Search Results (1,019)

(1) Background: University food service establishments are complex environments, where high turnover and handling practices create conditions for microbial persistence. Food-contact surfaces (FCSs) and handlers’ hands (FHs) function as dynamic reservoirs, facilitating the circulation of contaminants within these institutional settings. This study aimed to characterise the microbiological contamination of FCSs and FHs in university food service establishments in Northern Portugal and to evaluate their role as interconnected environmental reservoirs within the indoor built environment. (2) Methods: A total of 590 samples were analysed from two universities in Northern Portugal (L1, L2), comprising 380 FCS and 210 FH samples. Aerobic colony counts (ACCs), Enterobacteriaceae, and Moulds and yeasts (MYs) were analysed according to ISO methods. FH samples were additionally screened for Escherichia coli and Staphylococcus spp. (3) Results: Overall, 35.5% of FCSs were classified as non-compliant, according to microbial criteria based on guideline values from the National Health Institute Dr. Ricardo-Jorge (INSA), with non-compliance primarily driven by elevated ACCs and MYs. Based on a Generalised Linear Model (GLM), establishment types (canteens vs. cafes) were associated with Enterobacteriaceae levels (p = 0.016), whereas ACCs and MYs were not significantly associated with district, establishment type, or functional surface category (p > 0.05). Differences between left and right hands showed small effect sizes, and location was a highly significant determinant of hand hygiene acceptability. (4) Conclusions: FCSs and FHs act as relevant contamination reservoirs in these settings. The results indicate that Enterobacteriaceae levels on FCSs differed between establishment types, while ACCs and MYs showed no significant variation across the evaluated environmental factors. Marked differences in hand hygiene acceptability between campuses support the implementation of targeted interventions, including the optimisation of cleaning and disinfection protocols, the structured training of food handlers, and the routine microbiological monitoring of surfaces and hands to improve institutional food safety. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

In this paper, a novel multiband microwave resonator is proposed and investigated for non-invasive glucose sensing applications. The structure is based on a compact, planar spiral-like geometry fed by a Coplanar waveguide (CPW) transmission line, designed to support multiple resonant modes through nested concentric rings. A full electromagnetic model was developed to predict the resonance behavior analytically, achieving excellent agreement with Computer Simulated Technology (CST) simulations across four resonant frequencies (2.7, 6.44, 8.0, and 12.8 GHz). The sensor demonstrated high glucose sensitivity at multiple frequencies, with peak values reaching 0.05 dB/mg/dL and 0.038 dB/mg/dL at 10.1 GHz and 6.22 GHz, respectively. To enhance conformability and skin contact, the antenna was further transformed into a semi-cylindrical flexible form suitable for finger-wrapping. Despite the mechanical deformation, the structure preserved its resonance while offering enhanced near-field interaction with biological tissues. The folded sensor achieved a sensitivity of 0.032 dB/mg/dL at 5.25 GHz and a peak gain of 6.05 dB, validating its robustness for wearable deployment. The clear correlation between reflection magnitude and glucose level (with R > 0.99) confirms the sensor’s potential as a passive, multiband, and non-invasive glucose monitoring platform. The physics-informed residual deep learning framework significantly enhances prediction accuracy, achieving an RMSE of 0.28 mg/dL, MARD of 0.13%, and confining 100% of both training and holdout predictions within the <5% ISO-like risk region, thereby ensuring robust and clinically reliable non-invasive glucose estimation. Full article

Broiler respiratory rate (RR) in cage systems is a core physiological indicator of health and stress. However, real-time, non-invasive continuous RR monitoring is difficult in a high-density breeding environment, thereby limiting precise poultry health management. This study developed a feasible non-contact broiler RR measurement method to address this gap. The proposed method integrates infrared thermal imaging and phase-based video magnification (PBVM). Using cage-reared white-feathered broilers as subjects, we selected the thoracodorsal and tail regions as regions of interest (ROI), applied PBVM to amplify subtle respiratory-related body surface movements, and extracted RR features via the Fast Fourier Transform (FFT). Two validation experiments were conducted under controlled laboratory conditions. One was an RR dynamic monitoring experiment covering the entire life cycle (4 to 36 days), which analyzed video data of 198 individual quiet broilers. The other was a multi-gradient heat stress experiment with temperature increases of +2 °C, +4 °C, and +5 °C, and analyzed video data of 162 individual quiet broilers. The method achieved favorable measurement accuracy: in the whole-life-stage experiment, the mean absolute error (MAE) was 0.036 Hz, the mean absolute percentage error (MAPE) was 4.461%, and the coefficient of determination (R²) reached 0.961; in the heat stress experiment, the MAE was 0.042 Hz, the MAPE was 3.270%, and the R² reached 0.928. Linear regression analysis confirmed that healthy broiler RR decreased linearly with increasing age, and verified that RR showed a stepwise response to thermal challenge with a positive correlation between RR increase and temperature increment, accompanied by growth stage specificity. This study provides a feasible non-invasive approach for broiler RR monitoring, offering preliminary reference data for early heat stress detection and sustainable poultry production. Full article

