Search Results (5,239)

To address the challenges of small foreign matter size, severe occlusion, and complex backgrounds in Pu-erh ripe tea processing, this study drew inspiration from primate visual mechanisms and proposed an improved YOLOv13-based network, AE-YOLOv13-S. To mitigate loss of fine details, the weakening of discriminative features, and the frequent occurrence of missed and false detections, the Adaptive Sparse Self-Attention Network was introduced to optimize the backbone of the network, inspired by the sequential cognitive pattern of primates involving target search, local verification, selective integration, and final decision making. To address insufficient long-range semantic associations and the submergence of fine-grained differences in background noise, Emulating Self-Attention with Convolution was employed to optimize part of the Conv modules of the network, drawing on the hierarchical information processing mechanisms of primates from peripheral perception to central fine analysis. In response to the limitations of bounding boxes, such as approximate target enclosure, the large amount of geometric supervision noise, the obvious localization deviation, and delayed model convergence, a Scale-based Dynamic Loss, inspired by primate visual perception mechanisms, was introduced to optimize the network’s loss function. The results showed that, during training, compared with the baseline, AE-YOLOv13-S achieved lower training loss values: Box Loss declined by 6.76%, Cls Loss by 6.52%, and DFL Loss by 8.65%. On the validation dataset, the model demonstrated reductions of 6.58%, 16.39%, and 8.33% for these respective metrics. After the overall improvements, AE-YOLOv13-S achieved increases of 1.43, 4.85, and 2.69 percentage points in precision, recall, and mAP@50, respectively, with only a 0.3 G increase in FLOPs. The improved model can classify and detect foreign matter in Pu-erh ripe tea efficiently and accurately, providing not only a new technical pathway for foreign matter detection in tea processing but also a practically meaningful technical solution for intelligent quality control and food safety assurance in the tea processing chain. Full article

This work investigates a hardware-aware, circuit-level emulation of BB84-like circuit primitives on noisy intermediate-scale quantum (NISQ) processors. The motivation is to evaluate whether BB84-like basis sifting and intercept–resend-induced QBER behavior remain observable when selected BB84 operations are mapped to parallel single-qubit circuits on gate-based devices. The proposed mapping represents Alice’s preparation, optional Eve intercept–resend emulation, and Bob’s measurement as processor-internal circuit layers; it is therefore an on-chip emulation and not an end-to-end optical QKD implementation. Experiments combine real IBM superconducting processors with Qiskit, Cirq, and Azure/Q# simulator-based or noise-modeled evaluations. Baseline QBER was first calibrated for each backend, and intercept–resend experiments then produced a clear QBER separation from the no-eavesdropper condition. The observed sifted-bit utilization was close to the expected 50% BB84 basis-matching reference, while the constant-depth circuit structure supported scalable raw/sifted-bit generation before any classical post-processing. These observations are treated as implementation-level consistency checks and backend-dependent experimental metrics, rather than as new BB84 protocol-level results. Finite-shot uncertainty, calibration drift, and backend-specific noise are treated as limitations of the proposed QBER-based evaluation rule rather than as deployment-level security guarantees. Because the study does not implement a physical quantum channel, authenticated classical communication, error correction, privacy amplification, finite-key security analysis, or general QKD attack models, the reported metrics should be interpreted as raw/sifted-bit experimental metrics and QBER-based disturbance evaluation for BB84-like NISQ emulation, not as secure key rates, secure throughput, or practical QKD deployment results. Full article

