increase (p = 0.0093). Overall, climate warming affect spring wheat yield in cool area, and increasing temperature that was below the optimum temperature can benefit wheat productivity. [ABSTRACT FROM AUTHOR]

Achieving high classification performance in electroencephalogram (EEG)-based brain–computer interfaces (BCIs) often entails a large number of channels, which impedes their use in practical applications. Despite the previous efforts, it remains a challenge to determine the optimal subset of channels in a subject-specific manner without heavily compromising the classification performance. In this article, we propose a new method, called spatiotemporal-filtering-based channel selection (STECS), to automatically identify a designated number of discriminative channels by leveraging the spatiotemporal information of the EEG data. In STECS, the channel selection problem is cast under the framework of spatiotemporal filter optimization by incorporating a group sparsity constraints, and a computationally efficient algorithm is developed to solve the optimization problem. The performance of STECS is assessed on three motor imagery EEG datasets. Compared with state-of-the-art spatiotemporal filtering algorithms using full EEG channels, STECS yields comparable classification performance with only half of the channels. Moreover, STECS significantly outperforms the existing channel selection methods. These results suggest that this algorithm holds promise for simplifying BCI setups and facilitating practical utility. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate alterations to brain activity and functional connectivity in patients with tinnitus, exploring neural features in the transition from acute to chronic phantom perception. Twenty‐four patients with acute tinnitus, 23 patients with chronic tinnitus, and 32 healthy controls were recruited. High‐density electroencephalography (EEG) was used to explore changes in brain areas and functional connectivity in different groups. When compared with healthy subjects, acute tinnitus patients had a significant reduction in superior frontal cortex activity across all frequency bands, whereas chronic tinnitus patients had a significant reduction in the superior frontal cortex at beta 3 and gamma frequency bands as well as a significant increase in the inferior frontal cortex at delta‐band and superior temporal cortex at alpha 1 frequency band. When compared to the chronic tinnitus group, the acute tinnitus group activity was significantly increased in the middle frontal and parietal gyrus at the gamma‐band. Functional connectivity analysis showed that the chronic tinnitus group had increased connections between the parahippocampus gyrus, posterior cingulate cortex, and precuneus when compared with the healthy group. Alterations of local brain activity and connections between the parahippocampus gyrus and other nonauditory areas appeared in the transition from acute to chronic tinnitus. This indicates that the appearance and development of tinnitus is a dynamic process involving aberrant local neural activity and abnormal connectivity in multifunctional brain networks. [ABSTRACT FROM AUTHOR]

Here, a piezoresistive sensor was fabricated by combining graphene and the veins of Magnolia henryi. The sensor displayed a high sensitivity, with a gauge factor of 238.09, and a wide and appropriate linear working range, with a linear coefficient of 99.4% when subjected to mechanical strain from 0% to 12%. It also showed a long-term repeatability of 0.0078% per second when subjected to 1000 cycles of periodic mechanical strain. The resistance only fluctuated between 154 ohm and 161 ohm when the environmental temperature changed from 30 °C to 70 °C. These results show that the multi-level structure of the sensor played a critical role in the piezoresistive mechanism. The performance of the developed sensor was ascribed to the veins' multi-level structure at the microscale and the doping of boron atoms from the aforementioned veins into the graphene crystal structure at the atomic scale, thereby altering the structure of its electronic energy bands and electron density. This introduced holes and formed a p-type semiconductor. [ABSTRACT FROM AUTHOR]

Background: The role of bone tissue engineering is to regenerate tissue using biomaterials and stem cell-based approaches. Combination of two or more cell types is one of the strategies to promote bone formation. Endothelial progenitor cells (EPCs) may enhance the osteogenic properties of mesenchymal stem cells (MSCs) and promote bone healing; this study aimed to investigate the possible mechanisms of EPCs on promoting osteogenic differentiation of MSCs. Methods: MSCs and EPCs were isolated and co-cultured in Transwell chambers, the effects of EPCs on the regulation of MSC biological properties were investigated. Real-time PCR array, and western blotting were performed to explore possible signaling pathways involved in osteogenesis. The expression of osteogenesis markers and calcium nodule formation was quantified by qRT-PCR, western blotting, and Alizarin Red staining. Results: Results showed that MSCs exhibited greater alkaline phosphatase (ALP) activity and increased calcium mineral deposition significantly when co-cultured with EPCs. The mitogen-activated protein kinase (MAPK) signaling pathway was involved in this process. p38 gene expression and p38 protein phosphorylation levels showed significant upregulation in co-cultured MSCs. Silencing expression of p38 in co-cultured MSCs reduced osteogenic gene expression, protein synthesis, ALP activity, and calcium nodule formation. Conclusions: These data suggest paracrine signaling from EPCs influences the biological function and promotes MSCs osteogenic differentiation. Activation of the p38MAPK pathway may be the key to enhancing MSCs osteogenic differentiation via indirect interactions with EPCs. [ABSTRACT FROM AUTHOR]

Background: Pulmonary hypertension is common in chronic kidney disease (CKD) patients. However, the prognostic value of pulmonary hypertension in Chinese predialytic CKD patients is rarely reported. We evaluated the relevant factors and prognostic value of pulmonary hypertension in CKD patients. Methods: This retrospective cohort study enrolled 1092 predialytic patients from The Third Affiliated Hospital of Sun Yat-Sen University from May 1st, 2011, to December 31st, 2016. Data of interest were retrieved from electronic medical records. Pulmonary hypertension was defined as pulmonary arterial systolic pressure (PASP) ≥ 35 mmHg by echocardiology. All participants were followed from the date of the first echocardiography examination. The primary endpoints were all-cause mortality and cardiovascular mortality. The secondary endpoint was end-stage renal disease (ESRD) defined as starting renal replacement therapy. Results: The prevalence of pulmonary hypertension was 15.9% in the study population. For CKD stage 1, 2, 3a, 3b, 4 and 5, the prevalence was 6.0%, 9.6%, 17.2%, 13.3%, 20.7% and 26.6%, respectively. Older age, lower left ventricular ejection fraction, anemia and higher pulse pressure were independently associated with pulmonary hypertension in CKD patients. In multivariate Cox regression analysis, pulmonary hypertension was the independent risk factor for cardiovascular mortality, but not of all-cause mortality and ESRD. Conclusions: Pulmonary hypertension is not rare in early CKD patients. Patients with older age, anemia, higher pulse pressure and compromised heart function were more likely to comorbid pulmonary hypertension. Pulmonary hypertension maybe a sign of worse cardiovascular outcome in CKD patients. [ABSTRACT FROM AUTHOR]