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Assessments of hydrogen project viability typically focus on evaluating specific sites for development, or providing generic cost-estimates that are independent of location. In reality, the success of hydrogen projects will be intimately linked to the availability of local energy resources, access to key infrastructure and water supplies, and the distance to export ports and energy markets. In this paper, we present an economic model that incorporates assessments of these regional factors to identify areas of high economic potential for hydrogen production - the so-called "Economic Fairways" for such projects. In doing so, the model provides a tool that can be used to inform investors and policy makers on the available opportunities for hydrogen development and their infrastructure requirements. The model includes analysis of the regional economic potential for both blue and green hydrogen projects. It accounts for hydrogen production from renewable (wind and solar) sources, as well as non-renewable sources (steam-methane reformation and coal gasification) combined with carbon capture and storage. Results from case studies conducted with the tool are presented, illustrating the potential for hydrogen production across Australia.

Enhanced geothermal systems (EGS) have a great potential to extract geothermal energy and have attracted much interest. In this paper, based on a 3D thermal-hydrologic model considering CO2 sequestration, the influences of reservoir heterogeneity and anisotropy on CO2 sequestration and heat extraction in CO2-based EGS are investigated. Different heterogeneous reservoirs and homogeneous reservoir are compared, and different ratios among reservoir permeability components are compared. The results show that greater reservoir heterogeneity enhances CO2 sequestration and restrains heat extraction. Higher ratio between horizontal (x and y directions) and vertical permeability components enhances CO2 sequestration and heat extraction, and vertical permeability component has a little effect. With the increasing ratio between x-directional (perpendicular to the line of the injection well and the production well) and y-directional (perpendicular to x direction) reservoir permeability components (i.e. kx:ky), both CO2 sequestration amount and steady-state heat extraction rate first increase and then decrease, and thermal breakthrough time increases, showing that there exists an optimum kx:ky, which is about 1:1. The results of this paper indicate that reservoir heterogeneity and anisotropy have important influences on CO2 sequestration and heat extraction.

Hydrogen is the most effective and sustainable carrier of clean energy, and liquid-phase hydrogen storage materials with high hydrogen content, reversibility and good dehydrogenation kinetics are promising in view of "hydrogen economy". Efficient, low-cost, safe and selective hydrogen generation from chemical storage materials remains challenging, however. In this Review article, an overview of the recent achievements is provided, addressing the topic of nanocatalysis of hydrogen production from liquid-phase hydrogen storage materials including metal-boron hydrides, borane-nitrogen compounds, and liquid organic hydrides. The state-of-the-art catalysts range from high-performance nanocatalysts based on noble and non-noble metal nanoparticles (NPs) to emerging single-atom catalysts. Key aspects that are discussed include insights into the dehydrogenation mechanisms, regenerations from the spent liquid chemical hydrides, and tandem reactions using the in situ generated hydrogen. Finally, challenges, perspectives, and research directions for this area are envisaged.

The automatic recognition of facial expressions is challenging for many reasons, and there is still scope for research in the control of sample quality, the construction of effective feature extraction model and multi feature fusion. This study proposes a facial expression recognition method that combines transfer learning, attention mechanism, and feature fusion technology. In this method, various features are extracted from several pre-trained deep learning networks; the recognition weight of each sample is then learned via a quality attention network. This weight can be used to distinguish the quality of samples and suppress the contribution of low-quality samples in the training process. Finally, for a variety of features, this study proposes a fusion method based on feature clustering and correction. The experimental results indicate that the proposed method can significantly improve the accuracy of expression recognition and achieve state-of-the-art recognition accuracy for RAF-DB and FER2013+ datasets.

