Search Results (108)

This paper investigates the precision and stochastic error propagation of navigation solutions obtained from a low-cost microelectromechanical system inertial measurement unit (MEMS-IMU) under static conditions. A modern smartphone equipped with an MEMS-IMU was rigidly mounted at a calibrated fixed location to establish a zero-reference scenario, and inertial measurements were collected while the device remained stationary. The dataset was divided into 75 non-overlapping segments, each comprising 30 s of data sampled at 10 Hz, to enable statistically robust analysis. For each segment, velocity and position, which are theoretically zero under static conditions, were computed using strapdown inertial mechanization. A comprehensive statistical framework was then applied to characterize the stochastic behavior of both the raw inertial measurements and the derived navigation states. The methodology first assessed data normality, stationarity using the Augmented Dickey–Fuller (ADF) test, and variance homogeneity using Bartlett’s test. Subsequently, ARIMA models were identified and validated using the Ljung–Box (LB) test, while power spectral density (PSD) analysis provided complementary frequency-domain characterization. In addition, a multivariate, non-negative least squares variance component estimation (NNLS-VCE) method was employed to jointly estimate the variance components of multiple navigation state variables. The results demonstrate that the accelerometer and gyroscope measurements along all three axes are well characterized as stationary white-noise processes, with standard deviations in the order of ${10}^{-2} \mathrm{m}/{\mathrm{s}}^2$ and ${10}^{-4} \mathrm{rad}/\mathrm{s}$, respectively. The estimated velocity random walk (VRW) coefficients are $\left[0.197,0.201,0.160\right] \mathrm{m}/\mathrm{s}/\sqrt{\mathrm{h}}$, while the corresponding angular random walk (ARW) coefficients are $\left[0.009,0.012,0.008\right] \mathrm{rad}/\sqrt{\mathrm{h}}$. In contrast, the derived velocity and position estimates exhibit random walk behavior caused by error accumulation in the inertial mechanization process and are best represented by ARIMA$(0,1,0)$ and ARIMA$(0,2,0)$ models, respectively, consistent with the corresponding Allan variance analysis. After 30 s of static navigation, the average standard deviations of the ENU velocity estimates are ${\sigma }_v=[0.77,0.44,0.29] \mathrm{m}/\mathrm{s}$, while the corresponding position standard deviations are ${\sigma }_p=[1.30,0.69,0.46] \mathrm{m}$. The proposed framework provides a comprehensive approach for the stochastic modeling, precision assessment, and error characterization of low-cost MEMS-IMU navigation systems. Full article

This paper presents a wolf-pack-inspired distributed encirclement framework for heterogeneous unmanned aerial vehicle–unmanned surface vehicle (UAV–USV) teams operating with limited communication and intermittent visual sensing. The proposed growth-based visual connectivity encirclement (GB-VCE) method separates high-level role evolution from low-level motion execution. Each agent accumulates local evidence from target observation, visual connectivity contribution, motion stability, and platform characteristics, enabling smooth transitions among leader, relay, searcher, and encircler roles. Visual links and relay relations are treated as coordination resources rather than auxiliary sensing constraints, allowing target-related information to propagate through the team without centralized fusion or persistent all-to-all communication. Simulations with paired random seeds show that GB-VCE improves encirclement accuracy, target-information coverage, convergence consistency, and heterogeneous role complementarity compared with fixed-role, static-leader, UAV-only, and USV-only baselines. The results indicate that biomimetic role growth and visual connectivity preservation provide an interpretable and robust coordination principle for air–sea cooperative encirclement. Full article

