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In order to improve the accuracy and efficiency of hydrogen energy storage system (HESS) risk assessment, the study proposes a risk portfolio assessment model based on kernel principal component analysis-tuna swarm optimization-least squares support vector machine (KPCA-TSO-LSSVM) algorithm. Firstly, the original data were downscaled using KPCA to extract principal components with at least 98% information content. These principal components use the extracted principal components as inputs to the model. Secondly, TSO is used to implement the optimization of parameter settings for LSSVM. Thirdly, the applicability of the proposed KPCA-TSO-LSSVM in HESS risk assessment is verified by case analysis. Finally, the superiority of the model proposed is verified by comparing TSO, whale optimization algorithm (WOA) and particle swarm optimization (PSO). The results show that KPCA-TSO-LSSVM performs optimally in HESS risk assessment. The classification time of the test sample is shorter at 0.0416 s. The accuracy is higher at 97%. Therefore, the proposed model can effectively identify HESS risks, reduce the operational risks of HESS, and improve the stability and reliability of the system. • Kernel principal component analysis extracts principal components of 98% or more. • Tuna swarm algorithm optimises parameters of least squares support vector machine. • Case study validates the applicability of KPCA-TSO-LSSVM in risk assessment. • Multi-method comparisons confirm that KPCA-TSO-LSSVM is the best with 97% accuracy. [ABSTRACT FROM AUTHOR]

Although inorganic salts are considered outstanding thermal storage materials, drawbacks such as low thermal conductivity, corrosion issues, and leakage severely hinder their practical industrial application. Porous ceramics are suitable to serve as the supporting skeleton for inorganic salt due to good corrosion resistance, high thermal conductivity and high porosity. In this study, secondary aluminum dross (SAD) and ferronickel slag (FNS) were used as raw materials to prepare cordierite-mullite porous ceramic (CMC). The NaNO 3 /CMC composites were synthesized using spontaneous melting-infiltrating method, and their thermal storage performance were studied in detail. The results indicated that CMC had a favorable pore structure with high porosity and micro-sized pores, as well as high compressive strength exceeding 30 MPa. Excellent chemical compatibility between CMC and NaNO 3 was demonstrated by XRD analysis. The latent heat of the NaNO 3 /CMC5, NaNO 3 /CMC15 and NaNO 3 /CMC30 composites, as determined by DSC, were 76.4 J/g, 76.7 J/g and 73.6 J/g, respectively, accounting for 43.5%,43.6% and 41.9% of pure NaNO 3. The supercooling of NaNO 3 was significantly decreased after infiltration into CMC supporting skeleton. A 100-cycle thermal cycles experiment was conducted to verify the long-term thermal reliability of the NaNO 3 /CMC composites, including latent heat and heat transfer property. The melting latent heat of the NaNO 3 /CMC5, NaNO 3 /CMC15 and NaNO 3 /CMC30 decreased by nearly 5.6%, 3.7% and 2.9%, respectively, after 100 thermal cycling tests, indicating the NaNO 3 /CMC composites had good thermal reliability. The thermal conductivity of all the NaNO 3 /CMC composites exceeded 1.8 W m−1 K−1 at 350 °C. Additionally, the compressive strength of all the composites showed only minimal decrease after thermal cycling test, remaining higher than 100 MPa. The NaNO 3 /CMC composites offer promising prospects in the medium temperature thermal storage utilization field, such as industrial waste heat recovery, owing to low production cost, high thermal conductivity and high mechanical strength. • Excellent chemical compatibility between CMC and NaNO 3 was verified. • The latent heat of NaNO 3 /CMC composite was more than 40% of that of pure NaNO 3. • Good thermal reliability of NaNO 3 /CMC composite was demonstrated. • The thermal conductivity of NaNO 3 /CMC composite exceeds 1.8 W/(m·K) even after 100 thermal cycles. [ABSTRACT FROM AUTHOR]

Wind energy is an important renewable clean energy resource. However, the stochastic and volatile nature of wind power brings significant challenges to the power system's reliable and secure operation. Accurate and reliable wind power prediction is critical for the integration of wind power into the grid. The existing wind power forecasting (WPF) methods lack an assessment of the reliability of the predicted results, which may result in a financial penalty for the wind energy producers. An accurate prediction with reliability measurement is urgently needed to encounter the intricate nature of the problem. In this paper, a Bayesian framework-based bidirectional gated logic unit (BiGRU) method was proposed for ultra-short-term wind power forecasting. First, an encoder-decoder (ED) architecture was combined with a BiGRU time series modeling and feature–temporal attention (FT-Attention) to improve the accuracy of wind power prediction. Then, two uncertainty losses were applied to improve the model's performance further. The proposed method obtains the uncertainty of forecast results, which effectively eliminates the untrusted results. Our proposed method demonstrated promising results for ultra-short-term wind power forecasting due to its competitive performance compared with traditional forecasting methods. • A novel ultra-short-term WPF method is proposed with a deep Bayesian model. • Quantifying the uncertainty of WPF is a unique ability of our proposed framework. • Aleatoric uncertainty loss and epistemic uncertainty loss are introduced. • Temporal attention and feature attention are designed to enhance the accuracy of WPF. [Display omitted] [ABSTRACT FROM AUTHOR]

