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In this paper, we propose the CO2 laser polishing method to prepare a novel type of incline plane-shaped long period fiber grating (IPS-LPFG) for strain measurement. This structure is achieved by periodic incline plane-shaped structures polished on single mode fiber. The incline plane-shaped structure introduced asymmetric thickness of the side plane, which enhances the strain sensitivity of the sensor effectively. Experimental results illustrate that the compact strain sensor with a length of 5 mm achieves a high strain sensitivity of 15 pm/ $\mu \varepsilon $ at the range of 0- $900~\mu \varepsilon $ , and the temperature sensitivity of it is 54.5 pm/°C. Moreover, the proposed strain sensor has good mechanical properties and high repeatability, which demonstrates the potential in optical fiber sensing.

Limited and censored time-to-failure data are common in practical life testing, which may lead to a lack of knowledge, i.e., epistemic uncertainties, and makes it difficult to evaluate product reliability accurately. Under this situation, the large-sample based probability theory is not appropriate anymore. To deal with such problem, in this paper, the uncertainty theory based reliability evaluation is proposed to cope with the limited and censored time-to-failure data, where two types of life testing censoring (type-I and type-II censoring) and two types of time-to-failure data (precise and interval data) are considered. Firstly, the lifetime model and reliability evaluations based on uncertainty theory are presented. Then, the uncertain statistics method is presented for each combination of censoring types and data types in life testing, which includes four steps: order statistics construction, belief degree construction according to the Laplace principle of indifference, parameter estimation based on the uncertain principle of least squares, and uncertain hypothesis testing. Two simulation studies and two practical cases with comprehensive and in-depth discussion are conducted to illustrate the practicability and effectiveness of the proposed method. The results show that the proposed method can quantify epistemic uncertainties properly and provide more stable and accurate mean time to failure results under limited and censored time-to-failure data compared with probability and Bayesian probability methods.

Messenger RNA (mRNA) vaccine is a promising candidate in cancer immunotherapy as it can encode tumor-associated antigens with an excellent safety profile. Unfortunately, the inherent instability of RNA and translational efficiency are major limitations of RNA vaccine. Here, we report an injectable hydrogel formed with graphene oxide (GO) and polyethylenimine (PEI), which can generate mRNA (ovalbumin, a model antigen) and adjuvants (R848)-laden nanovaccines for at least 30 days after subcutaneous injection. The released nanovaccines can protect the mRNA from degradation and confer targeted delivering capacity to lymph nodes. The data show that this transformable hydrogel can significantly increase the number of antigen-specific CD8+ T cells and subsequently inhibit the tumor growth with only one treatment. Meanwhile, this hydrogel can generate an antigen specific antibody in the serum which in turn prevents the occurrence of metastasis. Collectively, these results demonstrate the potential of the PEI-functionalized GO transformable hydrogel for effective cancer immunotherapy.

This article prepared a simvastatin-NLCs for the treatment of arteriosclerotic occlusive disease of lower limbs. Taking the size distribution, polydispersity coefficient, encapsulation efficiency and drug loading of simvastatin-NLCs as evaluation indicators, various prescription factors of simvastatin-NLCs were investigated. The in vitro release behavior and stability of simvastatin-NLCs were also investigated. A hyperlipidemia rat model was established using high-fat diets. SD rats fed ordinary diet were set as normal control groups. 20 rats, 20 in the simvastatin group and 20 in the simvastatin nanocarrier group. After 5 weeks of drug intervention, the rats were sacrificed and the aorta was taken to determine the smooth muscle cell apoptosis rate. Studies have shown that simvastatin nanocarriers can more effectively reduce blood lipids in hyperlipidemia rats, increase the rate of smooth muscle cell apoptosis in hyperlipidemia rats, and delay the onset of atherosclerosis.

The effect of C-alloying on phase constituent, microstructure and mechanical behaviour of a novel Co-free (Fe36Mn36Ni9Cr9Al10)100-x C x (x = 0, 1.5, 2.5, 3.5, 5, 6) low-cost high entropy alloy (HEA...

Hypothesis G-quadruplex structure has raised increasing attention in supramolecular chemistry as an effective template for ordered functional materials. Thus, it is of practical significance to advance our understanding regarding G-quadruplex structures. Typically, G-quadruplex structures are formed in the presence of suitable metal ions. New methods to construct such structures need to be explored. Experiments The supramolecular assembly between CTAB and a guanosine derivative at different molar ratios was systematically studied, including assembly mechanisms, morphology, and macroscopic properties. Cationic surfactants with different alkyl chains were studied as control experiments. Findings A novel strategy to construct G-quadruplex with the promotion of the cationic surfactant CTAB is presented in this work. The structure-property relationships of G-quadruplex gels are characterized by rheology and shrinkage ratio experiments. MacKintosh’s theory was used to rationalize the relationship between gel elasticity and water content. The transition of G-quadruplex structures could be easily enabled by modulating CTAB concentration, which promotes the phase transition from gel/sol biphase to homogeneous sol phase. This work will provide a new viewpoint for the construction and modulation of G-quadruplex structures.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling , which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.

