A Fully Decentralized Hierarchical Transactive Energy Framework for Charging EVs with Local DERs in Power Distribution Systems

The penetration rates of both electric vehicles (EVs) and distributed energy resources (DERs) have been increasing rapidly as appealing options to address the global problems of carbon emissions and fuel supply issues. However, uncoordinated EV charging activities and DER generation result in operational challenges for power distribution systems. Therefore, this article has developed a hierarchical transactive energy (TE) framework to locally induce and coordinate EV charging demand and DER generation in electric distribution networks. Based on a modified version of the alternating direction method of multipliers (ADMMs), two fully decentralized (DEC) peer-to-peer (P2P) trading models are presented, that is, an hour-ahead market and a 5-min-ahead real-time market. Compared to existing P2P electricity markets, this research represents the first attempt to comprehensively incorporate alternating current (ac) power network constraints into P2P electricity trading. The proposed TE framework not only contributes to mitigating operational challenges of distribution systems, but also benefits both EV owners and DER investors through secured local energy transactions. The privacy of market participants is well preserved since the bid data of each participant are not exposed to others. Comprehensive simulations based on the IEEE 33-node distribution system are conducted to demonstrate the feasibility and effectiveness of the proposed method

Impact of glycaemic control on circulating endothelial progenitor cells and arterial stiffness in patients with type 2 diabetes mellitus

Topics: Basic science, translational and clinical researchPoster PresentationThis journal supplement contains abstracts from the 17th MRC; Dept. of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong KongINTRODUCTION: Patients with type 2 diabetes mellitus (DM) have increased risk of endothelial dysfunction and arterial stiffness. Levels of circulating endothelial progenitor cells (EPCs) are also reduced in hyperglycaemic states. However, the relationships between glycaemic control, levels of EPCs and arterial stiffness are unknown. METHODS: We measured circulating EPCs and …published_or_final_versionThe 17th Medical Research Conference (MRC), Department of Medicine, University of Hong Kong, Hong Kong, 14 January 2012. In Hong Kong Medical Journal, 2012, v. 18 suppl. 1, p. 63, abstract no. 10

Development and Validation of a Clinical Model for Predicting Delay in Postoperative Transfer Out of the Post-Anesthesia Care Unit: A Retrospective Cohort Study

Guang-Hong Xie,1,* Jun Shen,2,* Fan Li,2 Huan-Huan Yan,2 Ying Qian3 1Department of Operating Room, The First People’s Hospital of Lianyungang, The Affiliated Hospital of XuZhou Medical University, Lianyungang, Jiangsu, 222002, People’s Republic of China; 2Department of Breast Surgery, The First People’s Hospital of Lianyungang, The Affiliated Hospital of XuZhou Medical University, Lianyungang, Jiangsu, 222002, People’s Republic of China; 3Department of Operating Room, Wuxi People’s Hospital, Wuxi, Jiangsu, 214063, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying Qian Department of Operating Room, Wuxi People’s Hospital, No. 299 Qingyang Road, Liangxi District, Wuxi, 214063, Jiangsu, People’s Republic of China, Tel +8615190209358, Email [email protected]: We aimed to analyze the factors related to delay in transfer of patients in the post-anesthesia care unit (PACU) and to develop and validate a prediction model for understanding these factors to guide precise clinical intervention.Methods: We collected data from two cohorts of 1153 and 297 patients who underwent surgery and were treated in the PACU at two time points. We examined their clinical features and anesthesia care data using analytical methods such as logistic regression, Random Forest, and eXtreme Gradient Boosting (Xgboost) to screen out variables and establish a prediction model. We then validated and simplified the model and plotted a nomogram. Using LASSO regression, we reduced the dimensionality of the data. We developed multiple models and plotted receiver operating characteristic (ROC) and calibration curves. We then constructed a simplified model by pooling the identified variables, which included hemoglobin (HB), alanine transaminase (ALT), glucose levels, duration of anesthesia, and the minimum bispectral index value (BIS_min).Results: The model had good prediction performance parameters in the training and validation sets, with an AUC of 0.909 (0.887– 0.932) in the training set and 0.939 (0.919– 0.959) in the validation set. When we compared model 6 with other models, the net reclassification index (NRI) and the integrated discriminant improvement (IDI) index indicated that it did not differ significantly from the other models. We developed a scoring system, and it showed good prediction performance when verified with the training and validation sets as well as external data. Additionally, both the decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the potential clinical efficacy of the model in guiding patient interventions.Conclusion: Predicting transfer delays in the post-anesthesia care unit using predictive models is feasible; however, this merits further exploration.Keywords: delayed transfer, machine learning, nomogram, post-anesthesia care unit, predictive mode

