Nowadays, with the wide installation of distributed energy resources and independent energy storage systems, prosumers as a new type of electricity market entity have emerged. Since numerous prosumers can significantly impact the carbon emission of the power grid, this paper proposes an improved carbon emission flow method for the power grid with prosumers. This method can accurately clarify the detailed distribution of electrical carbon emission flow in power grids. First, based on the power flow, prosumers’ impacts on the electrical carbon emission are quantified from three aspects that include the carbon emission sources, the network flow, and the indirect carbon emission individuals. Then, an improved power carbon emission flow model is proposed, in which the complex carbon emission intensity of prosumers is derived emphatically. Finally, case studies based on the IEEE 30-bus system verify the feasibility of the proposed method. This method provides a measurement basis for further research considering electrical carbon emissions.
The restoration of the modern alternating current-direct current (AC–DC) hybrid power grid after a blackout is difficult and complex. By using the partitioning restoration strategy and the support of high voltage direct current (HVDC) system, the restoration speed of the blackout power grid can be greatly accelerated. Aiming at this problem, this paper proposes a partitioning method for the power grid restoration with multiple line commutated converter-based HVDC (LCC-HVDC) systems. Firstly, by introducing the system strength indices to quantify the interaction between AC and LCC-HVDC systems in the restoration process, a partitioning model considering multiple LCC-HVDCs is proposed. Wherein, the transmission power is optimized to make full use of LCC-HVDCs’ support capability. Then, by combining the system strength characteristic and community structure characteristic of the power grid, a partitioning method based on an improved Girvan Newman (GN) algorithm is proposed. Finally, the results are studied based on a modified IEEE 39-bus system and a realistic partial power grid of southwest China. Compared with two typical partitioning methods for the AC power grid restoration, the results show that the proposed method can effectively use the DC support power and speed up the restoration progress.
Continuum manipulators have infinite degrees of freedom and high flexibility, making it challenging for accurate modeling and control. Some common modeling methods include mechanical modeling strategy, neural network strategy, constant curvature assumption, etc. However, the inverse kinematics of the mechanical modeling strategy is difficult to obtain while a strategy using neural networks may not converge in some applications. For algorithm implementation, the constant curvature assumption is used as the basis to design the controller. When the driving wire is tight, the linear controller under constant curvature assumption works well in manipulator position control. However, this assumption of linearity between the deformation angle and the driving input value breaks upon repeated use of the driving wires which get inevitably lengthened. This degrades the accuracy of the controller. In this work, the Koopman theory is proposed to identify the nonlinear model of the continuum manipulator. Under the linearized model, the control input is obtained through model predictive control (MPC). As the lifted function can affect the effectiveness of the Koopman operator-based MPC (K-MPC), a novel design method of the lifted function through the Legendre polynomial is proposed. To attain higher control efficiency and computational accuracy, a selective control scheme according to the state of the driving wires is proposed. When the driving wire is tight, the linear controller is employed; otherwise, the K-MPC is adopted. Finally, a set of static and dynamic experiments has been conducted using an experimental prototype. The results demonstrate high effectiveness and good performance of the selective control scheme.
In this paper, we propose TRouter, a thermal-driven PCB routing framework via a machine learning model. The model is designed to capture the long-range spatial information from the PCB layout and predict thermal distribution. The information contains pads, vias, components and wire segments. A gradient in each grid cell obtained from the backpropagation is integrated into a full-board routing algorithm to guide thermal-aware wire detour and via punching. To achieve a significant speedup, we construct a conflict graph according to whether overlapping among convex hulls of nets. A greedy-based method is adopted to remove non-root nodes from all nodes. Then a task graph is constructed to improve the parallelism. We conduct experiments on open-source benchmarks to illustrate our TRouter can achieve significant speedup and lower-temperature designs, compared with a state-of-the-art PCB routing algorithm.
Lacking self-repair abilities, injuries to articular cartilage can lead to cartilage degeneration and ultimately result in osteoarthritis. Tissue engineering based on functional bioactive scaffolds are emerging as promising approaches for articular cartilage regeneration and repair. Although the use of cell-laden scaffolds prior to im-plantation can regenerate and repair cartilage lesions to some extent, these approaches are still restricted by limited cell sources, excessive costs, risks of disease transmission and complex manufacturing practices. Acellular approaches through the recruitment of endogenous cells offer great promise for in situ articular cartilage regeneration. In this study, we propose an endogenous stem cell recruitment strategy for cartilage repair. Based on an injectable, adhesive and self-healable o-alg-THAM/gel hydrogel system as scaffolds and a biophysio-enhanced bioactive microspheres engineered based on hBMSCs secretion during chondrogenic differentiation as bioactive supplement, the as proposed functional material effectively and specifically recruit endogenous stem cells for cartilage repair, providing new insights into in situ articular cartilage regeneration.