A novel optic-fiber torsion and temperature sensor has been proposed and fabricated by sandwiching a segment of a triangular shaped single-mode fiber between two multimode fibers. This structure provides the sensitivity of torsion, which results from the destruction of the rotational symmetry of the optical fiber. Experimental results show that the polished multimode single-mode multimode (MSM) sensor has the ability of determining the twist direction and the amount of torsion with sensitivities of 0.196 nm/(rad $\cdot \,\,\text{m}^{-1}$ ) and −0.18 nm/(rad $\cdot \,\,\text{m}^{-1}$ ) at dip 1455 nm and dip 1505 nm, respectively. In temperature measurement, the sensitivities provided by the two dips above are 0.058 nm/° and 0.072 nm/°, respectively. The stability and the feasibility of the proposed sensor are experimentally verified.

At present, a variety of lubricating oils are available for R290 room air conditioners (RACs). R290 is highly miscible with mineral oils (MOs), while it is partially miscible with some alkylene oils (PAGs). In this paper, the oil retention behavior of a 1.5 HP R290 RAC with two miscible MOs and two partially miscible PAGs was studied. Firstly, the OCR of a R290 rotary compressor with PAG VG60 or MO VG100 was measured in compressor performance test platform, and it was 0.66–0.88%. Then the oil retention and its mass distribution within the RAC using PAG VG60 or MO VG100 were measured by Remove and Weight Technique (RWT) and Mix and Sample Technique (MST). And then, the oil retention when using PAG VG40 or MO VG56 was measured by RWT for further comparison. Results showed that the oil retention was 1.9–8.2 g, only 0.79%–3.9% of the total oil quantity. The oil mass in heat exchangers was nearly proportional to the local refrigerant mass, while that in gas line was greatly affected by the viscosity grade of oil. It was verified that the miscibility effect of the R290/PAG mixture was mild on this R290 RAC, and the viscosity grade of oil was the most critical parameter for the oil retention in the gas line.

Noise attenuation for ordinary images using machine learning technology has achieved great success in the computer vision field. However, directly applying these models to seismic data would not be effective since the evaluation criteria from the geophysical domain require a high-quality visualized image and the ability to maintain original seismic signals from the contaminated wavelets. This paper introduces an approach equipped with a specially designed deep learning model that can effectively attenuate swell noise with different intensities and characteristics from shot gathers with a relatively simple workflow applicable to marine seismic data sets. Three significant benefits are introduced from the proposed deep learning model. First, our deep learning model doesn't need to consume a pure swell-noise model. Instead, a contaminated swell-noise model derived from field data sets (which may contain other noises or primary signals) can be used for training. Second, inspired by the conventional algorithm for coherent noise attenuation, our neural network model is designed to learn and detect the swell noise rather than inferring the attenuated seismic data. Third, several comparisons (signal-to-noise ratio, mean squared error, and intensities of residual swell noises) indicate that the deep learning approach has the capability to remove swell noise without harming the primary signals. The proposed deep learning-based approach can be considered as an alternative approach that combines and takes advantage of both the conventional and data-driven method to better serve swell-noise attenuation. The comparable results also indicate that the deep learning method has strong potential to solve other coherent noise-attenuation tasks for seismic data.

Accelerated degradation testing (ADT) aids the reliability and lifetime evaluations for highly reliable products. In engineering applications, the number of test items is generally small due to finance or testing resource constraints, which leads to the rare knowledge to evaluate reliability and lifetime. Consequently, the epistemic uncertainty is embedded in ADT data and the large-sample based probability theory is no longer appropriate. In this paper, we introduce the uncertainty theory, which is a theory different from the probability theory, to account for such uncertainty due to small samples and build up a framework of ADT modeling. In this framework, an uncertain accelerated degradation model is first proposed based on the arithmetic Liu process. Then, the uncertain statistics for parameter estimations are presented correspondingly, which is completely constructed on objectively observed ADT data. An application case and a simulation case are used to illustrate the proposed methodology. With further comparisons to the Wiener process based accelerated degradation model (WADM) and the Bayesian-WADM, the sensitivities of these models to sample sizes are explored and the results show that the proposed model is superior to the other two probability-based models under the small sample size.

As a revolution in networking, the Internet of Things (IoT) aims at automating the operations of our societies by connecting and leveraging an enormous number of distributed devices (e.g., sensors and actuators). One design challenge is efficient wireless data aggregation (WDA) over the dense IoT devices. This can enable a series of the IoT applications ranging from latency-sensitive high-mobility sensing to data-intensive distributed machine learning. Over-the-air (function) computation (AirComp) has emerged to be a promising solution that merges computing and communication by exploiting analog-wave addition in the air. Another IoT design challenge is battery recharging for dense sensors which can be tackled by wireless power transfer (WPT). The coexisting of AirComp and WPT in the IoT system calls for their integration to enhance the performance and efficiency of WDA. This motivates the current work on developing the wirelessly powered AirComp (WP-AirComp) framework by jointly optimizing wireless power control, energy and (data) aggregation beamforming to minimize the AirComp error. To derive a practical solution, we recast the non-convex joint optimization problem into the equivalent outer and inner sub-problems for (inner) wireless power control and energy beamforming, and (outer) the efficient aggregation beamforming, respectively. The former is solved in closed form while the latter is efficiently solved using the semidefinite relaxation technique. The results reveal that the optimal energy beams point to the dominant Eigen-directions of the WPT channels, and the optimal power allocation tends to equalize the close-loop (down-link WPT and up-link AirComp) effective channels of different sensors. The simulation demonstrates that the controlling WPT provides additional design dimensions for substantially reducing the AirComp error.

