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MicroRNA-141-3p (miR-141-3p) has been found to be altered in the brain following a stroke. Herein, we investigate the impact of miR-141-3p on the apoptosis of neural stem cells (NSCs) in mice with middle cerebral artery occlusion (MCAO) and the potential mechanisms involved. Eight-week-old mice were injected intracerebroventricularly with miR-141-3p, antagomir-141-3p, or agomir negative control 2 h before MCAO, and animal behavior tests and infraction volume measurements were performed 24 h later. MCAO-mediated brain injury and NSCs apoptosis were observed by HE, TTC, and TUNEL staining. The expression of cleaved caspase-3 and Ki67 was detected by western blotting. The luciferase reporter assay proved that miR-141-3p in combination with its target gene PBX homeobox 1 (PBX1). Exogenous miR-141-3p (agomir-141-3p) treatment increased infraction volume and brain edema and damaged neurological functions compared to control mice. Agomir-141-3p increased miR-141-3p expression in brain tissue of mice with MCAO and suppressed PBX1 expression. The effects of the agomir-141-3p-induced apoptosis in NSCs treated with oxygen-glucose deprivation (OGD)/reoxygenation (R) were abolished by PBX1 overexpression. The results from UCSC and JASPAR database showed that prokineticin 2 (PROK2) was a transcription factor of PBX1. The expression of PROK2 was transcriptionally regulated by PBX1 using RT-PCR and western blot assays. The effects of the apoptosis-promoting caused by PBX1 inhibition in NSCs treated with OGD/R were reversed by PROK2 inhibition. In conclusion, the miR-141-3p/PBX1/PROK2 axis might be a novel therapeutic target for the apoptosis of NSCs in MCAO.

Participants in electricity markets are becoming more proactive because of the fast development of DERs and DSM, which also boosts the emergence of P2P market mechanisms. Moreover, the market is also required to operate in a real-time scheme in response to changes in generation and load to maintain power balance. Therefore, a practicable real-time P2P market mechanism is in urgent need. However, it is technically challenging to deploy P2P mechanisms in real-time, since they most often involve a heavy computation burden, while the time available for negotiation in real-time is very short. Our core contribution is to design a novel asynchronous online optimization framework to enable the real-time P2P market negotiation mechanism, which can greatly reduce the computation and communication burden from two aspects. First, a novel online consensus ADMM algorithm is proposed, which can greatly reduce the computation complexity since only one iteration is performed for each agent in every time period. Second, the market operates in an asynchronous mode so that all agents can freely trade without waiting for idle neighboring agents. The sublinear regret upper bound is proved for our online algorithm, which indicates that social welfare can be maximized in the long run on time average.

Carbon emissions from buildings account for approximately half of China's total social carbon emissions. Focusing only on the carbon emissions of building operation tends to neglect the carbon emissions of other related parts of the building sector, thus slowing down the progress of carbon peaking in the building sector. By applying life-cycle analysis to calculate carbon emissions throughout the building's life cycle, the performance of carbon emissions at each stage of building materials, construction, operation and end-of-life demolition can be identified, so that carbon reduction strategies in building design can be selected.. This paper constructed a method for calculating the carbon emissions of green buildings in whole-building life cycle, and conducted a summary analysis of the carbon emissions of 33 projects that were awarded green building certification. The study found that the Chinese Assessment Standard for Green Buildings has a significant effect on reducing the carbon emissions of buildings in whole-building life cycle. Compared with the current average operational carbon emissions of buildings in China, the carbon intensity of green public buildings is 41.43% lower under this standard and the carbon intensity of green residential buildings is 13.99% lower. A carbon correlation analysis of the provisions of the current Chinese Assessment Standard for Green Buildings was conducted, comparing the changes in the carbon intensity of buildings before and after the revision of the standards. The study concluded that the new version of the standards has a greater impact on public buildings than residential buildings, the requirement of carbon emission reduction in the production stage of building materials is strengthened in terms of carbon emission during the whole-building life cycle. This study addresses the current problem of unclear carbon emission reduction effect of green buildings.

SUMMARY The stability of partial differential equations determines the properties of their solutions. This study focuses on the stability analysis of the equations describing wave propagation in fluids-saturated porous media. We briefly introduce the stability analysis method for the wave propagation equations and discuss the adverse effects on the solutions. In this way, the first part of this paper is mainly devoted to the analysis of the Tuncay and Corapcioglu's (TC) model, which describes the dynamic behaviour of porous media saturated with two immiscible fluids. It is pointed out that the TC model allows spatially bounded but time-exponentially exploding solutions and may yield unstable numerical results. Based on the deduced unstable factors, we construct a stable equivalent fluid (SEF) model. We rigorously analyse the stability of the SEF model using the energy method. For predicting the influence of saturation on wave velocity, the robustness of this model is preserved due to its consistency with the original TC model. Furthermore, the numerical simulations of the wavefields show that the results of the TC model exponentially increase with time after the initial effective wave signal, which does not occur in the SEF model curves. This indicates the necessity of considering the stability from a mathematical point of view during the construction of physical model. It could be useful to merge the mathematical stability theory with the geophysical wave propagation modelling theory.