In recent years, the incidence of cardiovascular disease has been increasing year by year, has gradually developed into a global health problem, in the world wide concern. Non - cardiac injury is one of the most common cardiovascular diseases. The rehabilitation of patients with non-cardiac myocardial injury is related to their life and quality of life. Rehabilitation exercise is helpful to improve the therapeutic effect of patients. The purpose of this paper is to explore the specific effects of rehabilitation exercise on non-cardiac myocardial injury and to promote the full play of the role of rehabilitation exercise in the treatment of noncardiac myocardial injury. First to illustrate the application of optical microscope, and then from the specific reflection of non cardiac myocardial damage and formation mechanism are introduced, based on the cases were retrospectively analyzed experiment, to explore the rehabilitation exercise in the cardiac effects of myocardial injury treatment, and on the basis of this puts forward the corresponding scientific rehabilitation exercise plan. Experimental results show that compared with the rehabilitation exercise intervention before and after rehabilitation exercise intervention, non cardiac myocardial injury in the therapeutic effect of 17% or so, in the treatment of speed increased by about 21%, in the recurrence rate was reduced by 17% or so, so sports in promoting the cardiac myocardial injury treatment has good effect.

This paper shows 100% renewable electricity by 2050 in Australia is both technically and economically feasible. Modelling the transition from today’s electricity generation mix to 2050 using a capacity expansion model with hourly variability in wind and solar potential input, detailed transmission, ramp rate and inertia constraints, we show that the least-cost power system will consist mostly of wind, solar PV, pumped hydro energy storage and upgraded transmission networks. We demonstrate that the 100% renewable system is not only cost-competitive, but also equally robust in terms of reliability and security compared to the longstanding fossil fuel dominated power system.

Ontology classification, the problem of computing the subsumption hierarchies for classes (atomic concepts), is a core reasoning service provided by Web Ontology Language (OWL) reasoners. Although general-purpose OWL 2 reasoners employ sophisticated optimizations for classification, they are still not efficient owing to the high complexity of tableau algorithms for expressive ontologies. Profile-specific OWL 2 EL reasoners are efficient; however, they become incomplete even if the ontology contains only a small number of axioms that are outside the OWL 2 EL fragment. In this paper, we present a technique that combines an OWL 2 EL reasoner with an OWL 2 reasoner for ontology classification of expressive SROIQ. To optimize the workload, we propose a task decomposition strategy for identifying the minimal non-EL subontology that contains only necessary axioms to ensure completeness. During the ontology classification, the bulk of the workload is delegated to an efficient OWL 2 EL reasoner and only the minimal non-EL subontology is handled by a less efficient OWL 2 reasoner. The proposed approach is implemented in a prototype ComR and experimental results show that our approach offers a substantial speedup in ontology classification. For the wellknown ontology NCI, the classification time is reduced by 96.9% (resp. 83.7%) compared against the standard reasoner Pellet (resp. the modular reasoner MORe).

Metal magnetic memory (MMM) technique can evaluate early damages of ferromagnets and search possible defect locations, while just classifies the defect types roughly. To promote study in this area, the magnetic gradient tensor (MGT) of the self-magnetic leakage field (SMLF) on the fracture zone of crack and stress concentration was measured using a tri-axis magnetometer. From measured results, both the plane and the vertical characteristics of SMLF distributions were discussed. To remove the influence of the measuring direction on experimental results, a new parameter of the analytical signal of magnetic gradient tensor (AMGT) was introduced to determine the location and boundary of the defect. Then, the vertical features were acquired by measuring the plane distributions of AMGT under different lift offs. Through analyzing the vertical features, it was concluded that change rule of the maximum AMGT can be used to predict the defect type. At last, the explanation of the relationship between the vertical feature and the defect type was discussed, which can give some useful inspirations to researchers on magnetic leakage field testing.

Due to reduction of feed in tariff, the exploration of design solutions for smart cost-effective energy system is required to enable the consumers to select the optimum capacity of renewable energy system. This study investigates an innovative reward-penalty mechanism which is specifically designed to promote net zero energy buildings and net zero energy communities. Two different strategies (i.e. the building-level-based strategy and the community-level-based strategy) are developed regarding the application of the proposed reward-penalty mechanism. The electricity loads of family-houses in Ireland ranging from 1,286 kWh/y to 36,802 kWh/y are used to study the effect of the proposed strategies. The results show that both strategies lead to a similar cost versus zero-energy-building level relationship, i.e. a higher profit is related to a higher zero-energy-building level whilst a heavier financial penalty is related to a lower zero-energy-building level. The difference between the costs for the entire community (100 family-houses) predicted by the two different strategies is observed to be less than 10%. The building-level-based strategy is observed to produce the change rate of cost versus zero-energy-building level curve that often deviates significantly from the expected parabolic curve designed in this study, thus the community-level-based strategy provides an advantage over the building-level-based strategy. In addition, the Monte Carlo sampling method is used to quantify the uncertainty of the proposed reward-penalty function. © 2021 Elsevier Ltd

