Search Results (1,427)

Retinal disorders, such as diabetic retinopathy, cataract, and glaucoma, are among the leading causes of vision loss and blindness worldwide. The use of normal data in diagnostic studies provides a basis for distinguishing between pathological and healthy conditions. Complete and accurate diagnosis of these conditions is essential for effective treatment and prevention of recurrence. This study focuses on the VGG19 model and transfer learning to classify retinal conditions such as normal, diabetic, cataract, and glaucoma. A publicly available dataset from Kaggle consisting of labeled retinal images is used for training and evaluation. The data used in this study consists of 400 retinal images, each consisting of 100 images per class, where there are four classes consisting of normal eyes, cataract, diabetic retinopathy and glaucoma. In 50 epochs of training, Adam optimization and softmax function activation, the modeling performance measured using the confusion matrix, including the accuracy, precision, recall and F1 score, achieves accuracy results of 0.91 for 320 training data and 0.88 for 80 validation data. The loss value is 0.18 for the training data and 0.31 for the validation data. Using the test data, the values of the cataract class are 0.94 for precision, 0.8 for recall, and 0.86 for the F1 score. The values are 0.91 for precision, 1.00 for recall and 0.95 for the F1 score in the diabetic retinopathy class. For glaucoma, the scores are 0.74 for precision, 0.85 for recall, and 0.79 for the F1 score. The normal class has scores of 1.00 for precision, 0.9 for recall and 0.95 for the F1 score. Given the performance test results shown above, VGG19 modeling for diagnosing retinal disease provides quite good results. Future research can expand this research by combining additional datasets and exploring other neural network architectures to improve the diagnostic performance. Full article

We report high-resolution, cavity-enhanced direct frequency comb Fourier transform spectroscopy of cold acetylene (C₂H₂) molecules in a planar supersonic jet expansion. The experiment is based on a near-infrared frequency comb with a 300 MHz effective repetition rate, matched to a high-finesse enhancement cavity traversing the jet. The rotational and translational cooling of acetylene was achieved via expansion in argon carrier gas through a slit nozzle. By interleaving successive mode-resolved spectra measured at different comb repetition rates, we retrieved full absorption line profiles. Spectroscopic analysis reveals sharp, Doppler-limited transitions corresponding to a jet core rotational temperature below 7 K. Frequency comb and cavity stabilization were achieved through active Pound–Drever–Hall locking and mechanical vibration damping, enabling a spectral precision better than 2 MHz, limited by the vibrations induced by the pumping system. The demonstrated sensitivity reaches a minimum detectable absorption of 7.8 × 10⁻⁷ cm⁻¹ over an 18 m effective path length in the jet core. This work illustrates the potential of cavity-enhanced direct frequency comb spectroscopy for precise spectroscopic characterization of cold supersonic expansions, with implications for studies in molecular dynamics, reaction kinetics, and laboratory astrophysics. Full article

Background: Eravacycline exhibits potent activity against multidrug-resistant pathogens and holds promise for the management of hospital-acquired and ventilator-associated pneumonia (HAP/VAP). However, sensitive and robust bioanalytical methods to quantify eravacycline in human pulmonary epithelial lining fluid (ELF) for pharmacokinetic (PK) and pulmonary penetration studies in these infections remain limited. Methodology: A simple, rapid, and sensitive LC-MS/MS method was developed for the quantification of eravacycline in bronchoalveolar lavage fluid (BALF). Using urea as a volume normalizer, ELF concentrations were calculated from the eravacycline concentrations in BALF. This method was applied in a clinical study evaluating the pulmonary penetration after intravenous infusion in patients with HAP and VAP. Results: The developed LC-MS/MS method exhibited good linearity in the range of 1–200 ng/mL for quantifying eravacycline in BALF. In BALF, intra-day precision ranged from 1.4% to 6.0%, and inter-day precision from 1.6% to 9.9%, with accuracy between 98.0% and 102.4%. Matrix effects were within 97.4% to 107.6% for BALF samples from six different individuals, with extraction recoveries ranging from 103.5% to 107.2%. Stability studies demonstrated that eravacycline remained stable under various conditions, including storage at room temperature, freeze–thaw cycles, long-term (–70 °C) storage, and post-treatment handling. The method was successfully applied to clinical samples from four HAP or VAP patients, with measured eravacycline pulmonary penetration ratios of 4.29, 17.40, 5.22 and 4.70, indicating efficient pulmonary distribution. The measured eravacycline concentrations ranged from 0.0243 to 0.0436 μg/mL in BALF. The corresponding urea-corrected ELF concentrations ranged from 0.570 to 1.617 μg/mL. Conclusions: This study described a detailed and validated method for quantifying eravacycline concentrations in ELF from patients, providing a reliable analytical approach for investigating the pulmonary distribution of eravacycline. Full article

