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MAB phases represent a class of ternary layered transition metal borides that are structurally analogous to the well-established MAX phase, and they exhibit promising potential in various applications, including mechanics, catalysis, wave-absorption, batteries, and high-temperature environments. However, only a few MAB phases have been experimentally synthesized, making the exploration of new phases a pressing challenge. In this study, three potential Ti-based MABs (Ti2AlB2, Ti3AlB4 and Ti4AlB6) are predicted via first-principles calculations and density functional theory (DFT). The computational analyses confirm the thermodynamic and mechanical stability of these phases, with their lattice parameters determined, providing valuable references for future experimental endeavors. Crystal structure analysis reveals a distinctive feature of MABs, the B–B zigzag chains, which are absent in MAX phases, endowing them with superior in-plane strength. Mechanical analyses show that these compounds exhibit elastic anisotropy, which is closely related to the microstructure of Ti–B slices. Electronic studies confirm the metallic nature of these compounds, while the highly reactive Al atomic layer suggests a significant potential for selectively etching to yield two-dimensional (2D) MBene. Optical studies indicate that these compounds possess excellent polarization and dielectric loss capabilities, making them promising candidates for next-generation materials in photo-thermal conversion, sensing and wave-absorbing devices.

Investigations have shown that storage bugs seriously harm grains during storage. In the interim, essential oils (EOs) have been proven to be a good botanical pesticide. The anti-Lasioderma serricorne properties of Elsholtzia ciliata essential oil, which was obtained by steam distillation, were evaluated using DL-limonene, carvone, and their two optical isomer components using contact, repelling, and fumigation techniques. Simultaneously, the fumigation, contact, and repellent activities of carvone and its two optical isomers mixed with DL-limonene against L. serruricorne were evaluated. The results showed that E. ciliata, its main components (R-carvone, DL-limonene), and S-carvone exhibited both fumigations (LC50 = 14.47, 4.42, 20.9 and 3.78 mg/L) and contact (LD50 = 7.31, 4.03, 28.62 and 5.63 µg/adult) activity against L.serricorne. A binary mixture (1:1) of R-carvone and DL-limonene displayed an obvious synergistic effect. A binary mixture (1:1) of carvone and its two optical isomers exhibited an obvious synergistic effect, too. Furthermore, the repellent activity of the EO, carvone, and its two optical isomers, DL-limonene, and a combination of them varied. To stop insect damage during storage, E. ciliata and its components can be utilized as bio-insecticides.

Spatio-temporal forecasting is of great importance in a wide range of dynamic systems applications, such as earth science, transport planning, etc. These applications rely on accurate predictions of spatio-temporal structured data reflecting real-world phenomena. A stunning characteristic is that the dynamical system is not only driven by some physics laws but also impacted by the localized factor in spatial and temporal regions. One of the major challenges is to infer the underlying causes, which generate the perceived data stream and propagate the involved causal dynamics through the distributed observing units. Another challenge is that the success of machine learning-based predictive models requires massive annotated data for model training. However, the acquisition of high-quality annotated data is objectively manual and tedious as it needs a considerable amount of human intervention, making it infeasible in fields that require high levels of expertise. To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics. To deal with data-acquisition constraints, an active learning mechanism with Kriging for actively acquiring the most informative data is proposed for ST-PCNN training in a partially observable environment. Our experiments on both synthetic and real-world datasets exhibit that the proposed ST-PCNN with active learning converges to near-optimal accuracy with substantially fewer instances.

This study focuses on predicting harmful algal bloom (HAB) events in Lake Okeechobee, a shallow lake in Florida. A spatiotemporal deep learning model is employed to predict the levels of cyanobacteria Microcystis aeruginosa present in the lake for a single-day and a 14-day prediction horizon. Datasets collected from remote sensing (i.e., satellite images from January 2018 to December 2020) and from a physics-based simulation model (i.e., daily simulation from January 2018 to December 2020) are available. Owing to the low quality of remote sensing data caused by various environmental and technical issues, the two available datasets are fused together to create a multisource hybrid dataset for deep learning model training. A convolutional long-short term memory (ConvLSTM) deep neural model is trained on the datasets, and the results of the predictions are compared to the true cyanobacterial index for that time period. Findings include the following: 1) the deep learning model, ConvLSTM, shows promising performance for short- and mid-term HAB forecasting; and 2) the hybrid dataset that fuses remote sensing with physics-based modeling (a.k.a. modeling based on fundamental physical and biogeochemical principles) speeds up the model learning and improves its performance significantly. The proposed methodologies are reliable and cost-effective and could be used to forecast algal bloom occurrences in shallow lakes with limited sparse observations.

