Search Results (1,413)

This paper proposes a compressive-sensing (CS) acquisition algorithm for low-power, high-dynamic GNSS receivers based on low-dimensional time-domain measurements, a non-iterative compressive-domain direct-projection peak-search pipeline, and a coherence-optimized sensing-matrix design. Unlike most existing GNSS-CS acquisition approaches that rely on explicit sparse-recovery formulations (e.g., OMP/BP/LS-type iterative reconstruction) to identify the delay–Doppler support—often incurring substantial computational burden and acquisition latency—the proposed method performs peak detection directly in the compressive measurement domain and is supported by unified Gram-matrix optimization and perturbation/detection analyses. Specifically, the measurement Gram matrix is optimized on the symmetric positive-definite (SPD) manifold to obtain a diagonally dominant and well-conditioned structure with reduced inter-column correlation, thereby bounding reconstruction-induced perturbations and preserving the main correlation peak. Simulation results show that the proposed scheme retains the low online complexity characteristic of direct-projection baselines while achieving a 2–3 dB acquisition sensitivity gain, and it requires substantially fewer operations than iterative OMP-based CS acquisition schemes whose cost scales approximately linearly with the sparsity level K. The proposed framework enables robust, low-latency acquisition suitable for resource-constrained GNSS receivers in high-dynamic environments. Full article

As the world’s leading carbon emitter, China’s ability to reach its pledged carbon peak by 2030 is pivotal for its own green transition and global climate governance. This research proposes a novel integration of spatial proximity neural networks with attention-enhanced geographically weighted neural network regression. This new model integrates spatial dependencies and an attention mechanism into the traditional geographically weighted neural network regression framework. The model demonstrates good performance in forecasting carbon emissions (coefficient determination = 0.904, root mean square error = 48.927). Using this model, alongside population, GDP, total energy consumption, and other influencing factors, the research integrated scenario forecasting to project China’s total carbon emissions from 2023 to 2040. Three policy-relevant scenarios—baseline, low-carbon, and extensive development—were set to forecast and analyze various potential outcomes under uncertain conditions. Under the baseline scenario, China’s emissions peak in 2029 at 9926.26 Mt; the low-carbon scenario advances the peak to 2027 at 9688.88 Mt; whereas an extensive growth path delays the peak to 2032 at 10,347.70 Mt. These findings underscore the urgency of optimizing energy structure, curbing fossil fuel dependence, and balancing economic growth with the deep decoupling of emissions. This research offers policymakers a robust, spatially explicit tool for evaluating future trajectories under diverse development pathways. Full article

Remaining Useful Life (RUL) prediction for aeroengines represents a core challenge in Prognostics and Health Management (PHM), with significant implications for condition-based maintenance, operational cost reduction, and flight safety enhancement. Current deep learning-based approaches encounter three major limitations when handling multi-source sensor data: conventional convolution operations struggle to model heterogeneous sensor feature distributions, leading to computational redundancy; simplistic multimodal fusion strategies often induce semantic conflicts; and high model complexity hinders industrial deployment. To address these issues, this paper proposes a novel Partial Convolution Attention-enhanced CNN-LSTM Network (PALC-Net). We introduce a partial convolution mechanism that applies convolution to only half of the input channels while preserving identity mappings for the remainder. This design retains representational power while substantially lowering computational overhead. A dual-branch feature extraction architecture is developed: the temporal branch employs a PConv-CNN-LSTM architecture to capture spatio-temporal dependencies, while the statistical branch utilizes multi-scale sliding windows to extract physical degradation indicators—such as mean, standard deviation, and trend. Additionally, an adaptive fusion module based on cross-attention is designed, where heterogeneous features are projected into a unified semantic space via Query-Key-Value mappings. A sigmoid gating mechanism is incorporated to enable dynamic weight allocation, effectively mitigating inter-modal conflicts. Extensive experiments on the NASA C-MAPSS dataset demonstrate that PALC-Net achieves state-of-the-art performance across all four subsets. Notably, on the FD003 subset, it attains an MAE of 7.70 and an R² of 0.9147, significantly outperforming existing baselines. Ablation studies validate the effectiveness and synergistic contributions of the partial convolution, attention mechanism, and multimodal fusion modules. This work offers an accurate and efficient solution for aeroengine RUL prediction, achieving an effective balance between engineering practicality and algorithmic sophistication. Full article

