Search Results (767)

Railway and metro inspection planning requires an ordered set of engineering intervals for follow-up investigation. This study combines persistent scatterer interferometric synthetic aperture radar (PS-InSAR) and small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) to prioritize intervals by joint relative prominence and assess ranking uncertainty. The Shilihe–Bagou corridor of Beijing Subway Line 10 was analyzed using 48 ascending Sentinel-1A scenes from 2021 to 2024. Rates were centered on each product’s corridor median and aggregated into paired route bins. The smaller method-specific percentile rank defined joint prominence, while paired block bootstrap quantified rank variability. PS-only and SBAS-only rankings shared only two of their first five intervals. The joint same-direction shortlist comprised the two Huoqiying–Bagou units, Changchunqiao–Huoqiying, Cishousi–Chedaogou, and Lianhuaqiao–Gongzhufen. Spatial and representation tests retained four or five baseline members, and all three boundary-allocation tests retained all five. The main Huoqiying–Bagou interval had a baseline 95% rank interval of 1–3 and, together with Changchunqiao–Huoqiying, remained selected throughout the tested spatial settings. The short open-cut Huoqiying–Bagou unit remained prominent but changed to opposite relative directions under the 200 m grid. These results distinguish persistent priorities for targeted field review from intervals requiring closer examination of spatial sampling and cross-method differences. Full article

Coordinated multi-unmanned aerial vehicles (UAVs) planning requires the joint consideration of spatial paths, flight speeds, arrival times, and separation constraints. This study develops an improved whale optimization algorithm (IWOA) for offline path–speed planning in known static environments. The method combines continuous spherical-increment encoding with deterministic initialization, nonlinear adaptive weighting, population migration, and a heavy-tailed candidate. Among eight algorithms, IWOA achieved the best mean rank of 1.38 on 29 2017 IEEE Congress on Evolutionary Computation (CEC2017) functions and a mean rank of 1.25 across four repeated single-UAV scenarios. Eight algorithms were also evaluated in four 230-dimensional, five-UAV scenarios using 30 runs, a population of 30, and 500 iterations, for 960 runs in total. The penalty-only IWOA obtained the lowest median final fitness in three scenarios, but no solution passed the post hoc feasibility screen. We therefore introduced a feasibility-priority variant with corrected angular and geometric checks, normalized violation magnitudes, continuous-time separation checks, and Deb-style ordering. In a separate 240-run comparison, Improved Whale Optimization Algorithm with Feasibility-Priority (IWOA-FP) and Whale Optimization Algorithm with Feasibility-Priority (WOA-FP) each returned 120/120 modeled-feasible terminal incumbents from a shared, prevalidated feasible warm start. Conditional objective comparisons favored IWOA-FP in Map 4, Complexity Setting 1 (M4-C1) and Map 4, Complexity Setting 2 (M4-C2) and WOA-FP in Map 3, Complexity Setting 2 (M3-C2), with neither method favored in Map 3, Complexity Setting 1 (M3-C1). These results show that IWOA is competitive under fixed-generation testing and that explicit feasibility-priority selection can preserve and optimize a feasible plan. Validation remains limited to the tested static model, and neither measures unseeded feasibility discovery nor certifies flight safety. Full article

Background/Objectives: Patient-centred practices do not by themselves show whether an individual’s priorities influence care. This review asks how organizational decoupling theory can inform a traceable response to those priorities and locate a loss of influence. Methods: A theory-informed critical narrative review used a purposive analytical bibliography of 63 publications. The authors’ preliminary categories, professional experience, and organizational theory guided interpretation. A source-to-claim matrix separates published findings, conceptual arguments, and proposed mechanisms. The framework was refined through comparison with literature, without independent inductive coding. Results: Three candidate mechanisms concern influence over service design, the expression and recognition of patient goals, and the organizational response to experience and outcome data. They may overlap and do not exhaust the possible explanations. A proposed loop links the goal, decision, action, outcome, and review. Its assessment distinguishes evidence of lost influence from justified plan retention and insufficient information. A hypothetical care pathway and a research programme specify how to test these distinctions while accounting for changing preferences, implementation fidelity, and documentation burden. Conclusions: The contribution is an account of where patient influence may be lost and what a considered response would involve. The three mechanisms and the order of the loop are conceptual propositions. Their reliability, explanatory value, and effects on care require independent evaluation before use as performance indicators. Full article

