Search Results (393)

This paper presents the Micro Video Guidance Sensor Version 2 (uVGS-2), a ROS-based vision navigation framework for real-time six-degrees-of-freedom pose estimation in drones, spacecraft, and autonomous robotic platforms operating in GNSS-denied environments. The system evolves from the previous Smartphone Video Guidance Sensor (SVGS) architecture through a modular C++ implementation, including advanced image preprocessing, deterministic blob sorting, and an optimized perspective-4-point solver using a Lie-algebra-based analytical Jacobian formulation. The proposed architecture achieves computationally efficient photogrammetric state estimation using onboard camera and processor resources, enabling deployment in resource-constrained systems. Experimental validation was conducted in NASA’s Astrobee free-flying robot, both at the International Space Station (ISS), for SVGS, and by ground testing through real-time sensor-fusion with Astrobee’s graph-based localizer (Astroloc), for uVGS-2. Results demonstrate robust centimeter-level accuracy in relative position and attitude estimation under illumination disturbances, partial occlusions, and intermittent loss of line-of-sight. The framework can be used in robotic platforms and autonomous UAV operations, including precision landing, formation flight, and cooperative navigation in environments where GNSS signals are unavailable or intermittent. Full article

With the widespread deployment of IoT devices, deploying efficient network traffic classification models on resource-constrained edge nodes is critical for real-time boundary security. However, traditional lightweight models primarily rely on macro-level structural pruning, which often sacrifices crucial feature extraction capabilities when handling complex heterogeneous traffic, leading to a severe imbalance between parameter compression and detection accuracy. To overcome this bottleneck, we propose TinyFlowNet, an ultra-lightweight multi-module fusion architecture. To prevent the parameter explosion inherent in combining CNN, LSTM, and Transformer modules, TinyFlowNet innovatively adopts an extreme operator-level reconstruction strategy. By introducing debiased computations, affine-free normalization, and a customized micro-self-attention mechanism, it comprehensively strips away underlying redundant parameters. Simultaneously, an integrated parameter-free regularization mechanism is introduced to compensate for the representational capacity lost under this extreme compression, ensuring robust spatio-temporal feature fusion. Comprehensive evaluations on the custom X-IDS-20 balanced dataset alongside the complex CICDarknet2020 and ToN_IoT public datasets demonstrate that TinyFlowNet achieves exceptional accuracies of 95.31 percent, 99.53 percent, and 97.13 percent, respectively. Furthermore, it exhibits formidable robustness against extreme class imbalances by securing a peak Matthews Correlation Coefficient of 0.9465 and an outstanding PR-AUC of 0.9834, all while strictly confining the parameter count to merely 74,600. Crucially, actual on-device hardware profiling on a commercial edge device corroborates its deployment viability, exhibiting a minimal dynamic memory footprint of 8.26 MB, an average inference latency of 0.79 ms, and a processing throughput exceeding 1200 FPS. Compared to a standard heavy Hybrid CNN-LSTM-Transformer baseline, TinyFlowNet achieves superior detection accuracy while drastically reducing the parameter footprint by over 99.3% and computational FLOPs by 95.8%. Furthermore, against mainstream lightweight benchmarks like DistilBERT and heavy baselines such as LSTM, TinyFlowNet reduces parameters by 61.4% to 94% while simultaneously achieving absolute accuracy leaps and accelerating inference speed by nearly 4× over MobileNetV2, establishing a highly efficient new paradigm for intelligent edge defense. Full article

