Search Results (4,462)

Quantum gate estimation and tomography pipelines routinely combine intrinsically defined likelihoods with priors or regularization terms specified in local Euclidean coordinates. This practice implicitly replaces the Haar reference measure on $SU\left(2\right)$ with Lebesgue measure, specifying a different statistical model rather than a reparametrization of the intended one. We show that omitting the associated chart-volume factor alters the optimization objective itself, modifying its gradient field and stationary-point structure. The gradient discrepancy $\nabla L_G-\nabla L_E=-\nabla log{J}_{exp}$ is nonzero for all $v\ne 0$ so that flat-coordinate surrogate objectives can converge to points that are non-stationary for the corresponding Haar-consistent objective even in regimes where local Gaussian approximations are assumed valid. We prove a formal non-equivalence proposition and validate a leading-order Fisher-information correction analytically and numerically. Large-scale multi-start optimization experiments ($N=11,900$ runs) demonstrate that the discrepancy is regime dependent and most pronounced under moderate-to-strong regularization or limited data. The fix requires a single-line modification to any gradient-based optimizer. These results identify reference-measure selection as an explicit modeling decision with direct consequences for optimization and inference in gate-set tomography, randomized benchmarking, and Bayesian gate estimation on curved parameter manifolds; quantitative validation is restricted to single-qubit systems, though the mechanism extends to any regularized optimization on a curved parameter manifold. Full article

Respiratory disease classification using lung sound recordings remains challenging due to signal interference, heterogeneous acquisition conditions, and substantial overlap among clinically related acoustic patterns. This study presents a framework for respiratory disease classification using NMF-enhanced log-mel spectrograms and deep neural classifiers. Respiratory sound recordings from two publicly available datasets were harmonized into a unified label space comprising Asthma, Bronchiectasis, Bronchiolitis, COPD, Healthy, Pneumonia and URTI. Following signal standardization and fixed-length segmentation, a non-negative matrix factorization (NMF)-based enhancement stage was applied to increase the salience of respiratory components prior to log-mel spectrogram generation. The proposed classifier was a convolutional recurrent neural network (CRNN) that combined convolutional feature extraction, bidirectional recurrent modelling, and attention-based temporal aggregation. For comparison, RDLINet, a conventional CNN, ResNet, and a YOLO-style backbone were implemented under the same preprocessing and training framework. Experimental results demonstrated that the proposed CRNN achieved the best overall performance, attaining 96.14 ± 0.50% accuracy and 94.05 ± 1.21% Macro-F1 on the unified seven-class cohort. Class-wise analysis, confusion-matrix evaluation, and output-space visualization further showed that the CRNN provided more balanced recognition across disease categories and clearer class separation than competing architectures. These findings indicate that NMF-enhanced spectro-temporal modelling combined with convolutional recurrent learning offers an effective approach for automated multi-class respiratory disease classification. Full article

Persistent urban–rural inequality remains a major challenge for sustainable regional development, especially in countries where rural communities still face limited access to markets, employment, and public services. This study examines whether rural road network density promotes inclusive regional growth in China. Using county-level panel data from 2013 to 2024, we construct an inclusive regional growth index that combines economic output, nighttime-light-measured economic activity, rural income, and the urban–rural income gap. rural road network density is measured by the length of county, township, and village roads per 100 square kilometers. Two-way fixed-effects models, mechanism tests, robustness checks, instrumental-variable estimation, and heterogeneity analysis are employed. The results show that rural road network density significantly improves inclusive regional growth. Dimensional analysis indicates that higher rural road network density increases real GDP per capita, strengthens nighttime-light-measured economic activity, raises rural income, and reduces the urban–rural income gap. Mechanism analysis shows that these effects operate through labor mobility, market access, and non-agricultural industrial development. The results remain robust to alternative road measures, lagged specifications, outlier treatment, sample restrictions, and instrumental-variable estimation. Heterogeneity analysis further shows that the effects are larger in central-western counties, low-accessibility counties, and less-developed counties. These findings suggest that rural road network density is not only a transport infrastructure indicator but also a key spatial condition for promoting sustainable and inclusive regional development. Full article