Although drug-loaded polymethyl methacrylate (PMMA) bone cement had been widely used in the vertebroplasty and arthroplasty, the mismatch between the drug release cycle and clinical demand, as well as low drug release rate and single therapeutic effect restricted the use of PMMA bone cement as the drug carrier in clinical applications. In this study, poly(methyl methacrylate-acrylic acid)-alendronate [P(MMA-AA)-ALN] drug-loaded nanoparticles with anti-osteoporosis function were synthesized and introduced into the gentamicin sulfate (GS) loaded PMMA bone cement. The rapidly expandable bone cement with water absorption pathways was constructed by adjusting the particle size of P(MMA-AA)-ALN nanoparticles. More importantly, the sustained release of GS and ALN were creatively achieved by their migration through the water absorption pathways composed of P(MMA-AA)-ALN nanoparticles. The results showed that the cumulative release ratios of GS and ALN in the composites were up to 74.67 ± 1.02 % and 75.23 ± 1.96 %. Meanwhile, the release cycles of GS and ALN were extended to 4 weeks and 15 weeks, effectively preventing infection, and matching the normal bone healing cycle. In addition, the swelling and dual drug release bone cement (SDBC) displayed superior antibacterial activity and biocompatibility.

Abstract A generalized policy‐iteration‐based solution to a class of discrete‐time multi‐player non‐zero‐sum games concerning the control constraints was proposed. Based on initial admissible control policies, the iterative value function of each player converges to the optimum approximately, which is structured by the iterative control policies satisfying the Nash equilibrium. Afterwards, the stability analysis is shown to illustrate that the iterative control policies can stabilize the system and minimize the performance index function of each player. Meanwhile, neural networks are implemented to approximate the iterative control policies and value functions with the impact of control constraints. Finally, two numerical simulations of the discrete‐time two‐player non‐zero‐sum games for linear and non‐linear systems are shown to illustrate the effectiveness of the proposed scheme.

Abstract Background Nociceptin/orphanin FQ (N/OFQ) has been revealed to play bidirectional roles in orofacial pain modulation. Calcitonin gene-related peptide (CGRP) is a well-known pro-nociceptive molecule that participates in the modulation of orofacial pain. We aimed to determine the effects of N/OFQ on the modulation of orofacial pain and on the release of CGRP. Methods Orofacial pain model was established by ligating springs between incisors and molars in rats for the simulation of tooth movement. The expression level of N/OFQ was determined and pain level was scored in response to orofacial pain. Both agonist and antagonist of N/OFQ receptor were administered to examine their effects on pain and the expression of CGRP in trigeminal ganglia (TG). Moreover, gene therapy based on the overexpression of N/OFQ was delivered to validate the modulatory role of N/OFQ on pain and CGRP expression. Results Tooth movement elicited orofacial pain and an elevation in N/OFQ expression. N/OFQ exacerbated orofacial pain and upregulated CGRP expression in TG, while UFP-101 alleviated pain and downregulated CGRP expression. N/OFQ-based gene therapy was successful in overexpressing N/OFQ in TG, which resulted in pain exacerbation and elevation of CGRP expression in TG. Conclusions N/OFQ exacerbated orofacial pain possibly through upregulating CGRP.

