Search Results (3,628)

The rapid digitalization of energy systems and the increasing integration of distributed energy resources, renewable energy technologies, and prosumer-oriented infrastructures have accelerated the development of IoT-enabled Smart Grids as a foundation for intelligent and adaptive energy management. Modern Smart Grids increasingly depend on the coordinated interaction of IoT architectures, artificial intelligence, distributed analytics, and decentralized control mechanisms to ensure reliability, scalability, and real-time operational flexibility. Despite extensive research activity, existing studies remain predominantly technology-centric, focusing on isolated architectural layers or individual intelligent methods without providing a unified system-level perspective on their coordinated operation and interoperability. This article presents a system-level integrative review and challenge-oriented comparative synthesis of intelligent operational and architectural foundations of IoT-enabled Smart Grids. The study analyzes data-driven, model-driven, knowledge-driven, agent-based, and hybrid-oriented intelligent paradigms within multi-layer IoT energy infrastructures. In addition, the research establishes a cross-layer mapping between Smart Grid operational challenges, enabling technologies, and corresponding analytical approaches while identifying interoperability constraints, scalability limitations, and coordination challenges associated with decentralized energy ecosystems. The conducted synthesis demonstrates that hybrid-oriented intelligent approaches represent the most promising direction for future Smart Grid evolution due to their ability to integrate AI, ML, digital twins, semantic reasoning, and decentralized multi-agent coordination within unified IoT architectures. The conducted comparative synthesis identifies the ongoing transition from isolated intelligent solutions toward integrated hybrid cyber–physical energy ecosystems and highlights key characteristics of future adaptive, interoperable, scalable, and explainable Smart Grid architectures. Full article

Small and medium-sized enterprises (SMEs) are particularly vulnerable when crisis support systems are delayed, unreliable, or difficult to access. This study examines how South African SMEs maintained strategic continuity during COVID-19 by developing the concept of relational substitution, defined as a process in which owner-managers compensate for weak formal support by internalizing continuity work within the employment relationship. The study is based on a secondary qualitative analysis of 16 semi-structured interviews with SME owners in the Western Cape, South Africa, complemented by a targeted evidence review to inform boundary-condition assessment. The findings show that owner-managers assembled relational continuity bundles that combined labor flexibility, retention intent, transparent communication, and visible well-being support. Owners presented these bundles as efforts to sustain cooperation, trust, and operational functioning when enacted through fairness logics such as voice, transparency, equal sacrifice, and relational care. The study contributes to SME resilience and management and organization studies by distinguishing relational substitution from sustainable human resource management, organizational justice, relational leadership, and institutional fragility, and by specifying the firm-level and institutional conditions under which this mechanism is more likely to support strategic continuity. Full article

The efficiency of airport resource allocation is improved through the establishment of a scientific multi-granularity configuration scheme for flight slot coordination parameters. In this study, a collaborative configuration method for hourly and 15 min coordination parameters is proposed, with Beijing Capital International Airport serving as a case study. Short-term traffic clusters are frequently omitted by traditional hourly parameters, thereby leading to sudden delay surges. First, local delays were extracted from March 2024 Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data. Subsequently, a delay prediction model was constructed through the integration of a non-stationary queuing model and a gradient boosting regression tree. Second, simulated timetables were generated via a Monte Carlo method under various parameter combinations. With a constant daily flight volume utilized as the experimental baseline, a mapping relationship was established between parameter combinations and expected local delays. Finally, feasible delay regions were delineated and interpretable configuration rules were extracted via a decision tree to maximize schedule flexibility. It was indicated by the results that at an hourly parameter of 70 flights, the target delay is maintained below 8 min by tightening the 15 min parameter to 19 flights. The findings suggest that average load is controlled by hourly parameters, while traffic clustering in high-load scenarios is effectively suppressed by 15 min parameters. A quantitative reference is provided by this method for the configuration of multi-granularity time parameters at hub airports. Full article

