Search Results (7,886)

As robotic systems evolve toward large-scale distributed architectures and cloud-edge collaboration, communication middleware has become a critical infrastructure that impacts system real-time performance and scalability. The traditional Robot Operating System 1 (ROS 1) communication architecture, which relies on a centralized master node, has limitations in dynamic network environments. Robot Operating System 2 (ROS 2) achieves decentralized communication through the introduction of DDS. However, the single Data Distribution Service (DDS) mechanism remains inadequate for cross-network communication and high-performance local data exchange. Addressing the current issue in ROS communication research: the coexistence of multiple mechanisms without a unified analytical framework or guidance for selection. This paper systematically traces the evolution of the ROS communication architecture from centralized to distributed systems. It constructs a unified analytical framework covering two dimensions: communication models and data transmission paths. Crucially, to overcome the unreliability of cross-protocol comparisons based on heterogeneous literature, this paper designs and executes a set of unified benchmark experiments on a controlled testbed. These experiments systematically evaluate the performance of two mainstream DDS implementations (CycloneDDS and FastDDS) across five key metrics: latency, throughput, jitter, scalability, and packet loss rate under load. Additionally, a comprehensive comparative analysis of the performance of three transmission modes is conducted. Based on this comprehensive evaluation, this paper summarizes the performance characteristics of different mechanisms and further proposes an optimization-based middleware selection method for quantitative communication mechanism selection under different workload and application requirements. This paper provides a systematic reference for the design and optimization of ROS communication systems and offers guidance for promoting the application of multi-middleware collaborative architectures in robotic systems. Full article

In reliability and survival studies, lifetime data are frequently subject to progressive Type-II censoring, leading to incomplete failure-time information and challenging statistical inference problems. In this study, statistical inference for the Rayleigh–Logarithmic (RL) distribution is developed under progressive Type-II censoring. The RL distribution provides a flexible lifetime model by combining a Rayleigh lifetime component with a logarithmically distributed number of latent failure causes. A competing-risk interpretation of the model is presented, and parameter estimation is carried out using both maximum likelihood estimation (MLE) and maximum product spacing (MPS) methods. The performance of the proposed inference procedures is investigated through extensive Monte Carlo simulations under different parameter settings and censoring schemes. The results indicate that both MLE and MPS provide reliable estimates, with estimation accuracy improving as the sample size increases. The methodology is further illustrated using simulated and real lifetime data sets and compared with classical lifetime distributions. The findings show that the RL distribution offers a flexible and effective framework for modeling progressively censored lifetime data, particularly in the presence of heterogeneous and latent failure mechanisms. Full article

Cross-scene Hyperspectral Image (HSI) classification faces substantial domain shifts caused by sensor heterogeneity, acquisition variation, and scene diversity. While benchmark annotations are assigned to individual center pixels, local patches often contain implicit multi-label semantics due to spectral mixing and spatial overlap. This mismatch distorts prediction structure, exacerbates generalization errors, and limits the effectiveness of standard domain generalization (DG) techniques focused solely on feature or prediction invariance. We propose Class-Aware Semantic Calibration (CASC), a systematic semantic structure calibration framework that addresses three complementary distortions induced by mismatched patch supervision: (i) Balance corrects class frequency bias via reweighted supervision; (ii) Separability enhances boundary decision stability through margin-based logit calibration; and (iii) Independence reduces domain-specific spurious co-occurrence via prediction covariance decorrelation. To preserve calibrated semantics under pseudo-source shift, we further introduce a complementary DualAlign (DA) module, which jointly aligns feature statistics and prediction distributions, enforcing consistency at both representation and semantic levels. Extensive experiments on three cross-scene benchmarks (Houston, Pavia, and WHU-Hi) demonstrate that CASC-DA consistently improves performance over strong baselines, achieving an average gain of 3.0% in overall accuracy and 4.9% in Kappa coefficient compared with the best-performing baseline on each dataset. These results underscore the importance of semantic structure calibration for domain-generalized HSI classification. Full article