Abstract MicroRNAs (miRNAs) are a class of small noncoding RNA molecules containing only 20–22 nucleotides. MiRNAs play a role in gene silencing and translation suppression by targeting and binding to mRNA. Proper control of miRNA expression is very important for maintaining a normal physiological environment because miRNAs can affect most cellular pathways, including cell cycle checkpoint, cell proliferation, and apoptosis pathways, and have a wide range of target genes. With these properties, miRNAs can modulate multiple signalling pathways involved in cancer development, such as cell proliferation, apoptosis, and migration pathways. MiRNAs that activate or inhibit the molecular pathway related to tumour angiogenesis are common topics of research. Angiogenesis promotes tumorigenesis and metastasis by providing oxygen and diffusible nutrients and releasing proangiogenic factors and is one of the hallmarks of tumour progression. CRC is one of the most common tumours, and metastasis has always been a difficult issue in its treatment. Although comprehensive treatments, such as surgery, radiotherapy, chemotherapy, and targeted therapy, have prolonged the survival of CRC patients, the overall response is not optimistic. Therefore, there is an urgent need to find new therapeutic targets to improve CRC treatment. In a series of recent reports, miRNAs have been shown to bidirectionally regulate angiogenesis in colorectal cancer. Many miRNAs can directly act on VEGF or inhibit angiogenesis through other pathways (HIF-1a, PI3K/AKT, etc.), while some miRNAs, specifically many exosomal miRNAs, are capable of promoting CRC angiogenesis. Understanding the mechanism of action of miRNAs in angiogenesis is of great significance for finding new targets for the treatment of tumour angiogenesis. Deciphering the exact role of specific miRNAs in angiogenesis is a challenge due to the high complexity of their actions. Here, we describe the latest advances in the understanding of miRNAs and their corresponding targets that play a role in CRC angiogenesis and discuss possible miRNA-based therapeutic strategies.

The spatial distribution and environmental risk assessment of eight hazardous elements in wetland soils in Yellow Sea coastal area of China were investigated. The concentrations of As, Hg, Pb, Cd, Cu, Cr, Zn, and Se varied between 6.35-46.14, 0.004-0.05, 18.90-62.07, 0.17-3.76, 11.34-76.79, 21.52-80.88, 39.25-136.86, 0.002-0.16 mg/kg, respectively. The spatial distributions analysis demonstrated that the high levels of As, Pb, Cd, and Zn were in Sheyang area. The results of correlation and PCA indicated that natural and anthropogenic source were major sources for Pb, Cr, Cu, Zn, and Se. As and Cd were from industrial and agricultural sources, and Hg was from natural source. The Cd indicated that 84% of sites experienced moderate and considerable contamination. The PER indices indicated that 68% of sites experienced considerable and high ecological risk. This study suggested that more attention should be paid to hazardous elements contamination of wetland soils in eastern China.

Background: Psoriasis vulgaris is an inflammatory skin disease. Observational studies have shown associations between circulating cytokine levels and psoriasis vulgaris. But the causal relationship between circulating cytokine and psoriasis vulgaris remains elusive.Methods: To assess the causal effects of cytokine levels on the risk of psoriasis vulgaris and vice versa, we performed a two-sample Mendelian randomization (MR) study by using the inverse-variance weighted (IVW), weighted median, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) in genome-wide association summary statistics of 41 circulating cytokines in up to 8,293 individuals and psoriasis vulgaris in 399,883 individuals.Results: We identified that increasing RANTES level induced an elevated risk of psoriasis vulgaris in IVW (β = 0.33, S.E. = 0.12, p = 0.006). This causal effect showed consistency across the weighted median (β = 0.35, S.E. = 0.15, p = 0.022) and MR-PRESSO method (β = 0.33, S.E. = 0.11, p = 0.028).Conclusions: Our results suggest a potential causal effect of elevated RANTES concentration on the increased risk of psoriasis vulgaris.

The influence of morphology of the Ti2Cu phase on biological corrosion and antibacterial properties of Ti-Cu alloy was investigated by micro-galvanic corrosion. Elongated “micro-galvanic cells” were formed between lamellar Ti2Cu phase (L-Ti2Cu) and α-Ti matrix due to the different Volta potentials. The corrosion rate (Vcorr) of L-Ti2Cu was twice that of granular Ti2Cu phase (G-Ti2Cu) because lamellar Ti2Cu phase had more galvanic interface. The release of Cu2+ ions in L-Ti2Cu was 55 % higher than G-Ti2Cu, reaching 99.5 % of the antibacterial rate in L-Ti2Cu and providing a great potential in clinical application for dental implants.