γ-photon tomography, which leverages the high penetration and electrical neutrality of high-energy γ-photons, offers a promising non-contact approach for industrial flow field monitoring. However, γ-photon flow-field images are inherently grayscale and exhibit probabilistic statistical imaging characteristics, leading to color banding artifacts when processed by mainstream colorization algorithms like DeOldify, which compromise structural continuity and visual consistency. To address this issue, this paper proposes a Structure Enhancement Colorization Network (SECN) model for γ-photon flow-field image colorization. A U-Net + GAN framework is employed, with ResNet101 as the generator backbone. It integrates structure-aware enhancement and multi-scale attention modules, while the discriminator incorporates enhanced blocks for improved boundary and texture discrimination. By adaptively fusing global–local features across channel and spatial dimensions, the SECN model effectively suppresses color banding artifacts and enhances structural consistency. To validate the effectiveness of the proposed algorithm, two CFD-simulated γ-photon flow-field image colorization scenarios—namely a large-scale vortex wake and a horizontal wake—are used as evaluation targets. In terms of image quality metrics, the proposed colorization algorithm achieves PSNR, SSIM, FID, and MAE values of 32.5831, 0.8612, 17.8514, and 0.0191, respectively, corresponding to improvements over DeOldify of 4.54%, 2.82%, 5.18%, and 11.16%. When considering information entropy, the proposed colorization algorithm achieves an average entropy value of 4.0257, marking a 4.44% increase compared to DeOldify’s 3.8543, demonstrating superior information preservation and reduced uncertainty in reconstructing complex probabilistic structures. Furthermore, from the perspective of parameter inversion, the temperature inversion MAPE is 7.60%, which is a significant reduction of 18.42% compared to that of DeOldify. Full article

This study investigates the fracture initiation and propagation mechanisms of continuously reinforced concrete–asphalt (CRC+AC) composite pavements under the synergistic effects of diurnal temperature fluctuations and non-uniform tire loading. A three-dimensional (3D) thermo-mechanical coupled finite element (FE) model was developed, with its underlying mechanical framework validated through laboratory-scale model tests conducted at 20 °C. The experimental results, involving strain monitoring at varying depths, demonstrated a high degree of consistency with numerical predictions in terms of spatial strain distribution, thereby ensuring the model’s reliability in capturing interlayer load-transfer efficiency. Building upon this validated mechanical foundation, numerical simulations were extended to analyze the low-temperature fracture response. The numerical results indicate that the maximum longitudinal and transverse tensile stresses in the asphalt layer are concentrated at the pavement surface, whereas the maximum shear stress occurs at a depth of 2–3 cm near the leading and trailing edges of the wheel load. Under low-temperature gradients, the Mode I stress intensity factor (K_I) at the crack tip exhibits a distinct diurnal opening–closing–reopening pattern, peaking at approximately 220 kPa·m^1/2 during the early morning hours (05:00–06:00). Furthermore, numerical simulations reveal the significant sensitivity of shear-sliding to axle loads; specifically, the peak Mode II stress intensity factor (K_II) increases monotonically from 190 to 230 kPa·m^1/2 as the axle load rises from 10 t to 16 t. Under non-uniform contact pressure, longitudinal cracking is primarily characterized by a mixed Mode I and Mode II mechanism driven by coupled tensile and shear stresses, whereas transverse cracking is dominated by Mode II shear failure. These findings suggest that implementing targeted traffic restrictions for overloaded vehicles during identified high-risk time windows can significantly enhance the structural durability and service life of composite pavements in cold regions. Full article

