Search Results (65)

The remote sensing scene classification (RSSC) task plays a pivotal role in Earth observation missions, yet its progress remains constrained by the scarcity of high-quality labeled imagery. This article introduces a self-supervised learning (SSL) paradigm to address this challenge. First, for pseudo-label construction, a large set of long-interval satellite revisit imagery is collected and processed with pixel-level registration. The SIFT inliers retained during registration serve as saliency priors to guide asymmetric masking across views. This produces positive pairs that preserve global scene consistency while introducing controlled object-level ambiguities. Second, we propose a progressive layer-wise contrastive learning framework (MTC-Net) that couples the pseudo-label with the network’s representational hierarchy, forming a curriculum from local texture robustness to global semantic invariance. A dual-attention module with spatial–channel branches is further embedded to recalibrate intermediate features. The learning paradigm encourages the model to perform cross-view contextual reasoning rather than relying on pixel-wise correspondences. Experiments on three widely used datasets demonstrate that MTC-Net achieves competitive classification accuracy under limited-label settings, while ablation and visualization studies validate the effectiveness of establishing scene-level invariance through multi-temporal contrastive alignment. Full article

Exploring plant nutrient allocation and stoichiometry is critical to understanding the adaptive strategies of endangered trees in heterogeneous habitats. This study determined the concentrations of carbon (C), nitrogen (N), phosphorus (P), and potassium (K) in seven organs (leaves, current-year twigs, perennial branches, phloem, xylem, transport roots, and absorptive roots) of Parrotia subaequalis from eight wild populations in the Dabie Mountains and then analyzed the stoichiometric characteristics, chemical plasticity, and allometric relationships of these elements among organs. The concentrations of C, N, P, and K ranged from 416.31–513.61, 4.74–12.99, 0.74–12.89, and 2.62–10.34 mg g⁻¹, respectively. Belowground organs had significantly higher C:P, N:P, and N:K ratios than aboveground ones (by 72.83%–740.30%). Perennial organs (xylem, phloem, perennial branches) showed higher C concentrations and C:N, C:P, C:K, and P:K ratios but lower N, P, and K concentrations than current-year ones (leaves, current-year twigs). Xylem, phloem, and perennial branches exhibited the lowest coefficient of variation and plasticity index. N, P, and K exhibited isometric scaling (α = 0.96–1.04) between absorptive and transport roots. Leaf N and P were positively correlated with K (R² ≥ 0.53). Organ age is a critical determinant influencing the variation in stoichiometric characteristics of the organs. Overall, P. subaequalis adapts to nitrogen-limited wild habitats by adjusting N, P, and K nutrient up-take rates and allocation ratios across current-year organs. Full article