Condition-based maintenance has been developed as a very efficient strategy for guaranteeing multicomponent system performance and preventing unexpected failures. However, there are shortcomings in the existing condition-based maintenance optimization models. First, the existing models do not utilize the accelerated degradation testing (accelerated degradation testing) data obtained at the stage of component development. Second, most of these models assume perfect repair instead of imperfect repair. Third, the degradation models used in these condition-based maintenance models cannot consider the epistemic uncertainty. Motivated by these problems, this paper presents a new condition-based maintenance optimization model for multicomponent systems with imperfect repair. An integrated degradation prediction framework utilizing both ADT data and field data is presented to timely update the parameters in the proposed model. In order to solve the proposed multivariable, nonlinear programming model, a novel genetic algorithm with self-crossover operation and shift-mutation operation is developed. Numerical examples and comparisons are conducted to evaluate the performance of the proposed model. Results show that the proposed model can evaluate the degradation process of components accurately and achieve lower total maintenance cost.

Mutant TP53 is a promising therapeutic target in cancers. Considering the current challenges facing the clinical treatment of cancer, as well as the urgent need to identify novel therapeutic targets in osteosarcomas, we aimed to evaluate the clinical significance of mutant TP53 in osteosarcoma patients and to explore the therapeutic effect of targeting mutant TP53 in osteosarcomas. We performed a meta-analysis to investigate the relationship between mutant TP53 and the overall survival of patients with osteosarcoma. A CRISPR-Cas9 system and a TP53 inhibitor, NSC59984, were also used to specifically knock-out and inhibit mutant TP53 in the human osteosarcoma cell lines, KHOS, and KHOSR2. The meta-analysis demonstrated that mutations in the TP53 gene could be used to predict a poor 2-year survival in osteosarcoma patients. We also demonstrated that the expression of mutant TP53 in human osteosarcoma cell lines can be efficiently knocked-out using CRISPR-Cas9, and this decreased the proliferation, migration, and tumor formation activity of these osteosarcoma cells. Moreover, drug sensitivity to doxorubicin was increased in these TP53 knock-out osteosarcoma cells. NSC59984 also showed similar anti-tumor effects as CRISPR-Cas9 targeted TP53 in the osteosarcoma cells in vitro. We have also demonstrated that the knock-out or inhibition of mutant TP53 decreased the expression of the oncogene IGF-1R, anti-apoptotic proteins Bcl-2, and Survivin in osteosarcoma cells. Collectively, these results suggest that mutant TP53 is a promising therapeutic target in osteosarcomas. Therefore, further studies exploring novel strategies to target mutant TP53 may help improve the treatment outcomes of osteosarcoma patients in the clinic. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility, mobile crowd sensing (MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named wirelessly powered crowd sensing (WPCS), which integrates MCS with wireless power transfer (WPT) for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an access point (AP) transfers energy to multiple mobile sensors (MSs), each of which performs data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the threshold-based structure with respect to a defined crowd-sensing priority function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms., This work has been submitted to the IEEE for possible publication

Struvite crystallization is one of the most promising routes for phosphorus recovery from phosphorus-rich sludge. However, the adsorption of alginic acid, a surrogate of extracellular polymeric substances in phosphorus-rich sludge, on struvite crystals has been proved to significantly inhibit struvite crystallization. To comprehensively understand the inhibitory mechanism, the adsorption behavior was evaluated at various adsorption time, the concentration of alginic acid, pH and ionic strength in the present study. Subsequently, adsorption equilibrium isotherm, kinetic and thermodynamics models were employed to fit the adsorption data. Interestingly, the adsorption mechanism was explored by X-ray photoelectron spectra (XPS) and Fourier transform infrared spectroscopy analysis (FTIR), which was further elucidated by triple-layer model (TLM). The results indicated that the adsorption capacity of struvite crystals almost reached a plateau value within the first 30 min. Meanwhile, the adsorption capacity was significantly reduced with increasing pH, but slightly increased with increasing ionic strength. Moreover, the adsorption process obeyed pseudo-second order kinetic model and Langmuir adsorption isotherm model, whereas intra-particle diffusion was a main rate-limited step. Furthermore, hydrogen bonding and surface complexation between alginic acid and struvite crystals dominated the adsorption mechanism. Additionally, alginic acid underwent surface reactions by virtue of two outer-sphere surface complexes and one inner-sphere surface complex. The findings presented herein will facilitate understanding of the inhibitory mechanism of organic matters on struvite crystallization from phosphorus-rich sludge.

Hydrogels based on G-quadruplexes (G-hydrogels) were prepared using guanosine 5′-monophosphate disodium salt, GMP, with a hyperbranched poly(ethylenimine), PEI, containing abundant –NH2 groups. The cation–dipolar interaction between Na+ ions and GMP-quartets and the hydrogen bonding between the phosphate groups of GMP and the –NH2 groups of PEI gave rise to the hydrogels at an appropriate pH range of 1.7–4.3. X-ray diffraction (XRD) measurements and fluorescence enhancement of Thioflavin T (ThT) indicated the formation of the G-quadruplexes. The G-hydrogel showed high adsorption capacity for the contaminant, picric acid (PA), in water, ascribed to its plentiful –NH2 groups in the network structures.

