Search Results (34,131)

Pilot workload is a critical factor influencing flight safety. This study collects both subjective and objective data on pilot workload using the NASA-TLX questionnaire and electroencephalogram acquisition systems during simulated flight tasks. The raw EEG signals are denoised through preprocessing techniques, and relevant EEG features are extracted using time-domain and frequency-domain analysis methods. One-way ANOVA is employed to examine the statistical differences in EEG indicators under varying workload levels. A fusion model based on CNN-Bi-LSTM is developed to train and classify the extracted EEG features, enabling accurate identification of pilot workload states. The results demonstrate that the proposed hybrid model achieves a recognition accuracy of 98.2% on the test set, confirming its robustness. Additionally, under increased workload conditions, frequency-domain features outperform time-domain features in discriminative power. The model proposed in this study effectively recognizes pilot workload levels and offers valuable insights for civil aviation safety management and pilot training programs. Full article

Given the central importance of maritime logistics to global trade, accurate and efficient vessel motion forecasting is essential for strengthening supply chain resilience and improving operational efficiency. However, traditional physical and statistical models often fail to effectively capture the multivariate, noisy, and strongly coupled nature of maritime dynamics. In this manuscript, we adapt the DSformer architecture for ship motion forecasting, leveraging its dual sampling and dual-attention design to address the multi-scale and cross-variable dependencies inherent in maritime data. Across three real-world datasets, the adapted DSformer reduces prediction error by 23% and training time by 70% compared with 13 state-of-the-art (SOTA) baselines. Moreover, we identify a consistent relationship between sampling strategies and sea states, where dense sampling performs best under stable conditions, whereas moderately sparse sampling with multi-head attention improves robustness under turbulent environments. These results apply the algorithm’s new capabilities to the daily management of maritime logistics. By adapting the architecture to real-world operational settings and optimizing its key parameters, the approach enables efficient, real-time vessel forecasting and decision support across global supply chains. Full article

Background: Catechol-O-methyltransferase (COMT) catalyzes catecholamine O-methylation and contributes to dopamine turnover, potentially influencing levodopa requirements in Parkinson’s disease (PD). We evaluated whether the Gothelf COMT haplotype—and its constituent variants rs2075507, rs4680 (Val158Met), and rs165599—differ in frequency between PD cases and controls. We then tested associations between these variants and clinical phenotypes, with a prespecified focus on levodopa equivalent daily dose (LEDD). Finally, we examined whether haplotype structure and allele-specific context (e.g., background-dependent effects) help explain observed genotype–phenotype relationships in the PD cohort. Aim: Analysis of the rs2075507, rs4680 and rs165599 at individual and haplotype level between control and diseased groups. Furthermore, analysis of association of individual SNPs or haplotype level with clinical outcomes. Subjects and methods: Fifty-five individuals with Parkinson’s disease (PD) and fifty-three neurologically healthy controls were enrolled at a single center. Genomic DNA was isolated from peripheral blood, and three COMT variants—rs2075507 (promoter), rs4680/Val158Met (coding), and rs165599 (3′UTR)—were genotyped by Sanger sequencing. Allele, genotype, and tri-marker haplotype frequencies were estimated, and case–control differences were evaluated. Within the PD cohort, associations with clinical outcomes—primarily levodopa equivalent daily dose (LEDD)—were analyzed using multivariable linear models. Statistical tests were two-sided, with multiplicity control as specified in the corresponding tables. Results: The rs2075507 polymorphism showed a robust additive association with LEDD; each A allele predicted higher dose (LEDD ≈ +1331 mg/day, p = 0.001) after adjusting for age and sex. The tri-haplotype test did not show significant association with LEDD. Nevertheless, rs2075507 SNP strongly marked downstream backgrounds: in AA carriers, rs4680–rs165599 haplotypes were enriched for Val (G) and rs165599-G; in GG carriers, for rs165599-A with mixed Val/Met; and GA was A-loaded at both loci. Exact tests confirmed that AA and GG differed in rs4680–rs165599 composition, whereas GA vs. GG was not significant. Conclusions: The promoter variation at rs2075507 may represent the genetic contributor to levodopa dose requirements when modeled with SNP–SNP interactions, with its effect is modified mostly by rs165599 polymorphism. Tri-haplotypes do not independently predict LEDD. The rs4680 (coding) and rs165599 (3′UTR) context appears to fine-tune rather than determine dosing needs, mainly via interaction with rs2075507 SNP. Full article

