Biogas composed of methane and carbon dioxide derived from palm oil mill effluent (POME) is a promising renewable energy source; however, the design of serial separation processes faces considerable challenges in upgrading and compressing bio-methane. This study designed a layered adsorption column for removing H2O via pressure-temperature swing adsorption (PTSA) and analyzed various effects on the performance of PTSA units and the POME upgrading process combining water scrubbing and PTSA. A layered adsorption column, composed of activated alumina and zeolite 4A, was investigate for its H2O removal performance, using a bench-scale laboratory apparatus and numerical simulation. Based on breakthrough curves, the optimal packing ratio was 30 % activated alumina and 70 % zeolite 4A by height, and the column was sufficiently regenerated at > 150 degrees C with 10 % of product stream. Numerical simulations supported the experimental results and the effects of different packing configurations on the performance of the layered adsorption column. However, H2O removal, the primary factor used for assessing the adsorption column, was not solely affected by CH4 recovery and the purity of CH4 during integrated POME upgrade. Further simulations based on the experimental data showed that the CH4 recovery and purity of CH4 from the integrated system was enhanced by applying a high-activatedalumina-content layered column. This study highlights the contrasting effects of the packing ratio of layered adsorption columns on the performances for independent PTSA units and integrated processes. The findings contribute to developing scalable and energy-efficient bio-methane or bio-CNG production processes, aligning with global efforts toward sustainable energy solutions.

E-commerce marketplace platforms have evolved into integral digital intermediaries that shape online transactions in competitive environments. Companies continuously endeavor to improve e-service quality, customer satisfaction, and e-trust to gain a competitive advantage. This study aimed to identify the relationships between e-service quality, customer satisfaction, e-trust, and continuous usage intention in e-commerce marketplace platforms. Moreover, this study examined the roles of customer satisfaction and e-trust as mediators. We estimated nine hypothesized relationships using a structural equation modeling technique. Data from 311 users were used in the data analysis. The results are as follows: First, e-service quality significantly and positively affects customer satisfaction, e-trust, and continuous usage intention. Second, customer satisfaction has a significant and positive impact on e-trust and continuous usage intention. Third, e-trust has a significant and positive impact on continuous usage intention. Finally, both customer satisfaction and e-trust serve as significant mediating factors in the relationship between e-service quality and continuous usage intention. These insights hold strategic importance for e-commerce marketplace platform operators, allowing them to formulate service strategies and policies tailored to enhance user experience, foster trust, and drive continued usage, thereby strengthening their market position and ensuring sustained success.

Abstract Welding processes pose significant challenges and risks to both humans and the environment, as well as the machines involved. To mitigate the potential negative impacts of welding, we suggest the development of a four-camera system that can replace human involvement in the assessment and evaluation of welded products. In this research paper, we introduce an algorithm based on Mask-RCNN and Green’s theorem-based classifier, designed to assess the quality of welding beads. For the optimization of our model, we trained the enhanced model on COCO weights using various backbone networks, including VGG16, ResNet101, MobileNet, Inception, and EfficientNet. Our findings revealed that Effi-cientNet reduced computational time compared to the other options. Due to constraints in data availability, we treated the problem as a single-class classification task. We addressed the issue of imbalanced datasets by incorporating a classifier with a decision threshold of 75% to distinguish between correctly and incorrectly welded beads. Furthermore , our proposed algorithm is versatile and adaptable, functioning effectively under different environmental conditions, with various types of cameras, diverse brightness settings, and multiple resolutions. Based on a thorough evaluation using performance metrics and the specified decision threshold, our algorithm achieved impressive results, a mean recall of 100%, precision of 100%, an accuracy of 99.56%, and an Intersection over Union (IoU) score of 98.92%. Additionally, the algorithm is capable of detecting and assessing the quality of welding beads within a mean processing time of 0.55 seconds per image, obtained from 172 frames. Based on these promising results, we recommend the adoption of our proposed algorithm for real-time applications that involve the detection and evaluation of welding bead quality, especially when integrated with the four-camera system we have developed. JEL Classification: D8 , H51

Thing of interest (ToI) in a photograph may be perceived as smaller than being perceived from the real scene due to the discrepancy between the imaging principles in the camera and human perception. When using existing image resizing approaches to enlarge the ToI in the input image, the resulting image may have problems, such as loss of distance sense, composition collapse, failure to preserve salient object shapes, etc. In this study, we propose a ToI resizing method based on seam carving method. The proposed method adopts an energy function, which takes image composition preservation into consideration. Furthermore, to prevent salient objects from being edited, the state-of-the-art deep learning model for salient object detection (SOD) has been adopted in the proposed method. To confirm the performance of the proposed method, a subjective evaluation experiment was conducted in this study. The experimental result shows that the effectiveness of the proposed method in terms of the preservation of perceptual size and perceptual distance of the ToI.