Background: HS is a chronic inflammatory skin disease in which inflammatory nodules and abscesses coexist with tunnels, fibrosis, and scarring. This dual biology explains why medical therapy often improves inflammatory dissease activity without fully addressing fixed tissue damage, whereas surgery can achieve durable local control but does not treat diffuse inflammatory burden. Contemporary international guidelines increasingly endorse multimodal and medicosurgical care. Objective: To critically compare the evidence supporting medical and surgical management of HS, with emphasis on outcomes, indications, limitations, and clinical decision-making relevant to contemporary practice. Methods: A structured review was undertaken using PubMed/MEDLINE, the Cochrane Library, and major dermatology guideline sources, with searches updated to 7 May 2026. Priority was given to clinical guidelines, systematic reviews and meta-analyses, randomized controlled trials, and higher-quality observational studies. Evidence was synthesized narratively because endpoints, populations, and follow-up intervals differed markedly across medical and surgical studies. Results: Medical evidence is strongest for biologic therapy in moderate-to-severe inflammatory HS. Weekly adalimumab improved week-12 HiSCR in the phase 3 PIONEER trials; secukinumab improved week-16 and week-52 outcomes in SUNSHINE/SUNRISE; and bimekizumab improved week-16 HiSCR50 in BE HEARD I/II. Surgical evidence is strongest for wide excision in structurally advanced disease, particularly when compared with local excision or incision and drainage. Meta-analytic data consistently show lower recurrence after wide excision than after local excision, and lower recurrence after flap or graft reconstruction than after primary closure. Combined therapy is increasingly supported: peri-operative adalimumab improved outcomes in SHARPS, and surgery plus adalimumab outperformed adalimumab alone in a pragmatic 12-month RCT. Conclusions: HS is best managed by matching treatment to disease phenotype. Medical therapy is essential for inflammatory control; surgery is essential for persistent tunnels, fibrosis, and scarred regional disease. The strongest overall clinical position is an integrated, multidisciplinary model in which systemic therapy reduces inflammatory load and surgery definitively treats irreversible tissue damage. Full article

Reference guided anime editing is challenging when the target requires both rendering style transfer and local structural change. Existing diffusion stylization methods that do not require training usually assume a fixed content layout, while drag-based editors deform local structures without enforcing a separate style reference. Directly combining them is unstable: reference attention can disrupt handle tracking during dragging, whereas stylization after dragging can weaken the edited structure. This paper proposes AnchorHandoff, a temporally coordinated diffusion framework for joint drag and style editing. Drag optimization is performed with style injection disabled, followed by a short interval without style injection that lets the edited structure stabilize. A predicted clean sample from this state after dragging is then used as an anchor: content queries are refreshed from the anchor, and reference style keys and values are replayed on the edited layout. Soft correspondences from intermediate attention features guide style injection toward compatible regions without parsers or segmentation labels. On a curated anime benchmark, controlled comparisons, ablations, and a blind study with 36 participants show that AnchorHandoff reduces residual tracking error and feature structure distortion while maintaining comparable distribution level style alignment. The method remains limited under very large structural changes, but the results highlight temporal handoff as an important factor in joint anime structure and style editing. Full article

Vehicular platoons under realistic scenarios are prone to actuator saturation, model uncertainties, and external disturbances, which degrade transient tracking and spacing stability. Conventional prescribed-performance control (PPC) strictly requires initial errors to lie within a predefined envelope, while finite/fixed-time schemes cannot directly assign the settling-time bound. To resolve these limitations, this paper proposes a practical predefined-time sliding-mode adaptive platoon control strategy under input saturation constraints. Specifically, a smooth hyperbolic-tangent approximation combined with a mean-value-theorem-based gain formulation is utilized to handle saturation nonlinearity and simplify stability analysis. A novel initial-error transformation is developed to eliminate the stringent envelope constraint on the original initial tracking error. Furthermore, a predefined-time sliding variable and an adaptive compensation mechanism are synthesized to guarantee that tracking errors converge into a bounded neighborhood of the origin within a user-specified time. Numerical simulations and comparisons with predefined-time sliding-mode and PID controllers demonstrate that the proposed strategy eliminates initial error restrictions and suppresses chattering. Compared to the alternative schemes, the proposed method restricts the maximum tracking error within 0.05 m—representing reductions of approximately 77% and 91%, respectively—and shortens the settling time to within 2 s. These results validate its effectiveness for robust cooperative platoon control. Full article

Vibration-based fault diagnosis is widely used for rotating machinery health monitoring, but practical diagnosis is often limited by scarce fault labels and uncertain measurement length. Longer vibration records can improve decision reliability but increase sensing and computational cost, whereas overly short records may yield unreliable predictions under noise and measurement corruptions. This paper studies few-shot fault diagnosis as a measurement-constrained decision task, in which the model identifies the fault class and determines when sufficient vibration evidence has been acquired. We propose a measurement-efficient diagnosis framework that combines prior knowledge from unlabeled healthy signals, physically constrained augmentation of scarce labeled samples, and adaptive early stopping in a shared one-dimensional feature extractor. The framework is evaluated on the UORED-VAFCLS and Paderborn University bearing datasets under 6-, 8-, and 10-shot settings with controlled corruption levels. Results show robust diagnostic performance with fewer acquired vibration windows than with fixed-length inference. In the representative PU-Hard 8-shot setting, the proposed method achieves 80.26% accuracy with an average of 1.2432 acquired windows and reduces the evaluation cost J from 0.3929 to 0.2596 compared with fixed four-window inference. These results indicate that adaptive measurement improves the accuracy–cost trade-off in few-shot vibration diagnosis. Full article

