Search Results (457)

To enhance the sustainability of temporary office buildings, energy-saving and emissions-reduction technologies, as well as the optimization of photovoltaic (PV) energy storage systems in such structures, are of great importance. In this study, a distributed energy storage system was developed for a temporary office building in Jincheng, China. Measurements showed climatic factors had the greatest effect on building energy consumption due to the building envelope’s low thermal performance and airtightness. The air conditioning system accounted for the highest proportion (87%) of building energy consumption. The PV system’s peak output occurred in the morning due to illumination conditions and module orientation. On this basis, a time-of-use (TOU)- and state-of-charge (SOC)-aware scheduling strategy was developed for the PV-ESS of the temporary office building to improve renewable-energy utilization and reduce user-end electricity cost. Unlike purely theoretical optimization studies, this work focuses on the practical application and validation of the scheduling framework in a real temporary office building using monitored data. The electricity cost decreased by 0.3 RMB/kWh, and the revenue from electricity sales during the scheduling period increased by 0.03 RMB/kWh after model optimization. The optimized scheduling strategy resulted in significantly fewer charge–discharge cycles of the storage battery, substantially decreasing the battery’s storage capacity and the system’s investment costs. Full article

Selectively modulating specific brain-rhythm bands with physical stimuli helps both to reveal neural mechanisms and to provide non-pharmacological treatment avenues for brain disorders. This study proposes and implements a multi-channel transcranial magneto-acoustic stimulation driving system based on amplitude-modulated (AM) sine waves (AM-TMAS) intended to supply a reliable hardware platform for noninvasive, focal low-frequency rhythmic electrical stimulation of deep-brain structures. The driving system implements a 64-channel AM module based on an FPGA plus high-speed DACs. Multi-channel precision is achieved via a unified high-speed clock and a global UPDATE trigger. To overcome the large separation between envelope and carrier frequencies, we developed a high-fidelity AM waveform generation method based on DDS + LUT + envelope multiplication. The algorithm first centers the carrier samples to preserve waveform symmetry, then applies LUT-based envelope coefficients and fixed-point envelope multiplication, enabling high-precision AM outputs with carrier frequencies from 100 kHz to 2 MHz and envelope frequencies from 0.1 Hz to 100 kHz. We tested the system’s rhythmic multi-channel AM output performance across frequencies and also measured magneto-acoustic-coupled rhythmic electrical signals produced by the AM-TMAS driving setup. Any single channel reliably produced high-fidelity AM waveforms with a 500 kHz carrier and 8 Hz/40 Hz envelopes; the measured carrier was 499.998 kHz with excellent frequency stability. Both envelope and carrier frequencies are flexibly tunable. At the nominal 500 kHz carrier, envelope fidelity was further quantified: the extracted envelopes achieved NRMSEs of 1.0795% (8 Hz) and 1.9212% (40 Hz), confirming high-fidelity AM synthesis. Under a 0.3 T static magnetic field, the AM-TMAS driving system generated rhythmic electrical responses in physiological saline that carried the expected 40 Hz envelope. The proposed AM-TMAS driver achieves high accuracy in AM waveform generation and robust multi-channel performance, and—when combined with an external static magnetic field—can produce rhythmically modulated magneto-acoustic electrical stimulation. This platform provides a practical technical tool for brain-function research and the development of rhythm-targeted neuromodulation therapies. Full article

