Search Results (3,212)

Utilizing multi-source meteorological data for lightning identification is crucial for monitoring severe convective weather. However, several key challenges persist in this field: dimensional imbalance and modal competition among multi-source heterogeneous data, model training bias caused by the extreme sparsity of lightning samples, and an imbalance between false alarms and missed detections resulting from complex background noise. To address these challenges, this paper proposes a lightning identification network guided by physical priors and constrained by supervision. First, to tackle the issue of modal competition in fusing satellite (high-dimensional) and radar (low-dimensional) data, a physical prior-guided asymmetric radar information enhancement mechanism is introduced. This mechanism uses radar physical features as contextual guidance to selectively enhance the latent weak radar signatures. Second, at the architectural level, a multi-source multi-scale feature fusion module and a weighted sliding window–multilayer perceptron (MLP) enhanced decoding unit are constructed. The former achieves the coupling of multi-scale physical features at a 2 km grid scale through cross-level semantic alignment, building a highly consistent feature field that effectively improves the model’s ability to detect lightning signals. The latter leverages adaptive receptive fields and the nonlinear modeling capability of MLPs to effectively smooth spatially discrete noise, ensuring spatial continuity in the reconstructed results. Finally, to address the model bias caused by severe class imbalance between positive and negative samples—resulting from the extreme sparsity of lightning events—an asymmetrically weighted BCE-DICE loss function is designed. Its “asymmetric” characteristic is implemented by assigning different penalty weights to false-positive and false-negative predictions. This loss function balances pixel-level accuracy and inter-class equilibrium while imposing high-weight penalties on false-positive predictions, achieving synergistic optimization of feature enhancement and directional suppression. Experimental results show that the proposed method effectively increases the hit rate while substantially reducing the false alarm rate, enabling efficient utilization of multi-source data and high-precision identification of lightning strike areas. Full article

With the rise of Financial Operations (FinOps), cloud resource management requires the enforcement of strict budgetary guardrails rather than soft cost objectives. However, discrete Virtual Machine (VM) types often cause structural infeasibility, which existing methods fail to address. We formulate the Budget-Constrained VM Resizing problem under temporal hard constraints and establish the NP-hardness of the scalarized problem as a completeness result. To solve this, we propose the Budget-aware Dual (BD) solver, which utilizes a dual variable as a shadow price to dynamically steer candidate decisions toward budget feasibility without opaque penalty tuning. Extensive experiments demonstrate that BD significantly improves budget feasibility and operational stability compared to the baselines. In the run-rate setting, BD reduces candidate budget violations to zero once the budget enters feasible regimes at and substantially reduces operational churn, decreasing the change rate from 53.95% to 7.80% in an oscillatory workload scenario. BD also exhibits near-linear scalability and remains more than 100× faster than NSGA-II at large fleet sizes. This framework provides a theoretically grounded and scalable approach for balancing economic efficiency, operational stability, and strict budget compliance. Full article

To address the challenges of coordinated optimization in building integrated energy systems (IES) under the dual-carbon targets—characterized by strong multi-energy coupling, significant uncertainty in renewable generation, and stringent safety constraints—a novel safe deep reinforcement learning algorithm, Safe-DDPG, is proposed. Traditional deep reinforcement learning methods often suffer from high constraint-violation risk and limited policy reliability due to coupled objectives in building IES optimization. To overcome these limitations, a dual-channel critic architecture is designed to independently evaluate and decouple economic and safety objectives. In addition, a dynamic safety–penalty mechanism based on logarithmic barrier functions is introduced, together with an adaptive exploration strategy, enabling dynamic balancing between economic cost and constraint satisfaction according to system states during training. Experimental results demonstrate that, compared with mainstream algorithms, Safe-DDPG achieves substantial improvements across multiple key performance indicators: safety violations are reduced by up to 96.7%, average daily operating costs decrease by 18.5%, and cumulative rewards increase by more than 30%. Ablation studies further confirm the effectiveness and necessity of each core component. Two DRL methods from reference papers are reproduced, and their performance is compared with the proposed method in the existing experimental results, showing that the proposed method has significant advantages in reward value and economic cost. This work provides a safe, reliable, and efficient reinforcement-learning-based approach for optimization and scheduling of building energy systems under complex operational constraints. Full article

