Search Results (161)

To address the common issues of high small object miss rates, frequent false positives, and poor real-time performance in PCB defect detection, this paper proposes a multi-scale fusion algorithm based on the YOLOv12 framework. This algorithm integrates the Global Attention Mechanism (GAM) into the redesigned A2C2f module to enhance feature response strength of complex objects in symmetric regions through global context modeling, replacing conventional convolutions with hybrid weighted downsampling (HWD) modules that preserve copper foil textures in PCB images via hierarchical weight allocation. A bidirectional feature pyramid network (BiFPN) is constructed to reduce bounding box regression errors for micro-defects by fusing shallow localization and deep semantic features, employing a parallel perception attention (PPA) detection head combining dense anchor distribution and context-aware mechanisms to accurately identify tiny defects in high-density areas, and optimizing bounding box regression using a normalized Wasserstein distance (NWD) loss function to enhance overall detection accuracy. The experimental results on the public PCB dataset with symmetrically transformed samples demonstrate 85.3% recall rate and 90.4% mAP@50, with AP values for subtle defects like short circuit and spurious copper reaching 96.2% and 90.8%, respectively. Compared to the YOLOv12n, it shows an 8.7% enhancement in recall, a 5.8% increase in mAP@50, and gains of 16.7% and 11.5% in AP for the short circuit and spurious copper categories. Moreover, with an FPS of 72.8, it outperforms YOLOv5s, YOLOv8s, and YOLOv11n by 12.5%, 22.8%, and 5.7%, respectively, in speed. The improved algorithm meets the requirements for high-precision and real-time detection of multi-category PCB defects and provides an efficient solution for automated PCB quality inspection scenarios. Full article

This article presents a novel off-cloud anchor sharing framework designed to enable seamless device interoperability for Mixed Reality (MR) multi-user and multi-platform applications. The proposed framework enables local storage and synchronization of spatial anchors, offering a robust and autonomous alternative for real-time collaborative experiences. Such anchors are digital reference points tied to specific positions in the physical world that allow virtual content in MR applications to remain accurately aligned to the real environment, thus being an essential tool for building collaborative MR experiences. This anchor synchronization system takes advantage of the use of local anchor storage to optimize the sharing process and to exchange the anchors only when necessary. The framework integrates Unity, Mirror and Mixed Reality Toolkit (MRTK) to support seamless interoperability between Microsoft HoloLens 2 devices and desktop computers, with the addition of external IoT interaction. As a proof of concept, a collaborative multiplayer game was developed to illustrate the multi-platform and anchor sharing capabilities of the proposed system. The experiments were performed in Local Area Network (LAN) and Wide Area Network (WAN) environments, and they highlight the importance of efficient anchor management in large-scale MR environments and demonstrate the effectiveness of the system in handling anchor transmission across varying levels of spatial complexity. Specifically, the obtained results show that the developed framework is able to obtain anchor transmission times that start around 12.7 s for the tested LAN/WAN networks and for small anchor setups, and to roughly 86.02–87.18 s for complex physical scenarios where room-sized anchors are required. Full article

In wireless sensor networks, evolutionary algorithms have emerged as pivotal tools for addressing complex localization challenges inherent in non-convex and nonlinear maximum likelihood estimation problems associated with received signal strength (RSS) measurements. While differential evolution (DE) has demonstrated notable efficacy in optimizing multimodal cost functions, conventional implementations often grapple with suboptimal convergence rates and susceptibility to local optima. To overcome these limitations, this paper proposes a novel enhancement of DE by integrating opposition-based learning (OBL) principles. The proposed method introduces an adaptive scaling factor that dynamically balances global exploration and local exploitation during the evolutionary process, coupled with a penalty-augmented cost function to effectively utilize boundary information while eliminating explicit constraint handling. Comparative evaluations against state-of-the-art techniques—including semidefinite programming, linear least squares, and simulated annealing—reveal significant improvements in both convergence speed and positioning precision. Experimental results under diverse noise conditions and network configurations further validate the robustness and superiority of the proposed approach, particularly in scenarios characterized by high environmental uncertainty or sparse anchor node deployments. Full article

