Search Results (58,215)

Background: T-cell redirecting therapies (TCRTs), including CAR T-cell therapies and bispecific antibodies, have transformed treatment for relapsed/refractory multiple myeloma (MM), yet real-world adoption remains limited by awareness and access barriers across patients, caregivers, and physicians. Methods: We conducted a cross-sectional study (June–September 2025) using IRB-approved REDCap surveys distributed to patients with MM (via MyChart), caregivers of TCRT recipients (via email), and community physicians (via email). Surveys assessed TCRT familiarity, perceived barriers, and access challenges. The primary objective was to compare TCRT utilization across racial groups among previously treated patients. Results: A total of 428 respondents participated (346 patients, 51 caregivers, 31 physicians). Among patients, 18% were Black. Overall, 26% were unfamiliar with TCRT, with higher unawareness among Black patients (32% vs. 25%) and those with ≤high school education (42% vs. 23%). Higher education was associated with twice the odds of TCRT utilization (p = 0.05). Among patients reporting prior MM treatment, TCRT utilization was similar across racial groups (39% Black vs. 38% non-Black). Among patients reporting TCRT receipt, Black patients more frequently reported receiving therapy through clinical trials compared with non-Black patients (44% vs. 34%). Among caregivers, 53% reported effects on mental/emotional wellness, 61% moderate-to-high stress during treatment, and 33% provided >40 h/week of care during month one. Among physicians, 71% referred patients for TCRT, but 48% reported <25% of referrals resulted in treatment. Patient hesitancy was the most cited referral barrier. Conclusions: Awareness gaps disproportionately affect Black patients and those with lower education. Caregivers experience substantial emotional and time burden. Patient hesitancy remains the primary barrier to TCRT referrals. Clinical trials may serve as an alternative access pathway for some patients. Multi-level interventions targeting education, caregiver support, and care coordination are needed to improve equitable TCRT access. Full article

Emissivity is a critical parameter in infrared temperature measurement and varies significantly among different materials. Infrared thermography has been widely used for the inspection of substation equipment. However, substations contain a large number of devices with complex structures, making it impractical to assign a separate emissivity value to each device or component. This limitation can significantly affect temperature measurement accuracy. To address this issue, this paper proposes an intelligent multi-emissivity temperature correction method for infrared images of substation equipment. First, a temperature–emissivity correction function is established. Then, a total of 2189 infrared images of substation equipment are collected, and the main equipment components are annotated at the pixel level. Subsequently, an equipment component segmentation model based on DeepLabv3+ is trained. Finally, different emissivity values are assigned to different component regions for temperature correction, and corrected infrared pseudo-color images are regenerated. In the experiment, the temperature values before and after correction are compared with thermocouple measurements. In the present validation experiment, the average deviation between the corrected infrared temperature and the thermocouple measurement was reduced by 79.2% compared with that before correction. Full article

Declining fertility and the shift away from three-generation co-residence have weakened traditional family-based child-rearing support in Japan, drawing attention to the role of networks in local communities. Using data from a citizens’ attitude survey in a regional Japanese city (Ube City; n = 1293), this study examines how residents’ perceptions of community networks—specifically mutual support and community vitality—are associated with satisfaction with the local child-rearing environment. Conventional mean-based approaches risk overlooking qualitative heterogeneity in such evaluations. Using cross-sectional secondary data, we combined multiple regression with latent class analysis (LCA) to explore this heterogeneity. Findings show that perceptions of community networks were positively associated with satisfaction across all models (composite index b = 0.412, p < 0.001). The strongest associations were with “learning opportunities” and “community-wide education” (b = 0.477–0.478). Three-generation households did not uniformly report higher satisfaction compared with nuclear-family households. The exploratory LCA identified three qualitatively distinct patterns, with an “evaluation-pending” group (intermediate scores, frequent “don’t know” responses) comprising over 40% of respondents. This heterogeneity suggests that average scores may obscure the experiences of those directly engaged in child-rearing and that municipal surveys could include items identifying respondents’ basis for evaluation. Full article