Background/Objectives: Artificial intelligence (AI) can support lesion detection in gadolinium-based contrast agent-enhanced (GBCA-enhanced) breast MRI. However, its effectiveness on virtual contrast-enhanced (vCE) images remains unclear. This feasibility study evaluated the publicly available MAMA-MIA nnU-Net model trained on GBCA-enhanced data using an independent cohort of both GBCA-enhanced and vCE breast MRI. Methods: This IRB-approved retrospective study included the publicly available nnU-Net model trained on n = 1506 MAMA-MIA breast MRI scans and a cohort of n = 2126 in-house 3T breast MRI scans. A generative adversarial network (Pix2Pix-GAN) was developed on n = 1870 of the in-house scans and used to generate vCE data on the remaining independent n = 256 in-house cases. The MAMA-MIA nnU-net was applied to both GBCA-enhanced (GBCA) and corresponding vCE images. Ground-truth segmentations of malignant lesions served to calculate the Dice score, Hausdorff distance, and lesion dimension differences. Results: The final test set comprised n = 250 cases (n = 69 malignant, n = 181 benign). Lesion detection rates were 91% (n = 63/n = 69; 95% confidence interval (CI): 82.3–96.0%) for GBCA and 84% (n = 58/n = 69; 95% CI: 73.7–90.9%) for vCE. Two lesions missed in GBCA were identified by vCE. The Hausdorff distances were similar (GBCA: 6.4 (IQR: 3.2–9.3; 95% CI: 5.2–7.8) mm; vCE: 6.7 (IQR: 3.9–9.7; 95% CI: 5.3–8.0) mm, p = 0.564). The Dice scores showed minor differences (GBCA: 0.829 (IQR: 0.723–0.900; 95% CI: 0.786–0.865) vs. vCE: 0.826 (IQR: 0.720–0.857; 95% CI: 0.770–0.836); p < 0.001). vCE images had slightly higher non-target tissue segmentation (median 6072 mm³ vs. 5754 mm³). Conclusions: A GBCA-trained algorithm demonstrated some cross-domain transferability to vCE images, albeit with a reduced case-level sensitivity of 84% (95% CI: 73.7–90.9%) vs. 91% (95% CI: 82.3–96.0%). Based on these preliminary results, further research, including larger cohorts and more diverse datasets, is warranted. Full article

Topological photonic crystals provide an important platform for robust light transport and light-field manipulation. To meet the demands for developing multifunctional and densely integrated photonic circuits, it is necessary to flexibly control light flow with multi-degrees of freedom while maintaining strong topological protection. In this work, we investigate multifunctional topological photonic crystals based on hybrid topological domain walls, which support valley-polarized chiral edge states (VCES). Based on hybrid domain walls, we design two types of compact topological photonic devices. By exploiting direction-selective coupling between valley edge states (VES) and VCES, we construct nonreciprocal coupled waveguides with a nonreciprocal transmission ratio of 10 dB and output-port isolation ratio of more than 30 dB. Moreover, through different configurations of the direction of external magnetic field, we construct a multi-channel selective routing device that enables the configurable transport of valley-polarized electromagnetic waves among multiple channels. Hybrid topological waveguides provide a foundation for designing novel photonic devices, offering the potential for realizing multifunctional integrated topological photonic networks in both classical and quantum regimes. Full article

To address the need for dynamic and static parameter testing of IGBT modules, an integrated test platform was developed. In production-line applications, consistency differences in key parameters between the upper and lower bridge arms of manufactured modules were found to be difficult to quantify using a single metric. To overcome this limitation, a multi-parameter consistency evaluation method was proposed. Based on more than 400 sets of production-level measurements obtained from newly manufactured IGBT modules from the same batch, eight key parameters, including E_on, E_off, dv/dt, di/dt, V_CES, I_CES, V_GEth and I_GES, were selected to construct paired samples of the upper and lower bridge arms. A dimensionless consistency index (CI) was introduced to quantify relative deviations between bridge arms, and consistency characteristics were analyzed using raw-parameter boxplots, CI boxplots, and paired scatter plots in terms of distribution, discrepancy quantification, and sample-wise pairing. The results show that different parameters exhibit different levels of distributional difference and relative deviation between the two bridge arms under identical test conditions, and that the three analyses reveal mutually consistent trends. The proposed method provides an effective basis for within-batch quality assessment, bridge-arm matching, abnormal sample identification, and production process troubleshooting. Full article