Urban ecological resilience (UER) has become a central concern in rapidly urbanizing regions where development pressures increasingly interact with ecological constraints. Focusing on the Guanzhong Plain Urban Agglomeration (GPUA), a semi-arid urban agglomeration in western China, this study examines the non-static and locally heterogeneous drivers of UER across 11 prefecture-level cities from 2000 to 2023. UER is measured through resistance, adaptability, and recovery. An extended STIRPAT model, Elastic Net with stability selection, two-way fixed-effects period interactions, and Geographically and Temporally Weighted Regression (GTWR) are integrated to identify robust drivers, test post-2011 shifts, and estimate city-year local associations. Residual Moran’s I diagnostics and Spatial Lag GTWR (SLM-GTWR) are used as supplementary checks. The results show that UER remains relatively stable at the aggregate regional level but becomes increasingly divergent across cities. Ten robust drivers are retained, with fiscal investment intensity, human capital, medical and health level, and total energy consumption emerging as key variables. Period heterogeneity results indicate that fiscal investment becomes more favorably associated with UER after 2011, while the marginal association of energy consumption weakens. GTWR reveals clear local heterogeneity: human capital shows the most stable positive association, medical and health level remains generally negative, fiscal investment is positive but context-dependent, and energy consumption is predominantly negative but locally differentiated. Supplementary spatial diagnostics suggest that the GTWR specification captures the main spatiotemporal structure of UER, while spatial-lag checks broadly support the robustness of the local coefficient patterns, although estimates of spatial interaction remain sensitive to how inter-city linkages are defined. These findings indicate that UER drivers are dynamic rather than fixed, with resilience formation shaped mainly by governance-regime shifts and localized heterogeneity. The study contributes a sequential screening–heterogeneity framework for identifying non-static resilience drivers and suggests that resilience governance should combine stage-sensitive policy adjustment, place-based intervention, and regional coordination where ecological functions and environmental risks cross administrative boundaries. Full article

This paper introduces and investigates two new classes of contraction mappings—weak enriched $\mathfrak{F}$-contractions and weak enriched ${\mathfrak{F}}^{\prime }$-contractions, in the context of convex metric space (CMS) and convex G-metric space (CGMS). From a given self-mapping, the study constructs a new mapping via different convex combinations, termed the k-fold averaged mapping. The paper establishes that if the underlying space is complete and certain conditions are satisfied, then the k-fold averaged mapping possesses a unique fixed point, and the corresponding iterative scheme converges to this fixed point. It is further shown that the fixed point set of the original mapping is always contained in the fixed point set of k-fold averaged mapping, and under further conditions, both sets of fixed points are equal. These results broaden the scope of fixed point theory in convex metric settings by introducing and exploring these new contraction mappings. Several examples are provided to illustrate the applicability and effectiveness of the theoretical findings. Full article

This paper considers the acceleration of the residual alternating direction implicit (RADI) iteration for solving large-scale low-rank Riccati matrix equations arising from time-invariant control systems. A direct attempt to accelerate the ADI iteration by treating the feedback gain matrix as a fixed-point iterate typically leads to a relatively slow convergence of the norm of the residual matrix. To address this issue, we combine the feedback gain matrix with the residual matrix as the input of the RADI iteration and develop an accelerated RADI scheme based on this reformulation. The convergence of the accelerated RADI algorithm is established under a relatively mild assumption. Numerical experiments from engineering applications demonstrate that the proposed accelerated RADI algorithm with properly selected parameters is able to attain a prescribed residual level with fewer iterations and less computational time than the RADI method. Full article

Agent-based neural models often encode transmission within neuron state updates, which can make it difficult to separately log and quantify spatial recruitment patterns, delay structure, and environment-mediated feedback effects. We present LANA (Local Adaptive Neural Agents), a dual-agent neural agent-based model in which neurons and propagating signals are represented as distinct interacting entities embedded in a dynamic environmental field. The model combines discrete leaky integrate-and-fire neuron dynamics, mobile signal agents, synaptic links with distance-dependent delays, and a bounded environment-to-neuron feedback mechanism. LANA is intended as a normalized phenomenological mesoscopic framework for mechanism-level comparison rather than as a circuit-specific biophysical reconstruction. To support interpretability and reproducibility, we report a compact internal verification block for the implemented operators, including delay propagation, environmental decay and diffusion, threshold activation, and refractory enforcement. We then compare the full LANA model against a matched neuron-only baseline and summarize spatial recruitment using first-spike maps, cumulative recruitment times, and wavefront speed as a secondary descriptive metric. Finally, we evaluate two controlled operating regimes, a resting regime (S1) and a hyperexcitable regime (S2), under fixed network size, stimulation schedule, and matched random seeds. Relative to the baseline, the full model sustains and spreads activity more effectively and provides spatially resolved recruitment summaries, including first-spike timing and cumulative recruitment measures, that are not available in the same form when transmission is represented only through neuron-level updates. Relative to S1, S2 exhibits earlier activation, higher firing activity, stronger environmental accumulation, and faster cumulative recruitment. Local and factorial sensitivity analyses further identify the parameters that most strongly govern these regime differences. Together, these results position LANA as a normalized mesoscopic and computationally tractable framework for studying how excitability, transport state dynamics, delayed coupling, and environment-mediated feedback jointly shape emergent activity in controlled simulation settings. 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

