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A network is a set of nodes connected via edges, with possibly directions and weights on the edges. Sometimes, in a multi-layer network, the nodes can also be heterogeneous. In this perspective, based on previous studies, we argue that networks can be regarded as the infrastructure of scientometrics in the sense that networks can be used to represent scientometric data. Then the task of answering various scientometric questions related to this data becomes an algorithmic problem in the corresponding network.

Not all knowledge is equally important in teaching and learning, yet identifying important knowledge remains a fundamental challenge in curriculum design. Traditional methods rely on expert judgment and lack systematic, reproducible criteria. This study demonstrates that node-level concept network analysis can provide operational definitions for important knowledge by deconstructing their characteristics into quantifiable structural features. Using a mathematics concept network, we analyzed both Big Ideas and International Baccalaureate (IB) key concepts, examining whether their characteristics can be systematically deconstructed into network indicators. The analysis successfully deconstructs important knowledge characteristics into structural features, with the deconstruction process demonstrating stability across independent optimization algorithms. While specific indicators (such as degree centrality) emerge from the analysis, the fundamental contribution is establishing the analytical framework itself. This research illustrates the potential of concept network analysis for addressing fundamental challenges in curriculum design and knowledge-focused educational research.

The global shift toward solar photovoltaic (PV) and wind power is crucial to climate mitigation, yet climate change may intensify extreme low-production (ELP) events and affect power reliability. Here, we assess future ELP changes under low (SSP1-2.6), intermediate (SSP2-4.5), and high (SSP3-7.0) greenhouse gas and air pollutant emissions scenarios. Even under SSP1-2.6, rising ELP risks are projected to affect more than one-third of global regions, expanding to nearly two-thirds under SSP3-7.0, regardless of whether systems rely on PV, wind, or both. Increases in ELP for wind power are nearly inevitable, with over 75% of currently installed areas experiencing 14.0–24.5% greater production anomalies by the late century. PV power diverges strongly across scenarios, shifting from a 14.8% decrease in anomaly under SSP1-2.6 to a 26.4% increase under SSP3-7.0, particularly in East Asia. Additionally, climate-induced risks disproportionately narrow the benefits of PV development in low- and lower-middle-income economies, where ELP risks rise at 1.8 times the global rate under SSP3-7.0. Our results underscore the need for coordinated mitigation and adaptation to secure power reliability in a changing climate. This work shows that climate change is projected to unevenly intensify extreme low-production events in solar and wind power systems worldwide, highlighting the need for mitigation and adaptation to ensure future power reliability.

The autonomous navigation of unmanned surface vehicles (USV) depends mainly on effective ship target detection to the nearby water area. The difficulty of target detection for USV derives from the complexity of the external environment, such as the light reflection and the cloud or mist shield. Accordingly, this paper proposes a target detection technology for USV on the basis of the EfficientDet algorithm. The ship features fusion is performed by Bi-directional Feature Pyra-mid Network (BiFPN), in which the pre-trained EfficientNet via ImageNet is taken as the backbone network, then the detection speed is increased by group normalization. Compared with the Faster-RCNN and Yolo V3, the ship target detection accuracy is greatly improved to 87.5% in complex environments. The algorithm can be applied to the identification of dynamic targets on the sea, which provides a key reference for the autonomous navigation of USV and the military threats assessment on the sea surface.

The Chinese visceral adiposity index (CVAI) is a recently developed indicator of visceral adiposity. We investigated the predictive value of the CVAI for the development of dysglycemia (pre-diabetes and type 2 diabetes) and compared its predictive power with that of the Visceral adiposity index (VAI) and various anthropometric indices. This community-based study included 2,383 participants. We assessed the predictive power of adiposity indices by performing univariate and multivariate binary logistic regression analysis and calculating the area under the receiver-operating characteristic (ROC) curve according to their quartiles. Logistic regression analysis showed that individuals in higher CVAI quartiles at baseline were more likely to develop dysglycemia than those in lower CVAI quartiles. The area under the ROC curve for CVAI was significantly higher than that of other adiposity indices. In addition, among the various adiposity indices tested, the CVAI had the greatest Youden index for identifying dysglycemia in both genders. Our data demonstrate that the CVAI is a better predictor of type 2 diabetes and pre-diabetes than the VAI, BMI, waist circumference, waist-to-hip ratio and waist-to-height ratio in Chinese adults.