Background: This paper presents an analysis and a structured framework for improving inventory accuracy in an automotive factory, considering the current context of global disruptions. In 2023, the company recorded 20,340 inventory adjustments (1695 per month) and a 0.24% monthly net value discrepancy (EUR 256,594 YTD), with a baseline absolute discrepancy of 2.21% of sales. The project aimed to reduce adjustments to below 700 per month and the net value discrepancy to 0.1%. Methods: The research followed the Six Sigma methodology’s Define, Measure, Analyze, Improve and Control (DMAIC) phases, integrating Root Cause Analysis (RCA) and Failure Mode and Effects Analysis (FMEA) to enhance inventory accuracy in manufacturing operations. Results: Implementation significantly improved inventory accuracy: monthly adjustments decreased from 1695 to 971, the highest RPN was reduced from 576 to 144, and the absolute discrepancy-to-sales ratio stabilized at 0.98% (a 56% improvement). Financial variance was reduced to EUR 1948.10 in Q4 2024, while organizational discipline, role clarity and process control also increased. Conclusions: The integrated DMAIC–RCA–FMEA framework proved effective and replicable, enabling systematic identification of root causes, targeted corrective actions and sustainable KPI-driven improvements. The results demonstrate a scalable approach to inventory optimization that supports operational resilience and supply chain performance. Full article

With the proliferation of mobile devices, identifying previously unseen mobile applications has become a critical challenge in network security. Traditional application identification approaches rely heavily on fixed training categories and limited traffic features, making them ineffective in real-world environments. To address this problem, we propose KZGNN, a knowledge-enhanced zero-shot graph neural network for mobile application identification. KZGNN first constructs a unified mobile application knowledge graph that integrates high-level semantic metadata with fine-grained network behavior, enabling structured representation of application characteristics. Building on this, KZGNN introduces a relation-aware dual-channel propagation mechanism that separates semantic relations and behavioral interactions into dedicated GNN pathways and adaptively fuses them through attention. To support zero-shot recognition, KZGNN projects node embeddings and category semantics into a shared embedding space, where a structure-preserving constraint maintains global semantic geometry and improves generalization to unseen categories. Experiments on a dataset of 160 mobile applications show that KZGNN outperforms nine state-of-the-art traffic classification baselines and achieves a 5.2% improvement in identifying unseen application categories, demonstrating its effectiveness for mobile application identification in zero-shot scenarios. Full article

Projected climate changes in the Mediterranean exceed those in most European regions, yet their effects on vegetation remain uncertain. We investigated vegetation changes in the Agri Valley (Basilicata, Italy) using 318 plots, including 40 resurveys. Community-weighted Ellenberg indicator values (EIVs) and plant ecological groups were combined with long-term hydroclimatic anomalies reconstructed via the BIGBANG model (1951–2024), providing a long-term climatic baseline for interpretation. Significant shifts emerged in several EIVs, with clear habitat-specific patterns. Forests showed decreasing light and increasing moisture values, reflecting a higher presence of forest-associated species, though some diagnostic taxa declined. Grasslands exhibited increasing aridity, with a growing contribution of dry-grassland species and a decline in winter therophytes. Climatic analyses revealed pronounced long-term warming, accelerating after the 1980s, while annual precipitation remained highly variable without a monotonic trend. Recent years were marked by intensified drought, evidenced by declining SPEI values (2013–2022) and a higher frequency of dry months (SPEI ≤ −1). The convergence of vegetation responses, species turnover, and climatic anomalies supports climate-driven community trajectories. Despite limited land-use data, this multi-indicator framework effectively detects early ecological responses and identifies vulnerable habitats, providing valuable insights for the conservation and management of Mediterranean mountain ecosystems under ongoing climate change. Full article