Branch prediction errors can reduce superscalar throughput by more than the error frequency alone suggests because a single misprediction can trigger redirect, squash, refetch, refill, and window recovery, which collectively disrupt sustained instruction-level parallelism. This paper presents a quantitative framework that relates prediction accuracy to realized parallelism loss in out-of-order superscalar processors. The framework separates prediction-error frequency, effective recovery cost, and unrealized issue capacity using prediction accuracy (Acc), misprediction rate (MR), effective branch penalty in cycles per misprediction (BP), parallelism loss ratio (PLR), the ratio-based branch sensitivity factor $BSF=PLR/MR$, and the slope-based branch sensitivity factor $\mathit{S-BSF}=\mathit{\partial }PLR/\mathit{\partial }MR$. BSF measures how strongly a particular processor configuration and workload convert prediction errors into lost issue capacity, whereas S-BSF provides a more stable sensitivity estimate when MR approaches zero. The framework is evaluated using timing-detailed gem5 O3 simulations on RV64GC workloads. The evaluation includes controlled branch microbenchmarks and six GAPBS graph workloads, allowing the proposed metrics to be examined under both mechanism-isolating and complex workload conditions. Two complementary controlled sweeps are used. At a fixed processor structure, predictor family and predictor level are varied to determine whether changing the predictor strengthens or weakens the relationship between MR and IPC/PLR. At a fixed predictor configuration, issue width and an effective front-end-depth proxy are varied to measure how the microarchitecture amplifies the performance cost of the remaining prediction errors. Thus, issue width is treated as an amplification variable for branch-prediction failures rather than as an independent performance topic. At the fixed structural point, Tournament and BiMode predictors show strong monotonic MR–PLR relationships on the high-branch benchmark, with Spearman coefficients of 1.00 and 0.98, whereas the Local predictor exhibits nearly unchanged MR but materially different IPC and PLR across levels. This demonstrates that the mapping from MR to throughput depends on predictor family and configuration rather than being invariant. In the controlled structural sweep, increasing issue width from 4 to 8 raises PLR by 37.6% and BSF by 55.0% on the high-branch benchmark, even though MR remains in the same order of magnitude. On GAPBS workloads, the lowest-MR configuration is not always the highest-IPC configuration, confirming that effective branch penalty and parallelism loss must be considered together with prediction frequency. These numerical findings are conditional on the evaluated single-thread gem5 DerivO3CPU model, RV64GC binaries, predictor implementations, memory hierarchy, and workload set. They characterize predictor–microarchitecture interactions in this controlled simulation environment and should not be interpreted as universal constants for all processors or applications. Full article

Audio-visual emotion recognition (AVER) is central to affective computing systems that require reliable, real-time interpretation of human emotions. However, many existing multimodal models treat feature learning and deployment efficiency separately, limiting their ability to preserve hierarchical facial relationships, capture long-range speech dynamics, and operate with low latency in distributed settings. This study proposes a latency-aware hybrid Transformer–capsule network for audio-visual emotion recognition in a simulated edge–fog–cloud environment. The visual stream employs a CNN–Capsule branch to retain spatial hierarchies in facial expressions, while the audio stream uses a CNN–Transformer branch to learn local spectral patterns and long-range temporal dependencies from speech. A cross-modal Transformer fusion module integrates complementary emotional cues, and a latency-aware task-allocation mechanism allocates preprocessing, inference, and training-related operations across edge, fog, and cloud layers according to workload, node capacity, and communication delay. Unlike approaches that optimize multimodal representation learning and distributed deployment as separate problems, the proposed framework adopts a deployment-aware co-design in which spatial visual representation, temporal acoustic modeling, multimodal interaction, and deterministic latency-aware task allocation are coordinated within a unified processing pipeline. The framework is evaluated on RAVDESS, CREMA-D, and SAVEE using a subject-independent protocol. Experimental results show an average accuracy of 91.5%, an F1-score of 90.7%, an MCC of 0.894, and an AUC of 0.950. The framework further incorporates a deterministic latency-aware task-allocation mechanism for coordinating operations across edge, fog, and cloud resources. Physical-device deployment and comprehensive resource profiling remain subjects for future validation. Full article

This editorial introduces the Special Issue “Mathematical Methods and Operation Research in Planning, Scheduling and Supply Chain Operations Management” of the journal Mathematics. The Special Issue gathers nine peer-reviewed papers that, together, illustrate the breadth of contemporary operations research applied to the planning and control of modern industrial systems. The accepted contributions span the full methodological spectrum—from exact combinatorial optimisation (constraint programming, branch and bound, and mixed-integer programming) through heuristics, metaheuristics and reinforcement learning to worst-case performance analysis and stochastic models—and they address problems across the whole operations hierarchy, ranging from single- and parallel-machine scheduling, flow shops and flexible job shops to inventory systems and supply-chain logistics, with a recurrent emphasis on sustainability, robustness and real-world applicability. This editorial summarises the scope of the Special Issue and the individual contributions, and outlines some cross-cutting trends and directions for future research. Full article

