Advancing Open Science

Accurate, scalable, and outlier-robust state estimation (SE) is critical for large AC power systems with mixed SCADA and PMU measurements. This paper proposes D-BSE-L1, a distributed robust state estimator for the bilinear AC model. The method combines the bilinear state estimation framework with a convex weighted least absolute value (WLAV) loss so that all area subproblems become convex linear or quadratic programs coordinated by ADMM, and a cache-enabled Cholesky factorization is used to accelerate the third-stage linear solves. Simulations on the IEEE 14-, 118-, and 1062-bus systems show that D-BSE-L1 achieves estimation accuracy comparable to its centralized bilinear counterpart. Under severe bad-data conditions, its advantage over weighted least squares with the largest normalized residual test (WLS + LNRT) is pronounced: with 10% 1.5× bad data, the voltage magnitude and angle MAEs are about 62% and 54% of those of WLS + LNRT, and with 5% 5× bad data, they further drop to roughly 43% and 51%, while requiring only about one-tenth of the CPU time. On the 1062-bus system, D-BSE-L1 maintains the MAE of the centralized estimator but reduces runtime from 2.46 s to 0.72 s, providing a scalable, hyperparameter-free, and robust solution for partitioned state estimation in large-scale power grids.

Innovation in the new energy industry serves not only as a key accelerator for the global green and low-carbon energy transition but also as a core driving force of the ongoing energy revolution. This study utilizes data on publications, patents, and the spatial distribution of representative innovation enterprises in the new energy industry of the Yangtze River Delta urban agglomeration from 2009 to 2023 to construct a multilayer science–technology–industry innovation network. Social network analysis is employed to examine its evolutionary dynamics and structural characteristics, and the Quadratic Assignment Procedure (QAP) is used to investigate the factors shaping intercity innovation linkages. The results reveal that the multilayer innovation network has continuously expanded in scale, gradually forming a multi-core radiative structure with Shanghai, Nanjing, and Hangzhou at the center. At the cohesive subgroup level, the scientific and technological layers exhibit clear hierarchical differentiation, where core cities tend to engage in strong mutual collaborations, while the industrial layer shows a hub-and-spoke pattern combining large, medium, and small cities. In terms of layer relationships, the centrality of the scientific layer increasingly surpasses that of the technological and industrial layers. Inter-layer degree correlations and overlaps also display a strengthening trend. Furthermore, differences in regional higher education scale, urban economic density, and geographic proximity are found to exert significant influences on scientific, technological, and industrial innovation linkages among cities. In response, this study recommends enhancing the leadership role of core cities, leveraging the bridging and intermediary functions of peripheral cities, and promoting application-driven cross-regional innovation collaboration, thereby building efficient science–technology–industry networks and enhancing intercity innovation linkages and the flow of innovation resources, and ultimately promoting the high-quality development of the regional new energy industry.

Digital transformation is reshaping work and management, yet evidence on how technological innovation interacts with workplace well-being, leadership, organizational culture, and human-centered management remains fragmented. This study aims to integrate these strands of research by examining how innovation and digitalization affect employee well-being and motivation in organizational contexts. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines, with a protocol registered on INPLASY. The search was performed in the Scopus database and identified 287 eligible studies (1989–February 2025). Bibliometric keyword co-occurrence analysis using VOSviewer (1.6.20), combined with qualitative content and thematic analysis, led to five clusters: (1) innovation and well-being; (2) leadership pathways to workplace well-being; (3) work motivation and job satisfaction; (4) human-centered management in technological progress; and (5) organizational culture. The results show that organizations reconciling innovation and people’s well-being tend to adopt leadership styles and cultures grounded in ethical values, inclusion, psychological safety, and balanced work demands and resources, operationalized through human-centered management practices. These findings offer an integrated framework that goes beyond an instrumental view of technology and provide guidance for leaders, HR professionals, and policymakers designing digital transformation strategies that foster responsible innovation and promote sustainable, health-promoting work environments.

This paper explores the integrability of the Akbota equation with various types of solitary wave solutions. This equation belongs to a class of Heisenberg ferromagnet-type models. The model captures the dynamics of interactions between atomic magnetic moments, as governed by Heisenberg ferromagnetism. To reveal its further physical importance, we calculate more solutions with the applications of the logarithmic transformation, the M-shaped rational solution method, the periodic cross-rational solution technique, and the periodic cross-kink wave solution approach. These methods allow us to derive new analytical families of soliton solutions, highlighting the interplay of discrete and continuous symmetries that govern soliton formation and stability in Heisenberg-type systems. In contrast to earlier studies, our findings present notable advancements. These results hold potential significance for further exploration of similar frameworks in addressing nonlinear problems across applied sciences. The results highlight the intrinsic role of symmetry in the underlying nonlinear structure of the Akbota equation, where integrability and soliton formation are governed by continuous and discrete symmetry transformations. The derived solutions provide original insights into how symmetry-breaking parameters control soliton dynamics, and their novelty is verified through analytical and computational checks. The interplay between these symmetries and the magnetic spin dynamics of the Heisenberg ferromagnet demonstrates how symmetry-breaking parameters control the diversity and stability of optical solitons. Additionally, the outcomes contribute to a deeper understanding of fluid propagation and incompressible fluid behavior. The solutions obtained for the Akbota equation are original and, to the best of our knowledge, have not been previously reported. Several of these solutions are illustrated through 3-D, contour, and 2-D plots by using Mathematica software. The validity and accuracy of all solutions we present here are thoroughly verified.