Multispectral imaging, a non-invasive technique to measure retinal oxygen saturation levels, is limited due to its time-consuming and expensive nature. In this paper, we present R3ST (Retinex-based Semantic Spectral Separation Transformer), an innovative end-to-end method for RGB fundus image spectral reconstruction. Our proposed model leverages RGB images to reconstruct multispectral images, making retinal oximetry more accessible for diagnostics. Existing spectral reconstruction methods face challenges in achieving high accuracy and generalizing across camera sources, affecting diagnostic results. R3ST addresses these issues through an unsupervised semantic spectral separation module that strives to encourage the separate reconstruction of pixels with different semantics as much as possible. Additionally, we introduce an oxygen saturation loss to improve reconstruction accuracy within the relevant regions. To overcome the limitations of generalization properties, our model incorporates a self-supervised Retinex reflectance extraction module. This simulates various imaging systems and extracts reflectance information from fundus images for reconstruction. Extensive experiments demonstrate R3ST's superior performance in spectral reconstruction accuracy, making retinal oximetry a more viable, cost-effective diagnostic option that provides essential eye health and systemic health information. The code of R3ST is available at https://github.com/telesc0pe/R3ST. [ABSTRACT FROM AUTHOR]

This study provided a value-added utilization route for industrial solid waste by converting them into porous ceramics to act as supporting skeleton for PCMs. Steel slag (SS), ferronickel-slag (FNS) and Fly ash (FA) was used as main raw material to prepared anorthite-cordierite (AC) porous ceramics. Five groups of formula with different phase content were designed by adjusting the amount of SS and FNS addition. FactSage 8.0 software was applied to calculate the equilibrium phase composition of AC at high temperature, which provided guidance for the actual high temperature synthesis process. The prepared AC porous ceramics were found to have good mechanical strength and high porosity. Good chemical compatibility between AC and NaNO 3 was demonstrated and the infiltration ratio was varied from 42% to 45%. In addition to high thermal storage capacity, AC has been found to help reduce the supercooling of NaNO 3 and helps to improve the thermal storage performance. After 100 thermal cycles of melting and freezing, NaNO 3 /AC C-PCMs could maintained high than 97% of latent heat. Thermal transfer properties of AC were significantly enhanced after molten NaNO 3 infiltrated. The thermal conductivity of NaNO 3 /AC40 C-PCMs at 25 °C reached 1.60 W·(m·K)−1, which is more than three times that of pure NaNO 3. The good thermal storage performance of NaNO 3 /AC C-PCMs makes it potential candidate in high-temperature thermal storage fields like industrial waste heat recovery and solar energy storage. In a word, the application potential of metallurgical slag in the field of energy saving was well demonstrated by this study. • Thermal transfer properties of AC improved with increase of cordierite phase. • AC0 is suitable as thermal insulation materials due to low thermal conductivity of 0.3 W·(m·K)-1. • AC was proved to have good chemical compatibility with NaNO 3. • Heat storage loss of NaNO 3 /AC was less than 3% even after 100 thermal cycles. [ABSTRACT FROM AUTHOR]

Photovoltaic storage system (PVSS) has been spawned with the combined application of photovoltaic (PV), energy storage (ES) and energy blockchain (EB), which has also made important contributions to the energy structure adjustment, energy transaction security and ecological environment protection. The establishment of a reasonable task matching mechanism can further improve the operation efficiency and quality of the system under the EB environment, and this paper consequently constructs a task matching model of PVSS based on GA-CLOUD-GS algorithm. Firstly, the decision-making committee is set up and the index system is determined for the to-be-matched task. Secondly, the Genetic Algorithm (GA) comprehensive weighting method is introduced to integrate the subjective and objective weights to achieve the weight optimization. Thirdly, the cloud model is used to obtain the preference order and priority order of the to-be-matched subjects. Fourthly, the Gale-Shapley (GS) algorithm is used to obtain the task matching results. Finally, the operability, the stability and the effectiveness of the model are separately verified through numerical simulation, sensitivity analysis and comparative analysis. Corresponding suggestions for the task matching of PVSS in the EB environment are also put forward according to the research content. • The necessity, superiority, and application scenarios of collaborative development between PVSS and EB are clarified, and the importance of research on task matching mechanism is identified. • A task matching mechanism model based on the GA-CLOUD-GS algorithm is established, the feasibility, stability, and effectiveness of the established model were verified. • Relevant suggestions are provided for the development of PVSS in the EB environment. [ABSTRACT FROM AUTHOR]

