Konyang University (건양대학교; RR: Geonyang), located in Nonsan, South Korea, was founded in 1991.Konyang University has campuses in Nonsan and Daejeon. The Nonsan campus is the main campus with over 10,000 undergraduate students, eight colleges with more than forty majors, mostly in applied fields About 230 of the students are foreign students. The Daejeon campus has programs in Western medicine, medical technology, and nursing. The university has about 700 graduate students. It is accredited by the Korean Council for University Education .In 2011, Konyang was one of four private ROK universities that began offering military science as a major.The university president is Dr. Kim Hi-soo, recipient of the 2006 South Korean Mugunghwa Order of Civil Merit Medal (국민훈상무궁화장) prize for outstanding citizen, for his work in fostering education.In 2013, as part of a major reorientation of the nation's tertiary educational system and in part in response to the significantly changing demographics, the country's government announced a change in financial support to both public and private universities. Schools that have not been performing well would have their support drastically cut. At the same time, institutions could apply or grants to support specific projects. The winning schools were announced in mid-2014. Konyang had submitted seven different projects. All of them were funded in full, for a total of 49 billion won in support.
Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due to limited access to dual-energy X-ray absorptiometry (DXA) in many areas. Artificial intelligence (AI) offers new opportunities to improve osteoporosis screening and management, but unvalidated tools pose risks of inconsistent care. This consensus was developed to provide regionally harmonized guidance on the safe, effective, and equitable use of AI in osteoporosis care. Purpose The aim of this work was to establish expert consensus recommendations on the role of AI in osteoporosis screening and management in the Asia-Pacific region. Key objectives were to define appropriate applications of AI (e.g., imaging-based bone assessment and fracture risk prediction) and specify minimum standards for validation and reporting, addressing region-specific implementation challenges and ensuring that AI use aligns with clinical guidelines and ethical principles. Methods This consensus was developed through multidisciplinary collaboration among experts across the Asia-Pacific region. Each participant reviewed draft statements, contributed feedback during virtual meetings, and provided insights based on clinical experience and current evidence. Consensus was reached iteratively until full agreement was achieved for all statements. The process integrated global best practices and regional adaptations, drawing from peer-reviewed studies, international AI guidelines, and local fracture registry data. The final recommendations emphasize the validation, transparency, and ethical implementation of AI within regional healthcare systems, ensuring compatibility with local regulations. Ultimately, twelve consensus statements were established to guide the responsible use of AI for osteoporosis screening and management in the Asia-Pacific region. Results The panel produced 12 consensus statements covering the role of AI as an adjunct for opportunistic osteoporosis screening rather than a diagnostic tool, requirements for imaging quality and AI model transparency, standards for validation and performance reporting, integration of AI with clinical risk stratification, demonstration of clinical utility in real-world settings, adherence to data protection laws and ethical AI principles, training of clinicians in AI use, strategies for implementation and monitoring (including post-market surveillance and feedback loops), and recognition of technical, clinical, and equity limitations of AI. All 12 statements give extensive recommendations for using AI to improve osteoporosis management while ensuring patient safety, accuracy, and equity. Conclusion This first Asia-Pacific consensus on AI in osteoporosis concludes that AI, when appropriately validated and implemented, can help bridge the osteoporosis care gap by identifying high-risk patients who would otherwise remain undiagnosed, thus facilitating earlier intervention. It emphasizes that AI should complement-not replace-standard diagnostic methods and clinical judgment. The guidance emphasizes validation, transparency, and ethical oversight to facilitate early intervention while minimizing risks associated with unvalidated or premature AI adoption.
