Search Results (7,282)

Continuum robots offer inherent compliance and dexterity for operation in confined and unstructured environments; however, achieving hybrid multi-segment functionality typically requires application-specific redesign and tightly coupled architectures. To address this limitation, this study proposes a reconfigurable hybrid continuum robot architecture based around a multi-lumen central integration backbone that supports multiple actuation modalities and robot configurations. The proposed design combines external tendon-driven disk modules for proximal actuation with a pneumatically actuated distal tip, while internal lumens allow routing of pneumatic lines and the insertion of optional stiffening elements without structural interference. The reconfigurability of the architecture is demonstrated through two configurations: Concept-1, a two-segment hybrid system, and Concept-2, a miniaturized three-segment configuration achieved by reducing the disk diameter and extending tendon actuation to the backbone. Experimental evaluations are conducted to characterize segment-wise actuation, coupled deformation behavior, and workspace capabilities, hysteresis response, tip contact force, and phantom-based target reachability. Results show that the integration of tendon-driven and pneumatic actuation significantly expands and reorients the reachable workspace. Additional functional tests showed repeatable loading–unloading behaviour of the tendon-driven segment, a maximum pneumatic tip contact force of approximately 0.45 N, and successful access to five representative targets within a stomach-like phantom using Concept-2. A kinematic model based on a constant-curvature formulation is validated against experimental data, yielding root-mean-square errors (RMSE) of 5.44 mm and 6.12 mm for Concept-1 and Concept-2, respectively. These results demonstrate consistent model accuracy across different configurations and scales. Overall, the proposed architecture enables modular, scalable, and reconfigurable hybrid continuum robots, providing a flexible framework for applications ranging from large-scale manipulation to gastroscopy-inspired minimally invasive procedures. Full article

Utilizing underwater vehicles for hydropower infrastructure inspection is increasingly vital. However, these GNSS-denied and confined environments pose significant navigation challenges: Inertial Navigation Systems (INSs) suffer cumulative drift, Doppler Velocity Logs (DVLs) face acoustic blind zones near walls, and visual navigation frequently fails in highly turbid waters. To address these issues, this paper proposes a tightly coupled multi-source (INS/acoustic/optical/vision) navigation algorithm leveraging prior wall geometry constraints. Developed within an Error-State Kalman Filter (ESKF) framework, the model seamlessly accommodates sensor spatiotemporal heterogeneity. To overcome optical failures, a structural surface constraint model is innovatively constructed using single-beam sonar ranging. The core contribution involves transforming sonar ranging data into 6-DOF spatial pose constraints based on the dam’s planar characteristics, effectively bounding the localization drift perpendicular to the surface. Field experiments at the hydropower station dam demonstrate that under extreme conditions with total visual failure, the proposed algorithm effectively constrains critical motion degrees of freedom. By maintaining the wall-tracking error within 0.08 m (Root Mean Square Error, RMSE)—which effectively represents the relative localization error given the known absolute position of the structural wall—this method significantly enhances the operational robustness and precision of close-wall inspections in extreme underwater environments. Full article

Rapid urbanization in tropical Asia has led to a critical loss of green cover, exacerbating urban environmental challenges. While green roofs offer a promising Nature-based solution, their implementation in Asian countries is hindered by the prevalence of sloped roofs and high structural conversion costs. This research addresses this gap by developing a novel, lightweight vegetative roof tile designed as a direct structural replacement for conventional roofing materials in Sri Lanka. Existing roofing systems were studied, followed by a laboriousness study to determine the optimum tile dimensions. To meet these requirements, a modular tile measuring 900 mm × 1200 mm with a wave-shaped corrugated profile (a 10 mm rise and a 200 mm pitch) was engineered using SolidWorks 2024 and ABAQUS 2024 to meet Eurocode standards. Field investigations into plant health helped to finalize the depth of the roof tile as 2.5 cm. Following root penetration testing, fiber-reinforced plastic was selected for the tile structure to ensure durability while maintaining a total saturated weight of 52.5 kg/m². Biological testing demonstrated robust greening performance, with Axonopus compressus and Zoysia matrella achieving 100% survival rates and over 80% canopy coverage. This design methodology can be adapted across tropical Asia, contributing significantly to regional green infrastructure development and sustainable building practices. Full article