Accurate, non-contact monitoring of rumination behavior is essential for assessing dairy cow health and welfare, as well as for optimizing feeding strategies and herd management in modern precision livestock farming. However, practical deployment in commercial barns faces challenges such as occlusions, variable lighting, and dynamic cow movements. To address this, we developed a robust, automated vision-based framework for continuous rumination monitoring. The core of our system integrates an enhanced object detection algorithm with a robust tracking module, specifically improved to capture subtle behavioral features and maintain identity under complex conditions. Evaluated on a comprehensive dataset collected from commercial settings under various lighting and occlusion scenarios, our framework achieved high detection accuracy (mAP of 96.26%) and reliable tracking performance (multi-object tracking accuracy of 99.2%). This demonstrates its suitability for real-time, on-farm deployment. The study provides a practical, end-to-end solution for fine-grained behavioral analysis in complex environments, offering a tool that can enhance welfare assessment and support decision-making in dairy farm management. The methodological approach is also adaptable to other precision livestock monitoring tasks. Full article

Traditional vision-based vibration measurement is fundamentally constrained by the low sampling rates of area-scan cameras and the noise sensitivity of existing motion magnification algorithms. To overcome these spatiotemporal barriers, we propose a high-fidelity framework that integrates ultra-high-speed line-scan imaging with a 1D Complex Morlet Wavelet Phase-Based Video Magnification (CMW-PVM) algorithm. By extracting and manipulating the localized phase of 1D spatial signals, CMW-PVM effectively decouples structural dynamics from background noise while eliminating the computational redundancy associated with 2D spatial pyramid methods. Simulations demonstrate that CMW-PVM significantly extends the linear magnification range (up to $\alpha \approx 35$) while preserving exceptional structural fidelity (FSIM $>0.87$) under severe noise conditions (SNR = 10 dB). Experimental validation against a laser Doppler vibrometer (LDV) reveals near-perfect kinematic accuracy, with a relative amplitude error of only 1.65%. Furthermore, at a 100 Hz high-frequency excitation, the system successfully resolves microscopic displacements (≈10 μm) without temporal aliasing—enabled not by violating sampling theory but by leveraging the high physical line rate of the line-scan sensor. This establishes a robust, non-contact, and computationally efficient paradigm for broadband, micro-amplitude vibration monitoring in industrial environments. Full article

The construction of suspension bridges in mountainous expressways often involves tunnel-type anchorages in close proximity to shallow-buried bifurcated tunnels, particularly in soft rock strata with dense overlying structures. This proximity poses significant challenges to construction safety and stability. This study aims to investigate the influence of tunnel-type anchorage construction on the ground surface, surrounding rock, and adjacent bifurcated tunnels under such complex conditions. It was hypothesized that the anchorage load transfer and deformation mechanisms would significantly affect the adjacent tunnel, with potential cumulative effects due to the twin-anchor configuration. To address this, a combined approach of in situ scaled model testing (1:10 scale) and three-dimensional numerical simulation was employed. The model test incorporated monitoring of deformation and stress at key locations (anchor plug, rock mass, and anchor–rock interface) under incremental cable loads. Quantitative results from the model test indicate that at the design load (1P, equivalent to 2016.84 kN per anchor), deformations were minimal (e.g., maximum anchor displacement 0.35 mm). The anchor–rock interface exhibited limited slip (max 0.06 mm at 1P), and contact stresses were highest in the rear part of the anchor plug, indicating a non-uniform load transfer. Under overload conditions, the system reached yield at 7P and peak strength at 10.5P, with measured ground surface cracks up to 5 mm. Numerical simulations, calibrated against the experimental data, revealed that under increasing load (up to 10P), the plastic zones around the two anchors progressively expanded and eventually coalesced, leading to a characteristic “inverted trapezoid” failure pattern propagating to the surface, accompanied by shear failure along the 14° bedding plane. The combined results quantify the progressive interaction between the twin anchorages and the surrounding rock, highlighting the critical role of the anchor–rock interface and the cumulative effect of twin anchors on ground deformation and potential failure mechanisms. This research provides a scientific basis for the design and construction of tunnel-type anchorages in similar challenging geological and spatial settings. Full article