Background and Objectives: Early diagnosis of acute myocardial infarction (AMI) remains challenging due to the “diagnostic blind window” of conventional protein biomarkers and the limited sensitivity of electrocardiograms in non ST-segment elevation myocardial infarction (NSTEMI). Cardiospecific circulating microRNAs, specifically the microRNA-208 (miR-208) family, have emerged as promising candidates to bridge this gap. This systematic review and meta-analysis evaluated the diagnostic accuracy of circulating miR-208 and outlines a proposed conceptual framework to guide its clinical translation. Materials and Methods: PubMed and Embase were systematically searched up to June 24th, 2026, for clinical studies evaluating the diagnostic performance of circulating miR-208a and/or miR-208b against standard reference definitions for AMI. Risk-of-bias assessment using the QUADAS-2 tool was performed independently by two reviewers. Pooled sensitivity and specificity were estimated using bivariate random effects modeling, and sources of heterogeneity were explored via subgroup analyses. Results: Forty-one studies enrolling 6306 participants were included in the qualitative synthesis, of which 14 were eligible for meta-analysis. The pooled sensitivity and specificity of circulating miR-208 for AMI detection were 0.89 (95% CI: 0.81–0.94) and 0.90 (95% CI: 0.83–0.94), respectively. Marked between-study heterogeneity was observed. Subgroup analyses revealed significantly higher diagnostic accuracy in isolated STEMI (sensitivity: 0.95) or NSTEMI (sensitivity: 0.93) cohorts compared to mixed chest pain populations (sensitivity: 0.65; p < 0.0001). Specificity dropped from 0.90 with healthy controls to 0.80 when using non-AMI controls (p = 0.002), indicating spectrum bias. Funnel plots suggested prominent small-study effects. Conclusions: Circulating miR-208 exhibits a powerful biological signal for the early detection of cardiomyocyte injury, but its standalone clinical utility is constrained by methodological heterogeneity and publication bias. Rather than an immediate clinical tool, future prospective translation requires evaluating this biomarker within the standardized miAMI framework—conceptually prioritizing future investigation of the hyper-acute (<2 h) window, absolute quantification to resolve normalization variability, and integration into multi-marker point-of-care panels. Full article

The rapid surge of Electric Two-Wheelers (E2Ws) in high-density urban villages imposes severe strain on low-voltage residential distribution networks. Unlike formal Electric Vehicles, E2W charging is decentralized and highly constrained by short pedestrian walking thresholds, frequently forcing users to adopt non-compliant “fly-wire charging” when public facilities are scarce. Traditional top-down load models fail to capture these localized, micro-behavioral single-phase grid impacts. To address this deficit, this study proposes a GIS-integrated “Digital Sandbox” simulation framework that projects individual behavioral mutations directly onto feeder networks via a hyper-granular “particle tracking” mechanism, treating each E2W as an autonomous agent. As a case study focused on a representative urban-village area, we validate the model using field data from the site. A 30-day simulation reveals that unmanaged fly-wire charging generates a peak load of 17.64 kW (nearly double the public station peak) and accounts for 38.9% of aggregate energy consumption—concentrated within the top 10 buildings and coinciding with evening peaks, inducing severe phase imbalance. While the numerical results are case-specific, the framework itself is transferable to other service areas through re-calibration against local data. This foundational digital twin blueprint shifts E2W planning from guesswork to particle-level risk prediction. Full article

Computational biomechanics has grown substantially alongside imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI), which together enable high-fidelity biomodels of both hard and soft tissues. Most such biomodels, however, are represented as continuous homogeneous solids, limiting their capacity to reproduce the internal architecture of living tissues. Micro-finite element (μFE) analysis has addressed this limitation for bone at sub-millimetric scales using micro-CT data, but its adoption remains constrained by scanner availability, computational cost, and workflow complexity. This work proposes a methodological framework, termed intrabiofidelity, as a taxonomic descriptor complementary to biofidelity that characterizes the degree to which a biomodel reproduces the internal morphology and morphometry of a tissue. A reproducible pipeline based on ScanIP^® segmentation of MRI-derived DICOM data, SolidWorks^® solidification, and ANSYS^® Workbench finite element analysis is presented, through which a macro-scale trabecular representation is extracted from the distal femoral cancellous bone and integrated into a knee biomodel. Two numerical analyses were performed under an equivalent bipodal-standing load with orthotropic material properties for cortical and trabecular bone: one with external biofidelity only (Case 1), and one incorporating macro-scale intrabiofidelity in the trabecular bone (Case 2). The introduction of intrabiofidelity produced a substantial redistribution of peak von Mises stress between compartments. Trabecular peak stress increased from 2.66 to 12.10 MPa (a 4.5-fold elevation), while cortical peak stress decreased from 56.25 to 45.97 MPa (an 18.3% reduction), whereas the volume-averaged stress remained essentially unchanged in both tissues, indicating that intrabiofidelity primarily affects local concentrations rather than the bulk stress state. Principal stress data further revealed that the trabecular region transitions from a low-stress, predominantly compressive state in Case 1 to one in which substantial local tensile and compressive concentrations of comparable magnitude coexist in Case 2. The proposed methodology provides an accessible workflow for macro-scale integration of internal bone architecture using routinely available MRI data and commercial FEA software, and introduces intrabiofidelity as a terminological complement useful for teaching and for systematically documenting the fidelity of computational biomodels. Full article

