Search Results (37,289)

Defects in welds can affect the structural safety and reliability of workpieces. Currently, the method of using phased array ultrasonic inspection technology for non-destructive testing of weld structures with high detection efficiency, good sensitivity, and good visualization of the results is widely used. However, the defective A-scan data collected by the ultrasonic phased array detector inevitably contain noise data, including the test piece material structure noise, equipment noise, and environmental noise, which undoubtedly affects the analysis of the A-scan signal. In addition, when defects are interpreted, the presence of noise also interferes with the process, which affects the accuracy of the interpretation. Therefore, to enhance the accuracy of defect identification based on phased array ultrasonic inspection technology, we must prevent the series of consequences caused by misjudgments. In this study, ultrasonic phased array inspection experiments were carried out, and the specific process flow of ultrasonic phased array inspection of flat plate butt welds was summarized. Utilizing pre-fabricated flat plate butt specimen blocks containing five types of typical defects, defect A-sweep signals based on ultrasonic phased array inspection were obtained. Combining the sparrow optimization algorithm (SSA), variational mode decomposition (VMD), and wavelet packet decomposition (WPD), a defect signal noise reduction method based on parameter optimization was studied. A noise reduction study was carried out using the noise-added simulated signal, and the results indicated that the noise reduction method proposed in this paper had a better noise reduction effect and the proposed method could effectively retain the detailed features of the ultrasonic phased array defective A-scan signal and realize the noise reduction processing of the defective A-scan signal. Full article

Accurate water vapor information is crucial for improving the quality of numerical weather forecasting. Previous studies have incorporated tropospheric water vapor data obtained from a global navigation satellite system (GNSS) into numerical weather models to enhance the accuracy and reliability of rainfall forecasts. However, research on evaluating forecast accuracy for different rainfall levels and the development of corresponding forecasting platforms is lacking. This study develops and establishes a rainfall forecasting platform supported by the GNSS-assisted weather research and forecasting (WRF) model, quantitatively assessing the effect of GNSS precipitable water vapor (PWV) on the accuracy of WRF model forecasts for light rain (LR), moderate rain (MR), heavy rain (HR), and torrential rain (TR). Three schemes are designed and tested using data from seven ground meteorological stations in Xi’an City, China, in 2021. The results show that assimilating GNSS PWV significantly improves the forecast accuracy of the WRF model for different rainfall levels, with the root mean square error (RMSE) improvement rates of 8%, 15%, 19%, and 25% for LR, MR, HR, and TR, respectively. Additionally, the RMSE of rainfall forecasts demonstrates a decreasing trend with increasing magnitudes of assimilated PWV, particularly effective in the range of [50, 55) mm where the lowest RMSE is 3.58 mm. Moreover, GNSS-assisted numerical weather model shows improvements in statistical forecasting indexes such as probability of detection (POD), false alarm rate (FAR), threat score (TS), and equitable threat score (ETS) across all rainfall intensities, with notable improvements in the forecasts of HR and TR. These results confirm the high precision, visualization capabilities, and robustness of the developed rainfall forecasting platform. Full article

Underwater images, as a crucial medium for storing ocean information in underwater sensors, play a vital role in various underwater tasks. However, they are prone to distortion due to the imaging environment, which leads to a decline in visual quality, which is an urgent issue for various marine vision systems to address. Therefore, it is necessary to develop underwater image enhancement (UIE) and corresponding quality assessment methods. At present, most underwater image quality assessment (UIQA) methods primarily rely on extracting handcrafted features that characterize degradation attributes, which struggle to measure complex mixed distortions and often exhibit discrepancies with human visual perception in practical applications. Furthermore, current UIQA methods lack the consideration of the perception perspective of enhanced effects. To this end, this paper employs luminance and saliency priors as critical visual information for the first time to measure the enhancement effect of global and local quality achieved by the UIE algorithms, named JLSAU. The proposed JLSAU is built upon an overall pyramid-structured backbone, supplemented by the Luminance Feature Extraction Module (LFEM) and Saliency Weight Learning Module (SWLM), which aim at obtaining perception features with luminance and saliency priors at multiple scales. The supplement of luminance priors aims to perceive visually sensitive global distortion of luminance, including histogram statistical features and grayscale features with positional information. The supplement of saliency priors aims to perceive visual information that reflects local quality variation both in spatial and channel domains. Finally, to effectively model the relationship among different levels of visual information contained in the multi-scale features, the Attention Feature Fusion Module (AFFM) is proposed. Experimental results on the public UIQE and UWIQA datasets demonstrate that the proposed JLSAU outperforms existing state-of-the-art UIQA methods. Full article

