Advancing Open Science

Power management strategies for microgrids are typically designed around quantitative performance metrics such as cost, efficiency, and reliability. While effective in many settings, these approaches often do not fully account for qualitative, human-centric considerations, such as the relative importance or criticality of different loads. This limitation is especially relevant in remote or community-based energy systems, and becomes more pronounced in wind-powered microgrids, where variable generation and limited resources require flexible and context-aware operational decisions. In this work, a qualitative-driven power management framework is proposed that incorporates stakeholder-defined qualitative indices into microgrid energy allocation. A community–importance (CI) index is used to represent qualitative needs as normalized weighting factors, which are then used to guide power redistribution during supply–demand imbalances. The framework is demonstrated using a wind-powered microgrid with heterogeneous load types and is evaluated under different operating scenarios. The results show that the proposed approach supports prioritized and socially informed power allocation while preserving overall system feasibility. Rather than replacing conventional quantitative optimization, the framework acts as a complementary decision-support layer and is particularly well suited for microgrids serving remote or resource-constrained communities where qualitative priorities play an important role in operational planning.

Interior permanent magnet synchronous motors (IPMSMs) are widely adopted in electric vehicles due to their high torque density and efficiency, and they require flux-weakening operation to achieve high-speed performance under certain driving conditions. However, the traditional current vector control (CVC)-based flux-weakening strategies suffer from performance degradation when motor parameters, such as inductances and flux linkage, vary with temperature and operating conditions. To address this issue, this paper proposes a robust flux-weakening control strategy against multiple parameter variations. First, three sequential sliding-mode observers (SMOs) that form a sliding-mode observer suite (SMOS), whose stability is analyzed using Lyapunov theory, are designed to estimate the flux linkage, q-axis inductance, and d-axis inductance, respectively. Second, an error-analysis extraction (EAE) is developed to refine the parameter estimation accuracy by analytically solving a set of linear equations derived from observer results. Third, the accurately estimated parameters are applied to the CVC framework to generate adaptive reference currents, achieving robust and stable flux-weakening control performance. Finally, simulation and experiment are conducted to demonstrate that the proposed strategy effectively enhances control performance under multiple parameter variations.

The paper presents an optimal control strategy selection for an induction motor during voltage start-up, focusing on limiting the maximum current and starting torque surges at a given start-up time. Optimizing the operation of a soft-start system can be achieved using a simple system that assumes a linear voltage increase from a specific initial value. The advantage of this approach is the elimination of transducer systems for measuring voltages and currents. That approach requires simulating the drive system to select the optimal variant for a given system based on maximum and minimum torque values, maximum current, and optimal starting time. Transient calculations, for example, for the induction motor, were performed using both the analytical and circuit-field methods for different U/f time characteristics under varying motor load conditions, and the simulation results were compared with measurements.

This paper reports a design-rationale account of building and deploying a small ecosystem of AI-driven educational conversational agents with distinct pedagogical personas. Two strands target school contexts: (i)Talk to Bill, a historically grounded Shakespeare interlocutor intended to support close reading, contextual understanding, and interpretive dialogue; and (ii)Here to Help, a set of UK GCSE subject- and exam-board-specific tutors designed for formative practice in recognised question formats with feedback and iterative improvement. The third strand comprises six complementary assistants for an undergraduate human–computer interaction (HCI) module, each bounded to a workflow-aligned role (e.g., Empathise-stage coaching, study planning, course operations), with guardrails to privilege process quality over answer generation. We describe how persona differentiation is mapped to established learning, engagement, and motivation theories; how retrieval-augmented generation and provenance cues are used to reduce hallucination risk; and what early deployment observations suggest about orchestration, integration, and incentives. The contribution is a transferable, auditable rationale linking theory to concrete dialogue and UI moves for multi-persona tutoring ecosystems, rather than a claim of causal learning gains.