The Variable Cycle Engine (VCE) is a key enabling technology for addressing the economic and environmental challenges of next-generation supersonic civil aircraft. This paper presents a multidisciplinary design analysis and optimization (MDAO) approach to quantitatively assess the potential benefits of Variable Cycle Engines (VCE) in the conceptual design of supersonic civil aircraft. In this approach, component-level models of a conventional Mixed-Flow Turbofan (MFTF) and a double-bypass VCE with a Core Driven Fan Stage (CDFS) are integrated into the MDAO process. Employing a multi-point optimization strategy, the engine design parameters and off-design control schedules are first determined. Subsequently, for each given engine design (MFTF and CDFS VCE), the airframe geometry parameters are optimized to minimize the aircraft Maximum Take-off Weight (MTOW). The application of this approach is illustrated through a case study of a medium-sized supersonic civil transport. The results indicate that, under the assumption of identical weights for the VCE and the MFTF, the design with the VCE reduces the MTOW by 2.8%, block fuel consumption by 5.7%, and total mission Nitrogen Oxides (NOx) emissions by 24.2% compared to the design with the MFTF. Additionally, lateral noise and flyover noise during the take-off phase are decreased by 2.2 EPNdB and 1.9 EPNdB, respectively. To account for the potential weight increase caused by the structural complexity of the VCE, a parametric weight sensitivity analysis is conducted. Results show that the VCE retains its advantages in MTOW, fuel efficiency, noise, and emissions for weight penalty factors up to 1.15. Full article

One of the main causes of failure in Insulated Gate Bipolar Transistor (IGBT) modules used in high-power conversion applications is thermal-stress-induced degradation. In this paper, an experimental testing setup for thermal stress and real-time degradation monitoring, as well as a deep neural network (DNN)-based lifetime prediction of IGBT modules under thermo-electrically stressed inverter operation, is proposed. A two-level SVPWM inverter is implemented to create a hybrid power cycling test platform that imposes well-defined junction-temperature swings representative of real-world operation by combining controlled electrical loading and active induction heating with water cooling. Throughout the aging process, on-state voltage and module temperature are constantly monitored to identify degradation precursors associated with thermo-mechanical fatigue. A physics-based Coffin–Manson lifetime model is fitted using failure datasets to characterize temperature-dependent lifetime behavior. An offline deep neural network (DNN) is trained on degradation trajectories derived from on-state collector–emitter voltage (V_ce,on) to predict remaining useful lifetime. This approach uses partial degradation histories for accurate early-life prediction. The proposed DNN model for competitive and computationally efficient lifetime prediction is validated experimentally on several IGBT modules under different thermal stresses, and its accuracy is compared with other prediction methods. Full article

Artificial intelligence (AI) marketing technologies are reshaping customer engagement in service sectors, yet their performance within integrated digital ecosystems remains poorly understood. Existing research often examines AI tools in isolation, overlooking how the holistic quality of the virtual customer experience (VCE) shapes their impact on consumer decisions, particularly in intangible service contexts such as telecommunications. This study addresses this gap by investigating the influence of four AI technologies—chatbots, dynamic pricing, voice search, and visual search—on purchasing decisions, with VCE tested as a critical moderating mechanism. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and survey data from 487 telecommunications customers in Saudi Arabia, the findings confirm significant positive direct effects for all four AI tools. Moreover, the VCE significantly amplifies these individual relationships and further strengthens their combined contribution to decision quality, enabling the model to explain 71.2% of the variance in purchasing decisions. The results indicate that competitive advantage in AI-enabled service markets depends not on deploying isolated technologies, but on orchestrating a coherent, high-quality virtual experience ecosystem. By integrating the Technology Acceptance Model (TAM) and Stimulus–Organism–Response (SOR) framework, this study advances the theoretical understanding of how AI and experience design jointly enhance digital decision-making. Practically, it underscores the need for managers to prioritize integrated VCE design to drive sustainable consumption and strengthen customer loyalty in increasingly digital service environments. Full article

