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Video-based learning has successfully become an effective alternative to face-to-face instruction. In such situations, modeling or predicting learners' flow experience during video learning is critical for enhancing the learning experience and advancing learning technologies. In this study, we set up an instructional scenario for video learning according to flow theory. Different learning states, i.e., boredom, fit (flow), and anxiety, were successfully induced by varying the difficulty levels of the learning task. We collected learners' electrocardiogram (ECG) signals as well as facial video, upper body posture and speech data during the learning process. We proposed classification models of the learning state and regression models to predict flow experience by utilizing different combinations of the data from the four modalities. The results showed that the model performance of learning state recognition was significantly improved by the decision-level fusion of multimodal data. By using the selected important features from all data sources, such as the standard deviation of normal to normal R-R intervals (SDNN), high-frequency (HF) heart rate variability and mel-frequency cepstral coefficients (MFCC), the multilayer perceptron (MLP) classifier gave the best recognition result of learning states (i.e., mean AUC of 0.780). The recognition accuracy of boredom, fit (flow) and anxiety reached 47.48%, 80.89% and 47.41%, respectively. For flow experience prediction, the MLP regressor based on the fusion of two modalities (i.e., ECG and posture) achieved the optimal prediction (i.e., mean RMSE of 0.717). This study demonstrates the feasibility of modeling and predicting the flow experience in video learning by combining multimodal data. [ABSTRACT FROM AUTHOR]

This article introduces a new method for balancing the state of charge (SOC) in a dual-bus battery system architecture. The system consists of multiple battery cells or modules connected in series to provide high voltage output. Additionally, low-power flyback converters are connected in series with each battery cell or module at the inputs, and their outputs are connected in parallel to provide lower voltage output. The SOC balancing algorithm ensures that the lower voltage output remains at a desired reference value by adjusting the average duty cycle of each power converter, while also balancing the rate of charge or discharge of each battery cell or module. This SOC balancing process does not affect the normal operation of the high voltage power output. In other words, the dual output (high voltage and low voltage) of the battery system can function independently, and the balancing current only flows through the low voltage power path. Experimental results from a prototype are provided and discussed to validate the proposed dual-bus battery system and controller. [ABSTRACT FROM AUTHOR]

Surmounting obstacles during continuously climbing in a complex environment is an important issue for pole-climbing robots. An obstacle-surmounting strategy is presented for a pole-climbing robot. The force and moment applied on the pole-climbing robot in static status were analyzed, and the analysis of pole-climbing robot’s upward vertical climbing was conducted. The climbing execution has four steps: loosening the lower gripper, curling up, striding forward, and clamping the upper gripper. To obtain the information of obstacle crossing accurately, the obstacle-surmounting conditions were analyzed in detail. We modeled the striding linkage with thickness and obtained the Denavit–Hartenberg coordinates of each vertex. The model of the grippers with thickness was proposed and the Denavit–Hartenberg coordinates of each vertex of the grippers were obtained. Then single-step negotiating an obstacle and multistep negotiating an obstacle were proposed. Experiments were conducted to verify the effectiveness of the obstacle-surmounting strategy.

Food self-sufficiency has long been regarded as essential for understanding and managing urban and regional food systems; however, few studies have examined the food self-sufficiency of megacity regions within a comprehensive framework that distinguishes different types of agricultural land (i.e., arable land, horticultural landscapes, and waters). To fill these gaps, we took the Pearl River Delta as a case study and quantified the foodsheds of different types of agricultural land by calculating the land footprint of food consumption. On this basis, food self-sufficiency is defined as the ratio of available and required agricultural area for regional food demand. The results indicated that the self-sufficiency level provided by the arable land in the Pearl River Delta is low and cannot realize self-sufficiency at the regional and urban levels. The horticultural landscapes can provide self-sufficiency at the regional level, whereas the regions with water cannot, as their foodsheds extend over the boundary of the Pearl River Delta. For arable land, establishing a localized regional food system requires expanding the foodshed size. These findings provide evidence that megacity regions may face increasing difficulties in achieving self-sufficiency in the near future. This research can improve policymakers' understanding of the sustainability and resilience of regional food systems in megacity regions. [ABSTRACT FROM AUTHOR]

