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A palladium-catalyzed cross-coupling of benzene sulfonyl hydrazides with benzyltrimethylammonium salts via C–S bond and C–N bond cleavage is reported. This reaction proceeds under mild reaction conditions and provides a new strategy for the synthesis of various diarylmethanes with broad functional group tolerance in moderate to good yields.

The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT shows significant improvement in a range of downstream NLP tasks, but the capabilities and limitations of ChatGPT in terms of recommendations remain unclear. In this study, we aim to conduct an empirical analysis of ChatGPT's recommendation ability from an Information Retrieval (IR) perspective, including point-wise, pair-wise, and list-wise ranking. To achieve this goal, we re-formulate the above three recommendation policies into a domain-specific prompt format. Through extensive experiments on four datasets from different domains, we demonstrate that ChatGPT outperforms other large language models across all three ranking policies. Based on the analysis of unit cost improvements, we identify that ChatGPT with list-wise ranking achieves the best trade-off between cost and performance compared to point-wise and pair-wise ranking. Moreover, ChatGPT shows the potential for mitigating the cold start problem and explainable recommendation. To facilitate further explorations in this area, the full code and detailed original results are open-sourced at https://github.com/rainym00d/LLM4RS.

Time-series 1-D signals are ubiquitous in industrial applications for monitoring and control. However, it is lacking of efficient tools to deal with simultaneously multiple time-series 1-D signals. To this end, in this article, a novel theory of image formation is proposed that converts multiple 1-D signals to 2-D images and takes advantages of convolutional neural network for feature extraction and classification of a sequence of images. A case study is carried out for the classification of working conditions in photovoltaic power systems. In total, 23 1-D signals are mapped to a sequence of 2-D images to derive six different models through image formation-based deep learning. They are tested through the outdoor experiments under time varying working conditions. We discover that physical implication in 2-D images affects significantly the classification performance such that 2-D images with the clustered currents or voltages tend to create better results while randomly arranged image patterns are prone to generate worse results. Excellent performance with an accuracy 96.09% is guaranteed when physical advantages are incorporated in the proposed tools. Driven by the deep learning approaches, the proposed tools are promising for complicated industrial applications.

The cutting-edge monitoring technique of photovoltaic power systems tends to employ cloud servers for big data analysis in the wake of smart sensors and internet of things (IoT). Various mismatch phenomena of photovoltaic arrays are generated due to the external and internal interactions, which would be identified by an appropriate monitoring approach. However, high dimensional sequential data provided by multiple sensors challenges the existing monitoring technologies. This paper proposes a dimension reduction technology mapping multiple sequence signals to a sequence of images which are processed further by a convolutional neural network (CNN), resulting in a novel condition monitoring system for photovoltaic array systems. Firstly, multiple sources of 1-dimensional time-series data are rearranged to construct 2-dimensional time-series images. Then, the CNN algorithm automatically extracts the underlying graphical features from data of 2-dimensional images for condition monitoring. Experiments were carried out upon self-made solar power stations to verify the effectiveness of the proposed method for real-world solar power systems. It shows that the data driven approach could identify effectively key operation conditions from the historical data with a negligible loss of features at the presence of mismatched phenomena.

Additional file 1. Search Strategy (Pubmed).

Additional file 2. Table S1. Revised Cochrane risk of bias tool for randomized controlled trial (RoB2.0). Figure S1. Risk of bias summary in 1 RCT.

