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Facial recognition technology (FRT) has become a significant topic in CSCW owing to widespread adoption and related criticisms: the use of FRT is often considered an assault on privacy or a kind of neo-phrenology. This discussion has revolved around uses of FRT for identification, which are often non-voluntary, in particular for surveillance wherein people are (by and large) unwittingly recognized by FRT systems. At the same time, we have also seen a rise of forms of FRT for verification (e.g., passport control or Apple's Face ID), which typically are overt and interactive. In this paper we study an interactive FRT system used for guest check-in at a hotel in China. We show how guests and bystanders engage in 'self-disciplining work' by controlling their facial (and bodily) comportment both to get recognized and at times to avoid recognition. From our analysis we discuss the role of preparatory and remedial work, as well as dehumanization, and the importance of CSCW paying closer attention to the significance of interactional compliance for people using and bystanding facial recognition technologies.

Polydopamine (PDA)-based Fenton agents attract increasing attention in tumor photothermal-enhanced chemodynamic therapy (CDT) due to their good biocompatibility and excellent loading capacity. However, PDA tends to eliminate the Fenton reaction-generated hydroxyl radical (∙OH) by its strong reducibility, which is an intractable hinder to the efficacy of CDT that need to be solved. Herein, a kind of mesoporous PDA-gold-manganese dioxide (MPDA-Au-MnO

The fuel consumption, productivity and operability are three important performance criteria for construction machinery. Better fuel economy sometimes means reduced productivity while higher productivity requires more powerful powertrain and thus consumes more fuel. It is challenging to compromise these performances in a single machine. However for working sites with multiple machines running together, it is possible to balance fleet fuel consumption and productivity with proper site operation strategy. In this study a site operation strategy for wheel loader/truck loading and transportation cycle is developed. A comprehensive criterion balancing the trade-off between fleet fuel consumption and fleet productivity – the fleet operating cost, is proposed. The strategy takes both fleet configuration parameter (number of wheel loaders and trucks) and operating parameter (wheel loader cycle time) into considerations. The fleet fuel consumption is calculated based on vehicle powertrain models instead of empirical estimation. The fleet fuel consumption, productivity and fleet operating cost with different numbers of wheel loader/truck and wheel loader cycle times are obtained. The optimal number of wheel loader/truck and wheel loader cycle time to achieve minimum fleet operating cost are identified.

Electric system planning with high variable renewable energy (VRE) penetration levels has attracted great attention world-wide. Electricity production of VRE highly depends on the weather conditions and thus involves large variability, uncertainty, and low-capacity credit. This gives rise to significant challenges for power system planning. Currently, many solutions are proposed to address the issue of operational flexibility inadequacy, including flexibility retrofit of thermal units, inter-regional transmission, electricity energy storage, and demand response (DR). Evidently, the performance and the cost of various solutions are different. It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source. In this study, the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed. Two types of DRs, namely interrupted DR and transferred DR, were modeled. Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization. Clustered unit commitment constraints for accommodating variability of renewables were incorporated. Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.

Since the 1990s, China has formalized its short-term foreign aid training for foreign officials and technological personnel. This type of training often lasts for 21 days and participants from invited countries arrive in China for a period of condensed study, with all fees covered by the Chinese government. By the end of 2009, China had organized more than 4000 short-term training programs for over 120,000 personnel from more than 50 countries. Along with the establishment of the Twelfth Five-Year Plan and the constructional needs of the Belt and Road Initiative, China has gradually increased the export of its cultural products in foreign aid training. Surprisingly, such national-scale training is largely omitted from current scholarly research. Employing the “fragmented authoritarianism” model, we look at the administrative structure of China’s foreign aid training and provide rudimentary research into the field.