The popularity of science videos is critical for the dissemination of knowledge, and predicting the popularity of a video is a hot topic among researchers. The existing research is mainly based on the videos' content (e.g., theme) or video-related external information (e.g., comments). However, videos with different popularity can bring different learning states and viewing experiences (emotional awakening, flow experiences). In this paper, we utilize participants' learning states when they watch videos to predict the popularity of the videos, depending on two modal indicators, i.e., contact physiological indicators recorded by a Biopac MP150 polygraph and noncontact gesture indices recorded by a Kinect V2 body camera which can obtain data of head position. We propose two classification prediction models with each modality of indicator and filter out the indicators that make sense for modeling. Results show that the meaningful indicators in the physiological modality through stepwise logistic regression are the standard deviation of normal to normal R-R intervals (SDNN) and high-frequency heart rate variability (HF). We find that participants had higher SDNN and lower HF when watching science video with high popularity (compared with low popularity one), and the accuracy rate of classification model is 81.6%. In a same way, the selected meaningful indicators in the gesture aspect are the maximum, the standard deviation (SD) of distance between the participants' head and Kinect. We find that the maximum and SD of head distance are smaller when participants studied highly popular science videos (contrary to the less popular videos) and the accuracy rate is 73.7%. Combining the four indicators for modeling by the direct input method, the accuracy rate is 78.9%, and the SD of head distance is probably the most important indicator for predicting popularity of videos. These results indicate that it is feasible to predict video popularity by learning states. [ABSTRACT FROM AUTHOR]

The bacteria pathogen Xanthomonas oryzae pv. oryzae (Xoo) infects rice and causes the severe disease of rice bacteria blight. As the central regulator of the salic acid (SA) signaling pathway, NPR1 is responsible for sensing SA and inducing the expression of pathogen-related (PR) genes in plants. Overexpression of OsNPR1 significantly increases rice resistance to Xoo. Although some downstream rice genes were found to be regulated by OsNPR1, how OsNPR1 affects the interaction of rice-Xoo and alters Xoo gene expression remains unknown. In this study, we challenged the wild-type and OsNPR1-OE rice materials with Xoo and performed dual RNA-seq analyses for the rice and Xoo genomes simultaneously. In Xoo-infected OsNPR1-OE plants, rice genes involved in cell wall biosynthesis and SA signaling pathways, as well as PR genes and nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes, were significantly upregulated compared to rice variety TP309. On the other hand, Xoo genes involved in energy metabolism, oxidative phosphorylation, biosynthesis of primary and secondary metabolism, and transportation were repressed. Many virulence genes of Xoo, including genes encoding components of type III and other secretion systems, were downregulated by OsNPR1 overexpression. Our results suggest that OsNPR1 enhances rice resistance to Xoo by bidirectionally regulating gene expression in rice and Xoo. [ABSTRACT FROM AUTHOR]

Drought stress is one of major factors resulting in maize yield loss. The roles of abscisic acid (ABA) have been widely studied in crops in response to drought stress. However, more attention is needed to identify key ABA-related proteins and also gain deeper molecular insights about drought stress in maize. Based on this need, the physiology and proteomics of the ABA-deficient maize mutant vp5 and its wild-type Vp5 under drought stress were examined and analyzed. Malondialdehyde content increased and quantum efficiency of photosystem II decreased under drought stress in both genotypes. However, the magnitude of the increase or decrease was significantly higher in vp5 than in Vp5. A total of 7051 proteins with overlapping expression patterns among three replicates in the two genotypes were identified by Multiplex run iTRAQ-based quantitative proteomic and liquid chromatography-tandem mass spectrometry methods, of which the expression of only 150 proteins (130 in Vp5, 27 in vp5) showed changes of at least 1.5-fold under drought stress. Among the 150 proteins, 67 and 60 proteins were up-regulated and down-regulated by drought stress in an ABA-dependent way, respectively. ABA was found to play active roles in regulating signaling pathways related to photosynthesis, oxidative phosphorylation (mainly related to ATP synthesis), and glutathione metabolism (involved in antioxidative reaction) in the maize response to drought stress. Our results provide an extensive dataset of ABA-dependent, drought-regulated proteins in maize plants, which may help to elucidate the underlying mechanisms of ABA-enhanced tolerance to drought stress in maize.

