As artificial intelligence (AI) systems are increasingly adopted in recruitment practices, applicants' responses to AI-mediated interviews have become an important issue for organizations. Understanding how applicants interpret these systems is relevant for organizational attractiveness and employer branding. Drawing on social exchange theory and signaling theory, this study examines the role of AI interview explainability in shaping applicants' evaluations of organizations. It proposes that explainability influences organizational attractiveness through two parallel mechanisms: perceived organizational support and perceived innovativeness. Survey data were collected from 196 job applicants with experience in AI-based interviews. The results show that higher perceived explainability of AI interviews is associated with stronger perceptions of organizational support and organizational innovativeness. Both perceptions are positively related to organizational attractiveness. These findings support a dual-mediation model and suggest that explainable AI interview systems communicate both supportive intentions and technological capability to applicants. By focusing on applicants' perceptions, this study contributes to the growing literature on AI use in human resource management. It highlights the importance of explainable system design in shaping early applicant reactions. The findings also provide practical implications for organizations seeking to implement AI-based recruitment tools that are transparent, credible, and attractive to potential applicants.
Maintaining bridge functionality requires timely retrofits to address both corrosion resistance and seismic performance. In general, retrofit planning may involve multiple bridges within a given region under the oversight of a bridge management agency. Therefore, retrofit strategies should be efficiently planned for multiple bridges in a network. This study introduces an approach for optimal seismic bridge column retrofit planning of multiple bridges in a bridge network. The optimisation is based on maximising the real option value (ROV). Real options analysis is conducted through cost-benefit assessments (CBA) of individual bridges in a bridge network. The CBA integrates time-variant fragility analyses and risk assessments for all individual bridges in a network, both with and without retrofitting, into a cumulative benefit. To efficiently address the time-dependent fragility of all individual bridges in a network, machine learning is utilised. ROVs for individual bridges are determined based on cumulative benefits. Aggregating the cumulative benefits of individual bridges estimates the ROVs for groups of bridges. Using the proposed approach, the necessity and optimal timing for bridge column retrofits can be determined for individual bridges, bridge groups, and the overall bridge network. The proposed approach is illustrated using existing bridges in a network located in South Korea.
Chitinases from Trichoderma species exhibit strong antifungal activity and high biocontrol potential, yet their industrial utilization has been constrained by low heterologous secretion efficiency and costly purification processes. In this study, the Generally Recognized As Safe yeast Saccharomyces cerevisiae Y2805 was engineered to secrete chitinase Tch36 from a Korean isolate of T. atroviride, using a rice α-amylase signal peptide under a constitutive glyceraldehyde-3-phosphate dehydrogenase (GPD) promoter. When cultivated in glycerol–colloidal chitin medium, the recombinant yeast exhibited approximately a fivefold increase in measurable chitinase activity relative to the SC mock control, whereas the empty-vector control showed only a modest increase. The crude, non-concentrated culture filtrate (approximately 1000 U L⁻1) displayed statistically significant antifungal activity against 12 fungal species, including 9 phytopathogens and 3 opportunistic Aspergillus species. Among the phytopathogens, Fusarium graminearum was one of the most strongly inhibited species, showing approximately 40–50
Real-world evidence on the effectiveness and safety of herbal medicines for type 2 diabetes mellitus (T2DM) remains limited despite promising results from controlled studies. This study aimed to evaluate the clinical outcomes of Hyeoldanggaesun-tang, a traditional Korean herbal formulation, in routine clinical practice. This multicenter retrospective chart review included 31 adults with T2DM who received Hyeoldanggaesun-tang continuously for 6 months across four Korean medicine clinics. Clinical and laboratory data were extracted at baseline and at 3 and 6 months. The primary outcome was change in glycated hemoglobin (HbA1c). The secondary outcomes included liver enzymes, lipid profiles, blood pressure, body mass index, and safety indicators. Mean HbA1c decreased significantly from 8.85 ± 2.01
This study investigates the elastic response and stability of axially functionally graded (AFG) tapered pinned columns under centroidal and eccentric axial loading. The columns are modeled with a linearly varying Young's modulus to capture AFG properties and a linearly tapered cross-section. Using Bernoulli-Euler beam theory, governing differential equations for elastic deflections and buckled mode shapes are derived, with numerical solutions obtained via the Runge-Kutta and shooting methods. The effects of key parameters-including modular ratio, taper ratio, eccentricity, and axial load-on elastic behavior and critical buckling loads are evaluated. For eccentric loading, initial rotation, maximum lateral deflection, and maximum bending moment are numerically examined. Results indicate that higher modular and taper ratios enhance stiffness and reduce deformations, while larger axial loads and end moments amplify responses. Slenderness and aspect ratio significantly influence maximum stress. For centroidal loading, critical buckling loads and stress distributions are assessed under varying material and cross-sectional properties. The findings provide a comprehensive framework for designing AFG tapered columns, ensuring structural stability and safe performance under diverse loading conditions.
