Search Results (3,446)

The proliferation of large language model (LLM) agents has enabled increasingly complex multi-step automation; however, composing multiple agents into coherent systems introduces significant orchestration challenges that remain poorly documented. This survey examines LLM-based multi-agent orchestration from 2023 through early 2026 (literature cutoff: March 2026), with explicit attention to the evidence hierarchy used to interpret deployment claims. We propose a three-topology, one-adaptivity taxonomy—centralized, decentralized, and hierarchical coordination topologies, each optionally augmented with a dynamic–adaptive control axis—grounded in classical multi-agent systems theory and recent empirical evidence. We compare six leading frameworks (LangGraph, CrewAI, AutoGen/Microsoft Agent Framework, OpenAI Agents SDK, MetaGPT, and DSPy) along axes directly relevant to practitioners: state-management granularity, token-cost structure, failure-recovery options, and design philosophy. The emerging protocol stack is examined in terms of why MCP (agent-to-tool) and A2A (agent-to-agent) occupy complementary layers, how the ACP–A2A merger signals protocol convergence, and where ANP’s decentralized-discovery design fits. Production design considerations—state management, task planning, error handling, scalability, and security—are evaluated with reference to published benchmarks. Vendor-reported figures are marked † throughout and held to a documented evidence hierarchy, which separates them from peer-reviewed and government-evaluator measurements. We close by identifying eight open challenges and proposing a six-dimension evaluation framework for multi-agent coordination quality. This paper offers practitioners a decision framework covering taxonomy, framework selection, protocol adoption, and early operational pilots. Full article

This article examines the medium to long-term enrollment patterns of an AI-based platform designed to support children in learning robotics and participating in a national robotics competition in Romania. Drawing on registration and participation data covering students and teachers across urban and rural schools between 2020 and 2025, the study documents a consistent pattern: an initial period of high enrollment and rapid adoption followed by a steady decline over time. A key feature of the initiative is that hardware, platform access, and learning resources were provided entirely free of charge, allowing cost-related explanations for the decline to be set aside and structural and human factors to be examined directly. The paper makes two primary contributions. First, it proposes a System Dynamics framework grounded in innovation diffusion theory as a first-generation calibration model for understanding AI-enabled educational robotics adoption in a resource-constrained national context. The model is designed to be progressively tested and refined as anonymized aggregate data accumulates, and it relies exclusively on anonymized aggregated public data in accordance with GDPR requirements. Second, it advances the hypothesis that an AI-based educational platform, even one from which all financial barriers have been removed, will experience sustained enrollment decline in the absence of adequate human teacher involvement. The empirical trajectory and model outputs are consistent with this hypothesis and motivate further investigation. This represents a hypothesis-generating and framework-building paper. The framework reveals pronounced urban-rural disparities and differential outcomes by age of entry. All findings are presented as model-generated hypotheses rather than empirically demonstrated conclusions. The paper invites researchers gathering comparable data from similar initiatives in other countries to collaborate in testing and refining the model. The central conclusion is cautiously optimistic: AI may support robotics education adoption, but it is not a substitute for dedicated teachers, and without sustained investment in human capital, even a financially accessible platform is insufficient to maintain long-term enrollments. Full article

This study investigates the structural performance and shear capacity of reinforced concrete (RC) columns characterized by diverse material and detailing deficiencies. Using a numerical modeling approach for an 8-story RC building, the research evaluates the vulnerability of a critical ground-story corner column through a nonlinear static pushover analysis. The investigation systematically examines the impact of isolated variables, including low-strength concrete, insufficient transverse reinforcement spacing, inadequate concrete cover, and the use of plain bars. The analysis demonstrates that each deficiency, when evaluated independently, induces a shear demand that exceeds capacity. Furthermore, under combined deficiency scenarios, the Performance Ratio (PR) escalates to 4.17. Two primary strengthening strategies, Fiber Reinforced Polymer (FRP) wrapping and RC jacketing, were assessed for their effectiveness in restoring structural integrity. The results demonstrate that while FRP wrapping successfully reduces the PR values to safe limits (0.40–0.56) across all models, localized RC jacketing remains insufficient, with PR values exceeding the unity threshold. These findings highlight the superior efficiency of FRP in mitigating brittle shear failures in deficient RC structures and provide critical insights for element-based retrofitting practices. Full article

