Search Results (774)

Background: Colorectal cancer (CRC) is significantly associated with multiple metabolic diseases, with plasma metabolites potentially mediating this relationship. This large-scale metabolomics study aims to (1) quantify the genetic correlations and causal effects between 10 metabolic disease-related phenotypes and CRC risk; (2) identify the plasma metabolites mediating these effects; and (3) explore downstream regulatory genes and druggable targets. Methods: Using linkage disequilibrium score regression and two-sample Mendelian randomization, we assessed the causal relationships between each metabolic trait and CRC. A total of 1091 plasma metabolites and 309 metabolite ratios were identified and analyzed for mediating effects by a two-step MR approach. Colocalization analyses evaluated shared genetic loci. The findings were validated in the UK Biobank for metabolite-trait associations. The expression of candidate genes was explored using data from TCGA, GTEx, and GEO. A FADS1-centered protein–protein interaction (PPI) network was constructed via STRING. Results: BMI, waist circumference, basal metabolic rate, insulin resistance and metabolic syndrome exhibited both genetic correlation and causal effects on CRC. Five plasma metabolites—mannonate, the glucose/mannose ratio, plasma free asparagine, 1-linolenoyl-2-linolenoyl-GPC (18:2/18:3), and the mannose/trans-4-hydroxyproline ratio—were identified as shared central mediators. A colocalization analysis showed rs174546 linked CRC and 1-linolenoyl-2-linoleoyl-GPC. Validation in the UK Biobank confirmed the associations between phosphatidylcholine (the lipid class of this metabolite), adiposity measures, and CRC risk. An integrative analysis of TCGA, GTEx, and GEO revealed consistent upregulation of FADS1/2/3 and FEN1 in CRC, with high FADS1 expression predicting a poorer prognosis and showing the distinct cell-type expression in adipose and colon tissue. The PPI network mapping uncovered nine FADS1 interacting proteins targeted by supplements such as α-linolenic acid and eicosapentaenoic acid. Conclusions: This study systematically reveals, for the first time, the shared intermediary plasma metabolites and their regulatory genes in the causal pathway from metabolic diseases to CRC. These findings provide candidate targets for subsequent functional validation and biomarker development. Full article

Marine mammals are important ecological bio-indicators of marine ecosystems impacted by a plethora of anthropogenic and environmental threats. Genomics detects genetic variation, adaptation to environmental shifts, and susceptibility to diseases in marine mammal species. In this study, eDNA was utilized for the first time in the Pagasitikos Gulf over three consecutive years (2022–2024) in order to detect marine mammal species. Additionally, visual monitoring and eDNA results were compared to reveal the pros and cons of the two methodologies. The gulf was zoned into five different areas with respect to oceanographic features for sampling. DNA extraction was assessed by using a standard protocol of phenol–chloroform followed by PCR amplification using the 16S rRNA gene. A total of 5,209,613 highly filtered sequence reads were attributed to 108 species. Among these, Monachus monachus, Tursiops truncatus, and Ziphius cavirostris species were detected. This novel detection of Z. cavirostris in the relatively shallow waters of the Gulf of Pagasitikos raised the question of whether it was a random event or a new ecological trend. Z. cavirostris and M. monachus appeared to share the same marine areas within the gulf. In the era of the climate crisis, eDNA provides essential information on marine mammals’ ecological status, yields novel detections, and predicts behavioral changes essential to deep-diving species. Full article

To maintain product reliability and stabilize performance, it is essential to prioritize the identification and resolution of latent defects. Advanced products such as high-precision electronic devices and semiconductors are susceptible to performance degradation over time due to environmental factors and electrical stress. However, conventional performance testing methods typically evaluate products based solely on predefined acceptable ranges, making it difficult to predict long-term degradation, even for products that pass initial testing. In particular, products exhibiting borderline values close to the threshold during initial inspections are at a higher risk of exceeding permissible limits as time progresses. Therefore, to ensure long-term product stability and quality, a novel approach is required that enables the early prediction of potential defects based on test data. In this context, the present study proposes a machine learning-based framework for predicting latent defects in products that are initially classified as normal. Specifically, we introduce the Sigma Deviation Count Labeling (SDCL) method, which utilizes a Gaussian distribution-based approach. This method involves preprocessing the dataset consisting of initially passed test samples by removing redundant features and handling missing values, thereby constructing a more robust input for defect prediction models. Subsequently, outlier counting and labeling are performed based on statistical thresholds defined by 2σ and 3σ, which represent potential anomalies outside the critical boundaries. This process enables the identification of statistically significant outliers, which are then used for training machine learning models. The experiments were conducted using two distinct datasets. Although both datasets share fundamental information such as time, user data, and temperature, they differ in the specific characteristics of the test parameters. By utilizing these two distinct test datasets, the proposed method aims to validate its general applicability as a Predictive Anomaly Testing (PAT) approach. Experimental results demonstrate that most models achieved high accuracy and geometric mean (GM) at the 3σ level, with maximum values of 1.0 for both metrics. Among the tested models, the Support Vector Machine (SVM) exhibited the most stable classification performance. Moreover, the consistency of results across different models further supports the robustness of the proposed method. These findings suggest that the SDCL-based PAT approach is not only stable but also highly adaptable across various datasets and testing environments. Ultimately, the proposed framework offers a promising solution for enhancing product quality and reliability by enabling the early detection and prevention of latent defects. Full article