Silver has been widely used for surface modification to prevent implant-associated infections. However, the inherent cytotoxicity of silver greatly limited the scope of its clinical applications. The construction of surfaces with both good antibacterial properties and favorable cytocompatibility still remains a challenge. In this study, a structurally homogeneous dopamine–silver (DA/Ag) nanocomposite was fabricated on the implant surface to balance the antibacterial activity and cytocompatibility of the implant. The results show that the DA/Ag nanocomposites prepared under the acidic conditions (pH = 4) on the titanium surface are homogeneous with higher Ag+ content, while an obvious core (AgNPs)–shell (PDA) structure is formed under neutral (pH = 7) and alkaline conditions (pH = 10), and the subsequent heat treatment enhanced the stability of PDA–AgNPs nanocomposite coatings on porous titanium. The antibacterial test, cytotoxicity test, hypodermic implantation and osteogenesis test revealed that the homogeneous PDA–AgNPs nanocomposite coating achieved the balance between the antibacterial ability and cytocompatibility, and had the best outcomes for soft tissue healing and bone formation around the implants. This study provides a facile strategy for preparing silver-loaded surfaces with both good antibacterial effect and favorable cytocompatibility, which is expected to further improve the therapeutic efficacy of silver composite-coated dental implants. [ABSTRACT FROM AUTHOR]

Multi-part Dedicated Flow-line (MDF) is widely used to produce discrete products for high volumes with low varieties. Since frequent changes occur in customers' demand and rapid upgrading of machines, the line needs to reconfigure timely to remain competitive. A lot of researchers investigate how to reconfigure the line when facing these changes, and most of them adopts small adjustments such as machine resettlement or buffer size rearrangements to react. However, when great changes occur in product' machining process or machine's performance, it's time to reconfigure the line in a higher level by reselecting machining process of products and/or reselecting machines composition of the line. To fill this research gap, the paper proposes an effective Multi-part Dedicated Flow-line Reconfiguration Model (MDFRM) which can effectively react to great changes occur in products and machines. By optimal selection of Machining Process Path (MPP) for every product and machines composition for the new line, MDFRM can reconfigure MDF with objective of minimizing the products' cost and capital investment. To solve this problem, an Enhanced Equilibrium Optimizer (EEO) with three layers of code and three steps to handling boundaries and constraints based on Equilibrium Optimizer (EO) is presented. The proposed new model and the designed method are validated by numerical experiments and real case study. This paper will significantly help decision-makers make a scientific decision to optimally reconfigure MDF. [ABSTRACT FROM AUTHOR]

Fog manufacturing (FMfg) is an emerging real-life cloud manufacturing (CMfg) paradigm based on digital twin models, which perceive finely dynamic processes and provide on-demand optimization services. Service composition as a core plays a vital role in achieving the optimization goal. Service composition issues in actual CMfg are mostly multitask problems that need considering dynamic factors. Otherwise, the planned production process will deviate significantly from actual situations. However, such multitask service composition (MTSC) models have scarcely been studied. Thus, this article formulates a multiobjective MTSC model considering the crucial resource competition process between multitasks under multiple practical constraints (MPC). Then, to solve the MTSC–MPC, this article develops an adaptive multiobjective whale optimization algorithm (AMOWOA) containing well-designed strategies. Numerical experiments and application cases verify that AMOWOA outperforms the comparison algorithms. AMOWOA continually can optimize and adaptively adjust the service composition process based on the crucial manufacturing resource available time under actual constraints in FMfg environment. [ABSTRACT FROM AUTHOR]

