Search Results (339)

Background/Objectives: Accurate estimation of caloric intake from breastfeeding is essential for understanding infant nutrition during early life. However, most existing models rely on fixed assumptions and do not reflect the natural variability in feeding behaviors and human milk composition. This study aims to provide a realistic estimation of breast milk (BM) caloric intake throughout infancy using a probabilistic approach based on empirical data. Methods: A probabilistic model was developed using four variables: feeding frequency, volume per feeding, caloric density, and infant weight. Systematic reviews were conducted to inform the input values of the first three variables, and meta-analyses were performed when feasible. Infant weight was based on World Health Organization (WHO) growth standards. Variables were stratified by age and integrated into the model through appropriate probability distributions. Monte Carlo simulations were conducted to estimate caloric intake per kilogram of body weight, expressed both per day and per feeding, across all age groups. Results: The model showed a progressive decline in daily caloric intake per kilogram with age, consistent with decreasing feeding frequency and the introduction of complementary foods. In contrast, caloric intake per feeding increased with age. These findings align with WHO energy intake targets during exclusive breastfeeding and reflect expected physiological changes in infant growth and feeding behavior. Conclusions: This study provides a probabilistic framework for estimating BM caloric intake across infancy, accounting for interindividual and age-related variability. It offers a valuable research tool to support future studies on infant nutrition and feeding behavior using realistic, data-driven assumptions. Full article

In maritime target search missions, UAV swarm task scheduling faces several challenges. These include uncertainties in target states, the high-dimensional multimodal characteristic of the solution space, and dynamic constraints on swarm collaboration. In terms of target position estimation, existing methods ignore the spatiotemporal correlation of target movement. At the level of optimization algorithms, existing algorithms struggle to balance global exploration and local exploitation, and they tend to fall into local optima. To address the above shortcomings, this paper constructs a technical system of “state perception-strategy optimization-collaborative execution”. First, a Serial Memory Iterative Method (GMMIM) integrated with the Gaussian–Markov model is proposed. This method recursively corrects the probability distribution of target positions using historical state data, thereby providing accurate situational support for decision-making. As a result, task scheduling efficiency is improved by 5.36%. Second, the sliding window technique is introduced to improve the Grey Wolf Optimizer (GWO). Based on the convergence of the population’s optimal fitness, the decay rate of the convergence factor is dynamically and adaptively adjusted. This balances the capabilities of global exploration and local exploitation to ensure swarm scheduling efficiency. Simulations demonstrate that the optimization performance of the proposed FSW-GWO algorithm is 16.95% higher than that of the IPSO method. Finally, a dynamic task weight update mechanism is designed. By combining resource load and task timeliness requirements, this mechanism achieves complementary adaptation between swarm resources and tasks. Full article

Recent climate events have highlighted an increasing need for developing sustainable energetic infrastructures. Mainly, the evolution of existing industrial domains toward sustainability would involve major changes in energy management. As a consequence, research studies have analyzed various possibilities for extending sustainability, with space-based energy applications garnering increased interest as a potential solution to provide the necessary sustainable energy for existing industries. Therefore, this study researched the development of a reduced-complexity orbital ephemerides set to demonstrate the increase in heuristic optimization agent density. Additionally, the required translations were studied and applied to place a hypothetical charging station as a target on planet Earth while considering real-scale interplanetary measurements during studies and simulations. Furthermore, the rocket thruster control analysis validated the consistency of this hypothetical methodology by proving the potential of using simplified ephemerides in coarse optimization, therefore reducing optimization resources. Thus, the results of this study indicate the consistency of this hypothetical optimization process, as evidenced by the similarity between command and error signal outputs. By such means, this improves the probability of finding a global optimum, potentially providing improvements in various aerospace domains in the scenario of orbital calibration optimization. Several future directions were discussed for applying the main concept to real-world operations to assess its actual applicability. Full article

