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X-ray imaging is the primary diagnostic tool for clinical diagnosis of suspected fracture. Hand fracture (HF) is a world-leading health problem for children, adolescents and the elderly. A missed diagnosis of hand fracture on radiography may lead to severe consequences for patients, resulting in delayed treatment and poor recovery of function. Nevertheless, many hand fractures are fairly insidious, which often lead to misdiagnosis. In this dissertation, we propose GA_Faster R-CNN in which a guided anchoring method (GA) of GA_RPN is applied to detect and localize hand fractures in radiographs. Our new guided anchoring method makes the anchor generation more accurate and efficient, greatly improves the network performance, and saves computing power. In our work, Feature Pyramid Network (FPN) is used to solve the problem of tiny object detection which mostly appears at the joint of fingertips and knuckles. In addition, Balanced L1 Loss is applied to adapt to the imbalance of learning tasks. We evaluate the proposed algorithm on a HF dataset containing 3,067 X-ray radiographs, 2,453 of which are assigned as the training dataset and 614 as the testing dataset. The present framework achieved accuracies of 97%–99% and an average precision (AP) of 70.7%, thereby outperforming the previous state-of-the-art methods for detecting HF. As a consequence, the GA_Faster R-CNN has great potential for clinical applications.

The increasing penetration of cyber-physical system (CPS) technologies in industry is transforming this environment into a multifaceted system featuring a tight combination of its physical and computational elements, including their digital (virtual) representations, contributing to the concept of an industrial CPS. The German “Industry 4.0” is a key innovation program aimed at realizing industrial CPS. Industry 4.0 is characterized by digital industrial components interacting with each other to become systems meeting flexible industrial demands, e.g., order-controlled production. An asset administration shell (AAS), as defined in the context of the Reference Architectural Model for Industry 4.0 (RAMI 4.0), is a practical embodiment of the latest buzzword, digital twin, and can be realized with the integration of operation technologies and information and communication technologies. AASs offer an interoperable way to capture key information pertaining to assets, such as intrinsic properties, operational parameters, and technical functionalities, and to enable straightforward interaction over standardized, secure communication with other Industry 4.0 components. The goal of this article is to present the status quo of AAS development, to design an intuitive method for implementing AASs, and to develop an AAS-enabled digital solution for cyber-physical applications in the manufacturing sector. Last but not least, we demonstrate a case study featuring an Industry 4.0 application scenario, i.e., plug-and-produce.

The future manufacturing environment is foreseen to be increasingly diverse with multi-vendor industrial wireless devices deployed in the same geographical area. Thus, effective medium resource sharing mechanisms are urgently needed to enable the coexistence of those heterogeneous industrial wireless sensor networks (IWSNs). To accommodate such heterogeneity, this paper proposes a collaborative scheduling algorithm (CSA) for coordinating the activation of each coexisting IWSN while guaranteeing their respective real-time communication requirements. Specifically, the proposed CSA is able to help determine a unique data transmission instantance for each network node that periodically generates time-sensitive data, through which timely data delivery is guaranteed without the interference of each other.

By using reservoir theory, we investigate the evolution of an atom placed in photon superfluid and study the entanglement properties of two qubits interacting with photon superfluid. It is found that the atomic decay rate in photon superfluid changes periodically with position of the atom and the decay rate can be inhibited compared to that in usual electromagnetic environment without photon superfluid. It is also found that when two atoms are separately immersed in their own local photon-superfluid reservoir, the entanglement sudden death or birth occurs or not only depends on the initial state of the qubits. What is more, we find a possible case that the concurrence between two qubits can remain a constant value by choosing proper values of parameters of the system, which may provide a new way to preserve quantum entanglement.

Demand response (DR) has been acknowledged as an effective method to improve the stability, and financial efficiency of power grids. During operation of a DR program, there are usually multiple interactions among different grid entities, which complicates decision-making processes with respect to grid operations. Recently, reinforcement learning (RL) has attracted increasing attention for managing complex decision-making problems, owing to its self-learning capacity. Several theoretical RL-based approaches have been proposed for addressing various DR issues, but the practical feasibility of these theoretical approaches remains to be proven. In this paper, a conceptual architecture is firstly proposed to support DR management of a diversified facility in the context of a price-based DR environment. Secondly, exhaustive guidelines are provided to illustrate how to implement a multi-agent RL-based algorithm in the constructed DR management system. Afterwards, a laboratory-level testbed was set up to evaluate the effectiveness of the deployed DR algorithm. The experimental evaluation results show that the RL-based DR algorithm takes about 20s and 50 episodes to achieve optimal load control policy. By executing the optimal operation policy, the overall energy consumption during the highest price period (i.e., 15:00–18:00) is significantly reduced by 133.6% compared with the lowest price period (i.e., 02:00–05:00).