According to the National Academies, a week long forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies at a given location is a critical step toward understanding their effects on the gulf ecosystems as well as toward anticipating and mitigating the outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM). However, creating such a forecast has remained a challenging problem since LC behavior is dominated by dynamic processes across multiple time and spatial scales not resolved at once by conventional numerical models. In this paper, building on the foundation of spatiotemporal predictive learning in video prediction, we develop a physics informed deep learning based prediction model called—Physics-informed Tensor-train ConvLSTM (PITT-ConvLSTM)—for forecasting 3D geo-spatiotemporal sequences. Specifically, we propose (1) a novel 4D higher-order recurrent neural network with empirical orthogonal function analysis to capture the hidden uncorrelated patterns of each hierarchy, (2) a convolutional tensor-train decomposition to capture higher-order space-time correlations, and (3) a mechanism that incorporates prior physics from domain experts by informing the learning in latent space. The advantage of our proposed approach is clear: constrained by the law of physics, the prediction model simultaneously learns good representations for frame dependencies (both short-term and long-term high-level dependency) and inter-hierarchical relations within each time frame. Experiments on geo-spatiotemporal data collected from the GoM demonstrate that the PITT-ConvLSTM model can successfully forecast the volumetric velocity of the LC and its eddies for a period greater than 1 week.

As clean and low-carbon energy, wind energy has attracted the attention of many countries. The main bearing in the transmission system of large-scale wind turbines (WTs) is the most important part. The research on the condition monitoring of the main bearing has received more attention from many scholars and the wind industry, and it has become a hot research topic. The existing research on the condition monitoring of the main bearing has the following drawbacks: (1) the existing research assigns the same weight to each condition parameter variable, and the model extracts features indiscriminately; (2) different historical time points of the condition parameter variable are given the same weight, and the influence degree of different historical time points on the current value is not considered; and (3) the existing literature does not consider the operating characteristics of WTs. Different operating conditions have different control strategies, which also determine which condition parameters are artificially controlled. Therefore, to solve the problems above, this paper proposes a novel method for condition monitoring of WT main bearings by applying the dual attention mechanism and Bi-LSTM, named Dual Attention-Based Bi-LSTM (DA-Bi-LSTM). Specifically, two attention calculation modules are designed to extract the important features of different input parameters and the important features of input parameter time series, respectively. Then, the two extracted features are fused, and the Bi-LSTM building block is utilized to perform pre-and post-feature extraction of the fused information. Finally, the extracted features are applied to reconstruct the input data. Extensive experiments verify the performance of the proposed method. Compared with the Bi-LSMT model without adding an attention module, the proposed model achieves 19.78%, 2.17%, and 18.92% improvement in MAE, MAPE, and RMSE, respectively. Compared with the Bi-LSTM model which only considers a single attention mechanism, the proposed model achieves the largest improvement in MAE and RMSE by 28.84% and 30.37%. Furthermore, the proposed model has better stability and better interpretability of the monitoring process.

Increased global renewable power demands and the high energy density of ocean currents have motivated the development of ocean current turbines (OCTs). These compliantly mooring systems will maintain desired near-surface operating depths using variable buoyancy, lifting surface, sub-sea winches, and/or surface buoys. This paper presents a complete numerical simulation of a 700 kW variable buoyancy controlled OCT that includes detailed turbine system, inflow, actuator (i.e., generator and variable buoyancy), sensor, and fault models. Simulation predictions of OCT performance are made for normal, hurricane, and fault scenarios. Results suggest this OCT can operate between depths of 38 m to 329 m for all homogeneous flow speeds between 1.0-2.5 m/s. Fault scenarios show that rotor braking results in a rapid vertical OCT system assent and that blade pitch faults create power fluctuations apparent in the frequency domain. Finally, simulated OCT operations in measured ocean currents (i.e., normal and hurricane conditions) quantify power statistics and system behavior typical and extreme conditions.

The skeletal system and immune system seem to be two independent systems. However, there in fact are extensive and multiple crosstalk between them. The concept of osteoimmunology was created to describe those interdisciplinary events, but it has been constantly updated over time. In this review, we summarize the interactions between the skeletal and immune systems in the co-development of the two systems and the progress of certain typical bone abnormalities and bone regeneration on the cellular and molecular levels according to the mainstream novel study. At the end of the review, we also highlighted the possibility of extending the research scope of osteoimmunology to other systemic diseases. In conclusion, we propose that osteoimmunology is a promising perspective to uncover the mechanism of related diseases; meanwhile, a study from the point of view of osteoimmunology may also provide innovative ideas and resolutions to achieve the balance of internal homeostasis.

