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Abstract Using e + e − collision data collected by the BESIII detector at BEPCII corresponding to an integrated luminosity of 30 fb −1, we measure Born cross sections and effective form factors for the process e + e − → Ξ 0 Ξ ¯ 0 $$ {e}^{+}{e}^{-}\to {\Xi}^0{\overline{\Xi}}^0 $$ at forty-five center-of-mass energies between 3.51 and 4.95 GeV. The dressed cross section is fitted, assuming a power-law function plus a charmonium(-like) state, i.e., ψ(3770), ψ(4040), ψ(4160), ψ(4230), ψ(4360), ψ(4415) or ψ(4660). No significant charmonium(-like) state decaying into Ξ 0 Ξ ¯ 0 $$ {\Xi}^0{\overline{\Xi}}^0 $$ is observed. Upper limits at the 90% confidence level on the product of the branching fraction and the electronic partial width are provided for each decay. In addition, ratios of the Born cross sections and the effective form factors for e + e − → Ξ 0 Ξ ¯ 0 $$ {e}^{+}{e}^{-}\to {\Xi}^0{\overline{\Xi}}^0 $$ and e + e − → Ξ − Ξ ¯ + $$ {e}^{+}{e}^{-}\to {\Xi}^{-}{\overline{\Xi}}^{+} $$ are also presented to test isospin symmetry and the vector meson dominance model.

Abstract The processes h c → γP (P = η′, η, π 0) are studied with a sample of (27.12 ± 0.14) × 108 ψ(3686) events collected by the BESIII detector at the BEPCII collider. The decay h c → γη is observed for the first time with the significance of 9.0 σ, and the branching fraction is determined to be (3.77 ± 0.55 ± 0.13 ± 0.26) × 10 −4, while B $$ \mathcal{B} $$ (h c → γη′) is measured to be (1.40 ± 0.11 ± 0.04 ± 0.10) × 10 −3, where the first uncertainties are statistical, the second systematic, and the third from the branching fraction of ψ(3686) → π 0 h c . The combination of these results allows for a precise determination of R h c = B h c → γη B h c → γ η ′ , $$ {R}_{h_c}=\frac{\mathcal{B}\left({h}_c\to {\pi}^0\gamma \eta \right)}{\mathcal{B}\left({h}_c\to {\pi}^0\gamma {\eta}^{\prime}\right)}, $$ which is calculated to be (27.0 ± 4.4 ± 1.0)%. The results are valuable for gaining a deeper understanding of η − η′ mixing, and its manifestation within quantum chromodynamics. No significant signal is found for the decay h c → γπ 0, and an upper limit is placed on its branching fraction of B $$ \mathcal{B} $$ (h c → γπ 0) < 5.0 × 10 −5, at the 90% confidence level.

To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from transformers and generative pretrained transformer-2, to automatically generate Python programming remedial materials; subsequently, learning performance prediction models constructed using various machine learning methods are used to determine students' learning type, enabling the automatic generation of personalized remedial materials. The participants in this study were 78 students from a university in northern Taiwan enrolled in an 8-week Python course. Students in the experimental (n = 36) and control (n = 42) groups engaged in the same programming learning activities during the first 5 weeks of the course, and they completed a pretest of programming knowledge in Week 6. For the review activity in Week 7, the 36 students in the experimental group received personalized intervention, whereas those in the control group received traditional class tutoring. We examined the effect of the self-regulated learning and personalized intervention on the learning performance of students. Compared with the traditional class tutoring, the personalized intervention review activity not only helped students obtain higher learning performance but also prompted greater improvements in the following learning strategies: rehearsal, critical thinking, metacognitive self-regulation, effort regulation, and peer learning. We also observed that students' rehearsal and help-seeking learning strategies indirectly affected learning performance through students' note-taking in the provided e-book.

The performance a valve has been frequently estimated with numerical methods owing to limitations such as cost and place. In this study, for the triple-offset butterfly valves, the different sizes in various disc-opening cases was numerically conducted using different turbulence models of the two-equation turbulence models of k–ε, k-ω, and Reynolds stress model. The numerical calculations were validated against experimentally obtained valve flow test results. The numerical effect with the different turbulence models were analyzed with respect to the disc-opening cases. From the numerical analysis, the Reynolds stress model exhibits the most pronounced turbulence effects among the various turbulence models showing higher value of Reynolds normal stress near the valve disc region. The sensitivity of the turbulence model constants was examined using the 300 mm valve to observe the sensitivity of the turbulence model parameters in the two-equation turbulence models.

This special issue focus on underlying research with the use of human-centered AI (Artificial Intelligence), where the new design methods and tools can be leveraged and evaluated, hopes to advance AI research, education, policy, and practice to improve the human condition in education. This special issue intends to advocate an in-depth dialogue between researchers with diverse thoughts, genders, ethnicity, and cultures, as well as across disciplines, leading to a better understanding of human-centered AI. Beneficial interactions between researchers could enhance the adoption of human-centered AI in education. This special issue includes ten papers demonstrating how to augment human intelligence with machine intelligence. The ten papers feature human-centered AI in education, AI in language education, AI in learning analytics, ethical reasoning, AI in the clinical workplace, intelligent education robots, AI risk framework, intelligent course recommendation, education chatbots, and intelligent assessment. Together with the ten papers, we achieve a better understanding of the application of human-centered AI in education.