A new family of stochastic $\eta$-power log-logistic diffusion processes was introduced and defined based on the classical log-logistic diffusion model. The probabilistic characteristics of the proposed process were derived through an analysis of the associated stochastic differential equation (SDE), including its explicit expressions, the transition probability density function, and the conditional and non-conditional mean functions. The statistical inference of the model was studied, and parameter estimation was performed using the maximum likelihood method based on discrete sampling paths. The proposed probabilistic and statistical framework was applied to data on individuals using the Internet in the United States to assess the practical performance of the model. The empirical results demonstrated that the inclusion of a power in the process improved the goodness of fit compared with the classical formulation, providing better agreement with the observed data. Finally, a small Monte Carlo experiment was performed to examine the robustness of the estimation procedure. Full article

Shipping is a high-energy-intensive sector and a major source of climate-relevant and harmful air pollutant emissions. In response to growing environmental concerns, the sector has been subject to increasingly stringent regulations, promoting the uptake of alternative fuels and emission control technologies. Accurate and diverse emission factors (EFs) are critical for quantifying shipping’s contribution to current emission inventories and projecting future developments under different policy scenarios. This study advances the development of load-dependent EFs for ships by incorporating alternative fuels, biofuels and emission control technologies. The methodology combines statistical analysis of data from an extensive literature review with newly acquired on-board emission measurements, including two-stroke propulsion engines and four-stroke auxiliary units. To ensure broad applicability, the updated EFs are expressed as functions of engine load and are categorized by engine and fuel type, covering conventional marine fuels, liquified natural gas, methanol, ammonia and biofuels. The results provide improved resolution of shipping emissions and insights into the role of emission control technologies, supporting robust, up-to-date emission models and inventories. This work contributes to the development of effective strategies for sustainable maritime transport and supports both policymakers and industry stakeholders in their decarbonization efforts. Full article

High-dimensional regression with multivariate responses poses significant challenges when data are collected across multiple platforms, each with potentially correlated outcomes. In this paper, we introduce a multi-platform multivariate high-dimensional linear regression (MM-HLR) model for simultaneously modeling within-platform correlation and cross-platform information fusion. Our approach incorporates a mixture of Lasso and group Lasso penalties to promote both individual predictor sparsity and cross-platform group sparsity, thereby enhancing interpretability and estimation stability. We develop an efficient computational algorithm based on iteratively reweighted least squares and block coordinate descent to solve the resulting regularized optimization problem. We establish theoretical guarantees for our estimator, including oracle bounds on prediction error, estimation accuracy, and support recovery under mild conditions. Our simulation studies confirm the method’s strong empirical performance, demonstrating low bias, small variance, and robustness across various dimensions. The analysis of real financial data further validates the performance gains achieved by incorporating multivariate responses and integrating data across multiple platforms. Full article

With the development of mobile crowdsensing systems (MCSs), wireless network transmission efficiency has attracted widespread attention. Network coding can be used in wireless communication to improve network throughput and robustness, which allows intermediate nodes to perform arbitrary coding operations on data packets. However, the data packet in network coding systems is vulnerable to pollution attacks. The special operation of intermediate nodes makes some security protocols in traditional store-and-forward networks unavailable in network coding systems. To address this problem, an efficient certificate-based linearly homomorphic signature scheme against pollution attacks in network coding systems is presented. A novel homomorphic contraction mapping technique is introduced to reduce the computational cost of signature generation. In the proposed scheme, the computational cost of both signature generation and verification is independent of the data packet size. Furthermore, a construction is provided to simultaneously defend against both eavesdropping attacks and pollution attacks in unicast networks. The security of the certificate-based linearly homomorphic signature scheme is formally proved in the random oracle model (ROM), and the scheme is implemented using the Java Pairing-Based Cryptography (JPBC) library. Simulation results demonstrate that the scheme is efficient and practical for real-world deployments in public environments without requiring secure channels. Full article

Deep vision foundation models such as DINOv3 offer strong visual representation capacity, but their direct deployment in medical image segmentation remains difficult due to the limited availability of annotated clinical data and the computational cost of full fine-tuning. This study proposes an adaptation framework called StrDiSeg that integrates lightweight bottleneck adapters between selected transformer layers of DINOv3, enabling task-specific learning while preserving pretrained knowledge. An attention-enhanced U-Net decoder with multi-scale feature fusion further refines the representations. Experiments were performed on two publicly available ischemic stroke lesion segmentation datasets—AISD (Non Contrast CT) and ISLES22 (DWI). The proposed method achieved Dice scores of 0.516 on AISD and 0.824 on ISLES22, outperforming baseline models and demonstrating strong robustness across different clinical imaging modalities. These results indicate that adapter-based fine-tuning provides a practical and computationally efficient strategy for leveraging large pretrained vision models in medical image segmentation. Full article