Maintaining transient stability is a core requirement for ensuring safe operation of power systems. Hence, quick and accurate assessment of the transient stability of power systems is particularly critical. As the deployment of wide area measurement systems (WAMS) expands, transient stability assessment (TSA) based on machine learning with data of phasors measurement units (PMUs) also develops rapidly. However, unstable samples of the power system are rarely seen in practice which affects greatly the effectiveness of transient instability recognition. To address this problem, we propose a deep imbalanced learning-based TSA framework. First, an improved denoising autoencoder (DAE) is constructed to map the training set to hidden space for dimension reduction. Then, adaptive synthetic sampling (ADASYN) is further used to synthesize unstable samples in hidden space to balance the proportion of different classes. Third, the synthesized data are decoded into the original space to enhance the training set. Finally, an ensemble cost-sensitive classifier based on a stacked denoising autoencoder (SDAE) is designed to extract different feature patterns, and the SDAEs are merged with a fusion layer to classify the status of the power system. The simulation results of two benchmark power systems indicate that the proposed method can effectively improve the recognition accuracy of unstable cases by combining nonlinear data synthesis with ensemble cost-sensitive learning methods. Compared with other imbalanced learning methods, the proposed framework enjoys superiority both in accuracy and G-mean.

Synthetic materials allow easy processing and modification for bone defect repairing with a good immunological compatibility and minimal risks of infections and donor site morbidity. But compared with an autologous bone graft, these artificial scaffolds still have limitations in terms of the osteogenic capacity and mechanical properties including excellent strength and toughness of super-light natural bone. In this study, we aimed to construct a bone scaffold with both a biomimetic multi-level hierarchical structure and similar constituents of a natural bone using HA/collagen composite nanofibers. The electrospinning technique and rolling method were used for bottom-up synthesis of this artificial system from nano-level to micro-level and then to macro-level. MicroCT scanning and scanning electron microscopy confirmed successful preparation of the bone scaffold composed of fully assembled microscopic fibers. The mechanical properties of the mineralized scaffolds were double reinforced by a large number of newborn HA nano particles and the chemical bonds. The bonds were formed between HA and collagen within and between the layers of the scaffold's matrix membrane. The scaffold showed an excellent biocompatibility profile. These suggest the scaffold potential for use in bone tissue engineering. Keywords: Hierarchical microstructure, HA/collagen biomimetic implant, Rolling motifs, Mechanical properties, Biocompatibility

Various malignant tumors, including colorectal cancer, have the ability to form functional blood vessels for tumor growth and metastasis. Vasculogenic mimicry (VM) refers to the ability of highly invasive tumor cells to link each other to form vessels, which is associated with poor cancer prognosis. However, the antitumor VM agents are still lacking in the clinic. Astragalus Atractylodes mixture (AAM), a traditional Chinese medicine, has shown to inhibit VM formation; however the exact mechanism is not completely clarified. In this study, we found that HCT-116 and LoVo could form a VM network. Additionally, hypoxia increases the intracellular reactive oxygen species (ROS) level and accelerates migration, VM formation in colorectal cancer cells, while N-Acetylcysteine (NAC) could reverse these phenomena. Notably, further mechanical exploration confirmed that the matrix metalloprotease 2 (MMP2) induction is ROS dependent under hypoxic condition. On the basis, we found that AAM could effectively inhibit hypoxia-induced ROS generation, migration, VM formation as well as HIF-1α and MMP2 expression. In vivo, AAM significantly inhibits metastasis of colorectal cancer in murine lung-metastasis model. Taken together, these results verified that AAM effectively inhibits migration and VM formation by suppressing ROS/HIF-1α/MMP2 pathway in colorectal cancer under hypoxic condition, suggesting AAM could serve as a therapeutic agent to inhibit VM formation in human colorectal cancer.