Human-guided machine learning can improve computing intelligence, and it can accurately assist humans in various tasks. In education research, artificial intelligence (AI) is applicable in many situations, such as predicting students’ learning paths and strategies. In this study, we explore the benefits of repetitive practice of short-answer questions could enhance students’ long-term memory for subsequent improvements in learning performance. However, frequent authoring questions and grading requires teachers’ professionalism, experience, and considerable efforts. Therefore, this study using modern AI technologies, specifically natural language processing, to provide Automatic question generation (AQG) solution, a combined semantics-based and syntax-based question generation system: Hybrid automatic question generation (Hybrid-AQG) was proposed in this study. We assessed its functionality and student learning performance by asking 91 students to complete short-answer questions and then applied a process similar to the Turing test to evaluate the question and grading quality. The results demonstrated that modern AI technologies can generate highly realistic short-answer questions because: (1) Compared with the control group, the experimental group exhibited significantly better learning performance, implying that students acquired long-term memory of course knowledge through repetitive practice with machine-generated questioning. (2) The experimental group could better distinguish machine-generated and expert-authored questions. Nevertheless, both groups in distinguishing questions presented like guessing. (3) Machine grading was deficient in some respects; but the way students answer questions can be adapted for machine understanding through repetitive practice.

As addressed by Stephen Yang in his ICCE 2019 keynote speech (Yang, 2019), precision education is a new challenge when applying artificial intelligence (AI), machine learning, and learning analytics to improve teaching quality and learning performance. The goal of precision education is to identify at-risk students as early as possible and provide timely intervention on the basis of teaching and learning experiences (Lu et al., 2018). Drawing from this main theme of precision education, this special issue advocates an in-depth dialogue between cold technology and warm humanity, in turn offering greater understanding of precision education. For this special issue, thirteen research papers that specialize in precision education, AI, machine learning, and learning analytics to engage in an in-depth research experiences concerning various applications, methods, pedagogical models, and environments were exchanged to achieve better understanding of the application of AI in education.

BL Lacertae is a key monitoring target of the 1.26 m National Astronomical Observatory–Guangzhou University Infrared/Optical telescope. Within the monitored duration from 2020 September 4 to 2022 September 28, we report 13,948 observations at the g , r , and i bands ( g band: 4498, r band: 4866, i band: 4584). (1) In the monitored duration, this source is located in a very bright and variable state. The maximum variabilities are Δ m _g = 2.013 ± 0.073 mag at g band, Δ m _r = 1.900 ± 0.049 mag at r band, and Δ m _i = 2.279 ± 0.089 mag at i band. (2) Among the gri intraday lightcurves, there are 104 portions of data sets displaying intraday variabilities (IDVs), with the IDV timescales (Δ T ) being in the range of 15.84–375.84 minutes and the biggest variable value Δ m = 0.430 ± 0.041 mag. (3) The distributions of Δ T show frequency-dependent behavior, and with the frequency increasing, Δ T tend to be shorter. The variable rates ( $V=\tfrac{{\rm{\Delta }}m}{{\rm{\Delta }}T}$ ) from the g band are more intense than the values ( V _r ) from the r band, but are more stable than the values ( V _i ) from the i band. (4) On three days (2020 September 7, 2020 September 19, and 2022 September 7), we find the intraday periodic oscillations, whose periods are around 150 minutes, 232 minutes, and 150 minutes, respectively, and which might come from the source “flickering.” (5) Based on the distributions between flux densities ( F _gri ) and spectral indices ( α ), they show the bluer-when-brighter behaviors and some uneven locations, which should come from the ministructures of the jet, the shock-induced particle acceleration, or magnetic reconnection in the jet.

We projected incident surface solar radiation (SSR) over China in the middle (2040–2059) and end (2080–2099) of the 21st century in the Representative Concentration Pathway (RCP) 8.5 scenario using a multi-model ensemble derived from the weighted average of seven global climate models (GCMs). The multi-model ensemble captured the contemporary (1979–2005) spatial and temporal characteristics of SSR and reproduced the long-term temporal evolution of the mean annual SSR in China. However, it tended to overestimate values compared to observations due to the absence of aerosol effects in the simulations. The future changes in SSR showed increases over eastern and southern China, and decreases over the Tibetan Plateau (TP) and northwest China relative to the present day. At the end of the 21st century, there were SSR increases of 9–21 W m−2 over northwest, central, and south China, and decreases of 18–30 W m−2 over the TP in June–July–August (JJA). In northeast China, SSR showed seasonal variation with increases in JJA and decreases in December–January–February. The time series of annual SSR had a decreased linear trend for the TP, and a slightly increased trend for China during 2006–2099. The results of our study suggest that solar energy resources will likely decrease in the TP under future climate change scenarios.

