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Gamification is increasingly successful in the field of education and health. However, beyond call-centers and applications in human resources, its utilization within companies remains limited. In this paper, we examine the acceptance of gamification in a large company (with over 17,000 employees) across three generations, namely X, Y, and Z. Furthermore, we investigate which gamification elements are suited for business contexts, such as the dissemination of company principles and facts, or the organization of work tasks. To this end, we conducted focus group discussions, developed the prototype of a gamified company app, and performed a large-scale evaluation with 367 company employees. The results reveal statistically significant intergenerational disparities in the acceptance of gamification: younger employees, especially those belonging to Generation Z, enjoy gamification more than older employees and are most likely to engage with a gamified app in the workplace. The results further show a nuanced range of preferences regarding gamification elements: avatars are popular among all generations, badges are predominantly appreciated by Generations Z and Y, while leaderboards are solely liked by Generation Z. Drawing upon these insights, we provide recommendations for future gamification projects within business contexts. We hope that the results of our study regarding the preferences of the gamification elements and understanding generational differences in acceptance and usage of gamification will help to create more engaging and effective apps, especially within the corporate landscape.
Lithium-ion batteries exhibit slow voltage dynamics on the minute time scale that are usually associated with transport processes. We present a novel modelling approach toward these dynamics by combining physical and data-driven models into a Grey-box model. We use neural networks, in particular neural ordinary differential equations. The physical structure of the Grey-box model is borrowed from the Fickian diffusion law, where the transport domain is discretized using finite volumes. Within this physical structure, unknown parameters (diffusion coefficient, diffusion length, discretization) and dependencies (state of charge, lithium concentration) are replaced by neural networks and learnable parameters. We perform model-to-model comparisons, using as training data (a) a Fickian diffusion process, (b) a Warburg element, and (c) a resistor-capacitor circuit. Voltage dynamics during constant-current operation and pulse tests as well as electrochemical impedance spectra are simulated. The slow dynamics of all three physical models in the order of ten to 30 min are well captured by the Grey-box model, demonstrating the flexibility of the present approach.
Background:
Ankle braces aim to reduce lateral ankle sprains. Next to protection, factors influencing user compliance, such as sports performance, motion restriction, and users’ perceptions, are relevant for user compliance and thus injury prevention. Novel adaptive protection systems claim to change their mechanical behavior based on the intensity of motion (eg, the inversion velocity), unlike traditional passive concepts of ankle bracing.
Purpose:
To compare the performance of a novel adaptive brace with 2 passive ankle braces while considering protection, sports performance, freedom of motion, and subjective perception.
Study Design:
Controlled laboratory study.
Methods:
The authors analyzed 1 adaptive and 2 passive (one lace-up and one rigid brace) ankle braces, worn in a low-cut, indoor sports shoe, which was also the no-brace reference condition. We performed material testing using an artificial ankle joint system at high and low inversion velocities. Further, 20 male, young, healthy team sports athletes were analyzed using 3-dimensional motion analysis in sports-related movements to address protection, sports performance, and active range of motion dimensions. Participants rated subjective comfort, stability, and restriction experienced when using the products.
Results:
Subjective stability rating was not different between the adaptive and passive systems. The rigid brace was superior in restricting peak inversion during the biomechanical testing compared with the passive braces. However, in the material test, the adaptive brace increased its stiffness by approximately 400% during the fast compared with the slow inversion velocities, demonstrating its adaptive behavior and similar stiffness values to passive braces. We identified minor differences in sports performance tasks. The adaptive brace improved active ankle range of motion and subjective comfort and restriction ratings.
Conclusion:
The adaptive brace offered similar protective effects in high-velocity inversion situations to those of the passive braces while improving range of motion, comfort, and restriction rating during noninjurious motions.
Clinical Relevance:
Protection systems are only effective when used. Compared with traditional passive ankle brace technologies, the novel adaptive brace might increase user compliance by improving comfort and freedom of movement while offering similar protection in injurious situations.
The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.
Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. To reveal model weaknesses, adversarial attacks are specifically optimized to generate small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained by using adversarial examples during training, which in most cases reduces the measurable model attackability. Unfortunately, this technique can lead to robust overfitting, which results in non-robust models. In this paper, we analyze adversarially trained, robust models in the context of a specific network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from downsampling artifacts, aka. aliasing, than baseline models. In the case of robust overfitting, we observe a strong increase in aliasing and propose a novel early stopping approach based on the measurement of aliasing.
The aim of this paper is to identify indicators at country level that could prove useful in improving the effectiveness of fraud detection in European Structural and Investment Funds. We analyse data for 454 funds, belonging to the period 2014-2020, from the 28 countries that were members of the European Union in 2014. Explanatory results suggest the convenience of tracking funds, especially in countries with higher GDP and higher transparency levels, and the lesser relevance of the number of irregularities for countries with higher GDP and those receiving larger funds. Fraud and fraud detection rates in individual funds vary significantly across states. Federal states, such as the Federal Republic of Germany, are comparatively successful in detecting fraud in EU funds.
Appraising the Methodological Quality of Sports Injury Video Analysis Studies: The QA-SIVAS Scale
(2023)
Background
Video analysis (VA) is commonly used in the assessment of sports injuries and has received considerable research interest. Until now, no tool has been available for the assessment of study quality. Therefore, the objective of this study was to develop and evaluate a valid instrument that reliably assesses the methodological quality of VA studies.