The optimization of membrane permeability is a pivotal approach for mitigating late-stage failures in peptide drug development. By leveraging linker chemical diversity, stapled peptides utilize linker engineering to precisely modulate key physicochemical parameters—such as lipophilicity and conformational constraints—to overcome the desolvation energy penalty. This review systematically evaluates linker-based strategies for enhancing the permeability of stapled peptides, categorized into two primary dimensions: (1) high-throughput screening (HTS) compatibility, focusing on the integration of functionalized linkers into mRNA display, phage display, and DNA-encoded libraries (DELs) to identify lead scaffolds with inherent permeability potential during early discovery; and (2) post-screening structural refinement, covering rational design strategies including intramolecular hydrogen-bond (IMHB) shielding, “chameleonic” adaptations, and stimuli-responsive reversible stapling. Furthermore, we analyze the paradigm shift in assessment methodologies from qualitative imaging to quantitative cytosolic delivery assays, which have deepened our understanding of mechanisms such as the charge/lipophilicity threshold balance and metabolism-driven trapping. Overall, linker engineering provides a robust technical roadmap for developing the next generation of cell-permeable stapled peptide therapeutics. Full article

The rapid development of quantum computing poses severe threats to traditional blockchain security mechanisms, while existing full-quantum blockchains face challenges regarding high hardware costs and limited scalability. To address these issues, this paper proposes a secure and practical semi-quantum blockchain system. Specifically, a Semi-Quantum Delegated Proof of Stake consensus mechanism is constructed by integrating an adapted semi-quantum voting protocol with the Borda count method and a malicious behavior penalty model. Furthermore, a lightweight transaction verification framework is designed based on semi-quantum key distribution, enabling classical users with limited quantum capabilities to participate securely. Theoretical analysis demonstrates that the system achieves unconditional security against quantum attacks while maintaining high throughput. These results indicate that the proposed asymmetric resource design significantly lowers hardware barriers compared to full-quantum schemes, effectively balancing security, practicality, and cost-effectiveness for post-quantum blockchain networks. Full article

Magnetic resonance imaging (MRI) reconstruction from undersampled k-space data enables accelerated acquisition but leads to a severely ill-posed inverse problem. Although supervised deep learning methods have achieved strong performance, they typically rely on large paired datasets that are difficult to obtain in clinical practice. To address these limitations, we propose a dictionary-learning-inspired dAta-fRee deep generative modeling with Meta-Attention and implicit precoNditIoning for compressively sampled MRI (CS-MRI), termed ARMANI. Specifically, a meta-attention-augmented deep image prior (MA-DIP) generator performs a joint optimization over the latent input $\eta$ and the network parameter $\theta$, where $\eta$ is regularized via gradient-domain sparsity and $\theta$ is constrained by a ridge penalty, mirroring the adaptive estimation of sparse coefficients and an empirical sparsifying dictionary. Furthermore, we integrate a single-step pseudo-orthogonal projection to achieve implicit preconditioning, which modulates the loss landscape and mitigates ill-conditioning of the forward operator. Experimental results demonstrate that ARMANI consistently outperforms existing SOTA data-free and self-supervised methods, and, with limited training data, achieves performance comparable to or slightly better than the supervised benchmark MoDL, with effective artifact suppression and faithful recovery of fine structural details. Overall, ARMANI shows strong scalability and potential for practical deployment in fully data-free CS-MRI reconstruction scenarios. Full article

Exhaust after-treatment (EAT) thermal management remains a critical challenge for diesel engines operating under low-load conditions, where low exhaust temperatures delay catalyst light-off and reduce emission control efficiency. This operating regime is common in marine auxiliary engines and onboard diesel generator sets during hoteling, maneuvering, and partial-electrical-load conditions. Conventional strategies such as late fuel injection or exhaust throttling can increase exhaust temperature but often result in significant fuel consumption penalties. This study numerically investigates the combined use of late exhaust valve opening (LEVO) and cylinder deactivation (CDA) to enhance EAT thermal management with a reduced fuel penalty. A six-cylinder diesel engine is analyzed at a low-load condition (1200 RPM, 2.5 bar BMEP) using a calibrated one-dimensional engine simulation model. LEVO applied to all cylinders increases exhaust temperature to approximately 250 °C, but with a considerable increase in fuel consumption. When two cylinders are deactivated and the remaining cylinders operate with LEVO, airflow and pumping losses decrease, enabling higher exhaust temperatures at comparable fuel consumption levels. Despite a 30% reduction in exhaust mass flow rate, the higher exhaust temperature dominates EAT heat transfer. Consequently, the combined strategy increases EAT heat transfer by up to 143% and achieves exhaust temperatures approaching 295 °C. These results indicate that combined valve timing and load redistribution through CDA can improve the exhaust temperature–mass flow trade-off, providing a potential pathway for enhanced EAT warm-up during low-load operation within the limitations of the numerical model. Full article