This paper presents a comprehensive evaluation of control strategies for a highly energy-efficient plus-energy terraced housing complex equipped with photovoltaic generation, modulating ground-source heat pumps, electrical and thermal energy storage systems, and activation of building thermal mass. The study combines long-term monitoring data, annual simulations, and hardware-in-the-loop (HiL) experiments to assess modulating heat-controlled operation (HC), PV-controlled (PVC), and predictive control strategies, including simple predictive control (SPC) and model predictive control (MPC). The simulation results show that the baseline HC operation already achieves a high load cover factor (LCF), defined as the fraction of total electrical demand covered by local PV generation (direct use + battery discharge) of 65.6% and a seasonal performance factor (SPF) of the central heat pumps of 5.8. PVC increases LCF (71.0%) by shifting heat pump operation toward PV-rich periods but leads to elevated storage temperatures up to 5 K and a reduced SPF of 4.8. MPC further enhances LCF by 4–7 percentage points in simulated and HiL environments. However, its real-world performance is strongly influenced by forecast quality and the limited controllability of the heat pump system. In addition, building thermal mass activation is investigated as a complementary flexibility option. Simulation and monitoring results demonstrate that moderate room temperature set-point (2 K) increases during PV availability significantly improve LCF from 20% to 55% while maintaining thermal comfort. Overall, the findings indicate that in highly efficient plus-energy buildings, robust rule-based strategies combined with thermal mass activation can achieve a large share of the attainable benefits, while the added complexity of MPC must be carefully weighed against practical limitations. Full article

The increasing flow of documents in digital libraries, archives and electronic document management systems makes the standardization, adaptation and automation of the process of creating metadata an urgent scientific problem. Metadata directly affects the efficiency of document search, identification, semantic interpretation, long-term storage and intersystem exchange. However, while standardized description based on MARC21, a flexible approach to creating a dynamic field, and intelligent methods based on deep learning, cover these requirements separately, the issue of their full integration into a single methodological system has not been sufficiently resolved. In this study, an integrated hybrid model for describing electronic documents based on standardized, flexible, and intelligent metadata was proposed. A mixed electronic document corpus of 1500 documents was formed for evaluation. The corpus consisted of books, dissertations, scientific articles, archival documents, and heterogeneous electronic documents, with 300 samples selected from each group. Key metadata elements for each document were manually identified and used as ground truth. According to experimental results, the MARC21-based constructor achieved 96.8% structural compatibility and 95.6% metadata completeness, but the average description time was 6.8 min. The dynamic field approach achieved 93.4% structural compatibility and 94.1% metadata completeness, and reduced the description time to 4.1 min. The deep learning-based intelligent module achieved a structural matching score of 91.7%, a metadata extraction score of 93.8% F1, and reduced the processing time to 1.9 min. The proposed hybrid model achieved a structural matching score of 95.9%, a metadata F1 score of 95.1%, and an average description time of 2.3 min. The results showed that the hybrid model is a balanced solution between metadata quality, flexibility, and automation. Full article

