Search Results (518)

Background: Astilbin is a bioactive flavonoid with documented anti-inflammatory properties; however, its sustained interactions with the gut microbiota remain poorly understood. Fecal samples from three healthy donors were pooled and fermented with astilbin in an in vitro human colonic model over 7 days. This exploratory study aimed to characterize the temporal ecological shifts associated with prolonged astilbin exposure. Methods: Time-resolved 16S rRNA gene sequencing, PICRUSt2 functional prediction, BugBase phenotypic inference, and pseudo-targeted metabolomics were integrated to track microbial-metabolic dynamics. Results: Astilbin exposure was associated with a highly coordinated, three-stage microbial succession. Day 3 (D3) emerged as a putative inflection point, where the enrichment of pioneer degraders (Flavonifractor, Bacteroides) was temporally correlated with the appearance of polyphenol cleavage intermediates. This transition featured an early decrease in markers of proteolytic fermentation alongside a transient in vitro lipid-stress response. By D7, the community shifted toward a stable configuration enriched in butyrogenic taxa (Roseburia, Subdoligranulum, Megamonas), with progressive depletion of potentially opportunistic pathogens (Escherichia-Shigella). Multi-omics integration suggested that these structural successions were strongly associated with marked metabolic shifts. Inflammatory lipid markers (e.g., leukotriene B4) showed a characteristic “D3-burst/D7-clearance” pattern, whereas potentially barrier-protective metabolites, particularly 3-indolepropionic acid (3-IPA, log₂FC = 2.00) and urolithin B (log₂FC = 1.18), accumulated substantially. Conclusions: This exploratory study provides valuable high-resolution insights into astilbin’s potential as a dynamic ecological modulator. It outlines a temporal framework illustrating how the gut microbiota may shift from proteolytic fermentation toward 3-IPA-associated homeostasis. Although limited by a pooled fecal model and the absence of a vehicle control, these hypothesis-generating findings offer a solid foundation for future in vivo studies and mechanistic validations across diverse human cohorts. Full article

3D geovisualisation is increasingly used to represent spatial phenomena in engaging and interactive ways. However, 3D thematic methods and their user-centred evaluation remain challenging due to issues such as view distortion, variable scale, and complex interactions. Previous research emphasises that effective 3D thematic design requires careful treatment of visual variables and interactive camera control to mitigate overlap and occlusion. Building on this foundation, we present 3D-Geo-Vis, a web-based application with open-source code. The application visualises air temperature using seven methods of 3D geovisualisation and supports real-time adjustment of method-specific visual variables through a dedicated side panel. We report a usability study with 54 participants that combines (i) eye-tracking (Tobii Pro Spark, 60 Hz), (ii) interaction logging using our MapLogger tool, and (iii) a post-test questionnaire including the User Experience Questionnaire (UEQ) and open-ended feedback. Participants completed a structured scenario comprising free exploration and targeted analytical tasks. Across all sessions, MapLogger captured 15,457 interactions. The success rate of fully completed tasks was generally high for tasks involving the search for a specific value or modification of interface parameters (Task 2: 87.0%; Task 3: 85.2%), while the voxel-based analytical task showed slightly lower completion (Task 4: 77.8%), reflecting higher cognitive and interaction demands. UEQ results indicate a slightly positive overall user experience, with speed-related items rated most negatively. Triangulating gaze behaviour, interaction logs, and subjective feedback reveals key usability issues (e.g., insufficient salience of method switching, attention concentration on the side panel, and performance limitations of voxel rendering) and yields concrete recommendations for improving onboarding, feedback, and control discoverability in interactive 3D visualisation environments. These findings and resulting recommendations informed the development of the revised 3D-Geo-Vis 2.0 application. Full article