Background/Objectives: Artificial intelligence (AI) is entering interventional pulmonology (IP) faster than its potential risks have been systematically catalogued. We explored whether general-purpose large language models (LLMs), now widely consulted informally by patients and clinicians, could provide a rapid and structured means of enumerating and ranking candidate AI-related risks in IP, potentially contributing to risk awareness and hypothesis generation. Methods: Five LLMs (ChatGPT, Claude, Gemini, Grok, DeepSeek) were each queried once, in independent, memory-free sessions, with one standardised prompt requesting ten ranked AI-related risks with impact and likelihood scores (1–5); an informal repeat administration was performed, but output stability was not formally assessed. The resulting 50 risk statements were inductively coded, by an AI coder with independent human validation by two reviewers, into 15 constructs nested in 8 higher-order domains. Results: Mean self-assigned impact was 3.92 (SD 0.78) and mean likelihood 3.54 (SD 0.68). The eight domains comprised AI technical/perceptual accuracy (diagnostic, detection and navigational error); automation bias and over-reliance; generalisability, algorithmic bias and health equity; model and system reliability over time; erosion of procedural competence; explainability, transparency and accountability; cybersecurity, privacy and data integrity; and cognitive and workflow burden. Five domains were raised by all five models and the remaining three by four of five. Two domains-AI technical/perceptual accuracy and automation bias-together accounted for every model’s two highest-ranked risks, and no risk statement outside these two domains was ranked first or second by any model. Deskilling (erosion of procedural competence), although listed by all five models, was never ranked above third by any model, whereas practising IP specialists in our prior international survey rated it the single highest research priority. Conclusions: These exploratory and hypothesis-generating findings suggest that general-purpose LLMs may have potential as a rapid, low-burden means of enumerating and ranking candidate AI-related risks and broadening awareness of issues that may warrant further investigation in IP. The lower ranking of deskilling by the LLMs than by domain experts indicates that LLM-generated rankings may under-prioritise risks that clinicians consider most important. Although their outputs are not validated measures of clinical risk and do not replace expert appraisal, we believe they may provide a starting point for subsequent human-led risk assessment and prioritization in an area of research that remains largely unexplored yet is highly relevant to patient safety. Full article

Background: Chlorine dioxide (ClO₂), formulated as an aqueous solution (CDS), has been proposed as a therapeutic agent, yet its classification among oxidizing compounds is frequently confounded with chemically distinct species—sodium chlorite (NaClO₂) and sodium hypochlorite (NaOCl). Objective: This study aimed to synthesize the physicochemical, mechanistic and cellular basis of ClO₂ action and to delineate the distinctions between ClO₂ and related oxidants, with explicit separation of established data from hypothesized data. Methods: The methodology includes a narrative synthesis of electrochemical data, primary toxicological and clinical literature (PubMed) and the redox-biology literature on low-dose oxidant signaling. Results: ClO₂ is a neutral free radical that acts via one-electron transfer (E°′ = +0.95 V vs. SHE for the half-reaction ClO₂ + e⁻ → ClO₂⁻, essentially pH-independent over pH 4–8, pH-independent) without chlorination. Its antimicrobial action is attributable to oxidation of thiol-dependent enzymes and membrane constituents and to the inhibition of bacterial respiratory metabolism; host-cell sparing is consistent with differential glutathione-dependent buffering. Controlled human ingestion studies at low concentrations report a favorable short-term safety profile; conversely, the principal metabolite (chlorite) is associated with documented developmental toxicity in animals and regulatory caution by EPA/WHO. Low-dose ClO₂ exposure is hypothesized—but not yet directly demonstrated—to elicit dose-dependent adaptive redox responses and redox-regulatory modulation. Conclusions: The one-electron, non-chlorinating chemistry of ClO₂ provides a coherent mechanistic framework. The translation of this framework into clinical application requires prospective trials and direct mechanistic measurement, ideally conducted by independent investigators. The following mechanisms are hypothesized and have not yet been directly demonstrated in vivo: modulation of oxidative phosphorylation, mitochondrial oxygenation, and associated redox-regulatory signaling. Reported antimicrobial efficacy typically occurs in the low-ppm range (0.1–5 ppm), whereas adverse effects on mammalian cells in the reviewed literature are reported at concentrations at least one order of magnitude higher; direct head-to-head cytotoxicity comparisons remain scarce and are identified as a priority for future work. This differential thiol buffering—in particular, the higher glutathione content of many mammalian cells relative to many pathogens—has been proposed as a contributing basis for host-cell sparing; direct quantitative confirmation in vivo is not yet available. Bacterial species possess their own thiol-based defense systems (e.g., mycothiol in actinomycetes, bacillithiol in Firmicutes), and the interplay between ClO₂ and these systems requires further investigation. Full article

