Hybrid polymer/ZnO solar cells sensitized by PbS quantum dots

Poly[2-methoxy-5-(2-ethylhexyloxy-p-phenylenevinylene)]/ZnO nanorod hybrid solar cells consisting of PbS quantum dots [QDs] prepared by a chemical bath deposition method were fabricated. An optimum coating of the QDs on the ZnO nanorods could strongly improve the performance of the solar cells. A maximum power conversion efficiency of 0.42% was achieved for the PbS QDs' sensitive solar cell coated by 4 cycles, which was increased almost five times compared with the solar cell without using PbS QDs. The improved efficiency is attributed to the cascade structure formed by the PbS QD coating, which results in enhanced open-circuit voltage and exciton dissociation efficiency

Stabilisation of the high energy orbit for a nonlinear energy harvester with variable damping

The non-linearity of a hardening-type oscillator provides a wider bandwidth and a higher energy harvesting capability under harmonic excitations. Also, both low- and high-energy responses can coexist for the same parameter combinations at relatively high excitation levels. However, if the oscillator’s response happens to coincide with the low-energy orbit then the improved performance achieved by the non-linear oscillator over that of its linear counterpart, could be impaired. This is therefore the main motivation for stabilisation of the high-energy orbit. In the present work, a schematic harvester design is considered consisting of a mass supported by two linear springs connected in series, each with a parallel damper, and a third-order non-linear spring. The equivalent linear stiffness and damping coefficients of the oscillator are derived through variation of the damper element. From this adjustment the variation of the equivalent stiffness generates a corresponding shift in the frequency–amplitude response curve, and this triggers a jump from the low-energy orbit to stabilise the high-energy orbit. This approach has been seen to require little additional energy supply for the adjustment and stabilisation, compared with that needed for direct stiffness tuning by mechanical means. Overall energy saving is of particular importance for energy harvesting applications. Subsequent results from simulation and experimentation confirm that the proposed method can be used to trigger a jump to the desirable state, thereby introducing a beneficial addition to the performance of the non-linear hardening-type energy harvester that improves overall efficiency and broadens the bandwidth

Ultra-short-term load prediction of integrated energy system based on load similar fluctuation set classification

Due to the strong coupling characteristics and daily correlation characteristics of multiple load sequences, the prediction method based on time series extrapolation and combined with multiple load meteorological data has limited accuracy improvement, which is tested by the fluctuation of load sequences and the accuracy of Numerical Weather Prediction (NWP). This paper proposes a multiple load prediction method considering the coupling characteristics of multiple loads and the division of load similar fluctuation sets. Firstly, the coupling characteristics of multivariate loads are studied to explore the interaction relationship between multivariate loads and find out the priority of multivariate load prediction. Secondly, the similar fluctuating sets of loads are divided considering the similarity and fluctuation of load sequences. Thirdly, the load scenarios are divided by k-means clustering for the inter-set sequences of similar fluctuating sets, and the Bi-directional Long Short-Term Memory (BI-LSTM) models are trained separately for the sub-set of scenarios and prioritized by prediction. Finally, the effectiveness of the proposed method was verified by combining the multivariate load data provided by the Campus Metabolism system of Arizona State University

FURTHER APPLICATION OF STOCHASTIC RESONANCE FOR ENERGY HARVESTING

ABSTRACT I

Development characteristics and main controlling factors of Carboniferous volcanic reservoirs in the Shixi area, Junggar Basin

The Carboniferous volcanic reservoirs in the Shixi area of the Junggar Basin are complex and diverse. Identifying the characteristics and main factors controlling high-quality volcanic reservoirs is the key to increasing oil and gas reserves and production in this area. Through core observations, thin section identification, physical property and pore structure analyses, combined with production data, the main controlling factors and development modes of high-quality reservoirs were analysed. The results show that the Carboniferous strata in the Shixi area mainly contain andesite and dacite of overflow facies, followed by volcanic breccia and tuff of explosive facies. Volcanic reservoirs in the study area are high-porosity–low-permeability and medium-porosity–low-permeability reservoirs. Volcanic breccia of explosive facies has the best physical properties, showing the characteristics of high porosity and medium permeability. The reservoir space is mainly composed of gas cavities, corrosion pores and fractures, among which the corrosion pores are the most important reservoir spaces of the Carboniferous volcanic rocks. Lithology and lithofacies, weathering and corrosion, and fractures are the main factors controlling the development of high-quality volcanic reservoirs. Volcanic rocks that had experienced weathering and denudation for a long time developed a large number of secondary corrosion pores due to the corrosion of soluble minerals or volcanic ash. Fractures further improved the physical properties, causing volcanic rocks to eventually develop into weathering crust reservoirs. The physical properties of the volcanic rocks far away from the weathering crust were improved through primary gas cavities and structural fractures, and these volcanic rocks eventually developed into the inner reservoir