Acacia melanoxylon R.Br. demonstrates strong biological nitrogen–fixation capacity and favourable economic returns, making it a promising candidate for the development of subtropical forestry in South Asia. It is a fast–growing leguminous tree species widely promoted for cultivation in China, and it is also one of the ideal tree species for improving soil fertility in forest lands. What are the synergistic mechanisms between A. melanoxylon-Eucalyptus stands and pure Eucalyptus spp.? Current theories regarding A. melanoxylon–Eucalyptus systems remain relatively fragmented due to the lack of effective silvicultural measures, resistance studies, and comprehensive ecological–economic benefit evaluations. The absence of an integrated analytical framework for holistic research on A. melanoxylon–Eucalyptus systems makes it difficult to summarise and comprehensively analyse their growth and development, thereby limiting the optimisation and widespread application of their models. This study employed CiteSpace bibliometric analysis and qualitative methods to explore ideal tree species combination patterns, elucidate their intrinsic eco–economic synergistic mechanisms, and reasonably reveal their collaborative potential. This study systematically reviewed silvicultural management, stress physiology, ecological security, and economic policy using the Chinese and English literature published from 2010 to 2025. The narrative synthesis results indicated that strip intercropping (7:3) is widely documented as an effective model for creating vertical niche complementarity, whereby canopy light and thermal utilisation by A. melanoxylon species improve subsoil nutrient cycling by enhancing stand structure. A conceptual full–cycle economic assessment framework was proposed to measure carbon sequestration and timber premiums. Correspondingly, this conversion of implicit ecological services into explicit market values acted as a critical tool for decision–making in assessing benefit. A three–dimensional “cultivation strategy–physiological ecology–value assessment” assessment framework was established. This framework demonstrated how to move from wanting to maximise the output of an individual component to maximising the value of the whole system. It theorised and provided guidance on resolving the complementary conflict between “ecology–economy” in the management of sustainable multifunctional plantations.

The foundation of successful mineral exploration is precise bauxite horizon demarcation and grade estimation. Although core analysis is the industry standard method, it is costly, labor-intensive, and has a relatively low processing capacity. To overcome these limitations, this study constructed an Extreme Gradient Boosting (XGBoost) classifier based on the logging parameters of natural gamma logging (GR), natural gamma spectroscopy logging (GGL), three-lateral logging (LL3), and compensated density logging (CDN) in order to achieve the automation of ore layer identification and grade prediction. The karst-type bauxite in Lvliang, Shanxi, was used to validate the research. The model was trained using the data from four wells in Shenjiazhuang. The trained model was directly applied to a blind well in Xingxian without parameter adjustment. Strong cross-site generalization was demonstrated by horizon recognition, which achieved 98.18% accuracy, 96.62% precision, 91.49% recall, and an F1 score of 93.99%. Based on the Al/Si ratio (A/S) and the content of Al₂O₃, the grade prediction classifies the samples into three grades: high-, medium-, and low-grade. The Mean Absolute Errors (MAEs) for the prediction of high- and medium-grade subsets of Al₂O₃ were 0.906 and 1.643, respectively, and those for A/S were 1.224 and 1.146, respectively. And the coefficient of determination (R²) for each grade level was greater than 0.8. These results support XGBoost’s field applicability and resilience for intelligent bauxite exploration.

In the present study the stereoselective addition of malononitrile to trifluoromethyl arylketimines promoted by chiral iminophosphoranes was investigated. A panel of structurally diverse enantiopure bifunctional superbases, which include thiourea or squaramide unit and a basic site connected by a chiral scaffold, was tested in the asymmetric organocatalytic reaction, to afford an adduct featuring a quaternary stereocenter, in up to a 87/13 enantiomeric ratio. The product was then converted in a single step transformation into the corresponding enantioenriched α-CF₃ substituted quaternary aminoester, without any loss of stereochemical integrity. The absolute configuration of the final product was established by chemical correlation of the chiral compound with a known molecule. Preliminary computational studies were performed in order to elucidate the reaction mechanism and rationalize the stereochemical outcome of the reaction.

Knowledge-sharing is a deliberate exchange of information to enhance accessibility and reuse which is critical for improving healthcare delivery. This study assessed knowledge-sharing practices among dentists, pharmacists, and allied health professionals (AHPs) in nine public hospitals in South Africa’s Eastern Cape Province. A cross-sectional survey was conducted using purposive and stratified random sampling to recruit 99 participants. Data were collected via a validated questionnaire and analysed with SPSS v.22.0 using descriptive statistics. Respondents were predominantly female (77.6%) and aged 21–35 years (63.6%); AHPs comprised 65.7% of the sample. The results show a statistically significant association between profession and encouragement to adopt a global perspective (p = 0.017), while significant differences were observed between profession and encouragement to seek inter-team solutions (p = 0.020), and access to leadership-driven opportunities for interdisciplinary knowledge-sharing (p = 0.016). Despite observable patterns in the descriptive results, no other statistically significant differences by profession were observed for all other items. Collaboration with external communities and leadership-driven knowledge-sharing opportunities were also highest among dentists but limited overall. Adoption of information systems for knowledge exchange was low, particularly among pharmacists and AHPs. Participation in professional development and recognition of long-term knowledge-sharing strategies followed similar patterns. These findings highlight the need to strengthen leadership-driven opportunities for interdisciplinary knowledge-sharing and to develop targeted interventions to address specific gaps between professions.