Non-Halogenated-Solvent Processed and Additive-Free Tandem Organic Solar Cell with Efficiency Reaching 16.67%

Organic solar cells (OSCs) have recently reached a remarkably high efficiency and become a promising technology for commercial application. However, OSCs with top efficiency are mostly processed by halogenated solvents and with additives that are not environmentally friendly, which hinders large-scale manufacture. In this study, high-performance tandem OSCs, based on polymer donors and two small-molecule acceptors with different bandgaps, are fabricated by solution processing with non-halogenated solvents without additive. Importantly, the two active layers developed from non-halogenated solvents show better phase segregation and charge transport properties, leading to superior performance than halogenated ones. As a result, a tandem OSC with high efficiency of up to 16.67% is obtained, showing unique advantages in future massive production.     

高空间照明均匀度植物光源系统的设计

传统的植物照明设计只针对单一参考面均匀度进行评价，难以满足植物在整个生长过程中对均匀光照环境的需求。针对这一问题，首先提出了空间照明均匀度评价体系，并基于该体系设计了一种复合光源模块的立体化照明系统，以期构建照明均匀的植物生长空间。进一步利用Taguchi方法优化实验过程，在结合ANOVA分析的基础上，获得了最优结构参数。最后对所得最优解进行灯珠形状分析和植物生长过程中的照明效果测试。实验结果表明:最优结构可提供一个水平参考面照度均匀度为87.22%，混色均匀度为90.11%；竖直参考面照度均匀度93.02%，混色均匀度91.43%的均匀照明空间。该植物光源系统可满足植物生长过程中对均匀空间照明环境的需求。     

An Adaptive Neuro-Fuzzy Inference System and Black Widow Optimization Approach for Optimal Resource Utilization and Task Scheduling in a Cloud Environment

Wireless Personal Communications - With the enhancing demand of the cloud computing products, task scheduling issue has become the hot study topic in this area. The task scheduling issue of the...     

Optimized Path and Reduced Rule Caching Cost for Software Defined Network (SDN) Based Internet of Things (IOT)

Wireless Personal Communications - 2In Software Defined Network based Internet of Things (SDN-IoT), the path selected for forwarding the packets as per rules, should be Quality of Service (QoS)...     

Micro-expression recognition from local facial regions

MiE is a facial involuntary reaction that reflects the real emotion and thoughts of a human being. It is very difficult for a normal human to detect a Micro-Expression (MiE), since it is a very fast and local face reaction with low intensity. As a consequence, it is a challenging task for researchers to build an automatic system for MiE recognition. Previous works for MiE recognition have attempted to use the whole face, yet a facial MiE appears in a small region of the face, which makes the extraction of relevant features a hard task. In this paper, we propose a novel deep learning approach that leverages the locality aspect of MiEs by learning spatio-temporal features from local facial regions using a composite architecture of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The proposed solution succeeds to extract relevant local features for MiEs recognition. Experimental results on benchmark datasets demonstrate the highest recognition accuracy of our solution with respect to state-of-the-art methods.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Rapid preparation of Palygorskite/Al2O3 composite aerogels with superhydrophobicity,high-temperature thermal insulation and improved mechanical properties

Al₂O₃ aerogels are widely employed in heat insulation and flame retardancy because of their unique combination of low thermal conductivity and exceptional high-temperature stability. However, the mechanical properties of Al₂O₃ aerogel are poor, and the preparation time is considerably long. In this study, we present a simple and scalable approach to construct monolithic Pal/Al₂O₃ composite aerogels using solvothermal treatment instead of traditional solvent replacement, which remarkably shortened the preparation time. Subsequently, to obtain stable superhydrophobicity (θ > 152°), the Pal/Al₂O₃ aerogel was modified by gas-phase modification method. The obtained Pal/Al₂O₃ composite aerogels demonstrate the integrated properties of low density (0.078–0.106 g/cm³), low thermal conductivity (1000 °C, 0.143 W/(m·K)), good mechanical properties (Young's modulus, 1.6 MPa), and good heat resistance. The monolithic Pal/Al₂O₃ composite aerogels with improved mechanical performance and improved thermal stability can show great potential in the field of thermal insulation.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Approximation Optimal Vibration Control with Applications to Jacket Offshore Platforms with Nonlinear Dynamic Characteristics

International Journal of Control, Automation and Systems - The vibration control problem of offshore jacket platforms is studied. The model of offshore platforms with nonlinear interactions of the...     

EarNet: Biometric Embeddings for End to End Person Authentication System Using Transient Evoked Otoacoustic Emission Signals

Neural Processing Letters - Transient Evoked Otoacoustic Emissions (TEOAE) are a class of oto-acoustic emissions that are generated by the cochlea in response to an external stimulus. The TEOAE...