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm⁻¹, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm⁻¹. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems. Full article

There has always been high interest in predicting the solder joint fatigue life in advanced packaging with high accuracy and efficiency. Artificial Intelligence Plus (AI+) is becoming increasingly popular as computational facilities continue to develop. This study will introduce machine learning (a core component of AI). With machine learning, metamodels that approximate the attributes of systems or functions are created to predict the fatigue life of advanced packaging. However, the prediction ability is highly dependent on the size and distribution of the training data. Increasing the amount of training data is the most intuitive approach to improve prediction performance, but this implies a higher computational cost. In this research, the adaptive sampling methods are applied to build the machine learning model with a small dataset sampled from an existing database. The performance of the model will be visualized using predefined criteria. Moreover, ensemble learning can be used to improve the performance of AI models after they have been fully trained. Full article

Using stress elicitors in fruits and vegetables is considered a good strategy to increase the content of bioactive compounds in plant foods. However, bioactive compounds can affect the sensory characteristics of food products, and little is known about their shelf-life stability in fresh produce treated with elicitors. In the present work, carotenoids and polyphenols were quantified by spectrophotometric methods in red and green butterhead lettuce treated with elicitors that had previously been demonstrated to increase bioactive compounds: arachidonic acid (AA), methyl jasmonate (MJ), and Harpin protein (HP). The bioactive compounds were determined immediately and during three weeks after harvest. A descriptive sensory analysis was carried out, which included odor, taste, tactile, and visual attributes of control and elicitor-treated lettuce. Carotenoids showed greater shelf-life stability than polyphenols, and both were more stable in red than in green lettuce during the first two weeks of storage. The best elicitor was MJ, which increased phenolic compounds (red and green lettuce), anthocyanins, and carotenoids (red lettuce) through the storage period. Color intensity, crispness, wettability, and bitter taste were some of the primary sensory attributes in butterhead lettuce and were not affected by any treatment. Other organoleptic properties were also not affected by the elicitors. These results suggest that elicitation could improve the content of bioactive compounds, which is stable through the shelf-life of butterhead lettuce, without any adverse effect on the sensory properties. Full article

Unmanned aerial vehicles (UAVs) equipped with multispectral cameras offer great potential for applications in precision agriculture. A critical challenge that limits the deployment of this technology is the varying ambient illumination caused by cloud movement. Rapidly changing solar irradiance primarily affects the radiometric calibration process, resulting in reflectance distortion and heterogeneity in the final generated orthomosaic. In this study, we optimized the radiometric block adjustment (RBA) method, which corrects for changing illumination by comparing adjacent images and from incidental observations of reference panels to produce accurate and uniform reflectance orthomosaics regardless of variable illumination. The radiometric accuracy and uniformity of the generated orthomosaic could be enhanced by improving the weights of the information from the reference panels and by reducing the number of tie points between adjacent images. Furthermore, especially for crop monitoring, we proposed the RBA-Plant method, which extracts tie points solely from vegetation areas, to further improve the accuracy and homogeneity of the orthomosaic for the vegetation areas. To validate the effectiveness of the optimization techniques and the proposed RBA-Plant method, visual and quantitative assessments were conducted on a UAV-image dataset collected under fluctuating solar irradiance conditions. The results demonstrated that the optimized RBA and RBA-Plant methods outperformed the current empirical line method (ELM) and sensor-corrected approaches, showing significant improvements in both radiometric accuracy and homogeneity. Specifically, the average root mean square error (RMSE) decreased from 0.084 acquired by the ELM to 0.047, and the average coefficient of variation (CV) decreased from 24% (ELM) to 10.6%. Furthermore, the orthomosaic generated by the RBA-Plant method achieved the lowest RMSE and CV values, 0.039 and 6.8%, respectively, indicating the highest accuracy and best uniformity. In summary, although UAVs typically incorporate lighting sensors for illumination correction, this research offers different methods for improving uniformity and obtaining more accurate reflectance values from orthomosaics. Full article