Energy is the foundation and driving force of modernization. Renewable energy has been developed with the help of global environmental awareness. In recent years, supported by the relevant national industrial policies, China's photovoltaic market has developed rapidly and its production capacity is huge. However, the core of supporting the sustainable development of an industry lies in the continuous appreciation of the value of itself. In order to correctly understand the status quo and problems of PV industry value chain in China, based on the "Smile Curve" theory, taking the gross profit rate of PV industry listed companies as sample variables from 2015 to 2017, the research finds that its value chain shows a gradually deepening smile. And to explore the extent to which gross profit margin is affected by various factors, 15 variables are selected from six aspects: internal innovation power, growth potential, profitability, operational efficiency, scale strength and external environment, the Stepwise Regression method is used to determine the optimal model first. Further use Principal Component Analysis to confirm the motivations affecting the value-added of listed companies in the PV industry in the upper, middle and down reaches. The results indicate that only 6 variables have significant effects on the gross profit margin of the PV industry value chain, and there are big diversities in the main influencing factors of the value-added capacity of PV companies in different links, so they should seek their own value-added breakthroughs. • China's PV industry value chain conforms to the "smile curve" form. • The profitability can significantly influence the value of the entire industry. • Innovation is the key factor to the upper and downstream PV companies' value added. • The key factor to midstream PV companies' value added is operational efficiency. • Company's scale and external environment have synergistic effects on the value added. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the wind curtailment has become a serious problem in China and the government are actively seeking solutions to deal with this energy loss. Therefore, the use of non-grid-connected wind power has received great attention since it can be supplied to local end users equipped with energy storage and then mitigate wind curtailment. Since the non-grid-connected wind power and local power load have to confront dramatic power fluctuations, a hybrid energy storage system (HESS) including batteries and supercapacitors is applied. This paper proposes a multi-objective optimization model of HESS configuration in non-grid-connected wind power/energy storage/local user system. In this model, two decision variables, numbers of batteries and supercapacitors, are determined based on the objective of annual profit maximization and wind curtailment rate minimization. To solve this model, a non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain Pareto front and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) technique is applied to select the optimal solution from Pareto solutions. Finally, a wind farm in Hebei province is studied and discussed. A scenario analysis, a sensitivity analysis and a comparative analysis are performed to show the advantages of the proposed model. Highlights • The local consumption of non-grid-connected wind is a solution to wind curtailment. • Hybrid energy storage increases the profit and reduces the wind curtailment rate. • NSGA-II-VIKOR is effective to determine the optimal energy storage capacity. • Energy storage cost and objective weight both influence the optimization result. [ABSTRACT FROM AUTHOR]

Secondary aluminum dross (SAD) is a kind of hazardous waste with high aluminum content produced in the aluminum industry. From another point, SAD is also considered as a valuable aluminum resource, which can be used as high-quality raw material for value-added products such as refractory materials. In this study, a novel route for preparing corundum-spinel heat storage spheres (HSS) using washed residue of secondary aluminum dross (WSAD) as the main raw material was proposed. FactSage 8.1 software was used for the detailed thermodynamic calculation, which provided accurate theoretical guidance for the actual synthesis experiments. The effects of roasting temperature and formula on the main phases, compressive strength, porosity, and bulk density of HSS were systematically analyzed. The strengthening mechanism of the liquid phase on the sintering strength was revealed. The results showed that the HSS with good properties could be obtained with 90 wt% WSAD, 10 wt% kaolin, and 1 wt% bentonite. The HSS mainly consisted of corundum and spinel phases. The synthetic temperature of corundum-spinel ceramic was reduced by more than 200 °C. This study provides a novel way for value-added utilization of SAD as well as realizing the deep detoxification and the removal of nitrogen of it. [ABSTRACT FROM AUTHOR]

The removal of 4 typical pharmaceutically active compounds (PhACs) in sewage sludge, i.e. diclofenac (DCF), clofibric acid (CFA), carbamazepine (CBM), and triclosan (TCS), was evaluated during 3 integrated processes of anaerobic digestions (ADs). The integrated processes included ADs integrated with mixed enzymolyses (MEADs), ADs integrated with mixed enzymolyses together with ultrasonic irradiation pre-treatment (MEUADs), and ADs integrated with mixed enzymolyses together with mechanical rotary disc post-treatment (MEADRDs). The SRTs were set at 15 d. Gas chromatography-mass spectrometry (GC/MS) following solid-phase extraction was used to analyze and detect the target compounds. Under the mesophilic condition, the highest removal during MEAD and MEUAD was 67.6% and 77.1% of CFA, and 78.1% of CBZ during MEADRD. There was little differences between the removals of 4 PhACs during MEADRD, and all the removal rates were higher than 70%. Especially the removal of DCF increased from 40.6% during MEAD to 71.7% during MEADRD. The overall removal during MEADRD was highest with the increase by about 20.9% from that during MEAD. The highest removal during MEAD, MEUAD and MEADRD was 81.1%, 70.7% and 71.8%, respectively, of CFA under the thermophilic condition. MEADRD could realize the highest overall removal, up to 69.4% with the increase by 11.0% compared with MEUAD. The results showed that the integrated process, MEADRD, under both mesophilic and thermophilic condition was suitable for the effective removal of PhACs, and MEADRD under the mesophilic condition was a preferable choice from the energy-saving perspective. [ABSTRACT FROM AUTHOR]