The charged-lepton masses are free Yukawa-sector parameters in the Standard Model, whereas their measured pole-mass ratios display a highly structured hierarchy and satisfy the Koide relation to notable accuracy. This paper develops a conditional mathematical-physics proposal in which these dimensionless regularities arise from a charge-neutral parent carrier-defect architecture before effective Higgs–Yukawa read-out. The assumptions of the construction are stated explicitly as structural postulates and are separated from their derived consequences. The central rule assigns equal primitive weight to admissible internal sectors that are indistinguishable at the level where they first become exposed; protected sectors are removed before counting, and later refinements are conditional on previously selected sectors. Under this rule, the Koide relation follows as an equal-power theorem between the democratic parent component and the orthogonal branch-splitting component of the charged-lepton root-amplitude state. A minimal endpoint construction then yields a rapidly stabilizing charged tower for the electron–muon ratio. Because deeper charged terms are too small to remove the remaining residual, the framework assigns that residual to the continuation-dual neutral branch. The resulting neutral overlap gives a leading solar-angle target of 33.21 degrees and closes the electron–muon ratio at the present experimental precision; the Koide relation then fixes the corresponding tau ratios. The construction does not replace the Standard Model but is proposed as a selection rule for the boundary values of effective charged-lepton Yukawa parameters, with pole masses used because the claimed invariant is attached to completed asymptotic particle read-out. Running parameters, the absolute mass scale, and the full Pontecorvo–Maki–Nakagawa–Sakata (PMNS) matrix remain outside the present derivation. The proposal has explicit failure conditions: improved measurements can exclude the predicted tau ratios or solar-angle target, and the claimed conditional uniqueness fails if a different counting scheme satisfies the same postulates while producing different endpoint weights. Full article

Anthropogenic pressures increasingly threaten ecological connectivity and basin-scale ecological sustainability in peri-urban landscapes. This study proposes a Hybrid Ecological Typology framework for the Büyükçekmece Lake Basin (Istanbul, Türkiye), integrating Morphological Spatial Pattern Analysis (MSPA), Analytic Hierarchy Process (AHP), and Minimum Cumulative Resistance (MCR) analysis within a unified spatial decision-support system. The framework is applied to a 67,627.06 ha basin area, including a 25,976.99 ha terrestrial focus area. The results indicate a structurally heterogeneous landscape dominated by interior habitat zones (85.94%) distributed across 93 core patches. Despite this dominance, ecological connectivity is maintained through a highly fragmented network of 484 landscape elements, where limited bridge (0.08%) and branch (0.62%) structures highlight structural vulnerability. Edge-dominated zones (12.87%) further reflect strong anthropogenic fragmentation pressures. Connectivity analysis identifies 10 key habitat patches with dPC (Probability of Connectivity index) values exceeding 5% and 12 strategic ecological corridors supporting basin-scale ecological flows. The proposed hybrid typology delineates five functional planning categories: conservation areas (22.72%), ecological corridors (1.91%), restoration areas (1.63%), sustainable use areas (0.51%), and controlled development areas (8.11%). Although high-quality habitat cores dominate the basin, ecological connectivity remains spatially constrained, with bottleneck zones (0.89%) concentrated along transportation corridors that significantly reduce landscape permeability. Overall, the findings demonstrate that basin-scale ecological sustainability in peri-urban environments is governed not only by habitat quantity but also by the interaction between spatial configuration and resistance structures. The framework provides a transferable decision-support tool that bridges landscape ecology theory with spatial planning practice for basin management and ecological network design. Full article