Incentive-based demand response is an important flexibility resource for power systems with high-renewable energy penetration. However, practical incentive allocation depends not only on flexible capacity and user response uncertainty, but also on the credibility of customer baseline load (CBL), which directly affects response measurement, verification, and incentive settlement. To address this issue, this paper proposes a constrained reinforcement learning method with customer baseline credibility for dynamic resource allocation in incentive-based demand response. Based on user-side load measurements and demand response event records, the proposed framework evaluates user resources using flexible capacity, response reliability, response cost, and CBL credibility. The CBL credibility score reflects the measurement quality of the delivered response and is used as a pre-event allocation factor. Users are then grouped into different resource levels, and a group-level reinforcement learning agent dynamically determines incentive multipliers and response task allocation ratios. To improve feasibility, an action correction module revises raw policy outputs under budget, price, response capacity, and CBL risk constraints before implementation. Case studies are conducted using public industrial demand response measurements and open electricity-system time-series data. The results show that the proposed CBL-CRL method reduces the normalized total operating cost to 0.897, reduces the response tracking error to 0.108, and lowers CBL risk exposure to 0.087 under the normal scenario. Relative to the No-DR reference, CBL-CRL reduces the normalized total operating cost by 10.3 percent. Compared with MAPPO, the strongest learning-based baseline, CBL-CRL reduces the response tracking error by 10.7 percent and the CBL risk exposure by 40.8 percent, while maintaining the same renewable accommodation rate of 0.970. Compared with rule-based and learning-based baselines, CBL-CRL achieves a better balance between operational performance, incentive efficiency, action feasibility, and baseline-related settlement reliability. The results demonstrate that CBL credibility should not only be used for post-event settlement, but can also serve as an effective pre-event resource allocation factor for measurement-driven demand response programs. Full article

The aim of this article is to identify how research on smart buildings has evolved in the context of the energy transition, with particular emphasis on energy flexibility, grid interaction, market integration, and policy barriers. The study addresses a gap in previous reviews, which have often focused on individual technological domains, building automation, or smart-readiness assessment, while paying less attention to the conditions under which smart buildings become active energy-system resources. A systematic review protocol based on the PRISMA logic was combined with bibliometric mapping and qualitative synthesis. Bibliographic data were retrieved from Scopus on 28 February 2026 and covered 663 English-language journal articles published between 2015 and February 2026. A core set of 63 studies was selected through explicit cluster-based and relevance-based criteria for in-depth qualitative synthesis. The results show a gradual shift from component-level efficiency research towards system-level studies in which smart buildings are analyzed as flexible demand-side assets, distributed energy nodes, and participants in emerging market mechanisms. At the same time, the evidence base remains uneven: many studies rely on simulation or case-specific modeling, while empirical validation, interoperability, occupant behavior, business models, and regulatory implementation remain less mature. The article contributes by distinguishing observed bibliometric patterns from conceptual interpretation and by integrating technological, economic, behavioral, and regulatory evidence into a framework explaining the persistent implementation gap in smart building deployment. Full article

