Search Results (1,174)

Active-power ceilings create a control challenge in droop-controlled grid-forming converter networks because the electrical response is faster than the measurements and outer control. Projected and projection-free power limiting use filtered active power in the outer power–frequency channel and therefore cannot act directly on the first electrical power peak. This paper proposes constraint-activated projection-free control, which coordinates a shaped projection-free multiplier with a bounded resistance term in the capacitor-voltage reference driven by instantaneous terminal power. A general full-order dynamic model describes the converters, controllers, and network without tying the formulation to a particular benchmark. Local well-posedness is established, and the proposed controller is shown to preserve the constrained projection-free equilibrium and active-branch Jacobian, allowing the same full-order stability assessment. Across ten tested scenarios with unchanged controller parameters, the proposed controller reduces peak power exceedance by 38.7–55.4% and accumulated excess energy by 34.1–62.2%. The corresponding DC-buffer requirement decreases without activating the independent current limiter, which isolates the source-side power constraint from AC overcurrent. A network-level study demonstrates sequential transitions between one and two constrained sources while the remaining converter supplies the feasible power imbalance. Full-order stability verification, component studies, and parameter sweeps establish the role and useful range of each controller path. Full article

Extreme weather couples spatially correlated component damage with photovoltaic (PV) derating, changing electric-vehicle (EV) demand, repair delay, and time-varying network topology. This paper develops a coordinated restoration architecture for multi-area feeders containing PV, battery energy storage, and charging stations. Its weather-facing layer constructs joint outage-risk, renewable-error, charging-demand, and voltage-vulnerability descriptors. Those descriptors parameterize a two-stage mixed-integer second-order-cone program with a finite-support optimal-transport ambiguity set that remains well defined for discontinuous mixed-integer recourse. Regional actor–critic agents propose five-minute corrections around the hourly robust schedule; constrained projection, non-linear AC power-flow screening, emergency fallback, and margin-tightened re-optimization retain the authority to accept or reject each proposal. The evaluation uses public 33-node and 123-node feeders together with synthetic 240-node and 850-node stress networks. A pre-fit manifest allocates 240 records to training, 80 to validation, and 320 to final testing, while aggregate operational outcomes cover 50 random streams. Within this controlled benchmark, accepted schedules restore 93.6% of critical-load energy (SD 2.1 percentage points), serve 96.7% of total demand (SD 1.8 percentage points), retain 82–86% of EV service across hazard classes, and reduce the modeled 24 h objective by 25.8% relative to deterministic dispatch. The full pipeline records two to four candidate-stage voltage-limit events by hazard, and 4.9% of candidates undergo tightened re-optimization before accepted schedules reach zero reported AC voltage-limit violations. Between-method comparisons are descriptive and unpaired; the larger synthetic cases are structural stress tests rather than feeder-transfer tests. Full article

Background/Objectives: Background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM) represents enhancement of otherwise normal fibroglandular tissue. Although BPE is well established in breast MRI interpretation, its determinants, technical variability, diagnostic implications, and potential value as a breast cancer risk biomarker on CEM remain incompletely defined. This review summarizes the current evidence and outlines the steps required for responsible clinical translation. Methods: PubMed/MEDLINE was searched from database inception through April 2026 using terms related to CEM and BPE. Reference lists of eligible studies and relevant reviews were also screened. Human studies evaluating CEM-specific BPE in relation to biologic or hormonal determinants, breast density, technical factors, measurement methods, reproducibility, diagnostic performance, temporal variability, asymmetry, or breast cancer outcomes were synthesized narratively. Results: CEM BPE is influenced by age, menopausal status, menstrual and hormonal factors, lactation, endocrine therapy, breast density, contrast timing, view order, compression, positioning, imaging system, and post-processing. Because CEM is a projection-based technique acquired over several minutes, its BPE should not be considered physiologically interchangeable with MRI BPE. Moderate or marked BPE may reduce lesion conspicuity or mimic abnormal enhancement, particularly when asymmetric. Early studies suggest that higher CEM BPE may be associated with prevalent or subsequent breast cancer after adjustment for established risk factors, but findings remain heterogeneous and derive predominantly from retrospective, single-center cohorts. Quantitative and artificial-intelligence approaches are promising but require technical normalization, reproducibility testing, multicenter validation, and linkage to clinically meaningful outcomes. Conclusions: CEM BPE is a functional imaging feature shaped by patient biology, tissue composition, acquisition technique, and reader assessment. It should currently be reported and interpreted in context but should not independently alter screening, biopsy, or risk-management decisions. Translation into a clinically useful biomarker will require standardized measurement, longitudinal prognostic validation, demonstration of added value beyond established risk models, and prospective evidence that BPE-informed care improves outcomes without producing excessive harms or costs. Full article