In-Mold Electronics (IMEs) present a highly promising monolithic integration strategy for manufacturing miniaturized 3D MEMS optical tweezers, offering exceptional environmental adaptability and structural compactness. However, the precision of such optical systems is heavily constrained by the injection molding process. During the molding phase, high-pressure melt scouring and severe thermo-mechanical coupling frequently induce geometric misalignment, manifesting as node displacement, localized warpage, and residual stress accumulation in the embedded circuits. This displacement critically alters the cross-sectional area of conductive traces, leading to resistance fluctuations that can destabilize the driving current. According to American Wire Gauge (AWG) standards, ensuring the geometric fidelity of this sensor-CPU interconnect pathway is fundamental to maintaining signal integrity. To address these manufacturing bottlenecks, this study systematically investigates the process stability of IME circuits Cyclic Olefin Copolymer (COC) is strategically selected as the substrate material over Polycarbonate (PC) and Liquid Silicone Rubber (LSR) due to its ultra-high light transmittance, extremely low water absorption, and superior thermomechanical stability. Based on finite element simulation, a data-driven intelligent optimization framework is developed. Latin Hypercube Sampling (LHS) is first utilized to efficiently sample the multi-dimensional process space, comprising melt temperature, packing pressure, and packing time. To handle the non-stationary nature of process feedback signals, wavelet analysis is introduced to decouple high-frequency noise, extracting Wavelet Energy Entropy (WEE) as a highly robust dynamic metric for process stability. Subsequently, a hybrid NSGA-II-MOPSO multi-objective algorithm is deployed to cooperatively optimize the injection parameters. The simulation-based optimization results demonstrate a substantial enhancement in manufacturing precision. Under the optimal parameter configuration, the average node displacement of the embedded circuits decreases significantly from 0.034 mm to 0.014 mm, achieving a 58.82% reduction. Simultaneously, volumetric shrinkage drops from 5.755% to 4.832% (a 16.04% reduction), while residual stress is maintained well within the structural safety threshold of optical-grade polymers. By clarifying the deformation control mechanism during the manufacturing phase, this study provides a highly reliable, data-driven methodological framework for the precision mass production of micro-nano optical systems. Full article

Orbital remote sensing platforms increasingly rely on CNN-based object detection for real-time situational awareness. However, deploying these models on spaceborne edge devices is challenging because of stringent Size, Weight, and Power (SWaP) constraints. In addition, the branch-and-merge topology of conventional single-stage detectors increases on-chip memory usage and introduces pipeline stalls, limiting efficient FPGA implementation. To address these challenges, we proposed RS-YOLO, an object detection algorithm developed through a hardware–software co-design approach. Structural re-parameterization converts heterogeneous branches into a sequential stream of padding-free convolutions, producing a deterministic dataflow and reducing per-state combinational control complexity and data-path multiplexing overhead. To mitigate the high-entropy concentration at the center of the re-parameterized kernels, we further introduce a spatial heterogeneous quantization (SHQ) engine. The SHQ engine assigns 16-bit precision to the central coefficients while preserving vectorized 8-bit computation for peripheral elements, reducing quantization errors for small targets with minimal hardware overhead. Experimental results on the Xilinx Zynq-7020 platform show that the proposed system consumes only 2.24 W while achieving a mean Average Precision (mAP) of 0.887 on the NWPU VHR-10 dataset, representing a 1.4% decrease compared with the FP32 baseline. The system also achieves an energy efficiency of 15.19 GOPS/W, demonstrating an effective balance between hardware efficiency and detection performance for resource-constrained edge platforms such as micro-satellite payloads. Full article