A microcapsule platform was constructed for modulating immunization microenvironments and potentiating vaccination against cancer., Therapeutic cancer vaccines that harness the immune system to reject cancer cells have shown great promise for cancer treatment. Although a wave of efforts have spurred to improve the therapeutic effect, unfavorable immunization microenvironment along with a complicated preparation process and frequent vaccinations substantially compromise the performance. Here, we report a novel microcapsule-based formulation for high-performance cancer vaccinations. The special self-healing feature provides a mild and efficient paradigm for antigen microencapsulation. After vaccination, these microcapsules create a favorable immunization microenvironment in situ, wherein antigen release kinetics, recruited cell behavior, and acid surrounding work in a synergetic manner. In this case, we can effectively increase the antigen utilization, improve the antigen presentation, and activate antigen presenting cells. As a result, effective T cell response, potent tumor inhibition, antimetastatic effects, and prevention of postsurgical recurrence are achieved with various types of antigens, while neoantigen was encapsuled and evaluated in different tumor models.

In the field of Synthetic Aperture Radar (SAR) image interpretation, the Automatic Target Recognition (ATR) has always been the focus and hotspot in this field, which is also the research difficulty in this field. The SAR ATR is generally composed of three steps: extraction of Region of Interest (RoI), target identification, and target classification. The complex flow not only limits the efficiency of SAR ATR, but also makes the overall optimization of the model difficult to be carried out, which restricts the accuracy. This paper discusses Faster R-CNN and SSD adopted from the computer vision and shows how those approaches enable significantly improved performance for SAR ATR. The validity of the deep learning method in the field of automatic target recognition for SAR images and the nature of the university are verified, which lays the foundation for further research.

RFID (Radio Frequency Identification) offers a way to identify objects without any contact. However, positioning accuracy is limited since RFID neither provides distance nor bearing information about the tag. This paper proposes a new and innovative approach for the localization of moving object using a particle filter by incorporating RFID phase and laser-based clustering from 2d laser range data. First of all, we calculate phase-based velocity of the moving object based on RFID phase difference. Meanwhile, we separate laser range data into different clusters, and compute the distance-based velocity and moving direction of these clusters. We then compute and analyze the similarity between two velocities, and select K clusters having the best similarity score. We predict the particles according to the velocity and moving direction of laser clusters. Finally, we update the weights of the particles based on K clusters and achieve the localization of moving objects. The feasibility of this approach is validated on a Scitos G5 service robot and the results prove that we have successfully achieved a localization accuracy up to 0.25 m.

This paper presents a novel network decomposition method that can detect faults of linear analog integrated circuit (IC) in network. The nodal admittance matrix (NAM) of linear analog IC is a function of its internal component values, which can be used for fault detection. However, it is difficult to obtain the NAM of linear analog IC in network. We propose a network decomposition based method to calculate the NAM of the IC under test in network. The IC under test is fault free, if its NAM lies inside the tolerance limit. Otherwise, it is faulty. The effectiveness of the proposed method is validated through benchmark circuits.

In the magnetic flux leakage (MFL) non-destructive testing (NDT) technique, the velocity of the inspecting instruments could produce eddy currents during the sensing that might cause the distortion of the MFL signals. In this paper, the distortions of the sensing signals are analysed using a 2-D finite element model with velocity-induced eddy currents, and Radial Basis Function (RBF) neural networks are presented to compensate the velocity effects in order to obtain pure defect signals. This method has been verified by simulation.