The deformation mechanisms of twin boundaries in nanotwinned metallic materials are still unclear. Here the authors combine in situ transmission electron microscopy tensile tests and molecular dynamics simulations with a dislocation-based theoretical model to reveal the deformation mechanism of nanotwins.

We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of “cold” technology and “warm” humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

Interfacial properties between metal back contacts and solar cell absorbers play important roles in determining efficiencies, but studies of such properties in CZTS and CZTSe based solar cells are quite lacking from theoretical aspects. To fill such a blank and explore insights for improving energy conversion efficiencies, we have studied interfacial properties in CZTS and CZTSe solar cells. The natural band offsets between CZTX and the spontaneously formed MoX2 layer (X = S, Se) are obtained using our recently developed intermediate-phase method. We find that the band alignment between CZTS and MoS2 is actually harmful while the band alignment between CZTSe and MoSe2 is beneficial for carrier extractions. We further propose to engineer the back contact interface to improve CZTS solar cell efficiency by depositing a thin layer of Se on the Mo back contact prior to the CZTS deposition, thus avoiding the formation of the undesirable MoS2 layer.

Three-dimensional nanoporous graphene monoliths are utilized to prepare graphene-poly(vinylidene fluoride) nanocomposites with enhanced mechanical and electro-mechanical properties. Pre-treatment of the polymer (poly(vinylidene fluoride), PVDF) with graphene oxides (GOs) facilitates the formation of uniform and thin PVDF films with a typical thickness below 100 nm well coated at the graphene nano-sheets. Besides their excellent compressibility, ductility and mechanical strength, the nanoporous graphene-PVDF nanocomposites are found to possess high sensitivity in strain-dependent electrical conductivity. The improved mechanical and electro-mechanical properties are ascribed to the enhanced crystalline β phase in PVDF which possesses piezoelectricity. The mechanical relaxation analyses on the interfaces between graphene and PVDF reveal that the improved mechanical and electro-mechanical properties could result from the interaction between the –C=O groups in the nanoporous graphene and the –CF2 groups in PVDF, which also explains the important role of GOs in the preparation of the graphene-polymer nanocomposites with superior combined mechanical and electro-mechanical properties.

Pan-sharpening of very high resolution remotely sensed imagery need enhancing spatial details while preserving spectral characteristics, and adjusting the sharpened results to realize the different emphases between the two abilities. In order to meet the requirements, this paper is aimed at providing an innovative solution. The block-regression-based algorithm (BR), which was previously presented for fusion of SAR and optical imagery, is firstly applied to sharpen the very high resolution satellite imagery, and the important parameter for adjustment of fusion result, i.e., block size, is optimized according to the two experiments for Worldview-2 and QuickBird datasets in which the optimal block size is selected through the quantitative comparison of the fusion results of different block sizes. Compared to five fusion algorithms (i.e., PC, CN, AWT, Ehlers, BDF) in fusion effects by means of quantitative analysis, BR is reliable for different data sources and can maximize enhancement of spatial details at the expense of a minimum spectral distortion.

The spatiotemporal variations of surface air pollutants (O3, NO2, SO2, CO, and PM10) with four land-use types, residence (R), commerce (C), industry (I) and greenbelt (G), have been investigated at 283 stations in South Korea during 2002–2013, using routinely observed data. The volatile organic compound (VOC) data at nine photochemical pollutant monitoring stations available since 2007 were utilized in order to examine their effect on the ozone chemistry. The land-use types, set by the Korean government, were generally consistent with the satellite-derived land covers and with the previous result showing anti-correlation between O3 and NO2 in diverse urban areas. The relationship between the two pollutants in the Seoul Metropolitan Area (SMA) residence land-use areas was substantially different from that outside of the SMA, probably due to the local differences in vehicle emissions. The highest concentrations of air pollutants in the diurnal, weekly, and annual cycles were found in industry for SO2 and PMPM10, in commerce for NO2 and CO, and in greenbelt for O3. The concentrations of air pollutants, except for O3, were generally higher in big cities during weekdays, while O3 showed its peak in suburban areas or small cities during weekends. The weekly cycle and trends of O3 were significantly out of phase with those of NO2, particularly in the residential and commercial areas, suggesting that vehicle emission was a major source in those areas. The ratios of VOCs to NO2 for each of the land-use types were in the order of I (10.2) > C (8.7) > G (3.9) > R (3.6), suggesting that most areas in South Korea were likely to be VOC-limited for ozone chemistry. The pollutants (NO2, SO2, CO, and PMPM10 except for O3 have decreased, most likely due to the effective government control. The total oxidant values (OX = O3 + NO2) with the land-use types were analyzed for the local and regional (or background) contributions of O3, respectively, and the order of OX (ppb) was C (57.4) > R (53.6) > I (50.7) > G (45.4), indicating the greenbelt observation was close to the background.