Crystallization‐Induced Emission Enhancement of a Deep‐Blue Luminescence Material with Tunable Mechano‐ and Thermochromism

Organic luminescent materials with the ability to reversibly switch the luminescence when subjected to external stimuli have attracted considerable interest in recent years. However, the examples of luminescent materials that exhibit multiresponsive properties are rarely reported. In this work, a new stimuli‐responsive dye P1 is designed and synthesized with two identical chromophores of naphthalimide, one at each side of an amidoamine‐based spacer. This amide‐rich molecule offers many possibilities for forming intra‐ and intermolecular hydrogen bond interactions. Particularly, P1 has an intrinsic property of cocrystallizing with methanol. Compared with the pristine P1 sample, the as‐prepared two‐component cocrystalline material displays an exceptive deep‐blue emission, which is extremely rare among naphthalimide‐based molecules in the solid state. Furthermore, the target material exhibits an obvious mechanochromic fluorescent behavior and a large spectral shift under force stimuli. On the other hand, the cocrystalline material shows an unusual “turn off” thermochromic luminescence accompanied by solvent evaporation. Moreover, using external stimuli to reversibly manipulate fluorescent quantum yields is rarely reported to date. The results demonstrate the feasibility of a new design strategy for solid‐state luminescence switching materials: the incorporation of solvents into organic compounds by cocrystallization to obtain a crystalline state luminescence system.     

Understanding the galvanic corrosion of the Q-phase/Al couple using SVET and SIET

The galvanic corrosion of the Q-phase/Al couple in 0.1 M NaCl solutions has been studied using the scanning vibrating electrode technique (SVET), the scanning ion-selective electrode technique (SIET) and energy dispersive X-ray spectroscopy (EDX). The galvanic corrosion of the Q-phase/Al couple was found to be dependent on pH and immersion time. Current density maps obtained by SVET shows that the anodic oxidation processes emerge from Al in a localized manner in pH 2 and 6 solutions but is initiated in a uniform manner in pH 13 solution, whereas, the cathodic processes are more homogeneously distributed over the Q-phase at pH 2, 6 and 13. It is seen that the Q-phase remains cathodic in the Q-phase/Al couple in acidic, neutral and alkaline solutions indicating that the galvanic polarity of the Q-phase is independent of pH. The effect of the galvanic corrosion was largest at pH 2 and 13 compared to pH 6. The pH map obtained by SIET indicates that the galvanic activity of the Q-phase/Al couple proceeds via heavy alkalization of the Q-phase surface with the generation of appreciable amounts of OH⁻ ions. The enrichment of Cu indicated by EDX is responsible for the observed cathodic activity of the Q-phase in the Q-phase/Al couple.     

深冷处理对低碳高合金马氏体轴承钢力学性能及组织的影响

利用洛氏硬度计、X射线衍射仪、扫描电子显微镜及透射电子显微镜等研究了低碳高合金马氏体轴承钢深冷处理后的硬度变化及组织演化。结果表明:深冷处理促使部分残留奥氏体转变为马氏体,导致深冷处理后实验钢的硬度较淬火态硬度有所升高。经深冷处理后实验钢在0~100 h回火过程中的硬度均比未深冷处理实验钢的硬度高。深冷处理促使钢中碳原子偏聚并在回火过程中以碳化物的形式析出,与未经深冷处理的实验钢相比,经深冷处理的实验钢回火后马氏体基体中的含碳量更低,表明实验钢经深冷处理后在回火过程中析出更多的碳化物。透射电镜分析表明,实验钢在回火过程中析出的大量弥散分布的纳米级M2C和M6C型碳化物是实验钢长时间回火后保持高硬度的主要原因。     

融入注意力机制的轻量化可回收垃圾检测方法

目的 针对目前智能垃圾分类设备使用的垃圾检测方法存在检测速度慢且模型权重文件较大等问题,提出一种基于YOLOv4的轻量化方法,以实现可回收垃圾的检测。方法 采用MobileNetV2轻量级网络为YOLOv4的主干网络,用深度可分离卷积来优化颈部和头部网络,以减少参数量和计算量,提高检测速度;在颈部网络中融入CBAM注意力模块,提高模型对目标特征信息的敏感度;使用K-means算法重新聚类,得到适合自建可回收数据集中检测目标的先验框。结果 实验结果表明,改进后模型的参数量减少为原始YOLOv4模型的17.0%,检测的平均精度达到96.78%,模型权重文件的大小为46.6 MB,约为YOLOv4模型权重文件的19.1%,检测速度为20.46帧/s,提高了约25.4%,检测精度和检测速度均满足实时检测要求。结论 改进的YOLOv4模型能够在检测可回收垃圾时保证较高的检测精度,同时具有较好的实时性。     

食品包装材质对消费者绿色购买意愿影响的眼动研究

目的 探讨食品包装材质对消费者注意加工流程和绿色购买意愿影响的内在机制。方法 采用包装材质2(环保/普通)×食品类型2(实用品/享乐品)双因素实验设计,在货架购物场景下采用Tobii Glasses 2眼镜式眼动仪,采集被试者浏览货架不同食品时的眼动数据(平均注视时间、注视次数、热点图),并结合行为数据综合分析。结果 在平均注视时间上,包装材质与食品类型的交互作用显著,注视次数交互作用不显著;环保材质包装的食品获得更多关注,被试者偏好于选择环保包装的享乐食品。结论 包装材质与食品类型共同影响消费者的视觉感知效果与加工过程,环保包装对青年消费者的绿色购买意愿有正向促进作用。     

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.