Buttock claudication after endovascular aneurysm repair (EVAR) impairs recovery and quality of life, yet individualized preoperative risk tools are scarce. We conducted a retrospective dual-center cohort study of consecutive EVAR patients from Fuxing and Xuanwu Hospitals. The endpoint was new-onset postoperative buttock claudication. Missingness was quantified for each predictor and handled using complete-case analysis or model-based single imputation according to the extent of missingness. Data were split into training and held-out test sets at a 70:30 ratio with outcome stratification. Predictor screening, preprocessing, and hyperparameter tuning were performed within the training/resampling framework to minimize data leakage. Ten algorithms were tuned using stratified 10-fold cross-validation, and test set performance was assessed using discrimination, threshold-based metrics, calibration plots, calibration intercept/slope, Brier score, and decision-curve analysis. SHapley Additive exPlanations (SHAP) provided model-agnostic explanations. A web calculator was deployed. Among 272 patients, 71 (26.1%) developed claudication. Independent risk factors included aneurysm with iliac involvement (adjusted OR 4.04), male sex (3.26), unilateral (3.86) and bilateral internal iliac artery embolization (8.61), and hyperlipidemia (5.66); >2 distal internal iliac branches was protective (0.15). On the test set, the neural network achieved the highest AUROC (test ROC), with the highest sensitivity (0.810) and top F1 (0.557) at balanced specificity (0.617); CatBoost maximized accuracy (0.790) and specificity (0.900). Calibration was acceptable, and DCA showed positive net benefit across clinically plausible thresholds. SHAP confirmed physiologic directions and enabled case-level interpretation. An explainable machine learning framework accurately stratifies risk of buttock claudication after EVAR, highlighting the roles of internal iliac embolization, iliac involvement, and distal branch anatomy. The publicly available Shiny tool supports perfusion-aware planning and shared decision-making. Full article

Structural health monitoring (SHM) of fibre-reinforced composites requires a health indicator that is monotonically non-decreasing under the standard SHM assumption that no self-healing or maintenance-induced restoration event is active, derived from heterogeneous sliding-window observations of acoustic emission, strain, and fibre Bragg grating channels, with only the failure timestamp available per specimen. Conventional endpoint-supervised regressors attain high rank correlation with normalised life but produce jagged, non-monotone trajectories of limited engineering value. A method named SAMS-Net (Smoothness-Anchored Monotone Neural Differential Equation Network) is developed, in which a neural differential equation backbone is anchored by a two-level Pool-Adjacent-Violators (PAV) projection. A within-window projection is applied during training with a straight-through gradient, and an across-window projection is applied at inference, yielding a globally non-decreasing health indicator. A smoothness-stratified two-phase training schedule first trains on specimens whose per-specimen median local-smoothness coefficient exceeds 0.5, then fine-tunes on the full set. Across the present seventeen-specimen open-hole carbon-fibre dataset spanning two stress levels and six leave-one-specimen-out and cross-condition scenarios, SAMS-Net wins on every scenario on the canonical Prognostics and Health Management (PHM) Composite of monotonicity, trendability, and robustness, with margins of 0.22 to 0.48 against the strongest baseline, reproducible across three random seeds. Ablation reveals that the operative mechanism is the two-level PAV projection rather than the stochastic differential equation (SDE) inductive bias. A new control experiment in which the across-window PAV projection is applied at inference to the strongest baselines confirms that the projection accounts for a substantial share of the SAMS-Net margin, while the within-window training-time projection and a globally consistent prognosability metric retain a SAMS-Net advantage. Cross-site or cross-material transferability remains to be established in future work. Full article

Deep learning has exhibited potential in land use and land cover (LULC) classification applications. However, the effectiveness of deep learning remains constrained by the availability and quality of annotated training data. The persistent scarcity of labeled samples and spatial heterogeneity of remote sensing imagery hinder the robustness and generalization of trained models. Meta-learning, commonly referred to as “learning to learn”, is a paradigm that trains models over a distribution of tasks to acquire transferable knowledge, enabling rapid adaptation to new tasks with only a few labeled samples. This cross-task learning capability makes meta-learning a promising solution to data scarcity and spatial heterogeneity in the remote sensing context. This paper provides a systematic review of meta-learning applications in LULC classification, identifying a total of 70 relevant studies between 2018 and 2025. Three mainstream meta-learning paradigms (memory-augmented, optimization-based, and metric-based) are reviewed, and the applications are analyzed across four core challenges in LULC remote sensing: label scarcity, cross-region and cross-domain distribution shifts, temporal dynamics modeling, and multimodal data integration. The review reveals that optimization-based and metric-based methods dominate current research, with MAML and its variants being the most widely adopted due to the model-agnostic property, while memory-augmented methods remain underexplored. A consistent finding is that meta-learning outperforms conventional pre-training followed by fine-tuning under significant domain shifts across multiple data modalities. Current limitations, including computational overhead, episodic training constraints, and the lack of standardized evaluation protocols, are discussed. Future directions in cross-domain generalization, integration with foundation models, novel architectures, and standardized benchmarks are identified. Full article