Urban disaster screening requires transparent methods that can integrate heterogeneous open datasets without implying unsupported causal or probabilistic relationships. This study presents an open-data framework for screening flood-footprint population proxies and potential disruption to hospital accessibility in Bucharest, Romania. The analysis covers 104 archived hexagonal spatial units and combines a 100-year flood-depth scenario from the Joint Research Centre, WorldPop 2020 population estimates, hospital-routing outputs derived from OpenStreetMap, and a publicly available seismic screening surface from the European Facilities for Earthquake Hazard and Risk. Flood and seismic information are retained as distinct screening dimensions because the available data do not support modelling their causal interaction, temporal sequence, joint probability, earthquake-related infrastructure damage, or hospital capacity. For spatial units that remain connected to a hospital, a transparent two-domain service-priority index is calculated using flood-footprint population proxy and the potential change in hospital accessibility under the flood scenario. Units for which no hospital route is available are reported separately as binary service-disconnection alerts rather than being assigned an arbitrary numerical penalty. The robustness and interpretability of the framework are examined through network monotonicity, score boundedness, Pareto dominance, penalty-free rank invariance, and rank-acceptability analysis. The results are communicated using a Pareto frontier, rank trajectories across the full weight simplex, indicator-contribution decomposition, and an exact hypergeometric assessment of class overlap. The proposed framework provides a transparent first-order planning tool for identifying locations that may require more detailed investigation. It should be interpreted as a screening approach rather than as a probabilistic risk, cascading-hazard, infrastructure-damage, or service-loss model. Full article

Accurate core-loss prediction in electrical machines is difficult when local magnetic flux follows elliptical, circular, irregular, harmonic-rich, or three-dimensional trajectories. This structured review examines empirical and loss-separation formulations, vector and hysteresis models, flux-locus methods, finite-element (FE)-assisted workflows, analytical and magnetic-network field solvers, reduced-order control models, and machine learning (ML) extensions. The literature is compared using common engineering criteria: required material data and parameters, applicable excitation and material conditions, reported error, computational burden, rotational-field capability, and suitability for machine-level design or control. The evidence shows that scalar alternating-field models remain useful for rapid estimation, but their reliability deteriorates under strong rotationality, minor loops, DC bias, saturation, and pulse-width-modulated excitation. Vector and hysteresis models offer stronger physical fidelity but require richer data and substantially greater calibration and computational effort. Recent reduced-order and data-driven approaches can accelerate FE-level prediction; however, their validity is limited to the training or calibration domain. The review therefore recommends a layered framework in which vector-field extraction and flux-locus classification select an appropriate physics-based baseline, while ML is used for residual correction, surrogate prediction, and uncertainty estimation. Priority research needs include standardized multidimensional datasets, anisotropic and grain-oriented material models, transparent quantitative benchmarks, and loss-aware models suitable for optimization and control. Full article

Due to the widespread deployment of large-scale satellite constellations, Spatial Information Networks (SINs) exhibit increasingly high-order node and service coupling characteristics, rendering them vulnerable to node removal and cascading failures. Traditional graph-theoretic models fail to capture the inherent multi-node cooperative relationships within SINs, while conventional centrality metrics overlook service coupling features and cross-community propagation risks, leading to inaccurate key node identification. To address these issues, this paper proposes a key node mining method based on soft community partition and hypergraph interweaving degree. The method first constructs a hypernetwork to characterize the high-order associations among three types of services—communication, remote sensing, and navigation. Second, a probabilistic generative model, Hypergraph-MT, is introduced to detect overlapping communities and capture the multi-role attributes of satellites. Finally, the Interweaving Degree (IW) and its derived Bridge Score (IWBS) are defined to quantify node importance from two dimensions: load capacity and cross-community connectivity. Experimental results demonstrate that, compared with degree centrality and betweenness centrality, the proposed method increases the service interruption rate from 27.4% to 64.8% under a 5% removal ratio, and the service efficiency loss is elevated by more than 60%. In comparison with pure interweaving degree removal, IWBS triggers deeper cascading removal at a 30% removal ratio, reduces the removal cost by 8.9%, and improves the input–output ratio by 8.2% under high-intensity removal. Moreover, the algorithm exhibits linear scalability and strong robustness to parameter variations. The proposed method significantly enhances the accuracy and efficiency of key node identification in SINs, providing theoretical support for constellation deployment optimization and priority protection decision-making. Full article

