Search Results (30,212)

Power density and power efficiency are crucial for the design of high-performance computing servers. Buck converters exist due to their simplicity, but achieving a solution that combines high efficiency and high power density remains an ongoing research area in buck converter design. High-frequency switching, which reduces inductor size in buck converters, is a common method for achieving high power density; however, high-frequency switching introduces higher switching losses, hence the frequent use of GaN HEMTs, which have low switching losses. To achieve both high efficiency and high power density, this study proposes a compact buck converter design that pairs a D-type GaN HEMT with a low-voltage PMOS, termed a P-cascode GaN HEMT. We analyze different current switching modes and find that boundary conduction mode (BCM) can minimize inductor size while maintaining high power efficiency. This paper explores the theoretical basis of BCM and the P-cascode GaN HEMT, derives the operating conditions of BCM, estimates power efficiency, and proposes a high-power density buck converter solution. Simulation and experimental results show that the proposed design achieves 95% power efficiency in applications from 12 V to 3.3 V, while reducing the inductor size by a factor of 10 compared to continuous conduction mode (CCM) designs. Full article

Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. Full article

To be acceptable for precision agriculture applications, satellite imagery must be converted to surface reflectance. To be economical, the analytics must be delivered completely by automation and free of error to preserve farmer trust. CMAC (closed-form method for atmospheric correction) software was tested for this application along with established applications, Sen2Cor and FORCE—all three software packages seek to retrieve Sentinel-2 surface reflectance. Forty-three Sentinel-2 images were selected of farmland near Burley, Idaho, corrected by this software and evaluated as reflectance time series extracted from three irrigated corn fields. NDVI of irrigated corn presented an ideal test of precision and accuracy for surface reflectance retrieval. If accurate and precise, a plotted time series will smoothly display logistic growth during crop establishment followed by a plateau, then gradual senesce before harvest: divergences from this pattern indicate errors. CMAC followed the expected smooth pattern for this dataset while, in both FORCE and Sen2Cor, divergence occurred both above and below the CMAC time series for NDVI and from individual spectral band reflectance. These divergences were systematic and directly related to the degree of atmospheric effect—overcorrecting when clear, under-correcting when hazy. Only CMAC provided surface reflectance with the accuracy required for precision agriculture: applicable for Sentinel-2 as Tier 1 data and when haze or cloud- affected and unreliable, as Tier 2 infill from daily smallsat data. Additional analyses of the CMAC-corrected dataset were performed that were also applicable to Tier 2 daily-cadence smallsat data. Further analysis of this dataset indicated that, applied as NDVI, the application of broadband NIR, though sensitive to atmospheric water vapor, exhibited minimal errors compared to NDVI from narrowband NIR. These CMAC-corrected data provided an application to index crop start dates and were capable of distinguishing the uncorrectable data of cloud, cloud shadow, or extreme haze for removal under complete automation. Full article

Fairness and minority exclusion have emerged as the central concerns of contemporary Artificial Intelligence (AI) ethics. However, standard auditing and documentation practices often fail to capture harms affecting edge cases and marginalized groups. This article argues that this failure is structural: the act of “discretization”—converting continuous reality into discrete governance categories—inevitably produces a “residual.” Drawing on German Idealism (Kant, Fichte, Schelling) and continental philosophy (Dilthey, Gadamer, Merleau-Ponty), we reconceptualize residuals not as mere noise but as “surprising facts” that should trigger abductive hypothesis revision. We critique checklist-centered governance as a form of proceduralized auditing that can obscure these residuals. This article makes three key contributions: (i) a structural diagnosis of residual production using systems theory and topology; (ii) a philosophical reconstruction of abductive revision as a hermeneutic necessity; and (iii) an institutional design proposal—specifically, the Residual Ledger and Category Revision Protocols—to operationalize “Open Schema” governance. Full article

Although electric vehicles are being vigorously promoted around the world, the mileage anxiety problem is an important hindrance to their development. Thus, this paper proposes an auxiliary half-bridge dual active bridge (AH-DAB) converter between different electric vehicles, which is based on nonlinear virtual power predictive control. For the converter, characteristics of high power density, wide voltage gain range, and high efficiency are necessary. Firstly, an AH-DAB converter is applied to improve the control variable. Under this effect, the converter can switch between the half-bridge and the full-bridge converter. Secondly, a duty ratio design method is proposed to improve zero-voltage switching (ZVS) performance. Therefore, wide voltage gain range, decoupling of control variables, and high efficiency can be achieved in the nonlinear AH-DAB system. Thirdly, the nonlinear virtual power predictive control is proposed to ensure energy transfer between two electric vehicles. Based on this, the phase shift angle can be predicted and adjusted by ensuring that the actual power is consistently maintained close to the reference power. Moreover, the virtual power is generated to represent the reference power, which can reduce the number of current sensors. Finally, simulation and experiment results collectively show the wide voltage gain range and high efficiency of the proposed AH-DAB converter. Full article

