Search Results (5,571)

A parallel-stirred tank bioreactor system on a 10 mL-scale automated with a liquid handling station introduces significant benefits in bioprocess analysis and design regarding preserving time, cost, and workload, thereby enabling quick generation of bioprocess results that can be easily scaled up. Although up-to-date approaches enable the online analysis of individual reactors for pH, dissolved oxygen (DO), and optical density (OD), the automated calibration of a new online laser-based infrared OD sensor device and noise reduction are still required. Among the extensive research on the full-data smoothing tools, the Savitzky–Golay (Savgol) filter was determined as the most effective one. Scattered and transmitted online light values were successfully aligned with the reference at-line OD values measured at 600 nm by the liquid handler with a step time of a few hours. The growth of an engineered Gluconobacter oxydans designed for specific whole-cell oxidations has been investigated in two parallel batch process setups with varied sugar types at varying sugar concentrations, combinations of sugars, and altered concentrations of complex media. Simulation of real-time smoothing was applied with a Kalman filter. Rapid adaptation was observed within a few upcoming data points by altering the parameters for the estimation of the noise in the signal. For almost all tested reaction conditions, a successful alignment of the simulation of real-time smoothed online OD with at-line values was achieved. The best growth condition was determined in the presence of 120 g L⁻¹ glucose and 30 g L⁻¹ fructose with the tripled peptone concentration. Under these conditions, OD₆₀₀ increased by 109%, from 2.1 to 4.4, compared to the reference process. Full article

Integrated satellite-terrestrial networks are crucial for critical communications, yet the initial access for user equipment (UE) is hampered by signal blockage and dynamic loads, challenging traditional random access (RA) mechanisms in achieving low latency and high success rates. To address this, we propose a Sensing-aware Random Access (SaRA) mechanism. SaRA introduces a lightweight sensing micro-slot before the standard RACH procedure, leveraging the sensing signal to jointly determine an optimal access decision threshold and a candidate beam set. This proactively filters users with poor channel conditions and narrows the beam search space. We formulate the resource allocation as a constrained optimization problem and propose a practical, low-complexity algorithm. Extensive simulations validate that SaRA provides substantial gains in access latency and system access capacity under high-load conditions compared with the standard 3GPP FR2 RACH baseline, while maintaining competitive first-attempt success probability with minimal additional overhead. Full article

Accurate measurement of sea-surface winds is critical for climate science, physical oceanography, and the rapidly expanding offshore wind energy sector. Buoy-based platforms—moored meteorological buoys, drifters, and floating LiDAR systems (FLS)—provide practical alternatives to fixed offshore structures, especially in deep water where bottom-founded installations are economically prohibitive. Yet these floating platforms are subject to continuous pitch, roll, heave, and yaw motions forced by wind, waves, and currents. Such six-degree-of-freedom dynamics introduce multiple error pathways into the measured wind signal. This paper synthesizes the current understanding of motion-induced measurement errors and the techniques developed to compensate for them. We identify four principal error mechanisms: (1) geometric biases caused by sensor tilt, which can underestimate horizontal wind speed by 0.4–3.4% depending on inclination angle; (2) contamination of the measured signal by platform translational and rotational velocities; (3) artificial inflation of turbulence intensity by 15–50% due to spectral overlap between wave-frequency buoy motions and atmospheric turbulence; and (4) beam misalignment and range-gate distortion specific to scanning LiDAR systems. Compensation strategies have progressed through four recognizable stages: fundamental coordinate-transformation and velocity-subtraction algorithms developed in the 1990s; Kalman-filter-based multi-sensor fusion emerging in the 2000s; Response Amplitude Operator modeling tailored to FLS platforms in the 2010s; and data-driven machine-learning approaches under active development today. Despite this progress, key challenges persist. Sensor reliability degrades under extreme sea states precisely when accurate data are most needed. The coupling between high-frequency platform vibrations and turbulence remains poorly characterized. No unified validation framework or benchmark dataset yet exists to compare methods across platforms and environments. We conclude by outlining research priorities: end-to-end deep-learning architectures for nonlinear error correction, adaptive algorithms capable of all-sea-state operation, standardized evaluation protocols with open datasets, and tighter integration of intelligent software with next-generation low-power sensors and actively stabilized platforms. Full article