Network reliability refers to a probabilistic measure of a network system’s ability to maintain its intended service functionality within a specified time interval and under given operating conditions. Let $\Omega (n,m)$ be the set of all simple two-terminal networks on n vertices and m edges. If each edge operates independently with the same fixed probability $p\in [0,1]$, then the two-terminal reliability, denoted by $R_2(G,P))$, is the probability that there exists a path between two target vertices s and t. For a given number of vertices n and edges m, there are some graphs within $\Omega (n,m)$ that have higher reliability than others, and these are known as extremely optimal graphs. In this work, we determine the sets of extremely optimal graphs in two classes of two-terminal network with sizes $m={\displaystyle \frac{n(n-1))}{2}}-2$ and $m={\displaystyle \frac{n(n-1))}{2}}-3$, consisting of 2 and 5 networks, respectively. Moreover, we identify one class of graphs obtained by deleting some edges among non-target vertices in the complete two-terminal graph, and we count the number of graphs of this class with size ${\displaystyle \frac{n(n-1)}{2}}-\lfloor {\displaystyle \frac{n-2}{2}}\rfloor \le m\le {\displaystyle \frac{n(n-1))}{2}}-1$ by applying the Pólya counting principle. Full article

Since efficient path planning technology is the key to the safe and autonomous navigation of autonomous ground robots, and in the complex and asymmetrically distributed land environment, the existing path planning and obstacle avoidance technologies seem somewhat inadequate. Since efficient path planning technology is key to the safe and autonomous navigation of autonomous ground robots, an advanced double Q-learning algorithm based on self-supervised prediction and curiosity-driven exploration is proposed. The algorithm reduces the risk of overestimation and bootstrapping by adjusting the calculation method of the target Q value and optimizing the network structure. In addition, a priority experience replay is introduced to set the priority for the data in the experience pool, thereby increasing the probability that better data is extracted. Experience pool data with fewer training times can be used more effectively. Adding the curiosity network to the original neural network, each state is given an overall reward when performing diverse actions. This method enhances the exploration of unmanned ground mobile robots and can independently select the shortest path to the endpoint. In complex environments, compared with the Sparrow Search Algorithm, Dung Beetle Optimization Algorithm, and Particle Swarm Optimization Algorithm, the results of the proposed algorithm are reduced by 18.07%, 7.91%, and 5.56%, respectively. Therefore, it could better cope with the challenges brought by complex environments and solve the problem that the algorithm cannot converge in complex environments. Full article

This paper introduces a novel sensor fusion approach for the detection of multiple static and mobile targets by autonomous mobile agents. Unlike previous studies that rely on theoretical sensor models, which are considered as independent, the proposed methodology leverages real-world sensor data, which is transformed into sensor-specific probability maps using object detection estimation for optical data and converting averaged point-cloud intensities for LIDAR based on a dedicated deep learning model before being integrated through a large language model (LLM) framework. We introduce a methodology based on LLM transfer learning (LLM-TLFT) to create a robust global probability map enabling efficient swarm management and target detection in challenging environments. The paper focuses on real data obtained from two types of sensors, light detection and ranging (LIDAR) sensors and optical sensors, and it demonstrates significant improvement in performance compared to existing methods (Independent Opinion Pool, CNN, GPT-2 with deep transfer learning) in terms of precision, recall, and computational efficiency, particularly in scenarios with high noise and sensor imperfections. The significant advantage of the proposed approach is the possibility to interpret a dependency between different sensors. In addition, a model compression using knowledge-based distillation was performed (distilled TLFT), which yielded satisfactory results for the deployment of the proposed approach to edge devices. Full article

With the booming development of the low-altitude economy and the widespread application of Unmanned Aerial Vehicles (UAVs), integrated sensing and communication (ISAC) technology plays an increasingly pivotal role in intelligent communication networks. However, low-altitude platforms supporting ISAC, such as UAV swarms, are highly vulnerable to deception jamming in complex electromagnetic environments. Existing multistatic radar systems face challenges in processing slowly fluctuating targets (like low-altitude UAVs) and adapting to complex electromagnetic environments when fusing multiple pulse echoes. To address this issue, targeting the protection needs of low-altitude targets like UAVs, this paper leverages the characteristic of rapid amplitude fluctuation in frequency-agile radar echoes to analyze the differences between true and false targets in multistatic frequency-agile radar systems, particularly for slowly fluctuating UAV targets, demonstrating the feasibility of discrimination. Building on this, we introduce a neural network approach to deeply extract discriminative features from true and false target echoes and propose a neural network-based variable-length processing method for deception jamming discrimination in multistatic frequency-agile radar. The simulation results show that the proposed method effectively exploits deep-level echo features, significantly improving the discrimination probability between true and false targets, especially for slowly fluctuating UAV targets. Crucially, even when trained on a fixed number of pulses, the model can process input data with varying pulse counts, greatly enhancing its practical deployment capability in dynamic UAV mission scenarios. Full article