Aluminum (Al) is the most abundant metal element in the earth's crust, and is implicated in the pathogenesis of liver lesions. However, the mechanisms underlying Al3+-induced hepatotoxicity are still largely elusive. Based on analysis with native gel electrophoresis, Al3+ plus 8-hydroxyquinoline staining and LC-MS/MS, the proteins with high Al3+ affinity were identified to be carbamoyl-phosphate synthase, adenosylhomocysteinase, heat shock protein 90-alpha, carbonic anhydrase 3, serum albumin and calreticulin. These proteins are involved in physiological processes such as the urea cycle, redox reactions, apoptosis and so on. Then we established an Al3+-treated rat model for biochemical tests, morphology observation and Ca2+ homeostasis analysis, in order to evaluate the extent of oxidative damage, hepatic histopathology and specific indicators of Al3+-related proteins in liver. Our findings indicated the high-affinity interactions with Al3+ perturbed the normal function of the above proteins, which could account for the mechanism underlying Al3+-induced hepatotoxicity.

This paper presents a novel incentivizing strategy for a demand response (DR) aggregator to participate in an intra-day market organized by the grid operator (GO). By taking the market entry criterion into consideration, the proposed incentivizing strategy is able to help the aggregator determine the optimal incentive for each customer based on their identified characteristics or preferences. The hierarchical decision making procedure between the aggregator and customers is casted into a Stackelberg game. In accordance, an optimal incentivizing strategy can be ascertained for the aggregator, which further helps the aggregator elicit optimal load reductions from enrolled customers meanwhile maximizing its potential profit. Simulation results show that the proposed incentivizing strategy is effective in helping the aggregator determine optimal incentive for each customer in face of different external factors.

The reconstruction of high-quality images from limited angular sparse-view tomography data is desired in many fields, including low-dose computed tomography and nondestructive detection. In this study, two regularization terms based on the assumptions of image continuity and nonzero pixels are utilized to reconstruct an image from incomplete data. By transforming the original nonlinear reconstruction model into a linear programming, the proposed method can obtain the optimal solution rather than the local minimum point, which helps to accurately reconstruct the images. To demonstrate the effect of the proposed method, we designed a simulation experiment under limited angle, few-view, and limited angular sparse-view conditions, and evaluated the reconstruction results using the structural similarity index, peak signal-to-noise ratio, and cosine distance. The experimental results show that our method can effectively overcome the adverse effects of sparse-view conditions and a limited scanning range. In the tomographic results of our method, streak artifacts do not appear, even when using projection data from 20 views of a 180° scanning angular range.

A Weisskopf–Wigner theory has been used to investigate the spontaneous emission of a two-level atom placed in a photon superfluid. It is found that the atom decays exponentially. However, the atomic decay rate changes periodically with the position of the atom and it is minimal when the atom is located at the wave nodes. The largest decay rate of the atom in photon superfluid has the same order of magnitude as it is in vacuum of free space. Moreover, the analytical result shows that the decay of an atom in photon superfluid, compared with that in planar cavity without photon superfluid, will be inhibited. The physical origin of atomic decay inhibition is also discussed.

When the transformer works for a long time, its winding is gradually deteriorated with time, and the fault phenomena such as winding short circuit or circuit break lead to serious power supply accidents. Under high temperature conditions, this paper analyzes the degradation process of the winding, and determines that the resonant frequency can be used as testing index of its degradation process. Therefore, the resonant frequency is used to monitor the performance degradation state of the transformer winding and realize the advance prediction, which can effectively avoid accidents. Accurate prediction of system reliability is of plenty of importance to engineering systems for accomplishing the designate function and system safety management. As the concerned system is getting complicated and more sufficient health monitoring measurement is available, the traditional reliability prediction schemes resorting to only one kind of prediction approaches, model-based or data-driven, begin to show their limitations. This paper proposes a PF prognostic method by combining traditional model approaches. The effectiveness of the proposed method is verified by thermal degradation experiments. This method improves the reliability of power system and is conducive to the rapid development of smart grid.

This paper proposes a price-based demand response (DR) algorithm for energy management in a hierarchical electricity market. The pricing problem is formulated as a reinforcement learning (RL) model. Using RL, the service provider (SP) can adaptively decide the retail electricity price during the on-line learning process.

This paper presents a novel decision-making scheme for facilitating energy trading between two agent-based microgrids (MGs) in an autonomous way. The interactions during the energy trading process are appropriately analyzed using Stackelberg game theory, where a unique Stackelberg equilibrium (SE) is proven to exist in the game. To protect the private information of each MG, a fully distributed algorithm is proposed to find the SE through message exchanges, which yields the optimal trading price as well as the optimal trading quantity for participating MGs. Simulation results show that the proposed approach is effective for obtaining the optimal energy trading price and quantity, and also helps each MG arrive at maximal utility or minimal cost.

Aprium (Prunus armeniaca × salicina L. cv. Xingmei) fruits were treated with 1.0 μL/L 1-methylcyclopropene (1-MCP) for 12 h at 20 °C or dipped for 2 min into 0.5% (w/v) chitosan oligosaccharides (COS) solution, or treated with the combination of the both, then stored at 2 °C for up to 30 days. Aprium fruits exhibited a typical climacteric pattern in ethylene production and respiration rate. Treatment with 1-MCP or COS could retard the decline in firmness, titratable acidity and total soluble solids of aprium fruits during storage, as well as reduce ethylene production and respiration rate. In addition, COS treatment also had a pronounced effect on reduction of decay rate.