3D pattern film is a film that makes a 2D pattern appear 3D depending on the amount and angle of light. However, since the 3D pattern film image was developed recently, there is no established method for classifying and verifying defective products, and there is little research in this area, making it a necessary field of study. Additionally, 3D pattern film has blurred contours, making it difficult to detect the outlines and challenging to classify. Recently, many machine learning methods have been published for analyzing product quality. However, when there is a small amount of data and most images are similar, using deep learning can easily lead to overfitting. To overcome these limitations, this study proposes a method that uses an MLP (Multilayer Perceptron) model to classify 3D pattern films into genuine and defective products. This approach entails inputting the widths derived from specific points' heights in the image histogram of the 3D pattern film into the MLP, and then classifying the product as 'good' or 'bad' using optimal hyper-parameters found through the random search method. Although the contours of the 3D pattern film are blurred, this study can detect the characteristics of 'good' and 'bad' by using the image histogram. Moreover, the proposed method has the advantage of reducing the likelihood of overfitting and achieving high accuracy, as it reflects the characteristics of a limited number of similar images and builds a simple model. In the experiment, the accuracy of the proposed method was 98.809%, demonstrating superior performance compared to other models.

This study explores the intricate relationships between perceived value, customer satisfaction, trust, and loyalty in the context of the dynamic online entertainment platform industry. As the entertainment landscape has evolved from traditional formats to digital and interactive experiences, businesses face intense competition and the need to innovate to attract and retain users. This study introduces a comprehensive research model that defines perceived value in three dimensions: utilitarian, hedonic, and social. It also investigates the roles of customer satisfaction and trust as mediators in the connection between perceived value and loyalty. A survey of entertainment platform users reveals that enhancing utilitarian and hedonic values can increase customer satisfaction and that all three perceived value dimensions positively influence trust. Customer satisfaction partially mediates the relationship between utilitarian value and loyalty and fully mediates the relationship between hedonic value and loyalty; however, trust does not act as a mediator in this context. The theoretical implications enhance our understanding of these relationships while the managerial implications provide actionable insights for businesses seeking to refine their customer-focused approaches in the competitive online entertainment landscape.

PURPOSE:This study aimed to comprehensively explore and elucidate the intricate relationship between exercise and depression, and focused on the physiological mechanisms by which exercise influences the brain and body to alleviate depression symptoms. By accumulating the current research findings and neurobiological insights, this study aimed to provide a deeper understanding of the therapeutic potential of exercise in the management and treatment of depression. METHODS:We conducted a systematic review of the scientific literature by selecting relevant studies published up to October 2023. The search included randomized controlled trials, observational studies, and review articles. Keywords such as "exercise," "depression," "neurobiology," "endocrinology," and "physiological mechanisms" were used to identify pertinent sources. RESULTS:Inflammation has been linked to depression and exercise has been shown to modulate the immune system. Regular exercise can (1) reduce the levels of pro-inflammatory cytokines, potentially alleviating depressive symptoms associated with inflammation; (2) help in regulating circadian rhythms that are often disrupted in individuals with depression; and (3) improve sleep patterns, thus regulating mood and energy levels. CONCLUSION:The mechanisms by which exercise reduces depression levels are multifaceted and include both physiological and psychological factors. Exercise can increase the production of endorphins, which are neurotransmitters associated with a positive mood and feelings of well-being. Exercise improves sleep, reduces stress and anxiety, and enhances self-esteem and social support. The implications of exercise as a treatment for depression are significant because depression is a common and debilitating mental health condition. Exercise is a low-cost, accessible, and effective treatment option that can be implemented in various settings such as primary care, mental health clinics, and community-based programs. Exercise can also be used as an adjunctive treatment along with medication and psychotherapy, which can enhance treatment outcomes.