Background/Objective: Film coatings are vital components of many pharmaceutical products consumed orally in solid dosage form, and the optimization of the film coating unit operation is critical to the success of these products. It is essential to maintain adequate film coat thickness on tablets to ensure the elegance, mechanical integrity, and controlled-release functionality of active pharmaceutical ingredients. We aim to evaluate techniques for measuring the film coat thickness of tablets in the pharmaceutical drug product development space as current research primarily focuses on in-line methods at the manufacturing scale. Methods: A total of four tablet types, varying in size, shape, and coating thickness were assessed using Optical Coherence Tomography and X-ray Computed Tomography. The data was then compared to baseline reference values gathered by examining tablets with a Confocal Microscope. A second trial was performed using an alternative Optical Coherence Tomography instrument to verify the accuracy of the data. The methods were also evaluated on additional criteria utilizing a Pugh Matrix. Results: The initial Optical Coherence Tomography yielded measurements that were inconsistent with the values provided by the control for three of the four tablet types; however, the follow-up study provided values within an acceptable range. The X-ray Computed Tomography also provided accurate measurements but presented challenges for precision in relation to the instrument’s resolution capabilities. Based on the assessment of selected criteria, Optical Coherence Tomography is ideal for all clear-coated tablets, while X-ray Computed Tomography is better suited for small tablets with either opaque or clear coats. Conclusions: Optical Coherence Tomography, X-ray Computed Tomography, and the use of a Confocal Microscope are all viable methods for measuring the film coat thickness of tablets. Method selection is not absolute and depends on factors such as safety, ease of use, adaptability, and tablet characteristics. The results of this study will help provide guidance for selecting the most appropriate method for measuring the film coat thickness of a specific tablet. Full article

Background: Recreational running is a globally popular activity known for its physical and mental health benefits, including stress reduction and improved quality of life. However, many recreational runners lack structured guidance, increasing their risk of lower limb injuries, often linked to variations in foot posture. Although African populations are well known for their endurance running abilities, limited research has examined their foot biomechanics and injury risk. This study addresses this gap by investigating foot posture and structure among African male recreational runners in South Africa. Methods: A cross-sectional, quantitative design was employed. Data were collected using structured data collection sheets, capturing demographic information, Foot Posture Index (FPI) scores, and Three-Dimensional (3D) foot scans. FPI provided a clinical evaluation of foot posture, while 3D foot scans delivered precise structural measurements. The aim was to describe and compare the foot posture characteristics and bilateral load distribution patterns in this population, using the Foot Posture Index (FPI) and 3D foot scanning as complementary assessment tools. Results: Findings showed agreement between FPI and 3D foot scan results, with both tools identifying a high prevalence of flexible flat feet among participants. Each method captured unique aspects of foot posture: FPI offered a qualitative overview, while 3D scans provided detailed, quantitative insights. This dual-assessment approach supports the value of using complementary methods in clinical and sports settings. Conclusions: This study suggests that integrating FPI and 3D scanning enhances the accuracy of foot posture assessments. Despite limitations such as a moderate sample size, the findings support personalized clinical interventions and footwear design tailored to the unique biomechanics of Black African male runners. Full article

