Search Results (2,441)

This study evaluated the integration of non-invasive remote sensing and colorimetry to classify the maturity stages of Coffea arabica fruits across four varieties: Caturra Amarillo, Excelencia, Milenio, and Típica. Multispectral signatures were captured using a Parrot Sequoia camera at wavelengths of 550 nm, 660 nm, 735 nm, and 790 nm, while colorimetric parameters L*, a*, and b* were measured with a high-precision colorimeter. We conducted multivariate analyses, including Principal Component Analysis (PCA) and multiple linear regression (MLR), to identify color patterns and develop predictors for fruit maturity. Spectral curve analysis revealed consistent changes related to ripening: a decrease in reflectance in the green band (550 nm), a progressive increase in the red band (660 nm), and relative stability in the RedEdge and near-infrared regions (735–790 nm). Colorimetric analysis confirmed systematic trends, indicating that the a* component (green to red) was the most reliable indicator of ripeness. Additionally, L* (lightness) decreased with maturity, and the b* component (yellowness to blue) showed varying importance depending on the variety. PCA accounted for over 98% of the variability across all varieties, demonstrating that these three parameters effectively characterize maturity. MLR models exhibited strong predictive performance, with adjusted R² values ranging between 0.789 and 0.877. Excelencia achieved the highest predictive accuracy, while Milenio demonstrated the lowest, highlighting varietal differences in pigmentation dynamics. These findings show that combining multispectral imaging, colorimetry, and statistical modeling offers a non-destructive, accessible, and cost-effective method for objectively classifying coffee maturity. Integrating this approach into computer vision or remote sensing systems could enhance harvest planning, reduce variability in specialty coffee lots, and improve competitiveness by ensuring greater consistency in cup quality. Full article

Water loss during storage is a major cause of postharvest quality deterioration in cucumber, yet existing methods to monitor hydration are often destructive or require expensive instrumentation. We developed a low-cost, non-destructive approach for estimating fruit relative water content (RWC) using visible-light color imaging combined with an ensemble machine-learning model (Random Forest). A total of 1200 fruits were greenhouse-grown, harvested at market maturity, and equally divided between optimal and ambient storage temperature (10 and 25 °C, respectively). Digital images were acquired at harvest and at 7 d intervals during storage, and color parameters from four standard color systems (RGB, CMYK, CIELAB, HSV) were extracted separately for the neck, mid, and blossom regions as well as for the whole fruit. During storage, fruit RWC decreased from 100% (fully hydrated condition) to 15.3%, providing a broad dynamic range for assessing color–hydration relationships. Among the 16 color features evaluated, the mean cyan component (μC) of the CMYK space showed the strongest relationship with measured RWC (R² up to 0.70 for whole-fruit averages), reflecting the cyan region’s heightened sensitivity to dehydration-induced changes in pigments, cuticle properties and surface scattering. The Random Forest regression model trained on these features achieved a higher predictive accuracy (R² = 0.89). Predictive accuracy was also consistently higher when μC was calculated over the entire fruit surface rather than for individual anatomical regions, indicating that whole-fruit color information provides a more robust hydration signal than region-specific measurements. Our findings demonstrate that simple visible-range imaging coupled with ensemble learning can provide a cost-effective, non-invasive tool for monitoring postharvest hydration of cucumber fruit, with direct applications in quality control, shelf-life prediction and waste reduction across the fresh-produce supply chain. Full article