Biomimetic strategies have successfully been applied to confine multiple enzymes on scaffolds to obtain higher catalytic efficiency of enzyme cascades than freely distributed enzymes. However, the origin of high efficiency is poorly understood. We developed a coarse-grained, particle-based model to understand the origin of high efficiency. We found that a reaction intermediate is the key in affecting reaction kinetics. In the case of unstable intermediates, the confinement of multiple enzymes in clusters enhanced the catalytic efficiency and a shorter distance between enzymes resulted in a higher reaction rate and yield. This understanding was verified by co-encapsulating multiple enzymes in metal-organic framework (MOF) nanocrystals as artificially confined multienzyme complexes. The activity enhancement of multiple enzymes in MOFs depended on the distance between enzymes, when the decay of intermediates existed. The finding of this study is useful for designing in vitro synthetic biology systems based on artificial multienzyme complexes.

The weapon-target assignment (WTA) problem is a crucial decision issue in the process of cooperative aerial warfare (CAW). The decision strategy of fighter teams involved in the CAW is susceptible to the influence of the enemy fire attack and electronic interference, which will lead to both the antagonism and uncertainty of the decision making. In this paper, a novel antagonistic game WTA (AGWTA) model with uncertainty is introduced. The antagonism is described by a non-cooperative zero-sum game model conducted by two fighter teams. Then, a modified sensor data fusion method using belief entropy and similarity of sensor data is presented to manage the uncertainty of AGWTA. According to the characteristics of the AGWTA model, a decomposition co-evolution algorithm (DCEA-AGWTA) is proposed to obtain the non-cooperative Nash equilibrium (NCNE) strategy. The experimental results show that the modified sensor data fusion method contributes to higher reliability of target type identification and the AGWTA model is meaningful in the antagonistic and uncertain situation of CAW. In addition, the DCEA-AGWTA is effective and has a promising ability in finding the closest strategy to the NCNE strategy compared with the other three intelligent evolution-based algorithms.

Although renewable energy based power generation is the most promising and sophisticated solution to the future society owing to its small amount of emission of the greenhouse effect gas, it has a great disadvantage of varied output. For instance, the large amount of existing PVs already gives a serious destabilizing effect on the western Japanese power systems. It is of great concern how future power grid can be operated securely when great amount of renewable based power plants are installed. One of the promising countermeasures against this problem is the utilization of battery energy storage, where its highly costly impact, which comes from the necessary battery storage installation, can be mitigated if the electric vehicles are used for this stabilizing control. This paper proposes a new method for the above stated technical challenge. So called Euler equation in the field of calculus of variation is solved numerically satisfying equality and inequality constraints based on the assumed scenario of usable electric vehicles, providing the most desirable charge/ discharge plan. Numerical study is done to confirm the fundamental feasibility of the proposed method. This method is a great candidate to be applied to general cases of many realistic constraints.

Fatigue is one of the most typical failure mechanisms, which can be regarded as a degradation process. The non-linearity in fatigue damage due to shocks cannot be disregarded. The mechanical shock is a transient physical excitation, in which the structure is nonequilibrium caused by a sudden applied load with high stress level or an abrupt change in the direction or magnitude of velocity. After the shock, the rate of fatigue damage accumulated decreases for a long time. When shock exists, the degradation could be generally retarded. The retardation phenomenon has been proven by the experimental observation. Therefore, a new approach is proposed to evaluate the structural reliability, in which the fatigue strength degradation process considering the retardation effect is explained by a crack-closure based fatigue model. The lifetime will be extended due to the applied shock loads. This fatigue crack growth model is validated by the experimental data of aluminum alloy D16. In addition, considering the material parameter and the shock loads are indeterminate, the reliability assessment under the fatigue loading with shocks is performed. A case study is given and two shock scenarios are discussed. In the first scenario, shocks occur in a fixed time period; while in the second scenario, shock occurs with varying time periods. The results indicate that the proposed approach can account for the retardation and evaluated the fatigue damage more appropriately.

A cloud data center is a critical infrastructure whose service reliability is relevant for service delivery. Constituting the carrier of service for cloud computing, the IT architecture of a cloud data center plays an important role, since it directly relates to service reliability. However, most existing research focuses only on the connectivity of the IT architecture and on service considering only the processing procedure. In order to bridge the gap between the existing works and reality, a hierarchical colored generalized stochastic petri net is proposed to evaluate the service reliability by Monte Carlo simulation, which comprehensively considers the connectivity and performance of the IT architecture and the dynamic of service delivery. The modeling and simulation framework is applied to the cloud data center of an insurance company and cost-effective strategies are found to support the configuration, operation and maintenance of the cloud data center.

Conventional beam scanning systems employing a microlens array (MLA) suffer from the problem that only discrete diffraction angles can be addressed because of the periodic structure of the MLA. In this paper, an adaptive fiber-optics collimator (AFOC) that continuously adjusts the position of light source (optic fiber output) is used in front of the periodic structure as a moving linear phase shifter to overcome this discrete scanning angle problem. By introducing the AFOC into the beam scanning system employing MLA, a beam scanning system with continuous scanning capability and high resolution is fulfilled. Theoretical simulations and experimental results both demonstrate the continuous high-resolution scanning capacity of the beam scanning system employing both MLA and AFOC. The proposed beam scanning system is expected to find wide applications in space optical communication, optical interconnection, power projection, and coherent beam combining.