This paper presents a PSCAD-based analysis of short-circuit faults and protection characteristics in a real distribution-level microgrid that integrates a 1 MWh battery energy storage system (BESS) with a 500 kW power conversion system (PCS) and a 500 kW photovoltaic (PV) plant connected to a 22.9 kV feeder. While previous studies often rely on simplified inverter models, this paper addresses the critical gap by integrating actual manufacturer-defined control parameters and cable impedances. This allows for a precise analysis of sub-millisecond transient behaviors, which is essential for developing robust protection schemes in inverter-dominated microgrids. The PSCAD model is first verified under grid-connected steady-state operation by examining PV output, BESS power, and grid voltage at the point of common coupling. Based on the validated model, DC pole-to-pole faults at the PV and ESS DC links and a three-phase short-circuit fault at the low-voltage bus are simulated to characterize the fault current behavior of the grid, BESS and PV converters. The DC fault studies confirm that current peaks are dominated by DC-link capacitor discharge and are strongly limited by converter controls, while the AC three-phase fault is mainly supplied by the upstream grid. As an initial application of the model, an instantaneous current change rate (ICCR) algorithm is implemented as a dedicated DC-side protection function. For a pole-to-pole fault, the ICCR index exceeds the 100 A/ms threshold and issues a trip command within 0.342 ms, demonstrating the feasibility of sub-millisecond DC fault detection in converter-dominated systems. Beyond this example, the validated PSCAD model and associated data set provide a practical platform for future research on advanced DC/AC protection techniques and protection coordination schemes in real BESS–PV microgrids. Full article

Today, machine learning (ML) is generally considered a potent and efficient tool for addressing studies in various diverse domains, including image processing and event prediction on a timescale. ML represents complex relations between features, and these mappings between such features may be applied in simulations of time-dependent events, such as the behavior of animals. Still, ML inherently strongly depends on extensive and consistent datasets, a fact that reveals both the benefits and drawbacks of ML. In the use of ML, insufficient or skewed data can limit the ability of algorithms to accurately predict or generalize possible states. To overcome this limitation, this work proposes an integrated hybrid approach that combines machine learning with methods from cognitive science, here especially inspired by the ACT-R model to approach cases of missing or unbalanced data. By incorporating cognitive processes such as memory, perception, and attention, the model accounts for the internal mechanisms of decision-making and environmental interaction where traditional ML methods fall short. This approach is particularly useful in representing states that are not directly observable or are underrepresented in the data, such as rare behavioral responses for animals, or adaptive strategies. Experimental results show that the combination of machine learning for data-driven analysis and cognitive ‘rule-based’ frameworks for filling in gaps provides a more comprehensive model of animal behavior. The findings suggest that this hybrid approach to simulation models can offer a more robust and consistent way to study complex, real-world phenomena, especially when data is inherently incomplete or unbalanced. Full article

Monoacylglycerols (MAGs) are significant intermediate byproducts in the hydrolysis of oils and fats. The accumulation of MAGs not only reduces the quality and purity of the final products in biodiesel production and edible oil refining but also poses challenges for downstream separation processes. Therefore, the development of efficient biocatalysts for the specific MAG conversion is of great industrial importance. The lipase from Aspergillus oryzae (AOL) has shown potential for lipid modification; however, the wild-type enzyme (WT) suffers from poor solubility, tendency to aggregate, and low specific activity towards MAGs in aqueous systems, which severely restricts its practical application. In this study, a combinatorial protein engineering strategy was employed to overcome these limitations. We integrated fusion protein technology with rational design to enhance both the functional expression and catalytic efficiency of AOL. Firstly, the superfolder green fluorescent protein (sfGFP) was fused to the N-terminus of AOL. The results indicated that the sfGFP fusion tag significantly improved the solubility and stability of the enzyme, preventing the formation of inclusion bodies. The fusion protein sfGFP-AOL exhibited a MAG conversion rate of approximately 65%, confirming the positive impact of the fusion tag on enzyme developability. To further boost catalytic performance, site-directed mutagenesis was performed based on structural analysis. Among the variants, the mutant sfGFP-Y92Q emerged as the most potent candidate. In the MAG conversion, sfGFP-Y92Q achieved a conversion rate of 98%, which was not only significantly higher than that of sfGFP-AOL but also outperformed the widely used commercial immobilized lipase, Novozym 435 (~54%). Structural modeling and docking analysis revealed that the Y92Q mutation optimized the geometry of the active site. The substitution of Tyrosine with Glutamine at position 92 likely enlarged the substrate-binding pocket and altered the local electrostatic environment, thereby relieving steric hindrance and facilitating the access of the bulky MAG substrate to the catalytic center. In conclusion, this work demonstrates that the synergistic application of sfGFP fusion and rational point mutation (Y92Q) can dramatically transform the catalytic properties of AOL. The engineered sfGFP-Y92Q variant serves as a robust and highly efficient biocatalyst for MAG degradation. Its superior performance compared to commercial standards suggests immense potential for cost-effective applications in the bio-manufacturing of high-purity fatty acids and biodiesel, offering a greener alternative to traditional chemical processes. Full article