We study an agent who tracks several independent, unobserved, slowly drifting states and is paid by how well a chosen action matches each state but who can process only a bounded amount of information per period. The payoff environment is deliberately symmetric—quadratic matching losses, Gaussian drift, Gaussian observation noise—and the agent’s objective contains no asymmetry: we treat both the risk-neutral (linear) objective and the long-run log-growth (Kelly) objective. Within this symmetric environment, we show that the value of attentionis sharply asymmetric in the sign of the agent’s surprise. Because the matching payoff is maximized when action equals state, a surprisingly low payoff is strong evidence of a state mismatch that is worth correcting, whereas a surprisingly high payoff is evidence either of noise or of a match already achieved—in both cases carrying little decision-relevant information. We prove (Theorem 1) that the posterior expected mismatch, and hence the value of information, is strictly decreasing in the realized payoff, negligible for good surprises and rising steeply for bad ones, with a correspondingly asymmetric slope. We then show that an information-constrained agent optimally adopts a threshold attention policy (Theorem 2), which, under one explicit and standard bridge—that valuation inherits attention weight, as in salience and rational-inattention theories of choice—projects onto a reference-dependent value function with a kink at the expected payoff and a loss-side slope strictly steeper than its gain-side slope (Corollary 1): precisely the signature of loss aversion. The mechanism supplies the structure of loss aversion—its sign, its reference point, and how it varies with the environment—while its magnitude is one calibrated parameter that places the implied coefficient in the empirical range. Risk aversion follows as a corollary (Theorem 3): the kink induces first-order risk aversion over small symmetric gambles, inverting the usual hierarchy in which (second-order) risk aversion is primitive, and loss aversion is an add-on. The mechanism is immune to the Rabin calibration critique. Simulations benchmark the myopic policy against the computed optimum, map the mechanism’s robustness across noise tails, and locate the implied coefficient; we close with extensions to endogenous gain-seeking in convex (“gold-rush”) environments, population heterogeneity through learned priors, and a reading of hedonic affect as the Lagrange multiplier that prices a scarce attentional resource. Full article

Enhancement of carbon dioxide (CO₂) storage capacity in subsurface formations aids in offsetting the projected increase in carbon emissions. Consequently, various injection techniques should be explored to optimize the storage process. This study delves into CO₂-foam utilization in a saline carbonate aquifer for underground carbon storage (UCS) purposes. In order to depict the subsurface flow dynamics, a carbonate saline aquifer model, which incorporates heterogeneous properties, a natural fracture network, and geochemical reactions, was developed. Various subsurface flow dynamics were considered by generating multiple natural fracture network realizations. Afterward, the developed model was utilized to numerically simulate the UCS process for two hundred years, capturing the CO₂ inventory as well as fluid–fluid and fluid–rock interactions throughout the storage process. Introducing the foam enabled the injected CO₂ to penetrate deeper around the injection zone; hence, higher trapped CO₂ can be expected at the bottom of the aquifer. Despite the heterogeneity and natural fractures, CO₂ foam helped in enhancing the dissolved CO₂ distribution in the swept volume of the aquifer. The natural fracture network realizations demonstrated that the CO₂ foam can potentially limit the influence of natural fractures during the UCS process. Furthermore, the subsurface geochemical reactions tend to be altered due to the drop in the fluid–fluid and fluid–rock interactions. Overall, the findings suggest that CO₂ foam impacts surpass the heterogeneity and natural fracture network effects during the UCS. While the performed evaluation highlighted the CO₂ foam role within a carbonate saline aquifer, the workflow and outcomes of the study can be extended to various subsurface environments wherein a compatible CO₂ foam design can lead to CO₂ storage capacity enhancement. Full article