The classification position of substrates forming on the beds of aquaculture ponds remains a poorly resolved issue at the intersection of pedology, limnology, and aquaculture science. We examine how major international and national soil classification systems—the USDA Soil Taxonomy, the World Reference Base for Soil Resources (WRB), the German Bodenkundliche Kartieranleitung, the Australian Soil Classification (ASC), the Russian Soil Classification, and the classification systems of Brazil and China—approach the systematics of subaqueous soils and their aquaculture analogues. A systematic literature search was conducted across the Web of Science, Scopus, and Google Scholar databases covering the period from 1953 to 2025. Our analysis reveals that Soil Taxonomy provides the most developed taxonomic framework through specialized suborders (Wassents and Wassists), while the WRB offers the greatest flexibility via its qualifier system (subaquatic, limnic, and gleyic). The German classification uniquely assigns subaqueous soils to the highest taxonomic level (division) with a substantive typology that is directly applicable to pond substrates. The Australian classification contributes a three-part sulfidic material typology of practical significance for pond management. The Russian and Brazilian systems currently lack formal taxa for subaqueous soils, although recent proposals (e.g., Aquazems) may address this gap. The Chinese paddy soil model offers a conceptual bridge between subaqueous pedology and aquaculture. No existing system adequately addresses the specific anthropogenic impacts of aquaculture management on pond soil formation. Permanently inundated little-disturbed ponds fall within the subaqueous soil concept, whereas intensively managed, frequently drained or dredged ponds are better treated as anthropogenic soils with a subaqueous phase. We recommend the WRB (4th edition, 2022) as the most suitable framework for current classification of aquaculture pond soils while acknowledging that a multi-system approach may ultimately prove most effective. These findings carry particular relevance for countries of the former Soviet Union (CIS), where extensive pond aquaculture is practiced but pond substrates remain outside formal pedological classification. Full article

Introduction: Understanding population structure is essential for the effective management of marine resources. The bluefish (Pomatomus saltatrix) is a widely distributed and commercially important species along the Brazilian coast, yet its population structure in the South–Southeastern region remains unclear. Objectives: This study aimed to assess the body morphological variation and to infer the population structure of P. saltatrix along the South and Southeastern coasts of Brazil using a landmark-based geometric morphometrics method. Methodology: Individuals were collected between August and October 2024 from artisanal fisheries in Rio de Janeiro (n = 46), São Paulo (n = 37), and Santa Catarina (n = 29). A total of 12 landmarks were used to derive a truss network and extract 25 transformed distances (DTs). Data were analyzed using univariate (one-way ANOVA, followed by Tukey’s test if needed, or Kruskal–Wallis followed by Dunn’s test if needed) and multivariate statistics (PERMANOVA and Flexible Discriminant Analysis (FDA)). Results: Significant regional differences were detected for most DTs, except DT3, DT4, DT6, DT9, DT13, DT24, and DT25. PERMANOVA revealed significant differences among all sampling locations, including pairwise comparisons (p < 0.05). FDA achieved an overall reclassification success of 93% of individuals to their original location. Conclusion: Despite the high reclassification accuracy, the results support the existence of a single stock with spatial structuring rather than completely distinct population units, indicating that P. saltatrix in this region is not homogeneous. Full article

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules. Full article

Background: Confident chemical annotation in nontarget small-molecule mass spectrometry critically depends on the availability of high-quality tandem mass spectral (MS²) reference libraries. While community efforts have driven significant expansion of open-access repositories, technical challenges in assembling standardized, metadata-rich records continue to limit broader participation, underscoring the need for improved computational tools to assist contributors. Methods: To promote the creation and sharing of standardized reference MS² spectral records, we have developed Librarian, a free, open-access web application designed for rapid and scalable assembly of high-resolution MS² libraries. Librarian integrates automated retrieval and harmonization of chemical identifiers and metadata from PubChem, compound mixture design for high-resolution mass spectrometry (HRMS) acquisition, and assembly of curated MS² spectra into repository-ready records compatible with public spectral databases. Results: Through a simple in-browser interface, Librarian offers a flexible end-to-end workflow compatible with popular open-source pre-processing tools to lower technical barriers and facilitate broader community participation in library development. As a demonstration, we used Librarian to create and deposit a spectral library comprising over 1500 new MS² records into MassBank, which was further applied in retrospective analysis of environmental datasets. Conclusions: Librarian streamlines the creation of standardized, metadata-rich and repository-ready MS² reference records. Addressing a key bottleneck in community spectral library development and sharing, Librarian supports the continued growth of open-access resources for metabolomics, exposomics, and environmental mass spectrometry. The Librarian web application is publicly accessible via the SciLifeLab Serve platform. Full article