Jixin Chen,1,2,* Aifeng Liu,1,2,* Qinxin Zhou,1,2 Weijie Yu,1,2 Tianci Guo,1,2 Yizhen Jia,1,2 Kun Yang,1,2 Puyu Niu,1,2 Huichuan Feng1,2 1Department of Orthopaedic Surgery, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, 300381, People’s Republic of China; 2National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, 300381, People’s Republic of China*These authors contributed equally to this workCorrespondence: Aifeng LiuDepartment of Orthopaedic Surgery, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, No. 88 Changling Road, Xiqing District, Tianjin, 300381, People’s Republic of ChinaTel +86-13803091533Email draifeng@163.comBackground: Acupuncture has been widely used in the clinical management of osteoarthritis of the knee (KOA). Many systematic reviews (SRs) and meta-analyses (MAs) have reported its effectiveness in relieving pain. This overview aimed to summarize SRs and MAs on the effectiveness and safety of acupuncture for KOA and evaluate their methodological and evidence quality of the included SRs and MAs.Methods: We conducted a comprehensive literature search for SRs and MAs in four Chinese and four international databases from their inception until August 2021. Two researchers independently searched the reviews, extracted the data, and cross-checked the data. The Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR 2) tool was used to evaluate the methodological quality of the included SRs and MAs. The Grades of Recommendations, Assessment, Development, and Evaluation (GRADE) system was used to assess the quality of evidence for the outcomes of the included SRs and MAs.Results: A total of 14 SRs and MAs were included. The evaluation results of the AMSTAR 2 tool showed that the methodological quality of all the 14 SRs and MAs was critically low. The principal causes are the lack of a pre-registration proposal and a list of excluded studies and justify the exclusions, the report on the sources of funding, and the reasons for the study designs for inclusion. The results of the GRADE evaluation showed 25 of 46 outcomes were very low-level evidence. Seventeen were of low level, four were of moderate level and none were of high level. Most outcomes were downgraded in quality of evidence mainly because of publication bias and imprecision.Conclusion: The existing evidence suggests that acupuncture seems to be an effective and safe therapy for KOA. However, the deficiencies in the methodological quality and quality of evidence of the included SRs/MAs have limited the reliability of the conclusions. Therefore, further rigorous and comprehensive studies are warranted to verify the effectiveness and safety of acupuncture in KOA.Keywords: acupuncture, knee osteoarthritis, overview, AMSTAR 2, GRADE, systematic review

Unprecedented mismatch occur frequently in the photovoltaic (PV) array systems, challenging the classical monitoring systems. However, the study of artificial intelligence methods lacks some well-designed experiments for a systematic verification because mismatch are influenced by many aspects such as the PV array, the DC–DC converter, the DC–AC inverter, the loads as well as the environmental variables. The objective has two folds. One is to design some identical apparatuses to emulate the ‘same’ real-world solar power station that is operated under multiple mismatch for a controllable, repeatable and comparable experimental study. Another is to propose a novel condition monitoring strategy based on the backward propagation neural network and a decision-making formula. Some controllable indoor experiments and uncontrollable outdoor experiments are carried out to verify the ideas. Such work has never been studied before. The experimental results show that the derived monitoring systems identify and classify accurately different mismatch in both indoor tests and outdoor experiments. Moreover, real-time experimental results infer that the data-driven approach with the self-learning capabilities is adaptive to the environmental changes. Therefore, the supervised learning-based condition monitoring strategy is promising in the solar power industry in terms of operation management and performance enhancement.

Photovoltaic array systems operated in harsh and versatile environments generate complex nonlinear characteristics due to various mismatched phenomena, which undermines the system performance unprecedentedly and challenges the existing monitoring technologies. To this end, a class of modern data acquisition and smart condition monitoring systems are introduced with the advanced technologies such as sensor networks, smart combiner boxes and smart inverters. Furthermore, a novel condition monitoring approach for identifying and classifying mismatched photovoltaic arrays is proposed that consists of pre-filter, Elman neural network and decision-making rules to deal with various mismatches such as partial shading due to tree leaves or dirt dropping, and open circuit faults. Novel micro solar power stations are built and used to prove the concept via a comparable experimental study that has never done before. Experimental results show that applying pre-filters on time series input attributes improves the performance up to 3.70% and modifying the decision making formulae improves further the performance up to 1.78%. Eventually, the entire network could identify all of fault conditions if the parameters are chosen properly. Therefore, the proposed method is promising in solar power systems and the constructed apparatuses are useful in the study of modern photovoltaic power systems.

When a high-speed droplet impacts on mesh screens, part of the droplet penetrates the screen through its pores and generates smaller secondary drops, which spray downstream in a conical distribution. This instantaneous phase fragmentation phenomenon has been widely utilized in liquid spray applications and multiple-phase liquid separation. During droplet deformation, the intense liquid–gas fragmentation can lead to high nonequilibrium effect, which makes it hard to simulate by traditional fluid computational method. In this study, for the first time, we provided a numerical method to simulate the entire process of penetration dynamic behaviors. This 3D droplet-impact model based on MDPD (many-body dissipative particle dynamics) method exhibits high stability. A special solid–liquid boundary condition was proposed and successfully reduced the massive computational resources wasted on the solid mesh surface. To verify our model, the impacting of a droplet on a flat surface and on a mesh screen were simulated, respectively. The result showed a good match with our previous drop impact study and our experiment of the whole process about a droplet fragmented into hundreds of small drops. We further studied the mass transfer ratio (the ratio of penetrated drops to the initial droplet) and the ejection angle (the angle of the spray cone). The mass transfer ratio and ejection angle can be approximated as a function of Weber number, solid fraction and mesh number by summarizing the regular drop-penetrated behaviors over initial speed and mesh number.