To address the challenge of optimizing the layout of pseudolite anchor points in complex indoor environments with significant occlusions, this paper proposes a multi-objective particle swarm optimization algorithm (MG-MOPSO). The algorithm leverages a minimum geometric dilution of precision (GDOP) configuration to optimize anchor deployment, aiming to meet the high-precision requirements of indoor pseudolite positioning systems. Experimental results show that compared to the standard MOPSO, MG-MOPSO improves the convergence speed of two objective functions by 21.43% and 25.81%, respectively, and enhances optimization accuracy by 29.41% and 10%. Compared to the non-dominated sorting genetic algorithm II (NSGA-II), the convergence speed increases by 33.33% and 36.99%, while optimization accuracy improves by 36.84% and 29.41%. Moreover, MG-MOPSO outperforms both standard MOPSO and NSGA-II in terms of the Pareto front’s convergence and diversity, with improvements of 16.8% and 14.7%, respectively. Additionally, significant reductions are observed in average positioning error, maximum positioning error, and standard deviation across multiple test points. These results validate the effectiveness of the minimum-GDOP-based initialization and segmented weighting strategy, demonstrating the superior performance and broad applicability of the proposed MG-MOPSO algorithm in optimizing pseudolite layouts under complex indoor occlusion conditions. Full article

Self-amplifying RNA-based (saRNA) technology represents the last frontier in using synthetic RNA in vaccinology. Typically, saRNA consists of positive-strand RNA molecules of viral origin (almost exclusively from alphaviruses) where the sequences of structural proteins are replaced with the open reading frame coding the antigen of interest. For in vivo delivery, they are complexed with lipid nanoparticles (LNPs), just like current COVID-19 vaccines based on synthetic messenger RNA (mRNA). Given their ability to amplify themselves inside the cell, optimal intracellular levels of the immunogenic antigen can be achieved by delivering lower amounts of saRNA molecules compared to mRNA-based vaccines. However, the excessive intracellular accumulation of saRNA may represent a relevant drawback since, as already described in alphavirus-infected cells, the recipient cell may react by incorporating excessive RNA molecules into extracellular vesicles (EVs). These EVs can shed and enter neighboring as well as distant cells, where the EV-associated saRNA can start a new replication cycle. This mechanism could lead to an unwanted and unnecessary spread of saRNA throughout the body, posing relevant safety issues. This perspective article discusses the molecular mechanisms through which saRNAs can be transmitted among different cells/tissues. In addition, a simple way to control the possible excessive saRNA intercellular propagation through the co-expression of an EV-anchored protein inhibiting the saRNA replication is proposed. Based on current knowledge, a safety improvement of saRNA-based vaccines appears to be mandatory for their usage in healthy humans. Full article

Multi-scale thermal regulation of urban green spaces is critical for climate-adaptive planning. Addressing the limited research on key indicators and cross-scale synergies in high-density areas, this study developed an integrated framework combining multi-granularity grids and boosted regression tree (BRT) modeling to investigate nonlinear scale-dependent relationships between landscape parameters and land surface temperature (LST) in the central urban area of Shijiazhuang. Key findings: (1) Spatial heterogeneity and scale divergence: Vegetation coverage (FVC) and green space area (AREA) showed decreasing contributions at larger scales, while configuration metrics (e.g., aggregation index (AI), edge density (ED)) exhibited positive scale responses, confirming a dual mechanism with micro-scale quality dominance and macro-scale pattern regulation. (2) Threshold effects quantification: The BRT model revealed peak marginal cooling efficiency (0.8–1.2 °C per 10% FVC increment) within 30–70% FVC ranges, with minimum effective green patch area thresholds increasing from 0.6 ha (micro-scale) to 3.5 ha (macro-scale). (3) Based on multi-scale cooling mechanism analysis, a three-tier matrix optimization framework for green space strategies is established, integrating “micro-level regulation, meso-level connectivity, and macro-level anchoring”. This study develops a green space optimization paradigm integrating machine learning-driven analysis, multi-scale coupling, and threshold-based management, providing methodological tools for mitigating urban heat islands and enhancing climate resilience in high-density cities. Full article