Accurate assessment of shale wettability is crucial for optimizing fracturing design and enhancing shale gas recovery. However, conventional evaluation methods are often unreliable due to shale’s complex mineral composition and heterogeneous pore structure. This study investigated marine shale from the Sichuan Basin by establishing a multi-scale research framework that integrates macro-scale spontaneous imbibition evolution, micro-scale dynamic video observation, and interfacial property characterization. Using techniques including X-ray diffraction (XRD), nuclear magnetic resonance (NMR), and deep-field microscopy, we investigated the water distribution patterns and imbibition mechanisms. The results indicate that the mixed-wettability characteristics of shale are governed by the synergistic effects of mineral composition and pore structure. Specifically, hydrophilic surfaces facilitate stable adsorbed water film formation via hydrogen bonding and van der Waals forces, whereas hydrophobic surfaces inhibit water spreading. At the macro-scale, a distinctive “water ring” was observed immediately upon immersion. This phenomenon reveals a physical correlation between the mass per unit length of the water ring and the contact angle at the gas–solid–liquid interface. Based on this correlation, an innovative standard curve method was developed to evaluate rock wettability. This method allows for the inversion of the apparent contact angle by simply measuring the mass per unit length of the water ring, thereby overcoming the limitations of traditional optical methods that are constrained by surface roughness and pore structure. Consequently, a logical chain of “wettability → occurrence characteristics → imbibition patterns” was established. This work provides new insights and theoretical support for understanding fluid dynamics and optimizing fracturing fluids in unconventional reservoirs. Full article

Text entry remains a bottleneck for productivity-oriented Virtual Reality (VR), especially in scenarios where optical hand tracking is unstable because of self-occlusion, poor lighting, or out-of-view interaction. We present SlideRing, a dual-thumb wearable text-entry method that senses thumb-to-finger micro-gestures with two miniature Inertial Measurement Units (IMUs). SlideRing defines a 30-command interaction space from two hands, three target fingers, and five gesture types, then maps these commands to a full alphabetic keyboard through two complementary strategies: an ergonomic layout optimized for low movement cost and a QWERTY-compatible layout optimized for learnability. To decode subtle inertial signals, we design a dual-stream recognition model with a Statistical Feature Encoder, a Temporal Feature Encoder, and a context-aware gating module for joint finger–action classification. In offline evaluation, the model reaches 96.5% target-finger accuracy and 94.2% action-type accuracy. In a five-day text-entry study, the ergonomic layout improves from 7.43 to 15.75 words per minute (WPM), while the QWERTY-compatible layout improves from 10.55 to 15.25 WPM. The ergonomic layout reduces physical demand, whereas the QWERTY-compatible layout lowers initial mental load. These results suggest that IMU-based thumb-to-finger input has the potential to provide robust, low-visual-demand text entry for constrained VR environments. Full article

Magnetic actuation is a fundamental enabling technology for micro/nanorobotics and biomedical manipulation. However, the trade-off between magnetic field gradient, usable workspace, and efficient heat dissipation often conflicts and constrains its performance. Here, we present an enhanced electromagnetic manipulation system (EEMS) based on a compact, high-efficiency magnetic circuit and an optimized six-electromagnet configuration. By integrating high-permeability structural components and employing finite-element-based optimization, the system achieves a spherical workspace of 106 mm in diameter while maintaining strong and spatially controllable magnetic fields. Experimental results demonstrate magnetic flux densities up to 300 mT and a magnetic field gradient up to 9.5 T/m within the workspace, with a central magnetic field gradient of approximately 2 T/m under continuous operation at 3 A. Thermal simulations and measurements confirm safe operation below human body temperature without active cooling. Magnetic manipulation experiments in viscous environments further validate precise motion control and force balancing, highlighting the system’s potential for advanced magnetic manipulation and intelligent microrobotic applications. Full article