Dynamic job shops must absorb new orders, machine failures and processing time variations while operating under increasingly demanding energy and carbon constraints. In such settings, an offline schedule may become obsolete soon after release, especially when production and energy states evolve on different time scales. Digital twins, data-driven models and artificial intelligence methods now make it possible to sense shop floor changes, anticipate their effects and revise schedules through feedback. This review organises the emerging literature through a ‘four loops and one layer’ framework: perception, modelling and prediction, intelligent decision-making, and execution feedback form the operating cycle, while continuous learning spans successive scheduling rounds. Studies are examined along three distinct but related dimensions—dynamic events, green objectives and digital intelligence methods. Within this D-G-I framework, the literature reveals a move from static optimisation to adaptive scheduling, from efficiency-centred formulations to coordinated efficiency–energy–carbon objectives, and from stand-alone rules towards combinations of data, models and domain knowledge. Yet the evidence remains uneven. Data–model coupling is often weak, transfer across production settings is limited, and genuinely closed-loop industrial validation is rare. These limitations make explainable decision-making, cross-scenario adaptation and digital twin-enabled closed-loop optimisation central priorities for subsequent research. Full article

This research presents a comparative analysis of criteria weighting and multi-criteria decision-making methods in the framework of onshore wind farm siting. Nine criteria weighting and four multi-criteria decision-making ranking methods, constituting a total of thirty-six models, are assessed for the relative spatial siting suitability ranking of onshore wind farms in the Regional Unit of Euboea, Greece. To determine the weights of five selected assessment criteria (wind velocity, distance from protected areas, distance from road networks, distance from electricity network, and distance from settlements), five subjective methods, namely the Analytic Hierarchy Process, Rank Order Centroid, the Simos method, the Best–Worst method, and the Equal-Weight Method, and four objective methods, namely the standard deviation, the statistical variance procedure, Criteria Importance Through Inter-criteria Correlation, and the Entropy Weight Method, are used. Using each of the assessment criteria weights provided by these nine methods, the suitability ranking of onshore wind farms in the Regional Unit of Euboea (Greece) is obtained through four multi-criteria decision-making methods: the weighted sum method, the weighted product method, the Technique for Order Preference by Similarity to Ideal Solution, and the VIseKriterijumska Optimizacija I Kompromisno Resenje method. Spearman’s rank correlation coefficient measures the consistency among criteria priorities and alternative rankings, while weighting scheme scenario analysis assesses the impact of different weighting schemes on the final outcomes. Heatmap analysis and Borda count consensus ranking are employed to identify stable alternatives across various model combinations and reduce the influence of method-specific ranking extremes. Subjective methods for determining criterion weights yield the same prioritization of the assessment criteria, whereas the results from objective methods differ substantially. The selection of both criteria weighting and multi-criteria decision-making ranking methods influences the final prioritization of alternatives. The proposed methodological framework can help decision-makers detect uncertainty, prioritize cases for further assessment, and justify spatial siting decisions of wind farms more transparently. Full article