Wireless indoor positioning systems are challenged by the reliance on densely deployed hardware and exhaustive site surveys, leading to elevated deployment and maintenance costs that limit scalability. This paper introduces a novel positioning framework that enhances the existing In-Building Wireless (IBW) infrastructure by retransmitting Global Navigation Satellite System (GNSS) signals. Pseudorange residuals extracted from raw GNSS measurements, when mapped against known cable lengths, facilitate anchor identification and precise ranging. In parallel, directional and inertial measurements are derived from the channel state information (CSI) of cellular reference signals. Building upon these observations, we develop a Hybrid Adaptive Filter-Graph Fusion (HAF-GF) algorithm for high-precision positioning, wherein the adaptive filter modulates observation noise based on Line-of-Sight (LoS) conditions, while a factor graph optimization over multiple positional constraints ensures global consistency and accelerates convergence. Ray tracing-based simulations in a complex office environment validate the efficacy of the proposed approach, demonstrating a 30% improvement in positioning accuracy and at least a threefold increase in deployment efficiency compared to conventional methods. Full article

Recent advancements in deep-learning and computer vision technologies, coupled with the availability of large-scale remote-sensing image datasets, have accelerated the progress of remote-sensing object detection. However, large-scale remote-sensing images typically feature extensive and complex backgrounds with small and sparsely distributed objects, which pose significant challenges to detection performance. To address this, we propose a novel framework for accurate object detection, termed transparent mask background optimization for accurate object detection (TMBO-AOD), which incorporates a clear focus module and an adaptive filtering framework. The clear focus module constructs an empirical background pool using a Gaussian distribution and introduces transparent masks to prepare for subsequent optimization stages. The adaptive filtering framework can be applied to anchor-based or anchor-free models. It dynamically adjusts the number of candidates generated based on background flags, thereby optimizing the label assignment process. This approach not only alleviates the imbalance between positive and negative samples but also enhances the efficiency of candidate generation. Furthermore, we introduce a novel separated loss function that strengthens both foreground and background consistencies. Specifically, it focuses the model’s attention on foreground objects while enabling it to learn the consistency of background features, thus improving its ability to distinguish objects from the background. We employ YOLOv8 combined with our proposed optimizations to evaluate our model in many datasets, demonstrating improvements in both accuracy and efficiency. Additionally, we validate the effectiveness of our adaptive filtering framework in both anchor-based and anchor-free methods. When implemented with YOLOv5 (anchor based), the framework reduces the candidate generation time by 48.36%, while the YOLOv8 (anchor-free) implementation achieves a 46.81% reduction, both with maintained detection accuracy. Full article

Localizing underwater nodes when they cannot be tethered or float on the surface presents significant challenges, primarily due to node mobility and the absence of fixed anchors with known coordinates. This paper advocates a strategy for tackling such a challenge by using visible light communication (VLC) from an airborne unit. A novel localization method is proposed where VLC transmissions are made towards the water surface; each transmission is encoded with the Global Positioning System (GPS) coordinates with the incident point of the corresponding light beam. Existing techniques deal with the problem in 2D by assuming that the underwater node has a pressure sensor to measure its depth. The proposed method avoids this limitation and utilizes the intensity of VLC signals to estimate the 3D position of the underwater node. The idea is to map the light intensity at the underwater receiver for airborne light beams and devise an error optimization formulation to estimate the 3D coordinates of the underwater node. Extensive simulations validate the effectiveness of the proposed method and capture its performance across various parameters. Full article