Small-molecule Targeting AMPA-mediated Excitotoxicity Has Therapeutic Effects in Mouse Models for Multiple Sclerosis

Peer reviewe

On square-wave-driven stochastic resonance for energy harvesting in a bistable system

Stochastic resonance is a physical phenomenon through which the throughput of energy within an oscillator excited by a stochastic source can be boosted by adding a small modulating excitation. This study investigates the feasibility of implementing square-wave-driven stochastic resonance to enhance energy harvesting. The motivating hypothesis was that such stochastic resonance can be efficiently realized in a bistable mechanism. However, the condition for the occurrence of stochastic resonance is conventionally defined by the Kramers rate. This definition is inadequate because of the necessity and difficulty in estimating white noise density. A bistable mechanism has been designed using an explicit analytical model which implies a new approach for achieving stochastic resonance in the paper. Experimental tests confirm that the addition of a small-scale force to the bistable system excited by a random signal apparently leads to a corresponding amplification of the response that we now term square-wave-driven stochastic resonance. The study therefore indicates that this approach may be a promising way to improve the performance of an energy harvester under certain forms of random excitation

The Effect of Medical Choice on Health Costs of Middle-Aged and Elderly Patients with Chronic Disease: Based on Principal-Agent Theory

(1) Background: The discussion on how to reduce the health costs of chronic disease patients has become an important public health issue. Limited research has been conducted on how chronic disease patients’ medical choice of public and private medical institutions affect health costs. (2) Methods: This study used the panel data composed of the China Health and Retirement Longitudinal Survey (CHARLS) from 2011 to 2018, adopted the quasi-natural experimental research method, and set up a control group and an experimental group that chose public medical institutions and private medical institutions, to analyze the association between the medical choice and health costs of chronic disease patients. (3) Results: Compared with chronic disease patients who chose private medical institutions, patients who chose public medical institutions increased their total cost by 44.9%, total out-of-pocket cost by 22.9%, and decreased the total out-of-pocket ratio by 0.117%, total drug cost out-of-pocket ratio by 0.075%, and drug cost ratio by 0.102%. (4) Conclusions: According to the triple principal-agent relationships, the resource advantages given by the government to public medical institutions, the salary incentive system of medical institutions, and the information asymmetry advantage held by physicians may be important factors for the increase in health costs for chronic disease patients

Using mask R-CNN to rapidly detect the gold foil shedding of stone cultural heritage in images

Abstract As immovable stone cultural heritage is kept in the open air, they are more susceptible to damage, and damage detection is very important for the protection and restoration of cultural heritage. This is especially true for gold-overlaid stone cultural heritage, which is usually more complicated than ordinary stone carvings. However, the detection of cultural heritage damages is mainly based on expert visual inspection, which is often subjective, time-consuming, and laborious. This paper uses the Mask R-CNN algorithm to rapidly and accurately detect the gold foil shedding of stone cultural heritage through two-dimensional images. The research data are from the high-precision images of the Dazu Thousand-Hand Bodhisattva Statue (World Heritage, UNESCO) in Chongqing, China. After cleaning and augmentation, 1900 images are input into Mask R-CNN model for training. Finally, the average precision value (AP) for detecting gold foil shedding is found to be 0.967. In order to test the performance of the model, the new images that do not participate in the training period are used, and it is found that the model can still accurately detect the gold foil shedding even if there are interference factors. This is the first attempt to detect the damages of gold-overlaid stone cultural heritage based on a deep learning algorithm, and it has achieved good results