Solar power is an important renewable energy resource and acts as a major contributor to replacing fossil fuel generators and reducing carbon emissions. However, the intermittent power output due to the uncertain solar irradiance significantly challenges the economic integrations of solar generation within the existing power system, which calls for effective forecasting methods to improve the solar prediction accuracy. In this paper, a novel improved radial basis function neural network model is proposed and applied in forecasting the short-term solar power generation. A recent proposed meta-heuristic approach named competitive swarm optimization is adopted to train the non-linear and linear parameters of the radial basis function neural network model. The proposed model has been validated in nonlinear benchmark functions and then employed in forecasting the solar power generation of a real-world case study in the Netherlands. Numerical results demonstrate that the proposed competitive swarm optimized radial basis function neural network model could obtain higher accuracy compared to other counterparts and thus provides a useful tool for solar power forecasting.

The development of Global Energy Interconnection (GEI) is essential for supporting a wide range of basic data resources. The Global Energy Interconnection Development and Cooperation Organization has established a comprehensive data center covering six major systems. However, methods for accurately describing and scientifically evaluating the credibility of the massive amount of GEI data remain underdeveloped. To address this lack of such methods, a GEI data credibility quantitative evaluation model is proposed here. An evaluation indicator system is established to evaluate data credibility from multiple perspectives and ensure the comprehensiveness and impartiality of evaluation results. The Cloud Model abandons the hard division of comments to ensure objectivity and accuracy in evaluation results. To evaluate the suitability of the proposed method, a case analysis is conducted, wherein the proposed method demonstrates sufficient validity and feasibility. Keywords: Global Energy Interconnection, Data credibility evaluation, Cloud Model

T helper 17 (Th17) cells have a pathogenic effect in many autoimmune diseases. Inhibition of Th17 cells can alleviate the inflammatory damage in autoimmune diseases. Our previous study found that microRNA-590-3p (miR-590-3p) was involved in the differentiation of Th17 cells in systemic lupus erythematosus (SLE). Here, we demonstrated that an increase in Th17 cells was correlated with low expression of miR-590-3p in patients with SLE and in lupus mice. Upregulation of miR-590-3p reduced the differentiation and promoted apoptosis of Th17 cells. Subsequent experiments demonstrated that miR-590-3p promoted apoptosis in Th17 cells by inhibiting autophagy. Autophagy-related 7 (Atg7) was the direct target of miR-590-3p that blocked the autophagy pathway. Finally, treatment of MRL/lpr mice with miR-590-3p agomir ameliorated lupus nephritis and skin lesions. Our work revealed that miR-590-3p inhibited Th17 cells by suppressing autophagy and that increased miR-590-3p expression was able to ameliorate the clinical symptoms of lupus. Therefore, miR-590-3p may be a promising therapeutic target for SLE and other Th17 cell-dependent autoimmune diseases.

T helper 17 (Th17) cells are characterized by the secretion of the IL-17 cytokine and are essential for the immune response against bacterial and fungal infections. Despite the beneficial roles of Th17 cells, unrestrained IL-17 production can contribute to immunopathology and inflammatory autoimmune diseases, including multiple sclerosis, rheumatoid arthritis, and inflammatory bowel disease. Although these diverse outcomes are directed by the activation of Th17 cells, the regulation of Th17 cells is incompletely understood. The discovery that microRNAs (miRNAs) are involved in the regulation of Th17 cell differentiation and function has greatly improved our understanding of Th17 cells in immune response and disease. Here, we provide an overview of the biogenesis and function of miRNA and summarize the role of miRNAs in Th17 cell differentiation and function. Finally, we focus on recent advances in miRNA-mediated dysregulation of Th17 cell fate in autoimmune diseases.