The existence and uniqueness theorem of solutions provides an effective tool for the model validation of both deterministic and stochastic equations. The objective of this paper is to establish the existence and uniqueness of solutions for a class of Itô-Doob stochastic fractional differential equations under non-Lipschitz condition which is weaker than Lipschitz one and contains it as a special case. The solution is constructed with the aid of Carathéodory approximation. Moreover, the continuous dependence of solutions on the initial value is investigated in view of the stability of solutions in the sense of mean square. Finally, an example is given to illustrate the theory. [ABSTRACT FROM AUTHOR]

Electric vehicle sharing (EVS) has been recognized as a promising solution to growing issues of traffic congestion, air pollution and insufficient parking spaces. This study assesses service performance of EVS programs in order to help operators improve service quality and attract more customers. First, this paper identifies 22 criteria from the perspectives of electric vehicles, charging stations, payment and Internet services and establishes the evaluation index system. Then a VIsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) based approach is proposed with three main steps including processing the criteria values represented by crisp values, interval numbers and linguistic variables, determining integrated criteria weights and assessing alternatives by VIKOR method. Next, a case study is provided to verify the feasibility and effectiveness of the proposed method. Three candidate EVS programs in Beijing are evaluated and ranked according to their scores. A sensitivity analysis and a comparative analysis of different methods are also performed to show that the proposed method is capable to assess service performance of EVS programs. Finally, suggestions are made for program operators according to assessment result. [ABSTRACT FROM AUTHOR]

Installing a photovoltaic (PV) power plant at a proper location has been a critical problem for the system planners and investors. In this study, grey cumulative prospect theory is employed to study the site selection of PV power plants from the perspective of sustainability. First, considering the sustainable development, eight decision-making criteria are identified and divided into three categories: geographical conditions, economic profits and environmental benefits. The decision-making steps of grey cumulative prospect theory are then proposed to select the optimal alternative and in this method, an optimization model is used to obtain the importance of criteria. The proposed method has also been implemented in a case of four cities in Northwest China in which the optimal alternative, Yulin city, is selected with the highest prospect value. Finally, a comparison analysis of multi-criteria decision-making methods and a sensitivity analysis of all criteria are performed and the results show that the proposed method can be used effectively for the site selection of PV power plants. [ABSTRACT FROM AUTHOR]

"Photovoltaic + energy storage" is considered as one of the effective means to improve the efficiency of clean energy utilization. In the era of energy sharing, the "photovoltaic - energy storage - utilization (PVESU)" model can create a more favorable market environment. However, the various uncertainties in the construction of the PVESU project have become the main obstacles to the development of the PVESU model. This paper aims to evaluate the risk level of China's PVESU projects through an improved Cloud-TODIM (Cloud-an acronym in Portuguese for Interative Multi-criteria Decision Making) method. First of all, 18 critical risk factors are identified using the constructed five-dimensional risk analysis model. Secondly, the improved method is used to calculate the weight of the comprehensive criteria and rank 14 PVESU alternatives. Finally, the accuracy and feasibility of the hybrid method are verified by sensitivity analysis tests concluding the loss decay coefficient adjustment and comparison analysis with the Cloud-TOPSIS(Cloud-Technique for Order of Preference by Similarity to Ideal Solution) method. Meanwhile, in terms of energy storage, some suggestions are made for the future development of China's PVESU project. This study can also provide insightful enlightenment for PVESU project investors, risk management professionals and decision makers. • The highlights stated are as follows: • Construct an evaluation system of Photovoltaic - Energy storage - Utilization (PVESU) project risk assessment. • Contribute to adding five-dimensional risk analysis method to select critical risk factors. • Propose an improved Cloud-TODIM method to analyze the risk level of PVESU projects. • Extend the research on integrated projects on the field of clean energy and energy storage. [ABSTRACT FROM AUTHOR]