Driven by the increasing demand for high power density in modern power electronic converters, this paper proposes two novel packaging designs based on the concept of an overlaying chip placement structure, including the design with a two-layer substrate (designated as M2) and the one with a three-layer substrate overlaying structure (designated as M3). Electrical and thermal simulations demonstrate that M2 achieves a 32.78% volume reduction while incurring a 12.70% increase in average thermal resistance, and a 5.72% reduction in power loop parasitic inductance compared to the conventional packaging design (designated as M1), representing a balance between compact packaging and electrothermal performance. Meanwhile, M3 achieves an ultra-low loop inductance of 2.02 nH thanks to the mutual inductance cancellation effect; however, the physical volume is increased by 38.17%, and the thermal resistance is reduced by 1.59% compared to the M1 design. The prototype of the M1 power module has been fabricated for experimental validation. Double-pulse testing and steady-state thermal resistance measurements are conducted based on the M1 prototype to confirm the accuracy of the simulation model. Full article

Efficiently converting natural capital into economic assets is a critical challenge in urban–rural transformation, yet the interactive mechanism between institutional land reform and physical spatial restructuring remains underexplored. While traditional frameworks emphasize institutional design, this study identifies a “Spatial-First” mechanism where physical reconfiguration serves as a spatial mediator to catalyze property rights breakthroughs. Using an entropy-weighted coupling coordination model, we analyzed policy dynamics in Nanhai District, China, a unique “dual-pilot” zone, from 2020 to 2024. The results indicate a nonlinear leap in the Coupling Coordination Degree (D) from 0.100 to 0.978. We interpret this surge as a policy-driven shock during the intensive pilot phase, where substantive spatial integration (0.719) effectively bypassed high transaction costs inherent in collective tenure, outpacing institutional progress (0.281). However, an Ecological Lag was observed; the disproportionately low weighting of the ecological carrier index (7.09%) suggests that current gains are primarily driven by green industrialization rather than the expansion of absolute ecological stock. This study concludes that while spatial tools can effectively unlock natural capital value in the short term, long-term sustainability necessitates a strategic shift from administrative-led economic efficiency to market-based ecological restoration. Full article

Recently, distributed DC microgrids have gained prominence due to their modular design, scalability, and seamless integration with renewable energy sources. However, ensuring robust operation of distributed secondary control schemes remains challenging, particularly in the presence of unavoidable communication disruptions and parametric uncertainties encountered in practice. Most existing control strategies either assume ideal communication networks or address fault tolerance and communication constraints separately, which limits their applicability in realistic networked environments. This paper proposes an event-triggered fault-tolerant distributed secondary control framework for DC microgrids operating under communication faults. An embedded averaged model is incorporated to support fault-tolerant decision-making and to guide event-triggered communication updates. In addition, an auxiliary recovery mechanism is introduced, enabling neighboring converters to cooperatively compensate for information loss during communication interruptions without centralized supervision. Lyapunov-based stability analysis establishes boundedness and practical convergence of the closed-loop system under event-triggered updates and bounded disturbances while explicitly excluding Zeno behavior. The simulation results under communication fault scenarios demonstrate that the proposed approach achieves accurate DC bus voltage regulation with steady-state deviations below 1% while restoring proportional power sharing with an averaged error within 5%. The embedded model error remains bounded throughout the fault interval, and fault-tolerant control actions are triggered sparsely with well-separated inter-event times on the order of tens of milliseconds, thereby significantly reducing the communication burden. These results confirm the effectiveness and robustness of the proposed framework for the resilient operation of distributed DC microgrids under practical communication constraints. Full article

