Search Results (2,715)

Polyploidy plays a key role in plant evolution and breeding, but the reproductive biology of polyploid rubber trees (Hevea brasiliensis) remains poorly understood. Here, we investigated microsporogenesis and pollen morphology in triploid and tetraploid rubber trees using acetic acid–magenta staining. Both polyploids displayed largely regular meiotic progression but exhibited abundant tetrad-stage abnormalities, including monads, dyads, triads, and unequal polyads. Tetraploids additionally exhibited chromosome misalignment, lagging chromosomes, asynchronous segregation, and multipolar spindles, with abnormal tetrads accounting for 24.51%. A total of more than 26,000 pollen grains (15,009 triploid and 11,774 tetraploid) were analyzed. In the triploid, 95.21% were normal-sized (30.7–43.3 µm), 2.21% were large (putative 2n; mean 47.2 µm), and 2.59% were super-large (putative 4n; mean 64.2 µm). In the tetraploid, 95.23% were normal-sized (31.7–46.7 µm), 4.75% were small (mean 19.6 µm), and only 0.025% were large (mean 55.3 µm). Normal-sized pollen from both polyploids was comparable to haploid pollen from diploids. Most large pollen grains exhibited wrinkled or irregular morphology, suggesting non-viability. Thus, functional male gametes in rubber tree polyploids are mostly reduced, and unreduced pollen is rare or nonviable. This study provides the first detailed description of the morphological sequence of microsporogenesis in triploid rubber tree and a cytological basis for understanding the low male fertility potential in breeding programs. Full article

Vehicle-side weight is a key constraint in wireless power transfer (WPT) systems for electric vehicles, as it directly affects cost, installation, and vehicle integration. This paper presents a lightweight Vehicle Assembly (VA) based on a conventional Series–Series (SS) compensation topology and a phase-shift-controlled active rectifier, designed within the SAE J2954 framework. The architecture reduces vehicle-side passive components while enabling load adaptation through the rectifier conduction angle. A fixed-output-power time-domain methodology is used to compare two operating strategies. In the exact 2-ZVS mode, only two rectifier commutations satisfy the charge-based ZVS condition, whereas in the robust 4-ZVS mode all four commutations are constrained to achieve ZVS through joint optimization of the rectifier control parameters and switching frequency. In both cases, the primary DC voltage is adjusted to maintain a constant output power of 7.7 kW. Measured coupler parameters are used in the circuit model. Across the aligned position and two measured misalignment conditions, exact 2-ZVS achieves an estimated AC–AC resonant-link efficiency of 98.01–98.41% and a modeled DC–DC efficiency of 96.16–96.90%. Robust 4-ZVS remains feasible, but its higher circulating-current requirement reduces the corresponding efficiencies to 93.26–94.96%, respectively. Therefore, for the investigated system, exact 2-ZVS provides the best efficiency–soft-switching trade-off. The AC–AC metric includes only winding and capacitor-ESR losses. The DC–DC metric additionally includes the modeled conduction and output-capacitance transition losses of the primary inverter and active rectifier; gate-drive, control, and auxiliary losses are excluded. Full article

Masonry structures, frequently encountered both in traditional architecture and in historical buildings, occupy a significant place among structural system types. In these systems, the load-bearing elements typically consist of walls made of materials such as brick and stone. Performance evaluation of masonry structures requires more refined modeling processes due to their brittle behavior under seismic effects and their irregular geometric characteristics. In particular, historical masonry buildings require accurate analysis. The analysis critically depends on the precise identification of the actual geometry, material properties, and structural deficiencies. Within the scope of the study, the effectiveness of combining laser scanning technology with Building Information Modeling (BIM) in the structural analysis and condition assessment of masonry structures was investigated through a case study. Laser scanning point cloud data were processed and transferred into a digital environment. Subsequently, BIM-based software was employed to generate a three-dimensional model using the point cloud data of a historic structure located in Cappadocia, Türkiye. Then, the model was transformed into a finite element model for structural analysis. The finite element model was employed for modal characterization and preliminary structural assessment under self-weight. Throughout the process, axis controls of the walls, misalignments, irregularities, structural discontinuities, and possible structure damage were evaluated digitally to identify potentially vulnerable zones. This study applies the existing Scan-to-BIM-to-Finite Element workflow to complex historical masonry, providing a reliable and practical roadmap for geometric documentation and preliminary structural assessment of the building stock. Full article