The efficient use of low-quality crude oil is one short-term solution to overcoming the imminent energy crisis due to the depletion of fossil resources. Several methods have been developed to upgrade low-quality oil, the most important part of which is the removal of acidic components. High-acid crudes can be converted to lower-acid crudes using various methods based on physical and chemical principles. In this contribution, we suggest an efficient and economical method based on liquid–liquid extraction. The candidate solvent was screened from a thermodynamic analysis in a previous study, and the additives and process conditions were adjusted according to experimental results. As the final solvent choice, 1,6-hexanediol in an aqueous ammonia solution showed the best performance with the minimal amount of solvent (weight fraction of 0.6). Experiments showed that the proposed solvent can be used to treat real crude oil used in the industry. A continuous operation scheme was suggested with solvent regeneration and recycling combined with separation of the acidic residue. The results showed that the proposed extraction process can reduce 85% of the acidic component in high-acid crudes and that repeated use of the solvent is possible.

Welding is a crucial manufacturing technique utilized in various industrial sectors, playing a vital role in production and safety aspects, particularly in shear reinforcement of dual-anchorage (SRD) applications, which are aimed at enhancing the strength of concrete structures, ensuring that their quality is of paramount importance to prevent welding defects. However, achieving only good products at all times is not feasible, necessitating quality inspection. To address this challenge, various inspection methods were studied. Nevertheless, finding an inspection method that combines a fast speed and a high accuracy remains a challenging task. In this paper, we proposed a welding bead quality inspection method that integrates sensor-based inspection using average current, average voltage, and mixed gas sensor data with 2D image inspection. Through this integration, we can overcome the limitations of sensor-based inspection, such as difficulty in identifying welding locations, and the accuracy and speed issues of 2D image inspection. Experimental results indicated that while sensor-based and image-based inspections individually resulted in misclassifications, the integrated approach accurately classified products as 'good' or 'bad'. In comparison to other algorithms, our proposed method demonstrated a superior performance and computational speed.

Three-dimensional film images which are recently developed are seen as three-dimensional using the angle, amount, and viewing position of incident light rays. However, if the pixel contrast of the image is low or the patterns are cloudy, it does not look three-dimensional, and it is difficult to perform a quality inspection because its detection is not easy. In addition, the inspection method has not yet been developed since it is a recently developed product. To solve this problem, we propose a method to calculate the width of pixels for a specific height from the image histogram of a 3D film image and classify it based on a threshold. The proposed algorithm uses the feature that the widths of pixels by height in the image histogram of the good 3D film image are wider than the image histogram of the bad 3D film image. In the experiment, it was confirmed that the position of the height section of the image histogram has the highest classification accuracy. Through comparison tests with conventional algorithms, we showed excellent classification accuracy for 3D film image classification. We verified that it is possible with high accuracy even if the image's contrast is low and the patterns in the image are not detected.

The shear reinforcement of dual-anchorage (SRD) is used to enhance the safety of reinforced concrete structures in construction sites. In SRD, welding is used to create shear reinforcement, and after production, a quality inspection of the welding bead is required. Since the welding bead of SRD is inspected for quality by measuring both horizontal and vertical lengths, it is necessary to obtain this information for quality inspection. However, it is difficult to inspect the quality of welding beads using existing methods based on segmentation, due to the similarity in texture between the welding bead and the base material, as well as discoloration around the welded area after welding. In this paper, we propose an algorithm that detects the welding bead using an image projection algorithm for pixels and classifies the quality of the welding bead. This algorithm detects the position of welding beads using the brightness values of an image. The proposed algorithm reduces the amount of computation time by first specifying the region of interest and then performing the analysis. Results from experiments reveal that the algorithm accurately classifies welding beads into good or bad classes by obtaining all brightness values in the vertical and horizontal directions in the SRD image. Furthermore, comparison tests with conventional algorithms demonstrate that the classification accuracy of the proposed algorithm is the highest. The proposed algorithm will be helpful in the real-time welding bead inspection field where fast and accurate inspection is crucial.

A 3D film pattern image was recently developed for marketing purposes, and an inspection method is needed to evaluate the quality of the pattern for mass production. However, due to its recent development, there are limited methods to inspect the 3D film pattern. The good pattern in the 3D film has a clear outline and high contrast, while the bad pattern has a blurry outline and low contrast. Due to these characteristics, it is challenging to examine the quality of the 3D film pattern. In this paper, we propose a simple algorithm that classifies the 3D film pattern as either good or bad by using the height of the histograms. Despite its simplicity, the proposed method can accurately and quickly inspect the 3D film pattern. In the experimental results, the proposed method achieved 99.09% classification accuracy with a computation time of 6.64 s, demonstrating better performance than existing algorithms.