In view of the bottleneck problems existing in the 3D ultrasonic testing of aircraft composite laminated structures—including heavy reliance on manual operation, resulting in low detection efficiency, and the inability of traditional robotic arms to adapt to the testing of complex curved surfaces due to their dependence on predefined fixed trajectories—this paper proposes an automated 3D ultrasonic testing method based on 3D vision guidance for robotic arms. Firstly, the proposed Yolo-Mask model is adopted to realize the visual recognition and segmentation of composite component regions, after which the segmentation results are mapped to the depth map and further converted into the surface point cloud of the material. Secondly, on the basis of point cloud preprocessing and trajectory point extraction, the automatic planning of the robotic arm’s scanning trajectory is achieved, which drives the robotic arm to perform precise motion and to synchronously collect spatial pose and ultrasonic testing data. Finally, 3D reconstruction is completed via a fusion algorithm, and 3D images of the material’s internal structures are generated. Experimental verification shows that the proposed method achieves a Segm-mAP of 97.4%, a detection speed of 11.7 fps, and a 3D imaging error of less than 0.1 mm, thereby realizing fully automated detection throughout the entire process. This research provides an effective solution for the non-destructive testing of aircraft composite structures. Full article

Background/Objectives: This study aims to develop a comprehensive Clinical Decision Support System (CDSS) that integrates multi-sequence sacroiliac joint (SIJ) MRIs with rheumatological, clinical, and laboratory findings into the decision-making process for the early diagnosis of axial spondyloarthritis (axSpA), incorporating segmentation-supported explainability. Methods: Multi-sequence SIJ MRI data (T1-WI, T2-WI, STIR, and PD-WI) were analysed from 367 participants (n = 193 axSpA; n = 174 non-axSpA controls). Sequence-based classification was performed using VGG16, ResNet50, DenseNet121, and InceptionV3 models; additionally, a lightweight and parameter-efficient SacroNet architecture was developed. Slice-level probability scores were converted to patient-level scores using the Dynamic Top-K Averaging method. Image-based scores were combined with a logistic regression-based clinical risk score using weighted linear integration (0.60 image/0.40 clinical) and a conservative threshold (τ = 0.70). Grad-CAM was applied for visual interpretability. Furthermore, to support the diagnostic outcomes with precise spatial data, active inflammation in STIR and T2-WI sequences was segmented. For this purpose, the MDC-UNet model was employed and compared with baseline U-Net derivatives. Results: Sequence-specific analysis showed VGG16 performing best on T1-WI (AUC = 0.920; Accuracy = 0.878) and DenseNet121 on STIR (AUC = 0.793; Accuracy = 0.771). The SacroNet architecture provided competitive classification performance at the patient level despite its low number of parameters (~110 K). Furthermore, MDC-UNet successfully segmented active inflammation, yielding Dice scores of 0.752 (HD95: 19.25) for STIR and 0.682 (HD95: 26.21) for T2-WI. Conclusions: The findings demonstrate that patient-level decision integration based on multi-sequence MRI, when used in conjunction with clinical risk scoring and segmentation-assisted interpretability, can provide a feasible and interpretable DSS framework for the early diagnosis of axSpA. Full article

With the proliferation of multimodal content on social media, creators increasingly require tools that can retrieve both images and videos relevant to a single textual query. However, existing cross-modal retrieval methods are typically confined to binary (text–image or text–video) settings and struggle with fine-grained semantic alignment and spatiotemporal information imbalance. To address this issue, we propose UniTriM, a unified framework for text–image–video joint retrieval. First, UniTriM supports concurrent retrieval of semantically relevant images and videos given one textual input. To overcome the scarcity of text–image–video triplet data, we introduce a self-attention-based keyframe selection strategy that converts existing text–video datasets into triplet format. Second, we design a multi-granularity similarity alignment module that captures hierarchical semantics by modeling patch–frame–video and word–triple–sentence structures and jointly optimizes intra- and cross-granularity alignments to enhance fine-grained cross-modal correspondence. Third, to alleviate the inherent spatiotemporal information imbalance between static images and video-aligned text descriptions, we introduce a feature disentanglement module that disentangles spatial-related features from text and aligns them explicitly with image representations. Experiments conducted on three benchmark datasets MSR-VTT, MSVD, and DiDeMo demonstrate that UniTriM achieves state-of-the-art performance on joint retrieval tasks. Full article