Conventional evaluations of indoor environmental quality (IEQ) in office spaces are typically disproportionately influenced by expert experience, often overlooking the cognitive gap between decision makers (experts) and users (employees). To quantify and explain this discrepancy, this study develops a comprehensive evaluation framework including 20 IEQ indicators, grounded in Maslow’s hierarchy of needs. Using the Shenzhen Science Park as a case study, evaluation data were collected from 13 experts and 432 employees. The Analytic Hierarchy Process (AHP) and the Kano model were applied to calculate expert weights and employees’ nonlinear sensitivities, respectively, followed by the construction of an optimization matrix via Importance–Performance Analysis (IPA). The results reveal a notable cognitive gap: experts prioritize foundational physical elements regarding spatial technology, whereas employees place greater emphasis on factors such as privacy protection and flexible layouts. Both groups concur that “noise interference” and “lack of privacy” are the primary shortcomings of open-plan offices. Prospective assessments indicate that embodied AI-enabled robots currently remain in a “early adoption phase,” with employees showing no functional dependency on them. This study confirms that merely improving building physical performance does not proportionally translate to increased employee satisfaction. Spatial optimization should adopt a human-centric approach, emphasizing acoustic control and the reconfiguration of privacy boundaries to enhance the scientific allocation of resources. Full article

Background/Objectives: Incorporating parent training into cognitive-behavioral therapy for anxious youth has not been shown to significantly improve outcomes perhaps because these interventions have not addressed potential interfering psychological barriers to implementing parenting changes and rarely offer between-session support. There is growing evidence that Acceptance and Commitment Therapy (ACT) can target these psychological barriers and generate more flexible and adaptive behavioral repertoires in parents of children with a variety of presenting challenges. Methods: Following a pilot trial of “Acceptance and Commitment Therapy for Parents of Anxious Children (ACT-PAC)” a six-week group-based intervention focused on targeting psychological barriers to parenting change using mindfulness and acceptance approaches, we collected qualitative feedback from participants in two post-treatment phases by conducting individual interviews and a focus group with participants that completed the intervention. Results: Analysis of interview responses revealed that parents found ACT principles and processes to be helpful, and many also appreciated the ACT-PAC group setting that allowed parents to recognize their experiences were shared by others and to self-disclose in a non-judgmental space. Feedback from the focus group further provides preliminary evidence that ACT-PAC is acceptable to and feasible for parent participants and suggests modifications such as involving additional caregivers, making resources more readily available, and creative structural changes that may facilitate between-session practice. Conclusions: Results suggest that the group-based intervention can be both maintained and improved for future participants. Limitations to generalizability in light of possible selection bias and the small focus group sample size are addressed. Full article

As core structural components for deep-sea oil and gas exploitation, deep-sea risers are continuously subjected to wind, wave, and current loads, which readily induce vortex-induced vibration (VIV) and further trigger structural fatigue damage. Furthermore, the progressive exploitation of deepwater and ultra-deepwater oil and gas resources has exacerbated the complexity and risk of riser VIV, rendering it a critical engineering problem that urgently requires effective solutions. This paper presents a comprehensive review of numerical studies on deep-sea riser VIV, systematically elaborating the fundamental principles, research advances, and application scenarios of three mainstream numerical approaches: semi-empirical models, computational fluid dynamics (CFD) models, and computational structural dynamics (CSD) models. The respective accuracy advantages and inherent limitations of each numerical method are thoroughly analyzed. Additionally, this review focuses on key research hotspots and challenging issues, including VIV responses of flexible risers, dynamic fluid–structure boundary coupling, internal–external flow coupling effects, wake interference of multi-riser systems, efficient VIV prediction, and vibration suppression optimization. The current technical bottlenecks in existing research are clarified. This study aims to provide a systematic theoretical framework and methodological reference for subsequent numerical investigations and engineering applications of riser VIV, and offer technical support for the optimal structural design and safety risk prevention of deep-sea riser systems. Full article