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm. Full article

A shared commitment to standardising the process of hypothetically reconstructing lost buildings from the past has characterised academic research in recent years and can manifest at various stages of the reconstructive process and with different perspectives. This research specifically aims to establish a user-independent and traceable procedure that can be applied at the end of the reconstructive process to quantify the average level of uncertainty of 3D digital architectural models. The procedure consists of applying a set of mathematical formulas based on numerical values retrievable from a given scale of uncertainty and developed to simplify reuse and improve transparency in reconstructive 3D models. This effort to assess uncertainty in the most user-independent way possible will contribute to producing 3D models that are more comparable to each other and more transparent for academic researchers, professionals, and laypersons who wish to reuse them. Being able to calculate a univocal numerical value that gives information on the global average uncertainty of a certain reconstructive model is an additional synthetic way, together with the more visual false-colour scale of uncertainty, to help disseminate the work in a clear and transmissible way. However, since the hypothetical reconstructive process is a process based on personal interpretation, which inevitably requires a certain level of subjectivity, it is crucial to define a methodology to assess and communicate this subjectivity in a user-independent and reproducible way. Full article

The aim of the article was to compare the landscape and cultural value of cemeteries located around wooden churches on the Polish–Czech border in the Lower Silesian Voivodeship. Research regarding the history of the villages was undertaken, describing their development and the construction of the churches and the cemeteries so as to compare changes in cemetery spatial layout, architecture, and landscape. The villages involved were Grzmiąca, Kamieńczyk, Międzygórze, Nowa Bystrzyca, Rybnica Leśna, and Zalesie. The following analyses were conducted: assessment of the visual aspects of the landscape, evaluation of the architectural value, and assessment of land use. In the landscape of Lower Silesia, churchyard cemeteries, which bear witness to the rich past of this region, are gradually but remorselessly deteriorating. Adverse changes are occurring to their spatial layout, to church buildings, as well as in architecture and greenery. The article presents a new approach to the subject of sacred sites through multi-faceted research aimed at protecting the fragile and changing religious landscape. Full article

Objectives: This study was aimed at analyzing the efficacy on the improvement of the visual function of a dichoptic online cloud-based platform for the treatment of amblyopia in anisometropic children. Methods: A quasi-experimental (pretest–post-test) study was conducted in 23 subjects with ages from 5 to 15 years old with anisometropic amblyopia combined with additional presence (2 subjects) or not (21 subjects) of microtropia. A total of 30 home-based training sessions of 30 min per session with Bynocs^® platform were prescribed for 6 weeks. Results: Amblyopic eye logMAR visual acuity (VA) significantly improved from 0.28 ± 0.24 to 0.13 ± 0.20 after the 6-week treatment (p < 0.001). At baseline, 60.9% of participants had VA in amblyopic eye of 0.20 logMAR or worse, whereas this percentage decreased to 21.7% after treatment. Binocular function (BF) significantly improved from 2.82 ± 1.11 to 2.32 ± 0.94 (p < 0.001). Mean compliance was 92%, 87% and 93% at 2, 4 and 6 weeks of treatment, respectively. Conclusions: In conclusion, home-based dichoptic training with the digital platform evaluated is an effective method to improve amblyopic VA and stereoacuity in children with anisometropic amblyopia combined or not with microtropia. Full article

The increasing mental health challenges among elders demand specialized interventions, especially within Thai communities where resources are limited and stigma persists. While monks offer spiritual support, there is a gap in addressing complex mental health needs. This research aims to develop a monk-led elderly mental health counseling program in Thai Buddhist communities. From January 2023 to March 2024, this study underwent four phases. Initially, qualitative interviews with thirty-six monk and elder participants elucidated requirements. The program development integrated findings from the requirement study, the Solution-Focused Brief Therapy process, and Buddhist mindfulness principles to create a prototype. The quality assessment involved expert content validation, feasibility examination by stakeholders, and a small-scale pilot testing with five monks. Finally, the feasibility of the program was assessed with thirty-two monks. The study reveals three key components of the monk-led elderly counseling program focused on mental health: the counseling process known as MPS-MAV-PI (an Introduction to Mindfulness, Identifying Problems, Assessing the Severity, Mindfully Observing Thoughts and Emotions, Acceptance, Visualizing Success, Planning Strategies for Problem-solving, and Implementation and Subsequent Monitoring), the C-TIME strategy (Collaboration, Training Manual, Implementation, the Monitoring, and Evaluation), and the program manual. Moreover, feasibility assessments among monks show the high feasibility of the program for implementation. The monk-led counseling program holds promise in addressing these challenges, with high feasibility indicating potential effectiveness and scalability. Future research will prioritize evaluating its cost-effectiveness and overall effectiveness. Full article