The increasing adoption of electric delivery fleets introduces significant challenges related to uneven energy utilization and suboptimal scheduling efficiency. Vehicle-to-Vehicle (V2V) energy sharing presents a promising solution, but its effectiveness critically depends on precise matching and co-optimization within dynamic urban traffic environments. This paper proposes a hierarchical optimization framework to minimize total fleet operational costs, incorporating a comprehensive analysis that includes battery degradation. The core innovation of the framework lies in coupling high-level path planning with low-level real-time speed control. First, a high-fidelity energy consumption surrogate model is constructed through model predictive control simulations, incorporating vehicle dynamics and signal phase and timing information. Second, the spatiotemporal longest common subsequence algorithm is employed to match the spatio-temporal trajectories of energy-provider and energy-consumer vehicles. A battery aging model is integrated to quantify the long-term costs associated with different operational strategies. Finally, a multi-objective particle swarm optimization algorithm, integrated with MPC, co-optimizes the rendezvous paths and speed profiles. In a case study based on a logistics network, simulation results demonstrate that, compared to the conventional station-based charging mode, the proposed V2V framework reduces total fleet operational costs by a net 12.5% and total energy consumption by 17.4% while increasing the energy utilization efficiency of EV-Ps by 21.4%. This net saving is achieved even though the V2V strategy incurs a marginal increase in battery aging costs, which is overwhelmingly offset by substantial savings in logistical efficiency. This study provides an efficient and economical solution for the dynamic energy management of electric fleets under realistic traffic conditions, contributing to a more sustainable and resilient urban logistics ecosystem. Full article

This paper proposes a novel hybrid control strategy integrating a Finite Control Set Model Predictive Controller (FCS-MPC) with a universal droop controller (UDC) for effective load power sharing in inverter-fed microgrids. Traditional droop-based methods, though widely adopted for their simplicity and decentralized nature, suffer from limitations such as steady-state inaccuracies and poor transient response, particularly under mismatched impedance conditions. To overcome these drawbacks, the proposed scheme incorporates detailed modeling of inverter and source dynamics within the predictive controller to enhance accuracy, stability, and response speed. The UDC complements the predictive framework by ensuring coordination among inverters with different impedance characteristics. Simulation results under various load disturbances demonstrate that the proposed approach significantly outperforms conventional PI-based droop control in terms of voltage and frequency regulation, transient stability, and balanced power sharing. The performance is further validated through real-time simulations, affirming the scheme’s potential for practical deployment in dynamic microgrid environments. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

Background/Objectives: Allergic diseases (e.g., asthma, chronic urticaria) are increasing globally, but current anti-allergic drugs exhibit limitations in efficacy and safety. Traditional Chinese Medicine (TCM) emphasizes constitutional regulation for allergic diseases management. The allergic constitution prescription (ACP), a TCM formulation, lacks clear mechanistic insights. Methods: This study employs a novel network pharmacology approach integrating ultra-high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS) to identify ACP’s chemical components and compare its mechanisms with anti-allergic drugs. Chemical components of ACP were analyzed via UHPLC-Q-TOF-MS/MS, and allergic disease-related targets were collected from public databases. Anti-allergic drug targets were intersected with ACP-disease targets to identify unique and common pathways. Molecular docking and dynamics simulations assessed binding affinity between key compounds and core targets. Results: We identified 126 compounds in ACP. Compared to anti-allergic drugs, ACP targeted 10 unique and five common key pathways (e.g., MAPK signaling), 10 unique and nine common core targets (e.g., Tumor Necrosis Factor (TNF), IL-6), and 14 unique and 15 common key compounds. Simulations confirmed high binding affinity of ACP compounds to core targets. Conclusions: These findings highlight ACP’s potential multi-target mechanisms for allergic diseases treatment, identifying unique and shared pathways, targets, and compounds compared to anti-allergic drugs, offering new insights for further mechanistic studies. However, it is crucial to note that these mechanistic predictions and compound-target interactions are primarily derived from computational analyses, and experimental validation (e.g., in vitro or in vivo assays) is essential to confirm these computational findings. Full article