Condition monitoring and overheating warnings of the main bearing of large-scale wind turbines (WT) plays an important role in enhancing their dependability and reducing operating and maintenance (O&M) costs. The temperature parameter of the main bearing is the key indicator to characterize the normal or abnormal operating condition. Therefore, forecasting the trend of temperature change is critical for overheating warnings. To achieve forecasting with high accuracy, this paper proposes a novel model for the WT main bearing, named stacked long-short-term memory with multi-layer perceptron (SLSTM-MLP) by utilizing supervisory control and data acquisition (SCADA) data. The model is mainly composed of multiple LSTM cells and a multi-layer perceptron regression layer. By combining condition parameters into a characteristic matrix, SLSTM can mine nonlinear, non-stationary dynamic feature relationships between temperature and its related variables. To evaluate the performance of the SLSTM-MLP model, experimental analysis was carried out from three aspects: different sample capacity sizes, different sampling time segments, and different sampling frequencies. Furthermore, the model’s capability of online fault detection was also carried out by simulation. The results of comparative studies and online fault simulation tests show that the proposed SLSTM-MLP has better performance for temperature forecasting of the main bearing of large-scale WTs.

Background: CD44 is widely used as a putative cancer stem cells (CSCs) marker for colorectal cancer (CRC). However, the prognostic role of CD44 in CRC remains controversial.Methods: We performed a systematic review and meta-analysis to evaluate the association of various CD44 isoforms and overall survival (OS) and clinicopathological features of CRC patients.Results: A total of 48 studies were included in the meta-analysis. Total CD44 isoforms overexpression was significantly correlated with worse OS of patients with CRC (HR = 1.32, 95% CI = 1.08–1.61, P = 0.007). In a stratified analysis, a higher level of either CD44v6 or CD44v2 had an unfavorable impact on OS (HRCD44v6 = 1.50, 95% CI = 1.10–2.14, P = 0.010; HRCD44v2 = 2.93, 95% CI = 1.49–5.77, P = 0.002). Additionally, CD44 was shown to be associated with some clinicopathological features, such as lymph node metastasis (ORCD44 = 1.56, 95% CI = 1.01–2.41, P = 0.044; ORCD44v6 = 1.97, 95% CI = 1.19–3.26, P = 0.008; ORTotal CD44 isoforms = 1.57, 95% CI = 1.15–2.14, P = 0.004), distant metastasis (ORCD44 = 2.90, 95% CI = 1.08–7.83, P = 0.035; ORTotal CD44 isoforms = 1.89, 95% CI = 1.02–3.53, P = 0.044). Moreover, a high level of CD44 showed a possible correlation with poor differentiation (ORTotal CD44 isoforms = 1.44, 95% CI = 1.00–2.08, P = 0.051), elevated level of CD44v6 tend to be correlated with tumor size (OR = 1.71, 95% CI = 0.99–2.96, P = 0.056).Conclusions: This meta-analysis demonstrated that CD44 overexpression might be an unfavorable prognostic factor for CRC patients and could be used to predict poor differentiation, lymph node metastasis and distant metastasis.

Cenospheres are hollow and spherical particles extracted from fly ash. Controllable separation of cenosphere particles with different densities and sizes can be realized by controlling the density of a flotation solution of cenospheres. In this paper, cenospheres were corroded by hydrofluoric acid to produce perforated pores on their shell surfaces. Then, the cenospheres were mixed with water to prepare the cenosphere curing agent. Foam concrete was prepared using ordinary Portland cement (42.5 R), vegetable protein foaming agent, fly ash, cenospheres, and the cenosphere curing agent as raw materials. The water absorption rate of the cenosphere curing agent was 156.0 wt.%, and 85% of the loaded water was released at a relative humidity of 97.4%, which met the microenvironment requirements of foam concrete. Addition of the cenosphere curing agent during foam concrete preparation has the following positive functions for foam concrete: inhibiting autogenous shrinkage, promoting the hydration degree, improving the state of the interfacial transition zone between cenospheres and the hardened cement paste, reducing the number of connected pores, relieving the stress concentration on the pore walls, and enhancing the compression strength of the foam concrete.