[Purpose] The COVID-19 pandemic and its transition into an endemic phase have profoundly impacted physical health, well-being, mental health, education, and various aspects of society, including the economy and social networks. Home confinement, social distancing, and physical inactivity have exacerbated numerous health issues, including obesity, diabetes mellitus, hypertension, hyperlipidemia, cardiovascular diseases, depression, and poor sleep quality. A systematic review has revealed significant findings: Regular aerobic programs (such as cycling or walking at an intensity of 60–80% of HR max for 20–60 minutes per session, repeated 2–3 times a week) have proven effective in improving both physical and mental health, as well as immune function. This type of physical activity has been shown to increase immunological markers, including lymphocytes, leukocytes, neutrophils, monocytes, and interleukin-6 (IL-6), while reducing low-grade inflammation. Therefore, in this study we aimed to assess the impact of tailored exercise interventions on the physical and mental health of COVID-19 patients. Based on the results, we can establish exercise intervention strategies to mitigate the negative health consequences during and after the COVID-19 pandemic.[Methods] We conducted a search of the PubMed database from January 2020 to August 2023 using predefined search terms such as “COVID-19 and post-COVID-19,” “exercise intervention and immunity,” and “mental health.” By examining references, we explored the links between exercise interventions and the mental and physical health of COVID-19 patients.[Results] A tailored, multifaceted exercise intervention should be developed and implemented to address the existing mental challenges and enhance mental health during both the pandemic and the post-COVID-19 periods.[Conclusion] Breathing exercises and respiratory support techniques, including yoga, thoracic expansion exercises, airway clearance methods, and breathing control, are likely to be beneficial.

When using a desktop computer, people tend to adopt postures that are detrimental to their bodies, such as text neck and the L-posture of leaning forward with their buttocks out and their shoulders against the backrest of the chair. These two postures cause chronic problems by bending the cervical and thoracic spines and can have detrimental effects on the body. While there have been many studies on text neck posture, there were limited studies on classifying these two postures together, and there are limitations to the accuracy of their classification. To address these limitations, we propose an algorithm for classifying good posture, text neck posture, and L-posture, the latter two of which may negatively affect the body when using a desktop computer. The proposed algorithm utilizes a skeleton algorithm to calculate angles from images of the user's lateral posture, and then classifies the three postures based on the angle values. If there is sufficient space next to the computer, the method can be implemented anywhere, and classification can be performed at low cost. The experimental results showed a high accuracy rate of 97.06% and an F1-score of 95.23%; the L posture was classified with 100% accuracy.

3D film images appear three-dimensional based on the angle, and viewing position of incident light rays. However, if the pixel contrast of the image is low or the patterns are cloudy, images do not appear three-dimensional, and it is difficult to perform quality inspection because detection is not straightforward. To address this issue, we propose a method to calculate the width of pixels for a specific height from the histogram of a 3D film image and classify it based on a threshold. The proposed algorithm is based on the feature that the widths of pixels by height in the histogram of a realistic 3D film image is wider than the histogram of an unrealistic 3D film image. In the experiment, it was confirmed that the position of the height section of the histogram has the highest classification accuracy. Through comparison tests with conventional algorithms, our approach demonstrated excellent classification accuracy for 3D film image classification and was proven to have high accuracy even for images with low contrast and indiscernible patterns.

The procedures of white points detection and localization are practically complex on noisy images. In this paper, we propose an algorithm that detects and localizes white points on 3D film images. The proposed algorithm uses the fast Fourier transform to convert the binarized image into real and imaginary parts to obtain the number of white points along the horizontal and vertical. We determine the sorted coordinates of the white points by adding a brute-force solution to the coordinates obtained from the real part of the image. These sorted coordinates are obtained by subtracting the error between the Euclidean distances of the normalized coordinates along the vertical and horizontal direction. The proposed algorithm with and without brute-force achieved an average detection ratio of 0.98 and 0.88 respectively, while the others underperformed. We perform various experiments using the existing algorithms such as template matching, thresholding, and an iterative method to validate the performance of our algorithm. We also compare the rule-based algorithms that detect and localize objects in noisy images with the proposed one to determine the reliability of our algorithm. The experimental results indicate that the proposed algorithm performs better than the template matching, thresholding, and iterative algorithm.