The rational development of single-atom catalysts (SACs) for selective formic acid dehydrogenation (FAD) requires an atomic-scale understanding of metal–support interactions and electronic modulation. In this study, spin-polarized density functional theory (DFT) calculations were performed to systematically examine platinum-group SACs anchored on graphitic carbon nitride (g-C₃N₄). The findings reveal that Pd and Au SACs exhibit superior selectivity toward the dehydrogenation pathway, lowering the free energy barrier by 1.42 eV and 1.39 eV, respectively, compared to the competing dehydration route. Conversely, Rh SACs demonstrate limited selectivity due to nearly equivalent energy barriers for both reaction pathways. Stability assessments indicate robust metal–support interactions driven by d–p orbital hybridization, while a linear correlation is established between the d-band center position relative to the Fermi level and catalytic selectivity. Additionally, charge transfer (ranging from 0.029 to 0.467 e) substantially modulates the electronic structure of the active sites. These insights define a key electronic descriptor for SAC design and offer a mechanistic framework for optimizing selective hydrogen production. Full article

To address the challenge of precise picking point localization in morphologically diverse safflower plants, this study proposes PointSafNet—a novel three-stage 3D point cloud analysis framework with distinct architectural and methodological innovations. In Stage I, we introduce a multi-view reconstruction pipeline integrating Structure from Motion (SfM) and Multi-View Stereo (MVS) to generate high-fidelity 3D plant point clouds. Stage II develops a dual-branch architecture employing Star modules for multi-scale hierarchical geometric feature extraction at the organ level (filaments and frui balls), complemented by a Context-Anchored Attention (CAA) mechanism to capture long-range contextual information. This synergistic feature learning approach addresses morphological variations, achieving 86.83% segmentation accuracy (surpassing PointNet++ by 7.37%) and outperforming conventional point cloud models. Stage III proposes an optimized geometric analysis pipeline combining dual-centroid spatial vectorization with Oriented Bounding Box (OBB)-based proximity analysis, resolving picking coordinate localization across diverse plants with 90% positioning accuracy and 68.82% mean IoU (13.71% improvement). The experiments demonstrate that PointSafNet systematically integrates 3D reconstruction, hierarchical feature learning, and geometric reasoning to provide visual guidance for robotic harvesting systems in complex plant canopies. The framework’s dual emphasis on architectural innovation and geometric modeling offers a generalizable solution for precision agriculture tasks involving morphologically diverse safflowers. Full article

Unmanned Aerial Vehicles (UAVs) have progressively gained interest in recent years due to the wide range of related applications, from aerial communications and autonomous flight to agriculture and logistics. However, accurate 3D localization is crucial for enabling these kinds of applications, and commonly used tracking algorithms are often performing unsatisfactorily in critical scenarios like urban canyons and environments, characterized by dense multipath and line of sight obstruction. In this work we derive a novel 3D tracking algorithm which, despite its mathematical simplicity, can efficiently track moving targets handling asynchronous arrival of the anchor measurements or obstructions of line-of-sight links and outperforming commonly used algorithms like the Extended Kalman Filter (EKF) and the Particle Filter (PF). The proposed algorithm tracks the 3D position, velocity, and acceleration of a moving target through the combination of range measurements, between the target and different anchors, which become available in numbers and time instants not necessarily ordered as usually assumed in these applications. We denote this condition as asynchronous measurements, meaning that the ranging measurements are not available from all the anchors and they refer to different positions of the UAV during the tracking. We also show that our estimator is optimal among the linear ones, meaning that within this class, it minimizes the estimation error variance. Finally, we explore the accuracy that can be achieved in simulated scenarios defined by realistic UAV altitudes, velocities, and trajectories, as well as typical ranging errors of wideband localization systems. Full article