This paper unifiedly addresses two kinds of key quantum secure tasks, i.e., quantum versions of secret sharing (SS) and symmetric private information retrieval (SPIR) by using multi-target monotone span program (MMSP), which characterizes the classical linear protocols of SS and SPIR. SS has two quantum extensions; One is the classical-quantum (CQ) setting, in which the secret to be sent is classical information and the shares are quantum systems. The other is the quantum-quantum (QQ) setting, in which the secret to be sent is a quantum state and the shares are quantum systems. The relation between these quantum protocols and MMSP has not been studied sufficiently. We newly introduce the third setting, i.e., the entanglement-assisted (EA) setting, which is defined by modifying the CQ setting with allowing prior entanglement between the dealer and the end-user who recovers the secret by collecting the shares. Showing that the linear version of SS with the EA setting is directly linked to MMSP, we characterize linear quantum versions of SS with the CQ ad QQ settings via MMSP. Further, we introduce the EA setting of SPIR, which is shown to link to MMSP. In addition, we discuss the quantum version of maximum distance separable codes.
To solve the problems of discomfort and potential colon perforations of patients that arise when standard colonoscopes are used for colonoscopy, an electromagnetically actuated soft-tethered colonoscope robot (EASCR) is here introduced. Owing to EASCRs’ highly nonlinear and complex application environments, the hybrid vision/magnetic-force tracking control for these types of robots remains a challenging research issue, and the lack of current constraints may also give rise to safety concerns. Therefore, a hybrid vision/magnetic-force fast convergent dual neural network (DNN) tracking controller for an EASCR with current constraints is developed to alleviate patient discomfort and ensure the safe and smooth progression of colonoscopy. First, EASCR motion/vision and electromagnetically actuated force nonlinear coupling models are established, and a quadratic programming visual servo-tracking control scheme with current constraints is designed. Second, a novel DNN solver for the nonlinear control scheme is developed, and its convergence in finite time is strictly proved. The results of simulations and experiments indicate that the designed control method can well control EASCRs with current constraints to achieve tracking tasks, and it has a stronger anti-disturbance ability, faster convergence, and higher convergence accuracy than existing methods.
The perception of object's deformability in unstructured interactions relies on both kinesthetic and cutaneous cues to adapt the uncertainties of an object. However, the existing tactile sensors cannot provide adequate cutaneous cues to self‐adaptively estimate the material softness, especially in non‐standard contact scenarios where the interacting object deviates from the assumption of an elastic half‐infinite body. This paper proposes an innovative design of a tactile sensor that integrates the capabilities of two slow‐adapting mechanoreceptors within a soft medium, allowing self‐decoupled sensing of local pressure and strain at specific locations within the contact interface. By leveraging these localized cutaneous cues, the sensor can accurately and self‐adaptively measure the material softness of an object, accommodating variations in thicknesses and applied forces. Furthermore, when combined with a kinesthetic cue from the robot, the sensor can enhance tactile expression by the synergy of two relevant deformation attributes, including material softness and compliance. We demonstrate that the biomimetic fusion of tactile information can fully comprehend the deformability of an object, hence facilitating robotic decision‐making and dexterous manipulation. This article is protected by copyright. All rights reserved
Background Self‐related information is difficult to ignore and forget, which brings valuable implications for educational practice. Self‐referential encoding techniques involve integrating self‐referencing cues during the processing of learning material. However, the evidence base and effective implementation boundaries for these techniques in teaching and learning remain uncertain due to research variability. Aims The present meta‐analysis aims to quantitatively synthesize the results from studies applying self‐referential encoding techniques in education. Methods The analysis was based on data from 20 independent samples, including 1082 students from 13 primary studies identified through a systematic literature search. Results Results from random effect models show that incorporating self‐referential encoding techniques improved learning ( g = .40, 95% CI [.18, .62]). Subgroup analysis showed that the valence of learning material serves as a significant boundary condition for this strategy. The students' cohorts, types of learning materials, and research context did not moderate the effect sizes. Conclusions Our results suggest that incorporating self‐referential encoding techniques on negative materials shows an aversive effect. Overall, there is a universal benefit to using self‐referential encoding techniques as an appropriate design guideline in educational contexts. Implications for teaching practice and future directions are discussed. Further studies are needed to investigate the effectiveness in more diverse educational and teaching situations.