As global urban development shifts towards stock upgrading and cultural tourism consumption, historic and cultural districts have become crucial spatial carriers for reshaping local identity and driving urban regeneration. Although the literature explores cultural value, current research remains limited to macro-scale assessments, leaving a gap in micro-scale, quantitative identification of spatial narrative capacity. To address this, the concept of ontological narrative is introduced, and a three-dimensional framework integrating physical space, functional formats, and historical events is constructed. Using event space as the unit of measurement, a mixed-methods approach combining spatial syntax, kernel density estimation, and the Analytic Hierarchy Process is applied to Chaozong Street in Changsha. The findings indicate that narrative intensity exhibits a spatial pattern of main-axis agglomeration and deep-alley attenuation. High-value nodes concentrate along primary streets with high accessibility. Conversely, narrative efficacy declines in branch alleys and functionally deficient zones. Furthermore, a four-quadrant diagnosis reveals widespread structural decoupling, such as high historical value paired with low vitality. This shows that historical assets require functional activation to become effective narratives. This research provides a precise analytical tool, grounded in node diagnosis, to counter homogenized urban renewal by fostering differentiated cultural expression. Full article

Bamboo forests are formed by the interlacing of multiple ramet systems. However, the interaction relationships between ramet systems remain unclear. To determine the effects of initial planting density on the construction of ramet systems in typical monopodial bamboos, and to clarify the differentiation rules of underground clonal architecture for bamboo species with different culm diameters, in this study, we selected large-diameter species Phyllostachys edulis and small-diameter species Phyllostachys praecox as study species. A pot experiment with root restriction was conducted using three density gradients of low, medium, and high. Rhizome morphology, underground bud bank dynamics, branching types, and the spatial distribution of the ramet systems were determined. The results showed that density significantly affected rhizome elongation, node allocation, the ratio of dormant buds to germinated buds, and branching hierarchy distribution of ramet systems, with pronounced differences between species. In P. edulis, longer rhizomes, higher dormant bud accumulation, and the branching hierarchies concentrated in low-to-moderate grades were observed at low density. In contrast, rhizome growth and branching were significantly inhibited with increasing density. For P. precox, multistage rhizome extension, higher sprouting activity, and wider branching distribution in the middle and posterior segments of the rhizomes occurred under low- and medium-density conditions. Under high density, however, the peak of dormant buds shifted backward, and a sprouting compensation effect occurred. The initial density reshaped the spatial architecture of ramet systems by altering the intensity of underground competition and preferentially inhibiting the development of new branches and high-grade rhizomes. This study focused on monopodial bamboos during the early establishment stage of mother bamboo development. First, it explored density-driven effects governing ramet system formation. Second, it elucidated contrasting clonal trade-off responses between large- and small-diameter bamboo species. The results will supplement theories on the population construction of woody clonal plants. In addition, they can guide rational close planting and targeted regulation of ramet systems in artificial bamboo stands. Full article

Extracting roads accurately from remote sensing images is important for map updates, traffic analysis, and infrastructure monitoring. Medium-resolution multispectral images can provide useful surface and background information, but when used alone, the spatial details are limited for retaining narrow roads, intersection structures, and fine road topologies. To address this problem, this paper proposes GeoRoad-UPerNet, a Geo-1-centered weakly supervised multispectral framework for road extraction. In this framework, Geo-1 serves as the primary 16-band multispectral source, Sentinel-2 Level-2A imagery serves as auxiliary contextual support, and OpenStreetMap (OSM) road information is converted into proxy supervision rather than dense manual ground truth. GeoRoad-UPerNet contains three modules: a Geo Spectral Semantic Stem (GSSS), a Geo-Auxiliary Gated Fusion module (GAGF), and a Road Semantic Multi-Task Head (RSMH). GSSS strengthens road-sensitive multispectral responses in the Geo-1 branch. GAGF injects Sentinel-2 context through a Geo-centered gate instead of symmetric channel concatenation. RSMH imposes restrained hierarchy- and material-aware semantic regularization on the shared decoder representation during training. On the fixed source-domain benchmark, the complete model achieves an IoU of 0.7204, an F1-score of 0.8375, a Precision of 0.8092, and a Recall of 0.8678 against OSM-derived proxy masks. Relative to the UPerNet-MiT-B3 early-fusion baseline, IoU, F1-score, and Precision increase by 6.29%, 3.65%, and 12.58%, respectively. These results indicate that role-aware multisource organization improves road extraction under proxy supervision and reduces boundary noise and background false positives. Full article