Background: Pathogenic BRCA1 and BRCA2 mutations confer a homologous recombination deficiency that underlies PARP inhibitor sensitivity. While BRCA1 mutation carriers more frequently develop triple-negative breast cancer (TNBC) and BRCA2 carriers hormone receptor-positive (HR+) disease, whether the specific protein domain harboring a pathogenic somatic mutation differs systematically between breast cancer subtypes remains uncertain. Apparent domain enrichment in earlier unfiltered analyses may be confounded by missense variants of uncertain significance (VUSs), which lack clinical actionability. Methods: We assembled three independent breast cancer cohorts via cBioPortal: TCGA-BRCA (brca_tcga_pub2015), METABRIC (brca_metabric), and MSK-CHORD (msk_chord_2024). All somatic BRCA1/2 mutations were mapped to UniProt-annotated functional domains and to Rebbeck-defined breast/ovarian cancer cluster regions (BCCR/OCCR). Per ENIGMA/ACMG guidance, pathogenic mutations (nonsense, frameshift, and canonical splice site) were analyzed inferentially, while missense and in-frame variants—predominantly VUSs—were only reported descriptively. Fisher’s exact tests with Benjamini–Hochberg FDR correction were applied across domain × subtype contingencies. Cohort heterogeneity was assessed via Cochran’s Q and I² statistics; pooled effect estimates were computed using inverse-variance fixed-effects meta-analysis. Results: A total of 394 somatic BRCA1/2 mutations were identified across the three cohorts (BRCA1 n = 166; BRCA2 n = 228), of which 147 (37.3%) met pathogenic criteria. Among 131 pathogenic mutations in HR+/HER2− or TNBC subtypes, 84 (64.1%) occurred in HR+/HER2− disease and 47 (35.9%) in TNBC. Domain-level distributions did not differ significantly between subtypes for any BRCA1 domain (BRCT: TNBC 20.0% vs. HR+ 18.8%, OR = 1.08, 95% CI 0.31–3.78, and FDR-adjusted p = 1.00) or BRCA2 domain (DBD: TNBC 17.6% vs. HR+ 30.8%, OR = 0.48, and FDR-adjusted p = 1.00). Cluster-region analyses (nine Rebbeck BCCR/OCCRs) similarly showed no significant enrichment. Post hoc power analysis indicated that the study could only reliably detect large effects (OR ≥ ~3.0 for the principal BRCT contrast), and formal equivalence testing (TOST) demonstrated equivalence within a prespecified ±20% margin for BRCA1 BRCT (TOST p = 0.031). Heterogeneity across cohorts was minimal (Cochran’s Q = 0.62, I² = 0.0%). Descriptive analyses of VUSs suggested the apparent enrichment of BRCA1 BRCT-localized missense variants in TNBC (31.8% vs. 17.9% in HR+), but this signal did not extend to pathogenic mutations. Conclusions: Within the statistical power available, our three-cohort analysis shows no evidence of large subtype-specific enrichment of pathogenic BRCA1/2 somatic mutations across protein domains or cluster regions; small to moderate effects cannot be excluded. Notably, the majority (64%) of pathogenic mutations occurred in HR+/HER2− disease, underscoring that BRCA1/2 testing should not be deprioritized in non-TNBC subtypes. The apparent BRCT enrichment observed in earlier unfiltered analyses appears to be driven by VUSs rather than pathogenic variants, highlighting the methodological necessity of pathogenicity filtering for clinically actionable inference. These findings provide cohort-scale supportive evidence for emerging clinical guidelines that recommend broader BRCA1/2 testing across breast cancer subtypes. Full article

Exploring the interaction relationship among multiple ecosystem services is vital for maintaining ecosystem function. However, traditional approaches are limited in their ability to: (i) characterize complex interactions and (ii) visualize the spatial connectivity of various ecosystem services delivered by social–ecological systems. To address these challenges, a framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by: (i) estimating the spatial distribution of multiple ecosystem services using the InVEST model, and (ii) generating network nodes and edges with geographical attributes based on the minimum cumulative resistance model and a multiresolution segmentation method. We conducted a case study in the Guangdong–Hong Kong–Macao Greater Bay Area and examined the topological features of the spatial networks using complex network indicators. For each network, winding and multiple edges connected adjacent nodes and formed continuous linkages across the entire study area, indicating that the proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. The different ecosystem service networks exhibited conspicuous spatial heterogeneity and generally maintained relatively high connectivity, as evidenced by their tree-like structure with winding pathways and the distribution of multi-edge nodes, indicating that each ES was predominantly connected with multiple other ecosystem services. Meanwhile, nodes with high values of degree centrality and clustering coefficient were mainly concentrated in coastal and mountainous regions. This study advances the representation of complex interactions among multiple ecosystem services from a spatial perspective, thereby facilitating a deeper understanding of the interaction mechanisms underlying ecosystem functioning. Full article

