Optimal sampling plan for clean development mechanism lighting projects with lamp population decay

This paper proposes a metering cost minimisation model that minimises metering cost under the constraints of sampling accuracy requirement for clean development mechanism (CDM) energy efficiency (EE) lighting project. Usually small scale (SSC) CDM EE lighting projects expect a crediting period of 10 years given that the lighting population will decay as time goes by. The SSC CDM sampling guideline requires that the monitored key parameters for the carbon emission reduction quantification must satisfy the sampling accuracy of 90% confidence and 10% precision, known as the 90/10 criterion. For the existing registered CDM lighting projects, sample sizes are either decided by professional judgment or by rule-of-thumb without considering any optimisation. Lighting samples are randomly selected and their energy consumptions are monitored continuously by power meters. In this study, the sampling size determination problem is formulated as a metering cost minimisation model by incorporating a linear lighting decay model as given by the CDM guideline AMS-II.J. The 90/10 criterion is formulated as constraints to the metering cost minimisation problem. Optimal solutions to the problem minimise the metering cost whilst satisfying the 90/10 criterion for each reporting period. The proposed metering cost minimisation model is applicable to other CDM lighting projects with different population decay characteristics as well

BMP-2 releasing mineral-coated microparticle-integrated hydrogel system for enhanced bone regeneration

Introduction: Large bone defects (LBD) caused by trauma, infection, and tumor resection remain a significant clinical challenge. Although therapeutic agents such as bone morphogenetic protein-2 (BMP-2), have shown substantial potency in various clinical scenarios, their uncontrollable release kinetics has raised considerable concern from the clinical viewpoint. Mineral-coated microparticle (MCM) has shown its excellent biologics loading and delivery potential due to its superior protein-binding capacity and controllable degradation behaviors; thus, it is conceivable that MCM can be combined with hydrogel systems to enable optimized BMP-2 delivery for LBD healing.Methods: Herein, BMP-2 was immobilized on MCMs via electrostatic interaction between its side chains with the coating surface. Subsequently, MCM@BMP-2 is anchored into a hydrogel by the crosslinking of chitosan (CS) and polyethylene glycol (PEG).Results and Discussion: This microparticle–hydrogel system exhibits good biocompatibility, excellent vascularization, and the sustained release of BMP-2 in the bone defect. Furthermore, it is observed that this microsphere–hydrogel system accelerates bone formation by promoting the expression of osteogenesis-related proteins such as RUNX2, osteopontin, and osteocalcin in bone marrow mesenchymal stem cells (BMSCs). Thus, this newly developed multifunctional microparticle–hydrogel system with vascularization, osteogenesis, and sustained release of growth factor demonstrates an effective therapeutic strategy toward LBD

Deep Hashing Based on Class-Discriminated Neighborhood Embedding

Deep-hashing methods have drawn significant attention during the past years in the field of remote sensing (RS) owing to their prominent capabilities for capturing the semantics from complex RS scenes and generating the associated hash codes in an end-to-end manner. Most existing deep-hashing methods exploit pairwise and triplet losses to learn the hash codes with the preservation of semantic-similarities which require the construction of image pairs and triplets based on supervised information (e.g., class labels). However, the learned Hamming spaces based on these losses may not be optimal due to an insufficient sampling of image pairs and triplets for scalable RS archives. To solve this limitation, we propose a new deep-hashing technique based on the class-discriminated neighborhood embedding, which can properly capture the locality structures among the RS scenes and distinguish images class-wisely in the Hamming space. An extensive experimentation has been conducted in order to validate the effectiveness of the proposed method by comparing it with several state-of-the-art conventional and deep-hashing methods. The related codes of this article will be made publicly available for reproducible research by the community

Optimal metering plan for measurement and verification on a lighting case study

M&V (Measurement and Verification) has become an indispensable process in various incentive EEDSM (energy efficiency and demand side management) programmes to accurately and reliably measure and verify the project performance in terms of energy and/or cost savings. Due to the uncertain nature of the unmeasurable savings, there is an inherent trade-off between the M&V accuracy and M&V cost. In order to achieve the required M&V accuracy cost-effectively, we propose a combined spatial and longitudinal MCM (metering cost minimisation) model to assist the design of optimal M&V metering plans, which minimises the metering cost whilst satisfying the required measurement and sampling accuracy of M&V. The objective function of the proposed MCM model is the M&V metering cost that covers the procurement, installation and maintenance of the metering system whereas the M&V accuracy requirements are formulated as the constraints. Optimal solutions to the proposed MCM model offer useful information in designing the optimal M&V metering plan. The advantages of the proposed MCM model are demonstrated by a case study of an EE lighting retrofit project and the model is widely applicable to other M&V lighting projects with different population sizes and sampling accuracy requirements.http://www.journals.elsevier.com/energy2017-01-31hb2016Electrical, Electronic and Computer Engineerin

Sox10+ adult stem cells contribute to biomaterial encapsulation and microvascularization.