Accurate patent valuation remains a persistent challenge in intellectual property management, particularly in the food industry, where technological homogeneity and rapid innovation cycles introduce substantial noise into observable performance indicators. Traditional valuation approaches, whether based on subjective expert judgment or citation-based metrics, often struggle to effectively reduce information uncertainty in this context. To address this limitation, this study proposes an objective, data-driven patent valuation framework grounded in information theory. We construct a multidimensional evaluation system comprising nine indicators across technological, legal, and economic dimensions and apply it to a large-scale dataset of 100,648 invention patents. To address the heavy-tailed nature of patent indicators without sacrificing the information contained in high-impact outliers, we introduce a square-root transformation strategy that stabilizes dispersion while preserving ordinal relationships. Indicator weights are determined objectively via Shannon entropy, capturing the relative scarcity and discriminatory information content of each signal, after which comprehensive value scores are derived using the TOPSIS method. Empirical results reveal that the entropy-based model assigns dominant weights to so-called “costly signals”, specifically PCT applications (29.53%) and patent transfers (24.36%). Statistical correlation analysis confirms that these selected indicators are significantly associated with patent value ($p<0.001$), while bootstrapping tests demonstrate the robustness of the resulting weight structure. The model’s validity is further evaluated using an external benchmark (“ground truth”) dataset comprising 55 patents recognized by the China Patent Award. The proposed framework demonstrates substantially stronger discriminatory capability than baseline methods, awarded patents achieve an average score 2.64 times higher than that of ordinary patents, and the enrichment factor for award-winning patents within the Top-100 ranking reaches 91.5. Additional robustness analyses, including benchmarking against the Weighted Sum Model (WSM), further confirm the methodological stability of the framework, with sensitivity analysis revealing an exceptional enrichment factor of 183.1 for the Top-50 patents. These findings confirm that the Entropy–TOPSIS framework functions as an effective information-filtering mechanism, amplifying high-value patent signals in noise-intensive environments. Consequently, the proposed model serves as a generalizable and theoretically grounded tool for objective patent valuation, with particular relevance to industries characterized by heavy-tailed data and high information uncertainty. Full article

Advanced glycation end products (AGEs) are important biomarkers associated with diabetes and metabolic disorders; yet existing detection methods are invasive and unsuitable for frequent monitoring. This study aimed to develop a non-invasive and portable AGEs detection device, optimize strategies for mitigating pigmentation-related interference, and evaluate its feasibility for metabolic assessment. The proposed system employs a 365 nm ultraviolet LED excitation source, an optical filter assembly integrated into an ergonomic dark chamber, and an eyelid-signal-based algorithm to suppress ambient light and skin pigmentation interference. Simulation experiments were conducted to evaluate the influence of different pigment colors and skin tones on fluorescence measurements. A clinical study was performed in 200 participants, among whom 42 underwent concurrent serum AGEs measurement as the reference standard. Predictive models combining corneal fluorescence signals and body mass index (BMI) were constructed and evaluated. The results indicated that purple and blue pigments introduced greater interference, whereas green and pink pigments had minimal effects. Device-derived AGEs estimates demonstrated good agreement with serum AGEs, with a mean error below 8%. A hybrid model incorporating BMI achieved improved predictive accuracy compared with single-parameter models. Participants with high-AGE dietary habits exhibited elevated fluorescence signals and BMI. These findings suggest that the proposed device enables stable and accurate non-invasive AGEs assessment, with potential utility for metabolic monitoring. Incorporating lifestyle-related parameters may further enhance predictive performance and expand clinical applicability. Full article