Enzymatic catalysis, which has been driving biological processes in a green, mild, and efficient manner for billions of years, is increasingly being used in industrial processes to manufacture chemicals, pharmaceuticals, and materials for human society. Since enzymes were discovered, strategies to adapt enzymes for use as catalysts for industrial processes, such as chemical modification, immobilization, site-directed mutagenesis, directed evolution of enzymes, artificial metalloenzymes, and computational design, have been continuously pursued. In contrast to these strategies, editing enzymes to easily integrate biocatalysis with chemocatalysis is a potential way to apply enzymes in industry. Enzyme catalyst editing focuses on fine-tuning the microenvironment surrounding the enzyme or achieving a new catalytic function to construct better biocatalysis under non-natural conditions for the enzyme.

With the increasing design requirements of grid-connected microgrid, the capacity configuration of microgrid should not only consider the cost, but also consider other design objectives, uncertainty of renewable energy and load demand. Therefore, a multi-objective function considering annual comprehensive cost and grid dependence is established. The k-medoids method was used to obtain the data and probability of typical and extreme days, which was added into the optimization as uncertain scenes. The Multi-Objective Cuckoo Search algorithm (MOCS) is used to solve the proposed model. Then the obtained non-dominated optimal solution set was treated with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, which can obtain the optimal compromise solution from the set. The optimal compromise solution is the final configuration of this Wind-PV-Battery microgrid.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), while tremendous labels are required for AI model training via supervised learning. To tackle this challenge, a novel framework of wireless crowd labelling is proposed that downloads data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. The integration of the rate-distortion theory and the principle of repetition labelling gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Aiming at maximizing the labelling throughput, this work focuses on optimizing the joint annotator-and-spectrum allocation (JASA). To develop an efficient solution approach, an optimal sequential annotator-clustering scheme is derived. Thereby, the optimal JASA policy can be found by an efficient tree search.

The weapon-target assignment (WTA) problem is a key issue in Command & Control (C2). Asset-based multiobjective static WTA (MOSWTA) problem is known as one of the notable issues of WTA. Since this is an NP-complete problem, multiobjective evolutionary algorithms (MOEAs) can be used to solve it effectively. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a practical and promising multiobjective optimization technique. However, MOEA/D is originally designed for continuous multiobjective optimization which loses its efficiency to discrete contexts. In this study, an improved MOEA/D is proposed to solve the asset-based MOSWTA problem. The defining characteristics of this problem are summarized and analyzed. According to these characteristics, an improved MOEA/D framework is introduced. A novel decomposition mechanism is designed. The mating restriction and selection operation are reformulated. Furthermore, a problem-specific population initialization method is presented to improve the efficiency of the proposed algorithm, and a novel nondominated solution-selection method is put forward to handle the constraints of Pareto front. Appropriate extensions of four MOEA variants are developed in comparison with the proposed algorithm on some generated scenarios. Extensive experiments demonstrate that the proposed method is effective and promising.

The robot-assisted natural orifice transluminal surgery (NOTES) typically involves applying the lengthy and slender continuum instruments/manipulators to get access to the target lesion sites, and then performs complex operations. However, due to the strong-nonlinearity of continuum robotic manipulators and the confined and tortuous anatomical paths, it’s difficult to establish accurate inverse kinematics (IK) model to achieve precise motion control and real-time shape sensing for accurately modeling their shapes. To tackle such difficulties, a model-free method based on neural network have been proposed to solve the IK problem and reconstruct the shape of a continuum manipulator at the same time using the training results from the electromagnetic (EM) tracking approach. For the IK problem, the relationship between the tip position and the corresponding cable lengths can be learned and for the shape estimation problem, the mapping from the cable lengths to the shape of the continuum robot can be established. A dataset of 500 random continuum manipulator postures was used to train the neural network, with recorded EM sensors-tracked positions logged synchronously to the cable lengths. Experiment results show the proposed model-free method could achieve high accuracy and reliable inverse kinematics and shape reconstruction outcomes.

In this paper, we provide a novel type of micro-direct methanol fuel cell (μDMFC) fabricated with two stainless steel plates as metal current collectors. In order to improve cell performance, two sheets of stainless steel mesh (SSM) are added between the membrane electrode assembly (MEA) and current collectors. To avoid electrochemical corrosion, titanium nitride (TiN) layers with thickness of 500 nm are deposited onto the surface of current collectors and stainless steel mesh. The results show that the embedded stainless steel mesh can remarkably reduce the interior impedance of the μDMFC. Moreover, compared with μDMFC without SSM, the novel cell exhibits a more outstanding performance, yielding a maximum power density of 54.4 mW cm−2 at 80 °C.

The amphiphilic derivatives of l -histidine with different alkyl chains, NIPCA, UIPCA, and TIPCA, have been designed and their self-assembly behaviors with citric acid were investigated. All l -histidine derivatives can form hydrogels with citric acid, and exhibit the increasing gelation ability and mechanical strength with the increasing chain length. The gelation ability is considered to be affected by the synergistic effect of electrostatic interaction, hydrogen bonding and hydrophobic interaction. The hydrogels exhibit excellent releasing selectivity for charged dyes in aqueous solution, enlightening people to utilize this kind of hydrogels as intelligent carriers to separate the mixtures of charged dyes.