Background: Tooth-supported fixed dental prostheses (FDPs) remain relevant when implant therapy is limited, but their mechanical behavior depends on material selection, connector design, retainer design, prosthesis configuration, and abutment support. This systematic review assessed how these factors affect fracture behavior and stress transmission in tooth-supported FDPs. Materials and Methods: PubMed/MEDLINE, Scopus, Web of Science Core Collection, and Dentistry and Oral Sciences Source were searched for English-language studies published from 1 January 2016 to 15 May 2026. Eligible studies were in vitro mechanical, fatigue, fracture-resistance, or finite element analysis (FEA) studies of tooth-supported FDP designs. Clinical studies were screened during eligibility assessment, but no clinical study met the final inclusion criteria for primary synthesis. In vitro components were appraised with the Quality Assessment Tool for In Vitro Studies (QUIN), and FEA components were appraised with the Risk-of-bias Framework for Dental Finite Element Analysis (ROBFEAD). Findings were synthesized narratively by evidence type and biomechanical theme. Results: Twenty-nine studies were included: 11 in vitro-only studies, 14 FEA-only studies, and four combined experimental and computational studies. No eligible clinical study met the final inclusion criteria. Zirconia-based systems were the most frequent focus. Their behavior depended on connector dimensions, connector shape, framework design, span, retainer configuration, loading direction, abutment selection, periodontal support, and bone support. Larger connector dimensions or greater connector height often improved fracture resistance or reduced modeled stress in zirconia models, but connector area alone did not explain performance across all materials and designs. Conservative FDPs were sensitive to retainer geometry, adhesive-interface behavior, connector design, and abutment support. Conclusions: Current evidence is limited to laboratory and computational studies. Tooth-supported FDP biomechanics should be interpreted as a material, design, and support system, not as a material effect alone. Full article

Buchwald–Hartwig C-N coupling is a central method for constructing (hetero)aryl–nitrogen bonds. Yet condition translatability is often problematic from one substrate to another, even among closely related substrates, especially for heteroaryl halides. In this work, we demonstrate an approach to solving this task using the six available bromoimidazo[1,5-a]pyridine regioisomers and a representative nucleophile panel comprising benzamide, aniline, morpholine, and benzylamine as a model study. A limited-scale HTE campaign was conducted with an in-house ligand set, a fixed palladium source, and two bases. Reaction performance was assessed by LCMS with internal standard calibration, and targeted hits were verified by preparative re-runs and NMR-confirmed product assignment. The resulting conversion and product yield maps reveal strong dependence on bromide position, nucleophile class, and ligand/base selection. The 6- and 8-bromo isomers show the broadest productive reactivity profiles, whereas the 5- and 7-isomers are active in narrow condition windows. The imidazole–ring 1- and 3-bromo isomers react readily but do not provide isolable, structurally confirmed target products. Methodologically, this work demonstrates that a compact HTE workflow can rapidly define useful ligand/base combinations for heteroaryl bromides while preventing misleading conclusions from conversion-only analysis. The same approach can be applied as an early-stage reactivity screen for other heteroaryl halide series. Full article

Spiking Neural P systems provide a rule-based model of distributed computation inspired by membrane computing, while kernel P systems use guarded transformations and structured control of rule applicability. This paper introduces Convolutive Kernel-Guarded Spiking Neural P systems ($\mathrm{CK}-\mathrm{SN}\mathrm{P}$ systems), a formal and trainable framework in which spike-rule applicability may depend on local kernel responses computed over ordered neighborhoods of spike multiplicities. The proposed model provides a general mechanism for local feature computation, combining explicit operational semantics with kernel-based predicates that can be fixed, selected, or embedded in trainable realizations. We define the syntax and transition semantics of the model, relate the construction to delay-free extended Spiking Neural P systems and kernel P systems under stated assumptions, and present a reproducible instantiation for electrocardiographic beat classification under a patient-independent protocol. The empirical study illustrates how CK–SN P local responses can be combined with RR, Gaussian, and Fourier descriptors and evaluated with classical and neural classifiers. Overall, the study clarifies both the formal role of guarded local computation and its practical use as an interpretable feature-generation mechanism. Full article