Sustainable port development in coastal regions necessitates robust frameworks for quantifying hydrodynamic risks under climate change. To bridge the gap between generic guidelines and site-specific resilience planning, this study proposes and applies a numerical modeling-based risk assessment framework. Within the context of the Port Master Plan, the framework is applied to the critical case of Takoradi Port in West Africa, employing a two-dimensional hydrodynamic model to simulate current fields under three current regimes, “Normal”, “Stronger”, and “Estimated Extreme” scenarios, for the first time. The model quantifies key hydrologic parameters such as current velocity and direction in critical zones (the approach channel, port basin, and berths), providing actionable data for the Port Master Plan. Key new findings include the following: (1) Northeastward surface currents, driven by the southwest monsoon, dominate the study area; breakwater sheltering creates a prominent circulation zone north of the port entrance. (2) Under extreme conditions, the approach channel exhibits amplified currents (0.3–0.7 m/s), while inner port areas maintain stable conditions (<0.1 m/s). (3) A stark spatial differentiation in designed current velocities for 2–100 years return periods, where the 100-year extreme current velocity in the external approach channel (0.87 m/s at P1) exceeds the range in the internal zones (0.01–0.15 m/s) by approximately 5 to 86 times. The study validates the framework’s utility in assessing hydrodynamic risks. By integrating numerical simulation with risk assessment, this work provides a scalable methodological contribution that can be adapted to other port environments, directly supporting the global pursuit of sustainable and resilient ports. Full article

This paper presents a novel high-order numerical method. The proposed scheme utilizes polynomial generating functions to achieve p order accuracy in time for the Grünwald–Letnikov fractional derivatives, while maintaining second-order spatial accuracy. By incorporating a short-memory principle, the method remains computationally efficient for long-time simulations. The authors rigorously analyze the stability of equilibrium points for the fractional vegetation–water model and perform a weakly nonlinear analysis to derive amplitude equations. Convergence analysis confirms the scheme’s consistency, stability, and convergence. Numerical simulations demonstrate the method’s effectiveness in exploring how different fractional derivative orders influence system dynamics and pattern formation, providing a robust tool for studying complex fractional systems in theoretical ecology. Full article

Length-based stock assessment methods are widely applied in data-limited fisheries, yet the effects of how length-frequency data are temporally grouped prior to analysis remain poorly examined. Temporal grouping is routinely used to increase sample size and approximate equilibrium conditions, but it may also alter the size structure presented to assessment models and bias inference. In this study, we evaluate how alternative temporal grouping schemes influence stock status inference within a single length-based framework, using the length-based spawning potential ratio (LBSPR) model as a diagnostic tool. Using a 30-year length-frequency dataset from a tropical purse seine fishery in the Northeast Atlantic as an illustrative case, we applied LBSPR under four practice-relevant temporal grouping schemes: full-period pooling, a broad regime-based scheme, decadal blocks, and five-year blocks. Life history parameters and model settings were held constant across schemes to isolate the effect of temporal grouping. A sensitivity analysis of biological parameters was conducted for the finest temporal scheme to contextualise robustness. Results show that temporal grouping alone can substantially alter the inferred status of the illustrative case. The fully pooled scheme produced an apparently favourable status signal, whereas finer temporal groupings revealed extended periods of inferred reproductive depletion, followed by a more recent recovery. Sensitivity analyses indicate that, while biological parameter uncertainty influences the magnitude of estimates, it does not overturn the dominant effect of temporal grouping on inferred status patterns. This study demonstrates that temporal grouping is not a neutral preprocessing step but a structural decision with the potential to conceal or reveal exploitation signals in length-based assessments. We argue that temporal grouping should be treated as an explicit sensitivity dimension in data-limited assessment workflows. By shifting attention from stock-specific outcomes to data-structuring choices, this work provides practical guidance for improving transparency and robustness in length-based stock status inference. Full article

Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article