Due to the fast development of distributed energy resources and demand-side response management, agents in electricity markets are becoming more proactive, which boosts the development of peer-to-peer (P2P) market mechanisms. However, to our knowledge, none of the existing works considers clearing both energy and reserve via a P2P market mechanism in order to compensate for the uncertainty originating from renewable generation and allocate the reserve cost induced by uncertainty fairly. In this article, a novel P2P joint energy and reserve market is proposed, where each agent can negotiate with neighboring agents to determine the quantities and prices of traded energy and reserve. We model the renewable generation uncertainty by versatile distribution and determine the required reserve based on a chance-constrained optimization approach. Then, a fully decentralized P2P market based on consensus alternating direction method of multipliers (ADMM) theory is proposed. In addition, to further lower the social cost, we exploit the correlation and complementarity among uncertainties and design a renewable community-based market, where all renewable agents share uncertainty information to community manager for calculating total required reserve. Finally, simulation results show the convergence performance, fairness and scalability of our market mechanism.

With the increasing penetration of renewable energy resources and plug-in electric vehicles (PEV) in the smart grid, the problem of phase imbalance in a three-phase distribution system becomes more challenging. In this paper, we propose a phase balancing (PB) scheme performed by PEVs. With PB remunerations introduced as financial incentives, PEV owners are encouraged to assist the distribution system operator in reducing the phase imbalance. This is achieved by exploiting the charging flexibility of PEV batteries. We formulate this problem into a non-smooth non-cooperative game, where each PEV owner autonomously minimizes his/her charging cost minus the PB remuneration, considering decisions of other PEV owners. Through transforming this game into a smooth extended one, we are able to solve this challenging problem. Specifically, existence of its Nash equilibrium (NE) is demonstrated, and a distributed algorithm is developed to achieve the NE. Convergence proof of this algorithm is also provided. Simulation results validate our proposed scheme, and illustrate that both PEV owners and the distribution system operator will benefit financially. Moreover, the power quality and system reliability of the distribution network can also be improved.

Considering that the development of science and technology depends on metal support, the EU, USA, and China have issued a critical metal list on the development report. However, the scarce and important mineral deposits on a global scale will not be enough to meet the huge needs of economic development in the future. Many fields such as renewable energy, high-performance computing, and AI all require critical metals as essential supports. A proper price regulation of such important metals will contribute to the fair price power on the international market. In this paper, the pricing history and strategy of critical metal support are fully studied and discussed. Since China has become a major consumer country, China should gain fair price power in the market of important metals.

Hot dry rock (HDR) is a geothermal resource with a high temperature that is widely distributed and has good potential as a clean and renewable energy source. To determine underground electrical structures and to predict granite reservoir distributions, the wide-field electromagnetic (WFEM) method has been applied to explore deep mineral resources and has advantages such as explorations at greater depths and at high resolutions. In this study, a WFEM investigation was carried out for HDR exploration in Gonghe Basin within Qinghai Province. Six parallel survey lines, each spaced apart by 1 km, were designed for WFEM data acquisition. After data processing and inversion, we mapped the subsurface resistivity distribution and divided the inversion resistivity of HDR in the Qiabuqia area into four layers. From the WFEM results, we inferred the location of HDRs, which was verified using drilling wells. HDRs were found at a depth between 3200 m and 3705 m in the well. Furthermore, with the calibration of drilling well GR1, we provided the relationship between temperature and inversion resistivity. From this relationship, the exploration areas with mining potential can be determined.

Seismic precursors prior to the failure of rocks are essential for probing the nucleation process and mitigating hazards. However, such precursory events before large landslides are rarely reported possibly due to the lack of near-source observations. The 2017 Nuugaatsiaq, Greenland landslide that was preceded by an abundance of small earthquakes and captured by a local seismic station is a notable exception and offers us a valuable opportunity to investigate how a large landslide initiated. Prior work suggests that accelerated creeping plays an important role during the landslide nucleation process. However, by analyzing the temporal evolution of the waveform similarities, waveform amplitudes, and inter-event times of the seismic precursors, we find that the Nuugaatsiaq landslide was very likely triggered by a series of accelerated and migratory small earthquakes approaching the nucleation area of the upcoming landslide, thus providing important insights into the failure initiation of massive landslides.