Osteosarcoma (OS), a highly aggressive bone malignancy, is hard to treat due to complex molecular mechanisms. This study aimed to identify key bioactive compounds from medicine–food homologous (MFH) substances for OS intervention. We analyzed GEO transcriptomic data to get 317 differentially expressed genes (DEGs), screened bioactive compounds from 106 MFH via dual databases, predicted compound–DEG protein interactions with GraphBAN, and filtered 11 core compounds through drug-likeness/toxicity evaluations. Regulatory networks identified 5 key target genes (SOST, ACACB, TACR1, GRIN2B, MPO), 10 key compounds (e.g., ellagic acid dihydrate) and 8 MFHs (e.g., Daidaihua). Molecular docking/MD confirmed stable complexes. GSEA/GSVA revealed pathway dysregulation (e.g., upregulated WNT signaling), and immune analysis showed altered infiltration of 5 cell subsets. 143B cell experiments and qRT-PCR validated findings. MFH-derived compounds, especially ellagic acid dihydrate, have multi-target anti-OS potential, laying a foundation for novel OS therapeutics. Full article

Free-roaming cats may contribute to zoonotic risk via parasites and other eukaryotic taxa, yet surveillance in protected island settings is limited and conventional coprology can miss low-intensity or degraded signals. We conducted a cross-sectional 18S rRNA metabarcoding survey to establish a baseline profile of potentially pathogenic eukaryotes in community cats from La Graciosa (Natura 2000, Canary Islands, Spain) prior to large-scale antiparasitic interventions. We analysed 152 faecal samples, including fresh samples collected during a high-throughput TNR campaign (n = 37) and dry environmental deposits (n = 115). Host amplification was reduced using a feline 18S blocking primer; libraries were sequenced with Oxford Nanopore technology; and taxonomy was assigned using SILVA-based classifiers with downstream filtering for veterinary/zoonotic relevance. After quality control, 72 eukaryotic taxa were retained and DNA from at least 24 potentially pathogenic taxa was detected. Dipylidium caninum was most frequent (74.3%; 113/152), and opportunistic fungi/yeasts were common (e.g., Pichia kudriavzevii 42.4%, Diutina catenulata 31.5%). Zoonotic protozoa showed low-to-moderate detection frequency (Acanthamoeba castellanii 13.3%, Toxoplasma gondii 7.9%, Balamuthia mandrillaris 4.6%). Overall richness did not differ between fresh and dry samples (p > 0.05), but fresh samples contained higher richness of potentially pathogenic taxa (p < 0.01). Full article

Background: Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders associated with cardiovascular complications, cognitive impairments, and reduced quality of life. Early and accurate diagnosis is essential. The present gold standard, polysomnography, is expensive and resource-intensive. This work develops a non-invasive machine-learning-based framework to classify four OSA severity groups (non, mild, moderate, and severe) using tracheal breathing sounds (TBSs) and anthropometric variables. Methods: A total of 199 participants were recruited, and TBS were recorded whilst awake (wakefulness) using a suprasternal microphone. The workflow included the following steps: signal preprocessing (segmentation, filtering, and normalization), multi-domain feature extraction representing spectral, temporal, nonlinear, and morphological features, adaptive feature normalization, and a three-stage feature selection that combined univariate filtering, Shapley Additive Explanations (SHAP)-based ranking, and recursive feature elimination (RFE). The classification included training ensemble learning models via bootstrap aggregation and validating them using stratified k-fold cross-validation (CV), while preserving the OSA severity and anthropometric distributions. Results: The proposed framework performed well in discriminating among OSA severity groups. TBS features, combined with anthropometric ones, increased classification performance and reliability across all severity classes, providing proof for the efficacy of non-invasive audio biomarkers for OSA screening. Conclusions: TBS-based model’s features, coupled with anthropometric information, offer a promising alternative or supplement to PSG for OSA severity detection. The approach provides scalability and accessibility to extend screening and potentially enables earlier detection of OSA, compared to cases that might remain undiagnosed without screening. Full article