The selective flotation separation of chalcopyrite from pyrite under low-alkalinity conditions remains a challenge due to their similar surface properties. In this study, a sulfonated dextrin (SD) was synthesized via chemical modification and evaluated as a selective depressant for pyrite in the chalcopyrite–pyrite flotation system. Micro-flotation experiments demonstrated that SD exhibited significantly superior selective depression for pyrite compared to unmodified dextrin. At pH 8.0, SD achieved over 95% depression of pyrite with negligible impact on chalcopyrite flotation. In mixed-mineral flotation, SD reduced pyrite recovery from 75.01% to 27.08% at a dosage of 100 mg/L, while chalcopyrite recovery increased from 80.56% to 87.65%. Contact angle measurements revealed that SD markedly enhanced the hydrophilicity of pyrite surfaces, whereas chalcopyrite surfaces retained strong hydrophobicity in the presence of SBX. Zeta potential and FTIR analyses indicated that SD strongly adsorbed onto pyrite surfaces through the introduced sulfonic groups (−SO₃⁻), effectively hindering subsequent SBX adsorption. XPS analysis further suggested chemical interactions between SD and oxidized iron species on pyrite surfaces, forming a stable organic–inorganic composite adsorption layer. These findings demonstrate SD to be a promising environmentally friendly depressant for selective copper–sulfur separation under low-alkalinity conditions, offering a viable alternative to conventional lime-based processes. Full article

Background: This study aimed to compare changes in motor coordination associated with two different motor practice models—multisport and single-sport soccer training—in children, without implying causal effects of one training context over the other. Methods: A quasi-experimental pre–post study (T0–T1) was conducted over 10 weeks in non-randomized, pre-existing groups. A total of 143 children (mean age: 6.21 ± 0.55 years; 107 males) were allocated to a Multisport Group (MG; n = 79) and a Single-sport Group (SG; n = 64). Motor coordination was assessed using the Körperkoordinationstest für Kinder (KTK), and raw scores (RAW SCORE) were used for analysis. Statistical analyses included t-tests, change-score (Δ) and effect-size analyses, and two-way repeated-measures ANOVA. Results: Both groups showed significant improvements over time (p < 0.001; d = 0.75 for MG, d = 0.77 for SG). However, the MG showed significantly greater improvements than the SG (Δ = 16.68 ± 6.14 vs. 12.28 ± 4.08; p < 0.001; d = 0.83). At T1, the SG still showed slightly higher absolute RAW SCORE values than the MG (p = 0.049; d = −0.33), although this between-group difference was markedly reduced compared to baseline (d = −0.59). The ANOVA revealed a very large effect of Time (F(1,141) = 1092.31, p < 0.001, η²p = 0.886), a small-to-medium effect of Group (F(1,141) = 7.51, p = 0.007, η²p = 0.051), and a large Time × Group interaction (F(1,141) = 24.18, p < 0.001, η²p = 0.146). A sensitivity analysis (ANCOVA) confirmed that, after adjusting for baseline scores, sex, and anthropometric variables, the Multisport group showed significantly higher adjusted T1 scores than the Single-sport group (adjusted mean difference = 3.96 points, 95% CI: 1.93–5.99), reversing the direction observed in the unadjusted T1 comparison. Given the non-randomized design and baseline group differences, findings reflect observed associations rather than causal effects. Conclusions: Both practice models were associated with improved motor coordination, with multisport participation associated with greater observed change over 10 weeks. These findings are consistent with a possible role of diversified motor experiences in coordination development, though this study cannot establish causality. Full article

To address the limitations of traditional Voltage-Current (VI) trajectory features in appliance load identification—such as the difficulty in distinguishing similar appliances, weakened amplitude information, and the absence of dynamic characteristics—this paper proposes a dual-stage cyclic training method for load identification that integrates multi-feature reconstruction with Particle Swarm Optimization (PSO). First, to overcome the high similarity of original VI trajectories, a PSO-based threshold optimization algorithm is designed to reconstruct VI trajectories through reflection operations and normalization, thereby enhancing the geometric morphological differences among similar appliances. Second, to supplement dynamic impedance information and energy level features, conductance-time trajectories and mean-square current color-block maps are introduced to characterize dynamic impedance variations and energy level differences, respectively. Finally, a three-channel classification network based on ResNet18 is constructed, where the reconstructed VI trajectories, conductance-time trajectories, and mean-square current color-block maps are fused via RGB channels as inputs, forming a closed-loop “threshold optimization—feature reconstruction—cyclic training” framework. Experimental results on the PLAID dataset demonstrate that the proposed method achieves an identification accuracy of 98.29% and a macro-averaged F1-score of 97.93%. Comparative experiments verify the effectiveness of the reconstructed VI trajectories, the complementarity of multi-feature fusion, and the superiority of the combinatorial optimization network, significantly improving the identification of multi-state and similar-condition appliances. Full article

