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Dominance of Leading Business Schools in Top Journals: Insights for Increasing Institutional Representation
Full text linkThe competitive push for business schools to publish in prestigious journals has resulted in a disproportionate number of papers in prestigious Management and Operations Research/Management Science (OR/MS) journals coming from a select group of institutions. Our analysis shows the Matthew effect of prestigious journals favors established schools with 51.2% of papers in 18 Management ABS 4* journals and 61.3% of papers in 3 OR/MS ABS 4* journals involving authors from the 100 top business schools identified by the University of Texas at Dallas (UTD). Citation patterns are similarly concentrated among papers authored by scholars from UTD-listed business schools, with nearly 80% of citations from 4* Management journals directed to equally rated 4* Management journals (67.8% for 4* OR/MS journals). An initial regression analysis suggested a positive correlation between the percentage of papers in a journal attributed to authors affiliated with those leading business schools and journal citation performance. However, further examination using multi-level regression adjusted for journal prestige, using the ABS and FT50 lists, showed a negative interaction effect on citation rates for papers from these schools in prestigious OR/MS journals. This insightful finding was confirmed by a post-hoc comparison revealing no significant citation advantage in prestigious journals for papers from leading business schools over those from a broader range of institutions. Thus, while leading business schools benefit from disproportionate space in prestigious journals, this does not translate to a citation advantage for the journals themselves, indicating no Matthew effect at the journal level driven by these schools. We argue that our findings show a unique opportunity for prestigious journals and business schools to expand collaborations with institutions in geographies historically underrepresented without a significant impact on the citation performance of those journals. This inclusion would only benefit research excellence, as our results demonstrate convergence in citation rates, citation patterns on external research areas, and topics across both subsets of papers—from leading institutions and those from a broader institutional spectrum—published in prestigious journals, indicating that diversifying contributions does not compromise the performance of these journals
Incorporating Insulin Into Alginate-Chitosan 3D-Printed Scaffolds: A Comprehensive Study on Structure, Mechanics, and Biocompatibility for Cartilage Tissue Engineering
Full text linkOsteoarthritis is a leading cause of disability worldwide, challenging current treatments to limited cartilage self-healing capacity. Cartilage tissue engineering (CTE) integrates cells, scaffolds, and signaling molecules, with Insulin being utilized as a differentiation biomolecule due to cost-effectiveness, dose-dependent influence on chondrogenesis, suitable biological activity, and ability to activate relevant receptors. Yet, administering differentiation biomolecules through conventional scaffolds poses a persistent challenge. Alginate (Alg) is commonly employed in CTE for its biocompatibility, though it lacks sufficient mechanical properties. Chitosan (Cs), while enhancing scaffold mechanical properties, but does not independently provide optimal support for chondrogenesis. While Alg-Cs scaffolds have garnered attention, challenges persist in achieving sustained differentiation biomolecules delivery and attaining suboptimal structural and biological properties for cartilage regeneration. This study utilizes advanced scaffolds by employing three dimensional (3D) printing technique to create Insulin-loaded Alg-Cs scaffolds, examining their structural, mechanical, and release properties, and assessing cell viability and chondrogenic differentiation through markers like COL1A1, COL2A1, SOX9 and ACAN. Following a 30-day implantation period, we also evaluate histological parameters. The findings revealed that the incorporation of a 20 (μg/ml) dose of Insulin into Alg-Cs 3D-printed scaffolds significantly enhanced the expression of these markers, indicating improved chondrogenic potential for cartilage regeneration. Histological analysis confirmed favorable biocompatibility and structural integrity of Insulin-loaded pure Alg and Alg-Cs scaffolds at drug loading levels of up to 20 and 10 μg, respectively. The hypothesis suggests that these advanced scaffolds can achieve controlled Insulin release, enhancing cartilage regeneration. This research aims to develop to yield mechanically optimized, bioactive 3D-printed scaffolds for regulated delivery of Insulin to promote cartilage regeneration
Morphing Shape Metastructures Using The Hybrid Position Feedback Control and Bistable Structural Elements
Full text linkThis research introduces a new multi-degree-of-freedom metastructure concept utilizing a hybrid position feedback controller. An arbitrary number of bistable segments, or structural elements, are connected serially or in parallel to form a distributed bistable structure, or a so-called metastructure. The hybrid controller, an unstable-then-stable second-order single-degree-of-freedom system, leverages the resonant mode of a bistable system to destabilize it and dynamically induce snap-through between equilibria. The metastructure inherits multiple bistable positions, maintaining equilibrium shapes without consuming power and enabling many equilibrium configurations. Two geometric configurations are proposed and analyzed using a modified hybrid feedback control approach, which rejects forces from neighboring degrees of freedom and linearizes the system around a user-specified target equilibrium. Despite the added complexity compared to the previous approach by the authors, the modified controller facilitates robust control. Parametric analyses identify controller parameters for successful shape adaptation. The simplicity of the baseline hybrid controller enables physical implementation with simple circuit elements integrated into the structure
Advanced Nanocomposite-Based Electrochemical Sensor for Ultra-Sensitive Dopamine Detection in Physiological Fluids