The realization of carbon neutral goal is inseparable from the development of new energy industry, and scientific and effective policy support can accelerate the progress of the goal. In this paper, the policy driven ability of China's photovoltaic industry in the background of carbon neutral is evaluated. Firstly, the evaluation system is established by the improved diamond model. Then, the policy evaluation standard is formulated according to the Interval Type-2 Fuzzy sets (IT2FS). Finally, the weight of each index is determined by using the fuzzy OWA operator weighting method (F-OWA). Then, the policy driving ability of China's photovoltaic industry is evaluated by fuzzy matter-element extension method (F-MEEM), and the effectiveness of the evaluation results is further verified by weight sensitivity analysis. According to the evaluation results, policy support plays an important role in the development of photovoltaic industry. This paper also gives policy suggestions for the development of China's photovoltaic industry under the background of carbon neutral from the macro, meso and micro perspectives. • China's PV industry evaluation system is established by the improved diamond model. •IT2FS and F-OWA are used to process indicators. •The policy driving ability of China's PV industry is evaluated by F-MEEM. •The effectiveness of the evaluation results is further verified by weight sensitivity analysis. •The results are analyzed in detail, and corresponding suggestions are put forward. [ABSTRACT FROM AUTHOR]

• Two novel concepts of UPIoTs and non-grid-connected wind power are explored in China. • ISM is improved with G1 method to overcome the shortcomings of traditional method. • 26 influence factors about how UPIoTs has impacts on wind power are identified. • The relationship between identified factors is researched by proposed model. • Suggestions about how to promote the consumption of wind power are proposed. In the last decade, the scale and speed of development for wind power in China presented an explosive growth and has been maintaining a leading position worldwide. However, rapid development, with addition of the nature of clean energy itself, is followed by the problem of energy consumption such as wind curtailment. To improve the energy efficiency, a new pattern of wind power generation, namely the non-grid-connected wind power, is gradually accessible to the public. At the same time, proposes a novel concept of ubiquitous power Internet of things (UPIoTs) to improve the energy-using environment and quality. In the context of above proposal, to research how UPIoTs promotes the consumption of wind power especially has become a crucial task. The purpose of this paper is to identify influence factors through literature review and expert interview on the basis of relationship between the referenced two. During the process of exploring effects among factors, the improved interpretative structural modeling (ISM) coupled with fuzzy order relation analysis method (fuzzy G1 method) is constructed and adopted to make up for the shortcomings of traditional method. According to the results, three factors are considered as the most influential and some suggestions are given from these three aspects, aiming at making a contribution to the solutions in theory and in practice. [ABSTRACT FROM AUTHOR]

With the increasingly severe situation of environment and gradually depleted energy, wind power has been put on the schedule and comes to an efficient way of power supply and sustainable development. However, wind power generation depends on weather to a great extent, throwing great threats to demand side and supply side. To ensure the normal operation of power system, load forecasting especially for the short term has become a necessary phase. Considering from external environment, this paper aims to identify the influence factors of wind power load and conduct short-term load forecasting with random forest coupling interpretative structural modeling, so as to realize a more accurate and reliable prediction. The data containing information about climate and power load is gathered and put into the calculation of case study. Results show that proposed model performs well, which can be verified and validated through scenario analysis, sensitivity analysis, and comparison analysis. Furthermore, according to the results of factors identification and load forecasting, some suggestions are put forward to deal with the environmental impact on wind power. Image 1 • A novel prediction model combining interpretative structural modeling with random forest is constructed for load forecasting. • Interpretative structural modeling gets improvements by transformation from qualitative to quantitative. • The environmental influence factors of wind power are identified and the hierarchy structures between them are divided. • The proposed model is verified by scenario analysis, sensitivity analysis, and comparison analysis. • Suggestions about how to deal with the impact of external environment on wind power are proposed based on the results. [ABSTRACT FROM AUTHOR]

• The paraffin adsorption capacity of MBFS almost doubled compared with the BFS. • The modified mechanism of Ca(OH) 2 on BFS was investigated. • Prepared C-PCMs is chemically and thermally stable and without paraffin leakage. • Paraffin/MBFS C-PCMs have good thermal reliability even after 100 thermal cycles. As widely used in building materials, blast furnace slag (BFS) has the potential to prepare composite phase change materials (C-PCMs), which can be applied in thermal energy storage field. However, the defects of insufficient porosity and small pore volume of the BFS have severely limited the PCMs adsorption capacity. In this study, the BFS was modified by Ca(OH) 2 to improve the PCMs adsorption capacity. The results showed that the porous structure of BFS was significantly improved after modification. And the apertures of BFS were distinctly enlarged and vast irregular slit-shaped pores emerged, which significantly increased the paraffin adsorption capacity. The MBFS could hold about 26.4% paraffin when 10% Ca(OH) 2 was added as modifier, which was almost double compared with the BFS. The melting temperature and latent heat of paraffin/MBFS C-PCMs were determined as 51.9 °C and 36.4 J/g. And the prepared C-PCMs could maintain good thermal reliability even after 100 thermal cycles, which presented a potential application in the thermal energy storage fields like building energy conservation and solar energy storage. [ABSTRACT FROM AUTHOR]