The periodontal ligament (PDL) is a fibrous connective tissue that anchors the tooth-root surface to the alveolar bone with specific orientations. It plays a crucial role in functional restoration, optimal position stabilities, biomechanical stress transmission, and appropriate tissue remodeling in response to masticatory loading conditions. This pilot study explored spatial microarchitectures to promote PDL orientations while limiting mineralized tissue formation. A computer-designed perio-complex scaffold was developed with two parts: (1) PDL-guiding architectures with defined surface topography and (2) a bone region with open structures. After SEM analysis of micropatterned topographies on PDL-guiding architectures, perio-complex scaffolds were transplanted into two-wall periodontal defects in the canine mandible. Despite the limited bone formation at the 4-week timepoint, bone parameters in micro-CT quantifications showed statistically significant differences between the no-scaffold and perio-complex scaffold transplantation groups. Histological analyses demonstrated that the PDL-guiding architecture regulated fiber orientations and facilitated the functional restoration of PDL bundles in immunohistochemistry with periostin and decorin (DCN). The perio-complex scaffold exhibited predictable and controlled fibrous tissue alignment with specific angulations, ensuring spatial compartmentalization for PDL tissues and bone regenerations. These findings highlighted that the perio-complex scaffold could serve as an advanced therapeutic approach to contribute periodontal tissue regeneration and functional restoration in tooth-supporting structures. Full article

Terrestrial and satellite communications, tactical data links, positioning, navigation, and timing (PNT), as well as distributed sensing will continue to require precise timing and the ability to synchronize and disseminate time effectively. However, the supply of space-qualified clocks that meet Global Navigation Satellite Systems (GNSS)-level performance standards is limited. As the awareness of potential disruptions to GNSS due to adversarial actions grows, the current reliance on GNSS-level timing appears costly and outdated. This is especially relevant given the benefits of developing robust and stable time scale references in orbit, especially as various alternatives to GNSS are being explored. The onboard realization of clock ensembles is particularly promising for applications such as those providing the on-demand dissemination of a reference time scale for navigation services via a proliferated Low-Earth Orbit (pLEO) constellation. This article investigates potential inter-satellite network architectures for coordinating time and frequency across pLEO platforms. These architectures dynamically allocate radio resources for clock data transport based on the requirements for pLEO time scale formations. Additionally, this work proposes a model-based control system for wireless networked timekeeping systems. It envisions the optimal placement of critical information concerning the implicit ensemble mean (IEM) estimation across a multi-platform clock ensemble, which can offer better stability than relying on any single ensemble member. This approach aims to reduce data traffic flexibly. By making the IEM estimation sensor more intelligent and running it on the anchor platform while also optimizing the steering of remote frequency standards on participating platforms, the networked control system can better predict the future behavior of local reference clocks paired with low-noise oscillators. This system would then send precise IEM estimation information at critical moments to ensure a common pLEO time scale is realized across all participating platforms. Clock steering is essential for establishing these time scales, and the effectiveness of the realization depends on the selected control intervals and steering techniques. To enhance performance reliability beyond what the existing Linear Quadratic Gaussian (LQG) control technique can provide, the minimal-cost-variance (MCV) control theory is proposed for clock steering operations. The steering process enabled by the MCV control technique significantly impacts the overall performance reliability of the time scale, which is generated by the onboard ensemble of compact, lightweight, and low-power clocks. This is achieved by minimizing the variance of the chi-squared random performance of LQG control while maintaining a constraint on its mean. Full article

Phishing webpage detection is critical in combating cyber threats, yet distinguishing between benign and phishing webpages remains challenging due to significant feature overlap in the representation space. This study introduces a reinforced Triplet Network to optimize disentangled representation learning tailored for phishing detection. By employing reinforcement learning, the method enhances the sampling of anchor, positive, and negative examples, addressing a core limitation of traditional Triplet Networks. The disentangled representations generated through this approach provide a clear separation between benign and phishing webpages, substantially improving detection accuracy. To achieve comprehensive modeling, the method integrates multimodal features from both URLs and HTML DOM Graph structures. The evaluation leverages a real-world dataset comprising over one million webpages, meticulously collected for diverse and representative phishing scenarios. Experimental results demonstrate a notable improvement, with the proposed method achieving a 6.7% gain in the F1 score over state-of-the-art approaches, highlighting its superior capability and the dataset’s critical role in robust performance. Full article