Background/Objectives: Adenoid cystic carcinoma (ACC) is a heterogenous disease and defining aggressive subtypes to inform surveillance and treatment strategies remains clinically pertinent. NOTCH pathway-activated ACC is associated with worse outcomes. We describe the distribution of tumour mutational burden (TMB) and characterise its relationship with NOTCH gain-of-function (GoF) mutation and survival in locally advanced or recurrent/metastatic (LA-R/M) ACC. Methods: 124 tumours from the UK NHS (NHS group) and 139 tumours from cBioPortal; MSK metTropism (MSK group) were evaluated as independent cohorts. TMB (mut/Mb) was calculated either by F1CDx or its precursor FoundationOne NGS in the NHS group, or MSK-IMPACT NGS in the MSK group. NOTCH1/2 mutations were classed as GoF if predicted to disrupt the NRR/PEST domains. Overall survival (OS) was measured from diagnosis of unresectable LA-R/M ACC. Results: Median TMB was 1.26 mut/Mb (IQR 0–2.52) in the NHS group and 1.96 (IQR 0.87–3.46) in the MSK group. NOTCH1/2 GoF mutation was seen in 16/124 (13%) in the NHS group and 22/139 (16%) in the MSK group. Median TMB for tumours with NOTCH1/2 GoF mutation was 2.52 mut/Mb (IQR 1.82–3.84) in the NHS group and 4.38 mut/Mb (IQR 3.33–4.89) in the MSK group. For tumours with NOTCH1/2 GoF mutation, median OS reduced with TMB > median of 2.52 mut/Mb (0.7 yrs vs. 2.6 yrs, p = 0.02) in the NHS group. Conclusions: LA-R/M ACC has a low TMB profile overall. Median TMB was higher in NOTCH-activated ACC in both the NHS and MSK groups and TMB may have value in further stratifying patients with LA-R/M ACC. Full article

Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance—Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud–edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier “Teacher–Student” paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity “Teacher,” employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV’s edge digital twin. This resource-constrained edge tier relies on a localized “Student” network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. Full article

This paper presents a multidimensional analysis of Historic Building Information Modeling (HBIM) segmentation, offering a roadmap towards standardization, a key dimension towards broader adoption within the Cultural Heritage (CH) sector. HBIM faces multiple challenges related to the lack of standardized protocols and varying definitions of Level of Detail (LOD) across applications. Amid the advancements of the fourth industrial revolution, integrating Building Information Modeling (BIM) improves sustainability and digital governance, aligning with the sustainable development agenda. Despite increasing academic interest, the implementation of HBIM remains limited, primarily due to the complexities and heterogeneities inherent in CH artifacts. This study begins with a purely qualitative strategy. Then, it introduces multidimensional and hierarchical clustering analysis to classify the unique characteristics of various HBIM applications such as segmentation, input, and data-capturing media. At the same time, it is a tool for fine-tuning keyword-based selection criteria, which is crucial in systematic or semi-systematic surveys in HBIM segmentation. The thematic analysis output is interrupted just before the conceptualization step, and theme extraction is diverted to correspondence analysis implemented in R, an open-source statistical package. Among the key findings of this paper is the classification of four distinct HBIM application clusters, revealing how specific workflows align with data acquisition methods, input formats, and Level of Detail (LOD) requirements. The analysis exposes critical standardization bottlenecks hindering wider-scale industry adoption, highlighting that challenges are domain-specific. Strong evidence shows that 3D modeling has not reached the required maturity level, with persisting challenges distributed non-uniformly within the applications spectrum. Finally, AI-driven automation relates with poor LOD outcome. Full article

Frequent engineering changes in manufacturing require worker instructions to be updated quickly and reliably. In many production environments, however, update handling still depends on manual comparison procedures, delayed communication, or repeated traversal of large document collections, limiting responsiveness during ongoing production changes. This paper presents a hierarchical hash-based method for change detection in structured manufacturing documents as the computational core of a worker assistance system for near-real-time instruction updates in the context of in-line qualification. Heterogeneous instruction data are transformed into canonical hierarchical document structures, from which SHA-512 digests are generated at multiple structural levels. During repeated comparison operations, document-state evaluation is reduced to digest comparison, while structural differences can be localized through hierarchical refinement of affected substructures. The method is integrated into a system architecture that combines predecessor-linked version management with role-specific filtering for controlled dissemination of relevant instruction updates. The approach was implemented in an automotive assembly use case involving structured work instructions and evolving production documentation. The evaluation demonstrates that the proposed approach reduces repeated comparison effort relative to conventional field-wise traversal methods while maintaining the ability to localize structural changes through hierarchical refinement. The reported results focus on computational behavior and implementation feasibility in structured manufacturing environments rather than hardware-specific throughput benchmarks. Overall, the results indicate that hierarchical comparison of structured instruction states provides a practical basis for change-aware worker assistance and controlled propagation of instruction updates in evolving manufacturing environments. The evaluation focuses on repeated-comparison scenarios in structured manufacturing settings and does not address semantic interpretation of detected changes or large-scale distributed deployments. Full article