Remote sensing referring segmentation aims to extract the exact region of an object in an aerial image based on a natural language description, but it remains challenging because remote sensing scenes cover large areas, many objects look similar, and the descriptions are often long and detailed. Existing attention-based models are computationally expensive on large images and may underuse fine-grained language cues, which can lead to inaccurate or incomplete masks. To address this, we present MSMamba, an efficient framework built on a state space model for stable long-range context modeling over large spatial grids. We further strengthen language grounding by identifying descriptive words in the expression and using them to guide visual features from coarse localization to boundary refinement. In addition, we design a scale-aware decoding strategy that fuses multi-scale features with adaptive gating to better handle severe size variation and thin structures. Experiments on four public benchmarks show that MSMamba consistently improves segmentation quality. On RefSegRS, MSMamba improves Pr@0.8 on the test set by 25.53% and increases mIoU by 6.65%. On RRSIS-HR, MSMamba improves Pr@0.8 by 9.09% and increases mIoU by 3.02%. These results suggest that combining a state space model with structured language guidance and scale-aware fusion is a practical alternative to attention-only designs for remote sensing referring segmentation. Full article

Although the green belt around Greater Lomé (GBGL) is a vital ecological buffer, it is currently facing significant degradation. This decline appears to be associated with a combination of various socioeconomic uses by the local community and formal operations of established businesses. Grounded in the cultural materialism framework, this study aims to contribute to a better understanding of the dynamics of the socioeconomic uses of the green belt around Greater Lomé in a context of degradation and investigates the dynamics of these socioeconomic uses and their environmental impacts through a multidisciplinary methodology. This approach combines anthropological analysis based on field observation, 53 semi-structured interviews and 5 focus groups, a quantitative questionnaire survey (n = 384) and an analysis of land use and land cover (LULC) dynamics derived from Landsat imagery (2003–2023). The results reveal six main types of socioeconomic uses of the GBGL (notably land transactions, agriculture, breeding and grazing, exploitation of wood energy, timber and utility wood, sand mining, and waste disposal), which lead to complex social dynamics ranging from conflicts to alliances among stakeholders. The LULC dynamics analysis indicates a staggering 468.26% expansion in built-up areas over the last 20 years, at the expense of swamp vegetation/gallery forest (−76.79%), tree-and-shrub savanna (−53.47%) and plantations (−49.43). This study provides a scientific basis supporting the urgent necessity to establish the GBGL as a legally protected entity and argues in favour of an inclusive management model that is designed to reconcile the socioeconomic survival needs of local populations with sustainable preservation of essential ecosystem services. Full article

Ground-surface temperature (GST) in maritime Antarctic ice-free areas is influenced by atmospheric forcing, snow cover, surface energy and topography. Previous PERMATHERMAL studies in Livingston and Deception Islands have shown changes in air and ground-surface thermal regimes, with fewer cold conditions, greater thawing influence and strong snow-cover modulation. However, the interval in which GST responds effectively to radiative and topographic forcing remains poorly explored. We characterize the station- and season-specific timing of the thermally effective GST thawing period and evaluate topographic and modeled potential controls on its thermal intensity and cumulative effect around the Spanish Antarctic Station Juan Carlos I, Hurd Peninsula, Livingston Island. Onset and end were objectively delimited by using three consecutive days with daily mean GST > 0.5 °C and daily thermal amplitude > 1.0 °C. Hourly GST records from six PERMATHERMAL stations were combined with potential radiation, potential insolation and topographic variables derived from a high-resolution UAV-based DEM. Accumulated thawing degree days were strongly influenced by period duration. Mean thermal intensity was primarily associated with elevation, while mean modeled potential radiation provided additional explanatory power only when combined with elevation. This UAV–GIS–GST approach provides a simple framework for assessing local surface–atmosphere coupling in remote Antarctic ice-free areas. Full article