Multi-camera vehicle sensing provides a cost-effective solution for 3D environmental perception in intelligent vehicles. A practical challenge is that temporal fusion becomes unreliable when the ego-vehicle undergoes aggressive motion. Historical camera features are commonly aligned by rigid ego-pose warping, but residual motion-induced displacement can still degrade object localization, heading estimation, and velocity sensing. This paper presents GCL-BEV, a motion-aware temporal compensation framework for multi-camera vehicle sensor systems. The proposed framework uses synchronized surround-view cameras and ego-motion measurements as coupled sensing inputs. First, a Geometric-Aware Feature Enhancement (GAFE) module converts ego-motion priors into motion-conditioned BEV sampling offsets, allowing the visual sensing representation to compensate for local temporal misalignment before fusion. Second, a View-Consistency Learning (VCL) objective imposes a training-time equivariance constraint so that the sensor representation remains consistent under planar viewpoint perturbations. Across 10 random seeds on nuScenes, GCL-BEV achieves 57.80% ± 0.15 NDS and 46.22% ± 0.16 mAP with a ResNet-101 backbone. Compared with BEVDet4D, it reduces the mean Average Orientation Error by 5.4% and shows smaller degradation from steady driving to high-turn scenarios, indicating improved robustness for dynamic vehicle sensing. Full article

Background/Objectives: While Lebanon’s Vision 2030 prioritizes strengthening Lebanon’s chronically constrained, fragmented, and shocked primary healthcare system, implementation is hindered by weak sensing mechanisms, misaligned spatial distribution, and poorly integrated referral pathways. This study develops a preliminary, context-specific, systems-level healthcare policy assessment and framework for primary healthcare reform by applying biomimicry principles derived from the drought-resistant root architecture of grapevine (Vitis vinifera). Methods: Publicly available data from the Lebanese Ministry of Public Health, national assessments, and non-governmental organization registries were synthesized to examine mismatches between healthcare need and service capacity. We developed a preliminary healthcare Need Score for Lebanon’s eight governorates based on clinical demand, capacity strain, and socioeconomic vulnerability. An Agent-Based Approach within a biomimicry framework was used to translate grapevines’ biological strategies into healthcare policy design principles. Results: Structural deficiencies were identified in Lebanon’s primary healthcare system, including absence of standardized need-based sensing, uneven geographic service distribution, weak-referral integration, and limited adaptive feedback. Our preliminary Need Score was used four clinical demand indicators for iron deficiency, neonatal and maternal deaths, pre-mature births and birth defects, and disease incidence. Service-capacity strain indicators were primary healthcare center density and staffing. Finally, socioeconomic vulnerability indicators were percentages for non-Lebanese population and food insecurity. The proposed policy framework can translate frontline stress signals to rule-based responses, guide adaptive redistribution of capacity, and structurally anchor primary healthcare centers to referral hubs through defined catchments and electronic referral loops. Conclusions: Biomimicry can shed insight into how to improve primary healthcare governance under chronic scarcity. Need Scores and biological resilience principles can strengthen primary healthcare systems in low-resource settings while aligning with healthcare policy objectives like those in Lebanon’s Vision 2030. Full article

Early-stage offshore wind development in the Gulf of Tonkin (Bac Bo Gulf) requires environmental information beyond conventional wind-resource mapping. Waves, currents, directional relationships, and extreme conditions all affect structural loading, operability, and the reliability of preliminary feasibility assessments. Wind, wave, current, and bathymetric data for 1993–2024 were analysed at three representative fixed-bottom screening sites: northern Gulf of Tonkin (NGT), central Gulf of Tonkin (CGT), and southern Gulf of Tonkin (SGT). The analysis characterises long-term conditions, seasonal variability, directional misalignment, wind-conditioned sea states, and marginal extremes with quantified uncertainty. The central site was the most energetic, with a mean wind speed of 7.97 m s⁻¹ at 150 m and a 50-year marginal wind return level of 37.67 m s⁻¹; the southern site had the strongest background current and the most persistent wind–wave misalignment. These differences do not follow a simple north–south gradient, so a single regional environmental description would misrepresent at least one of the three sites. The resulting conditional, directional, event-associated, and marginal-extreme statistics were organised into an IEC-oriented screening framework rather than treated as completed design-load-case inputs. This framework gives developers a basis for comparing site-specific environmental conditions before committing to costly site investigations. However, joint environmental modelling, water levels, tidal and storm currents, site measurements, and load-response analysis remain necessary before detailed design and certification. Full article