Rainfall is a critical factor inducing slope instability, and accurate prediction of the factor of safety (FOS) of slopes under rainfall conditions is of paramount importance for disaster prevention and mitigation. Conventional numerical simulation methods incur high computational costs, while individual machine learning models are often insufficient to adequately capture the nonlinear spatiotemporal evolution characteristics of multiple factors under coupled multi-physics fields. To address these limitations, this paper proposes a Transformer–LSTM prediction framework. First, a fluid–structure coupling model for rainfall-affected slopes is constructed using COMSOL, and multi-factor orthogonal experiments are performed to generate multi-dimensional time-series data. Subsequently, a Transformer–LSTM fusion deep learning model is built, in which LSTM is employed to extract the temporal dynamic characteristics of rainfall infiltration, and the self-attention mechanism of the Transformer is leveraged to enhance feature extraction and global dependency modeling of key disaster-causing factors. Experimental results demonstrate that the Transformer–LSTM model significantly outperforms traditional PSO-LSTM, PSO-SVM, and standalone Transformer or LSTM models in terms of both prediction accuracy and generalization capability. Its coefficient of determination (R²) remains above 0.94, and key evaluation metrics—including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)—attain the lowest values among the compared models. Furthermore, the SHAP (SHapley Additive exPlanations) interpretability framework is introduced to quantitatively elucidate the model’s predictive decision-making and to establish a physically grounded causal mapping with geotechnical mechanisms. It is confirmed that effective cohesion and slope angle exert a dominant interactive effect on the degradation of slope stability, providing data-driven support for wide-area monitoring of rainfall-induced landslides. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article

High-resolution spatiotemporal sea ice concentration (SIC) estimates are essential for Arctic navigation and ice analysis, but existing observational products are often too coarse, and physics-based models are computationally expensive. This study proposes a data-driven framework that couples Light Gradient Boosting Machine (LightGBM) temporal prediction with Kriging-based spatial interpolation to reconstruct SIC fields over the Northern Sea Route sector. LightGBM is trained on a grid-based SIC time series with engineered features representing persistence, seasonality, and short-term variability, enabling multi-horizon forecasting across large spatial grids. The predicted SIC fields are then refined using Ordinary Kriging (OK) and Co-Kriging (CK) with Gaussian and spherical semi-variogram models. Prediction performance is evaluated using root mean square error, and interpolation accuracy is assessed through cross-validation. Results show that, for high-latitude regions and resolutions finer than 0.25° × 0.25°, OK with a spherical semi-variogram achieves lower interpolation errors than CK and Gaussian-based alternatives. By sequentially coupling temporal learning and spatial refinement, the proposed framework improves temporal continuity, spatial structure, and error quantification, providing high-resolution SIC information suitable for large-scale Arctic ice analysis and navigation support. Full article

In the present study, we investigated how soybean yield is enhanced upon editing of the gene GmJAG1 and the consequent influence on the structure and function of the rhizosphere microbiome. Field trials revealed that gene-edited (GE) soybeans had a 55.22% increase in yield without concomitant changes in root length. Metagenomic sequencing of the rhizosphere soil microbiome showed that, compared with the corresponding non-edited line (CK), the alpha diversity of the GE groups remained unaltered, whereas beta diversity differed significantly at the soybean reproductive (R2) stage. Notably, the rhizosphere microbiome of GE soybeans at the R2 stage exhibited enrichment of functional pathways related to transport, amino acid biosynthesis, and central metabolism. These findings suggest that GmJAG1 editing may shape the functional profile of the rhizosphere microbiome, which could potentially contribute to yield gains. This work offers a novel microbiological perspective for understanding the mechanisms by which yield may be improved in GE crops. Full article

