Search Results (81)

This study evaluates calibration strategies for predicting soluble solids content (SSC) in Ambrosia apples using a low-cost, portable short-wave near-infrared (SW-NIR) spectrometer (640–1050 nm) under interactance acquisitions. Replicate sampling spectral curves exhibited notable variability due to asymmetric illumination, peel color heterogeneity, and probe contact inconsistencies. Regression models were comparatively built on averaged spectra compared with those trained directly on multiply sampled replicate spectra, applying piecewise Savitzky–Golay smoothing and detrending as pretreatment. Variable selection was performed via uninformative variable elimination (UVE) and backward interval partial least squares (BiPLS). Models calibrated on replicate spectra demonstrated superior generalization to unseen replicate measurements, despite slightly higher cross-validation errors. The BiPLS model on replicate spectra achieved the best predictive performance (mean RMSEP = 0.677 °Brix, Rp = 0.796, RPD = 1.656), with improved trueness (lower relative absolute bias) and precision (lower relative standard deviation). For comparison, the BiPLS model on averaged spectra yielded a mean RMSEP = 0.899 °Brix, Rp = 0.593, RPD = 1.25; the replicate-spectra strategy thus reduced the RMSEP by 24.7% and increased Rp and RPD accordingly. This suggests that for low-cost NIR instruments, using replicate sampling spectral modeling combined with interval variable selection can provide better prediction performance and achieve the purpose of on-site sorting in food quality analysis. Full article

Seed dimensions (length and width), surface color, frictional properties (static and kinetic coefficients of friction), thousand-seed weight, and angle of repose are five important categories of seed physical properties. Conventional methods generally measure these properties separately and rely heavily on manual operation, resulting in limited applicability and difficulty in balancing measurement efficiency and accuracy. To address these limitations, this study developed an automated method and an integrated platform incorporating automatic feeding, individual-seed positioning, state recognition, parameter acquisition, and cyclic control. After a single sample loading, the platform sequentially processed individual seeds, measured their dimensions, surface color, and static and kinetic coefficients of friction, and accumulated the measured seeds for subsequent thousand-seed weight and angle-of-repose determination, thereby enabling continuous automated measurement of the five categories of physical properties. Experiments were conducted using maize kernels, red kidney beans, and sunflower seeds to evaluate the measurement performance, repeatability, and cross-material adaptability of the platform. The standard deviations of repeated seed-dimension measurements were below 0.042 mm for all three seed types. The first moments of H and S in the HSV color space and a* and b* in the CIELAB color space were selected for color analysis and exhibited relatively low sensitivity to illumination variation under the tested imaging conditions. Within the stable-sliding intervals, the coefficients of determination for the relationship between actual seed displacement and squared time were all greater than 0.99, supporting the approximation of uniformly accelerated translational motion and the calculation of sliding acceleration for kinetic-friction determination. When the sample size used for thousand-seed weight estimation exceeded 50 seeds, further reductions in the coefficient of variation and average relative error were no greater than 0.2 percentage points. The angle-of-repose measurements distinguished differences in the pile formation characteristics of the three seed types. All three seed types completed the entire measurement procedure, corresponding to a platform adaptation success rate of 100%. In the cross-material validation, the coefficients of variation of all evaluated repeated-measurement indicators were no greater than 2.5%. These results indicate that, under the tested material and experimental conditions, the platform exhibited good operational stability, measurement repeatability, and cross-material adaptability, providing an effective approach for the automated and integrated measurement of multiple seed physical properties. Full article

Background/Objectives: This study aimed to evaluate the reproducibility of 3D facial skeletal surface models generated from repeated computed tomography (CT) and cone bean CT (CBCT) scans. Methods: Four hydrated-dry skull specimens, with soft-tissue simulation using water, were scanned twice within seconds using a single CT scanner. Eight skulls were scanned twice using two CBCT scanners with different settings. An experienced operator segmented all facial skeletal surfaces using a visually estimated optimal threshold. 3D models from repeated scans were superimposed on the forehead, zygomatic area, and maxilla using a best-fit algorithm. Deviations between superimposed models were assessed through distances in predefined areas and color-coded maps, attributed to segmentation errors or tomographic volume generation inaccuracies. Two facial surfaces from each acquisition setting with the largest deviations were resegmented using the original threshold value. Results: Repeated threshold determinations showed no significant differences (p = 0.266; median difference: −6.0, IQR: 39.5). Significant differences were noted between CT and CBCT scanners, but not among CBCT scanners. The median Mean Absolute Distance (MAD) for CBCT was 0.059 mm (IQR: 0.032) versus 0.016 mm (IQR: 0.007) for CT. Color-coded maps confirmed higher consistency in CT and Newtom models, with the low-radiation Planmeca protocol achieving comparable reproducibility. Differences primarily arose from image generation parameters, not threshold estimation. Conclusions: CT provides slightly more consistent 3D facial skeletal surface models. However, CBCT scanners, including those using low-radiation protocols, also demonstrate high reproducibility, reinforcing their reliability in diagnosing and planning treatment for facial morphology variations. Full article