This study investigates the design and performance of lime mortars incorporating multi-phase change material (multi-PCM) systems as thermally responsive rendering materials for building-envelope applications under variable conditions. Moving beyond conventional single-PCM lime mortar approaches, this work proposes a controlled multi-PCM design framework in which a fixed total PCM dosage is distributed across selected phase-transition windows. Mortars combining PCMs with different transition temperatures (5–25 °C and 18–25 °C) were produced using two PCM types: silica-supported form-stable systems and polymeric-shell microencapsulated systems supplied as powders or aqueous slurries. All formulations contained 20% PCM and were optimized with polymeric additives, including a polycarboxylate ether-based superplasticiser and a starch-derived adhesion enhancer, to ensure suitable workability and applicability as rendering materials. Microstructural analyses showed that form-stable PCMs generated more heterogeneous pore structures, whereas polymeric-shell microencapsulated systems maintained pore structures similar to PCM-free mortars. Mortars containing metakaolin exhibited enhanced mechanical performance and durability, in some cases outperforming reference mortars, highlighting the importance of matrix refinement in the successful incorporation of multi-PCM systems. Thermal characterization revealed that form-stable systems produced broader phase transitions due to component interactions, while polymeric-shell microencapsulation preserved distinct transitions and enabled a wider, more controllable activation range. Under dynamic thermal conditions (−10 to 50 °C), all multi-PCM mortars demonstrated effective temperature buffering, achieving reductions of up to 1.5 °C during heating and 1.1 °C during cooling. Environmental and economic analyses highlighted that the benefits of PCM incorporation depend on matching PCM transition temperatures to specific climatic and application requirements. These findings position multi-PCM lime mortars as a promising route towards climate-adapted, thermally responsive renders with distributed and tailorable activation profiles. Full article

The pore architecture and textural properties of heterogeneous catalysts affect their intrinsic and extrinsic kinetics, selectivity, and resistance to deactivation. Modelling allows the cheaper and quicker design of new catalyst products, and the optimization of the operation of existing ones. This work particularly reviews major and recent developments in pore network models (PNMs), including image-derived versions, which are a key tool for determining the impact of pore structure and mass transport on catalyst performance. It also briefly considers related areas of multi-scale modelling, first-principles modelling of active sites with DFT, intermediate-scale microkinetic modelling, and recent developments in machine-learning-based approaches. It has been seen that, for some reaction systems, PNMs can predict effectiveness factors a priori, and deliver optimized pore network designs. However, this survey also highlights issues with current models including omission of key controlling structures due to insufficient prior pore characterization, lack of the often-substantial evolution of the pore structure over the catalyst life-stages due to various on-going physical processes, and the neglect of the often-heterogeneous spatial distribution of active sites. Further, this review also considers novel experimental techniques that demonstrate, and remedy, gaps often left out from the current modelling approaches. Full article

Pavement damages along the Qinghai–Xizang Highway (QXH) in permafrost regions are jointly controlled by geographical and engineering factors, leading to higher damage rates than in non-permafrost regions. However, the overall development trend of these damages and the spatial–temporal patterns have not been systematically quantified. To analyze the spatial distribution of different pavement damages, reveal the spatial–temporal associations, and analyze the spatial heterogeneity of the driving factors, three field surveys were conducted in 2014, 2019 and 2024, with records of seven major pavement damages. Statistical analyses were used to examine the relationships among single and co-occurring damages. Then, a novel geographical model, combining a graph convolutional network with multi-scale geographically weighted regression (GCN-MGWR), was further developed to treat the QXH as a linear geographic unit and to assess the spatial heterogeneity and relative contribution of different influencing factors. The results show that the mean pavement damage ratios in permafrost regions during the three surveys are 4.21%, 6.82%, and 4.74%, respectively, with crack-type damages (transverse, longitudinal, and block cracking) exhibiting the highest occurrence rates. The three strongest pairs of correlations are transverse and longitudinal cracking (0.584), transverse and block cracking (0.570), and waving and rutting (0.622). The primary factors influencing crack-type damages are embankment thickness, mean annual ground surface temperature (MAGST), elevation and existing damages. Transverse and longitudinal cracking show a pronounced increase with rising MAGST, and embankment thickness below 1 m or above 4 m significantly contribute to the development of both crack types (SHAP > 0.5). Overall, the evolution of crack-type damages has shifted from being primarily controlled by geographical factors to being controlled by the combined influence of engineering and geographical factors during 2014–2024. The factor contributions identified by the GCN-MGWR model provide quantitative support for the regional adaptive design and specific maintenance of roadway in permafrost regions. Full article