The optimal allocation of global energy resources is a cardinal direction of the energy system, and the forecasting of renewable energy generation power prediction is the basis of energy interconnection. In power forecasting, it is necessary to integrate multiple information to improve the accuracy. Thus, bringing data that has no impact on the outcome and leads to computationally intensive data. Conventional principal component analysis (PCA) can downscale data with no temporal order. However, the data of meteorological parameters are with high temporal and spatial resolution. Therefore, it needs to be extended to complex principal component analysis (CPCA). Simultaneously, the unknown historical output of the regional power grid poses difficulties in predicting the generation power. This paper extracts the spatio-temporal features of renewable energy from typical local grids in the world based on CPCA. Through the interconnection relationship between different regions, this paper establishes a renewable energy generation prediction model. The validity and accuracy of the model are verified in MATLAB with domestic and foreign regional power grids as examples.

Northeast Asia is the most developed region in Asia with large energy demand, and plays an important role in the global economic development. Northeast Asia has been facing severe challenges in ensuring energy security, protecting the environment, and coping with climate change, because of their high dependency on fossil fuels and imports of oil from outside the region, and inverse distribution between energy resources and demand. In order to actively respond to climate change, promote the transition to low-carbon energy and sustainable development in the region, achieve the grand purpose of economic prosperity, social progress and ecological protection, this study is conducted with a focus on power grid interconnection in Northeast Asia. Based on the historical energy and power data in Northeast Asia, this paper studies the development trends of energy and power demand in future by combining qualitative and quantitative methods. Considering the distribution of clean energy bases, this paper proposes an energy interconnection scheme in Northeast Asia with high clear energy penetration scenario. To form the Asia-Europe energy interconnection, the construction of the Asia-Europe interconnection channels is briefly analyzed in this paper.

In recent years, global warming impact are becoming increasingly severe due to the dramatic green house emission and severe environmental problem. The large integration of PEV and RGs directly affect the supply and demand balance of power grid, which bring challenges to the secure and economic operation of power system. This study proposes a novel parallel social learning particle swarm optimization method for solving the large scale power system scheduling problem with significant integration of RGs and PEVs. The novel algorithm combines the real value and binary decision variables obtained by social learning particle swarm optimization algorithm, aiming to solve large scale mixed integer unit commitment problem considering charging and discharging management of PEV with large RGs integration. To verify the effectiveness of the proposed algorithm, numerical examples are analyzed for multi scale unit numbers and various cases of RGs and PEVs. The results show that the proposed parallel social learning particle swarm optimization method has superior performance in solving UC problems considering new energy sectors. In addition, the case studies shows that the integration of new energy sources and flexible demand side management of plug-in electric vehicles have great potentials to alleviate power grid load and bring considerable economic benefits.

After a blackout, it is essential to restore the blackout area rapidly to minimize possible losses. In parallel restoration, the blackout area is first partitioned into several subsystems, which will then be restored in parallel to accelerate the restoration process. In order to ensure restoration reliability, each subsystem should have enough generation power and satisfy a set of constraints before triggering the parallel restoration process. This paper models this as a constrained optimization problem and proposes a partitioning strategy to solve it in three steps. In the first step, some existing methods and expert knowledge are used for initialization of the partitioning process. The second step ensures the satisfaction of modeled constraints. The third step operates greedily to find suitable partitions for parallel restoration. The proposed strategy is implemented and evaluated on IEEE 39- and 118-bus power systems. Evaluation results show that it provides adequate subsystems for parallel restoration. Unlike some existing partitioning strategies, the proposed strategy can be used to partition a power system into multiple subsystems in a single execution.