This paper investigates the impact of a regional innovation-driven platform (Qinchuangyuan Innovation-driven Platform) on entrepreneurial confidence, particularly in technology-based small and medium-sized enterprises (TSMEs) during their start-up period. By analyzing data collected from 132 TSMEs, this study explores how regional innovation-driven platforms influence entrepreneurial confidence. The main findings are as follows: First, the results of ordinary least squares (OLS) regression reveal that the innovation-driven platform significantly improves entrepreneurial confidence, and the results of propensity score matching (PSM) remain still positive. Second, we conduct instrumental variable (IV) estimation as supplementary robustness evidence for potential endogeneity concerns, using whether an enterprise participates in market expansion activities and whether an enterprise uses government support services as two instrumental variables. Third, the innovation-driven platform is mediated by entrepreneurial satisfaction with the business environment and entrepreneurial satisfaction with the government, thereby enhancing entrepreneurial confidence. This paper provides a new perspective for assessing business development through entrepreneurial confidence rather than traditional performance metrics and provides a valuable reference for the development and optimization of innovation-driven platforms in similar regional contexts, especially in supporting sustained entrepreneurial activity, technology transformation, and regional economic resilience. Full article

This article introduces a new concept of compatible contraction in metric spaces. By utilizing graph properties, namely being orbitally edge-preserving, we establish the existence of fixed points under these conditions. Furthermore, under specific assumptions on the mapping, the uniqueness of the fixed point is guaranteed. We provide illustrative examples to clarify the theoretical developments. To demonstrate the practical utility of this framework, we apply it to specific classes of integral and differential equations, specifically focusing on nonlinear fourth-order boundary value problems. We show that these problems satisfy the proposed contraction criteria, ensuring their solution via iterative methods. Numerical experiments on various fourth-order boundary value problems validate the effectiveness of our approach. Full article

Bubble entropy has established its own place in the research community, representing a new and promising definition of entropy. Based on the work required to order a vector in an embedding space of dimension m, Bubble entropy gives a physical interpretation of what the metric actually computes. In this work, Bubble entropy is evaluated based on its ability to classify RR time series, the time series most commonly considered for entropy-based analysis in the field of biomedical engineering. For this purpose, it is compared with three other definitions of entropy: the most widely used Sample entropy and Approximate entropy, the most relative to Bubble entropy, and also the widely used Permutation entropy. Signals from healthy individuals, in sinus rhythm, are compared with signals from cardiac patients, and machine learning methods are applied to calculate the classification accuracy that each method can achieve. The classifiers chosen are k-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Gaussian Naive Bayes. Feature evaluation methods are also employed to serve as additional measures of effectiveness. Bubble entropy generally manages to achieve better results than Sample entropy, Approximate entropy and Permutation entropy, both in terms of classification accuracy and feature ranking. Full article

Deterministic and probabilistic models of measured quantities, processes, and fields in production process control systems, as well as physical and probabilistic measures, enable the formation of measurement results and confer them the properties of objectivity and reliability. The issue of improving and developing models and measures in measurement methodology plays an increasingly important role in achieving high measurement accuracy in control systems and the reliability of decision-making by expert systems in production processes. The measurement result is formed by many factors, most of which are random in nature. The stochastic approach in measurement theory is particularly important for the measurement of probabilistic physical quantities and for the construction of decision rules for expert systems. Probabilistic measures play a key role in both the measurement of physical quantities and the construction of decision rules when using a stochastic approach. The main contribution of this paper is a measure-centred formulation of stochastic measurement and decision support, in which physical and probabilistic measures are treated as an explicit intermediate layer between the model and the algorithm. This is not presented as a new entropy or distance metric, but as a methodological integration that clarifies uncertainty handling, improves traceability of measurement results, and supports decision rules for production-process monitoring. The approach is illustrated on air-quality monitoring data from a real control system. Full article