Visual density, defined as the number of identifiable elements per unit area within a visual design, significantly influences consumer perceptions. This study investigates the effects of varying visual densities in pizza packaging, encompassing both food-related and decorative elements, on consumers’ expectations regarding taste and texture, ultimately influencing their purchase decisions. We conducted a controlled experiment where participants were presented with pizza boxes of differing visual densities. Participants rated their expectations regarding the taste and texture of the pizza, as well as their purchase intentions. Additionally, we measured consumption frequency to evaluate its moderating influence on the observed effects. Results indicate that high-visual-density packaging significantly heightened expectations of taste and texture, independent of the element’s nature—whether food-related or decorative. Enhanced sensory expectations fully mediated the relationship between visual density and purchase intentions. Additionally, high consumption frequency amplified the effect of high visual density on sensory expectations and purchase intentions. These findings contribute to sensory marketing theory by highlighting the importance of visual density in packaging design and the role of consumption frequency. They provide practical implications for food packaging strategies aimed at enhancing consumer experience and satisfaction. Full article

This paper presents a case of a 35-year-old female patient diagnosed with Complex Regional Pain Syndrome (CRPS) type I and treated over a two-month period with a novel low-frequency sound-transduced focal pulsed stimulus. The patient received 21 treatments consisting of focally applied sound sweeps in the 15–100 Hz range. Outcome measures included the Visual Analogue Scale for pain, five physical assessment parameters, medication, and the Pain Catastrophizing Scale. A follow-up was conducted at six months. The results show that the patient’s low-back pain level was substantially reduced after treatment and after six months. CRPS-related peripheral pain was strongly reduced but had some rebound after six months. The low-frequency sound-transduced focal pulsed stimulus shows potential as a non-invasive treatment for CRPS and deserves controlled clinical trials. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is becoming more prominent globally due to an increase in the prevalence of obesity, dyslipidemia, and type 2 diabetes. A great deal of studies have proposed potential treatments for MASLD, with few of them demonstrating promising results. The aim of this study was to investigate the potential effects of (-)-epicatechin (EPI) on the development of MASLD in an in vitro model using the HepG2 cell line by determining the metabolic viability of the cells and the levels of PPARα, PPARγ, and GSH. HepG2 cells were pretreated with 10, 30, 50, and 100 μM EPI for 4 h to assess the potential effects of EPI on lipid metabolism. A MASLD cell culture model was established using HepG2 hepatocytes which were exposed to 1.5 mM oleic acid (OA) for 24 h. Moreover, colorimetric MTS assay was used in order to determine the metabolic viability of the cells, PPARα and PPARγ protein levels were determined using enzyme-linked immunosorbent assay (ELISA), and lipid accumulation was visualized using the Oil Red O Staining method. Also, the levels of intracellular glutathione (GSH) were measured to determine the level of oxidative stress. EPI was shown to increase the metabolic viability of the cells treated with OA. The metabolic viability of HepG2 cells, after 24 h incubation with OA, was significantly decreased, with a metabolic viability of 71%, compared to the cells pretreated with EPI, where the metabolic viability was 74–86% with respect to the concentration of EPI used in the experiment. Furthermore, the levels of PPARα, PPARγ, and GSH exhibited a decrease in response to increasing EPI concentrations. Pretreatment with EPI has demonstrated a great effect on the levels of PPARα, PPARγ, and GSH in vitro. Therefore, considering that EPI mediates lipid metabolism in MASLD, it should be considered a promising hepatoprotective agent in future research. Full article