This study aims to develop a methodology for implementing solar photovoltaic systems (SSFV) in Caribbean hotels. It begins with an analysis of building characteristics to design and size the SSFV, considering panel support structures, system layout, and grid integration. The methodology also evaluates economic and environmental impacts at both company and national levels. Machine learning analysis identified the variables (Degree Days (DG) and Hotel Days Occupied (HDO)) $HDO\times DG$ as key determinants of energy consumption, with a high coefficient of determination ($R^2$ = 0.97). Implementing a target energy-saving line achieved a 5.3% reduction (1028 kWh) relative to the baseline. Using a genetic algorithm to optimize the SSFV azimuth angle increased photovoltaic energy production by 14.75%, enhancing efficiency and installation area use. Economic assessments showed a challenging scenario for hotels, with a negative internal rate of return of −10%, a 17 year payback period, and a net present value of USD 20,000. However, on a national scale, significant annual savings of USD 225,990.8 from reduced fuel imports were projected. Additionally, carbon emissions reductions of 18,751.4 tons ($tC{O}_2$) were estimated. The findings highlight the feasibility and benefits of SSFV implementation, emphasizing its potential to improve energy efficiency, reduce costs, and enhance sustainability in the Caribbean hotel sector. Full article

Soil erosion in the purple soil region presents severe challenges with complex driving mechanisms. At the same time, evaluation and prediction of runoff and sediment dynamics are lacking for natural vegetation restoration in bare areas. The Mann–Kendall and Pettitt tests were employed to identify abrupt shift points in runoff and sediment dynamics, utilizing monitoring data from the Suining Soil and Water Conservation Experimental Station over the period from 1984 to 2018. Therefore, the research periods were divided into a baseline period (1984–1992) and an evaluation period (1993–2018). Subsequently, encompassing rainfall, runoff, sediment, topography, soil properties, and vegetation parameters, a Water Erosion Prediction Project (WEPP) model was established to quantify the reduction benefits of runoff and sediment during the period of forest restoration. We found that the calibrated WEPP model demonstrated satisfactory performance based on Nash–Sutcliffe efficiency coefficients (NSE > 0.5) and determination coefficients (R² > 0.5) for runoff and sediment simulations. The WEPP model and double-mass curve analysis method revealed that forest restoration reduced runoff and sediment by more than 80%. It is recommended to implement artificial vegetation restoration before reaching the threshold for natural vegetation restoration to achieve soil and water conservation goals. Full article

Resource endowment serves as the foundational condition and strategic pillar for the sustainable improvement of rural project quality, determining the capacity for sustainable development. Clarifying the intrinsic mechanisms through which resource endowment influences the sustainable improvement of rural project quality not only demystifies the “black box” of resource conversion but also reshapes the project development paradigm centered on endowment matching. Based on panel data from 30 provinces in China spanning from 2015 to 2024, this paper empirically examines the impact of resource endowment on the sustainable improvement of rural project quality using a double machine learning model. The results indicate that resource endowment has significant promoting effect. Furthermore, the baseline regression results remain robust after various robustness checks, including adjustment to the research sample, reestablishment of machine learning model, and endogeneity tests involving the introduction of instrumental variable and lagged core variable. Mechanism analysis indicates that resource endowment primarily achieves promoting effect through government attention. Heterogeneity analysis indicates that the impact of resource endowment varies depending on geographic location and the type of project. The SHAP method is also employed to reveal the key factors driving the sustainable improvement of rural project quality in resource endowment. Full article