Membraneless organelles (MLOs) are likely assembled via liquid‐liquid phase separation (LLPS). The liquid‐like MLOs afford multifold peculiarities including high dynamics, reversibility and responsiveness. It is common to see fast, drastic and reversible formation and dissolution events of MLOs, as well as transition into more stable glassy or gel‐like states, which suggests a metastable assembly state. Moreover, the alteration of metastability of LLPS is linked with cellular pathology. Here, we review the ‘metastability’ related to MLOs driven by liquid phase separation, from multifaceted regards including energy state, molecular interactions, molecular structure, phase transition, as well as the associations with diseases. This review can help to advance the insight into properties and pathogenesis associated with LLPS of biological matter. This article is protected by copyright. All rights reserved.
Accommodating services at the network edge is favorable for time-sensitive applications. However, maintaining service usability is resource-consuming in terms of pulling service images to the edge, synchronizing databases of service containers, and hot updates of service modules. Accordingly, it is critical to determine which service to place based on the received user requests and service refreshing (maintaining) cost, which is usually neglected in existing studies. In this work, we study how to cooperatively place timely refreshing services and offload user requests among edge servers to minimize the backhaul transmission costs. We formulate an integer non-linear programming problem and prove its NP-hardness. This problem is highly non-tractable due to the complex spatial-and-temporal coupling effect among service placement, offloading, and refreshing costs. We first decouple the problem in the temporal domain by transforming it into a Markov shortest-path problem. We then propose a light-weighted Discounted Value Approximation (DVA) method, which further decouples the problem in the spatial domain by estimating the offloading costs among edge servers. The worst performance of DVA is proved to be bounded. 5G service placement testbed experiments and real-trace simulations show that DVA reduces the total transmission cost by up to 59.1% compared with the state-of-the-art baselines.
According to Pierre Noras argument on the relationship between memory and history, these two concepts are not synonyms but antonyms. In the modern world, critical history begins from rational reflection, and it represses memory, which correlates more to the personal narrative field. The memory theory gives us the insight to reexamine the narration in literary works reflecting the war memory. This paper demonstrates different voices in narrating the individualized traumatic memory of the Nanking Massacre through the comparative reading between fictional works of Murakami Haruki and Geling Yan, Killing Commendatore and The Flowers of War. Shujuan, the protagonist in the novella of Yan, is both the observer and survivor of the massacre. Her storyline obtains two heterogeneous narratives that respectively belong to the field of personal memory and history; while her responsibility of memorizing the unrecorded experience makes her to pick a historical view to narrate her past. Boku, the first-person protagonist in Murakamis fiction, on the other hand, is a contemporary character with no experience or direct memory of the war. However, he gets connected with the war memory through the Murakami-style surreal experience. His experience demonstrates how personal memory can console the trauma after the war, usually neglected by the grand history. In the paper, I argue that although the two authors are from different cultural backgrounds and apply different styles, their narrations that focus on the personal memory field show the consoling power that connects the past and present and heals the trauma left by the war. Further, their fictions still contribute to the political and historical discussions on Nanking, presenting the power against revisionists and bridging the conflicts inside the discussions around such a traumatic historical issue.
Focusing on the U.S. market, this research selects ten high-capitalization U.S. stocks in different industries and uses stock price forecasting in machine learning as well as Monte Carlo simulation to explore the efficient frontier of assets. Besides, this paper builds a portfolio with equal weight, maximum Sharpe ratio, and minimum volatility criteria, respectively. The results show that the Exxon Mobil Corporation possesses the largest proportion of the maximum Sharpe ratio portfolio, and the UnitedHealth Group Inc. accounts largest weights for the portfolio with the minimum volatility. In addition, this paper also compares the cumulative returns of the three investment portfolios with the important index NASDAQ of the US stock market. The results indicate that that the above mentioned three portfolios are all better than the benchmark index and can obtain a higher return. The results may shed light on some investors' approach to portfolio management during this extraordinary time.