This study aims to guide the management and service of highways towards a more efficient and intelligent direction, and also provides intelligent and green data support for achieving sustainable development goals. The forecasting of traffic flow at highway stations serves as the cornerstone for spatiotemporal analysis and is vital for effective highway management and control. Despite considerable advancements in data-driven traffic flow prediction, the majority of existing models fail to differentiate between directions. Specifically, entrance flow prediction has applications in dynamic route guidance, disseminating real-time traffic conditions, and offering optimal entrance selection suggestions. Meanwhile, exit flow prediction is instrumental for congestion and accident alerts, as well as for road network optimization decisions. In light of these needs, this study introduces an enhanced heterogeneous spatiotemporal graph network model tailored for predicting highway station traffic flow. To accurately capture the dynamic impact of upstream toll stations on the target station’s flow, we devise an influence probability matrix. This matrix, in conjunction with the covariance matrix across toll stations, updated graph structure data, and integrated external weather conditions, allows the attention mechanism to assign varied combination weights to the target toll station from temporal, spatial, and external standpoints, thereby augmenting prediction accuracy. We undertook a case study utilizing traffic flow data from the Chengdu-Chengyu station on the Sichuan Highway to gauge the efficacy of our proposed model. The experimental outcomes indicate that our model surpasses other baseline models in performance metrics. This study provides valuable insights for highway management and control, as well as for reducing traffic congestion. Furthermore, this research highlights the importance of using data-driven approaches to reduce carbon emissions associated with transportation, enhance resource allocation at toll plazas, and promote sustainable highway transportation systems. Full article

Graph Neural Networks (GNNs) have shown strong performance in link prediction, a core task in graph analysis. However, recent studies reveal their vulnerability to backdoor attacks, which can manipulate predictions stealthily and pose significant yet underexplored security risks. The existing backdoor strategies for link prediction suffer from two key limitations: gradient-based optimization is computationally intensive and scales poorly to large graphs, while single-node triggers introduce noticeable structural anomalies and local feature inconsistencies, making them both detectable and less effective. To address these limitations, we propose a novel backdoor attack framework grounded in the principle of homophily, designed to balance effectiveness and stealth. For each selected target link to be poisoned, we inject a unique path-based trigger by adding a bridge node that acts as a shared neighbor. The bridge node’s features are generated through a context-aware probabilistic sampling mechanism over the joint neighborhood of the target link, ensuring high consistency with the local graph context. Furthermore, we introduce a confidence-based trigger injection strategy that selects non-existent links with the lowest predicted existence probabilities as targets, ensuring a highly effective attack from a small poisoning budget. Extensive experiments on five benchmark datasets—Cora, Citeseer, Pubmed, CS, and the large-scale Physics graph—demonstrate that our method achieves superior performance in terms of Attack Success Rate (ASR) while maintaining a low Benign Performance Drop (BPD). These results highlight a novel and practical threat to GNN-based link prediction, offering valuable insights for designing more robust graph learning systems. Full article