OBJECTIVES: To establish and verify a simple noninvasive model based on the left gastric vein (LGV) to predict the grade of esophageal varices (EV) and high-risk EV (HEV), to facilitate clinical follow-up and timely treatment. METHODS: We enrolled 320 patients with B-viral cirrhosis. All patients underwent endoscopy, laboratory tests, liver and spleen stiffness (SS), and ultrasonography. HEV were analyzed using the χ2 test/t test and logistic regression in the univariate and multivariate analyses, respectively. EV grades were analyzed using the variance/rank-sum test and logistic regression. A prediction model was derived from the multivariate predictors. RESULTS: In the training set, multivariate analysis showed that the independent factors of different EV grades were SS, LGV diameter, and platelet count (PLT). We developed the LGV diameter-SS to PLT ratio index (LSPI) and LGV diameter/PLT models without SS. The area under the receiver operating characteristic curve of the LSPI for diagnosis of small EV, medium EV, large EV, and HEV was 0.897, 0.899, 0.853, and 0.954, respectively, and that of the LGV/PLT was 0.882, 0.890, 0.837, and 0.942, respectively. For the diagnosis of HEV, the negative predictive value was 94.07% when LSPI < 19.8 and the positive predictive value was 91.49% when LSPI > 23.0. The negative predictive value was 95.92% when LGV/PLT < 5.15, and the positive predictive value was 86.27% when LGV/PLT > 7.40. The predicted values showed similar accuracy in the validation set. DISCUSSION: Under appropriate conditions, the LSPI was an accurate method to detect the grade of EV and HEV. Alternatively, the LGV/PLT may also be useful in diagnosing the varices when condition limited.

The self-assembly of C 22 -tailed surfactants in ethanol (a common solvent favoured for both the alkyl tail and the head-group) induced by the addition of water (a non-solvent for the tail) has been studied using light-scattering measurements. These surfactants self-associated into core–shell micelles in ethanol upon the addition of water. However, the coronas of the micelles cannot envelop the cores particularly well since the cores were swollen by the solvent: these micelles were thereby further associated into secondary aggregates with an average radius of greater than 100 nm. Upon an increase in water content, the core contracted and the exposure of the alkyl tails was lessened: this reduced the extent of secondary aggregation. The surfactant with a more hydrophilic head-group promised the addition of more water. If the water content was relatively high, secondary aggregation may be avoided and complete core–shell micelles could be obtained upon the addition of some additives that filled the gaps in the corona. The results represented the development of a new self-assembly approach to traditional surfactants, by which the solubility thereof was enhanced owing to the use of a common solvent. Therefore, the self-assembly of those surfactants insoluble in water could be examined by this method.

The Global Energy Interconnection (GEI) is an original solution proposed by China to address the global challenges of clean energy, climate change and sustainable development. As an ambitious vision for energy industry, the construction of GEI will face a lot of challenges. The research on environment, economy, energy, electricity, politics, finance, and other fields involving GEI must be improved. Thus, a comprehensive platform is required to supply data and tools for GEI research. This paper proposes the design idea, realization method and applications of the GEI digital research platform. By solving the problems about data, simulation, management and display, the platform improves the GEI analysis and research capabilities, meanwhile strongly supporting the construction of advanced think tank, promotion of GEI concept, and the landing of developing strategies.

In this paper, the use of chaos in power-line communication (PLC) with blind equalization of the multipath effects is considered. After investigating equalization methods for chaotic signals, we propose an minimum phase-space volume–extended Kalman filter (EKF) equalization method to fulfill the practical requirements. First, an EKF-based method is used to replace the inverse filter to overcome the channel noise. Second, a nearest neighbor search method and a fast system identification method are used to reduce the computational complexity. Computer simulations are used to confirm the efficiency of the proposed method. Furthermore, real PLC channel experiments are conducted to demonstrate the effectiveness of this method.

Due to the inherent noise-like characteristic of chaotic signals and their sensitivity to the initial value, chaotic direct sequence spread spectrum (CD3S) signals have the advantages of a low probability of intercept (LPI) and a high level of security. Demodulation of non-cooperated CD3S signals is then a challenging issue. If the signal is sent though multipath channels, it is even more difficult for the receiver to demodulate it blindly. Based on the existing theories and methods, we focus more on signals passing through multipath channels. This paper presents an approach to achieve blind equalization and demodulation of CD3S signals through multipath channels. Multiple unscented Kalman filters (UKFs) are used to equalize and demodulate the CD3S signals for the unknown channel. This method can effectively demodulate the signals without any knowledge of the chaotic transmitter’s parameters, initial value, state equation, or the channel coefficients, even when the signal is severely distorted by the multipath channel. Simulation results demonstrate that this method gives faster convergence and better demodulation performance than existing methods for various channel conditions.