To address the existing challenges in mold slag thickness measurement—such as the susceptibility of contact sensors to high-temperature degradation and the limitation of non-contact methods to detecting only the upper slag surface—this study proposes an integrated approach that fuses millimeter-wave radar and eddy current sensors for measuring mold slag thickness in a continuous casting mold. The method innovatively combines two sensing principles: the millimeter-wave radar employs an improved FFT-CZT² high-precision ranging algorithm to perform high-resolution scanning of the solid slag upper surface, reconstructing its topography (error: $\pm$1 mm), while Mel-frequency cepstral coefficients (MFCC) are applied to extract features from the radar intermediate-frequency signals, combined with an enhanced PSO-BP neural network algorithm to predict the thickness of the solid slag layer (error: $\pm$5 mm). Concurrently, an eddy current sensor monitors the liquid slag–molten steel interface position (error: $\pm$1 mm). Through dual-sensor data fusion, the upper surface topography data and solid slag thickness obtained from the radar are spatially registered in three dimensions with the molten steel level information derived from the eddy current sensor. This integration ultimately enables the non-contact synchronous measurement of three key parameters within the mold: solid slag layer thickness, liquid slag layer thickness inversion, and molten steel level. Furthermore, by reconstructing the upper slag surface morphology, the method successfully resolves practical issues such as uneven material distribution, local material deficiency, or excessive feeding. Preliminary experimental verification confirms that the proposed method maintains stable performance even under high-temperature and complex environmental conditions. It thus provides a real-time, accurate, and full-cross-section monitoring solution for mold slag in continuous casting, offering significant practical value for the development of smart steel plants. Full article

(1) Background: Hospitalized preterm infants often require months of vital signs monitoring in the neonatal intensive care unit. Today, wired sensors are essential for survival, but are associated with numerous disadvantages including sensor dislocations, skin trauma and hygiene risks. Non-contact vital sign monitoring would therefore represent a significant improvement in the care of hospitalized neonates. (2) Objective: This study aims to lay the foundation for non-contact radar-based monitoring of the respiratory rate, which could be used in the neonatal intensive care unit. (3) Methods: We developed a radar-based vital parameter monitoring system for recording the respiratory rate of premature infants in a pediatric incubator. The novel system employs a four-channel I/Q FMCW radar with compact, application-specific antennas optimized to cover the defined area of interest on the infant’s thorax. As a proof-of-principle study, the system was tested in six anesthetized newborn piglets. (4) Results: Using the radar-based system, thorax movements were detected and the respiratory rate was calculated. We observed a high accordance between the signals of respiration detected by the novel radar sensor with the signals of the cable-bound monitor in resting piglets. (5) Conclusions: The novel radar sensor is suited for measuring respiration in the piglet model. In future, the sensor should be optimized in order to improve its robustness against disturbances body movements and in order to allow detection of heartbeat. Full article

Texture characteristics are critical quality evaluation indicators for soft foods. Traditional texture profile analysis (TPA) relies on probe–sample contact and may cause irreversible structural damage, limiting its application in nondestructive or online detection. In this study, a non-contact and nondestructive Controlled Airflow–Laser Texturemeter (CAFLT) system was developed to achieve rapid multi-parameter texture characterization. The system integrates programmable airflow loading with laser displacement sensing to implement a TPA-like double-cycle loading protocol, simultaneously acquiring time–applied airflow pressure (T–AP) and time–displacement (T–D) responses. Gelatin–maltose composite gels with graded Bloom strengths (CL50–CL250) were used as model samples. Texture-related descriptors were extracted using a dual-curve feature framework and compared with traditional TPA measurements. The CAFLT system produced a double-peak response pattern resembling that of traditional TPA and showed clear monotonic trends with increasing gel strength. Hardness_CAFLT exhibited a strong correlation with the reference TPA hardness value (r = 0.97). In addition, Gumminess_CAFLT showed a positive association with traditional gumminess (r = 0.87), but should be interpreted within the CAFLT-specific loading framework. Multivariate principal coordinates analysis further demonstrated clear multivariate discrimination among samples. Additionally, the time-domain descriptor t_Peak1 showed a strong power-law relationship with Bloom strength ($R^2=0.96$), indicating enhanced sensitivity to mechanical differences under small-deformation conditions. Overall, the CAFLT system provides a feasible approach for non-contact, nondestructive, and quantitative texture evaluation of soft foods, and shows strong potential for real-time quality monitoring and intelligent food inspection. Full article