Background/Objectives: Limited health literacy among older adults with noncommunicable diseases (NCDs) remains a major challenge in community and primary-care settings. This action research aimed to develop and evaluate a community-based health literacy training model for volunteer caregivers for the elderly (VCEs) to support the prevention and control of diabetes and hypertension among older adults in the community. Materials and Methods: This study was conducted in a primary care-based community setting and comprised two phases: Phase 1 (model development) and Phase 2 (implementation and evaluation). The primary analytic sample consisted of 38 volunteer caregivers for the elderly, each providing home-based health education to one older adult (n = 38). The intervention combined structured health literacy education based on the K-shape framework (Knowledge, Comprehension, Thoughtful Inquiry, Decision-making, and Implementation) with SKT meditation/exercise. The program was delivered weekly over 8 weeks. Outcomes included health literacy (20-item scale) and disease prevention and control behaviors (12-item scale), assessed at baseline, immediately post-intervention, and 1 month after program completion. Results: Among VCEs, mean health literacy scores increased significantly from baseline to post-intervention and were further improved at 1-month follow-up (p < 0.001), indicating sustained gains in health literacy. Preventive behavior scores also increased significantly from baseline to post-intervention (p < 0.001); however, no additional improvement was observed at 1 month compared with immediately after the program (p > 0.05). The magnitude of improvement suggested a meaningful effect of the intervention on health literacy, while behavioral changes appeared to plateau after program completion. Conclusions: The community-based training model effectively and sustainably improved health literacy among volunteer caregivers for the elderly. Although preventive health behaviors improved immediately after the intervention, no further gains were observed at 1 month, suggesting that ongoing reinforcement may be required to sustain behavioral change. This model supports the role of community participation in primary care-based NCD prevention among older adults. Full article

This study, based on the specifications of implantable medical devices for wireless power transfer, presents a bootstrap-comparator rectifier circuit design characterized by high voltage conversion efficiency, high power conversion efficiency, and improved reliability. The design is implemented using a 0.18 µm process to achieve superior VCE and PCE performance. The input signal is a 2 MHz, 3.3 V sine wave, producing an output voltage of 2.94 V with a maximum operating current of 5 mA. At an output load of $R_L=8\mathrm{k}\Omega$, the maximum voltage conversion efficiency (VCE) reaches 89.02%, while the maximum power conversion efficiency (PCE) is 84.73% at $R_L=500\Omega$. The temperature rise ($\Delta T$) is 0.22–0.45 °C. Full article

Bond wire degradation represents the predominant failure mechanism in IGBT modules, accounting for approximately 70% of power converter failures and posing significant reliability challenges in modern power electronic systems. Existing monitoring techniques face inherent trade-offs between measurement accuracy, implementation complexity, and electromagnetic compatibility. This paper proposes a physics-constrained ensemble learning framework for non-intrusive bond wire health assessment via V_ce-on prediction. The methodological innovation lies in the synergistic integration of multidimensional feature engineering, adaptive ensemble fusion, and domain-informed regularization. A comprehensive 16-dimensional feature vector is constructed from multi-physical measurements, including electrical, thermal, and aging parameters, with novel interaction terms explicitly modeling electro-thermal stress coupling. A dynamic weighting mechanism then adaptively fuses three specialized gradient boosting models (CatBoost for high-current, LightGBM for thermal-stress, and XGBoost for late-life conditions) based on context-aware performance assessment. Finally, the meta-learner incorporates a physics-based regularization term that enforces fundamental semiconductor properties, ensuring thermodynamic consistency. Experimental validation demonstrates that the proposed framework achieves a mean absolute error of 0.0066 V and R² of 0.9998 in predicting V_ce-on, representing a 48.4% improvement over individual base models while maintaining 99.1% physical constraint compliance. These results establish a paradigm-shifting approach that harmonizes data-driven learning with physical principles, enabling accurate, robust, and practical health monitoring for next-generation power electronic systems. Full article

At present, the UWB-assisted VIO scheme only uses range measurements to estimate the anchor position. The accuracy of the anchor location estimation algorithm can be affected by factors such as the trajectory being a straight line or having a small curvature, as well as changes in multi-observation noise. To address these problems, we propose an adaptive UWB anchor location estimation algorithm leveraging Unmanned Ground Vehicle (UGV) multi-source observations. The key innovations include the following: (1) a novel anchor initialization method that incorporates both distance and angles, including azimuth and elevation measurements, to overcome the limitation of the approach that relies solely on range for straight or small-curvature trajectories; (2) an adaptive nonlinear optimization anchor location estimation algorithm that dynamically adjusts measurement weights and addresses the accuracy decreasing under time-varying noise characteristics in both distance and angle measurements caused by environmental disturbances. In this paper, the robustness and anchor position estimation accuracy of the proposed algorithm are validated through simulation and UGV real experiments. Full article