Edge intelligence enables significant latency reduction and enhances the timeliness of model-based fault diagnosis. However, existing deep learning-driven bearing fault diagnosis models are ill-suited for deployment on edge devices, primarily due to three critical limitations: (1) Lightweight models typically exhibit inadequate anti-noise performance, failing to meet the reliability requirements of real-world engineering scenarios. (2) Models with superior anti-noise capabilities often demand high-performance hardware for operation, thereby restricting their deployment on resource-constrained edge devices. (3) These models adopt a fixed input length, which makes it difficult to guarantee diagnostic accuracy across diverse application scenarios—attributed to variations in sampling frequencies, bearing parameters, and other relevant factors. To address these challenges, this paper proposes a lightweight anti-noise diagnostic framework (LADF) for edge-intelligent bearing fault diagnosis in complex engineering environments. The LADF comprises three core modules: a dynamic input module (DIM), a lightweight network module (LNM), and a denoising branch. Specifically, the DIM is designed based on the envelope spectrum, leveraging its inherent demodulation characteristics to dynamically adapt to input signals across diverse scenarios. Group convolution and layer normalization are employed to construct the LNM, ensuring robust diagnostic performance while achieving efficient computation. The denoising branch constrains the feature extractor via a loss function, enabling it to learn generalized fault features under varying noise environments and thereby enhancing the anti-noise capability of the framework. Finally, the proposed LADF is validated through test rig experiments on two datasets of train axle box bearings. Comparative analysis with state-of-the-art models demonstrates that the LADF achieves superior diagnostic stability and anti-noise performance while maintaining a more lightweight architecture, making it well-suited for edge deployment in railway bearing fault diagnosis. Full article

Electromyographic (EMG) signals encode complex spatiotemporal dynamics reflecting neuromuscular coordination and pharmacological modulation. This study introduces a unified Hankel–topological framework for reconstructing and analyzing long-duration EMG recordings acquired from pigs under pharmacological influence, and for quantifying their bioelectrical organization. The method couples low-rank Hankel representations—capturing temporal redundancy and smoothness—with topological continuity constraints that stabilize activity packets defined by 5 s silence intervals. Six pigs were recorded across four experimental sessions (24 h each; four channels), and envelope reconstruction was performed using an ADMM-based solver. Quantitative analysis revealed consistent post-drug reductions in the packet rate ($-24.9\%$), the mean duration ($-2.3$ s), the amplitude ($-0.16$ a.u.), the effective Hankel rank ($-3.0$), and topological diversity ($\Delta {\beta }_0=-1.2$; all $p<0.01$). Deeper channels exhibited stronger suppression (interaction $p<0.02$), suggesting depth-dependent neuromuscular effects. The proposed framework unifies dynamical, statistical, and topological perspectives on EMG structure and yields interpretable biomarkers of neuromuscular inhibition and recovery. More broadly, it provides a generalizable signal processing methodology for analyzing structured, noisy physiological time series beyond EMG. Full article

Improving the energy efficiency of the building envelope is critical for global decarbonization, yet a gap remains in the comprehensive thermophysical characterization of carbon-enhanced Expanded Polystyrene (EPS). This study evaluates the impact of expansion ratios and moisture content on the thermal behavior of two commercial EPS grades, EPS-A (12.7 ± 0.5 kg/m³) and EPS-B (16.0 ± 1.1 kg/m³), investigating the counterintuitive role of graphite (1.4–1.8 wt.%) in enhancing the thermal insulation properties. Thermal conductivity and diffusivity were independently determined via Transient Plane Source (TPS) and Heat Flow Meter (HFM) methods across a 10–50 °C range, while specific heat capacity (c_p) was analyzed using HFM and Differential Scanning Calorimetry (DSC) through the sapphire comparison method and Temperature-Modulated DSC (TOPEM^®). Methodologically, it was found that standard HFM protocols are unsuitable for c_p determination in low-density foams, yielding an average relative error of ±29%; conversely, the sapphire comparison method provided the most reliable results in agreement with theoretical expectations. Results indicate that the efficacy of graphite as a radiative shield is closely coupled with cellular morphology, proving significantly more effective in the higher expansion grade (EPS-A, 70 wt.% open porosity) than in the denser EPS-B. Furthermore, 30-day water immersion tests revealed that the higher open porosity of EPS-A facilitates increased water uptake of 144 ± 17 wt.% (compared to 97 ± 7 wt.% for EPS-B), causing the geometric densities of the two grades to converge and fundamentally altering thermal transport mechanisms. The study concludes that accurate thermal modeling of carbon-enhanced insulation requires careful selection of testing parameters, particularly when accounting for moisture-induced degradation in high-porosity systems. Full article