Accurate identification of weak nodes is a prerequisite for online security assessment, preventive control, and resilience enhancement in modern power systems. However, conventional single-layer graph-learning models mainly emphasize local neighborhood aggregation and are insufficient for characterizing vulnerability propagation from equipment-level disturbance to regional congestion and system-level transfer constraints. This paper proposes a mechanism-aware hierarchical graph-learning framework for weak-node identification in complex interconnected power grids. We emphasize that attention, fusion, and gating operations are standard neural-network mechanisms and are not claimed as new generic deep-learning blocks. The contribution of this paper is the power-system-specific formulation: constructing an electrically meaningful local-supernode hierarchy, defining reproducible mechanism-based node and branch-vulnerability proxies, and interpreting weak-node rankings through node–line–corridor coupling evidence. In the validated implementation, a local graph convolutional encoder and a supernode/global graph convolutional encoder generate 32-dimensional local embeddings and 16-dimensional global embeddings, which are concatenated and decoded by a 48 → 24 → 1 multilayer perceptron to obtain node vulnerability scores. Experiments are conducted on reproducible IEEE benchmark data generated from pandapower standard systems, with representative comparisons on the IEEE 57-bus, 145-bus, and 300-bus systems and a detailed structural interpretation on the IEEE 145-bus case. The present results validate the ability of the implemented local–global hierarchical model to reproduce the proposed mechanism-based vulnerability proxy on representative small- and medium-scale benchmarks. Full article

We introduce a quantum multiplexer (GHZ MUX) architecture that enables deterministic routing of an unknown qubit from a single sender to one of $2^n$ receivers using only local tripartite Greenberger–Horne–Zeilinger (GHZ) states arranged in a binary tree. At each level of the hierarchy, a Bell-basis measurement and classical feed-forward propagate the encoded quantum information along a selected branch while maintaining the appropriate Pauli correction frame. Unlike quantum routing architectures that rely on globally entangled multipartite states, the proposed design composes small GHZ clusters into a modular teleportation hierarchy that requires only local entanglement generation and coherence. This structure achieves full input–output connectivity while preserving deterministic routing control and experimental feasibility for near-term small-scale quantum networks. Beyond routing functionality, we show that the same GHZ-tree structure naturally supports hidden-destination communication. We formalize this extension as the Hidden-Secret GHZ-Tree Routing (HS-GTR) protocol, in which the final receiver remains unknown to external observers and the transmitted quantum state may optionally be protected by a quantum one-time pad. This construction demonstrates that hierarchical GHZ routing can serve not only as a quantum switching architecture but also as a building block for privacy-preserving communication and multi-receiver key establishment in distributed quantum networks. Full article

Robust brain tumor classification from magnetic resonance imaging (MRI) remains challenging due to complex structural heterogeneity and subtle inter-class variability. Beyond predictive accuracy, conventional convolutional neural networks predominantly rely on texture-dominant features and fixed receptive fields, which may limit the extraction of clinically meaningful structural information. This study proposes a morphology-aware multi-scale deep representation learning framework that embeds morphological inductive bias directly within hierarchical feature extraction. The proposed architecture synergistically integrates trainable morphological operations with multi-scale convolutional feature learning inside a unified residual framework, supported by an in-block morphological refinement mechanism and a morphology-aware downsampling module. Unlike prior approaches that treat morphological operators as preprocessing or auxiliary branches, the proposed design incorporates differentiable dilation and erosion into the core feature hierarchy to guide structure-aware representation formation. The model was evaluated using five-fold cross-validation and an independent test set, achieving an overall test accuracy of 99.31% with consistently high macro-averaged precision, recall, F1-score, and AUC values. Grad-CAM analysis further demonstrates that the learned representations emphasize clinically relevant tumor regions, supporting interpretable structural knowledge extraction. Ablation studies confirm that performance improvements arise from the synergistic integration of multi-scale learning and morphology-aware refinement. Overall, embedding structural inductive bias within multi-scale deep representation learning enhances robustness, stability, and interpretable knowledge extraction for brain tumor MRI analysis. Full article