With the increasing penetration of renewable energy generation (REG) in novel distribution systems, active distribution networks (ADNs) integrated with microgrids (MGs) play a crucial role in enhancing the flexibility of regulation resources and promoting the accommodation of REG. To meet the operational requirements for efficient collaboration between ADNs and MGs under different dispatch time scales, this paper proposes a collaborative optimal dispatch strategy for multi-microgrid active distribution systems based on a hybrid game and variable time scales. Firstly, a transaction operation framework is constructed for the distribution network operator (DNO) and a multi-microgrid alliance (MMA), considering the peer-to-peer (P2P) transaction mode. On this basis, a day-ahead hybrid game model with a two-layer structure is constructed, the upper layer is a master–slave game with the DNO as the leader and the MMA as the follower, while the lower layer is a cooperative game for MGs within the MMA. An asymmetric Nash bargaining strategy based on contribution degree in P2P transactions is introduced to ensure equitable benefit allocation among cooperative MGs. Secondly, an intra-day rolling optimization model for reactive power and voltage based on variable time scales is proposed, which enhances the system’s responsiveness to real-time source–load power fluctuations by dynamically adjusting the dispatch time scale. Finally, the alternating direction method of multipliers (ADMM), integrated with a strategy separation mechanism, is adopted to efficiently solve the hybrid game model involving numerous 0–1 variables. The case study results indicate that, under the proposed strategy, the MMA’s power purchase cost from the DNO and ESS operational cost are decreased by 9.7% and 11.6%, respectively, while the system’s average deviation rate of node voltage decreases by 0.82%. Therefore, the proposed collaborative dispatch strategy can not only effectively reduce the system’s operational cost and ensure voltage stability but also significantly promote the accommodation of REG. Full article

This paper proposes a genetic algorithm (GA) for the Dual-Resource Flexible Job Shop Scheduling Problem with Partial Resource Allocation (DRFJSSP-PRA), a particular variant of a dual-resource constrained scheduling problem that has not been fully explored due to its intricate nature. The DRFJSSP-PRA poses a challenging scheduling problem, having several applications in many industries, including food, chemistry and pharmaceutics. The proposed algorithm is applied to real-world scheduling instances in pharmaceutical quality control. The objective function considered is the total completion time. The GA is compared with three state-of-the-art algorithms. For small- and medium-size instances, the proposed algorithm achieves optimal or near optimal results for the majority of the instances tested. For large-sized instances, the proposed GA outperforms all the other algorithms, in all of the tested instances. Thus, the experimental results show that the proposed GA achieves competitive results for any type of instance. The proposed algorithm also has the ability to optimize production processes through scheduling, leading to potential cost savings, increased efficiency, and improved competitiveness. Full article

This study examines punctuation as a paralinguistic resource in digital political discourse, focusing on its role in encoding stance in a corpus of tweets produced by Donald Trump. Drawing on a dataset of 1000 tweets, the analysis combines quantitative and qualitative approaches to identify patterns in the use of exclamatory, interrogative, and mixed punctuation forms. The findings suggest that punctuation functions systematically as a mechanism of textual amplification, shaping the expression of evaluation, emotional intensity, and interactional meaning. Repeated exclamation marks are closely associated with heightened stance and emphasis, while interrogative forms frequently function rhetorically to signal doubt and challenge rather than to request information. Mixed forms further demonstrate the flexibility of punctuation in encoding multiple layers of meaning within a single utterance. The analysis also shows that punctuation operates in close interaction with lexical intensifiers and capitalization, forming clusters of meaning that reinforce communicative force. By foregrounding punctuation as a meaningful semiotic resource, the study contributes to a more nuanced understanding of how written discourse adapts to the constraints of digitally mediated communication. It argues that punctuation should be treated as an integral component of pragmatic and discourse-analytic inquiry and highlights its role in the construction of stance in contemporary digital political discourse. Full article