In vapor-cell atomic clocks, a buffer gas is employed to slow the collision rate of atoms with the vapor-cell’s walls, which dephases the atomic coherence and thereby contributes to the 0-0 hyperfine transition’s linewidth. However, the buffer gas also gives rise to a temperature-dependent pressure shift in the hyperfine transition, Δν_hfs. As a consequence, the clock’s frequency develops a temperature dependence, manifesting as a clock environmental sensitivity, which can degrade the clock’s long-term frequency stability. To mitigate this problem, it is routine to employ a buffer-gas mixture in a vapor cell. With an appropriate choice of buffer gases, d[Δν_hfs]/dT = 0 at a vapor temperature T_c, “zeroing out” the clock’s buffer-gas temperature sensitivity. Unfortunately, T_c depends on the exact mix of buffer-gas partial pressures, and if not properly achieved, T_c will be far from the vapor temperature that yields useful atomic clock signals, T_o. Therefore, understanding buffer-gas partial pressures in sealed vapor cells is crucial for optimizing a vapor cell clock’s performance, yet, to date, there have been no easy means for measuring buffer-gas partial pressures non-destructively in sealed glass vapor cells. Here, we demonstrate an optical technique that can accurately assess partial pressures in binary buffer-gas mixtures. Moreover, this technique is relatively simple and can be easily implemented. Full article

This study investigates fiber content and orientation in prefabricated slab elements made of ultra-high-performance fiber-reinforced concrete (UHPFRC), using novel non-destructive measurement using a coil’s quality factor, where the coil is put to one side of the specimen only. This allows the analysis of slab specimens of arbitrary size. That then allows an accurate quality control of elements made in the prefabrication industry. This study presents an experimental campaign designed to evaluate the non-destructive principle’s accuracy and practical feasibility. Twenty-five large slab specimens were made in an industrial prefabrication plant using various casting methods to introduce different flow-induced fiber parameters. The slabs were subjected to this non-destructive testing, then destructive bending tests and CT scanning to tie the results together and validate the non-destructive results. The results showed that the coil’s quality factor values correspond well to the distribution (concentration) and orientation of fibers, and the method reliably reveals potential defects of the material and can predict the element’s mechanical properties. Full article

Background: Breast cancer is the most common cancer diagnosis and the second leading cause of cancer-related death in women. Breast cancer is a major health burden worldwide. Advances in breast cancer detection and treatment have contributed to improving the rate of survival, although mortality rates remain significantly high. Despite all these advances, more efficient diagnostic methods and effective treatments are necessary. Colchicine is a natural alkaloid with strong antimitotic activity, but its potential effects on extracellular matrix components in cancer remain poorly understood. Objective: This study aimed to investigate the influence of colchicine on glycosaminoglycan (GAG) concentrations and cell viability in MCF-7 breast cancer cells cultured in a three-dimensional (3D) hollow fiber bioreactor system. Methods: Magnetic resonance imaging (MRI) was applied as a non-invasive technique to quantify GAG levels through fixed charge density (FCD) and T₁ relaxation mapping. MCF-7 HER-2-overexpressing and HER-2-negative cells were treated with 1000 nM colchicine for 72 h, and cell viability was assessed in parallel with GAG measurements. Results: Colchicine significantly reduced cell viability and altered GAG concentrations. HER-2-overexpressing MCF-7 cells exhibited higher baseline GAG levels than HER-2-negative controls, and colchicine decreased the GAG content in both lines. Conclusions: Colchicine reduces viability and modifies GAG concentrations in 3D cultures of MCF-7 cells. The use of MRI provides a reproducible, non-destructive tool for monitoring extracellular matrix changes, offering a novel methodological approach for studying drug effects in physiologically relevant cancer models. Full article

Laser-accelerated electron beams, in the so-called Very High-Energy Electron (VHEE) energy range, are of great interest for biomedical applications. For instance, laser-driven VHEE beams are envisaged to offer suitable compact accelerators for the promising field of FLASH radiotherapy. Radiobiology experiments carried out using laser-driven beams require the real-time knowledge of the dose delivered to the sample. We have developed an online dose monitoring procedure, using an Integrating Current Transformer (ICT) coupled to a suitable collimator, that allows the estimation of the delivered dose on a shot-to-shot basis under suitable assumptions. The cross-calibration of the measured charge with standard offline dosimetry measurements carried out with RadioChromic Films (RCFs) is discussed, demonstrating excellent correlation between the two measurements. Full article

