Artiles
Open Access
Article
30 March 2026Sexual Dimorphism in the Association Between Status Symbols and Body Height in the Early Medieval Avar Population from the Csokorgasse Burial Ground (Vienna, Austria)
The relationship between material culture and body height, commonly used as a proxy for reconstructing economic conditions and social stratification, has not previously been examined for early medieval Avar populations. Therefore, this study investigates the association between estimated body height and grave goods, funerary characteristics, and activity-related indicators interpreted as markers of elevated social status in 148 male and 136 female individuals from the Avar burial ground Csokorgasse (Vienna, Austria). In addition, diachronic changes in body height from the late 6th to the late 8th century CE, a period marked by substantial transformations in subsistence strategies and lifestyle, are assessed. Overall, body height shows a slight but statistically insignificant decrease over time in both sexes. Among males, individuals interred in equestrian graves together with horses were on average more than 6 cm taller than males buried without horses. Similarly, males identified as warriors based on the presence of weapons as grave goods were significantly taller than those without weapons. Multipart belt sets, commonly interpreted as indicators of high-status males, display only a weak and statistically insignificant positive association with body height. In contrast, patterns observed among females differ markedly: Of the categories examined, only jewelry shows a statistically significant association with body height, with shorter women being buried with a greater quantity of jewelry. Thus, whereas male body height is positively associated with several markers of elevated social status, no comparable pattern can be identified for females. These results indicate a pronounced sex-specific divergence in the relationship between biological status, as reflected by body height, and socially expressed status in early medieval Avar society.
Open Access
Article
30 March 2026A Fast Acting Quantized Energy Balance Criterion for Power System Instability Detection Based on WAMPAC GOOSE Pulses Induced by Small Speed Perturbations
A newly developed stability assessment tool for a power system is proposed in this paper based on estimating the kinetic energy-time variations. It aims to introduce a practical alternative to the Equal Area Criterion (EAC) method that is valid for multi-swing cases. It utilizes the Generic Object Oriented Substation Event (GOOSE) packets launched due to angle variations during swing by the Intelligent Electronic Devices (IEDs) measuring the generator bus angle. The scheme maps the GOOSE packets to quantized energy levels. The detector IED receives the launched GOOSE from disturbed generators through the Wide Area Monitoring, Protection and Control (WAMPAC) System and evaluates the system stability accordingly. The areas under the positive energy intervals above the time axis determine the stability for the oscillatory swing. It has been proven that the area under positive energy levels is proportional to the number of GOOSE packets emitted during these intervals. For the fast monotonic swing, the quantized energy pattern shows quasi-stable intermediate energy levels between two high energy levels, where the scheme detects the transition to the second higher level as an indication of instability, with enough time in advance for corrective measures. The scheme is Phasor Measurement Unit (PMU)-independent, thus eliminating the burden and cost of synchronization requirements. The new scheme has been tested using the IEEE 39 Bus System. The results show the scheme’s capability to predict instability 87 ms prior to its occurrence, which is an adequate time for remedial action.
Open Access
Article
30 March 2026Evaluation of Power Grid Investment Effectiveness in New Power Systems Considering Decision Psychology and Sustainable Development: An Empirical Study Based on Chinese Urban Power Grid Simulation
The evaluation of investment effectiveness in power grids oriented towards new-type power systems is a critical issue for advancing grid transformation and enhancing the scientific basis of investment decision-making. To address the current challenges—such as single-dimensional evaluation, strong subjectivity in index weighting, and insufficient consideration of risks and decision-makers’ psychological factors—this paper aims to construct a hybrid evaluation framework that comprehensively reflects both objective data and subjective decision-making preferences. First, a comprehensive evaluation index system is established, encompassing four dimensions: low-carbon performance, safety, economic efficiency, and intelligence. Second, an innovative integration of the Back Propagation Neural Network (BPNN), the CRITIC method, and the Entropy Weight Method (EWM) is conducted. The combination weights are determined through game theory to scientifically quantify the importance of each index. Based on this, the Improved Cumulative Prospect Theory (ICPT) is introduced to characterize decision-makers’ psychological behavior under uncertainty. Furthermore, by combining Grey Relational Analysis (GRA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), an ICPT-GRA-TOPSIS comprehensive evaluation model is constructed. An empirical study of 13 typical urban power grids in China reveals that the proposed model can effectively identify the strengths and weaknesses of investment effectiveness across different regions, categorizing them into development tiers such as “multi-objective collaborative leading type”, “key breakthrough but unbalanced type”, and “system-lagging type”. More importantly, the sensitivity analysis of decision-making psychology demonstrates that the evaluation of investment strategies is highly dependent on decision-makers’ risk attitudes and value orientations. This provides critical quantitative decision-making references for formulating differentiated, precise investment strategies for power grids, offering significant theoretical and practical value for guiding power grid enterprises in optimizing resource allocation and supporting the construction of new-type power systems.