• Construct an evaluation system of energy storing node optimization selection. • Contribute to adding four subnetworks using BOCR with this study's concern. • Extend the research on energy storage enterprises selection. • Extend the research on overall influential factor evaluation and ranking analysis. Energy storage has developed into a key solution to the problem of energy abandonment, which has been paid close attention. In the era of energy sharing, there is a general trend of cooperation between energy storage companies and power suppliers. This paper evaluates energy storage node enterprises to make their alliance more valuable with power generation node enterprises. First, this paper expounds the importance of sustainable energy storing node optimization selection and identifies 23 criteria from the perspective of benefits, opportunities, costs and risks to establish the evaluation index system. Then a hybrid ANP-BOCR -TOPSIS method is proposed to evaluate the index system including determining the integrated criteria weights and ranking four alternatives by TOPSIS method. Next, a case study is used to verify the accuracy and feasibility of the hybrid method. Through the validation with DEMATEL and comparative analysis of three integrated methods, it is concluded that the hybrid ANP-BOCR-TOPSIS method can be applied to the evaluation of clean energy further research. Finally, the suggestions and conclusions are given in the end. [ABSTRACT FROM AUTHOR]

With the continuous attention on clean energy and energy abandonment, clean energy power generation – energy storage-energy using virtual enterprise (PGSU VE) centered on energy storage has been highly valued. The alliance can not only effectively integrate enterprise resources, but also efficiently adapt to the change of market environment. However, the various uncertainties in the alliance become a major obstacle to PGSU development. To ensure the healthy development of the alliance, risk evaluation has become a crucial step. This paper aims to identify risk factors through questionnaire survey and literature review and calculate the overall risk levels of PGSU VEs with an integrated approach with fuzzy Analytic Hierarchy Process (fuzzy AHP). Results of factors identification consisted of market, social politics and environmental, capacity, cooperation and coordination, alliance formation and risk commitment and profit distribution risk categories. Overall risk levels of three VEs were calculated and ranked with proposed approach whose feasibility and consistency were verified through a comparative analysis with Best Worst Method (BWM) and Stochastic multi-criteria acceptability analysis (SMAA). Moreover, considering existing risks, some suggestions were put forward for future development of clean PGSU VE. The implications and limitations were presented in the conclusions. [ABSTRACT FROM AUTHOR]

• Characteristics of Cr(VI) adsorption on the VTM-HA were well investigated. • The vast majority of Cr(VI) was reduced to Cr(III) by HA-Fe(II) system. • The Cr(VI) was adsorbed on the Ti atom of VTM-HA via ionic bonds. • The reduced Cr(III) was deposited on the VTM-HA surface via complexation. • VTM-HA is a promising complex adsorbent for Cr(VI) pollution treatment. The Cr(VI) could be adsorbed and reduced by the humic acid (HA)-Fe(II) system structured on the V, Ti-magnetite (VTM) surface. The Cr(VI) removal process included adsorption and reduction stages. First, the Cr(VI) was adsorbed on the VTM-HA surface via the ionic bonds between the Ti atoms of VTM core and the O atoms of the HCrO 4 −. The adsorption of Cr(VI) is uniform, monolayer, and controlled by Cr(VI) diffusion. Subsequently, the adsorbed Cr(VI) was reduced by the HA-Fe(II) system on the VTM-HA surface. During the Cr(VI) reduction process, the HA and Fe(II) have a synergistic effect. The Cr(VI) was reduced to the Cr(III) by the HA and Fe(II). Meanwhile, the HA could also reduce Fe(III) to Fe(II), making Fe(II) continue to participate in the Cr(VI) reduction. The olefin, hydroxyl, and aldehyde groups of HA were the primary electron donors during the Cr(VI) reduction. The Fe(II) acted as an electron bridge, transferring the electron from HA to Cr(VI). The reduced Cr(III) was deposited on the VTM-HA surface via the complexation with the carboxyl and hydroxyl groups of HA. The results demonstrated that the Cr(VI) could be adsorbed, reduced and complexed by the HA-Fe(II) system on the VTM-HA surface synchronously. [ABSTRACT FROM AUTHOR]

• Extract the pulse signal and calculate the heart rate through the phone camera. • An improved adaptive peak extraction algorithm is proposed to detect the peaks of pulse signal. • The proposed method makes an important contribution in improving the accuracy of heart rate calculation and reducing the time consumption. As an important indicator of human health, heart rate is related to the diagnosis of cardiovascular diseases. In recent years, extracting the heart rate from the mobile phone image has become a research hotspot. However, the illumination intensity of the background, frame rate of the video, and resolution of the image influence heart rate detection accuracy. To overcome these problems, this study proposed a novel heart rate extraction method based on mobile video. Firstly, the mobile phone camera is engaged to record the finger video, the region of interest (ROI) is extracted through the iterative threshold, and the pulse signal is obtained according to the grayscale change of the resolution within the ROI. Then, a low-pass and a high-pass Butterworth filters are exploited to filter out the noise and interframes from the extracted pulse signal. Finally, an improved adaptive peak extraction algorithm is proposed to detect the pulse peaks and the heart rate derived from the difference in pulse peaks. The experimental results show that light intensity, frame rate and resolution all have an influence on the heart rate extraction accuracy, with the most obvious influence of light, the average accuracy of the experiment can reach 99.32 % under good lighting conditions, while only 72.23 % under poor lighting conditions. In terms of frame rate, increasing the frame rate from 30 fps to 60 fps, the accuracy is improved by 0.9 %. For the resolution, increasing the resolution from 1080 p to 2160 p, the accuracy is improved by 1.12 %. While comparing the proposed method with existing methods, the proposed method has a higher accuracy rate, which has important practical value and application prospects in telemedicine and daily monitoring. [ABSTRACT FROM AUTHOR]