Pipelines used in the petrochemical industries are an essential means of transporting materials economically, efficiently and safely. However, ferromagnetism can lead to cracking, corrosion and other defects due to operation, wear, stress and accidental damage. Moreover, because the pipelines work under the condition of high pressure and for long periods of time, if these defects cannot be discovered and repaired quickly, it will cause problems leading to lower transport efficiency, leaks and other issues. Most pipelines are built under the sea or underground, in the event there are some troubles, the repair cost will be very high. Magnetic flux leakage (MFL) is the most common technique used in pipeline inspection at present [1] . In this technique [2] , the wall of the pipelines is magnetised axially to near saturation flux density. If, at some point, the thickness of the wall is reduced by a defect, a higher fraction of the magnetic flux will ‘leak’ from the wall into the air inside and outside the pipe. The magnetic leakage field measured on the near side of the pipe contains information about the pipe condition. Then the identification of the defect can be implemented based on MFL signals by analysing the correlation between MFL signals and the defect geometry parameter

Magnetic flux leakage techniques are used extensively to detect and characterise defects in natural gas and oil transmission pipelines. Based on the least squares support vector machines (LS-SVMs) technique, this paper presents a novel approach for the three-dimensional (3-D) defect profile reconstructed from magnetic flux leakage signals. The basic theory of LS-SVM for function estimates is given. The hyper-parameters of the LS-SVMs problem formulations are tuned using a 10-fold cross validation procedure and a grid search mechanism, and applying the pruning algorithm to impose sparseness on the LS-SVMs. The training data are composed of the measured and simulated data. A mapping from MFL signals to 3-D profiles of defects is established, the reconstruction of 3-D profiles of defects from magnetic flux leakage inspection signals is achieved and 3-D error of reconstruction results is analysed. The experimental results show that the LS-SVM has high precision, good generalisation ability and capability of tolerating noise.

Large scale ontology applications require efficient reasoning services, of which ontology classification is the fundamental reasoning task. The special EL reasoners are efficient, but they can not classify ontologies with axioms outside the OWL 2 EL profile. The general-purpose OWL 2 reasoners for expressive Description Logics are less efficient when classifying the OWL 2 EL ontologies. In this work, we propose a novel technique that combines an OWL 2 reasoner with an EL reasoner for classification of ontologies expressed in DL SROIQ. We develop an efficient task decomposition algorithm for identifying the minimal non-EL module that is assigned to the OWL 2 reasoner, and the bulk of the workload is assigned to the EL reasoner. Furthermore, this paper reports on the implementation of our approach in the ComR system which integrates the two types of reasoners in a black-box manner. The experimental results show that our method leads to a reasonable task assignment and can offer a substantial speed up (over 50%) in ontology classification.

Nondestructive evaluation of the gas pipeline system is most commonly performed using magnetic flux leakage (MFL) techniques. The data obtained from MFL inspection of the pipes is contaminated by various sources of noise, including the pipe noise due to its material properties, lift-off variation of MFL sensor due to motion of pipe and the system noise. The noise can considerably reduce the detection ability of defect signals in MFL data. The lifting wavelet packet based on a novel threshold method is presented in this paper for minimizing the effects of various sources of noise results. The approach utilizes a novel threshold processing method coupled with preorder decomposition and the best basis postorder searching to improve the performance of the lifting wavelet packet. Compared with the performance of signal noise reduction under three kinds of threshold processing mode (TPM), the test results shows that the proposed method has the characteristics of fast operational speed, high space efficiency and good de-noising effectiveness. Iit is a valid on-line processing method for MFL inspecting signal.

The soul and premise of cultivation modes of compound application talents are correct attitudes of education and engineering. This paper assumes that science and engineering universities should adapt to the needs of time and substitute “the attitudes of engineering education” for traditional “attitude of technology education”. The aim is to cultivate compound application talents with solid scientific theory and technology, management skills, spirits of humanity. By discussing curriculum system reform, strengthening practice, emphasizing the guidance to major-oriented educations and innovation, this paper tends to provide an applicable systematic talents-cultivating mode for ordinary colleges of science and engineering.