Open Access
Article
30 March 2026Mechanical Properties of Developed Corncob-Urea Particles Hybrid Reinforced Polyester-Based Composites
This study investigates the development of hybrid-reinforced polyester composites using corncob and urea particles as reinforcement for sustainable applications. Composites were fabricated by the stir casting method with varying weight fractions of corncob and urea. The mechanical and physical properties of the developed composites were evaluated, while fracture surface morphology was examined using scanning electron microscopy (SEM). The burning rates of the samples were investigated to evaluate their flame-retardant potential. The results demonstrate that incorporating corncob and urea effectively enhances stiffness-related mechanical properties, including tensile and flexural moduli and hardness. Composite containing 12 wt% urea and 3 wt% corncob exhibited the highest flexural moduli and hardness with an improvement of 122% and 45%, respectively. Composite with 3 wt% corncob and 18 wt% urea has the highest flexural strength with an increase of 44%, composite with 9 wt% corncob and 18 wt% urea has the highest tensile modulus with an improvement of 22%. In addition, it was found that the presence of corncob and urea reduced burning rates, with the sample containing 15 wt% corncob and 18 wt% urea exhibiting the lowest burning rate, indicating better flame-retardant potential. Thus, the findings indicate that corncob–urea hybrid reinforcement offers a promising, sustainable approach to enhancing the mechanical stiffness and reducing the burning rate of polyester composites. These materials have potential for use in applications requiring improved durability and low burning rate potentials while reducing reliance on conventional synthetic additives.
Open Access
Article
30 March 2026CFD Investigation of Torque Generation in an Archimedes Screw Hydrokinetic Turbine
The Archimedes Screw hydrokinetic turbine (AST) is a promising technology for renewable energy generation in shallow, low-velocity, and bidirectional flows, but the mechanisms governing its torque production remain poorly understood. This study uses computational fluid dynamics (CFD) to investigate the performance and torque-generation mechanism of a three-flight AST inclined at 30° and operating in two configurations previously examined experimentally. Transient simulations were performed in ANSYS Fluent using a sliding mesh and flow-induced rotation approach within an unsteady Reynolds-averaged Navier–Stokes framework with the SST k–ω turbulence model. The results show that pressure forces dominate torque generation, while viscous contributions are comparatively small. Importantly, this behaviour is observed at a relatively low Reynolds number of approximately 4.5 × 104, indicating that Reynolds-number dependence becomes weak at Reynolds numbers substantially lower than those expected in practical deployments. For the first configuration, with the upstream edge of the turbine at the free surface, the CFD model predicted a maximum power coefficient of 0.85 at a tip speed ratio of 1.50, compared with an experimental value of 0.40 at 0.53. For the second configuration, with the downstream edge of the turbine at the free surface, the corresponding maximum power coefficient was 0.82 at a tip speed ratio of 1.51, compared with 0.34 at 0.54, as experimentally observed. The simulations also captured strong cyclic torque variations; the maximum variation in torque was over three times the mean value for both configurations. Comparison of the cavitation and pressure coefficients indicates little likelihood of cavitation at the experimental flow velocity but suggests possible cavitation onset at higher velocities.