Heterogeneity at the grain scale strongly influences hydraulic fracturing in crystalline rock; however, systematic studies quantifying its impacts on the evolution of injection pressure and crack propagation remain limited. To address this gap, we employ a hydro-mechanical phase-field model incorporating Voronoi-based microstructures to systematically quantify the effects of grain-scale heterogeneity on hydraulic fracturing. Two numerical experimental programs are designed to examine the effects of (i) mean grain size and (ii) mineral distribution under different axial stresses. The simulations reveal a close coupling between injection pressure and crack-length evolution, and both responses are strongly governed by grain-scale heterogeneity. When the fracture enters weak minerals, it advances rapidly and pressure drops; when it encounters on strong minerals, growth slows or arrests and pressure builds until a threshold triggers the next advance. Moreover, peak pressure statistics further indicate that mineral distribution dominates the response scatter, while axial stress plays a secondary role. Specifically, the mean peak pressures at 0 and 10 MPa are similar (about 14.31 and 14.21 MPa), whereas rearranging minerals within the same Voronoi tessellation changes peak pressure by more than 4 MPa. Higher peaks occur when strong minerals lie ahead of the initial crack tip, increasing resistance to initiation and early growth. Finally, the stress state modulates fracture trajectories: under low axial stress, fractures preferentially follow mineral boundaries, whereas higher axial stress strengthens macroscopic stress guidance and shifts the path toward a direction closer to being perpendicular to the maximum principal stress. This trend is consistent with energy minimization, since interface detouring under high axial stress incurs a larger elastic free energy penalty. Full article

Emergency messages and augmented reality (AR) are becoming integral to intelligent vehicular systems, but their existence poses significant challenges due to conflicting requirements. Emergency short messages demand ultra-low latency and strict reliability, while AR contents require larger data transfers with more flexible but still location-sensitive deadlines. To address this, a joint problem of roadside unit (RSU) assignment and transmission scheduling in multi-server, multi-user MEC-enabled vehicular networks is studied. The problem is formulated as an NP-hard optimization task and a two-stage framework is proposed. First, the penalty-minimizing RSU selection (PMRS) algorithm assigns requested content to RSUs by minimizing combined deadline and coverage penalties. Then a hybrid scheduling algorithm called deadline-aware priority scheduling (DAPS) is proposed, which integrates earliest-deadline-first and simulated annealing to prioritize emergency traffic while efficiently serving AR content. We benchmark the proposed framework against classical heuristics and metaheuristics. The results verify that the proposed approach can outperform the baseline methods under various realistic vehicular mobility and traffic conditions. Full article

Carbon capture and storage technologies are widely adopted, primarily in conventional power plants. Maritime transport must align with the 2050 targets and sharply reduce its environmental footprint. Onboard Carbon Capture and Storage (OCCS) appear to be an immediately feasible solution until alternative fuels are adopted and fully implemented. This study presents a regulatory compliance assessment and a techno-economic analysis of the implementation of OCCS. An LNG tanker was selected as a case study due to the inherent compatibility between LNG storage systems and CO₂ storage on board. The examined regulation includes the calculation of the corresponding penalties arising from the enforcement of the EU ETS, FuelEU Maritime, and the IMO NZF framework. The cost of installing the OCCS is also considered when evaluating the proposal’s sustainability. The results demonstrate that OCCS shows real promise in the fight against maritime transport emissions, but at present, it is not economically viable. Its viability depends mainly on clear regulatory guidelines and effective incentives that encourage its adoption, while offsetting investment and operating costs. Finally, the current study also seeks to resolve an ambiguity in the existing legislation that renders the OCCS a viable option. Full article

The accurate inference of hidden states from non-stationary physiological signals remains a significant challenge in stochastic process modeling. This paper proposes an Adaptive Sticky Hidden Markov Model (Sticky-HMM) framework designed to enhance the robustness of state decoding in noisy environments. To address the “state-flickering” issue inherent in traditional HMMs, we incorporate a “Sticky” parameter into the transition matrix, imposing a temporal penalty on spurious state switching to maintain continuity. Furthermore, we introduce a Dynamic Prior Strategy that adaptively calibrates self-transition probabilities by mapping frequency-domain features of the observed sequence to the model’s parameter space. The proposed decoding process employs a two-pass refinement strategy and the Viterbi algorithm in the logarithmic domain to ensure numerical stability. The model’s efficacy was validated using a high-fidelity dataset of simulated apnea events. This work provides a computationally efficient and mathematically rigorous approach that demonstrates strong potential for long-term respiratory health monitoring. Full article