Timely forecasting of maize productivity is essential to support precision agriculture and optimize management practices. In this study, we analyzed the potential of integrating ground-based measurements and UAV-derived spectral data for predicting maize grain yield (GY) under different fertilization conditions. Field data were collected at two key phenological stages: early vegetative stage (V7) and pre-harvest (R4). Ground-based measurements included SPAD, above-ground biomass (AGB), and leaf area index (LAI), while multispectral and hyperspectral imagery was acquired by drone. A series of Ordinary Least Squares (OLS) models was developed to evaluate the predictive performance of individual variables and their combinations. Model robustness was assessed using two validation strategies: Leave-One-Treatment-Out (LOTO) to assess model performance across the treatments included in the experimental design and random sampling to assess performance within the dataset. The results showed that yield prediction was less accurate during the early growth stages, where data fusion significantly improved the model’s accuracy (${\mathrm{R}}^2$ = 0.82; MAE = 6.36 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7 %). The predictive performance of VIs alone increased substantially in the pre-harvest stage, with the combination of red-edge indices and LAI proving to be the best model for late yield prediction (${\mathrm{R}}^2$ = 0.86; MAE = 6.56 q ${\mathrm{ha}}^{-1}$; $MAPE\approx$7%). Comparison of multispectral and hyperspectral data revealed comparable predictive performance, suggesting that multispectral sensors may already capture the key spectral information needed for yield forecasting. Furthermore, random validation consistently produced more optimistic results than the LOTO method, highlighting the importance of using validation strategies that explicitly account for the experimental design when evaluating model performance across the treatments included in the study. Overall, the present study demonstrates that yield prediction is highly dependent on the phenological stage and validation approach, and that integrating complementary data sources can improve model performance, particularly during the early growth stages. These findings should be interpreted as a proof-of-concept based on a single-site, single-season experiment with a limited sample size (n = 12), and therefore require further validation across multiple environments and growing seasons. Full article

Low-altitude unmanned-aerial-vehicle imagery exposes object detectors to a distinctive combination of tiny object footprints, dense instance layouts, abrupt scale variation, and weak texture under complex urban backgrounds. Existing detectors usually address these factors by adding larger backbones, denser feature pyramids, or heavier attention, but such independent additions often amplify background responses and dilute the fine localization cues needed by small targets. This paper proposes DFA-Det, a dynamic feature augmentation detector that treats low-altitude small-object detection as a coupled problem of context preservation, scale calibration, and content-aware refinement. The method first introduces a poly-kernel inception enhancement branch to preserve shallow structural details while expanding the effective receptive field. It then builds a Multi-Scale Interaction Encoder with Adaptive Feature Prior Learning and an Adaptive Feature Scaling Layer, where the latter contains a Bi-directional Channel Fusion Module that learns channel-wise evidence exchange between adjacent resolutions. Finally, a Hierarchical Refinement and Adaptive Fusion Module performs dynamic upsampling, semantic refinement, and adaptive fusion before the detection decoder. Experiments on the public VisDrone and CODrone benchmarks show that DFA-Det improves small-object precision, crowded-scene recall, and cross-scale robustness compared with representative two-stage, one-stage, transformer-based, and recent YOLO-family detectors. Extensive ablations, heatmaps, and qualitative comparisons indicate that the proposed modules cooperate as a coherent dynamic feature enhancement mechanism rather than isolated architectural attachments. Full article

Personal sound zones (PSZs) in cars allow occupants in different seats to enjoy distinct audio content without mutual interference. However, achieving sufficient acoustic isolation in real-world vehicle cabins remains challenging. In-vehicle experiments were conducted to enhance acoustic contrast using four headrest loudspeakers mounted on the rear-right seat to create dark zones at the front seats. Three arrangements of four headrest loudspeakers on a single seat were evaluated: longitudinal horizontal, lateral horizontal and vertical configurations. Compared with distributing four headrest loudspeakers across two seats, concentrating them on one seat yielded better acoustic contrast. When evaluated in the car using control points distributed on a horizontal plane, the vertical configuration exhibited superior performance among the tested arrangements, achieving an overall acoustic contrast of 12.4 dB over the 88–4525 Hz frequency band. This represents improvements of 3.9 dB over the two-loudspeaker setup and 0.9 dB over the two-seat-distributed four-loudspeaker arrangement. These results indicate that the vertical loudspeaker arrays on a single seat provide an effective approach for improving PSZ isolation in automotive environments. However, this enhanced acoustic isolation comes at the cost of increased array effort at low frequencies compared to the two-seat distributed configuration, as quantified in the study. Full article