Object-level point cloud extraction is relevant to robotic perception and may provide useful object-centric observations for mapping and SLAM. Many existing methods rely on per-frame instance segmentation, resulting in high computational cost and limited ability to extract multiple objects simultaneously in multi-object scenarios. This paper proposes an efficient MOT-assisted pipeline for multi-view object point cloud extraction. The pipeline first employs 2D multi-object tracking (MOT) to establish consistent object correspondences across views, and then combines monocular depth-based reconstruction with multi-view geometric association to estimate coarse object locations. An adaptive spherical proposal and a density-based refinement strategy are further introduced to extract clean object-specific point clouds while suppressing background noise and outliers. Experiments on the DTU, MVImgNet, and ScanNet++ datasets demonstrate the effectiveness of the proposed method. Relative to the unprocessed scene-level point cloud, the Chamfer Distance is reduced from $90.97$ mm to $12.52$ mm and Precision increases from $0.2998$ to $0.8776$ on DTU dataset Sequence 30. On MVImgNet, the Chamfer Distance decreases from $1.23$ m to $0.46$ m and Precision increases from $0.4777$ to $0.9337$, demonstrating effective removal of non-target scene points under real-world viewing conditions. Moreover, the proposed method reduces the cost of object-level association and provides competitive object-level extraction quality under the tested settings. Compared with the closely matched segmentation-based extraction, the proposed method provides lower measured object extraction runtime and built-in cross-view Track-ID association, while sacrificing some boundary accuracy. Full article

Bacterial disease is a major constraint on Magallana gigas larval production, yet hatchery microbiology has been characterised almost exclusively through the lens of Vibrio, and rarely across the whole production chain. We tracked bacterial communities throughout an entire larval rearing cycle at a commercial hatchery in northern China. A total of 78 samples were collected from the full production chain, from water intake to larvae. Bacterial communities were characterised using 16S rRNA (V4–V5) amplicon sequencing and culture-based isolation, with larvae sampled from three replicate tanks at six developmental stages. A protracted mortality event began at the D-veliger stage, with cumulative losses of roughly 40% before sinking ceased; ostreid herpesvirus 1 was not detected. The only taxon that rose above its healthy baseline during larval mortality was a single undescribed a single undescribed amplicon sequence variant (ASV) of the family Cryomorphaceae. Its relative abundance increased to a mean of 41% (with a tank-to-tank range of 33.7–51.6%) before disappearing once the mortality event concluded. No described species exceeds 92.2% 16S rRNA gene sequence identity to it, whereas its closest environmental relatives (97–98%) are, without exception, uncultured bacteria associated with marine invertebrates. It was an order of magnitude more abundant in larvae than in the surrounding water. Vibrio rose transiently at the onset of mortality but fell below its healthy-stage abundance while larvae were still dying. Tenacibaculum, by contrast, was the dominant genus of the rearing water yet was never recovered on Vibrio-selective medium and showed no association with mortality. Chlorination of the feed-room supply removed Tenacibaculum while leaving Vibrio uncontrolled. These findings reveal the limitations of the Vibrio-targeted, culture-based paradigm that has long dominated hatchery microbiology, as it overlooks both prevailing water-column organisms and mortality-linked taxa. Our results highlight the need for whole-community, culture-independent surveillance to better understand larval disease. Full article