Full text linkThis study presents a novel point-of-care electrochemical sensor for dopamine (DA) detection, featuring a flexible laser-induced graphene (LIG) modified with a unique nanocomposite comprising Nb4C3Tx MXene, polypyrrole (PPy), and iron nanoparticles (FeNPs). The LIG-Nb4C3Tx MXene-PPy-FeNPs is characterized by scanning electron microscopy to confirm the successful surface modification. The electrochemical performance of the fabricated sensor via cyclic voltammetry showed significant electrochemical activity upon Nb4C3Tx MXene-PPy-FeNPs nanocomposite modification of the LIG surface with an increased peak anodic current (Ipa) from 43 μA to 104 μA. The sensor demonstrated high electrocatalytic activity and a wide linear detection range of 1 nM to 1 mM DA with excellent sensitivity of 0.283 μA/nM cm−2, and an ultralow detection limit of 70 pM. The LIG-Nb4C3Tx MXene-PPy-FeNPs sensor exhibited good recovery in biological samples and a remarkable selectivity for DA, effectively distinguishing it from common interfering compounds such as uric acid, ascorbic acid, glucose, sodium chloride, and their mixtures. This flexible LIG-Nb4C3Tx MXene-PPy-FeNPs sensor platform provides a reliable and accurate approach for detecting DA, even in complex biological matrices at point-of-care applications highlighting its potential for advanced biosensing applications
A Spoofing Speech Detection Method Combining Multi-Scale Features and Cross-Layer Identification
Full text linkPre-trained self-supervised speech models can extract general acoustic features, providing feature inputs for various speech downstream tasks. Spoofing speech detection, which is a pressing issue in the age of generative AI, requires both global information and local features of speech. The multi-layer transformer structure in pre-trained speech models can effectively capture temporal information and global context in speech, but there is still room for improvement in handling local features. To address this issue, a speech spoofing detection method that integrates multi-scale features and cross-layer information is proposed. The method introduces a multi-scale feature adapter (MSFA), which enhances the model’s ability to perceive local features through residual convolutional blocks and squeeze-and-excitation (SE) mechanisms. Additionally, cross-adaptable weights (CAWs) are used to guide the model in focusing on task-relevant shallow information, thereby enabling the effective fusion of features from different layers of the pre-trained model. Experimental results show that the proposed method achieved an equal error rate (EER) of 0.36% and 4.29% on the ASVspoof2019 logical access (LA) and ASVspoof2021 LA datasets, respectively, demonstrating excellent detection performance and generalization ability
A Study of Differences in Enjoyment, Exercise Commitment, and Intention to Continue Participation Among Age Groups of Adult Amateur Golfers
Full text linkGolf is one of the leisure sports that offers various benefits and has become a popular public sport with participation from people of various age groups. A total of 262 questionnaires were distributed online, with 240 valid responses collected after excluding 22 with non-responses or partial answers. The results showed statistically significant differences in enjoyment by age group, except for recognition from others. Regarding physical health, those in their 60s and above had higher mean scores than other age groups, and those in their 50s scored higher than those in their 30s. Stress relief was greater among those in their 50s than among those in their 20s and 30s, while socialization was higher among those in their 50s than in their 20s. However, for skill improvement, participants in their 20s scored higher than those in their 50s and 60s and above. Exercise commitment and intention to continue participation also varied significantly by age group, with older participants generally scoring higher. In conclusion, the results of this study revealed significant differences in psychological factors among the age groups. Specifically, amateur golfers aged 50 and above showed higher mean scores in all variables compared to younger groups
Reliability of Popliteal Artery Flow-Mediated Dilation in the Seated Position
Full text linkFlow-mediated dilation (FMD), a noninvasive measure of endothelial function, is commonly measured in the popliteal artery in the supine and upright body positions. However, reliability studies of the measurement in these positions are not well studied. This study aimed to examine the trial-retrial and visit-to-visit reliability of popliteal artery FMD in the seated position. Popliteal artery FMD was measured in 20 healthy adults across two visits in the seated and prone positions to assess visit-to-visit reliability. Two measurements were taken for each body position at each visit to assess trial-retrial reliability. %FMD was calculated as the percent change from baseline diameter to peak diameter. The reliability of FMD was assessed via the intraclass correlation coefficient (ICC). Popliteal artery %FMD shows moderate-to-good reliability in the seated position (ICC: 0.67-0.89) and poor-to-moderate reliability in the prone position (ICC: 0.25-0.74) within and between visits. There were no significant differences in baseline diameter or minimum diameter between body positions, visits, or trials (p \u3e 0.05). %FMD and peak diameter following cuff deflation demonstrated no significant difference between body positions (p \u3e 0.05). Popliteal artery FMD demonstrates good reliability in the seated position, supporting the development of a standardized measurement protocol
Forecasting COVID-19 Cases, Hospital Admissions, and Deaths Based on Wastewater SARS-CoV-2 Surveillance Using Gaussian Copula Time Series Marginal Regression Model
Full text linkModeling efforts are needed to predict trends in COVID-19 cases and related health outcomes, aiding in the development of management strategies and adaptation measures. This study was conducted to assess whether the SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalizations, and deaths using copula-based time series modeling. SARS-CoV-2 RNA load in wastewater in Chesapeake, VA, was measured using the RT-qPCR method. A Gaussian copula time series (CTS) marginal regression model, incorporating an autoregressive moving average model and Gaussian copula function, was used as a forecasting model. Wastewater SARS-CoV-2 viral loads were correlated with COVID-19 cases. The forecasted model with both Poisson and negative binomial marginal distributions yielded trends in COVID-19 cases that closely paralleled the reported cases, with 90% of the forecasted COVID-19 cases falling within the 99% confidence interval of the reported data. However, the model did not effectively forecast the trends and the rising cases of hospital admissions and deaths. The forecasting model was validated for predicting clinical cases and trends with a non-normal distribution in a time series manner. Additionally, the model showed potential for using wastewater SARS-CoV-2 viral load as a predictor for forecasting COVID-19 cases