Implanted biomaterials and biomedical devices generally induce foreign body reaction and end up with encapsulation by a dense avascular fibrous layer enriched in extracellular matrix. Fibroblasts/myofibroblasts are thought to be the major cell type involved in encapsulation, but it is unclear whether and how stem cells contribute to this process. Here we show, for the first time, that Sox10+ adult stem cells contribute to both encapsulation and microvessel formation. Sox10+ adult stem cells were found sparsely in the stroma of subcutaneous loose connective tissues. Upon subcutaneous biomaterial implantation, Sox10+ stem cells were activated and recruited to the biomaterial scaffold, and differentiated into fibroblasts and then myofibroblasts. This differentiation process from Sox10+ stem cells to myofibroblasts could be recapitulated in vitro. On the other hand, Sox10+ stem cells could differentiate into perivascular cells to stabilize newly formed microvessels. Sox10+ stem cells and endothelial cells in three-dimensional co-culture self-assembled into microvessels, and platelet-derived growth factor had chemotactic effect on Sox10+ stem cells. Transplanted Sox10+ stem cells differentiated into smooth muscle cells to stabilize functional microvessels. These findings demonstrate the critical role of adult stem cells in tissue remodeling and unravel the complexity of stem cell fate determination

Cyanoacrylate Injection Compared with Band Ligation for Acute Gastric Variceal Hemorrhage: A Meta-Analysis of Randomized Controlled Trials and Observational Studies

Background. Cyanoacrylate injection (GVO) and band ligation (GVL) are effective treatments for gastric variceal hemorrhage. However, data on the optimal treatment are still controversial. Methods. For our overall analysis, relevant studies were identified from several databases. For each outcome, data were pooled using a fixed-effect or random-effects model according to the result of a heterogeneity test. Results. Seven studies were included. Compared with GVL, GVO was associated with increased likelihood of hemostasis of active bleeding (odds ratio [OR] = 2.32; 95% confidence interval [CI] = 1.19–4.51) and a longer gastric variceal rebleeding-free period (hazard ratio = 0.37; 95% CI = 0.24–0.56). No significant differences were observed between GVL and GVO for mortality (hazard ratio = 0.66; 95% CI = 0.43–1.02), likelihood of variceal obliteration (OR = 0.89; 95% CI = 0.52–1.54), number of treatment sessions required for complete variceal eradication (weighted mean difference = −0.45; 95% CI = −1.14–0.23), or complications (OR = 1.02; 95% CI = 0.48–2.19). Conclusion. GVO may be superior to GVL for achieving hemostasis and preventing recurrence of gastric variceal rebleeding but has no advantage over GVL for mortality and complications. Additional studies are warranted to enable definitive conclusions

Improvements to longitudinal Clean Development Mechanism sampling designs for lighting retrofit projects

An improved model for reducing the cost of long-term monitoring in Clean Development Mechanism (CDM) lighting retrofit projects is proposed. Cost-effective longitudinal sampling designs use the minimum numbers of meters required to report yearly savings at the 90% confidence and 10% relative precision level for duration of the project (up to 10 years) as stipulated by the CDM. Improvements to the existing model include a new non-linear Compact Fluorescent Lamp population decay model based on the Polish Efficient Lighting Project, and a cumulative sampling function modified to weight samples exponentially by recency. An economic model altering the cost function to a net present value calculation is also incorporated. The search space for such sampling models is investigated and found to be discontinuous and stepped, requiring a heuristic for optimisation; in this case the Genetic Algorithm was used. Assuming an exponential smoothing rate of 0.25, an inflation rate of 6.44%, and an interest rate of 10%, results show that sampling should be more evenly distributed over the study duration than is currently considered optimal, and that the proposed improvements in model accuracy increase monitoring costs by 21.4% in present value terms.Centre for New Energy Systems and the National Hub for the Postgraduate Programme in Energy Efficiency and Demand Side Management at the University of Pretoria.http://www.elsevier.com/locate/apenergyhb201

Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization

With the development of convolutional neural networks (CNNs), the semantic understanding of remote sensing (RS) scenes has been significantly improved based on their prominent feature encoding capabilities. While many existing deep-learning models focus on designing different architectures, only a few works in the RS field have focused on investigating the performance of the learned feature embeddings and the associated metric space. In particular, two main loss functions have been exploited: the contrastive and the triplet loss. However, the straightforward application of these techniques to RS images may not be optimal in order to capture their neighborhood structures in the metric space due to the insufficient sampling of image pairs or triplets during the training stage and to the inherent semantic complexity of remotely sensed data. To solve these problems, we propose a new deep metric learning approach, which overcomes the limitation on the class discrimination by means of two different components: 1) scalable neighborhood component analysis (SNCA) that aims at discovering the neighborhood structure in the metric space and 2) the cross-entropy loss that aims at preserving the class discrimination capability based on the learned class prototypes. Moreover, in order to preserve feature consistency among all the minibatches during training, a novel optimization mechanism based on momentum update is introduced for minimizing the proposed loss. An extensive experimental comparison (using several state-of-the-art models and two different benchmark data sets) has been conducted to validate the effectiveness of the proposed method from different perspectives, including: 1) classification; 2) clustering; and 3) image retrieval. The related codes of this article will be made publicly available for reproducible research by the community

Developmental trajectories of expert perception processing of Chinese characters in primary school children

Previous studies have demonstrated that inversion effect and left-side bias are stable expertise markers in Chinese character processing among adults. However, it is less clear how these markers develop early on (i.e., among primary school students). Therefore, this study aimed to investigate the development of the two markers by comparing primary school-aged students of three age groups (Grade 1, Grade 3, and Grade 5) and adults in tests of inversion effect (Experiment 1) and left-sided bias effect (Experiment 2). The results replicated that both effects during Chinese character processing were present among adults. However, more importantly, the effects were different among primary school-aged students in different grades: the inversion effect was found as early as in Grade 1, but the left-side bias effect did not emerge in Grade 1 and as approximated that of adults until Grade 3. The study suggested a potential dissociation in developing different aspects of expertise during Chinese character processing in early childhood