License plate recognition is a critical perception module in intelligent transportation systems, yet severe occlusion remains a fundamental challenge in unconstrained traffic scenes because the missing visual evidence is physically absent rather than merely degraded. Although multi-view observations provide complementary cues for recovering occluded characters, existing fusion strategies usually operate in the spatial domain and therefore suffer from feature misalignment, especially when Vision Transformers rely on local patch-wise positional encoding. To address this issue, we propose Wavelet-Enhanced Transformer, a domain-transformed multi-view recognition framework for occluded license plates. The central idea is to shift feature fusion from the spatial domain to the wavelet domain, where discrete wavelet transform decomposes encoded features into low-frequency structural components and high-frequency detail components. This transformation alleviates spatial semantic ambiguity caused by viewpoint variation and positional mismatch, enabling more robust cross-view feature aggregation. On top of this representation, we design an order-agnostic memory fusion mechanism to progressively accumulate complementary evidence from multiple partial observations, and introduce a discriminator-based stopping module to adaptively terminate unnecessary iterations for efficient deployment. Experiments on the CBLPRD-330k dataset and severe occlusion stress tests demonstrate the superiority of the proposed method. Under 20–50% character occlusion, our model maintains over 95% character accuracy and consistently outperforms mainstream baselines such as LPRNet, TrOCR, and PP-OCRv5 in character accuracy, length accuracy, and full-match rate (FMR). These results indicate that wavelet-domain feature fusion provides an effective and robust solution for highly occluded license plate recognition in complex traffic environments. Full article

Wireless power transfer has become an increasingly widespread technology in electric vehicle and charging-system applications. Advances in power electronics have led to significant improvements in system efficiency, and in parallel, extensive research has been conducted on energy storage technologies and coil designs. In this study, a split-coil structure using a dual inverter system is analyzed, aiming to achieve a flatter magnetic-flux distribution for electric vehicle charging systems and battery energy storage circuits or an increase in total flux density for power applications. A flatter flux distribution will make a significant improvement in electric vehicle charging systems under misalignment conditions. The key point based on these concepts is the dually driven split coil, which is analyzed as both a single-layer and a double-layer coil. The main objective of the present study is to investigate the magnetic-flux distribution and the performance of the proposed fully symmetric two-layer coil structure under nominally aligned conditions. Owing to the fully symmetric geometry, the presented one-dimensional magnetic-flux scan is sufficient for comparing the flux-distribution characteristics of the proposed and conventional coil structures. Following the demonstration that the single-layer split coil design was ineffective, theoretical analyses and experimental data indicate that the double-layer split-coil design provides 10% higher efficiency. Full article

The purpose of this study was to analyze the sensitivity of polyethylene stresses and strains, as proxies for wear and surface damage, to coronal and axial misalignment for a novel lumbar total joint replacement (LTJR) implanted at L5-S1. We hypothesized that these stresses and strains would remain below the levels associated with worst-case impingement in a spine wear simulator. A finite element model (FEM) of the L4-S1 spine was developed using CT-based anatomy from a representative Investigational Device Exemption study patient. An appropriately sized LTJR was virtually implanted at L5-S1 in a standardized baseline orientation, and component positioning was altered to achieve eight independent misalignment scenarios, spanning axial convergence angle, anterior–posterior offset, and coronal tilt. Physiologic forward-bending loads were applied, and polyethylene contact pressure, von Mises stress, and effective strain were recorded. Peak values for the baseline scenario were 35.0 MPa, 19.9 MPa, and 4.8%, respectively. Convergence angle and anterior–posterior offset had minimal effect, whereas coronal tilt produced the largest changes. Contact pressure and von Mises stress varied by <20% due to misalignment, whereas effective strain was more sensitive, increasing from 4.8% at baseline to 9.9% with 20 degrees of coronal tilt. All values were comparable to prior L4-L5 findings and remained well below impingement levels. Within the bounds of reasonable misalignment, the device maintained bearing congruency and did not approach conditions associated with accelerated wear. Full article