Abstract Assessment and evaluation are the essential processes of industrially manufactured products for the determination of the quality and quantity of products. They give justifications in a practical way about whether the machine is perfect or imperfect, which can lead to a better or poorer production. In this study, the authors propose an algorithm that uses morphological geodesic active contour and image processing techniques to perform segmentation and assess the performance of a robot used to manufacture welding beads. The algorithm has four parameters which are pre‐processed images, balloon force, smoothing parameter, and number of iterations. To pre‐process the images, the algorithm uses an inverse Gaussian gradient operator for edge detection and applies the histogram equalization method to level the distribution. To detect the external contour of the bead, the level set is initialized as the region of interest whereby a balloon force can inflate or deflate towards the edges. To smoothen the contour, a smoothing parameter is applied to convert the jagged lines into a curve over a reasonable number of iterations. Based on the experimental results, the authors’ algorithm used a fixed balloon force of −2, a smoothing parameter value of 4, and 40 iterations to segment images obtained from three different environments. The computation time for the segmentation and evaluation of one image was 0.70, 0.61, and 0.67 s for datasets with high brightness, low brightness, and normal brightness, respectively. Additionally, the authors’ proposed algorithm achieved an outstanding performance of 0.9954, 0.9843, 0.9892, and 0.9435 in terms of recall, precision, F‐measure, and IOU, respectively. To justify the performance of the authors’ proposed algorithm, the authors compared it with the existing algorithms and found that it worked better than all the others for segmentation, although it lagged behind the entropy‐based algorithm in terms of speed.

We experimentally studied removal of acid gas from natural gas via a pressure swing adsorption (PSA) process that employed a metal-organic framework adsorbent, MIL-101(Cr) to upgrade raw-quality natural gas to liquefied-quality natural gas. We hydrothermally synthesized MIL-101(Cr) in the presence of acetic acid, and then we extrudated it using a 15% carboxymethyl cellulose (CMC) binder. The adsorption isotherms of CO2 and CH4 in the pressure range 0-3 MPa and H2S in the pressure range 0-0.1 MPa for both powderand extrudate-form of the MIL-101(Cr) were measured. The CO2 and CH4 adsorption capacities at 3 MPa were found to be 26.31 and 8.89 mmol/g for powder-form and 19.26 mmol/g and 6.04 mmol/g for extrudate-form, respectively. The H2S adsorption capacities at 0.1 MPa were found to be 7.28 mmol/g for powder-form and 4.04 mmol/g for extrudateform. Feasibility tests for upgrading 2 vol% CO2-98 vol% CH4 and 2% CO2-0.02% H2S-97.98% CH4 into liquefied-quality natural gas (CO2 < 50 ppm and H2S < 4 ppm) via PSA were performed under various operating conditions (e.g., feed gas pressure (P-feed), amounts of purge gas (Q(purge)), and feed linear velocities (V-linear). In the case of 2 vol% CO2-98 vol% CH4, the extrudate-form exhibited 1.55 mmol CO2/g (i.e., 94% of the adsorption capacity at P-CO2 = 0.12 MPa), under the condition of V-linear = 0.127 m/s, Q(purge) = 178.97 cm(3)/g, and P-feed = 6 MPa. Upgrading 2% CO2 + 0.02% H2S + 97.98% CH4 via PSA was found to be difficult because of the strong adsorption of H2S to MIL-101 under the aforementioned conditions.

We synthesized an aluminophosphate (AlPO4)-based adsorbent that shows great cooling performance when used in a water adsorption chiller as an adsorbent. This adsorbent is crystalline microporous (CHA-type structure) material, which is composed of aluminum, phosphine, and oxygen atoms. It shows high water adsorption capacity (0.3 g(H2O) g(ads)(-1)) at 308 K under 12 Torr of water vapor pressure (typical adsorption conditions of the adsorption chiller), and almost zero adsorption capacity at 348 K under 42 Torr (typical desorption conditions). This adsorbent shows much larger working capacity between the adsorption and desorption steps than silico-aluminophosphate (SAPO(4)) adsorbent which has same physico-chemical properties as the AlPO4, except for the absence of silicon species in framework. The commercial water adsorbent, FAM-Z02 is known to have the same structure as this SAPO(4). We confirmed that AlPO4 can be easily synthesized in a large scale with a high hydrothermal stability, and easily pelletized to 0.6-1 mm sphere-shaped particles. The present AlPO4 adsorbent was tested in lab-made water adsorption chiller with 5 kW scale, and it shows 523 W kg(-1) of specific cooling power (SCP). This experimental value was in good agreement with SCP (513 W kg(-1)) derived from simulation. The SCP of the AlPO4 adsorbent is 28% larger than that of the SAPO(4) adsorbent calculated from simulation.