Marine oil spills severely threaten marine ecosystems, the coastal economy, and marine engineering structures. Because it enables all-weather and all-time acquisition of rich target information, fully polarimetric synthetic aperture radar (FP SAR) is widely used for monitoring marine oil spills. However, the differences in the scattering characteristics among oil types can cause variability in the information contained in the features extracted using FP SAR. Herein, RADARSAT-2 images obtained from a rare oil-on-water experiment conducted in the Norwegian North Sea were used to compare the distribution differences in polarimetric features based on the oil slick type and incident angle. Results showed that the incident angle exerted some influence on polarimetric features and the detection performance for oil spills with a low oil–water contrast, particularly at large incident angles. The polarimetric features related to scattering mechanisms exhibited good robustness and effectiveness across various incident angles. The polarimetric feature that combines the scattering entropy H and modified anisotropy A₁₂ exhibited strong overall performance and high suitability for extracting information on oil spills at different incident angles. This study demonstrates that incorporating appropriate polarimetric features according to the incident angle enables the identification of different oil slick types and facilitates oil spill detection and monitoring. Full article

Motorcycle noise along leisure routes constitutes a distinct environmental health burden poorly captured by standard road traffic noise indicators. This study derives source-specific exposure–response functions and examines non-acoustic predictors of residential motorcycle noise annoyance. A mixed-methods socio-acoustic design was applied in five study areas in Baden-Wuerttemberg, Germany. A community survey (N = 493) assessed long-term annoyance (12-month recall); a smartphone-based experience-sampling study (MotoApp; N = 213; ten days in summer 2022) collected hourly ratings. Acoustic measurements provided vehicle-specific L_Aeq,1h, L_AFmax,1h, and N₆₀; exposure–response functions were estimated using logistic regression and generalised estimating equations. In the long-term survey, 46.5% were highly annoyed by motorcycle noise, compared with 18.4% for passenger cars. Motorcycle exposure–response curves were markedly shifted; the 25–highly-annoyed threshold was reached approximately 16 dB lower in L_Aeq,1h than for passenger cars on weekends. Negative attitudes towards motorcycle riders, low coping capacity, and noise sensitivity were significant independent predictors. Source-specific assessment is necessary for motorcycle leisure routes. The 25–highly-annoyed criterion maps to approximately 52 dB L_Aeq,1h and 78 dB L_AFmax,1h on weekends, and to 25 N₆₀ events per hour—substantially below current road traffic guideline values—providing actionable thresholds for noise action planning. Full article

This study presents a modular AI-assisted workflow for converting single 2D interior images into textured 3D assets and for evaluating those assets when ground-truth 3D meshes are unavailable. The proposed pipeline combines object detection, instance isolation, monocular-depth estimation, image-to-3D generation, texture synthesis, mesh export, and cloud-based execution to support early-stage interior-design and real-estate visualization tasks. A reference-free validation protocol is introduced, based on rendered multi-view comparisons, silhouette Intersection-over-Union, automated captioning, and multimodal embedding similarity, and is complemented by a composite validation framework that benchmarks reconstructed scenes against 200 panoramic indoor scenes from the Structured3D dataset using Hungarian-matched placement, size, recall, and relative-distance metrics. The workflow was implemented and tested using contemporary computer-vision and generative 3D components, with Hunyuan3D 2.0 used as the main reconstruction model. Proof-of-concept experiments on a representative corpus of 178 synthetically generated single-object images spanning a range of interior furniture categories show comparable silhouette IoU for textured and non-textured outputs and indicate that texture-preserving renderings improve visual and semantic similarity scores across CLIP-based evaluations. The 200-scene dataset evaluation reveals stable spatial localization (placement error ≈ 1.18 m, relative-distance error ≈ 0.54 m) alongside systematic over-prediction and size-calibration errors. Beyond the applied pipeline, the study contributes a reference-free, ground-truth-free protocol for 3D-asset evaluation and a first quantified account of where object-centric single-image reconstruction is reliable—spatial placement—and where it is not—object scale and spurious detection—at interior-scene scale. The results demonstrate the feasibility of integrating perception, 3D reconstruction, semantic assessment, and scalable deployment into a single applied pipeline, while remaining proof-of-concept and requiring extension to larger object and scene corpora, baselines, real-photograph evaluation, and human-centered assessment before broad claims about general interior-scene reconstruction can be made. Full article