The Three-River Source Region (TRSR) is an environmentally fragile area in China and on the Qinghai–Tibet Plateau that is extremely vulnerable to climate change. The time lag between climate change and vegetation responses in high-altitude regions is a critical component of ecosystem–climate coupling that has not yet been fully quantified. The purpose of this project is to look into the influence of extreme climatic change in the TRSR on vegetation development, as well as to give data support for vegetation restoration and ecological security on the Qinghai–Tibet Plateau. This study used normalized difference vegetation index (NDVI) data from the TRSR from 2000 to 2024 to investigate the pixel-level lag effects of 12 ETCCDI (Expert Team on Climate Change Detection and Indices) extreme climatic indices. Pixel-level maximum Pearson correlation analysis was used to determine the ideal lag period and correlation direction within an 0–6-month lag window, and the lag ratios for seven vegetation kinds were quantified in stratified order. The results show that precipitation, and not temperature, is the primary climatic limiting factor for changes in NDVI in the TRSR. Furthermore, there is a clear distinction between the temperature- and the precipitation-driven lag responses: temperature extreme indices have shorter average response times and highly polarized correlation directions (positive correlation proportions range from 1.0% to 95.9%), whereas precipitation extreme indices have longer average lags and are predominantly positively correlated. Vegetation types have a significant impact on lag sensitivity: grassland and desert respond faster, reflecting shallower root depths and limited soil moisture buffering capacity, whereas meadows and shrubland have the longest lags, consistent with the water-holding capacity promoted by deeper root systems and higher soil organic matter. These findings contribute to our understanding of time-structured vegetation–climate coupling and provide a solid scientific foundation for proactive vegetation management in the TRSR to meet future extreme climate events. Full article

Construction contract durations are fixed during procurement, yet delivery often changes after risks materialize and formal extensions of time are approved. Although delays, extension-of-time claims, change orders, and risk-based duration estimation are well studied, less is known about how contract-amendment records can be converted into duration multipliers for planning. This paper develops a quantitative, document-based Risk-Calibrated Duration Multiplier framework linking initially contracted duration, approved extensions, and documented risk causes. The framework was applied to 197 signed works contracts from 60 projects within a broader portfolio of 63 EU-funded water and wastewater infrastructure projects, predominantly administered under FIDIC Red and Yellow Book conditions. The analysis combined duration multipliers, impact-weighted attribution of multi-risk amendments, risk-time coefficients, bootstrap uncertainty assessment, concentration indicators, benchmark regression models, and reconstruction validation. For completed contracts, the mean multiplier was 1.372, with P50, P80, and P90 values of 1.233, 1.635, and 1.886. Public-law procedural and design risk categories accounted for 60.9% of the total extension premium. The results show that contract-amendment records can be transformed into statistically interpretable planning parameters and used as a portfolio learning and contract-governance tool for more realistic infrastructure contract planning. Full article

In recent decades, residential mobility has emerged as a fundamental interpretative key lens for understanding contemporary urban transformations, particularly in polycentric and fragmented urban contexts. Movements between different residential settings reflect economic, social and cultural changes, impacting the organisation of urban spaces, the demand for services and mobility systems. In territories characterised by dispersed settlement patterns and strong functional polarisation, these dynamics tend to promote the intensive use of private means, with consequent negative impacts on environmental sustainability, social equity and economic efficiency. In response to these critical issues, there is growing interest in sustainable mobility models based on proximity and on the integration between daily travel, access to services and the quality of public space. Within this perspective, greenways are configured as hybrid infrastructures, capable of reorganising mobility while contributing to the regeneration of urban spaces. In the Caserta area, in the Campania region, the disused route of the former Alifana railway represents a topic of great interest, both for research and planning. Its potential strategic conversion into a greenway opens a broader perspective than that so far considered at the regional level, which has mainly focused on the infrastructure dimension. The paper analyses the strengths and weaknesses of an approach limited to infrastructural mobility, proposing a comparative evaluation of project scenarios—including the non-intervention hypothesis—both through the application of the MACBETH approach and preliminary parametric estimation of construction costs, in order to emphasise the importance of integrating social and environmental benefits, as well as quality of life, into decision-making processes. Full article