Key drivetrain components in wind turbines are prone to misalignment faults due to long-term operation under fluctuating loads and harsh environments. Because misalignment develops gradually rather than occurring instantly, reliable evaluation of structural designs and surface treatments requires long-duration, multi-sensor, and multi-condition experiments that generate massive heterogeneous datasets. Traditional data management relying on manual folders and USB drives is inefficient, redundant, and lacks traceability. To address these challenges, this study presents a dedicated misalignment experimental data management platform specifically designed for wind power applications. The innovation lies in its ability to synchronize vibration, electrostatic, and laser alignment data streams in long-term tests, establish a traceable and reusable data structure linking experimental conditions with sensor outputs, and integrate laboratory results with field SCADA data. Built on Laboratory Information Management System (LIMS) principles and implemented with an MVC + Spring Boot + B/S architecture, the platform supports end-to-end functions including multi-sensor data acquisition, structured storage, automated processing, visualization, secure sharing, and cross-role collaboration. Validation on drivetrain shaft assemblies confirmed its ability to handle multi-terabyte datasets, reduce manual processing time by more than 80%, and directly integrate processed results into fault identification models. Overall, the platform establishes a scalable digital backbone for wind turbine misalignment research, supporting structural reliability evaluation, predictive maintenance, and intelligent operation and maintenance. Full article

The effect of molecule–surface interaction strength on water becomes pronounced when pore size shrinks to the nanoscale, leading to the spatially varying viscosity and water slip phenomena that break the theoretical basis of the classic Lucas–Washburn (L-W) equation for the spontaneous imbibition of water. With the purpose of fulfilling the knowledge gap, the viscosity of nanoconfined water is investigated in relation to surface contact angle, a critical parameter manifesting microscopic molecule–surface interaction strength. Then, the water slip length at the nanoscale is determined in accordance with the mechanical balance of the first-layer water molecules, which enlarges gradually with increasing contact angle, indicating a weaker surface–molecule interaction. After that, a novel model for the spontaneous imbibition of nanoconfined water incorporating spatially inhomogeneous water viscosity and water slip is developed for the first time, demonstrating that the conventional model yields overestimations of 16.7–103.2%. Hydrodynamics affected by pore geometry are considered as well. The results indicate the following: (a) Enhanced viscosity resulting from the nanopore surface action reduces the water imbibition distance, the absolute magnitude of which could be 3 times greater than the positive impact of water slip. (b) With increasing pore size, the impact of water slip declines much faster than the enhanced viscosity, leading to the ratio of the nanoconfined water imbibition distance to the result of the L-W equation dropping rapidly at first and then approaching unity. (c) Water imbibition performance in slit nanopores is superior to that in nanoscale capillaries, stemming from the fact that the effective water viscosity in nano-capillaries is greater than that in slit nanopores by 5.1–22.1%, suggesting stronger hydrodynamic resistance. This research is able to provide an accurate prediction of spontaneous imbibition of nanoconfined water with microscopic mechanisms well captured, sharing broad application potential in hydraulic fracturing water analysis and water-flooding-enhanced oil/gas recovery. Full article

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair comparative analysis of three metaheuristic strategies: Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GAs). The manipulator dynamics are identified via a discrete-time recurrent high-order neural network (NN) trained online using an Extended Kalman Filter with adaptive noise covariance updates, allowing the model to accurately capture unmodeled dynamics, nonlinearities, parametric variations, and process/measurement noise. This neural representation serves as the predictive plant for the discrete-time SMC, enabling precise control of joint angular positions under sinusoidal phase-shifted references. To construct the optimization dataset, MATLAB^® simulations sweep the controller gains $(k_0^{*},k_1^{*})$ over a bounded physical domain, logging steady-state tracking errors. These are normalized to mitigate scaling effects and improve convergence stability. Optimization is executed in Python^® using integrated scikit-learn, DEAP, and scikit-optimize routines. Simulation results reveal that all three algorithms reach high-performance gain configurations. Here, the combined cost is the normalized aggregate objective $\tilde{J}$ constructed from the steady-state tracking errors of both joints. Under identical experimental conditions (shared data loading/normalization and a single Python pipeline), PSO attains the lowest error in Joint 1 ($7.36\times {10}^{-5}$ rad) with the shortest runtime (23.44 s); GA yields the lowest error in Joint 2 ($8.18\times {10}^{-3}$ rad) at higher computational expense (≈69.7 s including refinement); and BO is competitive in both joints ($7.81\times {10}^{-5}$ rad, $8.39\times {10}^{-3}$ rad) with a runtime comparable to PSO (23.65 s) while using only 50 evaluations. Full article