In this paper, we establish new fixed point results for Burton-type large contractions in complete b-metric spaces and introduce a Rakotch-type generalization in this setting. We establish existence and uniqueness results for fixed points, with an example to illustrate the applicability. Furthermore, an application to a fractional differential equation is presented. Our results generalize classical fixed point theorems and contribute to the theory of nonuniform contractions in generalized metric spaces. Full article

Urban systems are becoming increasingly complex due to rapid urbanization, socio-economic disparities, and climate change. Addressing these challenges requires innovative approaches that integrate data-driven methodologies with resilience planning. This paper presents a novel extension to the Urban System Abstraction Hierarchy (USAH) framework by integrating socio-economic indicators into a graph-based modeling environment, enabling a more holistic understanding of urban resilience. Our approach advances existing models by operationalizing multi-domain resilience through a graph-based framework that captures complex interdependencies across critical infrastructure, governance, finance, and vulnerable populations. Unlike prior USAH applications, which focused primarily on acute shocks, the proposed model captures interdependencies across infrastructure, environmental conditions, health systems, economic robustness, public finance, and social cohesion. Several graph metrics were analyzed including betweenness centrality, and system-level resilience metrics. Sensitivity testing of the indicator weighting scheme showed that increasing the influence of the network structure from 0.7 to 0.9 betweenness centrality shifts indicator importance toward structurally central nodes while reducing the influence of sub-indicator averages. However, the system-level resilience remained unchanged across scenarios. Beyond traditional centrality measures, we introduce new network metrics that identify system stabilizers, key policy leverage points, cross-domain dependencies, and overall structural fragility. Together, these measures transform the model from a descriptive mapping tool into a practical decision-support framework for resilience planning. Full article

The evolution of 5G networks has introduced new challenges in securing mobile infrastructures against increasingly sophisticated cyber threats. Intrusion detection in such environments has been widely studied using traditional datasets such as the Canadian Institute for Cybersecurity Intrusion Detection Systems CICIDS2017, the University of New South Wales-Network Behavior UNSW-NB15, and The Network Security Laboratory-Knowledge Discovery in Databases NSL-KDD; however, these benchmarks lack the architectural complexity and protocol diversity inherent to 5G networks. More recent research has adopted the 5G-NIDD dataset (5G Network Intrusion Detection Dataset), which provides realistic traffic generated from a live 5G testbed, including various attack scenarios targeting MEC servers and core network components. Nevertheless, existing works using 5G-NIDD often focus on limited subsets of attacks, rely on unsupervised or federated learning approaches, and lack comprehensive evaluations of supervised learning models. In contrast, this study leverages the entire 5G-NIDD dataset, encompassing all available attack scenarios, and conducts a systematic comparison of multiple supervised learning algorithms. A systematic evaluation of supervised learning algorithms is conducted using key performance metrics such as accuracy, precision, recall and F1-score to identify the most effective model for intrusion detection in 5G environments. Specifically, this study focuses on four supervised learning algorithms, K-Nearest Neighbors (KNNs), Support Vector Machines (SVMs), Logistic Regression (LR), and Naive Bayes (NB), to determine not only which achieves the highest detection accuracy but also which offers the best balance between predictive performance and computational efficiency in realistic 5G environments. To assess robustness and adaptability, the proposed models are further validated on two widely used benchmark datasets, namely CICIDS2017 and UNSW-NB15, as part of an extended analysis. This cross-dataset evaluation highlights each algorithm’s strengths and limitations under diverse network traffic conditions and attack scenarios. The results aim to validate the applicability of supervised learning approaches to intrusion detection in next-generation network infrastructures, while also emphasizing the importance of balancing predictive accuracy with computational efficiency for real-world deployment. Full article