By elevating stress levels, accelerated degradation testing (ADT) can obtain sufficient degradation data within limited time to predict the reliability and lifetime for highly reliable and long life products. In general, the degradation data collected in ADT have three kinds of characteristics: the time-stress-dependent structure, the random uncertainties caused by random effects in time dimension, and unit-to-unit variations, and the epistemic uncertainty caused by the small sample problem. However, existing acceleration degradation models based on Brownian motion with drift can successfully consider the time-stress-dependent structure and the random uncertainty, while failing to take the epistemic uncertainty into account. In this paper, based on the random fuzzy theory, a new random fuzzy accelerated degradation model and its corresponding statistical analysis method are proposed. The proposed model can take the above three kinds of characteristics into consideration simultaneously. The application case indicates that the proposed methodology is applicable for modeling the ADT data under small sample size. The simulation results show that the proposed methodology is more stable and slightly more conservative than the ADT model considering unit-to-unit variations. In addition, under small sample size (from 3 to 10), the proposed methodology is more stable and more accurate than the ADT model considering unit-to-unit variations.

Developing an accelerated acceptance sampling plan (AASP) is essential to product reliability demonstration, particularly for a product designed to have high reliability and long lifetime. Because of acceleration, a decision on lot acceptance for the product can be quickly made while significantly reducing the total cost of testing. Traditionally, the parameters of an acceptance probability function used in AASP are assumed to be known in advance. However, it is often difficult, if not impossible, to determine the exact values of these parameters. On the other hand, prior information, including earlier test results and expert opinions, may be available for potential uses in planning such tests. In this paper, Bayesian designs of AASP are considered by taking full advantage of prior information to reduce the uncertainty in these important parameters so that significant testing resources (e.g., sample size and testing time) can be saved. In particular, a product with a lognormal lifetime distribution is studied, and two types of Bayesian AASPs under Type-I censoring are developed based on the product's operating characteristic (OC) curve. The first Bayesian AASP considers the risks of the producer and the consumer simultaneously, while the second one considers only the consumer's risk. The optimal sampling plans aim at minimizing the total costs of testing. The proposed AASPs are illustrated using two numerical examples. Sensitivity analysis on the selection of prior distribution is also performed to validate the robustness of the proposed Bayesian AASPs. In addition, a comparison study shows that the proposed test plans outperform the traditional AASPs with respect to the use of testing resources.

Step-stress accelerated degradation testing (SSADT) aims to access the reliability of products in a short time. Bayesian optimal design provides an effective alternative to capture parameters uncertainty, which has been widely employed in SSADT design by optimizing specified utility objective. However, there exist several utility objectives in Bayesian SSADT design; for the engineers, it causes much difficulty to choose the right utility specification with the budget consideration. In this study the problem is formulated as a multi-objective model motivated by the concept of Pareto optimization, which involves three objectives of maximizing the Kullback-Leibler (KL) divergence, minimizing the quadratic loss function of p-quantile lifetime at usage condition, and minimizing the test cost, simultaneously, in which the product degradation path is described by an inverse Gaussian (IG) process. The formulated programming is solved by NSGA-II to generate the Pareto of optimal solutions, which are further optimally reduced to gain a pruned Pareto set by data envelopment analysis (DEA) for engineering practice. The effectiveness of the proposed methodologies and solution method are experimentally illustrated by electrical connector’s SSADT.

This work describes the gelation behaviors and properties of a biological molecule, guanosine 5′-monophosphate disodium salt (GMP), in the presence of trivalent lanthanide ions. Hydrogels composed of GMP-quadruplexes were prepared by adjusting pH, through which the protonation of phosphate group was controlled to tune the interactions between lanthanide ions and GMP. Within the pH region of 2–6, the electrostatic interaction between lanthanide ions and phosphate group is hindered and the cation-dipole interaction acts as the main driving force for the formation of G-quadruplexes. All the hydrogels were found consisting of three-dimensional network of the intertwining one-dimensional nanofibers formed by the stacking G-quartets induced by lanthanide ions. A significant fluorescence enhancement of thioflavin T (ThT), a fluorescent molecule, was found to be triggered by the G-quadruplex structures, for which the rotation of chromophoric groups on ThT molecules were prohibited due to the implant into the G-quadruplex structures.

Standby techniques of different types have been applied in a wide range of industries to improve system reliability. Since the reliabilities of the operating components are generally high in the initial period of the mission and the standby components are hardly needed, it is more reasonable to set a standby component into cold standby state at the beginning and switch it into the warm standby state after certain period. In this paper, we consider 1-out-of-N: G standby systems with components whose lifetimes can follow general distributions and investigate the optimal state transfer scheduling problem with the objective of maximizing system reliability at mission time. For system reliability evaluation, the system reliability functions are derived in a recursive way and the numerical methods based on the closed Newton-Cotes quadrature rules are proposed without using derivatives. The optimal state transfer scheduling is derived by using the meta-heuristic differential evolution algorithm. By identifying the optimal state transfer scheduling, the state transfer order of standby components can also be determined. Two numerical examples are provided to illustrate the proposed methodology and demonstrate its effectiveness.

How chemical environment of iron influence active species formation in heterogeneous Fenton processes is poorly understood. Herein, a series of metal organic frameworks (MOFs) with various substituents (UiO-66-X) were prepared as templates for iron chemical environment regulation. The results show that bisphenol A (BPA) degraded in the system modified with electron-donating groups (X = NH2, OH, OCH3) was predominantly attributed to HO . While Fe(IV) was detected in systems modified with electron-withdrawing groups (X = Cl, F, NO2). The introduction of electron-donating groups on FeOx@UiO-66-X increases the length of Fe-O bond from 2.06 to 2.12 A, and reduces the binding energy of H2O2 on FeOx, which were beneficial for HO generation. However, electron-withdrawing groups (X = Cl, F, NO2) reduces the Fe-O bond length to 1.99 A, and generates transition product Fe(IV) O. Consequently, this research contributes to the first mechanistic insight into active species regulation during Fenton-like processes by manipulating substituents on MOFs ligand.

