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The Power and influence of image and public relations in professional life
No full textClick on the DOI link to access this article at the publishers website (may not be free).[No abstract available
Exiting ergodicity
No full textAbstract for book: This innovative Handbook presents a comprehensive overview of the significance of complexity theory for understanding institutions. Eminent scholars cover the key tools and concepts of the field, including emergence, networks, ergodicity, and modularity, exploring their contributions to institutional formulation and evolution. Abstract for chapter: The bulk of dynamical models in economic theory are ergodic. Behavioral models constructed using expected utility theory, including all standard macroeconomic models, are ergodic. However, models must exit ergodicity to explain creative social phenomena like institutional emergence, self-organization, innovation, and catallaxy. Achieving theoretical escape velocity from ergodicity is no easy matter, but promises a rich new frontier to explore the formation, sustenance, and breakdown of economic institutions. This article is organized as an exposition for researchers new to ergodic theory and issues surrounding ergodicity and institutional economics
Comparable muscle fatigue responses across different low-load blood flow restriction protocols among women
No full textClick on the DOI link to access this article at the publishers website (may not be free).Purpose: Low-load blood flow restricted (LLBFR) resistance exercise has been demonstrated to accelerate acute muscle fatigue, but these responses may be dependent upon the protocol used. The purpose of this investigation was to examine fatigue characteristics following acute LLBFR resistance exercise with a 75-repetition (75-rep; 1 × 30, 3 × 15), 3 sets to failure (3×), and 1 set to failure (1×) protocols. Methods: Sixteen women randomly performed 75-rep, 3×, and 1× LLBFR protocols consisting of unilateral, submaximal (30% of maximal voluntary isometric contraction; [MVIC]), isokinetic (90°·s−1), leg extension muscle actions. Separate two-way, 3 (Condition [75-rep, 3×, 1×]) × 2 (Time [Pretest, Posttest]), repeated-measure ANOVA models were used to examine MVIC, peak twitch torque (PTT), surface electromyography amplitude (sEMG AMP), voluntary activation (VA), and Vwave/Mwave ratio. Results: There were no significant (p = 0.516–0.984) interactions for any of the fatigue characteristics. Collapsed across Conditions however, MVIC torque (21.1%), PTT (11.0%), sEMG AMP (1.9%), and VA (4.6%) decreased across Time (p < 0.001–0.011). There was no change in Vwave /Mwave ratio (p = 0.639–0.822). Conclusions: Despite differences in set and repetition schemes, all three LLBFR protocols induced comparable decreases in MVIC torque, PTT, sEMG AMP, and VA. The current results highlight the potential efficacy of a single set of LLBFR performed to volitional failure to provoke fatigue responses comparable to multiple set protocols. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025
Advanced Education Program in General Dentistry graduates 2013-2014
No full textSchool composite: students included in composite: Anna Grimmelsman, Jenelle Silvers, James Michael Womack, Valeriya Greenwood.Digitized by University Libraries' Technical Services Institutional Repository & Digitization group.Personal and non-profit use only. Contact [email protected] if you have any questions
Episode 36 – Coach Chris Lamb (V) and Coach Terry Nooner (WBB)
No full textJoin Wichita State President Rick Muma when he talks with women’s basketball Coach Terry Nooner and volleyball Coach Chris Lamb about their seasons, athletes and what it means to be part of Shocker Athletics.The “Forward Together” podcast celebrates the vision and mission of Wichita State University. In each episode, President Rick Muma will talk with guests from throughout Shocker Nation to highlight the people and priorities that guide WSU on its road to becoming an essential educational, cultural, and economic driver for Kansas and the greater good
Development and testing of a target program monitoring system
No full textPoster and abstract presented at the FYRE in STEM Showcase, 2025.Research project completed at the Department of Mathematics, Statistics and Physics.As part of the Neutrino Solar Orbiting Laboratory (nuSOL) mission, the SNAPPY CubeSat detector (a 3U nanosatellite) is expected to launch in low Earth orbit in 2025. It will be gathering data to support the detection of solar neutrinos, elusive subatomic particles that are created inside the Sun. In this project, an automated program monitoring system was developed with Python to ensure programs in the CubeSat function successfully. At 10-minute intervals, the system checks if a chosen target program is running. When the program is not detected, the system automatically restarts it to continue operation. If the target program fails to run after the first attempt, the system carries out two further restarts with a configured delay time to prevent overexertion on the system. After three failed attempts to restart the program, a lock file is created and diagnostics are logged. Varying restart delay times (1, 5, 15, and 30 seconds) were tested, confirming the downtime is proportional to the configured restart delay. Furthermore, the time the system takes to restart a program was found to be consistent between 2-3 seconds. Implementing this monitor system allows for the continuous collection of data in space. More development and testing will ensure long-term reliability
High-efficiency electrodialysis for enriching potassium nitrate with low concentrations
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Mechanical EngineeringNitrate is a toxic yet valuable ionic species, and nitrate capture from low-concentration streams presents an opportunity for resource recovery and a research challenge. Building on our previous study on nitrate concentrating at moderate concentration (7 mM), this study focused on even lower nitrate concentration down to 0.5 mM by advancing the electrodialysis system with tailored membrane choices and careful electrodialysis design aiming for efficient KNO3 concentrating.
First, comprehensive study was conducted on the counter-ion diffusion across nine commercial AEMs and CEMs, revealing distinctive diffusion behavior across different types of membranes, pinpointing FAS−PET−130 and FKL−PK−130 as the optimal pair in constructing ED cell for nitrate electrodialysis due to low counter-ion diffusion. Based on the counter-ion diffusion slope, the maximum concentrating ratio (Cc/Cs) of 126 for the membrane combination. With a safe design margin, a maximum ratio of 100 for Cc/Cs was adopted in the following electrodialysis study. Theoretical pair voltages were accounted for Donnan and Ohmic contributions between experimental observations and theoretical prediction. The source solution and concentrate solution were explored to ensure high coulombic efficiency under various pair voltages.
In particular, with an ultralow source concentration of 0.5 mM (i.e., 7 ppm nitrate-N), the successful nitrate concentrating was demonstrated with 50 mM of concentrate solution at 0.1 mA/cm2, with 96%−98% of coulombic efficiency. A low level of pair volage of −0.27 V at beginning to −0.85 V after 1,930 s was observed, which are close with theoretical predictions (−0.29 V to −0.70 V), confirming the highly efficient nitrate concentrating. These results demonstrate the feasibility of selective ion recovery from dilute streams and support future work on system durability and scale-up
A comparative analysis of network modelling approaches for predicting performance in large scale networks
No full textClick on the DOI link to access this article at the publishers website (may not be free).choosing the right node to measure is the key to success in large-scale networks; network modeling is a critical tool for detecting performance. The growing size and complexity of modern networks make accurately predicting their performance a crucial requirement for efficient and reliable operation. Numerous network modeling approaches, however, each with unique strengths and limitations, have emerged in response to this. This paper provides a comparative critique of these network modeling approaches and their performance prediction capabilities in large-scale networks. The first category is the analytical modeling approach, where mathematical equations represent network performance. An approach similar to the described process gives very accurate and detailed predictions but must rely on a theoretical understanding of how networks behave and the assumption of ideal networks. The second approach, simulation modeling, designs a virtual model of the network and lets you test it to predict its performance. This allows for greater flexibility, and real-world factors can be included in the heuristics; however, it is potentially very computationally intensive. The third method is machine learning, which involves algorithms that learn patterns in historical network data and create predictions. While this method is able to process intricate data sets and adjust to evolving network states, its precision relies heavily on the quality of the training data. © 2025 IEEE