Business Model Innovation in the Trading Card Grading Industry: Cross-National Insights from Pokémon Trading Card Game and Non-fungible Tokens

This study examines how firms in the Pokémon Trading Card Game (PTCG) grading industry adapt their business models in response to digital disruption. We employ a qualitative multiple-case design, investigating three leading grading companies – PSA (United States), CCIC (China), and SQC (Thailand) – through 30 in-depth interviews and supplemental document analysis. The findings reveal divergent strategies shaped by both dynamic capabilities and institutional contexts. PSA leverages scale and AI technology to enhance efficiency, CCIC focuses on legitimacy and incremental improvements under regulatory constraints, and SQC pursues exploratory digital initiatives (e.g., NFT-linked trials) to co- create value with its community. These patterns highlight the ambidexterity required for business model innovation in a digitizing niche service sector. The study contributes to business model innovation and digital transformation literature by demonstrating how national institutions and customer engagement influence innovation paths. Practical implications include lessons for balancing core business sustainability with transformative innovation in different regulatory environments

Strengthening ASEAN-Guangxi Trade Relations: Enhancing Regional Integration and Industrial Collaboration

A complex array of global disruptions—including the COVID-19 pandemic, the US-China trade war, the Russia-Ukraine conflict, retaliatory tariffs, economic stagflation, supply chain breakdowns, and the rise of artificial intelligence technologies—has significantly challenged the foundational structure of regional economic development. This study investigates the key barriers hindering ASEAN–Guangxi trade from achieving sustainable and accelerated economic growth. Trade data from 2019 to 2024 were analyzed, and empirical data were collected through structured questionnaires administered to 200 business practitioners and policymakers across ASEAN member states and Guangxi. The data were processed and validated using the Statistical Package for the Social Sciences (SPSS). The results indicate that enhanced regional integration and the presence of positive spillover effects are pivotal in promoting sustainable trade relations between ASEAN and Guangxi. These findings offer actionable insights for companies operating in the region and serve as a valuable reference for policymakers and future researchers seeking to strengthen regional economic cooperation. This study contributes to the literature by identifying integration and spillovers as critical drivers of resilient regional trade amid contemporary global uncertainties

6-Bromoindirubin-3′-Oxime (6BIO) Suppresses the mTOR Pathway, Promotes Autophagy, and Exerts Anti-aging Effects in Rodent Liver

Liver aging is associated with age-related histopathological and functional changes that significantly enhance the risk of numerous diseases or disorders developing in elderly populations. 6-Bromoindirubin-3′-oxime (6BIO), a potent inhibitor of glycogen synthase kinase-3 (GSK-3), has been implicated in various age-related diseases and processes, such as tumorigenesis, neurodegeneration, and diabetes. Recent studies have also revealed that 6BIO increases autophagy in yeast, mammalian cell lines, and dopaminergic neurons, which is one of the classical mechanisms strongly associated with liver aging. However, the impact or the mechanism of action of 6BIO in liver remains entirely unknown. Here, we find that 6BIO reduces oxidative stress, improves lipid metabolism, enhances autophagy, and significantly retards liver aging via modulating the GSK-3β pathway and mTOR pathway. Our findings suggest that 6BIO could be a potential agent to protect the liver in the field of anti-aging pharmacology

Clinical Pregnancy and Incidence of Ovarian Hyperstimulation Syndrome in High Ovarian Responders Receiving Different Doses of hCG Supplementation in a GnRH-Agonist Trigger Protocol

Objective. Ovarian hyperstimulation syndrome (OHSS) is a side effect of the exogenous human chorionic gonadotropin (hCG) hormones used to trigger oocyte maturation. High ovarian responders represent a population with a higher risk of OHSS and are characterized by an exaggerated response to gonadotropin administration. In this study, we compared clinical pregnancy and incidence of OHSS in high ovarian responders receiving different doses of hCG supplementation in a GnRH-agonist trigger protocol. Methods. A total of 205 high ovarian responders who were to undergo in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycles were recruited and randomly assigned to receive different doses of hCG supplementation in a GnRH-agonist trigger protocol: GnRH-a (n = 42), GnRH-a + 1000 IU hCG (n = 49), GnRH-a + 2000 IU hCG (n = 54), and GnRH-a + 3000 IU hCG (n = 60) groups. Results. The GnRH-a + 1000 IU hCG, GnRH-a + 2000 IU hCG, and GnRH-a + 3000 IU hCG groups had more oocytes retrieved, embryos, high-quality embryos, and a higher rate of high-quality embryos than the GnRH-a group ( p &lt; 0.05 ). The GnRH-a + 1000 IU hCG group demonstrated more oocytes retrieved, embryos, high-quality embryos, and a higher rate of high-quality embryos than the GnRH-a + 2000 IU hCG and GnRH-a + 3000 IU hCG groups ( p &lt; 0.05 ). No moderate and severe OHSS cases occurred in the GnRH-a and GnRH-a + 1000 IU hCG groups. The incidence rate of moderate and severe OHSS was remarkably lower in the GnRH-a group and GnRH-a + 1000 IU hCG groups than in the GnRH-a + 2000 IU hCG and GnRH-a + 3000 IU hCG groups ( p &lt; 0.05 ). The GnRH-a + 1000 IU hCG, GnRH-a + 2000 IU hCG, and GnRH-a + 3000 IU hCG groups had a higher clinical pregnancy rate than the GnRH-a group, showing no significant difference ( p &gt; 0.05 ). The GnRH-a + 1000 IU hCG, GnRH-a + 2000 IU hCG, and GnRH-a + 3000 IU hCG groups had a lower abortion rate than the GnRH-a group ( p &lt; 0.05 ). Conclusion. Based on the data obtained from this prospective study, we recommend 1000 IU hCG supplementation in a GnRH-agonist trigger protocol for high ovarian responders during IVF/ICSI cycles considering a higher rate of high-quality embryos, a lower incidence rate of moderate and severe OHSS, and a lower abortion rate.</jats:p