The carbon emissions trading market and direct power purchases by large consumers are two promising directions of power system development. To trace the carbon emission flow in the power grid, the theory of carbon emission flow is improved by allocating power loss to the load side. Based on the improved carbon emission flow theory, an optimal dispatch model is proposed to optimize the cost of both large consumers and the power grid, which will benefit from the carbon emissions trading market. Moreover, to better simulate reality, the direct purchase of power by large consumers is also considered in this paper. The OPF (optimal power flow) method is applied to solve the problem. To evaluate our proposed optimal dispatch strategy, an IEEE 30-bus system is used to test the performance. The effects of the price of carbon emissions and the price of electricity from normal generators and low-carbon generators with regards to the optimal dispatch are analyzed. The simulation results indicate that the proposed strategy can significantly reduce both the operation cost of the power grid and the power utilization cost of large consumers.

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.

A highly sensitive and specific LC-MS/MS method for the quantitation of largazole thiol, the active species of the marine-derived preclinical histone deacetylase inhibitor, largazole (prodrug), was developed and validated. Largazole thiol was extracted with ethyl acetate from human or rat plasma along with the internal standard, harmine. Samples were separated on an Onyx Monolithic C18 column by a stepwise gradient elution with 0.1% formic acid in methanol and 0.1% aqueous formic acid employing multiple reaction monitoring (MRM) detection. Linear calibration curves were obtained in the range of 12.5–400 ng/mL with 200 µL of human plasma. The overall intra-day precision was from 3.87% to 12.6%, and the inter-day precision was from 7.12% to 9.8%. The accuracy at low, medium and high concentrations ranged from 101.55% to 105.84%. Plasma protein bindings of largazole thiol in human and rat plasma as determined by an ultrafiltration method were 90.13% and 77.14%, respectively. Plasma drug concentrations were measured by this LC-MS/MS method. The pharmacokinetics of largazole thiol in rats was studied following i.v. administration at 10 mg/kg and found to follow a two-compartment model. Largazole thiol was rapidly eliminated from systemic circulation within 2 h. The established LC-MS/MS method is suitable for the analysis of largazole thiol in human plasma, as well.

Foam concrete with different dry densities (400, 500, 600, 700, and 800 kg/m3) was prepared from ordinary Portland cement (P.O.42.5R) and vegetable protein foaming agent by adjusting the water-cement ratio through the physical foaming method. The performance of the cement paste adopted, as well as the structure and distribution of air pores, was characterized by a rheometer, scanning electron microscope, vacuum water saturation instrument, and image analysis software. Effects of the water-cement ratio on the relative viscosity of the cement paste, as well as pore structure and strength of the hardened foam concrete, were discussed. Results showed that water-cement ratio can influence the size, distribution, and connectivity of pores in foam concrete. The compressive strength of the foam concrete showed an inverted V-shaped variation law with the increase in water-cement ratio.

BACKGROUND: Calcium phosphate cement (CPC) can be molded or injected to form a scaffold in situ, which intimately conforms to complex bone defects. Bioactive glass (BG) is known for its unique ability to bond to living bone and promote bone growth. However, it was not until recently that literature was available regarding CPC-BG applied as an injectable graft. In this paper, we reported a novel injectable CPC-BG composite with improved properties caused by the incorporation of BG into CPC. MATERIALS AND METHODS: The novel injectable bioactive cement was evaluated to determine its composition, microstructure, setting time, injectability, compressive strength and behavior in a simulated body fluid (SBF). The in vitro cellular responses of osteoblasts and in vivo tissue responses after the implantation of CPC-BG in femoral condyle defects of rabbits were also investigated. RESULTS: CPC-BG possessed a retarded setting time and markedly better injectability and mechanical properties than CPC. Moreover, a new Ca-deficient apatite layer was deposited on the composite surface after immersing immersion in SBF for 7 days. CPC-BG samples showed significantly improved degradability and bioactivity compared to CPC in simulated body fluid (SBF). In addition, the degrees of cell attachment, proliferation and differentiation on CPC-BG were higher than those on CPC. Macroscopic evaluation, histological evaluation, and micro-computed tomography (micro-CT) analysis showed that CPC-BG enhanced the efficiency of new bone formation in comparison with CPC. CONCLUSIONS: A novel CPC-BG composite has been synthesized with improved properties exhibiting promising prospects for bone regeneration.