Fas-associated protein with death domain (FADD) is a pivotal adaptor protein that functions in mediating cell death, cell cycle regulation, and particular in innate immunity by the main death receptors. In this study, a second FADD gene in sea cucumber Apostichopus japonicus (termed AjFADD-2) was cloned and its potential function in the innate responses was analyzed. The full-length cDNA of AjFADD-2 consists of 2405 bp and contains a 47 bp 5′-untranslated region (UTR), a 1629 bp 3′-UTR, and a 729 bp ORF encoding 242 amino acids. AjFADD-2 possesses two conserved domains of intracellular N-terminal death effector domain and an extracellular C-terminal death domain, which is different from the first cloned FADD gene in A. japonicus that only possesses the death domain. AjFADD-2 was examined in all sampled six tissues and was significantly induced in V. splendidus -challenged sea cucumbers and LPS-exposed coelomocytes. Subcellular localization detection showed that AjFADD-2 was primarily observed in the coelomocyte cytoplasm, and transferred to the nucleus post V. splendidus challenge. Consistently, AjFADD-2 knockdown significantly inhibited apoptosis in V. splendidus -challenged sea cucumbers and LPS-exposed coelomocytes. Taken together, our results provided evidence that AjFADD functioned as a positive regulator of coelomocytes apoptosis in response to pathogen V. splendidus challenge. • A novel FADD homolog was cloned and characterized in sea cucumber Apostichopus japonicus. • AjFADD-2 was mainly distributed in the cytoplasm and transferred into the coelomocytes nucleus post pathogen challenge. • AjFADD-2 knockdown significantly decreased the apoptosis levels in vivo and in vitro. [ABSTRACT FROM AUTHOR]

An ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC–MS/MS) method was developed to determine the fenofibric acid (FA) in human plasma and applied to a pharmacokinetic study of fenofibrate tablet (Lipanthyl® supra, 160 mg) on Chinese subjects which had not been reported. Bezafibrate was used as an internal standard (IS), and the plasma samples were precipitated by methanol. Multiple reaction monitoring (MRM) mode was used to quantitatively analyzed FA m/z 317.2 → 230.7 and the IS m/z 360.0 → 274.0 in the electrospray ionization (ESI) negative interface. The calibration curves were linear over the range of 50–30,000 ng/mL (r2 ≥ 0.996). The intra-day and inter-day precision (coefficient of variation, CV%) was less than 2.7 and 2.5%, respectively. The accuracy (relative error, RE%) ranged from −4.5 to 6.9%. The average recovery was higher than 86.2%, and the matrix effect was between 95.32 and 110.55%. The simple, rapid, and selectivity method was successfully applied to the pharmacokinetic study of fenofibrate tablets on Chinese subjects. [ABSTRACT FROM AUTHOR]

At present, with the emergence and development of cloud manufacturing (CMfg), the scale of services in CMfg platforms increases rapidly which provide the same or familiar functionality but different performance. Large-scale cloud service composition and optimization (CSCO) problems is one of the key issues for the implementation of CMfg. To deal with this NP-hard problem, a novel hybrid algorithm called Bee-Colony Simplex method hybrid Algorithm (ABCSA) for CSCO problems is proposed in this paper, which employs both the Simplex method and chaotic and global best guided strategy. The random-evolve Simplex method is proposed to maintain the algorithm work efficiently to keep the population diversity and avoid premature convergence. The global best guided and chaos searching strategy is proposed to avoid local optimization. To evaluate the effectiveness and efficiency, simulation and analysis of the experiments are carried out, and the results clearly prove the superior performance of ABCSA over existing intelligent optimization algorithms in the CSCO problems. [ABSTRACT FROM AUTHOR]