Infrared and visible image fusion needs to preserve visible texture details and infrared thermal saliency, yet emphasizing one modality may suppress or distort useful information from the other, while cross-modal differences may also contain noise, pseudo-textures, or locally incompatible boundaries. We propose ARC${}^3$Fusion, which reformulates image fusion as a reliability-calibrated consensus–complementarity–conflict process to achieve a more effective balance between visible texture detail and infrared target saliency. A progressive shared encoder and a modality-specific residual adapter first produce comparable yet modality-aware features. Cross-Modal Explainable Residual Decomposition then estimates jointly supported consensus and represents the information unexplained by the opposite modality as candidate residuals. Trustworthy Complementarity Verification evaluates infrared residuals using source intensity and edge evidence, while visible residuals are examined using source cues and learnable frequency-pattern evidence. Cross-Modal Conflict Estimation further characterizes local incompatibility through co-activation, reliability, amplitude imbalance, edge-strength mismatch, and orientation mismatch. Conflict-Aware Routing finally coordinates consensus and verified residuals according to these relation cues. Unlike conventional shared–private decomposition that directly preserves private features, ARC${}^3$Fusion treats modality-specific residuals as candidates that must be verified and conflict-coordinated before fusion. Experiments on LLVIP, MSRS, and TNO demonstrate consistent fusion performance. On LLVIP, ARC${}^3$Fusion achieves the best EN, SF, AG, VIF, and SCD values of 7.158, 14.467, 4.331, 1.136, and 1.229, respectively. These results indicate that verifying modality-specific residuals and coordinating local conflicts improves the joint preservation of visible texture details and infrared thermal saliency. Full article

Successful germination is a critical stage for the establishment and persistence of annual halophytes in Mediterranean saltmarshes, where highly variable salinity creates narrow recruitment windows. Seed heteromorphism is a common feature of annual halophytes and has been proposed to contribute to recruitment under variable environmental conditions. This study investigated how seed morphotype, NaCl concentration and population origin interact to shape germination responses in Salicornia perennans Willd. subsp. perennans. Seeds from three Tuscan coastal populations (Italy) were separated into two naturally occurring morphotypes (large and small) and exposed to four NaCl concentrations (0, 5, 10 and 20 g L⁻¹) in a fully factorial experiment. Seed morphotype emerged as the main determinant of germination performance: large seeds consistently achieved a higher final germination percentage and germination index than small seeds across populations and salinity treatments. Increasing salinity reduced final germination percentage and germination index but had no significant effect on mean germination time or the time required to reach 50% of final germination (T50) among seeds that germinated, which indicates that NaCl acted as a quantitative filter rather than a kinetic brake. Dose–response modeling yielded comparable EC50 estimates among populations for large seeds, where EC50 represents the salinity at which the fitted germination response reaches the midpoint between the lower and upper asymptotes, whereas the maximum germination capacity differed substantially. Unexpectedly, seeds from San Rossore, the site most influenced by freshwater inputs, maintained the highest germination under severe salt stress. Overall, our results identify seed morphotype as the primary determinant of germination success in S. perennans and find that it overrides differences among populations in their response to salinity. Full article

Urban traffic flow is difficult to forecast accurately because its evolution is non-linear and governed by dependencies that operate over different spatial and temporal ranges. This paper introduces the Multi-Perspective Spatio-Temporal Feature Fusion Model (MPSTFFM) to describe these dependencies through complementary views. The temporal signal is separated into a slowly varying trend and a residual fluctuation, while the spatial structure is represented by four graphs: first-order adjacency, second-order in-degree, second-order out-degree, and a data-adaptive graph. These graphs respectively encode physical road connectivity, common inflow sources, common outflow destinations, and latent spatial associations. Whereas the first three are constructed from the known network topology, the adaptive graph is learned together with the prediction model and can therefore identify correlations not expressed by physical links. Within each spatio-temporal view, self-attention captures dependencies over long ranges, and convolutional operations extract local patterns. The features learned from all views are subsequently fused into a high-dimensional representation used to predict future flow. Experiments on real-world datasets compare MPSTFFM with twelve methods published during the preceding five years. On these benchmarks MPSTFFM outperforms every baseline, lowering the average MAE, RMSE, and MAPE across the four datasets by 13.04%, 5.28%, and 9.59%, respectively, relative to the best baseline on each one. Full article