Search Results (10)

Xerosis, characterized by dry, rough skin, causes discomfort and aesthetic concerns, necessitating effective treatment. Traditional treatments often show limited efficacy, prompting the need for innovative therapies. This study highlights the efficacy of microbiome therapeutic LP51, derived from a healthy vaginal microbiome, in improving xerosis. A double-blind clinical trial involving 43 subjects with dry inner arm skin compared the effects of a 2.9% LP51 extract formulation to a placebo over 4 weeks. The LP51 group exhibited a significant increase in stratum corneum hydration (10.0 A.U.) compared to the placebo group (4.8 A.U.) and a 21.4% decrease in transepidermal water loss (TEWL), whereas the placebo group showed no significant change. LP51 also demonstrated benefits in enhancing skin hydration, improving the skin barrier, and exhibited anti-atopic, anti-inflammatory, and antioxidant properties. Safety was confirmed through in vitro cytotoxicity tests. These effects are attributed to the microbiome-safe component in LP51 and its role in improving xerosis, reflected by an increase in the xerosis-microbiome index, defined by the Firmicutes/Actinobacteria ratio. These findings position microbiome therapeutic LP51 as a promising novel treatment for xerosis. Full article

This research addresses the factors that impact the acceptance of AI-based technologies or products depending upon firm size in the construction industry, in which various corporates exist. In order to achieve the research goals, a technology acceptance model was applied to investigate the influencing factors in respect to adopting AI-based technologies or products. From the research results, technological and organizational factors were found to positively influence perceived usefulness and perceived ease of use. Corporate users perceived that technology is useful to their work and is easy to use when enough capital and education were invested prior to the company adopting AI-based technologies or products. It was found that perceived ease of use and perceived usefulness indicate satisfaction with new technology, and the higher the intention to use, the higher the satisfaction. In addition, as various information sharing and distribution channels increase, the frequency of use of new technologies or products also increases, not through traditional marketing, but through viral marketing via social media or promotion by influential persons or organizations. Furthermore, there are differences in the adoption of AI-based technologies or products depending on the size of the company. Full article

The number of people suffering from hair loss is increasing, and hair loss occurs not only in older men but also in women and young people. Prostaglandin D₂ (PGD₂) is a well-known alopecia inducer. However, the mechanism by which PGD₂ induces alopecia is poorly understood. In this study, we characterized CXXC5, a negative regulator of the Wnt/β-catenin pathway, as a mediator for hair loss by PGD₂. The hair loss by PGD₂ was restored by Cxxc5 knock-out or treatment of protein transduction domain–Dishevelled binding motif (PTD-DBM), a peptide activating the Wnt/β-catenin pathway via interference with the Dishevelled (Dvl) binding function of CXXC5. In addition, suppression of neogenic hair growth by PGD₂ was also overcome by PTD-DBM treatment or Cxxc5 knock-out as shown by the wound-induced hair neogenesis (WIHN) model. Moreover, we found that CXXC5 also mediates DHT-induced hair loss via PGD₂. DHT-induced hair loss was alleviated by inhibition of both GSK-3β and CXXC5 functions. Overall, CXXC5 mediates the hair loss by the DHT-PGD₂ axis through suppression of Wnt/β-catenin signaling. Full article

Hair follicle stem cells (HFSCs) utilize glycolytic metabolism during their activation and anagen induction. However, the role of pyruvate kinase M2 (PKM2), which catalyzes the final step of glycolysis, in hair regeneration has not been elucidated. In this study, we investigated the expression pattern and activity of PKM2 during the depilation-induced anagen progression in mice. We found that TEPP-46, a selective activator of PKM2, enhanced hair re-growth and proliferation of HFSCs. PKM2 expression was increased via up-regulation of Wnt/β-catenin signaling, which is involved in hair regeneration. Moreover, a combined treatment with KY19382, a small molecule that activates Wnt/β-catenin signaling, and TEPP-46 significantly enhanced hair re-growth and wound-induced hair follicle neogenesis (WIHN). These results indicate that simultaneous activation of the PKM2 and Wnt/β-catenin signaling could be a potential strategy for treating alopecia patients. Full article

The demand for categorising technology that requires minimum manpower and equipment is increasing because a large amount of waste is produced during the demolition and remodelling of a structure. Considering the latest trend, applying an artificial intelligence (AI) model for automatic categorisation is the most efficient method. However, it is difficult to apply this technology because research has only focused on general domestic waste. Thus, in this study, we delineate the process for developing an AI model that differentiates between various types of construction waste. Particularly, solutions for solving difficulties in collecting learning data, which is common in AI research in special fields, were also considered. To quantitatively increase the amount of learning data, the Fréchet Inception Distance method was used to increase the amount of learning data by two to three times through augmentation to an appropriate level, thus checking the improvement in the performance of the AI model. Full article