This paper develops a boundary-sensitive asymptotic theory for nonparametric conditional U-statistics smoothed by support-adapted asymmetric kernels when the response variable is subject to Missing-at-Random observation. The problem lies at the intersection of three well-established but traditionally separate lines of research: conditional U-statistics, asymmetric smoothing on constrained supports, and incomplete-data inference under MAR sampling. The contribution of the paper is not a novelty claim concerning any of these components in isolation. Rather, it consists in deriving a kernel-specific and MAR-aware limit theory for their simultaneous occurrence, where the estimators are nonlinear complete-case ratios of localized U-statistics and the localization devices are point-dependent approximate identities adapted to the geometry of the covariate support. The analysis covers three principal classes of support-respecting smoothers: Dirichlet kernels on the simplex, Bernstein polynomial smoothers, and multivariate beta kernels on hypercubes, with an additional extension to mixed continuous–categorical regressors. These smoothing schemes are not translation-invariant, and their local moments, effective support, normalizing constants and $L^2$-masses vary with the evaluation point, especially near the boundary. Consequently, their incorporation into conditional U-statistics requires more than a direct transfer of ordinary asymmetric-kernel regression theory. The numerator and denominator of the estimators are localized U-statistics whose stochastic expansions are governed by Hoeffding projections, including canonical components that must be controlled uniformly over the conditioning domain. Under regularity, smoothness and positivity assumptions adapted to the MAR setting, we establish uniform consistency, weak and strong uniform convergence rates, stochastic expansions and asymptotic normality. The results are obtained both on fixed compact subsets and on interior regions approaching the boundary, thereby identifying how support geometry enters the bias and stochastic normalizations. A central feature of the theory is the separation between the deterministic effect of complete-case sampling and its stochastic effect. For the complete-case estimator, the natural deterministic equivalent is obtained by replacing the design density f with the effective complete-case density $pf$, where p is the propensity score. Thus, the MAR mechanism may enter higher-order deterministic bias constants through the local design tilt, whereas the leading stochastic dispersion reflects the loss of effective information through propensity score factors. The precise variance constants and normalizing rates remain kernel-specific, depending on the local $L^2$-structure of the Dirichlet, Bernstein or beta smoothing device. The paper should therefore be viewed as a MAR extension and refinement of the complete-data asymmetric-kernel conditional U-statistic theory. It provides a common probabilistic architecture for several boundary-adapted smoothing schemes while retaining the kernel-dependent bias operators, variance constants, boundary regimes and Hoeffding-projection structures required for sharp asymptotic interpretation. Numerical experiments illustrate the finite-sample behavior predicted by the theory and highlight the interaction between support-adapted smoothing, boundary effects and incomplete response observation. Full article

Underground fluid injection is regarded as one of the important factors inducing seismic activity. This study therefore proposes a method to predict the maximum damage area and seismic magnitude induced by fluid injection, in order to quantify the relationship between stress disturbances in faults and induced seismic activity during fluid injection. This method involves analysing a three-dimensional geological model of fault permeability evolution in order to define the seismic rupture zone of faults during fluid injection projects. It also involves calculating the maximum damage area and seismic magnitude induced by injection and verifying the method’s effectiveness using field data. The results show that, during deep injection, continuous injection of fluid reduces the effective stress on the fault and increases the fracture area. Following the sudden cessation of injection, the rupture area and maximum seismic magnitude reach their peak values. During the initial stage of injection, seismic magnitude increases rapidly with the rupture radius of the fault, while the growth rate of seismic magnitude decreases during the stable injection stage. Once injection has ceased, the rupture range and seismic magnitude will gradually stabilise throughout the entire geological self-balancing stage. Periodic injection results in the largest fault rupture area, whereas linear growth injection induces the highest seismicity. Strike-slip faults exhibit the most significant increase in rupture area, whereas normal faults demonstrate more intense seismicity evolution. Low permeability, proximity to injection wells and direct well closure exacerbate instability, whereas linear slow closure is the safest option. These research results can inform seismic risk management in fluid injection engineering. Full article

Entrepreneurship education in film and the creative industries refers to a set of pedagogical approaches, curricula, and institutional frameworks designed to foster entrepreneurial mindsets, competencies, and practices among students and professionals operating within the cultural and creative industries (CCIs). Going well beyond conventional business training, entrepreneurship education in this context encourages learners to identify opportunities for value creation—cultural, social, and economic—to develop sustainable modes of creative practice, and to engage critically with the markets, institutions, and communities that constitute the contemporary creative economy. Within film studies and adjacent disciplines such as media production, design, music, and the visual arts, entrepreneurship education plays an increasingly prominent role in preparing graduates for careers characterised by self-employment, project-based work, portfolio careers, and the continuous negotiation of artistic autonomy with the imperatives of professional sustainability. This entry aims to compile and organise existing knowledge on entrepreneurship education as it applies to the CCIs, with particular attention to the film and audiovisual sector, drawing on academic literature, European policy frameworks, and empirical industry evidence. The entry uses a narrative literature review approach, synthesising scholarly works from the fields of education, cultural economics, and creative industry research alongside institutional documentation and policy instruments, in order to provide a systematic and accessible account of the current state of knowledge in this area. Full article