Retrieval-augmented generation has emerged as a core technological paradigm for addressing the bottlenecks of hallucinations and knowledge lag in large language models. However, many existing reviews focus on a single technical branch or vertical application scenario, making only scattered references to hardware, evaluation methods, and cross-industry empirical evidence, and lacking a systematic, end-to-end integration. This paper conducts research based on a total of 115 papers, comprising foundational literature from 1998–2019 and core RAG literature from 2020–2026, systematically cataloging end-to-end technologies and supporting solutions, establishing a quantitative hardware comparison table and comparing 11 categories of open-source and commercial APIs, constructing a two-tier, four-level standardized evaluation framework, and compiling empirical evidence and implementation challenges across eight industries from 2024 to 2026. Based on this, the paper identifies four major structural contradictions—the retrieval–creation trade-off, the geometric–semantic misalignment as a symmetry problem between representation space and semantic structure, the autonomy–reliability paradox, and evaluation blind spots—as a unified analytical framework for the five major technological strands. Finally, this paper proposes four research directions for practical implementation—differentiable joint optimization, hybrid geometric space learning, interpretable causal reasoning, and multidimensional diagnostic evaluation—providing a systematic reference for both theoretical research on RAG and its deployment in the private sector. Full article

This study explored pharmacy students’ lived experiences of mental health support structures at a University in South Africa. A qualitative phenomenological design was employed, with data collected through semi-structured interviews (n = 20) and document analysis of institutional mental health resources (2020–2024). Thematic analysis revealed five interrelated themes: awareness and visibility of support; barriers to access and utilization; institutional responses to mental health crises; cultural and contextual misalignment; and informal support and coping strategies. Although formal support services were available, students experienced them as fragmented, reactive, and insufficiently integrated into academic life. Barriers to access included long waiting times, stigma, and concerns about confidentiality. Institutional responses to crises were perceived as academically focused, with limited psychological follow-up. Students relied heavily on informal networks, including peers, family, and spirituality. These findings highlight a disconnect between institutional provision and student experience, underscoring the need for more integrated, culturally responsive, and student-centred approaches to mental health support within pharmacy education. Full article

In confined waters such as cylindrical aquaculture cages and ponds, underwater robots for cleaning, harvesting and other tasks that use single-beam mechanical scanning sonar for positioning and navigation are susceptible to multipath interference in complex water environments. Meanwhile, under extreme sea conditions, the severe attitude swaying of the robot and the slow-scanning characteristic of the sonar superimpose on each other, causing range stretching and helical deformation of the acoustic point cloud. To address these problems, this paper analyzes the deformation mechanism of single-beam sonar and proposes a spatiotemporal joint deformation compensation and localization-mapping method. First, an attitude-derived probabilistic confidence model is introduced as a lightweight robustness safeguard to characterize the geometric reliability of sonar echoes and reduce the contribution of low-confidence measurements during subsequent registration. Second, a beam-level spatiotemporal joint de-deformation algorithm is designed: the slant range in polar coordinates is flattened to eliminate nonlinear swaying deformation, and a beam-level displacement back-estimation based on the beam time offset and feedback velocity is employed to remove helical misalignment, thereby enhancing the underlying correction capability for dynamic deformation processes. Finally, a lightweight SLAM architecture that integrates keyframe-based dynamic sub-maps is constructed, where a confidence-weighted ICP is used to estimate the planar position with the heading provided by the compass and provide velocity-based closed-loop feedback, effectively mitigating the problem of global matching divergence caused by underlying dynamic deformations. Real-data-driven semi-physical disturbance tests based on measured pool data show that, under the injected ±45° roll disturbance and translational drift, the proposed method reduces the maximum point-to-reference error MaxAE from 1.059 m to 0.098 m. The reported mean internal registration residual decreases from 0.275 m for the traditional navigation odometry SLAM to 0.158 m for the proposed method, corresponding to a numerical reduction of approximately 42.5%. Under the evaluated conditions, the proposed method effectively mitigates point-cloud deformation and registration instability caused by robot swaying and slow-scanning sonar, while confidence weighting is retained as an auxiliary robustness mechanism for handling low-confidence correspondences. Full article