The long-term abuse of antibiotics such as norfloxacin (NOR) poses a significant threat to aquatic environments. The development of efficient and economical treatments is still a pain point in the industry. Herein, we reported a directly robust carbonization-pyrolysis method to synthesize iron and nitrogen co-doped biochar material (Fe@N co-doped biochar) that was first applied to NOR removal through persulfate (PS) activation. The catalytic performance and operating factors were systematically investigated. It was found that 10 mg/L NOR achieved 95% degradation within 20 min under optimal reaction conditions. The removal rate of NOR could still achieve 80% and almost 50% of NOR was completely mineralized after five cycles. Through combined electron-paramagnetic-resonance analysis, quenching experiments, and X-ray-photoelectron-spectroscopy tests, •OH, •SO4−, and 1O2 were confirmed as reactive oxygen species in catalytic reaction. Iron activated PS to produce •OH and •SO4− through electron transfer and nitrogen-containing functional groups (graphitic N, C-OH/C = N) accepted electrons from PS to generate 1O2. The radical pathway involving hydroxyl radicals and the nonradical pathways involving singlet oxygen together accounted for the rapid degradation of NOR. The degradation pathways were comprehensively established, including defluorination, decarboxylation, piperazine ring breakage and nalidixic ring transformation. This study shed light on a new mechanism of radical and nonradical co-dominated PS activation and proposed a simple and inexpensive antibiotic wastewater treatment system.

Bio-oil is produced as a promising fuel for improving the economics of biomass. However, bio-oil utilizations suffer from challenges due to its complex compositions. In this research three bio-oil samples (BSR, BPA and BFT) were blended with coal/char to prepare slurries as biofuels. The thermogravimetric analysis of combustion and ash compositions of biofuels were investigated. Results showed that bio-oils have more weight loss peaks than coal/char, and the Tm is concentrated in the range from 476.4 °C to 495.2 °C, which are much lower than char. The contribution of coal/char and bio-oils to biofuel blendings can be clearly seen in the combustion profiles. There is a synergistic effect between bio-oils and coal/char improving the combustion performance. Tb of lignite BSR slurry reduced up to around 40 °C in comparison with lignite. The temperature range for combustion became narrow. By blending BSR, the Ea of lignite, long flame coal and char reduced 5.65 kJ mol−1, 13.56 kJ mol−1 and 6.31 kJ mol−1, respectively. Lignite BSR slurry with the lowest activation energy presented best combustion characteristics among all biofuels. The improved performance may attribute to AAEMs contained in bio-oil and microexplosions occurred in combustion, and AAEMs also help form lower fusion temperature of ashes.

Atanassov's intuitionistic fuzzy set (IFS) is a generalization of a fuzzy set that can express and process uncertainty much better. There are various averaging operators defined for IFSs. In this paper, a new type of operator called an intuitionistic fuzzy entropy weighted power average ggregation operator is proposed. The entropy among IFSs is taken into consideration to determine the weights. What's more, the similarity is considered to measure the support degree between two elements of the IFS. Compared with other classical power average operators, the proposed operator is completely driven by data and fully takes into account the relationship among values. Finally, an illustrative example of multiple attribute group decision making is presented to show that the proposed operator is effective and practical.

A new approach to address the practical problems with condition evaluation/damage detection of structures is proposed based on the distinct features of a new damage index. The covariance of strain response function (CoS) is a function of modal parameters of the structure. A local stiffness reduction in structure would cause monotonous increase in the CoS. Its sensitivity matrix with respect to local damages of structure is negative and narrow-banded. The damage extent can be estimated with an approximation to the sensitivity matrix to decouple the identification equations. The CoS sensitivity can be calibrated in practice from two previous states of measurements to estimate approximately the damage extent of a structure. A seven-storey plane frame structure is numerically studied to illustrate the features of the CoS index and the proposed method. A steel circular arch in the laboratory is tested. Natural frequencies changed due to damage in the arch and the damage occurrence can be judged. However, the proposed CoS method can identify not only damage happening but also location, even damage extent without need of an analytical model. It is promising for structural condition evaluation of selected components.

In accelerated degradation testing (ADT), test data from higher than normal stress conditions are used to find stochastic models of degradation, e.g., Wiener process, Gamma process, and inverse Gaussian process models. In general, the selection of the degradation model is made with reference to one specific product and no consideration is given to model uncertainty. In this paper, we address this issue and apply the Bayesian model averaging (BMA) method to constant stress ADT. For illustration, stress relaxation ADT data are analyzed. We also make a simulation study to compare the $s\hbox{-}$ credibility intervals for single model and BMA. The results show that degradation model uncertainty has significant effects on the $p\hbox{-}$ quantile lifetime at the use conditions, especially for extreme quantiles. The BMA can well capture this uncertainty and compute compromise $s\hbox{-}$ credibility intervals with the highest coverage probability at each quantile.