Modern protection relays require accurate and fast phasor estimation under harsh transient conditions, including a decaying DC component, harmonics, interharmonics, noise, and frequency instability. The original CCHDF (Cleaned Characteristic Harmonic Digital Filter) produced a harmonic cleaned signal using the Biunivocal Frequency Relationship of Phasors (BFRP) technique, but relied on DFT, Hanning windowing, and peak detection to identify interharmonic components. This paper replaces that spectral estimation block with the Modified Covariance Method (MCM) estimator, a high resolution autoregressive (AR) spectral estimator capable of superior frequency, magnitude, and phase estimation of non-harmonic components even with a short data window. The result is an improved filter named MCCCHDF (Modified Covariance CCHDF), preserving the original algorithmic pipeline, but achieving higher accuracy and faster convergence in the presence of closely spaced harmonics/interharmonics and noisy decaying DC conditions. Full article

Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardware, high-density and flexible electrodes, and embedded acquisition modules combined with modern signal processing and machine learning have significantly enhanced the robustness and applicability of EMG-based systems. This review provides an integrated overview of EMG generation, acquisition standards, and preprocessing techniques, including adaptive filtering, wavelet denoising, and empirical mode decomposition. Feature extraction methods across the time, frequency, time–frequency, and nonlinear domains are compared with respect to computational efficiency and suitability for real-time systems. The review synthesizes classical and contemporary pattern-recognition approaches, from statistical classifiers to deep architectures such as CNNs, RNNs, hybrid CNN–RNN models, transformer-based networks, and graph neural networks. Key challenges, including signal non-stationarity, electrode displacement, muscle fatigue, and poor cross-user or cross-session generalization, are examined alongside emerging strategies such as transfer learning, domain adaptation, and multimodal fusion with IMU or FMG signals. Finally, the paper surveys rapidly growing EMG applications in prosthetics, rehabilitation robotics, human–machine interfaces, clinical diagnostics, and sports analytics. The review highlights ongoing limitations and outlines future pathways toward robust, adaptive, and deployable EMG-driven intelligent systems. Full article

The non-invasive recording of spinal cord neuronal activity, also known as electrospinography (ESG), using high-density surface electromyography (HD-sEMG) is a promising emerging biosensing modality. However, these recordings often contain electrocardiographic (ECG) artifacts that must be removed for accurate analysis. Given the emerging nature of ESG and the lack of dedicated signal processing methods, this study assesses the performance of seven established EMG denoising algorithms for their ability to preserve the broad spectral bandwidth needed for future ESG characterization: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD). Performance was quantified using six metrics: Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation ($\Delta$KR2). ESG data were recorded from nine healthy participants at brachial and lumbar plexus sites with various electrode configurations. ATS consistently outperformed all other methods in suppressing cardiac artifacts of varying shapes. Although it did not fully preserve low-frequency content, ATS achieved the best balance between artifact removal and signal integrity. Algorithm performance improved when ECG contamination was lower, especially in brachial plexus recordings with closer reference electrodes. Full article

At present, most of the switchgear partial discharge detection means are offline detection and cannot monitor multiple partial discharge sources online at the same time. Based on this, this paper investigates the application of ultrasonic technology in localized discharge fault localization in high-voltage switchgear, removes the background noise of localized discharge in switchgear by using soft and hard filtering; proposes a generalized cubic correlation algorithm on the basis of TODA, improves the accuracy of the time difference acquisition in the case of low signal-to-noise ratio; determines the number of multiple localized discharging power sources by using the single-channel signal blind source separation technique and singularity spectral analysis; and determines the number of multiple localized discharging power sources by using independent component analysis to separate them. As well as for the problem that TDOA cannot be directly applied to the localization of multiple partial discharge sources, independent component analysis is used to separate the mixed signals, and the disordered coordinate selection method is proposed to determine the coordinates of multiple partial discharge sources. The experimental results show that (1) the noise reduction method is able to remove the excess interference while preserving the localized discharge signals; (2) the improved generalized cubic inter-correlation algorithm is more resistant to interference and has less error than other time delay estimation algorithms. The localization error is reduced by 60 mm~68 mm compared to the basic correlation algorithm, 41 mm~47 mm compared to the twice correlation algorithm, and 17 mm~20 mm compared to the three times correlation algorithm, which is a big improvement compared to the pre-improved algorithm. (3) It is able to locate the multiple localized power sources, and the accuracy of the number of localized power sources reaches 88%. Full article