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

Manual single-slice lumbar quantitative computed tomography (QCT) depends on operator-driven slice selection and trabecular region-of-interest (ROI) placement. We developed a fully automated single-slice workflow for vertebral trabecular Hounsfield unit (HU) measurement that combines unsuitable-slice prescreening, dual-purpose segmentation, intra-patient slice-quality ranking, and a deterministic inner ROI rule. The pipeline includes an Eligibility Gate, QC-Envelope segmentation for broad, vertebral- and usability-preserving delineation, PairRank-Swin for best-slice selection, and dedicated trabecular segmentation for final quantitative analysis. In the independent external cohort, 4 cases were considered non-evaluable by both manual review and the pipeline, and 2 additional borderline-quality cases were manually measured but rejected by the pipeline; therefore, paired HU agreement analysis included 44 evaluable cases. Agreement remained high, with Pearson’s r = 0.987, Lin’s CCC = 0.985, mean bias −0.44 HU, and limits of agreement from −14.88 to +13.99 HU. Coverage was 84.1% within ±10 HU and 97.7% within ±15 HU. Ablation analysis showed that slice ranking and ROI erosion were the most critical components. In an open module-level baseline comparison, QC-Envelope segmentation substantially outperformed TotalSegmentator. This workflow provides high agreement with expert HU measurement while preserving reviewable intermediate outputs. Full article

Bacterial membrane vesicles (BMVs) are non-replicative, bilayered nanostructures secreted by both Gram-negative and Gram-positive bacteria. Rather than being passive byproducts of cell envelope turnover, BMVs are increasingly recognized as regulated particles that selectively package proteins, lipids, nucleic acids, and other bioactive molecules. Through these cargos, BMVs mediate a wide range of biological processes, including bacterial stress adaption, intercellular communication, virulence delivery, and host immune modulation. In this review, we integrate recent advancements in understanding the molecular mechanisms underlying BMV biogenesis and composition and discuss how their heterogeneity contributes to their functional diversity. Beyond their biological roles, we critically examine the translational potential of BMVs in vaccine development, targeted drug delivery, cancer therapy, diagnostic tools, and biotechnological applications. However, significant challenges related to their safety, efficacy, and large-scale production must be addressed to realize their full clinical potential. We review recent progress and ongoing obstacles in the use of BMVs across various biomedical applications and propose strategies for their clinical translation. Full article

Owing to its large flexible envelope, an airship is highly sensitive to environmental disturbances, such as wind gusts. Fluid–structure interaction induces structural deformation, which modifies the aerodynamic force distribution and introduces additional coupling effects. Furthermore, servo-elastic deformation alters the position and orientation of actuators mounted on the envelope, resulting in deviations between commanded and actual control forces. To address these issues, a composite control strategy integrating trajectory tracking and active elastic deformation suppression is proposed for a flexible airship equipped with multiple vectored propellers. Structural flexibility is explicitly incorporated into the dynamic model through modal decomposition, where the generalized coordinates and their time derivatives associated with deformation modes are included in the system state vector. A disturbance observer is developed to estimate actuator-level force deviations induced by elastic deformation, and the estimated disturbances are compensated in real time. Based on this formulation, a composite control framework, referred to as servo-elastic control, is established. The framework consists of a trajectory tracking controller and a displacement compensation module to achieve simultaneous motion regulation and structural deflection suppression. Numerical results demonstrate that the displacement at vectored thrust actuator attachment points is reduced to approximately 10% of that obtained using a trajectory tracking controller alone. The proposed method achieves significant deformation suppression without degrading position tracking performance, thereby enhancing control effectiveness and system stability of flexible airships. Full article

Gait is a sensitive marker of mobility decline and fall risk, motivating unobtrusive sensing methods that can extract spatiotemporal parameters outside specialized gait laboratories. This paper presents a physics-based comparison of two millimeter-wave frequency-modulated continuous-wave (FMCW) radar deployment paradigms using a low-cost, system-on-chip (SoC) 60 GHz Infineon BGT60TR13C radar sensor: (i) a fixed (tripod-mounted) corridor observer and (ii) a shoe-mounted body-centric configuration attached to the medial side of the left shoe. Four healthy adult author-participants performed repeated 30 s corridor trials under five gait styles (regular, slow, fast, simulated festination, and simulated freezing-of-gait), including brief pauses during turns; an empty-corridor recording was acquired to characterize static clutter. Step events were detected using peak-picking on foot-related velocity envelopes with adaptive thresholds, and step count, cadence, step time, and step-time variability were derived. Performance of the fixed and shoe-mounted configurations was quantitatively compared to video ground truth using mean absolute percentage error (MAPE) for step count estimation. Across all gait styles, the shoe-mounted FMCW radar consistently reduced step-count error relative to the fixed corridor-mounted configuration, with the largest gains under irregular patterns (e.g., festination: 37.1% fixed vs. 9.6% shoe-mounted). These findings highlight the advantages of body-centric millimeter-wave radar sensing and support low-cost SoC radar as a pathway toward wearable, privacy-preserving gait monitoring in real-world environments. Full article