Active fault-tolerant control of rotation angle sensor in steer-by-wire system based on multi-objective constraint fault estimator

Purpose Steer-by-wire (SBW) system mainly relies on sensors, controllers and motors to replace the traditionally mechanical transmission mechanism to realize steering functions. However, the sensors in the SBW system are particularly vulnerable to external influences, which can cause systemic faults, leading to poor steering performance and even system instability. Therefore, this paper aims to adopt a fault-tolerant control method to solve the safety problem of the SBW system caused by sensors failure. Design/methodology/approach This paper proposes an active fault-tolerant control framework to deal with sensors failure in the SBW system by hierarchically introducing fault observer, fault estimator, fault reconstructor. Firstly, the fault observer is used to obtain the observation output of the SBW system and then obtain the residual between the observation output and the SBW system output. And then judge whether the SBW system fails according to the residual. Secondly, dependent on the residual obtained by the fault observer, a fault estimator is designed using bounded real lemma and regional pole configuration to estimate the amplitude and time-varying characteristics of the faulty sensor. Eventually, a fault reconstructor is designed based on the estimation value of sensors fault obtained by the fault estimator and SBW system output to tolerate the faulty sensor. Findings The numerical analysis shows that the fault observer can be rapidly activated to detect the fault while the sensors fault occurs. Moreover, the estimation accuracy of the fault estimator can reach to 98%, and the fault reconstructor can make the faulty SBW system to retain the steering characteristics, comparing to those of the fault-free SBW system. In addition, it was verified for the feasibility and effectiveness of the proposed control framework. Research limitations/implications As the SBW fault diagnosis and fault-tolerant control in this paper only carry out numerical simulation research on sensors faults in matrix and laboratory/Simulink, the subsequent hardware in the loop test is needed for further verification. Originality/value Aiming at the SBW system with parameter perturbation and sensors failure, this paper proposes an active fault-tolerant control framework, which integrates fault observer, fault estimator and fault reconstructor so that the steering performance of SBW system with sensors faults is basically consistent with that of the fault-free SBW system. </jats:sec

Emerging Roles of Plant Circular RNAs

Circular RNAs (circRNAs) are covalently closed single-stranded loop RNA molecules with or without protein coding capability. CircRNAs were previously considered to be splicing intermediates or artifacts but are now found to be pervasively expressed in all eukaryotes studied with some demonstrated to have important molecular functions in various biological processes. CircRNA is now a hot study topic of molecular biology. In this review, we summarize the progress achieved so far on plant circRNAs, including identification and functional characterization, compare the similarities and differences of circRNAs between plants and animals, and discuss the challenges for confident detection and functional investigation of plant circRNAs. Similar to what have been found in animals, plant genomes contain a large number of circRNAs that potentially regulate a wide range of biological progresses related to plant development and biotic/abiotic responses. Despite only a few plant circRNAs have been functionally characterized, novel function/mechanism that has not been reported in animals was revealed, implying more exciting findings about plant circRNAs are expected in future studies.</jats:p

Simultaneous Quantification and Pharmacokinetic Study of Five Homologs of Dalbavancin in Rat Plasma Using UHPLC-MS/MS

Dalbavancin is a novel semisynthetic glycopeptide antibiotic that comprises multiple homologs and isomers of similar polarities. However, pharmacokinetic studies have only analyzed the primary components of dalbavancin, namely B0 and B1. In this study, an ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was developed to simultaneously determinate and investigate the five homologous components of dalbavancin, namely, A0, A1, B0, B1, and B2, in rat plasma. In this method, methanol was used to precipitate plasma, and a triple-bonded alkyl chromatographic column was used for molecule separation, using 0.1% formic acid-acetonitrile as the mobile phase for gradient elution. Targeted homologs were analyzed by a triple quadrupole mass spectrometer using positive electrospray ionization in multiple reaction monitoring mode. The linearity range was 50–2500 ng/mL with a high correlation coefficient (r2 &gt; 0.998). This method was successfully applied in the pharmacokinetic analysis of dalbavancin hydrochloride to investigate dalbavancin components in rats.</jats:p

Effective Eyebrow Matting with Domain Adaptation

We present the first synthetic eyebrow matting datasets and a domain adaptation eyebrow matting network for learning domain-robust feature representation using synthetic eyebrow matting data and unlabeled in-the-wild images with adversarial learning. Different from existing matting methods that may suffer from the lack of ground-truth matting datasets, which are typically labor-intensive to annotate or even worse, unable to obtain, we train the matting network in a semi-supervised manner using synthetic matting datasets instead of ground-truth matting data while achieving high-quality results. Specifically, we first generate a large-scale synthetic eyebrow matting dataset by rendering avatars and collect a real-world eyebrow image dataset while maximizing the data diversity as much as possible. Then, we use the synthetic eyebrow dataset to train a multi-task network, which consists of a regression task to estimate the eyebrow alpha mattes and an adversarial task to adapt the learned features from synthetic data to real data. As a result, our method can successfully train an eyebrow matting network using synthetic data without the need to label any real data. Our method can accurately extract eyebrow alpha mattes from in-the-wild images without any additional prior and achieves state-of-the-art eyebrow matting performance. Extensive experiments demonstrate the superior performance of our method with both qualitative and quantitative results.Computer Graphics ForumImage Detection and Understanding41