Addressing the urgent demand for high-precision pressure measurement in real-time barometric monitoring, aerospace, and industrial control, this paper presents a high-accuracy MEMS resonant pressure sensor based on electrostatic excitation and piezoresistive detection. The sensor incorporates a symmetric double-ended fixed-finger comb-drive resonator structure, driven into stable vibration at its natural frequency by an alternating electrostatic force. Piezoresistors integrated at the root of the resonant beams transduce the mechanical vibration into a frequency output, enabling precise external pressure measurement. Experimental results show that the developed sensor achieves an accuracy of 0.009% FS over a pressure range of 0–350 kPa across an operating temperature span from −30 °C to 50 °C, with a room-temperature repeatability error below 0.008% FS, demonstrating excellent measurement stability. Building on this performance, a real-time atmospheric pressure monitoring experiment was conducted, yielding a mean absolute percentage error of less than 0.05%, highlighting the sensor’s potential for engineering practicality. This work provides an effective technique for a high-precision, high-stability resonant pressure sensor, with clear potential for deployment in real-time barometric monitoring, aerospace, and industrial control applications. Full article

In 2025, the Democratic Republic of the Congo (DRC) experienced its 16th Ebola Virus Disease (EVD) outbreak, centered in the Bulape Health Zone of Kasai Province, amid multiple concurrent epidemics and limited health infrastructure. Genomic sequencing revealed a novel zoonotic spillover genetically related to the 1976 Yambuku strain. A Root Cause Analysis (RCA) using the “5 Whys” framework, integrating epidemiological data, genomic analysis, and surveillance reports, identified key contributors to delayed detection and response, with comparative insights drawn from the 2018–2020 North Kivu outbreak. The Mweka outbreak resulted in 28 confirmed, probable, or suspected cases and 15 deaths, including four healthcare workers. Root causes included inadequate ecological surveillance, weak community alert systems, diagnostic delays due to reliance on centralized laboratories, health system overload from concurrent outbreaks, and structural underfunding of preparedness and coordination. Unlike North Kivu, where security issues drove response delays, systemic and ecological vulnerabilities predominated in Mweka. These findings highlight how ecological and structural weaknesses facilitate novel Ebola spillovers and their escalation, emphasizing the need for sustained investment in One Health surveillance, decentralized diagnostics, and resilient public health governance to strengthen outbreak response capacity. Full article

Loading fluctuations cause structural responses such as deformations and vibrations on the propeller. Structural response of propellers results in vibrations on the shaft system or even the hull. Considering the demand for structural safety, the correlation between structural response of propellers and inflow conditions is numerically studied in the present paper. The interaction between the propeller and turbulence structures and vortex shedding from upstream structures is considered. Loading fluctuations on the propeller blade are obtained by a turbulence model of improved delayed detached eddy simulations (IDDESs). The deformations and vibrations of propeller blades fixed at their roots are captured considering fluid–structure interaction. Results show that the loading fluctuations and vibrations on the propeller contain tonal components occurring at harmonics of shaft frequency and broadband components. Inhomogeneous inflow amplifies pressure fluctuations as a product of space frequency and shaft frequency (SF). Inhomogeneous inflow also results in more intense fluctuations of velocity in the tip vortex at SF and blade wake at blade passing frequency and encounter frequency. As a result of loading fluctuations, the vibration of the blade is a superposition of excited vibrations and natural vibrations. Inhomogeneous inflow amplifies the vibrations at the encounter frequency. Resonance of the blade can be observed when the excited frequency approaches the first natural frequency. Full article