As cloud data center has caught the eye of the information-intensive society since its birth, it keeps on a flourishing development due to its advantages such as high availability and resource utilization, rapid elasticity and disaster recovery. However, as a complex system, it means the centralization of failures and risks, which exerts great influence on the service that cloud data center provides to customers. Thus, the service reliability of the cloud data center is always a key concern. In order to evaluate and further improve the service reliability of the cloud data center, modeling analysis is absolutely necessary but difficult to apply because of the complicated cloud control flows, massive-scale service sharing and complex real-world infrastructures. Focused on the active-active data center, which is a typical mode of the cloud data center, a methodology of the service reliability modeling and analysis based on IT infrastructure is proposed. In this methodology, the queuing theory and graph theory are used to formulate the service reliability model, but the Monte Carlo simulation is used for statistical evaluation. During the modeling, the operational process of the active-active data center is divided into two parts—the request stage and execution stage, which are modeled respectively. Then, the modeling and evaluation approaches are applied in a use case, which verifies the applicability and creditability of our approach. Meanwhile, by sensitivity analysis considering the variation of uncertain or key factors both internal and external, several parameters are identified as high-sensitive factors, which can enlighten service providers on service reliability improvement.

During the product life cycle, the lifetime information will be collected at each stage, mainly from different tests at the R&D phase, field usage, and maintenance. To comprehensively conduct reliability assessments, it generally requires the integration of multi-source datasets, even that from similar products. In this article, we considered the scenario that products have been arranged with several accelerated degradation tests (ADT) under different types of accelerated stresses with dependency. The obtained data is called incomplete ADT dataset with incomplete stress conditions which fails the traditional integration method for reliability assessments. A novel method is proposed to accomplish this task through mutually exclusive set (MES) theory. The probability assignments for each dataset are given through the union set of several MESs. Then, the multi-source ADT datasets are integrated with the assigned weights of probabilities. Finally, a simulation study and a real application are given to...

The influence of shale ash on the pyrolysis of Huadian oil shale was investigated. It was found that under the catalysis of shale ash, aliphatics in shale oil were partially converted to aromatics, and the long- chain aliphatics were converted to shorter ones. This effect could be improved by increased content of shale ash or by augmented temperature. Beyond the Diels-Alder reaction for the formation of aromatics, a novel reaction route of cyclization of aliphatics followed by dehydrogenation to aromatics was proposed. The component of CaO in shale ash plays an important role to the general performance of shale ash.

Degradation modeling and remaining useful life (RUL) prediction for products with multiple degradation features are hot topics in the prognostic and health management. The key to this problem is to describe the dependence among multiple degradation features effectively. In this paper, a multivariate degradation modeling approach based on the Bayesian dynamic linear model (BDLM) is proposed to calculate the RULs of degradation features, and the Copula function is employed to capture the dependence among RUL distributions. A combined BDLM is used to establish the multivariate degradation model, which includes two typical BDLMs, namely, the linear growth model and seasonal factors model. After the model parameters get calibrated by the maximum likelihood estimation, the model can predict the degradation process of features. Once the failure thresholds are given, the probability density function and cumulative distribution function (CDF) of RUL for each degradation feature can be obtained. Since these RUL distributions are not independent of each other, the Copula function is adopted herein to couple the CDFs. Finally, some practical testing data of a microwave component, which has two degradation features, are utilized to validate our proposed method. This paper provides a new idea for the multivariate degradation modeling and RUL prediction.

This paper presents the design and development of a new standard of Urban Transit, which replaced the traditional steel wheels with rubber tire and constraint within virtual track. This transit has been in revenue services at Zhuzhou, Hunan, Mainland of China in 2017. In order to facilitate developing the precision lane keeping control system, analyzing and building the 32 meters long transit dynamic models is necessary. This paper describes in three aspects: the transit dynamic model, the simulation procedure for validation testing, and the lane keeping controller design based on model predictive control method. Through these development procedure we can enhance the control system iterative rate and reliability to facilitate the new type of transit for narrow path lane keeping and precise docking at platform.

Manually driving a 32 meters long rubber-tire transit with highly intensive concentration could easily get driver fatigue. Alongside the vision navigation method, this paper describes an automatic path tracking control method. Firstly, introduce the automatic steering control principle which is based on image recognition with optical guidance. Then, specifically describes the path tracking controller design. Finally, the control effect is verified by both joint simulation of Trucksim-MATLAB/Simulink and the real vehicle tests. The results show that the precise docking clearance between the platform and the transit is kept at 10-20cm. When driving at high speed, the stable speed is up to 72km/h, and the lateral deviation is controlled within 0.15m.

The sensor-weapon&ndash, target assignment (S-WTA) problem is a crucial decision issue in C4ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the S-WTA schemes, which can greatly affect the operational effectiveness. In this paper, a mathematical model based on the synthetical framework of the S-WTA problem is established, combining the dependent and independent cooperative engagement modes of sensors and weapons. As this problem is a complex combinatorial optimization problem, a novel genetic algorithm is proposed to improve the solution of this formulated S-WTA model. Based on the prior knowledge of this problem, a problem-specific population initialization method and two novel repair operators are introduced. The performances of the proposed algorithm have been validated on the known benchmarks. Extensive experimental studies compared with three state-of-the-art approaches demonstrate that the proposed algorithm can generate better assignment schemes for the most of the benchmarks.

Osteosarcoma (OSA) is the most common primary bone malignancy overall and is responsible for considerable adolescent mortality. Approximately 850 patients are newly diagnosed with OSA in the United States each year. While the 5-year survival rate for localized OSA has improved from