BackgroundToilet flushing in public restrooms generates bioaerosols that may contain pathogenic microorganisms, posing potential risks for airborne infection transmission. Conventional control strategies such as ventilation and disinfection primarily function after contaminants have already dispersed, offering limited preventive efficacy.objectiveThis study is aimed at scientifically verifying the effectiveness of a negative-pressure toilet system applying the source control principle, which captures and removes contaminants at the point of generation.MethodsTo simulate bioaerosol behavior, ammonia (NH3) and hydrogen sulfide (H2S) were used as surrogate gases due to their comparable aerodynamic properties. The performance of the Etish-D1st system was evaluated under the official testing protocols of the Korea Conformity Laboratories (KCL), focusing on odor gas removal efficiency, suction flow rate, and noise level under operating and nonoperating conditions.ResultsWhen the system was activated, NH3 concentration inside the toilet was reduced to a nondetectable level (<= 5 ppm) within 10 min, whereas nonoperating conditions reached up to 45 ppm. The mean suction flow rate was 114 mL/s, and the average noise level was 48 dB, indicating both high collection efficiency and environmental compatibility for public use.ConclusionsThe negative-pressure system effectively contained gaseous contaminants at the emission source, reducing their concentrations to below the 5-ppm detection limit and preventing measurable dispersion into the surrounding air. These findings provide a scientific foundation for active prevention in public health engineering. Future policy integration into the Public Restroom Act and Building Facility Standards, along with R&D and certification support, would strengthen institutional adoption. The Etish-D1st system presents an innovative sanitary technology with potential to enhance restroom hygiene and establish a new international standard for infection prevention infrastructure.
This study examines the community-level determinants of persistently low fertility across Seoul's districts, a pivotal case in the Asia-Pacific demographic landscape. We advance a place-based, community-oriented framework by conceptualising fertility as a contextual outcome shaped by local institutional environments and collective social capital. Using Seoul's 25 districts as a bounded comparison, we analyse how social-enterprise capacity and municipal ESG performance (Environmental-Social-Governance) intersect with socioeconomic conditions to foster higher fertility. Utilising a unique integrated dataset of national vital statistics, ESG evaluations and administrative records, we apply fuzzy-set Qualitative Comparative Analysis (fsQCA). The analysis reveals no single necessary condition but identifies two sufficient pathways: (1) a synergy of robust environmental/governance performance and civic volunteering, which sustains fertility despite weaker marital and employment profiles; and (2) dense social-enterprise ecosystems coupled with strong environmental/social performance that offset governance deficits in diversity-intensive districts. These findings demonstrate equifinality, suggesting that effective demographic interventions require differentiated, place-based policy mixes rather than monolithic, uniform strategies.
carbon nitride (g-C3N4) photocatalyst has a relatively narrow band gap of 2.80 eV, which makes it active in the visible light range. Furthermore, the strong covalent bonds between carbon and nitrogen atoms provide excellent thermochemical stability. However, g-C3N4 photocatalyst still has a high band gap, which hinders its use as a visible light photocatalyst. To overcome this limitation, in this study, melamine was used as a starting material and the g-C3N4 photocatalyst was synthesized by a high-temperature reaction at 520 degrees C. To this, alkali metal hydroxides (LiOH, NaOH, KOH) were added, respectively, followed by heat treatment at 500 degrees C to synthesize alkali metal ion-doped M-g-C3N4 photocatalysts. The physical and chemical properties of the photocatalysts were then evaluated using various analytical equipments, including FT-IR, XPS, XRD, and UV-Vis spectrometer. XRD analysis confirmed a strong peak at 27.5 degrees, which indicated that the g-C3N4 photocatalyst was successfully synthesized. In addition, the alkali metal-doped M-g-C3N4 photocatalysts had narrower band gaps than the alkali metal-free g-C3N4 photocatalyst (2.80 eV), and among them, the band gap energy was found to be the lowest at 2.56 eV when K metal was doped. As the band gap energy decreases, electrons and holes can be easily generated by photoexcitation even with low-energy light, so it is judged that the alkali metal-doped M-g-C3N4 photocatalysts developed through this study can be used as effective visible-light photocatalysts.
The component specification problem—the mismatch between bioactivity observed in chemically unresolved extracts and the constituent-level chemical definition required for reliable artificial intelligence (AI) inference—remains a key bottleneck in computational natural product (NP) discovery. Without operational standards linking analytical evidence to AI strategy, even sophisticated models risk overinterpretation when applied to incompletely resolved extracts. Here, we introduce a data-quality-guided framework that aligns AI approaches with Metabolomics Standards Initiative (MSI) identification tiers. Informed by emerging case studies across MSI levels, this strategy enables researchers to match computational tools to available analytical resolution rather than exceed it. By integrating ethnopharmacological knowledge and multi-omics validation, the framework positions AI as a calibrated decision-support instrument for translational NP research.