• Two-phase anaerobic digestion of co-substrates (TPADCSs) studied with modified ADM1. • Optimal feedstock ratio of WAS to corn silage was 2:1 during mesophilic TPADCSs. • Acetate maximum specific uptake rate was calibrated to 29 d−1 in acidification phase. • Hydrogen maximum specific uptake rate was calibrated to 23 d−1 in methanogenic phase. • Good agreements achieved between measured data and simulation with the modified ADM1. Two-phase anaerobic digestion of co-substrates is a promising process to alleviate environmental pollution, product valuable methane gas and renew energy. A modified anaerobic digestion model No. 1 (ADM1) was proposed to characterize the mesophilic two-phase anaerobic digestion at the feedstock ratios of waste activated sludge to corn silage 1:1, 2:1, 3:1, respectively. The sensitivity analysis of the modified ADM1 was operated on the platform AQUASIM 2.0 while the kinetic parameter estimation and simulation was implemented on the platform MATLAB 7.0. The Monod maximum specific uptake rate for acetate (k m,ac) was calibrated to 29.0 d−1, and the half-saturation value for acetate (k s,ac) 0.40 kgCOD/m3 in the acidification phase while k m,ac 35.0 d−1, Monod maximum specific uptake rate for hydrogen (k m,h2) 23.0 d−1 and k s,ac 0.70 kgCOD/m3 in the methanogenic phase in the modified ADM1. The results from the batch tests showed the optimal feedstock ratio of waste activated sludge to corn silage was 2:1. Under this condition, chemical oxygen demand could reach the largest removal at around 51.0%, while Ammonia-nitrogen and total phosphorous increased by the maximal 192.0% and 71.5%. Besides, volatile fatty acids were lowest at about 0.27 kgCOD/m3, and the biogas flow was highest at about 47.6 mL/d. The pH values were always maintained at 6.8–7.2. Good agreements could be achieved between the measured data and the simulation with the modified ADM1. Therefore, the optimal and preferable operating factors during two-phase anaerobic digestion of co-substrates could be obtained from the simulation. The modified model will contribute to further investigating and predicting the characteristics of the process. [ABSTRACT FROM AUTHOR]

Abstract In recent years, wind power industry has been developing rapidly as the wind resources are clean, cheap and inexhaustible. However, it is difficult to supply steady wind power generation due to the strong randomness, volatility and uncontrollability of wind energy. Therefore, it is significant to propose an efficient wind power prediction model. In this paper, a short-term wind power prediction model is proposed based on data mining technology and improved support vector machine method. In this model, data mining is employed to investigate the relationship between wind speed and wind power output and then modify the invalid original data. Then, based on wavelet transform method, the high frequency parts of the original signal can be eliminated. Next, cuckoo search algorithm is used to optimize kernel function and penalty factor of support vector machine in order to improve the accuracy of the forecast result. Finally, a wind farm located in the Northwest China is selected to perform the case study. The results indicate that the proposed model has the best performance according to the values of several error assessment indexes, including mean absolute error, mean squared error and mean absolute percentage error. Highlights • A short-term wind power forecasting method is proposed. • A case study in Northwestern China is performed. • The prediction accuracy of the proposed method approaches to 90%. • The proposed method has the smallest values of three evaluation indexes. [ABSTRACT FROM AUTHOR]

As more new energy power is integrated into the power grid in a large scale, the security problem of the new energy power system (NEPS) has been paid more and more attention in China. Therefore, in order to identify the risk elements of NEPS in China and analyze their internal influence relations, this paper proposes a risk identification and analysis model of NEPS based on D numbers theory and decision-making trial and evaluation laboratory (DEMATEL) method. First, a risk element set is established including new energy resource risk, technical risk, demand side risk, grid side risk and human factor, and Crombach α reliability analysis is introduced to verify the rationality of this set. Then, D numbers theory is employed to integrate the expert evaluation information and the direct relation matrix is obtained. Next, based on DEMATEL method, the cause-effect relations and significance degree of risk elements are analyzed. The results indicate that C10, C15 and C14 are in the top three according to prominence values which are 6.455, 6.186, 5.854 respectively. Meanwhile 18 criteria are divided into 8 causal factors and 10 effect factors which are also analyzed by θ in total relation matrix. Finally, according the risk analysis results, managerial and policy implications are explored. This study concluded that the proposed method provides a strong basis for future academic research. [ABSTRACT FROM AUTHOR]