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article

The forthcoming joint NASA/ASI (National Aeronautics and Space Administration/Italian Space Agency) Surface Biology and Geology Thermal Infrared (SBG-TIR) mission will operate in a sun-synchronous polar orbit collecting data on a global scale. The mission will acquire thermal infrared observations together with limited visible and near-infrared (VNIR) observations, consisting of two spectral bands and one panchromatic channel. In this context, and particularly given the limited number of VNIR bands, accurate retrieval of Vegetation Fractional Cover (FC) and Leaf Area Index (LAI) is particularly relevant. This is because it enables the synergistic use of VNIR and TIR observations to support vegetation monitoring and surface energy flux estimation during the mission. This study evaluates different machine learning approaches under different configurations for the retrieval of FC and LAI using the VNIR observations expected from the SBG-TIR mission. Synthetic datasets generated with the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) radiative transfer model were used for model training and validation. Different input configurations were tested, including VNIR bands, the panchromatic channel, vegetation indices, and observation geometry variables. Model performance was assessed on independent test data, including uncertainty quantification. The optimal configuration, using Gaussian Process Regression (GPR), achieved RMSE values of 0.046 for FC and 0.053 m²/m² for LAI using a seven-channel input set, while yielding R² values greater than 0.9 for both variables. These results are consistent with previous studies, supporting the validity of the proposed approach. The trained models were subsequently applied to Sentinel-2 and evaluated against GBOV (Ground-Based Observations for Validation) reference measurements and standard Sentinel-2 biophysical products. The results showed strong statistical agreement with the Biophysical Processor implemented in the ESA Sentinel Application Platform (SNAP) toolbox, confirming the robustness of the proposed framework for operational estimation and mapping of FC and LAI in the context of the SBG-TIR space mission. Full article

Above-ground biomass (AGB) is a critical indicator for evaluating crop growth, with its large-scale monitoring being fundamental to precision agriculture. To improve the efficiency and reduce the cost of large-scale farmland monitoring, this study developed an unmanned aerial vehicle (UAV)–satellite collaborative inversion framework. The data, including rice AGB, UAV imagery, and satellite imagery, were collected in 2024. The proposed Distance-Correlation–Correlation-Feature-Selection (DC-CFS) algorithm was employed to select compact feature subsets for each growth stage. Subsequently, six machine learning models were compared to identify the optimal UAV-scale inversion model for each specific stage. Then, the AGB values simulated by the UAV-scale model were used to train the satellite-scale inversion model. A paddy field mask covering the entire district was generated using Segment Anything Model (SAM) and the temporal spectral variation pattern of rice, enabling county-scale AGB mapping. Research results indicate that the DC-CFS algorithm can effectively select a small number of low-redundancy features for each growth stage. The optimal UAV scale model type varies dynamically with growth stages, with ExtraTrees demonstrating overall superior performance. Except for the heading stage, the R² of the models remained above 0.69. Furthermore, the BayesianRidge algorithm also presents a viable and competitive alternative when computational efficiency is a consideration. At the satellite scale, eXtreme Gradient Boosting (XGBoost) and Extremely Randomized Trees (ExtraTrees) were identified as the optimal models for rice AGB estimation due to their stable performance across all stages, with R² values consistently above 0.74. Finally, rice growth classification maps and corresponding fertilization recommendations were generated based on the satellite-scale inversion results, providing technical support for precision agriculture practices. Full article

Upcycling biomass waste into value-added battery materials is crucial for sustainable energy storage. Here, we transform coffee grounds into high-performance hard carbon (HC) anodes for sodium-ion batteries (SIBs) via a synergistic pre-oxidation and acetylene chemical vapor deposition (CVD) strategy, which effectively reduces open pores and promotes structural stabilization. The resulting material exhibits features consistent with a closed-pore architecture. Pre-oxidation incorporates oxygen-containing functional groups that template accessible pores and expand the interlayer spacing during carbonization. Subsequent CVD covers surface pores and contributes to the stabilization of the pore structure. The optimized HC (COF300&1300@C) exhibits a balanced set of structural features, including a low specific surface area (2.1 m² g⁻¹), expanded interlayer distance (0.391 nm), and a well-regulated pore system with reduced surface area and controlled pore size. As a result, it delivers a reversible capacity of 298 mAh g⁻¹ with an ICE of 70%, and remarkable cycling stability (97% capacity retention after 500 cycles at 1C). This study elucidates the synergistic mechanism of pre-oxidation and CVD in reducing open pores and stabilizing the pore architecture, thereby yielding characteristics indicative of closed-pore behavior, and providing a novel and efficient approach for designing high-performance biomass-derived hard carbons for energy storage. Full article