Same-level-falls (SLFs) and falls-from-height (FFHs) remain major causes of severe injuries and fatalities on construction sites. Researchers are actively developing fall-prevention systems requiring accurate SLF and FFH detection in construction settings prone to false positives. In this study, a machine learning-based approach was established for accurate identification of SLF, FFH, and non-fall events using a single waist-mounted inertial measurement unit (IMU). A total of 48 participants executed 39 non-fall activities, 10 types of SLFs, and 8 types of FFHs, with a dummy used for falls exceeding 0.5 m. A two-stage feature extraction yielded 168 descriptors per data window, and an ensemble SHAP-PFI method selected the 153 most informative variables. The weighted XGBoost classifier, optimized via Bayesian techniques, outperformed other current boosting algorithms. Using 5-fold cross-validation, it achieved an average macro F1-score of 0.901 and macro Matthews correlation coefficient of 0.869, with a latency of 1.51 × 10⁻³ ms per window. Notably, the average lead times were 402 ms for SLFs and 640 ms for FFHs, surpassing the 130 ms inflation time required for wearable airbags. This pre-impact SLF and FFH detection approach delivers both rapid and precise detection, positioning it as a viable central component for wearable fall-prevention devices in fast-paced construction scenarios. Full article

Fibromyalgia (FM) is characterized by widespread musculoskeletal pain and tenderness, cognitive dysfunction, fatigue, and insomnia. Electroacupuncture (EA) has documented efficacy against FM-associated pain, while cannabinoid receptor 1 (CB1) plays a critical role in endogenous analgesia. Herein, we examined whether pain relief initiated by EA was linked with differing cerebellar CB1 levels and signaling in an intermittent cold stress (ICS) mouse model of FM. FM-like hyperalgesia and recovery were assessed by measuring mechanical and thermal nociceptive thresholds. Compared to control mice, ICS-induced FM-model mice exhibited a significantly reduced mechanical withdrawal threshold (2.3 ± 0.1 g) and shorter thermal withdrawal latency (4.0 ± 0.5 s), indicative of mechanical and thermal hyperalgesia. Both conditions were reversed by 2 Hz EA but not sham EA. Hyperalgesia was associated with reduced CB1 receptor expression and the enhanced activity of multiple nociceptive signaling pathways (PKA, PI3K, Akt, mTOR, ERK, and NF-kB) in the mouse cerebellum. The 2 Hz EA treatment reliably reversed these abnormalities, while the sham EA treatment did not. Intracerebroventricular injection of the CB1 agonist anandamide (AEA) recapitulated the effects of EA on pain thresholds, while the analgesic effects of EA were blocked by the CB1 antagonist AM251. Precise chemogenetic stimulation at the paraventricular nucleus (PVN) of the hypothalamus reliably induced FM pain. Chemogenetic inhibition at the PVN diminished FM through the CB1 pathway in the cerebellum. Our findings suggest that dysregulation of CB1 expression and aberrant hyperactivity of nociceptive signaling pathways in the cerebellum contribute to the etiology of FM and that the upregulation of CB1 signaling mediates the analgesic efficacy of EA. Full article

Urban footbridges are critical infrastructure increasingly challenged by vibration issues induced by crowd activity. Real-time monitoring of pedestrian dynamics is essential for evaluating structural safety, ensuring pedestrian comfort, and enabling proactive management. This paper proposes a lightweight, fully automated computer vision system for real-time monitoring of crowd dynamics on footbridges. The system integrates object detection, multi-target tracking, and monocular depth estimation to precisely quantify key crowd metrics: pedestrian flow rate, density, and velocity. Experimental validation demonstrated high performance: Flow rate estimation achieved 92.7% accuracy; density estimation yielded a 2.05% average relative error; and velocity estimation showed an 8.7% average relative error. Furthermore, the system demonstrates practical utility by successfully categorizing pedestrian behaviors using velocity data and triggering timely warnings. Crucially, field tests confirmed a minimum error of 5.56% between bridge vibration simulations driven by the system’s captured crowd data and physically measured acceleration data. This high agreement validates the system’s capability to provide reliable inputs for structural assessment. The proposed system establishes a practical technological foundation for intelligent footbridge management, focusing on safety, comfort, and operational efficiency through real-time crowd insights and automated alerts. Full article