Anomaly detection in vehicular networks focuses on cybersecurity, leaving physical traffic-flow anomalies at urban intersections underserved by IoT sensing. This paper benchmarks anomaly detection on five days of hourly vehicle counts from four consecutive signalized intersections in Kuwait City, with a vehicle-to-infrastructure (V2I) latency feasibility analysis. Anomalies are synthetically injected because verified incident labels are unavailable; scores reflect detectability under the injection protocol rather than validated incident detection. Across ten injection seeds, CUSUM is the most accurate (mean F1 0.945, perfect precision on every seed), Isolation Forest attains the highest recall (0.955), and the LSTM-AE reaches F1 0.347; on misaligned anomaly classes, the margin narrows, and the LSTM-AE matches CUSUM on gradual drift. A corridor rule localizes detected corridor anomalies (9/9, conditional on detection). Hourly aggregation alone imposes an expected 1800 s detection delay, over 130 times the 13.5 s V2I budget at 80 km/h. A sub-second Tier-1 edge detector, evaluated in traffic-calibrated simulation, detects surges within budget (median 6.8 to 9.1 s, robust to signal-cycle platooning), whereas flow-cutoff detection requires roughly 21 s and overnight hours remain a blind spot. Results support a two-tier edge-cloud design and provide, to our knowledge, the first such benchmark on real Gulf-region corridor count data. Full article

Australian rural education policy has undergone sustained reform, yet persistent gaps in teacher retention and outcomes suggest a fundamental misalignment between policy frameworks and rural realities. This paper argues that the misalignment is not a failure of implementation but of epistemology, and that it undermines policy’s claim to authority, that is, its claim to know what it governs. Tracing the policy trajectory across a decade of national teacher education reform, it shows how the “classroom ready” construct, built from metropolitan evidence, cannot recognize rural knowledge. Drawing on Fricker’s framework of epistemic injustice and its development by Medina, Dotson and Pohlhaus, it diagnoses two mechanisms of what it terms place-based epistemic exclusion. The first is testimonial injustice: rural practitioners’ deep contextual knowledge of what enables teachers to thrive is discounted when policy is formed. The second is hermeneutical injustice: the lived experience of marginalised rural students is barely represented in the research base, a scarcity that marks it as unrecorded rather than unheard. As a conceptual synthesis of three completed studies, the paper identifies a patterned gap in the evidence base rather than a causal relationship between that gap and rural educational outcomes. It proposes principles for an epistemically equitable approach within a federated policy system, arguing that epistemic equity is the condition under which policy’s evidentiary claim could be made good. Full article

Carbon inclusivity (CI) mechanisms face weak low-carbon adoption despite strong consumer intention in China. Prior studies explain low-carbon behavior mainly from individual psychological perspectives yet rarely investigate the intention–behavior gap in the digital–physical scenarios of CI mechanisms. Furthermore, single theoretical lenses such as the Motivation–Opportunity–Ability (MOA) framework or Social Practice Theory (SPT) have clear limitations in interpreting this gap. This study integrates the MOA framework with SPT to construct a novel “Motivation–Intention–Context–Practice–Behavior” (MICPB) model, framing behavioral change as a dynamic interplay of internal motivation, external opportunities, and evolving social practices. Different from studies adopting MOA or SPT in isolation, the MICPB model bridges individual psychological processes and routine social practices to unpack the intention–behavior gap. Adopting grounded theory to analyze 42 interviews from China’s CI pilot regions, we identify three core dimensions of the gap: limited internal competencies and emotional conflicts, fragmented external incentives and platform defects, and misalignment between low-carbon values and daily routines. The MICPB model reveals that the gap stems from misalignment across motivation, context and practice, and enriches MOA theory by incorporating practice-based dynamics. This integrated framework deepens understandings of barriers to consumer low-carbon participation under CI schemes. This study suggests coordinated policy and corporate efforts to restructure social practices, bridge the intention–behavior gap, and align digital governance with low-carbon transitions. Full article