In this paper, the minimum phase space volume (MPSV) method is modified to identify an autoregressive (AR) system driven by a chaotic signal blindly. After modification, the estimation speed is much faster than before, which makes the MPSV method more suitable for engineering applications. The simulation results show that, when comparing with the original MPSV method, the proposed method can obtain the same estimation result at a much higher speed.

Under laboratory condition, the compound materials of Poly (DL-lactic-co-glycolic acid)/Tricalcium phosphate [PLGA/TCP(L), with component ratio of 7:3] were fabricated by combining the thermally induced phase separation (TIPS) with solvent-casting particulate-leaching (SCPL) approach. On the other hand, rapid prototyping (RP) technique manufactured PLGA/TCP scaffolds [PLGA/TCP(RP)] were obtained. These two kinds of carriers were coated with collagen type I (Col I). The extracted bovine bone morphogenetic protein (bBMP) was loaded into carriers to establish biomimetic synthetic bones. PLGA/TCP(L) scaffolds, demineralized bone matrices (DBM) of bovine cancellous bone, PLGA/TCP(L) scaffolds, biomimetic synthetic bones and OsteoSet bone graft substitutes were investigated. Scanning electron microscopy revealed that the microarchitecture of PLGA/TCP(RP) scaffolds was much better than that of PLGA/TCP(L) scaffolds. The diameter of macropore of PLGA/TCP(RP) scaffold was 350 microm. The porosities of PLGA/ TCP(L) scaffolds, DBM, PLGA/TCP(RP) scaffolds and OsteoSet bone graft substitutes were 21.5%, 70.4%, 58.6% and 0%, respectively (P0.01). Modification of PLGA/TCP scaffolds with collagen type I [PLGA/TCP(L)-Col I and PLGA/TCP(RP)-Col I] essentially increased the affinity of the carriers to bBMP. Among these synthetic materials, PLGA/TCP(RP)-Col I-bBMP composite is promising as a novel bone graft substitute due to its advanced fabrication technique, good tri-dimensional microarchitecture and ideal components.

Tri-dimensional poly (DL-lactic-co-glycolic acid) (PLGA) scaffolds were fabricated using a rapid prototyping (RP) technique and the gene of human bone morphogenetic protein 2 (hBMP-2) was transferred into rabbit bone marrow stromal cells (MSCs) via recombinant adeno-associated virus vectors (rAAV-hBMP-2). Thirty-two PLGA scaffolds, size (4 mm X 4 mm X 4 mm), were coated with collagen type I and equally divided into 2 groups. In group A, each scaffold was loaded with 2 X 10(4) hBMP-2 (+) MSCs to establish a hBMP-2 (+) MSCs/PLGA composite. In group B, each scaffold was loaded with 2 X 10(4) hBMP-2 (-) MSCs to establish a hBMP-2 (-) MSCs/PLGA composite. The composites in both groups were cultured for subcutaneous implantation in nude mice. All animals were killed 30 days after implantation and the differentiation of composites was evaluated. As a result, MSCs infected with rAAV-hBMP-2 efficiently expressed hBMP-2 protein. RP-based PLGA scaffolds had ideal microarchitecture. The diameters of macropore and micropore of the scaffolds were 300 microm and 3-5 microm, respectively. At 3-5 days after culture, a number of seeding cells well grew on the scaffolds of both groups. The composites in group A had chondrogenesis ability in vivo and the expression of collagen type II was positive. In group B, however, only polymers and fiber tissues were predominantly found. The percentage of polymer remnant area was significantly lower in group A than in group B (P0.01). Our results therefore indicate that RP-based PLGA scaffolds efficiently coated with collagen type I have good biocompatibility with hBMP-2 (+) MSCs and the techniques developed in this study may favor cartilage tissue engineering.