In past decades, the macroeconomic stability of India has been tested repeatedly by major global disruptions, including oil price shocks, the 2008 global financial crisis and the COVID-19 pandemic. Analysing how macroeconomic variables respond to these shocks is essential for evaluating external vulnerability and policy resilience in emerging economies. Our study provides a comprehensive empirical investigation of the dynamic responses of wholesale price inflation, industrial output, oil prices and exchange rates in India by employing monthly data from January 1993 to December 2024. To examine long-run equilibrium relationships along with short-run adjustment dynamics, the present study employs co-integration analysis within a Vector Error Correction Model (VECM) framework. Further, we applied impulse response functions and forecast error variance decomposition to track volatility spillover mechanisms. Quantile regression and ARCH–GARCH models were further estimated to account for distributional heterogeneity and time-varying volatility. The findings of our study suggested stable long-run linkages among the selected variables, where oil price shocks emerged as a key external source of macroeconomic fluctuations. Short-run dynamics suggested that shocks in oil prices are transmitted primarily through inflation and exchange rate channels and then affect industrial output. Distributional estimates revealed the effects were stronger during stress periods, indicating tail risks that were not captured by the mean-based models. Lastly, volatility analysis confirmed persistent clustering, especially during phases of crisis. Overall, the findings suggest that India’s macroeconomic system remains externally sensitive, with adjustment mechanisms that operate gradually but come under strain during global disruptions. These results underscore the importance of energy risk management and crisis-responsive macroeconomic stabilisation policies. Full article

Meta-analysis is a statistical tool commonly used within systematic reviews to synthesize quantitative evidence, but individual studies with atypical results or disproportionate influence can materially affect pooled estimates, heterogeneity estimates, and the conclusions drawn from evidence syntheses. Conventional outlier and influence diagnostics for meta-analysis are useful, but their interpretation often relies on asymptotic reference values or informal rules of thumb, which may be inadequate when the number of studies is limited or heterogeneity is substantial. We introduce boutliers, an R package that implements bootstrap-calibrated outlier detection and influence diagnostics for fixed-effect and random-effects meta-analysis. The package provides leave-one-study-out diagnostics based on Studentized deleted residuals, relative changes in the variance of the pooled effect estimator, and relative changes in the between-study variance, together with a likelihood-ratio diagnostic based on a mean-shifted model. For each diagnostic measure, bootstrap reference distributions, critical values, and p-values are provided to support quantitative interpretation of influential studies. We describe the statistical framework, implementation, and practical use of the package and illustrate its application using a real published meta-analysis dataset on spinal manipulative therapy for chronic low back pain. The boutliers package provides accessible tools for incorporating uncertainty-calibrated influence diagnostics into routine meta-analytic practice. Full article

Background/Objectives: Popcorn (Zea mays L. var. everta) is an important specialty maize type; however, the genetic variation underlying popping-related quality traits remains insufficiently characterized in breeding. Methods: In this study, 18 popcorn inbred lines were analyzed using 25 simple sequence repeat (SSR) markers distributed across all 10 maize chromosomes, and 16 lines were further evaluated for popping performance and image-based flake morphology. Results: Substantial phenotypic variation was observed among the tested lines, with expansion volume ranging from 173.33 to 343.33 mL and expandability ranging from 16.79- to 32.46-fold. Image-based analysis of 957 popped kernels revealed continuous variation in flake circularity, indicating that flake morphology represents a quantitative trait rather than a strictly discrete classification. SSR analysis detected 2 to 11 alleles per locus, with polymorphism information content values ranging from 0.05 to 0.85, indicating moderate-to-high genetic diversity among the tested lines. Principal component analysis (PCA), unweighted pair group method with arithmetic mean (UPGMA) clustering, and population structure analysis revealed clear genetic differentiation and heterogeneous genetic backgrounds within the germplasm collection. Marker–trait association analysis identified several putative SSR loci associated with expansion efficiency, flake morphology, pericarp retention, and popping dynamics. Notably, marker M18 was putatively associated with both expansion volume and expandability. Conclusions: Based on these results, a conceptual framework was proposed in which popping-related traits were organized into partially independent but interconnected functional modules. Overall, this study provides SSR-based genetic information for popcorn germplasm characterization and offers preliminary marker resources for quality-oriented popcorn breeding. Full article