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Abstract Distributed photovoltaics in living environment harvest the sunlight in different incident angles throughout the day. The development of planer solar cells with large light-receiving angle can reduce the requirements in installation form factor and is therefore urgently required. Here, thin film organic photovoltaics with nano-sized phase separation integrated in micro-sized surface topology is demonstrated as an ideal solution to proposed applications. All-polymer solar cells, by means of a newly developed sequential processing, show large magnitude hierarchical morphology with facilitated exciton-to-carrier conversion. The nano fibrilar donor-acceptor network and micron-scale optical field trapping structure in combination contributes to an efficiency of 19.06% (certified 18.59%), which is the highest value to date for all-polymer solar cells. Furthermore, the micron-sized surface topology also contributes to a large light-receiving angle. A 30% improvement of power gain is achieved for the hierarchical morphology comparing to the flat-morphology devices. These inspiring results show that all-polymer solar cell with hierarchical features are particularly suitable for the commercial applications of distributed photovoltaics due to its low installation requirement.

The purpose of business sentiment analysis is to determine the emotions or attitudes expressed toward the company, products, services, personnel, or events. Text analysis are the simplest and most developed types of sentiment analysis so far. The text-based business sentiment analysis still has some unresolved challenges. For example, the machine learning algorithms are unable to recognize double meanings, jokes and allusions. The regional differences between language and non-native speech structures cannot be explained. To solve this problem, an undirected weighted graph is constructed for news topics. The sentences in an article are modeled as nodes, and the normalized sentence similarity is used as the link of the nodes, which can help avoid the influence of sentence length on the summary results. In the topic extraction process, the keywords are not limited to a single word, to achieve the purpose of improving the readability of the abstract. To improve the accuracy of sentiment classification, this work proposes a robust news mining-based business sentiment analysis framework, called BuSeD. It contains two main stages: (1) news collection and preprocessing, and (2) feature extraction and sentiment classification. In the first stage, the news is collected by using crawler tools. The news dataset is then preprocessed by reducing noises. In the second stage, topics in each article is extracted by using traditional topic extraction tools. And then a convolutional neural network (CNN)-based text analyzing model is designed to analyze news from sentence level. We conduct comprehensive experiments to evaluate the performance of BuSeD for sentiment classification. Compared with four classical classification algorithms, the proposed CNN-based classification model of BuSeD achieves the highest F1 scores. We also present a quantitative trading application based on sentiment analysis to validate BuSeD, which indicates that the news-based business sentiment analysis has high economic application value.

Energy cooperation is the forerunner and main engine of the Belt and Road Initiative. In the past ten years, hundreds of oil and gas cooperation projects have been operated in the countries along the Belt and Road and other countries by Chinese petroleum companies, with fruitful results achieved. The technical service and engineering construction is an important part of the cooperation. As the most important content for construction of oil and gas pipeline, hydrostatic testing is a most common method for verification of pipeline integrity. Based on the design experience of gathering pipelines in the oil and gas fields of the cooperation projects in Middle East and Africa, the 13 hydrostatic testing standards of the international organizations and petroleum corporations such as ASME, API, ISO, BSI, IPS, Saudi Aramco, ADNOC, Shell, Total and BP were sorted out, and the requirements for the test pressure and pressure holding time of the water quality test, strength test and tightness test were compared and analyzed. The research results are expected to provide reference for the design and construction of gathering pipelines in oversea oil and gas fields.

Pyroelectric infrared (PIR) sensors are much promising for device-free localization (DFL) due to their advantages of lower cost, low power consumption, and privacy protection. Most PIR-based localization methods usually assume some geometric models according to the detection principle of PIR sensors, which are however not accurate or robust due to the various cases of infrared radiation from human body, especially the case of multiple persons. Recently, deep learning is utilized in the PIR-based localization method (i.e. PIRNet Yang et al.) and well handles the complex infrared radiation even in the multi-person case. However, this method requires a high training cost, and has very weak generalization ability as it assumes the PIR sensors’ deployment in the testing environment is same to the deployment in training environment. To reduce the training cost and achieve high generalization ability, in this paper, we propose a robust method DeepPIRATES, which can be directly utilized in various deployment scenarios without retraining. DeepPIRATES combines deep learning and a geometric model. Specifically, DeepPIRATES divides the localization task into two steps. The first step utilizes a neural network to estimate the azimuth changes of multiple persons to a PIR sensor. Then, DeepPIRATES utilizes the persons’ azimuth changes to infer their locations based on a geometric model. Extensive experimental results show that DeepPIRATES can achieve similar localization accuracy as PIRNet, while does not require to be retrained when the sensor deployment changes.