Many ranking algorithms and metrics have been proposed to identify high-impact papers. Both the direct citation counts and the network-based PageRank-like algorithms are commonly used. Ideally, the more complete the data on the citation network, the more informative the ranking. However, obtaining more data on citation relations is often costly and challenging. In some cases, obtaining the citation counts can be relatively simple. In this paper, we look into using the additional citation counts but without additional citation relations to form more informative metrics for identifying high-impact papers. As an example, we propose enhancing the original PageRank algorithm by combining the local citation network with the additional citation counts from a more complete data source. We apply this enhanced method to American Physical Society (APS) papers to verify its effectiveness. The results indicate that the proposed ranking algorithm is robust against missing data and can improve the identification of high-quality papers. This shows that it is possible to enhance the effectiveness of a network-based metric calculated on a relatively small citation network by including only the additional data of the citation counts, without the additional citation relations.

Purpose To investigate the effectiveness of using node2vec on journal citation networks to represent journals as vectors for tasks such as clustering, science mapping, and journal diversity measure. Design/methodology/approach Node2vec is used in a journal citation network to generate journal vector representations. Findings 1. Journals are clustered based on the node2vec trained vectors to form a science map. 2. The norm of the vector can be seen as an indicator of the diversity of journals. 3. Using node2vec trained journal vectors to determine the Rao-Stirling diversity measure leads to a better measure of diversity than that of direct citation vectors. Research limitations All analyses use citation data and only focus on the journal level. Practical implications Node2vec trained journal vectors embed rich information about journals, can be used to form a science map and may generate better values of journal diversity measures. Originality/value The effectiveness of node2vec in scientometric analysis is tested. Possible indicators for journal diversity measure are presented.

Photovoltaic (PV) installations have rapidly and extensively been deployed worldwide as a promising alternative renewable energy source. However, weather anomalies could expose them to challenges in supply security by causing very low power production. Using reanalysis weather data from 1986 to 2021 and a high-resolution global inventory of PV installations, we assess the impact of extreme low-production (ELP) events across various regions. Our results reveal that regions between 60°N and 60°S experience an average of 27 ELP events annually, with 17% of these events being high-intensity. Regions with dense PV installations—including Southern China, Central and Northern Europe, Central and Eastern America, and Japan—are particularly affected. These areas, which collectively host approximately half of the global PV installations, see 44% of ELP events being high-intensity. Maintaining a daily backup supply equivalent to the average event intensity could recover 39% to 81% of events across different sites. This strategy helps ensure a stable energy supply despite the unpredictability of extreme weather events. Southern China, Central and N Europe, Central and Eastern America, and Japan are areas with dense photovoltaic installations, and they are particularly affected by extremely low production events, according to an analysis that uses weather data and an inventory of photovoltaic installations.

Using the Logit quantal response form as the response function in each step, the original definition of static quantal response equilibrium (QRE) is extended into an iterative evolution process. QREs remain as the fixed points of the dynamic process. However, depending on whether such fixed points are the long-term solutions of the dynamic process, they can be classified into stable (SQREs) and unstable (USQREs) equilibriums. This extension resembles the extension from static Nash equilibriums (NEs) to evolutionary stable solutions in the framework of evolutionary game theory. The relation between SQREs and other solution concepts of games, including NEs and QREs, is discussed. Using experimental data from other published papers, we perform a preliminary comparison between SQREs, NEs, QREs and the observed behavioral outcomes of those experiments. For certain games, we determine that SQREs have better predictive power than QREs and NEs.

It is a common phenomenon for scientists to follow hot topics in research and this phenomenon can generally be quantified by measuring the preference attachment of new papers. A similar phenomenon also exists when a paper chooses its references. However, the abovementioned method does not apply to measure the preference for hot papers. To solve this problem, in this paper, we propose to convert measuring a paper’s preference for hot papers into calculating the hotness obtained from a paper’s references. We propose a PageRank-like algorithm that considers the hotness propagation based on citation relationships between papers to measure the hotness transfer of individual papers. We apply this method to the American Physical Society journals and explore the hotness transfer performance of individual papers in physics. It is found that highly innovative papers, such as Nobel Prize-winning papers in physics, have a weaker hotness transfer degree than papers with the same number of citations. We explore the factors associated with the performance of hotness transfer indicators. We find that the larger the size or citation counts of the field are, the stronger the hotness transfer degree of the field is likely to be. The team size and the number of references can also affect the hotness transfer degree of individual papers. Finally, we find that the hotness transfer scores of papers show an increasing trend over time. Relevant empirical discoveries may be valuable for evaluating paper impact.