The macroscopic mechanical responses of cemented sand and gravel (CSG) are jointly affected by the mesoscopic properties of the aggregate, mortar matrix, and interfacial transition zone, while different parameters exhibit distinct magnitudes and directions of influence on strength, stiffness, and failure deformation. To establish a unified parameter priority across multiple macroscopic responses, a two-dimensional random aggregate finite element model was developed, and the elastic moduli and tensile strengths of the aggregate, mortar matrix, and interface were selected as six mesoscopic parameters. Bidirectional perturbations of ±5% and ±10% were introduced around the baseline state, and dimensionless local sensitivity coefficients were calculated using a central finite-difference formulation. Five statistically independent random aggregate realizations were further considered to evaluate the influence of mesostructural variability. The Ordered Weighted Averaging (OWA) operator and entropy weighting method were used to determine subjective and objective criterion weights, respectively, and a MULTIMOORA-based multi-response parameter-prioritization framework was established using the ratio system, reference point method, and modified multiplicative form. Across the five random realizations, the mean sensitivities of the mortar elastic modulus to the macroscopic elastic modulus and failure displacement are 0.5158 and 0.2664, respectively, while those of the interfacial tensile strength to compressive strength and failure displacement are 0.1550 and 0.4512, respectively. The ±5% and ±10% perturbations yield consistent parameter hierarchies, indicating stable local sensitivity results within the investigated neighborhood of the baseline state. Under the combined OWA–entropy weighting scheme, the multi-response parameter priority is ranked as mortar elastic modulus, interfacial tensile strength, aggregate elastic modulus, interfacial elastic modulus, mortar tensile strength, and aggregate tensile strength. The ratio system and modified multiplicative form yield identical rankings, and both show a Spearman rank correlation coefficient of 0.9429 with the reference point method. Weight-scenario analysis further shows that the mortar elastic modulus and interfacial tensile strength consistently remain the two highest-priority parameters, although their internal order varies with the criterion weights. The resulting ranking therefore represents a model- and evaluation-objective-dependent multi-response parameter priority and provides a quantitative basis for mesoscopic parameter screening and subsequent calibration of CSG numerical models. Full article

Stable circulation and limited mechanical loading are key requirements in the fluidized-bed coating of small vegetable seeds. For Chinese cabbage seeds, their small mass, irregular geometry, and mechanical sensitivity make it difficult to evaluate operating conditions using only global indicators, such as mean particle velocity and bed expansion height. In this study, a two-way coupled computational fluid dynamics–discrete element method (CFD-DEM) model was developed for a draft-tube fluidized-bed seed coater. Chinese cabbage seeds were represented by seven-sphere clumps, and an L9(3³) orthogonal array was used as a screening design to examine inlet air velocity, initial bed height, and bottom circulation inlet gap. The evaluation combined cycle time distribution (CTD), the global low-speed particle fraction R_s, the 95th-percentile normal contact force F_95,n, and the normalized high-percentile contact-load ratio η_c,95. Because the L9 array cannot resolve interactions or support a full quadratic model, factor effects were interpreted descriptively within the investigated range rather than as a confirmatory global optimization. Initial bed height produced the largest descriptive contribution to the circulation period, R_s, and F_95,n. Increasing inlet air velocity shortened the circulation period and reduced Rs but increased high-percentile contact loading. Across the nine cases, F_95,n ranged from 7.50 to 14.50 mN and η_c,95 from 0.18% to 0.35%; η_c,95 is used only as a normalized load ratio and not as a validated probability of seed damage. Case 4 ranked first under equal weighting and contact-load-priority weighting, whereas Case 7 ranked first under circulation-priority weighting. Case 4 is therefore described as a weight-dependent balanced candidate within the tested parameter range. Prototype experiments reproduced the ordering of circulation periods for three dry operating conditions, supporting qualitative consistency between simulated and observed circulation behavior. Visible breakage remained below 0.5%, but this observation provides only preliminary qualitative correspondence with the simulated contact-load trend. The proposed framework is intended for dry-stage screening and does not directly predict wet-coating quality, adhesion, agglomeration, or germination performance. Full article

The rapid development of artificial intelligence (AI) has created new opportunities for improving the performance, robustness, and efficiency of electrical machine drive systems. Among AI-based approaches, fuzzy logic control (FLC) has attracted considerable attention because of its ability to handle nonlinear dynamics, parameter uncertainties, and external disturbances without relying on an accurate mathematical model. This review systematically examines FLC-based control strategies for electrical machine drives, with particular emphasis on induction motors, switched reluctance motors, permanent-magnet synchronous motors, synchronous reluctance motors, and brushless DC motors. The review follows the PRISMA 2020 framework, and the selected studies are analyzed according to machine type, FLC architecture, control strategy, optimization method, implementation platform, and validation approach. The reviewed evidence indicates that FLC-based strategies can improve dynamic response, tracking accuracy, robustness, and torque regulation under the specific conditions reported in the literature. Hybrid approaches combining FLC with field-oriented control, direct torque control, sliding-mode control, model predictive control, neural networks, ANFIS, and optimization algorithms provide additional opportunities for adaptation and parameter tuning. However, the reported performance is strongly dependent on machine topology, controller architecture, tuning methodology, computational requirements, and validation platform. The review also identifies important limitations, including the lack of standardized benchmarking, computational complexity, dependence on expert knowledge, and limited HIL and experimental validation of several advanced approaches. Emerging directions include Type-2 and higher-order fuzzy systems, neuro-fuzzy and hybrid AI controllers, data-driven optimization, digital-twin-assisted control, edge computing, and hardware-oriented implementation. The objective of this review is to provide a structured and critical synthesis of the existing evidence, clarify the evolution and practical applicability of AI-driven FLC approaches, and identify research priorities for reliable, computationally efficient, and experimentally validated electrical machine control. Full article