Open Access
Article
27 March 2026Intra- and Inter-Watershed Variability in Benthic Macroinvertebrate Community Diversity, Taxa Richness, and Biotic Integrity: Citizen Scientist Sampling Within a Minnesota USA Region Dominated by Agriculture
Volunteer citizen scientists collected benthic macroinvertebrate samples from 35 streams throughout multiple watersheds in southeastern Minnesota, USA, during the period 1999–2013 to assess community diversity, taxa richness, and biotic integrity as indicators of water quality and general habitat conditions. In total, 452 invertebrate samples containing >46,000 organisms were collected, processed, and analyzed. Only 45% of the citizen scientists completed their 5-year sampling commitment. However, their samples generally demonstrated significant differences in total taxa richness, Ephemeroptera-Plecoptera-Trichoptera (EPT) taxa richness, Simpson and Shannon diversities, and a regional benthic index of biotic integrity (BIBI) within and/or among the watersheds examined. Streams in the two larger watersheds averaged significantly higher taxa richness and BIBI scores than those in smaller watersheds. Overall, streams in this region exhibited mostly poor or very poor biotic integrity based on their macroinvertebrate communities, indicating continued impacts from environmental stressors within these agricultural watersheds.
Open Access
Article
27 March 2026Collaborative Optimization of Berth Allocation and Marine Energy Utilization for Low-Carbon Ports
Ports, as key nodes for marine renewable energy consumption and integration with marine industries, are facing the dual pressures of low-carbon transformation and efficient energy utilization. To solve fossil fuel reliance and high carbon emissions from disconnected port berth scheduling and energy optimization, this study proposes a two-stage framework combining the improved Cuckoo Search Algorithm (ICSA) and Stackelberg game. In the first stage, a vessel-centric optimization framework is proposed, which integrates the time-of-use electricity pricing mechanism to coordinate ship operating decisions and port low-carbon objectives. The ICSA is employed to solve the low-carbon berth allocation problem, while synchronously generating the time-series load data of key port handling equipment. In the second stage, a demand response load matrix is established by fully exploiting the battery swapping characteristics of electric trucks and the cold load shifting capability of refrigerated containers. A tripartite Stackelberg game is then conducted among the port energy operator, distributed energy supplier, and port equipment aggregator to optimize energy pricing and multi-energy supply dynamically. Case studies show doubled shore power using vessels, 14% higher berth utilization, and 29.86% lower energy costs. Carbon emissions were significantly reduced, while the proportions of offshore natural gas and renewable energy saw notable increases. This study provides a new approach for the integration of marine energy into port operations, supporting the sustainable development of marine energy industries and the low-carbon transformation of coastal ports.
Open Access
Review
27 March 2026Artificial Intelligence in Photovoltaic Power Systems: A Bibliometric and Thematic Analysis of Knowledge Structures, Research Evolution, and Emerging Directions Toward Sustainable Energy Systems
Artificial intelligence (AI) has rapidly become a core enabling technology in photovoltaic (PV) power systems, supporting improvements in forecasting accuracy, operational control, fault diagnosis, and system-level energy management. Despite the rapid growth of this field, a comprehensive understanding of its intellectual structure, thematic evolution, and emerging methodological directions remains fragmented. To address this gap, this study develops an integrated bibliometric-thematic analysis framework to systematically map the knowledge structure, research trajectories, and methodological frontiers of AI applications in PV power systems. The analysis is based on 4752 peer-reviewed journal articles indexed in Scopus (2006–2025). It combines performance analysis, co-citation analysis, keyword co-occurrence analysis, and bibliographic coupling to answer five structured research questions. The results demonstrate that PV power forecasting constitutes the central intellectual backbone of AI-based PV research, with the highest citation concentration and the strongest thematic connectivity across clusters. Thematic evolution analysis reveals a clear methodological transition from conventional machine learning models toward hybrid deep learning architectures, uncertainty-aware prediction frameworks, and physics-based AI integration. Furthermore, emerging research frontiers are characterized by generative learning models, multi-source data fusion strategies, and resilience-oriented fault diagnostics, while critical gaps persist in benchmarking standardization, uncertainty quantification, system-level integration, and large-scale industrial deployment. Unlike prior reviews that focus on isolated technical applications, this study provides the first integrated performance analysis and science-mapping synthesis that connects intellectual foundations, thematic evolution, and frontier innovations across the entire AI-based PV ecosystem. The findings offer a structured research roadmap and actionable guidance for researchers, PV plant operators, and policymakers aiming to design intelligent, scalable, and resilient PV energy systems that support the global low-carbon transition.