This study examines upstream non-quality drivers and their root causes in development-to-production transition processes, with a focus on New Product Introduction (NPI) environments characterized by high technological and organizational complexity. Accelerated innovation and compressed development cycles increase exposure to early-stage organizational and process-related deficiencies that may later materialize as quality failures and cost overruns. While extensive research addresses downstream quality failures and the Cost of Poor Quality (COPQ), fewer studies focus on structured prioritization of upstream drivers at early NPI stages, where empirical failure-based data remain limited. Building on a previously developed Quality Deviation Index (QDI), this study applies the framework empirically within a complex NPI context to support structured prioritization of upstream organizational and process drivers. The analytical approach integrates root cause classification, frequency-based prioritization, and QDI-derived ranking to organize observed patterns among drivers, without introducing new quality metrics or inferential models. The findings illustrate how QDI-based prioritization can be applied to identify highly ranked upstream drivers within the examined context, thereby supporting early-stage organizational awareness and structured decision consideration prior to the manifestation of downstream quality failures. Sustainability-related aspects, such as waste generation and rework, are discussed strictly as interpretive downstream implications of improved early-stage prioritization rather than as empirically measured outcomes. Overall, the study provides a context-bound empirical illustration of how structured prioritization mechanisms may be applied in early-stage NPI environments characterized by high uncertainty and limited failure data, without implying statistical generalization beyond the studied setting. Full article

Water-sensitive papers (WSPs) are widely used for spray deposition assessment because they are inexpensive, simple to use, and suitable for field conditions. Combined with image analysis, they provide quantitative information on spray coverage and indirect insight into deposition structure. However, their interpretation is often oversimplified, particularly when percent coverage is treated as the sole indicator of spray quality. This paper presents a critical methodological review of image-based evaluation of spray deposition using WSPs, with emphasis on coverage-related metrics, spatial descriptors, droplet size estimation, and the main sources of uncertainty affecting their interpretation. The review also positions WSPs relative to other spray characterization techniques and discusses their practical role as proxy-based tools rather than direct measurement instruments. Representative WSP samples from previous field experiments are used exclusively to illustrate typical processing steps and methodological pitfalls, not to report new experimental results. In addition, the paper summarizes major segmentation approaches, discusses the interpretative value of selected deposition descriptors, and formulates practical recommendations for image acquisition, binarization, metric selection, sample exclusion, and reporting practice. It is concluded that WSP-based image analysis is most valuable for comparative and diagnostic assessment of spray deposition, provided that its methodological constraints are explicitly recognized and consistently reported. Full article

We apply the Noether symmetry condition to the time-dependent conformal Lagrangian $L=A\left(t\right)\left[{\displaystyle \frac{1}{2}}{g}_{ij}{\dot{x}}^i{\dot{x}}^j-KV\left(x\right)\right]$ with focus on the damped harmonic oscillator in Euclidean space, where $(A\left(t\right)=e^{\gamma t}$ and $V\left(x\right)$ is the oscillator potential. The Noether symmetries are generated by the homothetic algebra of the Euclidean metric and yield a complete classification of invariants. A key result is a family of conserved quantities parameterized by a constant $D\ne 2$, related to the potential multiplier K via the resonance condition $K=-2{\gamma }^{2}D/{(D-2)}^2$. This family includes the Bateman invariant as the special case $D=-4$. We demonstrate that the Ray-Reid method for constructing invariants via an auxiliary variable corresponds precisely to the choices $D=1$ and $D=4$ within our symmetry-based approach. Furthermore, each invariant in the family leads to a distinct quantum Hamiltonian via canonical quantization, resulting in different time-dependent effective masses and decoherence rates. The parameter D thus controls the quantum evolution of the damped oscillator, offering a unified framework that generalizes the Kanai–Caldirola and Bateman models and provides new experimentally testable predictions. Full article