Adverse imaging conditions such as fog, rain, and low light degrade the reliability of vision-based Personal Protective Equipment (PPE) detection systems on construction sites, yet most existing models are trained under clear-weather assumptions. This paper introduces a physics-based weather augmentation framework integrated with the YOLOv8n architecture to improve PPE detection robustness under adverse environmental conditions. The original Color Helmet and Vest (CHV) dataset was expanded from 1330 clear-weather images to 6650 images across five conditions using four physically grounded augmentation models: the Koschmieder atmospheric scattering model for fog, the Garg–Nayar streak model for rain, gamma-corrected attenuation with Poisson–Gaussian noise for low light, and a PSF-based glare model for bright sunlight. The weather-resistant model, a clear-weather baseline, and an augmented baseline were evaluated on the same 665-image weather-augmented test set. The weather-resistant model achieves 89.2% mAP50, a 5.7 percentage-point improvement over the clear-weather baseline (83.5%), with a nearly four-fold improvement in cross-condition stability (standard deviation 1.5% vs. 5.7%). Under matched training-data volume, the weather-resistant model still outperforms a conventionally augmented baseline across all five simulated conditions, indicating that these gains stem from physics-based modeling rather than larger training-data volume. The largest gain occurs under low light, where mAP50 improves from 73.4% to 87.9%. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis confirms that the weather-resistant model directs more attention toward PPE regions across all conditions, with the largest improvement under low light (+10.0 percentage points). The lightweight design (3.0 M parameters) and quantitative and qualitative validation on 205 annotated real-world construction site images under normal and low-light conditions provide preliminary evidence of practical applicability. Full article

The development of accessible and experimentally validated robotic systems for engineering education is a challenge, especially in academic environments where industrial manipulators are economically inaccessible. This paper presents the design, mechanical validation, and experimental evaluation of a robotic arm-based didactic module developed for the classification of objects according to color and morphology. The proposed system integrates a five-degree-of-freedom articulated configuration, a servomotor drive, motion planning with a trapezoidal speed profile, and a web-based control interface, enabling local and remote operation within an educational environment aligned with Industry 4.0 principles. The mechanical structure was designed using CAD modeling and validated through static structural analysis to ensure mechanical integrity and adequate safety factors. The selection of actuators was made considering the torque, angular velocity, and load requirements. A trapezoidal speed profile was implemented in order to ensure smooth trajectories and minimize positioning errors. Experimental validation was carried out through repetitive tests under controlled laboratory conditions, evaluating the accuracy and repeatability metrics. Statistical indicators such as mean error, standard deviation, and root mean square error (RMSE) were calculated. The results show the stable performance of the system, with low variability in multiple test cycles, confirming the viability of the proposed architecture for its implementation in automation and educational robotics laboratories. The integration of structural validation, motion control strategy, and experimental quantitative evaluation contributes to bridging the gap between theoretical teaching of robotics and its practical application, offering a scalable, low-cost platform for engineering training. Full article

Microplastic pollution has become a global concern due to its widespread impacts on organisms and ecosystems. While there have been a few studies quantifying microplastics in inland areas of Puerto Rico, none, to our knowledge, have studied nearshore coastal surface waters. This study, therefore, presents the first assessment of microplastic concentrations and descriptions in the surface waters of La Parguera Natural Reserve, southwestern Puerto Rico. Using 333-micron plankton net trawls, we found low mean ± standard deviation microplastic concentrations of 0.02 ± 0.07 microplastic particles m⁻³ (95% confidence interval = 0.01 to 0.04 microplastic particles m⁻³). The most prevalent polymers were high-density polyethylene (48%) and polyethylene (32%), followed by polypropylene (11%) and polystyrene (7%). The most common colors were white (50%), blue (34%), black (8%), red (5%), and colorless (3%). Subsequently, the common structures found were fragments (78%), filaments (12%), films (8%), and fibers (2%). No clear coastal gradient or seasonal patterns were detected (p < 0.05), and mean concentrations were similar to previously surveyed oceanic waters from the Caribbean, suggesting coastal sources of marine microplastics were minimal compared to oceanic sources. This study provides a foundational understanding of microplastics in the coastal waters of La Parguera Natural Reserve and provides critical baseline data for detecting potential future changes in microplastic concentrations. Full article