Pulmonary vascular proliferative lesions are rarely reported and poorly characterized in animals. In this study, we describe 13 cases identified in wild Korean raccoon dogs (Nyctereutes procyonoides), suggesting a higher-than-expected incidence in this species. Gross examination revealed villous projections within the lumina of pulmonary vessels, sometimes accompanied by pneumonia, hemorrhage, or Dirofilaria immitis (heartworm) infection. Most affected animals also presented with thick, dark gray cutaneous crusts associated with scabies infestation. Histopathologically, the lesions consisted of papillary proliferations within thickened vascular lumens. Special stains (Masson’s trichrome and Elastic Verhoeff–Van Gieson) demonstrated a single layer of endothelial cells lining fibromuscular and collagenous thick cores. Immunohistochemistry confirmed endothelial origin and benign proliferative nature, with positive expression of CD31, collagen types I, III, and IV, and proliferating cell nuclear antigen (PCNA). To date, pulmonary vascular proliferative lesions have not been well documented in N. procyonoides, and baseline pathological data, including findings from special stains, are lacking. These findings indicate that pulmonary vascular proliferative lesions may be underrecognized in raccoon dogs and suggest a likely association with chronic vascular injury related to parasitic infections. Further studies are warranted to elucidate the underlying mechanisms and contributing factors. Full article

The emergence of quantum computing threatens the security of classical cryptographic algorithms such as RSA and ECC. Post-quantum cryptography (PQC) offers mathematically secure alternatives, but migration is a complex, multi-year undertaking. Unlike past transitions (AES, SHA-2, TLS 1.3), PQC migration requires larger parameter sizes, hybrid cryptographic schemes, and unprecedented ecosystem coordination. This paper presents a structured expert synthesis of migration timelines, based on analysis of migration dependencies, historical precedents, and industry engagement. We analyze migration timelines for small, medium, and large enterprises, considering infrastructure upgrades, personnel availability, budget constraints, planning quality, and inter-enterprise synchronization. We argue that realistic timelines extend well beyond initial optimistic estimates: 5–7 years for small enterprises, 8–12 years for medium enterprises, and 12–15+ years for large enterprises under baseline assumptions. PQC migration is not a siloed technical upgrade but a global synchronization exercise, deeply intertwined with Zero Trust Architecture and long-term crypto-agility. These timelines are contextualized against expected arrival windows for fault-tolerant quantum computers (FTQC), projected between 2028 and 2033. We further analyze the “Store Now, Decrypt Later” threat model, crypto-agility frameworks, and provide comprehensive risk mitigation strategies for enterprises navigating this unprecedented cryptographic transition. Full article

Rising temperatures, extreme precipitation events such as excessive or insufficient rainfall, increasing levels of carbon dioxide, and associated climatic factors will persistently impact crop growth and agricultural production. The warming temperatures have reduced the agricultural crop yields. Rice (Oryza sativa L.) is the major food crop, which is particularly susceptible to the effects of climate change. It is very important to accurately evaluate the impacts of climate change on rice growth and rice yield. In this study, the rice growth during 1981–2018 (baseline period) and 2041–2100 (future period) were separately simulated and compared within the CERES-Rice model (v4.6) using high-quality weather data, soil, and field experimental data at six agro-meteorological stations in Hainan Province. For the climate data of the future period, the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios were applied, with carbon dioxide (CO₂) fertilization effects considered. The adaptation strategies such as adjusting planting dates and switching rice cultivars were also assessed. The simulation results indicated that the early rice yields in the 2050s, 2070s, and 2090s were projected to decrease by 6.2%, 11.8%, and 20.0% when the CO₂ fertilization effect was not considered, compared with the results of the baseline period, respectively, while late rice yields would decline by 9.9%, 23.4%, and 36.3% correspondingly. When accounting for the CO₂ fertilization effect, the yields of early rice and late rice in the 2090s increased 16.9% and 6.2%, respectively. Regarding adaptation measures, adjusting planting dates and switching rice cultivars could increase early rice yields by 22.7% and 43.3%, respectively, while increasing late rice yields by 20.2% and 34.2% correspondingly. This study holds substantial scientific importance for elucidating the mechanistic pathways through which climate change influences rice productivity in tropical agro-ecosystems, and provides a critical foundation for formulating evidence-based adaptation strategies to mitigate climate-related risks in a timely manner. Cultivar substitution and temporal shifts in planting dates constituted two adaptation strategies for attenuating the adverse impacts of anthropogenic climate change on rice. Full article