This study investigates the developmental trajectories of transcription and oral language skills in kindergarten students over the course of an academic year, with a focus on the influence of executive functions (EF) and home literacy practices (HLP). Hierarchical linear modeling (HLM) analyses revealed significant growth in transcription skills, with both EF and independent home literacy practices positively influencing baseline transcription scores. The interaction between independent home literacy practices and formal literacy practices at home further enhanced transcription skill development. In contrast, oral language skills were not influenced by either HLP or EF. These results suggest that EF plays a more prominent role in transcription development than oral language skills in early childhood, especially in transparent orthographic systems. The findings highlight the importance of cognitive and environmental factors in early literacy development, suggesting implications for educational practices, particularly in fostering effective home literacy environments

The COVID-19 outbreak has had a tremendous socioeconomic impact around the world, and although there are currently some drugs that have been granted authorization by the U.S. FDA for the treatment of COVID-19, there are still some restrictions on their use. As a result, it is still necessary to urgently carry out related drug development research. Deep generative models and cheminformatics were used in this study to design and screen novel candidates for potential anti-SARS-CoV-2 small molecule compounds. In this study, the small molecule structure of Molnupiravir which has been authorized by the U.S. FDA for emergency use was used to be a model in a similarity search based on the BIOVIA Available Chemicals Directory (BIOVIA ACD) database using the BIOVIA Discovery Studio (DS) software (version 2022). There were 61,480 similar structures of Molnupiravir, which were used as training dataset for the deep generative model, and then the reinforcement learning model was used to generate 6000 small molecule structures. To further confirm whether those molecule structures potentially possess the ability of anti-SARS-CoV-2, cheminformatics techniques were used to assess 38 small molecule compounds with potential anti-SARS-CoV-2 activity. The suitability of 38 small molecule structures was calculated using ADMET analysis. Finally, one compound structure, Molecule_36, passed ADMET and was unpatented. This study demonstrates that Molecule_36 may have better potential than Molnupiravir does in affinity with SARS-CoV-2 RdRp and ADMET. We provide a combination of generative deep neural networks and cheminformatics for developing new anti-SARS-CoV-2 compounds. However, additional chemical refinement and experimental validation will be required to determine its stability, mechanism of action, and antiviral efficacy.

Building energy consumption accounts for a significant portion of total society energy use, and photovoltaic technology is being rapidly deployed across the construction sector. In order to improve the efficiency with which photovoltaic shading devices capture solar energy, a numerical calculation model for the ideal tilt angle of these devices is constructed in this study. This model is based on clear-sky solar radiation calculation algorithms and solar radiation resources across different latitudes. In order to maximize solar radiation collection, an ideal control strategy for photovoltaic shading devices on buildings with varied orientations at different latitudes and in different months is derived through numerical simulations. The findings demonstrate that the building’s orientation has a significant role in determining how well photovoltaic shading systems use solar energy. In winter, the ideal tilt angle for south-facing facades increases by 10° for every 10° increase in latitude. And for every 25° rise in latitude, the ideal tilt angle increases by only around 10° in summer. By applying optimal regulatory strategies, solar radiation consumption efficiency of roughly 65% can be attained, providing a reference basis for boosting power generating efficiency and building energy saving.

In recent years, robots have started to be used in tasks involving human interaction. For this to be possible, humans must perceive robots as suitable interaction partners. This can be achieved by giving the robots an animate appearance. One of the methods that can be utilised to endow a robot with a lively appearance is giving it the ability to perform expressions on its own, that is, combining multimodal actions to convey information. However, this can become a challenge if the robot has to use gestures and speech simultaneously, as the non-verbal actions need to support the message communicated by the verbal component. In this manuscript, we present a system that, based on a robot’s utterances, predicts the corresponding gesture and synchronises it with the speech. A deep learning-based prediction model labels the robot’s speech with the types of expressions that should accompany it. Then, a rule-based synchronisation module connects different gestures to the correct parts of the speech. For this, we have tested two different approaches: (i) using a combination of recurrent neural networks and conditional random fields; and (ii) using transformer models. The results show that the proposed system can properly select co-speech gestures under the time constraints imposed by real-world interactions.