Infrared dim and small target detection aims to accurately localize targets within complex backgrounds or clutter. However, under extremely low signal-to-noise ratio (SNR) conditions, single-frame detection methods often fail to effectively detect such targets. In contrast, multi-frame detection can exploit temporal cues to significantly improve the probability of detection (Pd) and reduce false alarms (Fa). Existing multi-frame approaches often employ 3D convolutions/RNNs to implicitly extract temporal features. However, they typically lack explicit modeling of target motion. To address this, we propose a Dual Encoder–Decoder Multi-Frame Infrared Small Target Detection Network with Motion Encoding (DEMNet) that explicitly incorporates motion information into the detection process. The first multi-level encoder–decoder module leverages spatial and channel attention mechanisms to fuse hierarchical features across multiple scales, enabling robust spatial feature extraction from each frame of the temporally aligned input sequence. The second encoder–decoder module encodes both inter-frame target motion and intra-frame target positional information, followed by 3D convolution to achieve effective motion information fusion. Extensive experiments demonstrate that DEMNet achieves state-of-the-art performance, outperforming recent advanced methods such as DTUM and SSTNet. For the DAUB dataset, compared to the second-best model, DEMNet improves Pd by 2.42 percentage points and reduces Fa by 4.13 × 10⁻⁶ (a 68.72% reduction). For the NUDT dataset, it improves Pd by 1.68 percentage points and reduces Fa by 0.67 × 10⁻⁶ (a 7.26% reduction) compared to the next-best model. Notably, DEMNet demonstrates even greater advantages on test sequences with SNR ≤ 3. Full article

Most existing Simultaneous Localization and Mapping (SLAM) systems rely on the assumption of static environments to achieve reliable and efficient mapping. However, such methods often suffer from degraded localization accuracy and mapping consistency in dynamic settings, as they lack explicit mechanisms to distinguish between static and dynamic elements. To overcome this limitation, we present BMP-SLAM, a vision-based SLAM approach that integrates semantic segmentation and Bayesian motion estimation to robustly handle dynamic indoor scenes. To enable real-time dynamic object detection, we integrate YOLOv5, a semantic segmentation network that identifies and localizes dynamic regions within the environment, into a dedicated dynamic target detection thread. Simultaneously, the data association Bayesian mobile probability proposed in this paper effectively eliminates dynamic feature points and successfully reduces the impact of dynamic targets in the environment on the SLAM system. To enhance complex indoor robotic navigation, the proposed system integrates semantic keyframe information with dynamic object detection outputs to reconstruct high-fidelity 3D point cloud maps of indoor environments. The evaluation conducted on the TUM RGB-D dataset indicates that the performance of BMP-SLAM is superior to that of ORB-SLAM3, with the trajectory tracking accuracy improved by 96.35%. Comparative evaluations demonstrate that the proposed system achieves superior performance in dynamic environments, exhibiting both lower trajectory drift and enhanced positioning precision relative to state-of-the-art dynamic SLAM methods. Full article

Neovascular ocular diseases are caused by vascular endothelial growth factor A (VEGFA) overexpression. Thus, VEGFA inhibition is considered the main strategy for treating ocular neovascularization. However, existing anti-VEGF therapies have several limitations in stability and delivery efficiency. To overcome the limitations, exosome-based VEGF siRNA delivery technology has attracted attention since exosomes have the advantages of high in vivo stability and excellent intracellular delivery efficiency. Additionally, loading VEGFA siRNA into exosomes not only allows for targeting specific cells or tissues but can also improve therapeutic efficacy. Our research team purified and concentrated exosomes using chromatography techniques, added fluorescein amidite (FAM)-labeled VEGFA siRNA into exosomes, and observed the novel effect of drug delivery in vitro. This study successfully introduced hVEGFA siRNA-FAM into target cells, with high efficacy particularly at 48 h after treatment. Furthermore, the enhanced inhibition of VEGFA expression at 48 h post-treatment was confirmed. FACS analysis was performed using the apoptosis markers Annexin V-FITC (green) and PI-PE (red) to confirm the presence or absence of apoptosis. Both groups treated with hVEGFA siRNA-FAM-EXO (1) and hVEGFA siRNA-FAM-EXO (2) showed increased apoptosis as the exposure time passed compared to the untreated group (0 h). hVEGFA siRNA-FAM-EXO treatment effectively induced apoptosis. After 24 h, early apoptosis was 12.9% and 13.9% and late apoptosis was 1.5% and 3.7% in hVEGFA siRNA-FAM-EXO groups (1) and (2), respectively. After 48 h, early apoptosis was 23.9% and late apoptosis was 39.4% and 17.8% in hVEGFA siRNA-FAM-EXO groups (1) and (2), respectively, indicating a time-dependent pattern of apoptosis progression. Additionally, tube formation of human vascular endothelial cells (HUVECs) was induced to confirm the effect of VEGFA siRNA-loaded exosomes on the angiogenesis assay in vitro. Compared with controls, angiogenesis became significantly weakened in hVEGFA siRNA-FAM-EXO (1)- and hVEGFA siRNA-FAM-EXO (2)-treated groups at 48 h post-treatment and completely disappeared at 72 h, probably occurring due to decreased VEGFA, PIGF, and VEGFC in the intracellular cytosol and conditioned media secreted by VEGFA siRNA-FAM in HUVECs. In conclusions, FAM-tagged VEGFA siRNA was packed into exosomes and degraded over time after tube formation, leading to cell death due to a decrease in VEGFA, PIGF, and VEGFC levels. This study is expected to support the development of in vivo neovascularization models (keratitis, conjunctivitis, or diabetic retinopathy models) in the future. Full article