• Liposome encapsulation overcomes low aqueous solubility of 17-Hydroxy-jolkinolide B. • Hyaluronic acid modification enhances the targeting abilities of liposomal drugs. • The modified liposomal drugs suppress breast cancer metastasis by inhibit EMT process. Hyaluronic acid (HA)-coated liposomes were designed for the targeted delivery of 17-hydroxy-jolkinolide B (HA-Lip-HJB). HA-Lip-HJB had a particle size of 130.8 ± 1.9 nm, zeta potential of -52.36 ± 1.91 mV, and encapsulation efficiency of 89.2 ± 1.5 %. In vitro cell experiments indicated that modification of HA-Lip-HJB increased its cytotoxicity and cellular uptake via CD44 receptor-mediated endocytosis pathway. Of particular importance is that HA-Lip-HJB suppressed cell migration and invasion by inhibiting epithelial-mesenchymal transition (EMT) process. Moreover, the HA-Lip-HJB displayed notable growth inhibition on tumor spheroids. Furthermore, in vivo tissue distribution and anti-tumor experiments carried on BALB/C mice bearing 4T1 tumor indicated that HA-Lip-HJB had strong tumor targeting and tumor suppression abilities. The results also demonstrated that HA-Lip-HJB inhibited tumor cells migration and colonization on the lung. Therefore, HA-Lip-HJB is a promising formulation for metastatic breast cancer therapy. [ABSTRACT FROM AUTHOR]

Recently, oxidative stress is a common denominator in the pathogenesis of metal-induced neurotoxicity. Thus, antioxidant therapy is considered as a promising strategy for treating lead-related cognitive impairment. Here, we tested the hypothesis that astragaloside IV (AS-IV) ameliorates lead-associated cognitive deficits through Nrf2-dependent antioxidant mechanisms. Male Nrf2-KO and WT mice received drinking water with 2000 ppm lead and/or AS-IV by gavage for 8 weeks starting at 4 weeks of age. Morris water maze test and biochemical assays were employed to study cognition-enhancing and antioxidant effects of AS-IV. The signaling pathways involved were analyzed using RT-PCR and western blot technology. Significantly, AS-IV attenuated Morris water maze-based cognitive impairment in lead-intoxicated mice. Importantly, cognition-enhancing effect of AS-IV was lost in Nrf2-KO mice. In parallel, AS-IV suppressed lead acetate (PbAc)-induced oxidative stress, as measured by MDA. Mechanistically, AS-IV can up-regulate the expressions of the GCLc and HO-1 at the level of transcription and translation, but not SOD, TrxR activity, GCLm, Trx1, and NQO1 expression. Interestingly, AS-IV induced accumulation of Nrf2 in the nucleus, whereas Nrf2 mRNA levels were unchanged. Furthermore, AS-IV treatment resulted in elevated levels of phosphorylated Akt (active form) and phosphorylated GSK-3β (inactive forms) but decreased level of phosphorylated Fyn. Collectively, our findings indicate that AS-IV may target Nrf2 to attenuate lead-triggered oxidative stress and subsequent cognitive impairments, suggesting that AS-IV is a potential candidate for the treatment of lead-associated cognitive diseases. Unlabelled Image • AS-IV ameliorates PbAc-induced deficits of learning and memory. • AS-IV attenuates PbAc-induced oxidative stress in Nrf2-dependent manner. • AS-IV up-regulates the expressions of GCLc and HO-1 in mice brain. • AS-IV modulates Akt/GSK-3β/Fyn pathway in mice brain. [ABSTRACT FROM AUTHOR]

Pyrite cinder is a typical hazardous waste produced in sulfuric acid industry, which, however, contains a significant level of valuable metals, such as iron, copper, cobalt, etc. About 12 million tons of pyrite cinder are generated in China annually, while the utilization rate is less than 50% due to the complex relationship of various components. In this study, a process of phosphoric acid leaching was used to selectively separate and recover Cu, Co and Fe from the pyrite cinder. The effects of different leaching parameters on the recovery rate of valuable metals in phosphoric acid leaching systems were investigated. Under the optimal conditions, the results indicated that the leaching efficiency of copper and cobalt was above 82% and 99%, respectively. More than 98% of iron was transferred into insoluble FePO 4 ·2H 2 O, which could be used as precursor of battery-grade electrode material after a hydrofluoric acid purification process. Cu and Co in the leaching solutions were extracted efficiently by P204. • A novel method for high-value-added utilization of pyrite cinder was proposed. • Valuable metals of Cu and Co were selectively recovered efficiently. • Battery grade iron phosphate with uniform particle size was prepared. • No solid waste was produced during the comprehensive utilization process. [ABSTRACT FROM AUTHOR]