Cannabis sativa L. is a medicinal plant cultivated globally due to its remarkable historical and scientific relevance. Through the consumption of its flowers, also referred to as inflorescences, which contain a high content of cannabinoids, terpenes and polyphenols, the therapeutic properties of C. sativa can be harnessed. This study therefore aimed to determine the chemical profile, antioxidant and anti-inflammatory activities of the essential oils (EOs) obtained from the fresh and dried flowers of two C. sativa cultivars, Lifter and Cherrywine, grown in Komga, South Africa, to assess which cultivar has greater biological potential. The chemical profiles of the hydro-distilled EOs were analyzed by gas chromatography–mass spectrometry (GC-MS), while the in vitro antioxidant and anti-inflammatory activity of the EOs was analyzed using the DPPH and EAD methods, respectively. The identified constituents from the EOs were molecularly docked against NOX2 and NIK (NF-κB-inducing kinase) protein, which are implicated in oxidative stress. The afforded EOs were yellow (pale and bright yellow) in color with a sweet to mildly sweet aroma description. A total of 51 constituents were identified in both fresh and dry oils from the Lifter cultivar, while the Cherrywine cultivar contained a total of 44 constituents. Eighteen compounds, were found to be the main chemical constituents consistent in the flower EOs of both cultivars, notably, caryophyllene (10.71–19.96%), levo-β-pinene (1.37–13.21%), humulene (5.88–9.77%), caryophyllene oxide (4.32–7.49%), D-limonene (1.40–5.48%), α-pinene (2.22–5.22%), nerolidol (0.63–4.97%), cis-β-ocimene (0.22–4.37%), linalool (1.12–4.28%), selina-3,7(11)-diene (0.15–4.23%), humulene-1,2-epoxide (1.23–3.32%), guaiol (0.17–2.60%), (+)-β-selinene (1.20–2.51%), trans-α-bergamotene (0.68–2.37%), β-ocimene (0.90–2.27%), fenchol exo- (0.15–1.27), terpineol (0.14–1.38%) and α-terpineol (0.19–0.75%). The fresh Lifter flower oil (LFO) showed 50% inhibition at 100 μg/mL, with an IC₅₀ of 69.50 ± 4.05 µg/mL against DPPH, suggesting moderate to low radical scavenging activity. The maximum percentage inhibition response of DLFO, CFO and DCFO remained below 50% at all concentrations. The antioxidant activity of fresh LFO may be attributed to its overall chemical composition. The flower oils showed in vitro inhibition of protein denaturation; however, the high standard deviation relative to the mean IC₅₀ values limited the ability to rank the samples’ potencies. Further in silico studies on the putative constituents in the Lifter and Cherrywine cultivars revealed β-bisabolene and α-curcumene as potential molecular targets, with binding energy scores of −7.7 and −7.9 kcal/mol, respectively. Thus, the study findings highlight the promising biological importance of C. sativa inflorescences in the management of oxidative stress-related conditions. Further studies may investigate the influence of environmental growing conditions on their chemical composition, total ROS analysis, pharmacokinetic properties, and in vivo efficacy against oxidative damage to DNA, proteins and lipids. Evaluating the toxicity of the flower EOs is also recommended. Full article

Smartphone colorimetry has emerged as a low-cost and accessible approach for participatory environmental monitoring. In this feasibility study, mangrove soil samples collected at two depths (approximately 0 and 30 cm) and three distances from the shoreline (−10, 0, and 10 m) were analyzed using smartphone colorimetry. The redness (a*) and yellowness (b*) tended to decrease from the seaward side toward the landward side. The lightness (L*) showed a strong agreement with measurements obtained from a standard spectrophotometer, whereas systematic deviations were observed for chromatic coordinates, with underestimation of a* and overestimation of b* by the smartphone measurements. Soil colors were further examined alongside mineral composition determined by X-ray fluorescence (XRF) and organic matter characteristics obtained from thermogravimetric analysis (TGA). No systematic relationships were identified between color parameters and mineral composition or organic matter weight loss, highlighting the complex and multi-factorial nature of mangrove soil color. Although wetting generally reduced L* and b* values, the responses to increasing water content were not monotonic. These findings indicate that smartphone colorimetry is effective for capturing relative variations in soil lightness under controlled conditions, while emphasizing the need for calibration and cautious interpretation. The accessibility of smartphone-based measurements also suggests potential in public engagement. Full article