Due to high intraclass variability and subtle intercharacter differences, automatic Arabic handwriting recognition remains a challenging task, particularly for children’s handwriting. This study proposes a hybrid framework that combines class-conditional Wasserstein generative adversarial networks with gradient penalty (CWGAN-GP) for data augmentation and a convolutional neural network (CNN) enhanced with squeeze-and-excitation (SE) blocks for improved feature discrimination. Experiments were restricted to disconnected (isolated) characters from the Hijja dataset, which comprised 12,355 samples divided as follows: 80% for training (9884), 10% for validation (1236), and 10% for testing (1235). Training the CNN on real data alone yielded an accuracy of 93.47%, while incorporating CWGAN-GP-generated samples improved performance to 96.27%. Notably, the proposed SE-CNN trained with the CWGAN-GP-augmented data achieved the highest accuracy of 99.27%. This result demonstrates that the combination of advanced generative data augmentation and architectural refinement significantly enhances Arabic handwritten character recognition performance. Full article

Identifying the major themes and recurring motifs of an author’s long novels is a basic task in literary studies. To support this task in a scalable way while retaining within-novel narrative variation, we model an author corpus as a third-order nonnegative tensor indexed by work × narrative segment × vocabulary. For such narrative tensors, we propose a tailored nonnegative tensor factorization model that reduces redundancy among topics via an orthogonality-promoting penalty and promotes smooth topic variation along contiguous narrative segments via an ${\ell }_2$ total-variation penalty. We develop a block proximal linearization algorithm for the resulting optimization problem and show that every limit point of the generated sequence satisfies the KKT conditions. Experiments on Toni Morrison’s long novels, including comparisons of the results of the proposed model with those of NMF and LDA, suggest that the cross-work themes extracted by the proposed approach exhibit qualitative patterns broadly consistent with thematic concerns discussed in existing literary scholarship. Additional experiments on the corpora of Ernest Hemingway and Graham Swift provide further validation of the proposed model. Full article

In recent decades, microalgae biodiesel has been repeatedly presented as a fundamental pillar of future bioenergy systems in relation to fossil diesel. This is largely due to the high photosynthetic efficiency of microalgae, their high growth rates, and their ability to accumulate neutral lipids—particularly triacylglycerols (TAGs)—which constitute the main raw materials for biodiesel production. However, this route has not yet become economically competitive with conventional fuels and vegetable oils. In this context, the simultaneous increase in biomass productivity and TAG content remains essential to reduce the cost difference, but achieving these goals depends on a detailed understanding of lipid metabolism and its regulation under different environmental and nutritional conditions—and on overcoming the intrinsic trade-offs between growth and storage. Thus, this article aims to critically analyze the viability of microalgae biodiesel, seeking to identify the main factors that explain why this route has not yet become competitive with conventional fuels after decades of research. In parallel, the growing trend of multi-product microalgae biorefineries is examined, highlighting bottlenecks in downstream processing and product purification, as well as the inherent trade-offs between production strategies. Practical limitations related to biomass productivity per area, culture dilution, intracellular lipid storage, and vital steps such as transesterification are also discussed, which together impose high energy and operational penalties throughout the production chain. Finally, emerging trends and integrated approaches are discussed, with emphasis on strain and process co-optimization, as well as greater integration between cultivation and downstream operations, aiming to enable more efficient and realistically consistent microalgae biodiesel concepts. Full article

Total variation (TV)-based image denoising effectively suppresses noise while preserving edges, but it often introduces staircase artifacts in flat regions. To address this limitation, we propose a novel denoising model that combines adaptive fractional-order total variation with a minimax-concave (MC) penalty in the regularization term. The adaptive fractional-order TV alleviates staircase effects in homogeneous areas while preserving fine details in textured regions. The MC penalty provides a more accurate estimation of image sparsity, improving restoration fidelity compared to traditional L1-based regularization. The resulting model, termed AFTVMC, is efficiently solved using an alternating direction method of multipliers (ADMM). Extensive numerical experiments on synthetic and natural images demonstrate that AFTVMC outperforms classical TV, higher-order LLT, adaptive ATV, and state-of-the-art MCFOTV models in both objective metrics—peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)—and subjective visual quality, particularly in suppressing staircase artifacts and preserving complex texture details. Full article