Video Capsule Endoscopycapsule endoscopy (VCE) has emerged as a minimally invasive diagnostic tool for detecting and monitoring small bowel involvement in polyposis syndromes. VCE is included in the surveillance guidelines of Peutz-Jeghers syndrome. In the remaining familial polyposis syndromes, VCE may facilitate the early detection of polyps, when indicated, particularly in areas beyond the reach of conventional endoscopy, thereby aiding timely detection. Colon capsule endoscopy has been studied in symptomatic, screening and polyp surveillance populations and the second-generation colon capsule has demonstrated excellent detection rates for advanced neoplasia, however its role in colonic polyposis requires further research. The role of the panenteric capsule has not been explored in polyposis syndromes as a panintestinal examination. Despite its advantages, VCE has notable limitations; it may miss small, flat, or hidden lesions and lacks the capability for tissue sampling or therapeutic intervention. In the future, advances in imaging technology, extended battery life, and the integration of artificial intelligence (AI) are expected to further enhance the utility of VCE. Our review aims to focus on the applications of VCE in polyposis syndromes and future perspectives. Full article

Redox flow batteries (RFBs) are an emerging class of large-scale energy storage devices, yet the commercial benchmark—vanadium redox flow batteries (VRFBs)—is highly constrained by a modest open-circuit potential (1.26 V) while posing an expensive and volatile material procurement costs. This review focuses on recent progress in diversifying redox-active species to overcome these limits, highlighting chemistries that increase overall cell voltage, energy density, and efficiency while maintaining long cycle life and safety. The study dwells deeper into manganese-based systems (e.g., Mn/Ti, Mn/V, Mn/S, M/Zn) that leverage Mn’s high positive potential while addressing Mn(III) disproportionation reactions; iron-based hybrids (Fe/Cr, Fe/Zn, Fe/Pb, Fe/V, Fe/S, Fe/Cd) that exploit the low cost, and its abundance, along with membrane and electrolyte strategies to prevent the potential issue involving crossover; cerium-anchored catholytes (Ce/Pb, V/Ce, Eu/Ce, Ce/S, Ce/Zn) that deliver high operational voltage by implementing an acid-base media, along with selective zeolite membranes; and halide systems (Zn–I, Zn–Br, Sn–Br, polysulfide–bromine/iodide) that combine fast redox kinetics and high solubility with advances such as carbon-coated membranes, bromine complexation, and ambipolar electrolytes. Across these various families of RFBs, the review highlights the modifications made to the flow-fields, membranes, and electrodes by utilizing a zero-gap serpentine flow field, sulfonated poly(ether ether ketone) (SPEEK) membranes, carbon-modified and zeolite separators, electrolyte additives to enhance the voltage (VE%), and thereby energy (EE%) efficiency, while reducing the overall system cost. These modifications to the existing RFB technology offer a promising alternative to traditional approaches, paving the way for improved performance and widespread adoption of RFB technology in large-scale grid-based energy storage solutions. Full article

Small intestine tumors are rare. The four main groups include adenocarcinomas, neuroendocrine neoplasms (NEN), lymphomas, and mesenchymal tumors. The jejunum and ileum can only be examined endoscopically with device-assisted enteroscopy techniques (DAET), which are indicated only when specific clinical or imaging findings are present. The initial diagnosis of tumors of the small intestine is mostly made using computed tomography (CT). Video capsule endoscopy (VCE), computed tomography (CT) enterography, and magnetic resonance (MR) enterography are also time-consuming and costly modalities. Modern transabdominal gastrointestinal ultrasound (US) with high-resolution transducers is a dynamic examination method that is underrepresented in the diagnosis of small intestine tumors. US can visualize wall thickening, loss of wall stratification, luminal stenosis, and dilatation of proximal small-intestinal segments, as well as associated lymphadenopathy. This review aims to highlight the role and imaging features of ultrasound in the diagnosis of small-intestinal tumors. Full article