Global warming poses a serious threat to crops. Calcium‐dependent protein kinases (CDPKs)/CPKs play vital roles in plant stress responses, but their exact roles in plant thermotolerance remains elusive. Here, we explored the roles of heat‐induced ZmCDPK7 in thermotolerance in maize. ZmCDPK7‐overexpressing maize plants displayed higher thermotolerance, photosynthetic rates, and antioxidant enzyme activity but lower H2O2 and malondialdehyde (MDA) contents than wild‐type plants under heat stress. ZmCDPK7‐knockdown plants displayed the opposite patterns. ZmCDPK7 is attached to the plasma membrane but can translocate to the cytosol under heat stress. ZmCDPK7 interacts with the small heat shock protein sHSP17.4, phosphorylates sHSP17.4 at Ser‐44 and the respiratory burst oxidase homolog RBOHB at Ser‐99, and upregulates their expression. Site‐directed mutagenesis of sHSP17.4 to generate a Ser‐44‐Ala substitution reduced ZmCDPK7's enhancement of catalase activity but enhanced ZmCDPK7's suppression of MDA accumulation in heat‐stressed maize protoplasts. sHSP17.4, ZmCDPK7, and RBOHB were less strongly upregulated in response to heat stress in the abscisic acid‐deficient mutant vp5 versus the wild type. Pretreatment with an RBOH inhibitor suppressed sHSP17.4 and ZmCDPK7 expression. Therefore, abscisic acid‐induced ZmCDPK7 functions both upstream and downstream of RBOH and participates in thermotolerance in maize by mediating the phosphorylation of sHSP17.4, which might be essential for its chaperone function. [ABSTRACT FROM AUTHOR]

The prediction of chemical oxygen demand (COD) by ultraviolet–visible absorption spectrum is a common method. Many researchers use the absorbance at the characteristic wavelength to establish COD prediction models. However, selecting the characteristic wavelength is a problem. In this paper, the extreme values of absorption spectrum change rate, was proposed as a new characteristic parameter to determine the characteristic wavelengths. On this basis, a novel hybrid strategy for detecting COD in surface water was proposed. We first proposed to combine the first derivative method with the permutation entropy method (FDPE) to determine the characteristic wavelengths. Then we used partial least square (PLS) to establish a COD prediction model. Experimental results demonstrated the linear correlation coefficient ( R 2 ) of the FDPE_PLS was above 0.99 without turbidity interference. Secondly, a dual-wavelength method (DWM) was proposed to determine the turbidity values. The DWM used slopes of absorbance values at 400 nm and 600 nm to predict the turbidity values. Compared with the single-wavelength method, the DWM improves the measurement accuracy of turbidity. Finally, a new turbidity compensation method was proposed to compensate for the interference in the first derivative spectrum. After compensation, FDPE_PLS can predict COD concentrations accurately, whose R 2 was 0.99. [ABSTRACT FROM AUTHOR]

The low permeability of coal seams is the primary difficulty for gas production. Hydraulic cutting is one of the most effective technologies for increasing permeability and stimulating gas production. This paper investigates the hydraulic cutting styles in boreholes and the optimal equivalent borehole diameter after discharging the pulverized coal particles. This paper introduces the hydraulic cutting equipment and cutting styles and determines that the cutting cylinder is the most suitable style for a soft coal seam that is prone to gas outburst. The numerical simulation suggests that the radius of the stress-relaxed range increases quickly when the equivalent borehole radius is small, and the rate slows when the borehole radius becomes large. Then the result was verified by a field experiment in Pingmei coalfield. The gas drainage flowrate and stress-relaxed range greatly increased after initially outputting 2 tons of coal from the borehole, whereas the second outputting 2 tons of coal did not appreciably increase the stress-relaxed range and the flowrate. Too much or too little coal discharge is not optimal, and the borehole radius is recommend to be between 0.3 m and 0.4 m after the hydraulic cutting considering the pros and cons in Pingmei coalfield. The results are helpful for the engineering application of the hydraulic cutting technology, for increasing coal seam permeability, and for stimulating gas production. The method for finding the optimal coal discharge in this paper is also helpful for other coal mines. [ABSTRACT FROM AUTHOR]