This work presents an alternating sequential model predictive control (ASMPC) scheme applied to multimodular matrix converters. The proposed strategy alternately evaluates two control objectives: load current tracking and input reactive power minimization. The algorithm was implemented in MATLAB/Simulink on an architecture composed of two direct matrix converters in a multimodular configuration. The influence of parameter $N_2$ on system performance was analyzed under step changes in reference current of 30 A and 60 A. To this end, performance metrics such as THD and MSE were used, along with a descriptive statistical analysis including the mean, standard deviation, mean absolute deviation (MAD), and coefficient of variation (CV). Simulation results show stable performance for variations in $N_2$, with an input current THD of $8.10\%$ and load THD reduced to $1.00\%$, demonstrating improved harmonic performance compared with classical weighted MPC approaches. Full article

This study develops and tests an Engagement–Experience–Co-creation–Loyalty (EECL) framework explaining how hospitality brand engagement (HBE) is translated into multidimensional hotel loyalty through two parallel mechanisms: Hospitality brand experience (HBX) and hospitality value co-creation (HVCC). A variance-based PLS-SEM model with seven reflective latent constructs and 57 indicators was estimated using data from 1407 members of four global hotel loyalty programs; generational cohort was used only as a grouping variable in multi-group analysis, not as an additional construct. MICOM established measurement invariance across Generation Z, Millennials, Generation X, and Baby Boomers. HBE is positively associated with both HBX and HVCC, and both mechanisms transmit its relationship to cognitive, affective, and conative loyalty. These three attitudinal facets jointly predict action loyalty, supporting a parallel rather than strictly staged loyalty-formation logic in hotel loyalty-program contexts. Younger cohorts translate engagement more strongly into experience and co-creation, whereas older cohorts rely more on experience when forming cognitive loyalty. The study contributes a hospitality-specific, predictive, and cohort-sensitive explanation of how engagement is converted into hotel loyalty. Full article

As secondary educational environments face increasing volatility due to systemic resource constraints and pedagogical uncertainty, understanding the behavioral mechanisms of teacher agency has become paramount. While traditional teacher education has emphasized the execution of standardized curricula, the current era demands a fundamental shift toward adaptive expertise and psychological resilience. This study investigates the processes by which 28 secondary pre-service physical education teachers (PSTs) navigate instructional resource deficits through the lens of adaptive behavior (bricolage) and ecological teacher agency. Utilizing a qualitative case study design, I collected data from two universities in Seoul, South Korea, through reflective journals, revised lesson plans, and micro-teaching video analysis reports over a full 15-week semester. The results identified five coordinates of an adaptive instructional design compass: (1) Facing Constraints, (2) Resource Mining, (3) Contextual Engineering, (4) Simulation, and (5) Reflective Participation. These coordinates represent a transformative behavioral process where PSTs convert environmental deficits into professional assets. The findings reveal distinct adaptation styles based on psychological dispositions: the analytically oriented group (Group A) prioritized structural redesign through digital tools, while the narratively oriented group (Group B) utilized human-centric somatic metaphors and virtual rehearsals to bridge the epistemic void. Crucially, this research suggests that teacher adaptation is not a mere technical adjustment but a dynamic behavioral achievement of agency that ensures the long-term instructional quality of physical education. I propose that teacher education programs should incorporate “Safe Deficit” simulations—carefully calibrated instructional constraints—to trigger adaptive behavior and ensure that future educators can thrive in unpredictable pedagogical contexts without the risk of professional burnout. Full article

Expanded polystyrene (EPS) waste poses a major environmental concern due to its high volume, low density, and resistance to biodegradation. In this study, post-consumer EPS was reprocessed into continuous microfibers by Air Jet Spinning (AJS) using chloroform and chloroform/D-limonene as solvent systems. The effects of polymer concentration, air pressure, and solvent ratio on fiber formation were systematically investigated through rheological and surface tension analyses. The incorporation of 10 vol. % D-limonene improved jet stability and reduced bead formation, attributed to its lower volatility and favorable solubility with EPS, as supported by Hansen solubility parameters. SEM analysis confirmed uniform microfiber formation within a defined processing window. FTIR spectra indicated preservation of the polystyrene chemical structure, while TGA and DSC analyses were used to evaluate thermal behavior and assess potential residual solvent retention, particularly related to D-limonene. The results elucidate the interplay between solvent volatility, solution properties, and fiber morphology, establishing a sustainable processing framework for converting EPS waste into value-added fibrous materials via AJS. This work contributes to the United National Sustainable Development Goals, particularly SDG 12 (Responsible Consumption and Production) by promoting EPS waste valorization, and SDG 13 (Climate Action) through the partial replacement of conventional solvents with sustainable alternative. Full article