Agroforestry systems offer clear environmental and agronomic advantages, but their effect on plant–biotic stressor interactions remains poorly understood. Specifically, the shade from companion trees can create microclimates favorable to fungal diseases on herbaceous crops. This potential drawback may offset other benefits, highlighting the urgent need for advanced plant health monitoring in these systems. This study assessed the potential of hyperspectral reflectance to detect the single and combined effects of simulated tree shading and infection by the fungal pathogen Alternaria alternata on grain sorghum (Sorghum bicolor L. Moench) under rainfed field conditions. Sorghum was grown either under full light or 50% shading conditions. Half of the plots were artificially inoculated with an A. alternata spore suspension (2 × 10⁸ CFU mL⁻¹), while the others served as controls. Leaf and ground-canopy measurements were acquired with a full range spectroradiometer (VNIR-SWIR, 400–2,400 nm) and UAV imagery covered the VIS-NIR range (400–1,000 nm) before the onset of visible symptoms. Permutational multivariate analysis of variance of leaf and ground-canopy data revealed significant effects of shading (Sh), infection (Aa), and their interaction (p < 0.05), allowing early detection of infection two days before symptom appearance, while UAV data showed only singular significant effects. Partial least squares discriminant analysis accuracy reached 78% at the leaf level, 90% at the ground-canopy level, and 74% (Sh) and 75% (Aa) at the UAV scale. Furthermore, vegetation spectral indices derived from the spectra confirmed greater physiological stress in shaded and infected plants, consistent with disease incidence assessments. Our results establish scale-specific hyperspectral reflectance spectroscopy as a powerful, non-destructive technique for early plant health surveillance in agroforestry. This advanced optical sensing capability is poised to illuminate complex stressor interactions, marking a significant step forward for precision agroforestry management. Full article

Forest managers need regular accurate assessments of forest conditions to make informed decisions associated with harvest schedules, growth projections, merchandising, investment, and overall management planning. Traditionally, this is achieved through field-based sampling (i.e., timber cruising) a subset of the trees within a desired area (e.g., 1%–2%) through stratification of the landscape to group similar vegetation structures and apply a grid within each stratum where fixed- or variable-radius sample locations (i.e., plots) are installed to gather information used to estimate trees throughout the unmeasured remainder of the area. These traditional approaches are often limited in their assessment of uncertainty until trees are harvested and processed. However, the increasing availability of airborne laser scanning datasets in commercial forestry processed into Digital Inventories^® enables the ability to non-destructively assess the accuracy of these field-based surveys, which are commonly referred to as cruises. In this study, we assess the uncertainty of common field sampling-based estimation methods by comparing them to a population of individual trees developed using established and validated methods and in operational use on the University of Idaho Experimental Forest (UIEF) and a commercial conifer plantation in Louisiana, USA (PLLP). A series of repeated sampling experiments, representing over 90 million simulations, were conducted under industry-standard cruise specifications, and the resulting estimates are compared against the population values. The analysis reveals key limitations in current sampling approaches, highlighting biases and inefficiencies inherent in certain specifications. Specifically, methods applied to handle edge plots (i.e., measurements conducted on or near the boundary of a sampling stratum), and stratum delineation contributes most significantly to systematic bias in estimates of the mean and variance around the mean. The study also shows that conventional estimators, designed for perfectly randomized experiments, are highly sensitive to plot location strategies in field settings, leading to potential inaccurate estimations of BAA and TPA. Overall, the study highlights the challenges and limitations of traditional forest sampling and impacts specific sampling design decisions can have on the reliability of key statistical estimates. Full article