This paper aims to present, in a unified manner, the algebraic techniques of eigen-problem which are valid on both the quaternions and split quaternions. This paper studies eigenvalues and eigenvectors of the v-quaternion matrices by means of the complex representation of the v-quaternion matrices, and derives an algebraic technique to find the eigenvalues and eigenvectors of v-quaternion matrices. This paper also gives a unification of algebraic techniques for eigenvalues and eigenvectors in quaternionic and split quaternionic mechanics.

This paper aims to present, in a unified manner, algebraic techniques for linear equations which are valid on both the algebras of quaternions and split quaternions. This paper, introduces a concept of v-quaternion, studies the problem of v-quaternionic linear equations by means of a complex representation and a real representation of v-quaternion matrices, and gives two algebraic methods for solving v-quaternionic linear equations. This paper also gives a unification of algebraic techniques for quaternionic and split quaternionic linear equations in quaternionic and split quaternionic mechanics.

Owing to the fast deployment of distributed energy resources (DERs) and the further development of demand-side management, small agents in electricity markets are becoming more proactive. This may boost the development of peer-to-peer (P2P) market mechanisms. Meanwhile, since actual load and power generation may substantially deviate from schedules obtained at the day-ahead (forward) market stage, it is needed to rapidly reschedule the trades among agents to maintain power balance through a real-time market mechanism, also in a P2P framework. However, it is technically challenging to develop and operate such P2P market mechanisms in real-time, since they most often involve a heavy computational burden (e.g., based on iterative distributed optimization approaches), while real-time trading demands fast calculation. Our core contribution is hence to describe and analyze a novel online optimization framework to enable the real-time P2P market. It relies on online social welfare maximization using a novel online consensus alternating direction method of multipliers (OC-ADMM) algorithm. The computational complexity is then heavily reduced since only one iteration is performed for each agent at every time step in order to satisfy real-time requirements. We derive a sublinear non-stationary regret upper bound for our algorithm, which implies that social welfare will be maximized in the long run. Simulations based on a number of case studies show that our algorithm has good convergence performance, tracking ability, and high computational efficiency.

BACKGROUND The optimal medical regimen for managing hypertension during acute phase of lacunar infarcts has not yet been clarified in real world setting. The aim of this study was to evaluate blood pressure lowering regimens on neurological progression and clinical outcomes during the acute phase of lacunar infarcts. MATERIAL AND METHODS For this study, 411 patients with first-episode lacunar infarcts and hypertension within 24 hours of symptom onset were included. All patients received antihypertension therapies, with different regimens, as well as routine medication during first 7 days after onset. There were 6 proposed antihypertensive treatments: calcium channel blockers (CCB), angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), β-blocker (β-B), and diuretic drug (DD) alone or in combination. Neurological progression was defined as worsening by ≥1 point in the National Institute of Health Stroke Scale (NIHSS) for motor function. The outcome was assessed using the modified Rankin Scale (mRS): favorable outcome (mRS of 0-1) or unfavorable outcome (mRS 2-5). RESULTS Logistic regression analysis showed that combination therapy with CCB, ACEI/ARB, and β-B exhibited the lowest risk of deterioration (OR=0.48, P=0.019) and unfavorable outcomes (OR=0.50, P=0.022). Similarly, combination therapy with CCB, ACEI/ARB, and DD exhibited lower risk of deterioration (OR=0.63, P=0.033) and unfavorable outcome (OR=0.77, P=0.042) at 3 months. CONCLUSIONS Rational blood pressure lowering was beneficial to the functional outcomes of patients during acute phase of lacunar infarcts, and combination therapy was better than mono-drug therapy.

Three-dimensional seismic survey is widely applied, but 3-D filtering technology has yet to be fully utilized for the analysis of seismic field data. The common approach is to first filter inline and then crossline. However, an effective 3-D filtering method is expected to eliminate coherent noise, such as the ground roll. We propose a 3-D Butterworth filtering method in the time-space domain. Firstly, a Butterworth-type filter in the frequency-wavenumber-domain is designed to suppress the linear noise with a specific apparent velocity. Secondly, transforming this filter to the time and space domain yields 3-D partial differential equations (PDEs), which are applied to suppress the linear noise. Factorizing the finite-difference equations in a different direction other than decreasing the 3-D PDEs to 2-D PDEs produces a highly accurate and efficient algorithm. Designing the 3-D Butterworth filter, selecting the filtering parameters, and showing its application to synthetic data and a 3-D high-density onshore seismic field data from a region in western China are discussed in detail. Numerical experiments with 3-D high-density onshore seismic field data demonstrate that it is more effective than the 3-D frequency-wavenumber-wavenumber (FKK) filtering method.