Effective monitoring of surface damage in mining areas is vital for ecological restoration. Unmanned aerial vehicles (UAVs) have been widely used to obtain ground subsidence data owing to their low cost and ease of operation. The images captured by UAVs can generate dense image matching (DIM) point clouds, which, after screening, can be used to create a digital elevation model (DEM) required for deformation analysis. Existing filtering algorithms mainly rely on the spatial geometric features of point clouds and rarely utilize color information, which limits their accuracy in areas with vegetation coverage. To address this issue, this study proposes a H-PTD method that combines visible light with progressive triangulated irregular network densification (PTD). First, initial ground seeds are selected based on the H value in the HSV space. Subsequently, a triangulated irregular network (TIN) is constructed, and iterative densification is performed by evaluating the relationship between the target point and adjacent triangular faces, thereby achieving an accurate distinction between ground and non-ground. Evaluated on three terrain datasets and against five classical methods, the results indicate that the Total error in the H-PTD cross-matrix is controlled between 2.9% and 7.8%, and remains below 8% overall. The standard deviation of the DEM difference is around 0.02 m. Compared to other methods, H-PTD shows higher filtering accuracy and better terrain adaptability, making it more promising for monitoring mining areas and providing a more reliable tool for subsidence detection based on UAVs. Full article

This study presents the design and optimization of a digital-imaging afocal telescope system that integrates an afocal telescope architecture with an imaging optical subsystem. The proposed system employs a combination of spherical and aspherical optical elements to enhance imaging flexibility, reduce aberrations, and ensure effective system coupling. Proper pupil matching is achieved by aligning the exit pupil of the afocal telescope with the entrance pupil of the imaging system, ensuring minimal vignetting and optimal energy transfer. Circular apertures and lens elements are used throughout the system to simplify alignment and minimize pupil-matching errors. The complete system comprises three imaging optical subsystems and a digital camera module, each independently optimized to ensure balanced optical performance. The design achieves an overall magnification of 16×, with near-diffraction-limited quality confirmed by an RMS wavefront error of 0.0474λ and a Strehl ratio of 0.915. The modulation transfer function (MTF) reaches 0.42 at 80 lp/mm, while the distortion remains below 4.87%. Chromatic performance is well controlled, with maximum lateral color deviations of 1.007 µm (short-to-long wavelength) and 1.52 µm (short-to-reference wavelength), evaluated at 656 nm, 587 nm, and 486 nm. The results demonstrate that the proposed digital-imaging afocal telescope system provides high-resolution, low-aberration imaging suitable for precision optical applications. Full article

Drying is a widely utilized method for extending the shelf life of food products. This study applied four drying techniques—natural air drying (NAD), hot air drying (HAD), microwave drying (MD), and vacuum freeze drying (VFD)—to dehydrate water bamboo shoots (WBS), and investigated their effects on the quality and volatile organic compound (VOC) profiles of the dried WBS. The results showed that MD achieved the fastest drying rate, whereas NAD exhibited the slowest. Both MD and VFD samples possessed porous structures. However, the VFD-treated samples retained the highest levels of Vitamin C (VC) and total phenols, and showed the least color deviation compared to the fresh samples, which was closely associated with its low-temperature and vacuum processing conditions. A total of 52 VOCs were identified in this study. Among them, 3-methyl-butanal and 5-methyl-2-furfural were the most abundant VOCs in the treated samples. 3-methybutanal, pentanal, butanal, and 5-methyl-2-furfural were identified as the characteristic VOC markers for the HAD, NAD, VFD, and MD groups, respectively. In conclusion, this study determined that VFD effectively maintained the drying quality of WBS, with butanal identified as its characteristic VOC organic compound. These findings provide valuable insights for the practical production of high-quality dried WBS. Full article

The purpose of infrared and visible image fusion is to integrate their complementary information into a single image, thereby increasing the amount of information expression. However, previously used methods often struggle to extract information hidden in darkness, and existing methods—which integrate brightness enhancement and image fusion—can cause overexposure, image blocking effects, and color deviation. Therefore, we propose a visible light and infrared image fusion method, CDFFusion, for low-light scenarios. The premise is to utilize Retinex theory to decompose the illumination and reflection components of visible light images at the feature level before fusing and decoding the reflection features with infrared features to obtain the Y component of the fused image. Next, the proposed color mapping formula is used to adjust the Cb and Cr components of the original visible light image; finally, the Y component of the fused image is concatenated to obtain the final fused image. The SF, CC, Nabf, Qabf, SCD, MS-SSIM, and ΔE indicators of this method reached 17.6531, 0.6619, 0.1075, 0.4279, 1.2760, 0.8335, and 0.0706, respectively, on the LLVIP dataset. The experimental results show that this method can effectively alleviate visual overexposure and image blocking effects, and it has the smallest color deviation. Full article