Precise maintenance is vital for ensuring the safety of tunnel structures; however, traditional visual inspections are subjective and hazardous. Digital technologies such as LiDAR and imaging offer promising alternatives, but each has complementary limitations in geometric precision and visual representation. This study addresses these limitations by developing a three-dimensional modeling framework that integrates image and point cloud data and evaluates its effectiveness. Terrestrial LiDAR and UAV images were acquired three times over a freeze–thaw cycle at an aging, abandoned tunnel. Based on the data obtained, three types of 3D models were constructed: TLS-based, image-based, and fusion-based. A comparative evaluation results showed that the TLS-based model had excellent geometric accuracy but low resolution due to low point density. The image-based model had high density and excellent resolution but low geometric accuracy. In contrast, the fusion-based model achieved the lowest root mean squared error (RMSE), the highest geometric accuracy, and the highest resolution. Time-series analysis further demonstrated that only the fusion-based model could identify the complex damage progression mechanism in which leakage and icicle formation (visual changes) increased the damaged area by 55.8% (as measured by geometric changes). This also enabled quantitative distinction between active damage (leakage, structural damage) and stable-state damage (spalling, efflorescence, cracks). In conclusion, this study empirically demonstrates the necessity of data fusion for comprehensive tunnel condition diagnosis. It provides a benchmark for evaluating 3D modeling techniques in real-world environments and lays the foundation for digital twin development in data-driven preventive maintenance. Full article

Background/Objectives: Frailty in older adults limits social participation. We aimed to predict social participation in older individuals undergoing frailty health checkups using three machine learning (ML) models and identify key predictive factors through deep neural network (DNN) analysis. Methods: Overall, 301 older individuals were enrolled; 295 were included in the final analysis. The survey measured 18 attributes, including demographic, physical, cognitive, and social factors. Logistic regression (LR), nonlinear support vector machine (NLSVM), and DNN were used for prediction, with precision, accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) calculated as evaluation metrics. Results: Among 295 participants, 236 (80%) engaged in social activities, whereas 59 (20%) did not. The three models demonstrated complementary strengths: DNN provided the most balanced performance with superior sensitivity for detecting social participants; NLSVM showed the best overall discriminative ability but with higher false positive rates; and LR achieved the highest precision for correctly identifying participants but missed detecting social participants. AUC values ranged from 0.776 to 0.795 across models, indicating moderate discriminative performance. Contribution analysis revealed information-collection ability as the strongest predictor of social participation, followed by walking speed and number of cohabitants. Conclusions: ML models achieved moderate discriminative performance for predicting social participation among frailty-screened older adults. The DNN provided the most balanced performance. Each model exhibited distinct characteristics suitable for different screening purposes, with information-collection ability emerging as a key factor. The findings suggest that models must be carefully selected based on specific community health screening objectives. Full article

Poplar (Populus L. species), a fast-growing temperate species, forms plantations with high productivity and biomass, with its litter sustaining key functions in nutrient cycling, microbial diversity, and carbon storage. Litter microbial communities drive decomposition, particularly in early stages, this initial phase is characterized by the leaching of water-soluble carbon and nutrients from the litter, which creates a readily available resource pulse that facilitates rapid microbial colonization and activation. This process is followed by the activation of microbial enzymes and the immobilization of nutrients, collectively initiating the breakdown of more recalcitrant litter materials. Under rising global nitrogen deposition, we conducted a field randomized block experiment in 13-year-old pure poplar (Populus deltoides L. ‘35’) stands, with three nitrogen addition treatments: N0 (0 g N·m⁻²·yr⁻¹), N2 (10 g N·m⁻²·yr⁻¹), and N4 (30 g N·m⁻²·yr⁻¹). In the initial phase of litter decomposition, we measured the soil properties and litter traits, the litter microbial community composition, and its taxonomic and phylogenetic diversity indices. The results indicate that nitrogen addition altered microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), soil NO₃⁻-N, and accelerated litter decomposition rates. The microbial community in leaf litter responded to nitrogen addition with increased phylogenetic clustering (higher OTU richness and NRI), which suggests that environmental filtering exerted a homogenizing selective pressure linked to both soil and litter properties, whereas the microbial community in branch litter responded to nitrogen addition with increased taxonomic diversity (higher OTU richness, Shannon, ACE, and Chao1), a pattern associated with litter properties that likely alleviated nitrogen limitation and created opportunities for more taxa to coexist. The observed differences in response stem from distinct substrate properties of the litter. This study elucidates microbial taxonomic and phylogenetic diversity responses to nitrogen addition during litter decomposition, offering a scientific foundation for precise microbial community regulation and sustainable litter management. Full article