Real-time strawberry volume estimation is a pivotal technology for automated harvesting and precision grading. However, conventional contact methods are prone to damaging fruits, while existing vision-based approaches struggle to balance high accuracy with low computational overhead. To address these challenges, this study proposes a two-stage real-time volume estimation framework coupling a red-green-blue-depth (RGB-D) sensor with an “Instance segmentation–Principal-axis slicing” framework. First, to precisely extract target contours in complex backgrounds, we designed Deformable Feature Aware-YOLO (DFA-YOLO) based on the YOLO11-seg architecture. This model enhances the geometric perception of irregular fruit edges and effectively overcomes the challenges of background noise and multi-scale variations, providing high-precision masks for subsequent spatial mapping. Subsequently, a principal-axis-slicing algorithm extracts the mask’s centroid and principal axis, perpendicularly slicing the mask into infinitesimal micro-slices. By computing and accumulating the pixel-space volume of these slices, the system converts them into precise 3D physical volumes based on RGB-D depth mapping. The entire system was deployed on an NVIDIA Jetson Orin edge computing platform and validated in a greenhouse. Experimental results demonstrate that the estimated volume highly agrees with the true volume, achieving a coefficient of determination (R²) of 0.945 and a mean absolute percentage error (MAPE) of 9.0%. Under typical operating conditions (1–5 targets per field of view), the system maintains an overall frame rate of 8–15 FPS, requiring only 55 ms for single-fruit estimation. This method exhibits favorable stability and lightweight efficiency under the tested greenhouse conditions, offering a reliable solution for real-time non-destructive crop phenotypic monitoring in computationally constrained agricultural environments. Full article

Sustainable transport transformation increasingly depends on the configuration and performance of urban infrastructure systems. In coastal and port cities, decarbonizing transport is particularly complex due to spatial constraints, heritage protection requirements, and the coexistence of freight and passenger flows. In such environments, accessibility functions as a key indicator of transport infrastructure performance, reflecting how effectively transport systems enable low-carbon and multimodal mobility choices. Gdynia, a major Baltic port city in Poland, represents a context in which infrastructure limitations intersect with growing mobility demand. The concentration of port-related traffic, compact urban form, and limited opportunities for network expansion create structural conditions that may reinforce car dependency. This study examines infrastructure and accessibility challenges at the micro-scale of the Faculty of Navigation at Gdynia Maritime University, a centrally located campus with limited integration into public and active transport systems. Based on a survey of 342 respondents, including students and employees, the research analyzes modal split, travel time, and perceived barriers to sustainable mobility. The findings reveal infrastructure gaps in public transport connectivity, cycling network integration, and parking policy, collectively influencing transport behavior and constraining the shift toward low-carbon mobility. The study highlights the importance of infrastructure alignment, intermodal integration, and accessibility-based planning as prerequisites for smart and sustainable transport systems in coastal areas. Full article

Android malware detection is increasingly important for mobile and edge security because malicious applications may compromise user privacy, device reliability, and sensitive service transactions. However, single-view static detection methods often provide limited semantic coverage and are sensitive to noisy or obfuscated code, while many deep learning models remain too heavy for resource-constrained deployment. To address these challenges, this paper proposes a lightweight Android malware detection framework based on SDAE-guided multi-view static feature fusion. The framework extracts three complementary static views, namely API calls, permission requests, and system components, from AndroidManifest.xml and classes.dex. These views are independently denoised and compressed by stacked denoising autoencoders, then aligned as the R, G, and B channels of a pseudo-RGB representation. A compact MicroNet-SE classifier with squeeze-and-excitation blocks is used to recalibrate the fused semantic channels and perform malware classification. Experiments on the CICMalDroid 2020 and CIC-AndMal2017 datasets show that the proposed framework achieves 99.01% accuracy, 99.15% precision, 98.99% recall, and 99.07% F1-score, with only 99.6 k parameters and a model size of 1.26 MB. After conversion to TensorFlow Lite, the MicroNet-SE classifier achieves average on-device inference latencies ranging from 1.85 ms to 2.16 ms on two real mobile devices. The model also maintains stable performance under synthetic feature perturbations and practical APK-level obfuscation settings. These findings suggest that combining multi-view static semantics with denoising-based representation learning can improve both detection robustness and deployment efficiency. Overall, the results indicate that the proposed framework provides an effective and lightweight static screening component for Android malware detection in resource-constrained mobile and edge environments. Full article