Staphylococcus aureus is a major cause of skin and soft tissue infections, necessitating the development of new topical agents with rapid bactericidal activity and low resistance potential. Here, we evaluated the antibacterial activity of a pyrazolone copper complex (P-FAH-Cu-phen) against S. aureus, investigated its in vitro mode of action, and its assessed therapeutic efficacy in a murine model of S. aureus-infected skin trauma. P-FAH-Cu-phen exhibited potent bactericidal activity (minimum inhibitory concentration [MIC] 1.4 μg/mL; minimum bactericidal concentration [MBC] 2.8 μg/mL) and rapid killing (>91% eradication within 2.5 min), with no detectable MIC increase under the tested serial passaging conditions. Cell-envelope dysfunction was evidenced by increased supernatant alkaline phosphatase activity, elevated leakage of nucleic acids and proteins, and reduced membrane-associated Na⁺/K⁺- and Ca²⁺/Mg²⁺-ATPase activities. At sub-inhibitory concentrations, P-FAH-Cu-phen reduced haemolytic and coagulase activities, modulated virulence gene expression (sea, hla, agrA), and inhibited biofilm formation and biofilm-associated metabolic activity. In vivo, topical treatment accelerated wound closure and histopathological repair, increased hydroxyproline content, reduced bacterial burden, and lowered TNF-α and IL-10 levels in wound tissues. Collectively, P-FAH-Cu-phen shows multi-faceted anti-infective activity and exhibits further development as a topical candidate for S. aureus-infected skin wounds. Full article

Background and Objectives: Surface electromyography (EMG) is widely used to assess muscle activation. However, direct interpretation of its functional biomechanical consequences remains challenging. This study aimed to develop and evaluate an EMG-driven musculoskeletal simulation framework for investigating how controlled modifications of muscle activation patterns influence joint-level biomechanics in the upper limb. The objective was not to reproduce specific clinical pathologies but to enable systematic virtual scenario analysis of activation-dependent movement alterations. Materials and Methods: Surface EMG signals were recorded from five healthy adults (3 males, 2 females; age 22 ± 1 years) during cyclic elbow flexion/extension tasks using a wireless system (sampling frequency: 2000 Hz). Processed and normalized EMG envelopes were directly applied as prescribed neural inputs in forward dynamic simulations implemented in OpenSim, without optimization-based muscle recruitment. Controlled virtual scenarios were generated through parametric modification of activation signals to represent reduced activation capacity, increased antagonist co-activation, spasticity-like activation modulation, and tremor-like oscillatory modulation. Joint kinematics, joint moments, and movement stability were evaluated. A Movement Quality Index (MQI) was introduced as a comparative research metric integrating biomechanical performance indicators. Simulations were deterministic and analyzed descriptively. Results: Distinct activation modifications produced characteristic kinematic and kinetic responses. Reduced activation capacity decreased simulated joint moment output, increased co-activation altered joint moment timing and mechanical stability, and tremor-like oscillatory modulation generated periodic fluctuations in joint kinematics and kinetics. The MQI enabled quantitative differentiation between simulated scenarios and severity levels within the controlled modelling framework. Conclusions: The proposed EMG-driven forward dynamic simulation framework provides a methodological platform for controlled virtual scenario analysis of activation-dependent biomechanical changes. The findings highlight the sensitivity of joint-level mechanics to altered muscle activation patterns, within the deterministic modelling environment. The framework is intended for research-oriented biomechanical investigation and hypothesis testing rather than direct clinical diagnosis of neuromuscular disorders. Full article