Environmental factors like light, photoperiod, temperature, and water are vital for crop growth, and even slight deviations from their optimal ranges can cause seedling stress and reduce yield. Therefore, this study aimed to quantify seedling stress symptoms using image features analysis under varying light, photoperiod, temperature, and water conditions. Seedlings were grown under controlled low, normal, and high environmental conditions. Light intensity at 50 µmol m⁻² s⁻¹ (low), 250 µmol m⁻² s⁻¹ (normal), and 450 µmol m⁻² s⁻¹ (high), photoperiod cycles, 8/16 h (day/night) (low), 10/14 h (day/night) (normal), and 16/8 h (day/night) (high) day/night, temperature at 20 °C (low), 25 °C (normal), and 30 °C (high), and water availability at 1 L per day (optimal), 1 L every two days (moderate stress), and 1 L every three days (severe stress) were applied for 15 days. Commercial low-cost RGB, thermal, and depth sensors were used to collect data every day. A total of 1080 RGB images, which were pre-processed with histogram equalization and filters (Median and Gaussian), were used for noise reduction to minimize illumination effects. Morphological, color, and texture features were then analyzed using ANOVA (p < 0.05) to assess treatment effects. The result shows that the maximum canopy area for tomato was 115,226 pixels, while lettuce’s maximum plant height was 9.28 cm. However, 450 µmol m⁻² s⁻¹ light intensity caused increased surface roughness, indicating stress-induced morphological alteration. The analysis of Combined Stress Index (CSI) values indicated that the highest stress levels were 50% for pepper, 55% for tomato, 62% for cucumber, 55% for watermelon, 50% for lettuce, and 50% for pak choi. The findings showed that image-based stress detection enables precise environmental control and improves early-stage crop management. Full article

Adaptive filtering algorithms often suffer from biased parameter estimation and performance degradation in the presence of colored input noise and impulsive observation noise, both of which are common in practical sensor and communication systems. Existing bias-compensated least mean square (LMS) algorithms generally assume white Gaussian input noise, thereby limiting their applicability in real-world scenarios. This paper introduces a robust convex combination bias-compensated LMS (CC-BC-LMS) algorithm designed to address both colored Gaussian input noise and impulsive observation noise. The proposed algorithm achieves bias compensation through robust estimation of the input noise autocorrelation matrix and employs a modified Huber function to mitigate the influence of impulsive noise. A convex combination of fast and slow adaptive filters enables variable step-size adaptation, effectively balancing rapid convergence and low steady-state error. Extensive simulation results demonstrate that the proposed CC-BC-LMS algorithm provides substantial improvements in normalized mean square deviation (NMSD), surpassing state-of-the-art bias-compensated and robust adaptive filtering techniques by $4.48$ dB to $11.4$ dB under various noise conditions. These results confirm the effectiveness of the proposed approach for reliable adaptive filtering in challenging noisy environments. Full article

This study aimed to evaluate the effect of resin type and printing design on the dimensional accuracy of three dimensional (3D) printed orthodontic models, considering their clinical relevance for applications such as in-house aligner fabrication. Since low-cost Liquid Crystal Display (LCD) printers have been increasingly adopted in practice but data on their trueness and precision with different resins and print designs were limited, the study sought to provide evidence-based insights into their reliability. A mandibular model was designed using Blenderfordental (B4D, version 1.1.2024; Dubai, United Arab Emirates) software and fabricated with the Anycubic Photon Mono 7 Pro 14K (Anycubic, Shenzhen, China) LCD printer. The model was printed in vertical orientation using three different print designs at two layer thicknesses (50 µm and 100 µm). Four resins (Elegoo, Anycubic, eSUN, and Phrozen) were used, and each resin was printed with all three designs, yielding 126 models per resin and a total of 504 printed models. Dimensional deviations between the printed and reference models were assessed using root mean square (RMS) values and color-coded deviation maps. Significant differences in trueness were found among resins and print designs at both layer thicknesses (p < 0.001). At a layer thickness of 50 µm, eSUN and Anycubic showed superior trueness, whereas Phrozen exhibited the highest deviations. At a layer thickness of 100 µm, Anycubic, eSUN, and Phrozen generally performed better than Elegoo. Overall, printing at 100 µm yielded better performance than at 50 µm. Precision analysis revealed resin-dependent differences, with eSUN showing significantly higher precision than Elegoo at both layer thicknesses (p = 0.006 at 100 µm, p < 0.001 at 50 µm) and superior precision compared to Phrozen at 50 µm (p = 0.019). Both resin selection and print design significantly affect the dimensional accuracy of 3D-printed dental models. Full article