Open Access
Review
26 March 2026The Role of ILC2s in Tissue Injury and Repair
Group 2 innate lymphoid cells (ILC2s) are tissue-resident sentinels pivotal for maintaining barrier homeostasis and orchestrating type 2 immunity. Upon acute injury, alarmins rapidly activate ILC2s, which promote tissue repair by secreting amphiregulin, IL-5, and IL-13, driving epithelial proliferation and migration, anti-inflammatory macrophage polarization, and immune regulation. Under specific conditions, such as allergen immunotherapy, a subset of ILC2s can be induced to produce IL-10, further enhancing immune regulation and tissue repair. However, in chronic inflammatory or fibrotic diseases, such as asthma, atopic dermatitis, pulmonary and liver fibrosis, and cardiovascular disorders, persistent activation skews ILC2s toward a pathogenic state. Here, excessive cytokine production drives eosinophilia, mucus hypersecretion, and fibroblast activation, while microenvironmental cues can induce plasticity toward pro-inflammatory Group 1 innate lymphoid cell (ILC1)-like phenotypes. This review systematically details the dual, context-dependent roles of ILC2s across major organs, highlighting their function as critical regulators of the repair-fibrosis axis. We critically examine the sources of functional variability, including differences in injury models, disease chronicity, species-specific effects, and ILC2 subset definitions that may explain apparent contradictions in the literature. Where appropriate, we compare ILC2 functions with those of other immune cell types such as regulatory T cells (Tregs) and macrophages, emphasizing the unique and overlapping contributions of each population. Finally, we discuss emerging therapeutic strategies that aim to precisely inhibit pathogenic ILC2 responses or harness their reparative potential, offering promising avenues for treating a spectrum of chronic inflammatory and fibrotic diseases.
Open Access
Review
26 March 2026Sustainable Additive Manufacturing of Polymer Composites for Advanced Water Treatment: A Review
The increasing demand for clean water, coupled with growing concerns over energy consumption and environmental impact, has intensified the search for sustainable materials and fabrication strategies for water treatment technologies. Polymer composites have emerged as highly promising candidates due to their tunable chemistry, lightweight nature, and compatibility with functional fillers. At the same time, additive manufacturing (AM) offers unique advantages in terms of design freedom, material efficiency, and customizable architectures. This review provides a comprehensive assessment of sustainable polymer composites fabricated via additive manufacturing for advanced water treatment applications. Major AM techniques, including material extrusion, vat photopolymerization, material jetting, powder bed fusion, binder jetting, and sheet lamination, are critically evaluated with respect to their printability, design flexibility, and environmental footprint. Emphasis is placed on sustainable polymer matrices such as polylactic acid, polyhydroxyalkanoates, cellulose-based polymers, and recycled plastics, as well as eco-friendly fillers and functional additives, including biochar, lignin, chitosan, nanocellulose, clays, zeolites, hydroxyapatite, and functional nanomaterials (e.g., AgNPs, TiO2, ZnO, and graphene). The role of composite architecture, surface modification, and hierarchical porosity enabled by AM in enhancing adsorption, catalytic activity, and antimicrobial performance is highlighted. This review demonstrates that integrating sustainable materials with additive manufacturing enables the development of multifunctional, energy-efficient, and circular water treatment systems. The findings support the advancement of purification technologies aligned with the United Nations Sustainable Development Goals, particularly SDG 6, SDG 12, and SDG 13.