This study develops and experimentally validates a battery-coupled wind–photovoltaic power model for auxiliary electrical supply in small-vessel systems. The prototype integrates a 395 W CS6R-395MS monocrystalline photovoltaic module (CSI Solar Co., Ltd., Suzhou, Jiangsu, China), a 200 W FA200W horizontal-axis wind turbine (VEVOR, Rancho Cucamonga, CA, USA), maximum power point tracking (MPPT) power-conditioning stages, a 24 V/28 Ah AGM VRLA battery bank composed of four BAT212120086 batteries (Victron Energy B.V., Almere, The Netherlands), a 24 V DC bus, and a Phoenix 24/500 pure sine-wave inverter (Victron Energy B.V., Almere, The Netherlands), targeting non-propulsion navigation, communication, and lighting loads on a 5.7 m length overall (LOA) vessel. Field-acquired irradiance, cell temperature, incidence angle, PV voltage, wind speed, and rotor-speed data were used as time-dependent model inputs and compared with synchronized active-power measurements. Across the full 15–24 September 2025 experimental campaign, the maximum absolute relative error remained below 2.69%, while the aggregate statistical validation indices were ME = −0.1041 W, MAE = 0.3988 W, RMSE = 0.4931 W, and MAPE = 0.5946%. For the representative cloud-adverse case study conducted on 21 September 2025, the measured hybrid generation reached $E_{\mathrm{h}\mathrm{y}\mathrm{b}}=611.3 \mathrm{W}\mathrm{h}$ over 8.28 h, corresponding to $C_{\mathrm{R}\mathrm{E}\mathrm{S}}=102.1\%$ of the selected $E_{\mathrm{l}\mathrm{o}\mathrm{a}\mathrm{d}}=599 \mathrm{W}\mathrm{h}/\mathrm{d}\mathrm{a}\mathrm{y}$ auxiliary-load profile and to $C_{\mathrm{h}\mathrm{y}\mathrm{b}+\mathrm{b}\mathrm{a}\mathrm{t}}=158.1\%$ when the usable battery reserve at 50% depth of discharge (DOD) was included. Full article

Light detection and ranging (LiDAR) is widely used in robotics, autonomous vehicles, remote sensing, and object tracking, and a large number of commercial 3D LiDAR sensors with diverse specifications are now available. However, these sensors are typically sold with fixed specifications: their measuring range, field of view (FoV), angular resolution, number of scan points, and number of scan layers are all determined at the point of manufacture and cannot be adapted to the application. This rigidity forces the surrounding platform to absorb the cost of excess data, higher computation, larger post-processing storage, and false feature detections even when only a narrow region of interest is needed. To address these limitations, this paper presents 3D Customizable LiDAR (3D CS LiDAR), a novel reconfigurable 3D LiDAR architecture that exposes sensor specifications as run-time parameters. The paper describes the mechanical, electrical, and software subsystems of the developed sensor and evaluates its object-detection performance against a commercial 32-channel LiDAR under two experimental setups. Within the scope of the indoor evaluation reported here (1–3 m range, three geometric targets), the proposed reconfigurable 3D LiDAR provides denser on-target sampling and lower false negative rates than the commercial reference sensor in every tested configuration, indicating its potential for close-range indoor perception tasks such as robotic inspection and short-range obstacle detection. This work contributes to LiDAR technology by introducing a run-time-reconfigurable approach that addresses the specification rigidity of existing sensors and outlines the initial progress towards a full-fledged reconfigurable 3D LiDAR system, with outdoor operation and long-range characterisation identified as future work. Full article

Artificial intelligence (AI)-driven positioning and tracking systems combine geometric trajectories with behavior understanding in smart-city, healthcare, and autonomous environments. Skeleton sequences provide compact, privacy-preserving motion geometry, but labeled data are costly, and coordinate-only self-supervision cannot recover object, scene, or interaction cues. Vision-language transfer can supply these cues, but instance-level targets remain sensitive to noisy crops, incomplete descriptions, and ambiguous actions. We propose CrossVLS, which is a cross-modal vision-language-guided framework that transfers semantic knowledge from red, green, and blue (RGB) frames and generated language descriptions to a skeleton encoder during pretraining while retaining skeleton-only inference. CrossVLS replaces noisy instance-level transfer with a shared prototype space: skeleton, RGB, and language features are softly assigned to a common prototype bank through balanced optimal transport, and the resulting assignments define semantic soft targets for contrastive learning. A full-batch progressive training schedule gradually increases cross-modal guidance without splitting the physical batch, preserving the support set used to construct semantic targets. Experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD demonstrate strong performance under linear and semi-supervised evaluation using only the pretrained skeleton encoder at inference. These results show that prototype-mediated vision-language transfer can improve skeleton representations for the behavior-interpretation stage of positioning and tracking pipelines. Full article