Damage Detection Using Blind Source Separation Techniques

Blind source separation (BSS) techniques are applied in many domains since they allow separating a set of signals from their observed mixture without the knowledge (or with very little knowledge) of the source signals or the mixing process. Two particular BSS techniques called Second-Order Blind Identification (SOBI) and Blind Modal Identification (BMID) are considered in this paper for the purpose of structural damage detection or fault diagnosis in mechanical systems. As shown on experimental examples, the BMID method reveals significant advantages. In addition, it is demonstrated that damage detection results may be improved significantly with the help of the block Hankel matrix. The main advantage in this case is that damage detection still remains possible when the number of available sensors is small or even reduced to one. Damage detection is achieved by comparing the subspaces between the reference (healthy) state and a current state through the concept of subspace angle. The efficiency of the methods is illustrated using experimental data

Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction

It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local geometry of samples is well preserved for low dimensional data representation, 2) both the margin maximization and the classification error minimization are considered for sparse projection calculation, 3) the projection matrix of MEN improves the parsimony in computation, 4) the elastic net penalty reduces the over-fitting problem, and 5) the projection matrix of MEN can be interpreted psychologically and physiologically. Experimental evidence on face recognition over various popular datasets suggests that MEN is superior to top level dimensionality reduction algorithms.Comment: 33 pages, 12 figure

Natural Variation in Grain Selenium Concentration of Wild Barley, Hordeum spontaneum, Populations from Israel

Wild barley (Hordeum spontaneum), the progenitor of cultivated barley, is an important genetic resource for cereal improvement. Selenium (Se) is an essential trace mineral for humans and animals with antioxidant, anticancer, antiarthropathy, and antiviral effects. In the current study, the grain Se concentration (GSeC) of 92 H. spontaneum genotypes collected from nine populations representing different habitats in Israel was investigated in the central area of Guizhou Province, China. Remarkable variations in GSeC were found between and within populations, ranging from 0 to 0.387 mg kg−1 among the 92 genotypes with an average of 0.047 mg kg−1. Genotype 20_C from the Sede Boqer population had the highest GSeC, while genotype 25_1 from the Atlit population had the lowest. The mean value of GSeC in each population varied from 0.010 to 0.105 mg kg−1. The coefficient of variation for each population ranged from 12% to 163%. Significant correlations were found between GSeC and 12 ecogeographical factors out of 14 studied. Habitat soil type also significantly affected GSeC. The wild barley exhibited wider GSeC ranges and greater diversity than its cultivated counterparts. The higher Se grain concentrations found in H. spontaneum populations suggest that wild barley germplasm confer higher abilities for Se uptake and accumulation, which can be used for genetic studies of barley nutritional value and for further improvement of domesticated cereals

Preparation, structural characterisation and antibacterial properties of Ga-doped sol-gel phosphate-based glass

A sol-gel preparation of Ga-doped phosphate-based glass with potential application in antimicrobial devices has been developed. Samples of composition (CaO)(0.30)(Na2O)(0.20-x) (Ga2O3) (x) (P2O5)(0.50) where x = 0 and 0.03 were prepared, and the structure and properties of the gallium-doped sample compared with those of the sample containing no gallium. Analysis of the P-31 MAS NMR data demonstrated that addition of gallium to the sol-gel reaction increases the connectivity of the phosphate network at the expense of hydroxyl groups. This premise is supported by the results of the elemental analysis, which showed that the gallium-free sample contains significantly more hydrogen and by FTIR spectroscopy, which revealed a higher concentration of -OH groups in that sample. Ga K-edge extended X-ray absorption fine structure and X-ray absorption near-edge structure data revealed that the gallium ions are coordinated by six oxygen atoms. In agreement with the X-ray absorption data, the high-energy XRD results also suggest that the Ga3+ ions are octahedrally coordinated with respect to oxygen. Antimicrobial studies demonstrated that the sample containing Ga3+ ions had significant activity against Staphylococcus aureus compared to the control

A hybrid radiation detector for simultaneous spatial and temporal dosimetry

In this feasibility study an organic plastic scintillator is calibrated against ionisation chamber measurements and then embedded in a polymer gel dosimeter to obtain a quasi-4D experimental measurement of a radiation field. This hybrid dosimeter was irradiated with a linear accelerator, with temporal measurements of the dose rate being acquired by the scintillator and spatial measurements acquired with the gel dosimeter. The detectors employed in this work are radiologically equivalent; and we show that neither detector perturbs the intensity of the radiation field of the other. By employing these detectors in concert, spatial and temporal variations in the radiation intensity can now be detected and gel dosimeters can be calibrated for absolute dose from a single irradiation