Binary Waring decomposition seeks to express a homogeneous binary form as a minimal sum of powers of linear forms. In the binary setting, Sylvester’s theorem gives a classical algebraic route for rank determination and parameter recovery through structured Hankel/catalecticant matrices. Although this procedure is exact and interpretable in ideal arithmetic, practical rank identification may become unstable when the input coefficients are contaminated by noise or when the underlying roots are close to degenerate configurations. This paper develops a data-driven rank inference framework coupled with certified Sylvester reconstruction for robust binary Waring decomposition. The proposed method first converts the coefficient sequence into a Hankel-aware graph that captures recurrence-induced dependencies among polynomial coefficients. A graph neural network is then used to infer plausible rank candidates from this structured representation. Instead of accepting a single prediction directly, the framework performs explicit Sylvester reconstruction and algebraic residual verification for candidate ranks. To further improve decision reliability, a lightweight meta-verification module integrates reconstruction residuals, model confidence scores, and stability-related indicators to select the most credible rank. Experiments on large-scale synthetic binary forms show that the proposed meta-guided variant improves rank identification and verified reconstruction success relative to the one-shot hybrid solver under low-to-moderate noise while maintaining the transparency and auditability of classical symbolic–numeric computation. Additional stress tests indicate that performance can degrade under shifted sampling regimes; so, the method should be interpreted as a robust decision layer within the modeled problem class rather than as unconstrained real-world validation. Full article

Purpose in life is a central component of healthy ageing and psychological well-being, yet its meaning in later life has often been operationalised through standardized measures rather than being grounded in older adults’ lived experience. This qualitative study explores how older adults living in the community in Spain interpret their life purpose during the transitions of old age. Ten adults aged 65 years and older from Aragón and Andalucía participated in semi-structured interviews lasting 60–90 min. Data were analyzed using reflective thematic analysis on ageing supported by MAXQDA, supplemented with questions related to internalized ageism, psychological inflexibility, and quality of life to enrich the interpretive depth. Four themes were identified: (1) purpose as biographical continuity; (2) purpose as a dynamic and adaptive process; (3) legacy as a central axis towards meaning; and (4) the interplay between internal strengths and external conditions. Participants constructed purpose as rooted in identity, values, and life narratives rather than in distal goal attainment. Purpose was maintained through adaptive processes—including resilience, psychological flexibility, and meaning-centered coping—while internalized ageism and personal barriers emerged as life limitations. These findings suggest that purpose in later life is not diminished, but rather is reconfigured through processes of adaptation, value alignment, and social contribution. The study highlights the need for person-centred and process-based approaches that promote psychological flexibility and challenge deficit-based and ageist views of ageing. Full article

Background: Symbiotic fungi play essential roles throughout the entire cycle of orchid plants, including seed germination, seedling development, and maturation. Dendrobium officinale Kimura & Migo (Orchidaceae) (D. officinale) is a rare and highly valued traditional Chinese medicinal herb. Currently, artificial breeding using tissue culture technology is widely adopted and essential in the Dendrobium industry; however, this approach may impair or disrupt the plant’s ability to establish and maintain symbiotic relationships with mycorrhizal fungi. Methods: In this study, the fungal endophyte community (FEC) in the roots of D. officinale cultivated under four different modes was analyzed using high-throughput sequencing. Correlation analyses were also carried out to examine the relationships between bioactive compounds and the FEC. Results: (1) The FEC in D. officinale roots was dominated by Ascomycota and Basidiomycota, with significant differences in abundance, diversity, and community structure among cultivation modes; (2) the FEC under greenhouse cultivation differed significantly from those under tree epiphytic cultivation in terms of fungal nutritional types and dominant taxa; (3) six major mycorrhizal fungal taxa were identified in Dendrobium roots, although non-mycorrhizal fungi accounted for approximately 97% of the community; and (4) polysaccharide content in Dendrobium stems was positively correlated with certain root fugal endophytes (Exophiala, alaromyces, Pseudodactylaria, and Fellomyces). Conclusions: This study provides a foundation for understating the growth of D. officinale under different cultivation modes and highlights the relationship between bioactive compound accumulation and fungal endophyte communities. Full article