Laser powder bed fusion (L-PBF) enables the integral fabrication of thrust chamber–nozzle and injector assemblies, eliminating some assembly and welding interfaces and reducing structural redundancy. However, under this manufacturing route, as-built geometric deviations act more directly on functional features, creating new challenges for dimensional accuracy and performance stability. To address this issue, this study investigates an additively manufactured 200 N monopropellant thruster and conducts geometric-deviation characterization, deviation–performance mapping, precision allocation, and performance validation. Key forming deviations were characterized using a coordinate measuring machine, industrial CT, micro-focus CT, and confocal microscopy. A first-order geometric deviation–performance mapping model was established through CFD-based sensitivity analysis, and Monte Carlo simulation was used to evaluate performance risk. A priority evaluation system combining the process capability index (Cpk) and performance sensitivity was then developed to guide differentiated finish-machining allowance allocation, CAD model pre-compensation, and process optimization. After two closed-loop iterations, the Monte Carlo-predicted thrust nonconformance probability decreased from 8.5% to 1.2%, and the simulated injection-flow non-uniformity narrowed from ±8.7% to ±4.2%. Cold-flow measurements showed that injection-flow non-uniformity decreased from ±9.3% to ±4.8%. In the rated-condition hot-fire test of the compensated thruster, the measured steady-state thrust deviation was controlled within ±1.5%, while the throat-diameter Cpk increased from 0.56 to 1.45. These results demonstrate that the proposed “inspection–analysis–allocation–compensation” closed-loop method can integrate performance sensitivity with actual manufacturing capability and provide an implementable route for precision-resource allocation and engineering optimization of integrated additively manufactured propulsion components. Full article

Aims: Understanding how arrival order, nitrogen (N) availability and population density interact to shape plant invasion is critical for predicting and ameliorating biological invasions under global change. Chromolaena odorata is one of the most destructive invasive plants in tropical and subtropical regions, but the multifactorial drivers of its establishment and competitive dominance are not fully resolved. Methods: We constructed artificial communities in a common garden experiment and manipulated the arrival order of C. odorata (early, simultaneous and late arrival relative to native species), species density (high, medium and low), and N addition (0 vs. 10 g N m⁻² yr⁻¹), and then examined their effects on growth performance and competitive dominance. Results: Early arrival did not increase growth performance of C. odorata, but it substantially enhanced the competitive dominance of the invasive species in most communities. However, late arrival caused substantial reductions in height, biomass, and RDI of C. odorata across all communities, with the most severe declines observed in treatment groups that did not receive N addition. N addition disproportionately promoted the growth of C. odorata regardless of arrival order and enhanced its competitive advantage when it arrived later than native species. In low-density C. odorata communities, N addition exhibited the strongest compensatory effect against growth suppression of the invasive species resulting from late arrival. Importantly, when C. odorata arrived later than native species in plots receiving N addition, its height and biomass were comparable to plants that arrived at the same time as native species in groups without N addition across most communities. Conclusions: Arrival order is a critical determinant of C. odorata invasion success, with early arrival conferring competitive advantages and late arrival imposing severe competitive penalties due to competition with established native species. However, N addition can compensate for late-arrival disadvantages, particularly for low-density C. odorata, suggesting that N-enriched ecosystems may remain vulnerable to invasion, even when native communities are established. These results underscore the context-dependent nature of priority effects and highlight that managing colonization timing and nutrient inputs may be effective in similar localized settings, though future field-based, multi-season validation is clearly needed. Full article