How to improve the reliability has been a challenging task for transient protection, which has been affected seriously by the fault initial angle (FIA) and transient resistance (TR). Based on support vector machine (SVM), a novel smart transient protection method is presented in this study, which protects a bus and two transmission lines connected to the bus. The method is based on the measurements from the two close-in ends of the two lines. Hilbert–Huang transform is used to extracting instantaneous amplitude from the measured currents. The fault area is determined from the two instantaneous amplitude-integral (IA) of the transient fault currents, the difference of the two IAs, FIA, and the TR. SVM is utilised to determine the fault area. Two IAIs, difference of IAs, FIA and TR are treated as the input to SVMs. Faults with different FIA and TR are treated as a different class of fault, respectively. Correspondingly, a different criterion of discrimination is automatically employed by SVMs for a different fault class. The influence of FIA and TR is eliminated significantly, and the reliability of protection is enhanced substantially. The performance of the method is tested using ATP/EMTP with satisfactory results.

We applied Zelt's new frequency-dependent traveltime tomography (FDTT) method to 2D crosswell seismic field data from an eastern oil field in China. The FDTT uses the frequency content in the seismic waves in both the forward and inverse modeling steps. Although FDTT only uses a 300 Hz frequency to invert the dataset, the degree of matching between the inverted layers from FDTT and that of a sonic well logging curve is high, which shows that FDTT provides a high resolution reconstruction of subsurface structure through the simple use of the first-arrival traveltime data. The case study demonstrates that the FDTT algorithm is practical and can stand up to the complexities of a real 2D crosswell dataset. Additionally, we show that the FDTT method can create a high resolution long wavelength velocity model.

In a geophysical survey, one of the main challenges is to estimate the physical parameter using limited geophysical field data with noise. Geophysical datasets are measured with sparse sampling in a survey. However, the limited data constrain the geophysical interpretation. Traditionally, the field data has been interpolated using mathematical algorithm. In many cases, the estimated field data uncertainties are required to determine which earth models are consistent with the observations. A model-based data-estimation method can provide precise information for imaging and interpretation. The approach used in this paper is based on a stochastic partial differential equation, and it is employed to predict the geophysical data. With this statistical model-based approach, the sparse sample from a survey is used to estimate the underlying spatial surface, and it is assumed that the predicted geophysical data have the same probability density function as the observed data. Furthermore, this method can return the uncertainties of the prediction. Both the synthetic data and the gold mineral exploration field data cases illustrate that this approach leads to better results than traditional methods. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Marine controlled source electromagnetic (CSEM) data inversion for hydrocarbon exploration is often challenging due to high computational cost, physical memory requirement and low resolution of the obtained resistivity map. This paper aims to enhance both the speed and resolution of CSEM inversion by introducing structural geological information in the inversion algorithm. A coarse mesh is generated for Occam's inversion, where the parameters are fewer than in the fine regular mesh. This sparse mesh is defined as a coherence-based irregular (IC) sparse mesh, which is based on vertices extracted from available geological information. Inversion results on synthetic data illustrate that the IC sparse mesh has a smaller inversion computational cost compared to the regular dense (RD) mesh. It also has a higher resolution than with a regular sparse (RS) mesh for the same number of estimated parameters. In order to study how the IC sparse mesh reduces the computational time, four different meshes are generated for Occam's inversion. As a result, an IC sparse mesh can reduce the computational cost while it keeps the resolution as good as a fine regular mesh. The IC sparse mesh reduces the computational cost of the matrix operation for model updates. When the number of estimated parameters reduces to a limited value, the computational cost is independent of the number of parameters. For a testing model with two resistive layers, the inversion result using an IC sparse mesh has higher resolution in both horizontal and vertical directions. Overall, the model representing significant geological information in the IC mesh can improve the resolution of the resistivity models obtained from inversion of CSEM data.

Electromagnetic methods play an important role in mineral exploration and are widely used in the search for metallic resources such as copper, molybdenum, lead-zinc, bauxite, uranium, etc. In this paper, we focus on reviewing the application and development of electromagnetic methods, such as magnetotelluric (MT), audio magnetotelluric (AMT), controlled-source audio magnetotelluric (CSAMT), and transient electromagnetic (TEM), respectively. This paper also presents examples of electromagnetic methods applied to the exploration of metal deposits on land, airborne and in the marine environment. Furthermore, we discuss the future development of electromagnetic prospecting tools, the data processing, modeling and inversion, interpretation, and their application in complex geological environments. The future of successful mineral exploration using EM methods will be focused on concealed and deep target exploration, and possibly one day on resources on the seafloor.