Disruptions in supply networks have caused many logistical and planning challenges in the last few years. The previous predictability of the shipping times of raw materials changed drastically due to various global issues, which affected many production areas, including the wood industry. This work is motivated by a case study of a Central European plywood production facility, where supply-side disruptions caused difficulties in meeting deadlines for downstream companies of the construction and furniture industry. As a result, the objective of production planners shifted towards mitigating the financial burden caused by cancellation penalties. Three MILP (Mixed-Integer Linear Programming) models and a genetic algorithm were developed to tackle the scheduling of a plywood production plant with raw material shipments and order deadlines. The novelty of the considered problem lies in the flexibility of swapping order deadlines from the same client, which was inspired by the real-life deals of the aforementioned company. The methods were tested on 120 benchmark instances of different sizes generated from real industrial data. The genetic algorithm terminated within 60 s for all instances and found the optimal or best-known solution in 71 of 80 short-horizon instances, while also remaining efficient on larger 30-day cases. As the solution approach is not specific to plywood production, it can be applied to scheduling problems in other fields as well, where similar disruptions can develop, and the production process features are covered by the Multi-Mode Resource-Constrained Project Scheduling Problem class. Full article

Engineered geothermal system (EGS) cross-well flow of 30 L/s producing heat at a rate of Q~20 MW for 30 days was achieved by the UtahForge project in 2024. The cross-well flow doublet measured ℓ~400 m in length at L~100 m vertical offset. A first-order question is how sustainable the doublet’s 20 MW heat extraction is. Where once the answer would be framed in terms of pipe-like cubic-law flow along stress-aligned fault-scale planar heat exchange surfaces, UtahForge flow data rule out this heat exchange picture. The EGS flow data indicate aquifer-like volumetric cross-well flow with heat exchange at the grain scale. More specifically, the EGS flow data indicate no cross-well flow for a dozen hydrofrack attempts, while the 30 L/s flow occurred when the 400 m doublet wells were rendered effectively open to the crustal formation by drilling out all hydrofrack gear. An essential further observation is that the producer well flowed at only 70% of the injector rate: 30% of injected fluid was lost to flow heterogeneity in the cross-well volume. A four-step deconstruction of these observations explicitly characterizes the flow heterogeneous volume: (i) flow stimulation of the cross-well volume, (ii)wellbore-centric flow in/out of cross-well volume along the 400 m open well reach, (iii) heat advection in the cross-well volume, and (iv) sustainability-specific heat conduction into the cross-well volume. EGS stimulation process step (i) is attested by microseismic emissions (Meqs) registered on downhole sensors. Meq size and spatial correlations in turn reflect the flow heterogeneity of the cross-well volume. EGS step (iv), crustal heat conduction sustainability, is approximated by assuming radial heat energy extraction at rate Q/ℓ by a central line-sink of radius R < L/2. The line-sink analytic solution yields heat reservoir sustainability of ~3–10 years. Greater sustainability at Q/ℓ rate requires larger cross-well offsets L. The intimate relation between fluid flow and seismic emissions enables downhole seismic sensor data to image EGS flow stimulation activity. The future of EGS heat extraction depends to a large degree on feasible sizes of cross-well offset L in the flow-heterogeneous crust. Full article

This work presents a trajectory indexing pipeline for accelerating Social Spatio-Temporal Keyword Pattern (S²KP) queries over Multiple-Aspect Trajectory (MAT) data. An S²KP query forms a sequence of spatial, temporal, textual, and social-rating constraints over trajectory episodes. The constraints are formulated in the form of regular expressions, thus offering high expressiveness and flexibility in query formulation. In this paper, we enhance spatial pruning by enhancing a well-established MAT index, the Episode-Based Multiple-Aspect Trajectory (EMT) Dual Index. The EMT Dual Index is augmented with curve-based keys (Hilbert, Z-order, and Gray-coded Z-order mappings), so that spatially related entities are projected into one-dimensional key ranges, enabling additional subtree pruning through interval overlap while preserving exact final matching semantics. The intervals are induced by the numbering of cells generated by a curve. Our experimental study on two representative MAT datasets (one synthetic and one real) demonstrates the effectiveness of our proposal. Full article

This work investigates a sophisticated class of neutral fuzzy fractional functional differential equations (N3FDEs), where the fractional order $\alpha$ satisfies $0<\alpha \le 1$. We present a comprehensive analysis of the existence, uniqueness, and well-posedness of solutions under the generalized Hukuhara framework. First, we examine the existence and uniqueness of solutions under the generalized Hukuhara framework, providing an refined iterative formula for linear systems. We further verify the system’s well-posedness, proving that solutions remain stable and respond continuously to changes in initial data and parameters. Second, we introduce a novel spectral Vieta–Lucas projection method to approximate the solution. By leveraging the unique properties of Vieta–Lucas polynomials, we transform complex memory-dependent fuzzy equations into a streamlined algebraic system. Finally, numerical examples and error analysis show the method is accurate and efficient. Full article