Collaboration is increasingly treated as a core capability in contemporary working life, yet leadership-development research suggests that developmental efforts often remain too generic, weakly contextualized, and insufficiently connected to the conditions under which participants must learn and perform. This theory-informed exploratory study examines how Norwegian union representatives define, operationalize, and reflect on collaborative capability development within a semester-long university course. The study adopts a qualitative document design based on 25 written course reports produced by Parat union representatives enrolled in the course Collaboration for the Future Working Life at Kristiania University of Applied Sciences in autumn 2025. The reports are analyzed as structured reflective development documents using cross-case thematic analysis. Conceptually, the article draws on collaboration research, leadership development, self-directed learning, self-leadership, and job demands–resources theory. The findings indicate that participants conceptualized collaborative capability as a multidimensional professional capability combining dialogic competence, trust-building, psychological safety, role-based bridge-building, assertive boundary-setting, and self-regulation under pressure. Development was typically organized through iterative practice cycles of self-evaluation, feedback, goal setting, monitoring routines, micro-practices for attention and stress regulation, environmental redesign, implementation, reflection, and adjustment. At the same time, the reports suggest that collaborative development was constrained by time pressure, emotional exposure, cumulative role demands, and fluctuating energy. Reported outcomes were typically incremental, including clearer communication, increased awareness of triggers, stronger boundary-setting, more sustainable role professionalism, and improved presence under strain. The article contributes a bounded, context-sensitive account of collaborative capability development as a self-directed, self-regulated, and resource-sensitive process of professional becoming. It further develops two connected practical–theoretical models: the Performance Pyramid, which clarifies the developmental architecture from identity awareness to energy and capability regulation and performance enactment, and the Self-Leadership Wheel of Becoming, which functions as an operational scaffold for self-evaluation, goal setting, feasible program design, implementation, reflection, and revision. Rather than presenting these models as universally validated, the article positions them as heuristic and processual contributions for understanding and supporting capability development in collaboration-intensive roles. Full article

The No. VI ore block of the Galinge skarn system in the Qimantagh metallogenic belt, East Kunlun, contains proximal Fe-oxide mineralization and distal Pb–Zn sulfide mineralization that are spatially zoned and locally overprinted along faults and interlayer fracture zones. To constrain the controls on Fe–Pb–Zn zonation and overprinting within this ore block, we integrated LA–ICP–MS zircon U–Pb dating, zircon Lu–Hf isotopes, whole-rock major and trace elements, and in situ trace elements of magnetite, pyrite, chalcopyrite, pyrrhotite, and arsenopyrite. Zircon U–Pb ages indicate two Indosinian intrusive pulses: an early granodiorite at 235.1 ± 0.51 Ma and a younger granodiorite–quartz diorite at 229.52 ± 0.46 Ma. Excluding the hydrothermally altered sample ZK26804-805, the intrusive rocks are metaluminous, medium- to high-K calc-alkaline I-type granitoids mainly derived from remelting of ancient crustal material, with a greater juvenile crustal or mantle contribution in the younger phase. Magnetite is generally Zn-rich and Pb-poor, whereas late pyrite and chalcopyrite are enriched in Pb, Ag, Cd, and Bi; local Sb–As anomalies in magnetite and arsenopyrite indicate late hydrothermal overprinting. The Fe and Pb–Zn mineralization is best interpreted as staged products of one multipulse magmatic–hydrothermal system controlled not only by intrusive pulses but also by inherited structural pathways, host-rock reactivity, and evolving redox-sulfidation conditions. The interpretation of Sb–As enrichment in magnetite is therefore used cautiously because these elements may occur as lattice substitutions and/or micro- to nano-inclusions introduced or modified during retrograde alteration. Full article