The growing need for remote patient monitoring, accelerated by the global pandemic and an aging population, necessitates the development of advanced non-contact technologies for measuring vital signs. In this study, an integrated, non-contact system for accurately measuring heart rate (HR) and body temperature (BT) is developed and validated. The proposed system combines a 60 GHz radar sensor and infrared (IR) sensor for HR and BT measurements, respectively, enhanced with advanced signal processing and an AI-based computer vision algorithm. A Window Filter and a Peak Uniformity algorithm were applied to the raw radar signal to mitigate noise and motion artifacts. For Temp measurement, an IR sensor with a narrow five-degree field of view (FOV) was integrated with a YOLO Pose-based tracking system using a camera and servo motors to automatically orient the sensor towards the user’s face. The system was validated with 30 healthy adult participants, benchmarked against a MAX30102 PPG sensor and Braun ThermoScan 7 for BT and BT measurements, respectively. The advanced signal processing reduced the HR Mean Absolute Error from 13.73 BPM to 5.28 BPM (p = 0.002), while the AI-guided IR sensor reduced the BT MAE from 4.10 °C to 1.64 °C (p < 0.001). These findings demonstrate that integrating 60 GHz radar with AI-driven tracking provides a promising approach for home-based trend monitoring. Full article

Background and Objectives: Individuals with lower limb amputation are at increased risk of developing post-amputation complications that may affect rehabilitation outcomes. Assessing thermal symmetry between the residual and intact limbs, as well as activity-related temperature changes, represents a non-invasive and cost-effective approach for preliminary clinical evaluation. This study aimed to investigate regional skin temperature symmetry between residual and intact limbs at rest and following activity, and to examine whether the presence of phantom limb sensation or pain influences thermal patterns. Methods: Twenty-three individuals with unilateral lower limb amputation (mean age: 42.2 ± 15.1 years) participated in this cross-sectional study. The presence of phantom limb sensation and pain was recorded. Skin temperature measurements were obtained using a non-contact infrared thermometer under two conditions: “resting” and “post-activity” following a 10 min self-selected walking task, conducted after prosthetic removal. Measurements were acquired from the patella, tibialis anterior, and distal points of both the residual and intact limbs in both conditions. Results: Significant inter-limb differences were observed in the patellar and tibialis anterior regions, with higher temperatures at the patella and lower temperatures at the tibialis anterior on the residual limb. No significant differences were detected at the distal regions under either condition. Post-activity temperature increases were observed in the tibialis anterior and distal regions of the intact limb, whereas no comparable adaptation was observed on the residual limb. Thermal profiles did not differ between participants with and without phantom limb pain or sensation. Conclusions: These findings demonstrate that skin temperature dynamics in individuals with amputation are region-specific and influenced by functional activity. The absence of post-activity thermal adaptation in the residual limb may have clinical implications for rehabilitation monitoring. Incorporating regional thermal assessment into routine evaluation may support individualized rehabilitation strategies following lower limb amputation. Full article

This paper presents a novel dual-mode memristor-based ring oscillator designed for energy-efficient, wireless biomedical signal conditioning systems. The proposed architecture leverages a compact DTMOS memristor emulator, consisting of only two transistors and one capacitor, to replace the conventional NMOS pull-down devices in a three-stage PMOS ring oscillator. This integration enables two distinct operating modes within a single compact core: a fixed-frequency mode for stable clock generation and carrier synthesis, and a programmable chirp mode for frequency-modulated signal generation. The fixed-frequency mode achieves continuous tuning from 3.142 GHz to 4.017 GHz via varactor control, with an ultra-low power consumption of only 111 µW at 4.017 GHz. The chirp mode generates linear frequency sweeps starting from 0.8 GHz, with the sweep range independently controllable through the state capacitor value and the pulse width of the control signal (SW_Chirp). Designed in a standard 0.18 µm CMOS process, the oscillator exhibits a low phase noise of −87.82 dBc/Hz at a 1 MHz offset for the three-stage configuration, improving to −94.3 dBc/Hz for the five-stage design. The overall frequency coverage spans 0.8–4.017 GHz, representing a 133.6% fractional range. The calculated figure of merit (FoM) is −169.45 dBc/Hz. Experimental validation using a discrete CD4007 prototype confirms the oscillation principle, while comprehensive simulations demonstrate robust performance across process corners and temperature variations. With its zero-static-power memristor core, wide tunability, and dual-mode reconfigurability, the proposed oscillator is ideally suited for multi-standard wireless biomedical applications, including implantable telemetry, neural stimulation, ultra-wideband (UWB) transmitters, and non-contact vital sign monitoring. Full article