Auditory Brain–Computer Interfaces (BCIs) constitute the vital intervention for profound sensorineural hearing loss where the auditory nerve is compromised, yet their clinical efficacy remains restricted by substantial biological bottlenecks and limited spectral resolution. This review critically examines the evolutionary paradigm of auditory restoration, tracing the transition from static physical replacement to dynamic biological symbiosis. We systematically analyze physiological barriers across cochlear, brainstem, and cortical levels, elucidating how rigid interfaces provoke chronic tissue responses and why linear encoding protocols fail in distorted central tonotopy. The article synthesizes emerging methodologies in material science, demonstrating how soft, bio-integrated electronics and biomimetic topologies effectively address mechanical impedance mismatches. Furthermore, the trajectory of neural encoding is evaluated, highlighting the paradigm shift from traditional envelope extraction to deep learning-driven non-linear mapping and adaptive closed-loop neuromodulation. Finally, the potential of high-resolution modulation techniques, including optogenetics and sonogenetics, alongside AI-facilitated intent perception for active listening, is assessed. It is concluded that future neuroprostheses must evolve into symbiotic systems capable of seamlessly integrating with neural plasticity to enable high-fidelity cognitive reconstruction. Full article

A robust and cost-effective fiber-optic ultrasound sensor based on a slope-symmetric Fabry–Perot interferometer (FPI) is presented, employing dual-channel quadrature-biased heterodyne interrogation with an acousto-optic modulator (AOM). By introducing a 200 MHz frequency shift that yields an effective π/2 phase offset between the direct (unshifted) and frequency-shifted optical paths, the system ensures complementary sensitivity: when one channel operates at zero slope on the FPI transfer function (minimum sensitivity), the other resides at maximum slope, providing inherent immunity to laser wavelength drift and environmental perturbations. Experimental validation demonstrates reliable ultrasound detection across varying operating points. At quadrature extremes, one channel achieves peak amplitudes of ±2 V while the other is quiescent, whereas intermediate points enable simultaneous detection with amplitudes of ±1.5 V (AOM channel) and ±0.05–0.1 V (direct channel), accompanied by corresponding DC levels ranging from ~0.4 V to 1.6 V. The AOM channel utilizes simple envelope detection after 9.5–11.5 MHz bandpass filtering, maintaining low cost, though coherent mixing is suggested for enhanced weak-signal performance. The angle-symmetric FPI design, combined with gold-disk reflector adaptations and potential femtosecond laser micromachining, further reduces fabrication costs without sacrificing finesse or sensitivity. This quadrature-biased approach offers superior stability compared to single-channel systems, making it highly suitable for practical applications in photoacoustic imaging, nondestructive testing, and structural health monitoring. Full article

High-fidelity static map construction is essential for reliable autonomous navigation, yet dynamic environments introduce severe artifacts caused by moving objects (also referred to as dynamic artifacts) in accumulated maps. While geometry-based methods perform well on dense point clouds, their performance notably degrades on sparse 16-beam LiDAR due to the “Sparsity Trap”: dynamic objects are frequently missed by ray-based geometry, and purely geometric cues fail in radiometrically ambiguous scenarios. To address this, we propose RI-DVP, a physics–geometry dual-driven framework. Unlike conventional approaches, RI-DVP first performs a physics-inspired radiometric normalization that compensates for range attenuation and incidence-angle effects to establish a consistent signal baseline. Subsequently, a Dual-Residual Aggressive Removal (DRAR) module jointly exploits geometric residuals—bounded by a range-dependent spatial uncertainty envelope—and calibrated intensity residuals to detect geometrically indistinguishable objects. To balance recall and precision, a Hierarchical Static Reversion strategy (HSR) employs two-stage recovery to retrieve large-scale structures and correct fine-grained artifacts via topology-based adhesion reasoning. Experiments on SemanticKITTI and custom sparse datasets demonstrate that RI-DVP outperforms state-of-the-art geometric baselines, improving Dynamic Accuracy by over 36 percentage points in sparse scanning scenarios using a VLP-16 LiDAR sensor (Velodyne Acoustics, Inc., Morgan Hill, CA, USA) compared to baselines that fail under the sparsity trap while achieving real-time performance at approximately 15.3 Hz. Full article