Apple jam is a widely used all-season product. The quality of the jam is closely related to its sugar concentration, which affects its taste, texture, shelf life, and legal compliance with production requirements. Although traditional methods for measuring sugar, such as titration, enzymatic methods, and chromatography, are accurate, they are also invasive, destructive, and unsuitable for rapid screening. This study investigates a non-destructive and non-invasive alternative method that uses hyperspectral imaging (HSI) in combination with machine learning to estimate the sugar content in processed apple products. Eight cultivars were selected from the Central Asian region, recognized as the origin of apples and known for its rich diversity of apple cultivars. A total of 88 jam samples were prepared with sugar concentrations ranging from 25% to 75%. For each sample, several hyperspectral images were obtained using a visible-to-near-infrared (VNIR) camera. The acquired spectral data were then processed and analyzed using regression models, including the support vector machine (SVM), eXtreme gradient boosting (XGBoost), and a one-dimensional residual network (1D ResNet). Among them, ResNet achieved the highest prediction accuracy of R² = 0.948. The results highlight the potential of HSI and machine learning for a fast, accurate, and non-invasive assessment of the sugar content in processed foods. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

Premature deterioration of concrete structures in coastal areas requires a careful evaluation based on durability criteria. Electrical Resistivity (ER) serves as a valuable indicator of concrete durability, as it reflects how easily aggressive agents can penetrate its pores. This testing method offers several advantages; it is non-destructive, rapid, and more cost-effective than the chloride permeability test (RCPT). Furthermore, durable concrete typically necessitates larger quantities of cement, which contradicts the goals of sustainable concrete development. Thus, a significant challenge is to create concrete that is both durable and sustainable. This research explores the effects of pozzolanic additives, specifically Volcanic Ash (VA) and Sugarcane Bagasse Ash (SCBA), on the electrical resistivity of eco-friendly concretes exposed to the coastal conditions of the Gulf of Mexico. The electrical resistivity (ER) was measured at intervals of 3, 7, 14, 21, 28, 45, 56, 90, and 180 days across 180 cylinders, each with dimensions of 10 cm × 20 cm. The sustainability of the concrete was evaluated based on its energy efficiency. Three types of mixtures were developed using the ACI 211.1 method, maintaining a water-to-cement (w/c) ratio of 0.57 with CPC 30 R RS cement and incorporating various additions: (1) varying percentages of VA (2.5%, 5%, and 7.5%), (2) SCBA at rates of 5%, 10%, and 15%, and (3) ternary mixtures featuring VA-SCBA ratios of 1:1, 1:2, and 1:3. The findings indicated an increase in ER of up to 37% and a reduction in CO₂ emissions ranging from 4.2% to 16.8% when compared to the control mixture, highlighting its potential for application in structures situated in aggressive environments. Full article

Doppler tomography (DT) is a relatively new method that allows the imaging of cross-sections of an object. The method uses a two-transducer ultrasound probe that moves around or along the object in a specific way. Image reconstruction is performed on the basis of the detection of the so-called Doppler signal, which contains Doppler frequencies that identify the stationary heterogeneous structures inside the imaged cross-section of the object. The Doppler tomography method differs significantly from the popular blood flow velocity detection method and should not be confused with it. It can potentially be used to reconstruct 2D and 3D cross-sectional images of structures that reflect the ultrasound wave well, either in medicine for diagnostics or in industry for so-called non-destructive testing. This paper presents simulations of imaging using Doppler tomography. The method and algorithms that can be used for Doppler tomography imaging without the need for complicated measurement systems and calculations were proposed. The influence of selected parameters on DT imaging quality was investigated, and their optimal compromise values for specific conditions were determined. Ways to improve image quality were also discussed. Full article