Spatial intelligence in autonomous driving requires object-level 3D geometry, yet existing monocular mesh reconstruction methods usually operate with a fixed inference path and a single mesh parameterization, which limits their flexibility under heterogeneous resource constraints. To address this issue, we propose DyPRSI, a dynamic-parameterized framework for monocular vehicle 3D reconstruction that provides multiple predefined accuracy–latency operating points within a single model. DyPRSI inserts two early exits into a shared Res2Net–BiFPN trunk and associates each exit with an exit-specific mesh specification, forming a coarse-to-fine reconstruction hierarchy across network depth. To better match the efficiency requirements of shallow branches, DyPRSI adopts lightweight coordinate-classification keypoint decoding for EE1 and EE2, while retaining a heatmap-regression keypoint head in the Main branch to preserve the upper bound of reconstruction accuracy. Experiments on ApolloCar3D show that DyPRSI-Main achieves competitive reconstruction performance, whereas EE1 and EE2 substantially reduce end-to-end inference latency and provide useful alternatives under different resource requirements. Ablation studies further show that the speedup mainly comes from the lightweight branch-specific keypoint heads, while the exit-specific mesh settings help organize stable coarse-to-fine reconstruction behavior across branches. These results indicate that DyPRSI is a practical monocular vehicle reconstruction framework for resource-aware spatial intelligence. Full article

Addressing the thermal management challenges of prismatic aluminum shell lithium battery modules in electric vehicles under high-rate charge–discharge conditions, this study proposes a multi-objective optimization design method for integrated biomimetic liquid cooling plates. By integrating various highly efficient heat transfer structures from nature, including fractal-tree-like networks, leaf vein branching systems, and spider web radial distribution, a novel biomimetic liquid cooling plate topology was constructed. A multi-physics coupled numerical model considering electrochemical heat generation, thermal conduction, convective heat transfer, and thermal stress deformation was established. The NSGA-II algorithm was employed to globally optimize 12 design variables including channel geometric parameters, operating conditions, and structural dimensions, achieving collaborative optimization objectives of maximum temperature minimization, temperature uniformity maximization, pressure drop minimization, and structural lightweighting. The weight coefficients for the four optimization objectives were determined through the Analytic Hierarchy Process (AHP) with verified consistency (CR = 0.02 < 0.10), ensuring rational priority allocation aligned with automotive safety standards. The optimization results demonstrated that compared to the initial design, the optimal solution reduced the maximum temperature under 3C discharge conditions by 9.9% to 34.7 °C, decreased the temperature difference by 31.3% to 3.3 °C, lowered the pressure drop by 24.6% to 2150 Pa, reduced structural mass by 4.0%, and decreased maximum stress by 16.7%. Quantitative comparison with single biomimetic structures under identical boundary conditions showed that the integrated design achieved a 3.3% lower maximum temperature and 25.7% better flow uniformity than the best-performing single structure, demonstrating the synergistic advantages of multi-biomimetic integration. These synergistic performance improvements can be attributed to the hierarchical multi-scale architecture where fractal networks provide macro-scale flow distribution, leaf vein branches ensure meso-scale coverage, and spider web radials achieve micro-scale thermal matching. Long-term cycling tests conducted at 1C/1C rate with 25 ± 1 °C ambient temperature showed that the optimized design maintained a capacity retention rate of 92.3% after 1000 charge–discharge cycles, demonstrating excellent durability. The complex biomimetic channel structure can be fabricated using selective laser melting technology with minimum feature sizes below 0.3 mm, indicating promising manufacturing feasibility. The research findings provide theoretical guidance and technical support for the engineering design of high-performance battery thermal management systems. Full article