Magnetic particle imaging (MPI), as an emerging radiation-free, high-sensitivity molecular imaging technique, holds broad application prospects in fields such as medical diagnosis, angiography, and targeted drug tracking. However, traditional three-dimensional MPI reconstruction algorithms face a problem in balancing reconstruction speed and image resolution. A hybrid reconstruction algorithm (Full Hybrid) based on spatial density constraints and residual iterative optimization is proposed in this work. This paper simulates Lissajous trajectory scanning and the non-linear response of magnetic particles based on the three-dimensional MPI simulation framework. The proposed hybrid method first utilizes the X-space method to obtain a basic spatial prior, then introduces field-free point (FFP) trajectory density to impose spatial weighting constraints on the reconstructed image. Experimental results demonstrated that this hybrid algorithm performs better in the reconstruction of complex three-dimensional topological structures (an H-shaped phantom). Comprehensive evaluation demonstrated that the reconstructed outputs reach a peak signal-to-noise ratio (PSNR) of 12.85 dB, a structural similarity index measure (SSIM) of 0.7321, and a root mean square error (RMSE) of 0.2278. Ablation experiments and comparison experiments further reinforced the advantages of the proposed method. These results demonstrate the numerical feasibility of the proposed reconstruction method for a three-dimensional phantom and provide a basis for further evaluation under multiple simulation conditions and real-scanner measurements. Full article

We present a short-preamble digital signal processing (DSP) architecture for downstream Alamouti coherent-lite passive optical network (PON) reception using a simplified optical network unit (ONU) receiver. A four-branch gear-shifted least-mean-square (LMS) equalizer uses a large step during quadrature phase-shift keying (QPSK) preamble training and a small step during decision-directed payload tracking. In 50 GBaud 16-ary quadrature amplitude modulation (16-QAM) simulations over 20 km single-mode fiber, a 2048-symbol preamble is the first cold-start point whose mean bit-error rate (BER) falls below the selected uncoded BER target of ${10}^{-2}$. A 20-realization-per-point sweep gives a sub-1 dB penalty for 2048 symbols and a mean offline mean-square-error (MSE) settling time below 100 ns, with low-gain locking events retained. However, frame-level reliability improves with longer training: in an indicative 20-frame Monte Carlo test, 13 of 20 frames met the BER target with a 2048-symbol preamble, compared with 17 of 20 using 4096 symbols. For repeated frames to the same ONU after initial acquisition, warm-start tap reuse allows frames 4–5 in the tested sequence to fall below target with 512-symbol preambles, corresponding to a later-frame nominal coded payload rate of 163 Gb/s instead of 123 Gb/s. The results quantify the preamble/reliability/payload-rate trade-off and indicate that downstream Alamouti coherent-lite reception can support short-training equalizer acquisition after preamble synchronization with one balanced detector and one analog-to-digital converter. Full article

Dysgeusia is a clinically consequential, but still under-standardized, toxicity of cancer treatment. In the immunotherapy era, taste disturbances are increasingly relevant for patients receiving immune checkpoint inhibitors, chimeric antigen receptor (CAR) T-cell therapies and T-cell-redirecting bispecific antibodies, with G protein-coupled receptor family C group 5 member D (GPRC5D)-directed treatment in multiple myeloma representing a particularly instructive high-burden model. We performed a structured critical narrative review with evidence mapping. PubMed/MEDLINE was searched from database inception to June 2026, complemented by citation tracking in Google Scholar, ClinicalTrials.gov searches and guideline documents relevant to oncology nutrition, oral supportive care and cancer-related taste dysfunction. Search concepts covered cancer-related dysgeusia, immunotherapy-associated oral toxicity, GPRC5D/talquetamab-associated dysgeusia, oncology nutrition, oral–gut microbiome biology, natural taste modulators and miraculin-based interventions. Dysgeusia can reduce appetite, food enjoyment, dietary diversity and protein energy intake, thereby contributing to weight loss, malnutrition risk, distress, social withdrawal and, in severe cases, treatment modification or discontinuation. Available evidence is heterogeneous: general cancer-treatment-associated dysgeusia is supported by broader observational and interventional literature; immunotherapy-associated dysgeusia is less systematically characterized; and GPRC5D/talquetamab-associated dysgeusia represents the most clinically visible and target-specific immunotherapy-associated phenotype. Emerging pilot data suggest that dried miracle berry or miraculin-containing products may improve selected taste perception and nutritional parameters in cancer-related dysgeusia, but direct evidence in immunotherapy-associated dysgeusia is not yet established. We, therefore, propose a claim-disciplined precision supportive-care framework integrating systematic taste phenotyping, early nutritional risk assessment, oral health evaluation, microbiome-aware but hypothesis-generating endpoints, individualized flavor and texture adaptation, cautious use of natural taste modulators in selected patients and iterative monitoring of patient-centered outcomes. Future trials should test whether dysgeusia-focused nutritional and taste-modulating supportive care interventions can improve intake, quality of life and treatment persistence without compromising immunotherapy safety or efficacy. Full article