Efficiency of organic solar cells is determined by the physical properties of donors and acceptors in bulk heterojunction film. The authors optimise quaternary blends to realize a double cascading energy level alignment enabling efficient carrier dissociation and transport, achieving 18% efficiency.

Mixtures of nonfullerene acceptors (NFAs) are prepared to fine‐tune bulk heterojunction (BHJ) thin‐film morphologies. The acceptor phase resulting from these mixtures has unique physical properties with excellent optoelectronic processes that dictate the output of organic photovoltaic (OPV) devices. Remarkable short‐circuit current densities (JSC) and fill factors (FFs) are achieved due to the formation of better crystalline fibrils that suppress geminate recombination, leading to improved charge transport with enhanced crystallinity and aligned cascading energy levels confirm efficient exciton diffusion and dissociation, yielding more effective exciton recycling. The decreased Urbach energy and suppressed energetic disorder account for the improvement in the open‐circuit voltage (VOC). A maximum power conversion efficiency of 17.86% is obtained, underscoring the importance of using specific material interactions to produce a suitable morphology and manage energy loss, resulting in ideal organic solar cell (OSC) devices.

Abstract The donor/acceptor interaction in non‐fullerene organic photovoltaics leads to the mixing domain that dictates the morphology and electronic structure of the blended thin film. Initiative effort is paid to understand how these domain properties affect the device performances on high‐efficiency PM6:Y6 blends. Different fullerenes acceptors are used to manipulate the feature of mixing domain. It is seen that a tight packing in the mixing region is critical, which could effectively enhance the hole transfer and lead to the enlarged and narrow electron density of state (DOS). As a result, short‐circuit current (JSC) and fill factor (FF) are improved. The distribution of DOS and energy levels strongly influences open‐circuit voltage (VOC). The raised filling state of electron Fermi level is seen to be key in determining device VOC. Energy disorder is found to be a key factor to energy loss, which is highly correlated with the intermolecular distance in the mixing region. A 17.53% efficiency is obtained for optimized ternary devices, which is the highest value for similar systems. The current results indicate that a delicate optimization of the mixing domain property is an effective route to improve the VOC, JSC, and FF simultaneously, which provides new guidelines for morphology control toward high‐performance organic solar cells.

In nonfullerene acceptor- (NFA-) based solar cells, the exciton splitting takes place at both domain interface and donor/acceptor mixture, which brings in the state of mixing phase into focus. The energetics and morphology are key parameters dictating the charge generation, diffusion, and recombination. It is revealed that tailoringthe electronic properties of the mixing region by doping with larger-bandgap components could reduce the density of state but elevate the filling state level, leading to improved open-circuit voltage (VOC) and reduced recombination. The monomolecular and bimolecular recombinations are shown to be intercorrelated, which show a Gaussian-like relationship with VOC and linear relationship with short-circuit current density (JSC) and fill factor (FF). The kinetics of hole transfer and exciton diffusion scale with JSC similarly, indicating the carrier generation in mixing region and crystalline domain are equally important. From the morphology perspective, the crystalline order could contribute to VOC improvement, and the fibrillar structure strongly affects the FF. These observations highlight the importance of the mixing region and its connection with crystalline domains and point out the design rules to optimize the mixing phase structure, which is an effective approach to further improve device performance.

Purpose/Aim of the Study: The present study investigated the effect of surgical adhesives on the uterus of rabbits and the histomorphology alterations following occlusion, to improve the clinical treatment of abnormal fallopian tube with surgical adhesives for in vitro fertilization and embryo transfer (IVF-ET). Materials and Methods: The experimental rabbits received laparotomy and occlusion of the uterus by surgical adhesive adjacent to the two fallopian tubes, while the control rabbits only received laparotomy. The body weight, hysterosalpingography, and histomorphology were measured to evaluate the uterine occlusion at 1 and 6 months after surgery. Results: There was no significant difference in the mortality rate or body weight between the experimental and control groups. In the experimental group, 38 uterine cavities were identified in 19 rabbits, of which 97.37% were occluded, with expanded uterine cavity and tissue oppression at 1 month after surgery. In total, 33 uterine cavities out of the 36 in the control group were occluded, with proliferation of new stratified epithelial cells observed at 6 months after surgery. In the control group, 20 uterine cavities of 10 rabbits were observed to be absent of occlusion at 1 month after surgery, while 18 uterine cavities in the remaining 9 rabbits were also absent of occlusion at 6 months after the surgery. Conclusion: Surgical adhesives are effective in occluding the uterus of rabbits without adverse effects, supporting their potential clinical use to treat the occlusion in abnormal fallopian tubes prior to IVF-ET.