Radio occultation (RO) has become a key technique for monitoring the ionosphere by deriving electron density (Ne) profiles and total electron content (TEC) from GNSS signals. This study assesses the newly deployed PlanetiQ GNOMES constellation by validating its ionospheric Ne profiles and profile-based TEC against collocated measurements from ionosondes and the COSMIC-2 mission under both quiet and disturbed geomagnetic conditions. Data matching for the statistical validation uses conservative spatial thresholds of less than 1° in latitude and longitude and temporal limits of 30 min for ionosondes and 1 h for COSMIC-2, supported by a dedicated sensitivity analysis, whereas storm-time case studies apply tighter temporal collocation and explicit control of the ray path geometry. Quantitative agreement is evaluated using root mean square error (RMSE), mean and absolute mean differences, correlation coefficients, regression analysis, and normalized percentage differences for key F-layer parameters, including the maximum Ne of the F2 layer (NmF2), the peak height of the F2 layer (hmF2), and the critical frequency of the F2 layer (foF2), along with altitude-dependent Ne profiles. PlanetiQ shows strong consistency with ionosonde profiles, with RMSE ranging from 2.94 × 10⁴ to 2.76 × 10⁵ el/cm³, correlations typically exceeding 0.90, and normalized absolute mean differences often near or below about 10–20%, although lower correlations of about 0.31 and 0.69 are found at Poker Flat and Awase, respectively, reflecting complex local structures and regional variability. Comparisons with COSMIC-2 during quiet conditions yield RMSE values between 7.06 × 10⁴ and 2.16 × 10⁵ el/cm³, correlations from 0.94 to 0.99, and percentage differences that generally remain within a few tens of percent, while storm-time analyses show RMSE between 1.12 × 10⁵ and 3.70 × 10⁵ el/cm³ with correlations from 0.80 to 0.99, confirming robust agreement across a wide range of geophysical conditions. Regression results demonstrate slopes near 1.00 and correlation coefficients above 0.90 for NmF2 and foF2 between PlanetiQ and both ionosondes and COSMIC-2, whereas hmF2 exhibits larger scatter, particularly during geomagnetic disturbances; additional binning by spatial and temporal separation indicates that temporal mismatches generally have a stronger impact on discrepancies than horizontal distance. Overall, the results demonstrate that PlanetiQ ionospheric RO data provide accurate and consistent measurements of key ionospheric parameters, comparable to those from COSMIC-2 and ionosondes, and can reliably complement existing observing systems for monitoring ionospheric variability and space-weather impacts. Full article

This narrative review synthesizes current knowledge on MADS-Box 27 (OsMADS27), a member of the AGL17 clade in rice that has emerged as a regulatory node linking nitrate signaling, root development, and abiotic stress tolerance. Because most functional and mechanistic studies on OsMADS27 to date have been conducted in rice, this review is centered on Oryza sativa, with cross-species comparisons used for evolutionary and comparative context. Specifically, we summarize the gene and protein structure, phylogenetic position, expression profile, upstream and downstream regulation, and emerging functional significance of OsMADS27. OsMADS27 is a typical MIKC-type MADS-box protein with root-preferential expression, and its activity is strongly influenced by nitrate availability and miR444-mediated regulation. Evidence from functional genomics, transcriptomics, ChIP-based studies, and transgenic analyses suggests that OsMADS27 contributes to the regulation of root architecture, nitrate uptake, hormonal crosstalk, and stress-responsive pathways. Notably, OsMADS27 enhances salt tolerance through nitrate-dependent activation of downstream targets such as OsHKT1;1 and OsSPL7, contributing to ion homeostasis and salinity tolerance. Recent findings also suggest roles in grain size regulation and yield improvement, expanding its significance beyond root biology. This review compares OsMADS27 with AGL17-clade genes and highlights its value for crop improvement aimed at salinity tolerance and nitrogen use efficiency. However, important research gaps remain, particularly the limited field-level validation, the absence of integrated multi-omics analyses, and the lack of functional studies of OsMADS27 orthologs in non-rice crops. Overall, OsMADS27 represents promising rice-centered target for future biotechnology applications, while its translational relevance to other cereals remains to be established through orthology analysis and field-level evaluation. Full article