This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The goal was to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity (EDA), heart rate variability, skin temperature, and motion. Interpretable machine learning models, including linear classifiers, were trained to distinguish physiological states and rank feature relevance. The results revealed that even minor postural adjustments led to significant shifts in ANS markers, with phasic EDA and RR interval features showing heightened sensitivity. Surprisingly, micro-movements captured via accelerometry and transient electrodermal reactivity, specifically EDA peak-to-RMS ratios, emerged as dominant contributors to classification performance. These findings suggest that small-scale kinematic and autonomic shifts, which are often overlooked, play a central role in the physiological effects of yoga. The study demonstrates that wearable sensor analytics can decode a more nuanced and granular physiological profile of mind–body practices than traditionally appreciated, offering a foundation for precision-tailored biofeedback systems and advancing objective approaches to yoga-based interventions. Full article

The need for sustainable and long-term environmental monitoring has driven the development of energy-autonomous sensors, which either operate passively or integrate energy harvesting (EH) solutions. In many applications, the energy cost of data transmission is a critical factor in autonomous sensing systems. To address this challenge, optical passive sensors, which exploit changes in reflectivity to monitor physical parameters, offer self-sustained operation without requiring an external power source. Similarly, RF-based passive sensors, both chipless and with minimal circuitry, enable wireless monitoring with low power consumption. When more energy is available, EH techniques can be combined with active optical sensors. Infrared laser-based CO₂ sensors, as well as drone-mounted optical systems, demonstrate how EH can power precise environmental measurements. Beyond optics, other sensing modalities also benefit from EH, further expanding the range of self-powered environmental monitoring technologies. This review discusses the trade-offs between passive and EH-assisted sensing strategies, with a focus on optical implementations. The outlook highlights emerging solutions to enhance sensor autonomy while minimizing the energy cost of data transmission, paving the way for sustainable and scalable environmental monitoring. Full article

This paper presents the design and implementation of a 6-bit high-precision active vector-sum phase shifter (PS) operating in the E-band, fabricated using a 40 nm CMOS process. To generate high-quality in-phase and quadrature (I/Q) signals, a folded transformer-based quadrature generator circuit (QGC) employing inductive compensation is developed. Additionally, the series peaking enhancement technique is applied to improve overall gain and effectively extend the bandwidth. Measurement results demonstrate that the phase shifter achieves a 3 dB bandwidth from 72.3 GHz to 82.3 GHz. Within this range, the measured RMS phase error is merely 1.78–2.55 degrees without calibration, and the RMS gain error is 0.6–0.75 dB. The core area of the proposed phase shifter is 940 μm × 280 μm, and it consumes 57.2 mW of power with a 1.1 V supply. Full article

Droplet-based microlens fabrication using Ultra Violet (UV) curable polymers demands the precise measurement of three-dimensional geometries, especially for non-axisymmetric shapes influenced by electric field deformation. In this work, we present a polar coordinate-based contour fitting method combined with Hermite interpolation to reconstruct 3D droplet geometries from two orthogonal shadowgraphy images. The image segmentation process integrates superpixel clustering with active contours to extract the droplet boundary, which is then approximated using a spline-based polar fitting approach. The two resulting contours are merged using a polar Hermite interpolation algorithm, enabling the reconstruction of freeform droplet shapes. We validate the method against both synthetic Computer-Aided Design (CAD) data and precision-machined reference objects, achieving volume deviations below 1% for axisymmetric shapes and approximately 3.5% for non-axisymmetric cases. The influence of focus, calibration, and alignment errors is quantitatively assessed through Monte Carlo simulations and empirical tests. Finally, the method is applied to real electrically deformed droplets, with volume deviations remaining within the experimental uncertainty range. This demonstrates the method’s robustness and suitability for metrology tasks involving complex droplet geometries. Full article