Customizing diffusion models via Low-Rank Adaptation (LoRA) has become a standard approach for customized concept injection. However, synthesizing multiple customized concepts within a single image remains challenging due to the parameter pollution problem, where naive fusion leads to gradient conflicts and severe quality degradation. In this paper, we introduce ConWave-LoRA, a novel framework designed to achieve hierarchical disentanglement of object and style concepts in LoRAs. Supported by our empirical validation regarding frequency distribution in the latent space, we identify that object identities are predominantly encoded in high-frequency structural perturbations, while artistic styles manifest through low-frequency global layouts. Leveraging this insight, we propose a Discrete Wavelet Transform (DWT) based filtering strategy that projects these concepts into orthogonal optimization subspaces during contrastive learning, thereby isolating structural details from stylistic attributes. Extensive experiments, including expanded ablation studies on LoRA rank sensitivity and style consistency, demonstrate that ConWave-LoRA consistently outperforms strong baselines, producing high-fidelity images that successfully integrate multiple distinct concepts without interference. Full article

Climate change poses an unprecedented threat to global biodiversity, with birds serving as critical indicators of ecosystem responses. This study assessed the impacts of climate change on 29 endangered bird species in South Korea, a critical stopover region within the East Asian-Australasian Flyway (EAAF). Using Random Forest models, we predicted current (2010 baseline) and future species distributions under two climate scenarios (SSP2-4.5 and SSP5-8.5) for four time periods (2030s, 2050s, 2070s, and 2090s). Model performance was robust, with a mean AUC of 0.844 ± 0.122 across all species and 72.4% of species achieving AUC ≥ 0.80. Elevation emerged as the most influential predictor for 44.8% of species, followed by precipitation of the driest month (17.2%) and distance to water bodies (10.3%). Current species richness patterns showed spatial heterogeneity, with higher concentrations along coastal wetlands, particularly in the western and southern coasts and Jeju Island. Under SSP2-4.5, species richness patterns remained relatively stable through 2090, while SSP5-8.5 projected more dramatic shifts, particularly after 2070. Coastal regions and national parks exhibited differential responses, with some areas showing increases and others experiencing declines in species richness. High-elevation national parks, including Mt. Hallasan, Mt. Seoraksan, and Mt. Odaesan, demonstrated potential to serve as climate refugia, maintaining relatively stable species richness under both scenarios. Our spatial analysis at municipality and national park levels identified priority conservation areas and emphasized the need for climate refugium identification, habitat connectivity along elevational gradients, and adaptive management strategies. The findings provide actionable guidance for science-based conservation planning and contribute to international efforts to protect migratory birds along the EAAF. Urgent conservation measures are needed to safeguard coastal wetlands and establish ecological corridors to facilitate species range shifts under changing climatic conditions. Full article

The decarbonization of transport demands efficient, low-carbon alternatives to conventional fuels, particularly in regions where full electrification remains constrained. This study investigates the retrofitting of a 1.3 L Geely MR479Q spark-ignition engine for hydrogen operation, combining experimental measurements and one-dimensional numerical simulations in GT-SUITE. The baseline gasoline model was experimentally validated in 12 operating conditions and extended to the full map. In addition, the fuel was changed in the numerical model, and evaluations of hydrogen combustion through predictive sub-models considering mixture formation and pressure-rise limits were performed. Results show that the hydrogen engine operates stably within a wide air–fuel ratio window (λ = 1.0–2.7), with brake thermal efficiencies peaking at approximately 29%, surpassing gasoline operation by up to 5% in the mid-load range. However, port fuel injections cause a reduction in volumetric efficiency and maximum power output due to air displacement, a limitation that could be mitigated by adopting direct injection. A practical hydrogen conversion kit was defined—including injectors, cold-type spark plugs, electronic throttle, and programmable ECU—and the operational cost was analyzed. Economic parity with gasoline is achieved when hydrogen prices fall below ~6 USD kg⁻¹, aligning with near-term green-hydrogen projections. Overall, the results confirm that predictive numerical calibration can effectively support retrofit design, enabling efficient, low-emission combustion systems for sustainable transport transitions. Full article