Silicon nanocrystal solar cells on dielectric substrates

Photovoltaic (PV) has developed rapidly in the recent decades, and is now considered a competitive energy source. The Shockley-Queisser limit, however, places an upper limit on the energy conversion efficiency of a solar cell. As for a single junction crystalline Si (c-Si) solar cell, the theoretical limit is about 31%. To circumvent this limit, the application of silicon nanocrystals (Si NCs) in all-Si tandem solar cells attracts great interest. This thesis focuses on the investigation of solar cells with a mesa isolated p-i-n structure based on Si NCs in a silicon dioxide host matrix fabricated on dielectric substrates. The mesa isolated structure ensures that the signal from the solar cells originates entirely from the Si NC materials and not the substrate. The thesis begins with an investigation on the electroluminescence (EL) and photoluminescence (PL) of a Si NC device, where differences between the EL (1.27 eV) and PL (1.33 eV) peak energies and their corresponding FWHWs are observed. A model categorizing the Si NCs into two subsets is discussed and verified using atom probe tomography (APT), temperature dependent EL and PL. The dopant effects in Si NC materials are also investigated. Various characterization techniques are used including PL, Raman Spectroscopy, X-Ray Diffraction (XRD) and Electron Spin Resonance (ESR). To overcome the challenge of making anode and cathode contacts on a Si NC solar cell using reactive ion etching (RIE), photolithography and lift-off are employed in an alternative fabrication method. The advantages of the photolithography method include better control of isolation mesa fabrication and the avoidance of unpredictable damages caused by the device's exposure to highly energetic particles. The PL peak observed in the device fabricated via the photolithography method is 664 meV higher than the maximum splitting between the quasi Fermi energy levels extracted from the VOC-Temperature relation. The origin of this discrepancy is discussed. Limitations and potential improvements of the Si NC cell are investigated based on its optical and electrical performance

Improvements on Recommender System based on Mathematical Principles

In this article, we will research the Recommender System's implementation about how it works and the algorithms used. We will explain the Recommender System's algorithms based on mathematical principles, and find feasible methods for improvements. The algorithms based on probability have its significance in Recommender System, we will describe how they help to increase the accuracy and speed of the algorithms. Both the weakness and the strength of two different mathematical distance used to describe the similarity will be detailed illustrated in this article

Temporal and spatial variations of macrofouling organisms on ecological floating beds in Yundang Lagoon, China

Abstract(#br)Spatial-temporal variations of macrofouling organisms that attach to ecological floating beds (EFBs) in the Yundang Lagoon were investigated to identify factors that influence the appearance of macrofouling organisms. Results show that the composition, abundance, biomass, and dominance of macrofouling organisms on EFBs exhibited significant seasonal variation. Pearson correlation analysis indicates that the abundance and biomass of the bivalve Mytilopsis sallei showed negative correlation with root biomass ( p < 0.05) and particulate matter ( p < 0.05). Environmental (temperature and salinity, p < 0.05) and biological (bottom-up control) factors are the main drivers of population turnover. There were significant species differences of macrofouling organisms within the different parts of the lagoon, which were attributed to environmental characteristics such as hydrodynamics, dissolved oxygen, and the degree of eutrophication. Results of this study provide a basis for controlling macrofouling organisms, while improving the stability of EFBs and the efficiency of ecological restoration

DeftectNet: Joint loss structured deep adversarial network for thermography defect detecting system

In this paper, a novel joint loss Generative Adversarial Networks (GAN) framework is proposed for thermography nondestructive testing named Defect-Detection Network (DeftectNet). A new joint loss function that incorporates both the modified GAN loss and penalty loss is proposed. The strategy enables the training process to be more stable and to significantly improve the detection rate. The obtained result shows that the proposed joint loss can better capture the salient features in order to improve the detection accuracy. In order to verify the effectiveness and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer/plastic (CFRP) specimens. A comparison experiment has been undertaken to study the proposed method with other current state-of-the-art deep semantic segmentation algorithms. The promising results have been obtained where the performance of the proposed method can achieve end-to-end detection of defects

Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Purpose – An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS). Design/methodology/approach – Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data. Findings – The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine. Originality/value – The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS

MetaGPT: Meta Programming for Multi-Agent Collaborative Framework

Recently, remarkable progress has been made in automated task-solving through the use of multi-agents driven by large language models (LLMs). However, existing works primarily focuses on simple tasks lacking exploration and investigation in complicated tasks mainly due to the hallucination problem. This kind of hallucination gets amplified infinitely as multiple intelligent agents interact with each other, resulting in failures when tackling complicated problems.Therefore, we introduce MetaGPT, an innovative framework that infuses effective human workflows as a meta programming approach into LLM-driven multi-agent collaboration. In particular, MetaGPT first encodes Standardized Operating Procedures (SOPs) into prompts, fostering structured coordination. And then, it further mandates modular outputs, bestowing agents with domain expertise paralleling human professionals to validate outputs and reduce compounded errors. In this way, MetaGPT leverages the assembly line work model to assign diverse roles to various agents, thus establishing a framework that can effectively and cohesively deconstruct complex multi-agent collaborative problems. Our experiments conducted on collaborative software engineering tasks illustrate MetaGPT's capability in producing comprehensive solutions with higher coherence relative to existing conversational and chat-based multi-agent systems. This underscores the potential of incorporating human domain knowledge into multi-agents, thus opening up novel avenues for grappling with intricate real-world challenges. The GitHub repository of this project is made publicly available on: https://github.com/geekan/MetaGP

Mechanisms of action and synergetic formulas of plant-based natural compounds from traditional Chinese medicine for managing osteoporosis: a literature review

Osteoporosis (OP) is a systemic skeletal disease prevalent in older adults, characterized by substantial bone loss and deterioration of microstructure, resulting in heightened bone fragility and risk of fracture. Traditional Chinese Medicine (TCM) herbs have been widely employed in OP treatment owing to their advantages, such as good tolerance, low toxicity, high efficiency, and minimal adverse reactions. Increasing evidence also reveals that many plant-based compounds (or secondary metabolites) from these TCM formulas, such as resveratrol, naringin, and ginsenoside, have demonstrated beneficial effects in reducing the risk of OP. Nonetheless, the comprehensive roles of these natural products in OP have not been thoroughly clarified, impeding the development of synergistic formulas for optimal OP treatment. In this review, we sum up the pathological mechanisms of OP based on evidence from basic and clinical research; emphasis is placed on the in vitro and preclinical in vivo evidence-based anti-OP mechanisms of TCM formulas and their chemically active plant constituents, especially their effects on imbalanced bone homeostasis regulated by osteoblasts (responsible for bone formation), osteoclasts (responsible for bone resorption), bone marrow mesenchymal stem cells as well as bone microstructure, angiogenesis, and immune system. Furthermore, we prospectively discuss the combinatory ingredients from natural products from these TCM formulas. Our goal is to improve comprehension of the pharmacological mechanisms of TCM formulas and their chemically active constituents, which could inform the development of new strategies for managing OP