Aims Dissecting complex interactions among transcription factors (TFs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are central for understanding heart development and function. Although computational approaches and platforms have been described to infer relationships among regulatory factors and genes, current approaches do not adequately account for how highly diverse, interacting regulators that include noncoding RNAs (ncRNAs) control cardiac gene expression dynamics over time. Methods To overcome this limitation, we devised an integrated framework, cardiac gene regulatory modeling (CGRM) that integrates LogicTRN and regulatory component analysis bioinformatics modeling platforms to infer complex regulatory mechanisms. We then used CGRM to identify and compare the TF-ncRNA gene regulatory networks that govern early- and late-stage cardiomyocytes (CMs) generated by in vitro differentiation of human pluripotent stem cells (hPSC) and ventricular and atrial CMs isolated during in vivo human cardiac development. Results Comparisons of in vitro versus in vivo derived CMs revealed conserved regulatory networks among TFs and ncRNAs in early cells that significantly diverged in late staged cells. We report that cardiac genes (“heart targets”) expressed in early-stage hPSC-CMs are primarily regulated by MESP1, miR-1, miR-23, lncRNAs NEAT1 and MALAT1, while GATA6, HAND2, miR-200c, NEAT1 and MALAT1 are critical for late hPSC-CMs. The inferred TF-miRNA-lncRNA networks regulating heart development and contraction were similar among early-stage CMs, among individual hPSC-CM datasets and between in vitro and in vivo samples. However, genes related to apoptosis, cell cycle and proliferation, and transmembrane transport showed a high degree of divergence between in vitro and in vivo derived late-stage CMs. Overall, late-, but not early-stage CMs diverged greatly in the expression of “heart target” transcripts and their regulatory mechanisms. Conclusions In conclusion, we find that hPSC-CMs are regulated in a cell autonomous manner during early development that diverges significantly as a function of time when compared to in vivo derived CMs. These findings demonstrate the feasibility of using CGRM to reveal dynamic and complex transcriptional and posttranscriptional regulatory interactions that underlie cell directed versus environment-dependent CM development. These results with in vitro versus in vivo derived CMs thus establish this approach for detailed analyses of heart disease and for the analysis of cell regulatory systems in other biomedical fields.
Transcription factors (TFs) play key roles in regulating differentiation and function of stem cells, including muscle satellite cells (MuSCs), a resident stem cell population responsible for postnatal regeneration of the skeletal muscle. Sox11 belongs to the Sry-related HMG-box (SOX) family of TFs that play diverse roles in stem cell behavior and tissue specification. Analysis of single-cell RNA-sequencing (scRNA-seq) datasets identify a specific enrichment of Sox11 mRNA in differentiating but not quiescent MuSCs. Consistent with the scRNA-seq data, Sox11 levels increase during differentiation of murine primary myoblasts in vitro. scRNA-seq data comparing muscle regeneration in young and old mice further demonstrate that Sox11 expression is reduced in aged MuSCs. Age-related decline of Sox11 expression is associated with reduced chromatin contacts within the topologically associating domains. Unexpectedly, Myod1 Cre -driven deletion of Sox11 in embryonic myoblasts has no effects on muscle development and growth, resulting in apparently healthy muscles that regenerate normally. Pax7 CreER - or Rosa26 CreER - driven (MuSC-specific or global) deletion of Sox11 in adult mice similarly has no effects on MuSC differentiation or muscle regeneration. These results identify Sox11 as a novel myogenic differentiation marker with reduced expression in quiescent and aged MuSCs, but the specific function of Sox11 in myogenesis remains to be elucidated.
Metal–organic frameworks (MOFs) are promising electrocatalysts for clean energy conversion systems. However, developing MOF‐based electrodes with high performance toward oxygen evolution reaction (OER) is still challenging. In this work, a series of MOF film electrodes derived from Ni‐btz were prepared by employing the secondary growth strategy under solvothermal conditions. Fe and Co ions were also incorporated into the Ni‐btz framework to produce a trimetallic coupling effect to obtain enhanced OER activity. The as‐prepared FeCoNi‐btz/NF exhibited not only good stability but also excellent OER performance under alkaline conditions. Furthermore, the possible intermediates including metal oxides and metal oxyhydroxides were confirmed by X‐ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM).
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