Marine controlled-source electromagnetic (CSEM) is an efficient offshore hydrocarbon exploration method that has been developed during the last 18 years. Sea Bed Logging (SBL) and towed streamer electromagnetic (TSEM) are two different data acquisition systems. We compared these two methods by using 1D sensitivity modeling and 2D Occam&rsquo, s inversion. Based on this research, we tested the effect of frequency, offset range, water depth, reservoir size, and reservoir depth on the detection capability of the two acquisition methods in terms of sensitivity. In order to test the methodology clearly and simply, the geological model was extremely simplified for the inversion. The effect of these parameters on resolution was checked as another purpose. To easily evaluate our inversion results, a simple quantity was employed that we called the anomaly transverse resistance ratio. In the shallow water environment, both the SBL and the TSEM systems had a good sensitivity to the high resistivity targets. However, in the deep water environment, the SBL system had a low noise floor. Then, it could provide better detectability to the deep target. The TSEM had the advantage in terms of the horizontal resolution because of the dense in-line sampling of the electric field.

A new algorithm for busbar protection is proposed based on the Hilbert–Huang transform in this study. The algorithm decomposes currents of all lines connecting to the busbar into intrinsic-mode functions and calculates the instantaneous amplitude of the first intrinsic mode function (IMF). Then, the instantaneous amplitude is normalised to eliminate the impact of fault initial angle and transition resistance. When a fault occurs on the busbar, the initial travelling waves on head ends of the lines all have great instantaneous amplitudes; when a fault occurs on any line, the travelling wave on the faulted line is of great instantaneous amplitude, whereas the initial travelling waves on healthy lines are of small instantaneous amplitudes. Thus, through analysing the detected instantaneous amplitudes of travelling waves, a new busbar protection scheme can be constructed, which achieves a high reliability relative to other fault-travel-wave-based algorithms. According to simulation results on a 500 kV substation modelled in ATP-Draw, the graphical preprocessor to ATP-EMTP windows version, the proposed algorithm is effective in determining various types of faults.

In the Qinghai-Tibet railway ballast exploration, the main purpose is to detect frozen disease by Ground penetrating radar (GPR). New special GPR equipment was tested for the Qinghai-Tibet railway ballast exploration. For frozen disease detection on railway ballast, GPR has high resolution and efficient advantages compared with other geophysical methods. It is essential guaranteed to improve data processing accurately and effectively. In this paper, we employ predictive deconvolution methods to remove multiples to enhance signal noise ratio. Permafrost physical properties were studied, so that permafrost recognition algorithm would be given to detect frozen disease. Firstly, we test this algorithm at Central South University by GR-III GPR, where the results were shown an effective and efficient GPR equipment and permafrost recognition algorithm chosen. According to simulation of the geological data of the typical testing section of Qinghai-Tibet railway, predictive deconvolution method could remove most of multiples from synthetic data. At the end, we explored the permafrost on Qumar area on the Qinghai-Tibet highland. A high quality interpretation was given after the data processing.

The electromagnetic (EM) method is commonly used in mineral exploration due to the method&rsquo, s sensitivity to conductive targets. Controlled source audio-frequency magnetotellurics (CSAMT) is developed from magnetotelluric (MT) method with an artificial EM source to improve the signal amplitude. It has been used for mineral exploration for many years. In this study, we performed a case study of the CSAMT application for the Eagles-Nest lead&ndash, zinc (Pb&ndash, Zn) ore deposits in Jianshui, China. The Eagles-Nest deposit is located in southwest in China in forest-covered complex terrain, making it difficult to acquire the geophysical data. Based on the previous dual-frequency induced polarization (IP) results, we designed four profiles for the CSAMT data acquisition. After data processing and inversion, we mapped the subsurface resistivity distribution. From the CSAMT results, we inferred the location of the ore body, which was verified by the drilling wells. The Pb&ndash, Zn ore body was found at a depth between 373.70 m to 407.35 m in the well.

Increasing importance has been attached to the value of information technology (IT) infrastructure in today's organizations. The development of efficacious IT infrastructure capability enhances business performance and brings sustainable competitive advantage. This study analyzed the IT infrastructure capability in a holistic way and then presented a concept model of IT capability. IT infrastructure capability was categorized into sharing capability, service capability, and flexibility. This study then empirically tested the model using a set of survey data collected from 145 firms. Three factors emerge from the factor analysis as IT flexibility, IT service capability, and IT sharing capability, which agree with those in the conceptual model built in this study.

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach. Published version