Maritime safety, environmental protection, and efficient traffic management increasingly rely on data-driven technologies. However, leveraging Automatic Identification System (AIS) data for predictive modelling faces two major challenges: the massive volume of data generated in real-time and growing privacy concerns associated with proprietary vessel information. This paper proposes a novel, privacy-preserving framework for vessel traffic density (VTD) prediction that addresses both challenges. The approach combines the European Maritime Observation and Data Network’s (EMODNet) grid-based VTD calculation method with Convolutional Neural Networks (CNN) to model spatiotemporal traffic patterns and employs Federated Learning to collaboratively build a global predictive model without the need for explicit sharing of proprietary AIS data. Three geographically diverse AIS datasets were harmonized, processed, and used to train local CNN models on hourly VTD matrices. These models were then aggregated via a Federated Learning framework under a lifelong learning scenario. Evaluation using Sparse Mean Squared Error shows that the federated global model achieves promising accuracy in sparse data scenarios and maintains performance parity when compared with local CNN-based models, all while preserving data privacy and minimizing hardware performance needs and data communication overheads. The results highlight the approach’s effectiveness and scalability for real-world maritime applications in traffic forecasting, safety, and operational planning. Full article

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value. Full article

With the rapid growth of video platforms such as YouTube, Bilibili, and Dailymotion, an enormous amount of video content is being shared worldwide. In this environment, content providers are increasingly adopting methods that restructure videos around highlight scenes and distribute them in short-form formats to encourage more efficient content consumption by viewers. As a result of this trend, the importance of highlight extraction technologies capable of automatically identifying key scenes from large-scale video datasets has been steadily increasing. To address this need, this study proposes SPOT (Spatial Perceptual Optimized TimeSformer), a highlight extraction model. The proposed model enhances spatial perceptual capability by integrating a CNN encoder into the internal structure of the existing Transformer-based TimeSformer, enabling simultaneous learning of both the local and global features of a video. The experiments were conducted using Google’s YT-8M video dataset along with the MR.Hisum dataset, which provides organized highlight information. The SPOT model adopts a regression-based highlight prediction framework. Experimental results on video datasets of varying complexity showed that, in the high-complexity group, the SPOT model achieved a reduction in mean squared error (MSE) of approximately 0.01 (from 0.090 to 0.080) compared to the original TimeSformer. Furthermore, the model outperformed the baseline across all complexity groups in terms of mAP, Coverage, and F1-Score metrics. These results suggest that the proposed model holds strong potential for diverse multimodal applications such as video summarization, content recommendation, and automated video editing. Moreover, it is expected to serve as a foundational technology for advancing video-based artificial intelligence systems in the future. Full article

Urban parking spaces are key city resources that directly affect how easily people get around and the quality of their daily travel. Accurately predicting future parking space availability can improve the efficiency of using parking spaces. For instance, it can enhance smart parking applications like shared parking and EV charging scheduling. However, because parking behavior is dynamic and constantly changing, it is challenging to predict parking space availability over the medium-to-long term. This paper proposes a Scale-Fusion Transformer model (SFFormer) to address dynamic changes in parking spaces availability caused by complex parking behaviors, as well as challenges in medium-to-long-term prediction modeling. The three key innovations are as follows: (1) a scale-fusion module integrating short-term and long-term parking trends, (2) an adaptive data compression mechanism for multi-scale prediction tasks, and (3) a Transformer-encoder-based time pattern capturing architecture, which is adaptable to diverse parking lots and long-term prediction scenarios. Experiments on real parking datasets demonstrate that the SFFormer model significantly outperforms state-of-the-art models such as iTransformer, PatchTST, DLinear, and Autoformer. Full article

This paper introduces a parallelized approach to reconstruct Koopman computational graphs from the perspective of parallel computing to address the computational efficiency bottleneck in approximating Koopman operators within high-dimensional spaces. We propose the KPA (Koopman Parallel Accelerator), a parallelized algorithm that restructures the Koopman computational workflow to transform sequential time-step computations into parallel tasks. KPA leverages GPU parallelism to improve execution efficiency without compromising model accuracy. To validate the algorithm’s effectiveness, we apply KPA to a flight trajectory prediction scenario based on the Koopman operator. Within the CUDA kernel implementation of KPA, several optimization techniques—such as shared memory, tiling, double buffering, and data prefetching—are employed. We compare our implementation against two baselines: the original Koopman neural operator for trajectory prediction implemented in TensorFlow (TF-baseline) and its XLA-compiled variant (TF-XLA). The experimental results demonstrate that KPA achieves a 2.47× speed up over TF-baseline and a 1.09× improvement over TF-XLA when predicting a 1422-dimensional flight trajectory. Additionally, an ablation study on block size and the number of streaming multiprocessors (SMs) reveals that the best performance is obtained with the block size of 16 × 16 and SM = 8. The results demonstrate that KPA can significantly accelerate Koopman operator computations, making it suitable for high-dimensional, large-scale, or real-time applications. Full article