This paper presents a compact fractal-based super-wideband multiple-input multiple-output (MIMO) antenna for millimeter-wave (mmWave) 5G new radio (NR) and prospective 6G applications. The MIMO system comprises four Koch fractal monopole elements integrated with a modified shared ground plane. By adopting the second Koch iteration, the antenna achieves enhanced impedance bandwidth and stable radiation behavior compared with lower-order iterations. The elements are arranged in a polarization-diversity configuration within a 30 × 30 mm² footprint on a 0.8 mm-thick Rogers RO4835 substrate (ε_r = 3.5, δ = 0.0025). The proposed design provides an impedance bandwidth exceeding 14 GHz over 26.5–41 GHz, covering key bands at 28, 32, 38, and 40 GHz, while maintaining high inter-element isolation (around 30 dB over the operating range). The optimized ground modification enables a fully connected common ground and suppresses mutual coupling without additional decoupling structures. The antenna achieves 4–6 dBi realized gain with radiation efficiency exceeding 95%. MIMO performance metrics, including the envelope correlation coefficient (ECC), mean effective gain (MEG), and diversity gain (DG), confirm excellent diversity characteristics. The antenna is further evaluated under bending, demonstrating stable matching and isolation for conformal and wearable scenarios, and the concept is extendable to a non-planar 12-port configuration within the same footprint. Measured results agree well with simulations, validating the proposed design for wideband mmWave 5G/6G devices. Full article

To address the “3R” issues (resistance, resurgence, and residue) associated with chemical control of the sea buckthorn fruit fly (R. batava obscuriosa), this study proposes a novel physical barrier technology aimed at reducing pesticide application intensity, mitigating environmental pollution, and enhancing fruit quality. Yellow sticky traps were deployed to monitor adult occurrence dynamics and delineate the critical control window, while black polyethylene (PE) ground cover was installed on the orchard floor around the base of sea buckthorn trunks to prevent adult emergence from the soil. Control efficacy was evaluated by comparing adult trap catches and fruit infestation rates between the black PE ground cover treatment and the untreated control. Monitoring results revealed that adult emergence commenced on 29 June, entered the peak period on 9 July, attained maximum trap catch on 24 July, and persisted into the late emergence phase through mid-to-late August. Control data demonstrated that mean trap catches in the black PE ground cover treatment were lower than those in the control. From 2024 to 2025, fruit infestation rates declined from 74.5% and 62.3% in the control plot to 19.0~22.0% and 16.2~19.3% in the treatment plots, respectively, with control efficacy consistently exceeding 65%. This study demonstrates that black PE ground cover reduces adult abundance and fruit infestation rates of R. batava obscuriosa, with control efficacy consistently exceeding 65%. The observed effects are consistent with a soil-surface barrier effect and likely attributed to dual physical mechanisms: it may reduce adult emergence from the soil into the canopy and may obstruct mature larvae from entering the soil to pupate. This technology represents an environmentally sound, sustainable green control option suitable for integration into IPM programs for the sea buckthorn industry. Full article

Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through a multi-criteria analysis using geographic information systems (GIS) and hydrogeological field exploration, water balance modelling. Remote sensing datasets and evapotranspiration (ET) analyses were conducted for the 2014–2024 period, while field investigations, infiltration tests, and hydrochemical sampling were performed during the 2025 field campaign. The suitability testing was preliminarily performed in the Google Earth Engine (GEE; Google LLC, Mountain View, CA, USA) environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified, out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could potentially be added to groundwater recharge is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge, which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that the potential MAR rates range between 174 and 5282 m³/day depending on local hydrogeological conditions. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies. Full article