This paper presents a compute-efficient human-following robotic mobility system that integrates multimodal target recognition, LiDAR-based obstacle perception, hierarchical local planning, and low-level motion control into a unified perception–planning–control architecture. The system enables real-time target tracking and obstacle avoidance on resource-constrained embedded hardware. RGB-D vision and Tether Follow Sensors (TFS) are used for target recognition, while LiDAR provides local obstacle information. To reduce the computational burden of local planning, a Hierarchical Dynamic Window Approach (HDWA) is proposed. Unlike conventional DWA, which uniformly evaluates sampled motion candidates, HDWA applies Movement, Direction, and Detail dynamic windows according to obstacle conditions and target direction. This hierarchical structure reduces redundant trajectory evaluations by activating additional computation only when avoidance is required. Experimental validation on a low-cost embedded platform demonstrates that HDWA reduces the candidate-evaluation workload by 30.8–92.3% while maintaining stable target tracking and safe obstacle avoidance. In the left- and right-side avoidance scenarios, HDWA also reduced the path length by 6.5–7.8%, increased the average driving speed by 22.0–30.8%, and reduced the elapsed motion time by 24.4–28.5%. These results demonstrate the feasibility of the proposed compute-efficient system for human-following robots operating under embedded hardware constraints. Full article

Accurate and rapid prediction of waterflood front propagation and volumetric sweep efficiency remains challenging in ultra-low-permeability reservoirs because of strong heterogeneity, threshold pressure gradients, reservoir anisotropy, complex well-pattern geometry, and layer-dependent flow interference. In this study, an improved 3D streamtube model was developed for waterflood-front tracking and volumetric sweep evaluation in ultra-low-permeability reservoirs. The model incorporates experimentally constrained threshold pressure gradients, anisotropic coordinate transformation, dynamic streamtube flow-rate allocation, Buckley–Leverett-based non-piston displacement, interlayer interference correction, and irregular well-pattern adaptability. A unified calculation framework was established for both injector–producer and injector–fracture streamtube units, enabling 3D integration of layer-specific swept areas into volumetric sweep efficiency. The proposed model was validated against a commercial numerical simulator using a representative well group from Block A of the Changqing Oilfield. The predicted streamtube architecture and sweep-efficiency evolution agree well with numerical simulation results, with an average relative error of approximately 3.1%, while reducing the computational time from 1043 s to 1.42 s for a 30-year simulation. Sensitivity analysis demonstrates that threshold pressure gradient, well spacing, and inter-well connectivity are the dominant controls on sweep efficiency, whereas well-pattern type, interlayer heterogeneity, and reservoir anisotropy exert secondary but non-negligible effects. Field application further reveals a strongly layer-dependent waterflood behavior: the upper sand body preferentially propagates eastward, whereas the lower sand body advances mainly southward, producing a vertically asynchronous and laterally misaligned sweep pattern. These results show that the proposed model provides an efficient and physically interpretable tool for rapid waterflood-front prediction, refined waterflood optimization, and targeted production enhancement in heterogeneous ultra-low-permeability oil reservoirs. Full article