Signal intersections are key nodes in urban road traffic networks, and real-time queue length information serves as a core performance indicator for formulating effective signal management schemes in modern adaptive traffic signal control systems, thereby enhancing traffic efficiency. In this study, a roadside Light Detection and Ranging (LiDAR) sensor is employed to acquire 3D point cloud data of vehicles in the road space, which acts as an important method for queue length detection. However, during queue-length detection, vehicles in different lanes are prone to occlusion because of the straight-line propagation of laser beams. This paper proposes a queue-length detection method based on variations in vehicle point cloud features to address the occlusion of queue-end vehicles during detection. This method first preprocesses LiDAR point cloud data (including region-of-interest extraction, ground-point filtering, point cloud clustering, object association, and lane recognition) to detect real-time queue lengths across multiple lanes. Subsequently, the occlusion problem is categorized into complete occulusion and partial occlusion, and corresponding processing is performed to correct the detection results. The performance of the proposed queue length detection method was validated through experiments that collected real-world data from three urban road intersections in Suzhou. The results indicate that this method’s average accuracy can reach 99.3%. Furthermore, the effectiveness of the proposed occlusion handling method has been validated through experiments. Full article

Acoustic emission (AE) technology, a kind of non-destructive testing method, was used in this study to monitor the fracture process of Q245R steel in the single edge notched tension (SENT) test. The obtained AE signals were first processed by the sensor gauge method to distinguish the noise and signals related to a fracture. Based on the filtered data, it was found that the load-displacement curve and load–Crack Mouth Opening Distance (CMOD) curve of the fracture development were correlated with the characteristics of signals. In addition, an AE crack development index (CDI) was proposed to characterize different stages in the crack propagation process, and the results were verified by unloading compliance experiments. The results showed that the condition of structure can be well characterized by trends of cumulative counts and peak amplitudes of AE signals. In addition, stable cracks were found to occur when the load reached 92% of the ultimate load which produced AE signals with high counts, duration, and more high-amplitude signals. The proposed AE CDI of 40%max(CDI), 50%max(CDI), and 60%max(CDI) reflects the elastic, plastic, and stable crack propagation stages under monotonic tension, respectively, and remains stable even when the tensile loading method changes. Full article

This article presents an ontology-driven Digital Twin (DT) for hotel front-desk operations that fuses two real-time data streams: (i) physiological and activity signals from wrist-worn wearables assigned to staff, and (ii) 3D people-positioning and occupancy events captured by reception-area cameras using a proprietary implementation of Optical Camera Communication (OCC). Building on a previously proposed front-desk ontology, the semantic model is extended with positional events, zone semantics, and wearable-derived workload indices to estimate queue state, staff workload, and service demand in real time. A vendor-agnostic, property-based REST API specifies the DT interface in terms of observable properties, including authentication and authorization, idempotent ingestion, timestamp conventions, version negotiation, integrity protection for signed webhooks, rate limiting and backoff, pagination and filtering, and privacy-preserving identifiers, enabling any compliant backend to implement the specification. The proposed layered architecture connects ingestion, spatial reasoning, and decision services to dashboards and key performance indicators (KPIs). This article details the positioning pipeline (calibration, normalized 3D coordinates, zone mapping, and confidence handling), the wearable workload pipeline, and an evaluation protocol covering localization error, zone classification, queue-length estimation, and workload accuracy. The results indicate that a spatially aware, ontology-based DT can support more balanced staff allocation and improved guest experience while remaining technology-agnostic and privacy-conscious. Full article