To shorten the production cycle of Zaodan, this study first pickled Zaodan by a novel technology - vacuum decompression technology. Vacuum decompression technology could reduce the pickling time of Zaodan from 20 wk to about 9 wk. The protein content, moisture and pH of the Zaodan egg white gradually decreased with a concomitant increase in salt during the pickling process. The total sulfhydryl group (SH) group content of the egg white proteins was increased to 2.43×10-3 mol/L after being pickled for 30 d, whereas the content of disulphide bonds (SS) was reduced to 23.35×10-3 mol/L. The surface hydrophobicity was lowest after pickling for 30 d. In addition, great changes occurred in the secondary structure of the egg white proteins after pickling for 20 d. The disappearance of ovomucin was noticeable based on sodium dodecyl sulfate–polyacrylamide gel electrophoresis analysis.

AlCoCrFeNiTi high-entropy alloy coatings (HEACs) were prepared by mechanical alloying (MA) and laser cladding (LC) process on H13 hot-working die steel substrate. Phase evolution, microstructure, and mechanical properties of the alloyed powder and HEACs were investigated in detail. The final milling AlCoCrFeNiTi coating powders exhibited simple body centered cubic (BCC) phase and mean granular size of less than 4 &mu, m. With the increase of heat input of the laser, partial BCC phase transformed into minor face centered cubic (FCC) phase during LC. AlCoCrFeNiTi HEACs showed excellent metallurgical bonding with the substrate, and few defects. Moreover, the microhardness of AlCoCrFeNiTi HEACs reached 1069 HV due to the existence of the hard oxidation and the second phase grains, which are about five times that of the substrate. The laser surface cladding HEACs exhibited deteriorated tensile property compared with that of the substrate and the fracture generally occurred in the region of HEACs. The fracture mechanism of AlCoCrFeNiTi HEACs was dominated by the comprehensive influence of brittle fracture and ductile fracture.

A young nation with only half-century’s history, Singapore has in fact bred a Chinese spoken drama at the beginning of the twentieth century, a genre bearing the same title of spoken drama as in its source country of China, from the latter of which it was introduced and influenced. The research investigates into the original route of how such a new staging form has been distinctively brought out as a Chinese-speaking spoken drama of its own in the process of modernity upon its birth and formation at this multilingual and multicultural society in Southeast Asia.

Background PS916, chitosan derivative with shown activities in atherosclerotic and fatty liver, is being investigated as an anti-atherosclerotic agent in clinical trials in China. Methods Fluorescein-labeled PS916 (PS916-FTC) was prepared by the reaction with fluorescein isothiocyanate. The pharmacokinetics and bio-disposition of PS916-FTC were studied in rats after oral or intravenous administration. Results Analysis of the plasma, urine, fecal and tissue samples collected at intervals up to 72 h revealed that PS916-FTC exhibited moderate volume of distribution (Vss, 0.650~0.748 L/kg), and low clearance (60.9~107 mL/h/kg) after intravenous administration. The pharmacokinetics of PS916-FTC was characterized by low bioavailability (8.40%) after oral administration. The average accumulation ratio for PS916-FTC exposure after steady-state administration was 1.04. A two-compartmental pharmacokinetics model was employed. The urinary route was the major pathway (54.4%), and the fecal route was a minor pathway (6.29%) for PS916-FTC elimination after intravenous administration; the fecal route was the major pathway (79.0%) for PS916-FTC elimination after oral administration. Conclusion PS916-FTC was widely distributed to most tissues in rats; relatively high levels of PS916-FTC in kidney and liver were observed after intravenous or oral administration. These findings supported the understanding of pharmacokinetics and bio-disposition of PS916 in rats and provide relevant information for future design of clinical studies. Highlights 1) Fluorescein-labeled PS916 was successfully synthesized. 2) A rapid and sensitive analytical method of PS916-FTC was validated. 3) The pharmacokinetic of PS916-FTC in rats was investigated. 4) The bio-distribution of PS916-FTC in rats was investigated.

The paper proposes an infrared image synthesis model based on infrared physics and heat transfer. The model successfully reproduces the infrared images of the object under different viewing, ambient and internal conditions. Experimental examples illustrate the potential of our method.