Sustainable hydroponic strawberry cultivation requires precise, real-time management of nutrient solutions to reduce waste and improve resource efficiency for home growers. This work presents an artificial neural network (ANN)-based system that estimates and approximates optimal nutrient formulations for strawberry hydroponics, with a primary focus on nutrient and water savings. The ANN-based system is implemented on hardware for real-time operation and was compared against a traditional PID controller to evaluate performance in nutrient solution management. The ANN receives temperature, humidity, water pH, nutrient concentrations, and lighting as inputs and outputs recommended nutrient solution parameters. Training used gradient descent and reached convergence after an average of 2500 epochs, achieving a mean error of 0.001 and an operating frequency of 84 MHz. Mechanical design was optimized for compact 3D-printable assembly to facilitate adoption by small-scale and domestic producers. The validation with 15 min sampling demonstrated the ability of the system to maintain target nutrient conditions, producing measurable increases in seedling and fruit weight and size while substantially reducing water and nutrient waste. These results indicate that ANN-driven, real-time nutrient estimation can make precision hydroponics more accessible and sustainable for home growers. In the comparative analysis, the ANN-based system demonstrated superior performance compared to the PID controller in maintaining precise nutrient concentrations and reducing resource waste, achieving more accurate tracking of target nutrient levels. Full article

This paper proposes a three-dimensional prescribed-time hierarchical cooperative guidance law (3-D PTHCGL) for rapid and reliable head-on encirclement in cooperative interception of high-speed targets under incomplete directed communication topology, target maneuvers, and limited terminal engagement time. The proposed method consists of a distributed estimator layer (DEL) and a local controller layer (LCL), addressing three key issues in head-on encirclement formation: information acquisition, rapid convergence, and geometric configuration. In the DEL, a distributed prescribed-time estimator (DPTE) is developed to enable followers without direct communication with the leader to estimate the leader’s states from neighborhood information within a prescribed time. In the LCL, a prescribed-time extended state observer (PTESO) and a three-dimensional prescribed-time cooperative guidance law (3-D PTCGL) are designed to estimate target-maneuver-induced disturbances and unknown states, and to guarantee prescribed-time convergence of estimation and cooperative tracking errors. Furthermore, virtual line-of-sight (LOS) angles are introduced based on a three-dimensional head-on interception kinematic model to characterize the head-on encirclement configuration, and range-to-go together with radial relative velocity are adopted instead of time-to-go to reduce sensitivity to time-estimation errors. Simulation results demonstrate the effectiveness and robustness of the proposed method in achieving prescribed-time head-on encirclement and simultaneous attack without a speed advantage. Full article

Road transport is a leading source of urban nitrogen oxides (NO_x) and fine particulate matter (PM_2.5)—a public-health and urban-sustainability challenge—and Low-Emission Zones (LEZs) are Europe’s principal response. Yet most are governed statically, unable to track conditions changing by the hour and the street. A digital twin, treated as decision infrastructure rather than a 3D model, recasts the LEZ as an adaptive decision infrastructure: a closed loop of sensing, modelling and rule-based adjustment. We develop a scalable, five-phase lifecycle methodology with auditability and GDPR-by-design built in, and derive three falsifiable hypotheses—efficiency, data integration, responsiveness—defining a research agenda. We test only the first. A diagnostic reading of London’s ULEZ shows its unimplemented phases are precisely those that close the loop. A proof-of-concept on real hourly NO₂ from five London sites (2023–2024) tests efficiency: at equal abatement effort, adaptive targeting avoids significantly more elevated-pollution hours than a uniformly stricter policy (about 37% versus 27%; 95% CI excludes parity), the advantage rising with forecast quality. This demonstrates the mechanism in reduced form, not a generalizable figure for a deployed system. By making regulation more responsive and accountable, it advances the Sustainable Development Goals on health, sustainable cities and climate (SDGs 3, 11, 13). Full article