Infrared thermography is a common approach used in building inspection for identifying building envelope thermal anomalies that cause energy loss and occupant thermal discomfort. Detecting these anomalies is essential to improve the thermal performance of energy-inefficient buildings through energy retrofit design and correspondingly reduce operational energy costs and environmental impacts. A thermal bridge is an unwanted conductive heat transfer. On the other hand, an infiltration/exfiltration anomaly is an uncontrollable convective heat transfer, typically happening around windows and doors, but it can also be due to a defect that comprises a building envelope’s integrity. While the existing literature underscores the significance of automatic thermal anomaly identification and offers insights into automated methodologies, there is a notable gap in addressing an automated workflow that leverages building envelope component segmentation for enhanced detection accuracy. Consequently, an automatic thermal anomaly identification workflow from visible and thermal images was developed to test it, utilizing segmented building envelope information compared to a workflow without any semantic segmentation. Therefore, building envelope images (e.g., walls and windows) were segmented based on a U-Net architecture compared to a more conventional semantic segmentation approach. The results were discussed to better understand the importance of the availability of training data and for scaling the workflow. Then, thermal anomaly thresholds for different target domains were detected using probability distributions. Finally, thermal anomaly masks of those domains were computed. This study conducted a comprehensive examination of a campus building in Syracuse, New York, utilizing a drone-based data collection approach. The case study successfully detected diverse thermal anomalies associated with various envelope components. The proposed approach offers the potential for immediate and accurate in situ thermal anomaly detection in building inspections. Full article

With the rapid development of drone technology, target detection and estimation of radar parameters for maneuvering targets have become crucial. Drones, with their small radar cross-sections and high maneuverability, cause range migration (RM) and Doppler frequency migration (DFM), which complicate the use of traditional radar methods and reduce detection accuracy. Furthermore, the detection of multiple targets exacerbates the issue, as target interference complicates detection and impedes parameter estimation. To address this issue, this paper presents a method for high-resolution multi-drone target detection and parameter estimation based on the adjacent cross-correlation function (ACCF), fractional Fourier transform (FrFT), and deep learning techniques. The ACCF operation is first utilized to eliminate RM and reduce the higher-order components of DFM. Subsequently, the FrFT is applied to achieve coherent integration and enhance energy concentration. Additionally, a convolutional neural network (CNN) is employed to address issues of spectral overlap in multi-target FrFT processing, further improving resolution and detection performance. Experimental results demonstrate that the proposed method significantly outperforms existing approaches in probability of detection and accuracy of parameter estimation for multiple maneuvering targets, underscoring its strong potential for practical applications. Full article

Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for sensitive industrial data processing. In contrast to existing work that treats scheduling and privacy as separate concerns, this paper proposes a unified polymorphic heterogeneous security architecture that integrates hybrid event–time triggered scheduling with adaptive privacy-preserving arbitration, specifically designed to address the unique challenges of cloud–edge collaboration ICSs where both security resilience and privacy preservation are paramount requirements. The architecture introduces three key innovations: (1) a hybrid event–time triggered scheduling algorithm with credibility assessment and heterogeneity metrics to mitigate common-mode escape scenarios, (2) an adaptive privacy budget allocation mechanism that balances privacy protection effectiveness with system availability based on attack activity levels, and (3) a unified framework that organically integrates privacy-preserving arbitration with heterogeneous redundancy management. Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (${10}^{-2}$ probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments. Full article