Non-destructive testing methods are essential in civil engineering applications, such as evaluating the compressive strength of masonry. This paper presents a fuzzy signature model based on non-destructive in situ measurements and visual inspection, applying weighted geometric mean aggregation in the signature vertices determined by experts. The weights of the aggregation terms were optimized using the Monte Carlo method, genetic algorithm and particle swarm algorithm to ensure that the evaluation by the signature aligned with the results of destructive tests performed on existing masonry. The results of the methods were compared for single and multiple assembled masonry structures using the same objective function. All three methods provided relatively high confidence in finding the extreme values of the objective function on a generated dataset, which accounted for the correlations observed in actual measurements. Accordingly, validation based on real data yielded the expected results, thus demonstrating the model’s suitability for practical application. This study assessed the inherent, analyzing whether symmetric or asymmetric weight distributions affected evaluation consistency. While symmetric weighting simplified aggregation, asymmetry allowed local structural irregularities to be highlighted. In addition, the cost analysis of the optimization methods revealed a disparity in computational cost increments between the two approaches. The presented work outlines the advantages of the different methods and their applicability to structural assessment. Full article

As a key form of deep foundation in civil engineering, the concrete pouring quality of cast-in-place piles directly determines the integrity and long-term bearing performance of the pile body. Accurate monitoring of the pouring interface is critical to preventing defects such as mud inclusion and pile breakage. To address the limitations of existing monitoring methods for concrete pouring interfaces, this paper proposes a full-pulse acoustic monitoring method for the concrete pouring interface of cast-in-place piles. Firstly, by constructing a hardware system platform consisting of “multi-level in-borehole sound sources + interface acoustic wave sensors + orifice full-pulse receivers + ground processors”, differential capture of signals propagating at different depths is achieved through multi-frequency excitation. Subsequently, a waveform data processing method is proposed to realize denoising, enhancement, and frequency discrimination of different signals, and a target feature recognition model that integrates cross-correlation functions and signal similarity analysis is established. Finally, by leveraging the differential characteristics of measurement signals at different depths, a near-field measurement mode and a far-field measurement mode are developed, thereby establishing a calculation model for the elevation position of the pouring interface under different scenarios. Meanwhile, the feasibility of the proposed method is verified through practical engineering cases. The results indicate that the proposed full pulse acoustic monitoring method can achieve non-destructive, real-time, and high-precision monitoring of the pouring interface, providing an effective technical approach for quality control in pile foundation construction and exhibiting broad application prospects. Full article

This study focuses on the susceptibility and surface degradation of low-alloy carbon steel 42CrMo4 to corrosion and cavitation erosion, as this steel is widely used in marine environments with aggressive chemical species and harsh conditions. Due to its high strength and fatigue resistance, 42CrMo4 steel is often employed in offshore mechanical components such as shafts and fasteners as well as crane parts in ports and harbors. Various experimental methods, including corrosion and cavitation tests, were used to assess the steel’s surface integrity under extreme conditions. Surface changes were monitored using modern analytical tools for precise assessments, including image and morphological analyses, to quantify degradation levels and specific parameters of defects induced by corrosion and cavitation. Non-destructive techniques such as optical microscopy (OM), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and image analysis software were employed for the quantitative assessment of morphological parameters and elemental analysis. EDS analysis revealed changes in elemental composition, indicating corrosion products that caused significant mass loss and defect formation, with degradation increasing over time. The average corrosion rate of 42CrMo4 steel in a 3.5% NaCl solution reached a peak value of 0.846 mm/year after 120 days of exposure. Cavitation erosion behavior was measured based on mass loss, indicating the occurrence of different cavitation periods, with the steady-state period achieved after 60 min. The number of formed pits increased until 120 min, after which it decreased slightly. This indicates that a time frame of 120 min was identified as significant for changes in the mechanism of pit formation. Specifically, up to 120 min, pit formation was the dominant mechanism of cavitation erosion, while after that, as the number of pits slightly declined, the growth and merging of formed pits became the dominant mechanism. The cavitation erosion tests showed mass loss and mechanical damage, characterized by the formation of pits and cavities. The findings indicate that the levels of surface degradation were higher for corrosion than for cavitation. The presented approach also provides an assessment of the degradation mechanisms of 42CrMo4 steel exposed to corrosive and cavitation conditions. Full article