In the era of the Fourth Industrial Revolution, artificial intelligence (AI) is a core technology, and AI-based applications are expanding in various fields. This research explored the influencing factors on end-user’s intentions and acceptance of AI-based technology in construction companies using the technology acceptance model (TAM) and technology–organisation–environment (TOE) framework. The analysis of end-users’ intentions for accepting AI-based technology was verified by applying the structure equation model. According to the research results, the technological factors along with external variables and an individual’s personality had a positive influence (+) on the perceived usefulness and the perceived ease of use of end-users of AI-based technology. Conversely, environmental factors such as suggestions from others appeared to be disruptive to users’ technology acceptance. In order to effectively utilise AI-based technology, organisational factors such as the support, culture, and participation of the company as a whole were indicated as important factors for AI-based technology implementation. Full article

The paper examines that many human resources are needed on the research and development (R&D) process of artificial intelligence (AI) and discusses factors to consider on the current method of development. Labor division of a few managers and numerous ordinary workers as a form of light industry appears to be a plausible method of enhancing the efficiency of AI R&D projects. Thus, the research team regards the development process of AI, which maximizes production efficiency by handling digital resources named ‘data’ with mechanical equipment called ‘computers’, as the digital light industry of the fourth industrial era. As experienced during the previous Industrial Revolution, if human resources are efficiently distributed and utilized, no less progress than that observed in the second Industrial Revolution can be expected in the digital light industry, and human resource development for this is considered urgent. Based on current AI R&D projects, this study conducted a detailed analysis of necessary tasks for each AI learning step and investigated the urgency of R&D human resource training. If human resources are educated and trained, this could lead to specialized development, and new value creation in the AI era can be expected. Full article

Conventionally, the number of steel rebars at construction sites is manually counted by workers. However, this practice gives rise to several problems: it is slow, human-resource-intensive, time-consuming, error-prone, and not very accurate. Consequently, a new method of quickly and accurately counting steel rebars with a minimal number of workers needs to be developed to enhance work efficiency and reduce labor costs at construction sites. In this study, the authors developed an automated system to estimate the size and count the number of steel rebars in bale packing using computer vision techniques based on a convolutional neural network (CNN). A dataset containing 622 images of rebars with a total of 186,522 rebar cross sections and 409 poly tags was established for segmentation rebars and poly tags in images. The images were collected in a full HD resolution of 1920 × 1080 pixels and then center-cropped to 512 × 512 pixels. Moreover, data augmentation was carried out to create 4668 images for the training dataset. Based on the training dataset, YOLACT-based steel bar size estimation and a counting model with a Box and Mask of over 30 mAP was generated to satisfy the aim of this study. The proposed method, which is a CNN model combined with homography, can estimate the size and count the number of steel rebars in an image quickly and accurately, and the developed method can be applied to real construction sites to efficiently manage the stock of steel rebars. Full article

Construction waste generation along with the extensive consumption of natural resources has propelled researchers to investigate effective measures for minimising the waste. While several studies have shown that the structural design would be an influencing factor on the carbon dioxide emissions of a building, there is a lack of studies to corroborate the effect of different structural systems to generate waste during the construction stage. This article seeks to bridge some of the knowledge gaps regarding the waste generation from different structural systems during the construction phase in a building project in South Korea and demonstrate its potential for waste reduction. In this study, the amount of waste generation during the construction phase was calculated based on the quantities and the material loss rate of each building material to estimate the quantity of construction waste by the changes in the application of different structural systems for the slab of the studied model. The total waste generation during the construction phase of the different slab systems shows that the solid slab system produces the largest amount of construction waste, which is 101,361.385 kg. On the other hand, the void slab system generates 87,603.958 kg of the construction waste, which is the lowest amount among the four variables of this study. The additional purchasing costs due to the loss of construction materials indicate that the solid slab system would require 80,709.76 USD, which is the highest value of the four variables in this study. The void slab system would cost USD 50,054.12 for additional materials purchasing costs, which is approximately 38% lower than the solid slab system. Full article

Since the life cycle of a building spans more than 50 years, studies of the environmental impacts in the construction industry have focused on reducing the energy consumption and greenhouse gas emissions during the operation and maintenance phase. The products of the construction industry are assembled using various building materials manufactured outside of the construction site. Consequently, it is essential that the manufactured building materials be transported to the construction site using various types of transportation methods. However, there is a lack of studies that assess the pollutant emissions of road transport while executing a construction project. The purpose of this study is to investigate the changes in the road pollutant emissions when the old diesel vehicles for transporting building materials are replaced according to enhanced pollutant emission regulations. In this study, we found that approximately 89, 64, 77, and 64% of NO_x, VOC, PM, and CO, respectively, were emitted during transportation of building materials as a proportion of the emissions during the construction of the structure. The analyzed results also show that about 10, 35, 23, and 35% of NO_x, VOC, PM, and CO, respectively, were generated from material transportation as a proportion of the emissions from finishing the work. It is expected that a reduction in pollutant emissions from transporting building materials of up to approximately 64, 39, 49, and 27% of NOx, VOC, PM, and CO, respectively, can be achieved when vehicles registered before 2003 are replaced with ones that adhere to the tightened regulations. Full article