Methods
The Quality Appraisal for Sports Injury Video Analysis Studies (QA-SIVAS) scale was developed using a modified Delphi approach including expert consensus and pilot testing. Reliability was examined through intraclass correlation coefficient (ICC3,1) and free-marginal kappa statistics by three independent raters. Construct validity was investigated by comparing QA-SIVAS with expert ratings by using Kendall’s tau analysis. Rating time was studied by applying the scale to 21 studies and computing the mean time for rating per study article.
Results
The QA-SIVAS scale consists of an 18-item checklist addressing the study design, data source, conduct, report, and discussion of VA studies in sports injury research. Inter- and intra-rater reliability were excellent with ICCs > 0.97. Expert ratings revealed a high construct validity (0.71; p < 0.001). Mean rating time was 10 ± 2 min per article.
Conclusion
QA-SIVAS is a reliable and valid instrument that can be easily applied to sports injury research. Future studies in the field of VA should adhere to standardized methodological criteria and strict quality guidelines.
In this paper, the performance of different continuous-time and discrete-time models of the electrical subsystem of induction machines and permanent-magnet synchronous machines as well as methods based on them for decoupling the direct and
quadrature axis components of the stator current are investigated and compared. The focus here is on inverter-fed, pulse width modulated drives when operated with a relatively large product of stator frequency and sampling time, where significant
differences between the models and decoupling methods used come to light. Recommendations for a discrete-time model to be used uniformly in the future are made, as well as statements on whether feedforward or feedback decoupling structures are better suited and whether state controllers improve decoupling measures for very steep speed ramps. Simulation studies and measurement results support the statements made above.
Phytases are widely used food and feed enzymes to improve phosphate availability and reduce anti-nutritional factors. Despite the benefits, enzyme usage is restricted by the harsh conditions in a gastrointestinal tract (pH 2–6) and feed pelleting conditions at high temperatures (60–90 °C). The commercially available phytase Quantum® Blue has been immobilized as CLEAs using glutardialdehyde and soy protein resulting in a residual activity of 33%. The influence of the precipitating agent, precipitant concentration, cross-linker concentration and cross-linking time, sodium borohydride as well as the proteic feeders gluten, soy protein and bovine serum albumin (BSA) has been optimized. The best conditions were 90% (v/v) ethyl lactate as precipitating reagent, 100 mM glutardialdehyde and a soy protein concentration of 227 mg/L with a cross-linking time of 1 h. The intrinsically stable phytase remained its high thermal stability and temperature optimum. The phytase-CLEA achieved a 425% increase of residual activity under harsh acidic conditions between pH 2.2 and 3.5 compared to the free enzyme. The free and immobilized phytase were deployed in an in vitro assay simulating the acidic conditions in the gizzard of poultry at pH 2. The hydrolysis of phytate was monitored using a novel high-performance thin-layer chromatography (HPTLC) analysis and DAD scanner to study the InsPx fingerprint. All lower inositol phosphate pools (InsP1–InsP6) and free phosphate were separated and analyzed. The phytase-CLEA efficiently released 80% of the total phosphate within 180 min, whereas the free enzyme only released 6% in the same time under the same conditions.
This article presents the development, parameterization, and experimental validation of a pseudo-three-dimensional (P3D) multiphysics aging model of a 500 mAh high-energy lithium-ion pouch cell with graphite negative electrode and lithium nickel manganese cobalt oxide (NMC) positive electrode. This model includes electrochemical reactions for solid electrolyte interphase (SEI) formation at the graphite negative electrode, lithium plating, and SEI formation on plated lithium. The thermodynamics of the aging reactions are modeled depending on temperature and ion concentration and the reactions kinetics are described with an Arrhenius-type rate law. Good agreement of model predictions with galvanostatic charge/discharge measurements and electrochemical impedance spectroscopy is observed over a wide range of operating conditions. The model allows to quantify capacity loss due to cycling near beginning-of-life as function of operating conditions and the visualization of aging colormaps as function of both temperature and C-rate (0.05 to 2 C charge and discharge, −20 °C to 60 °C). The model predictions are also qualitatively verified through voltage relaxation, cell expansion and cell cycling measurements. Based on this full model, six different aging indicators for determination of the limits of fast charging are derived from post-processing simulations of a reduced, pseudo-two-dimensional isothermal model without aging mechanisms. The most successful aging indicator, compared to results from the full model, is based on combined lithium plating and SEI kinetics calculated from battery states available in the reduced model. This methodology is applicable to standard pseudo-two-dimensional models available today both commercially and as open source.
Teaching and learning concepts that are adapted to the constantly evolving requirements due to rapid technological progress are essential for teaching in media photonics technology. After the development of a concept for research-oriented education in optics and photonics, the next step will be a conceptual restructuring and redesign of the entire curriculum for education in media photonics technology. By including typical research activities as essential components of the learning process, a broad platform for practical projects and applied research can be created, offering a variety of new development opportunities.
Redesigning a curriculum for teaching media technology is a major challenge. Up-to-date teaching and learning concepts are necessary that meet the constant technological progress and prepare students specifically for their professional life. Teaching and studying should be characterized by a student-oriented teaching and learning culture. In order to achieve this goal, consistent evaluation is essential. The aim of the evaluation concept presented here is to generate structured information regarding the quality of content-related, didactic and organizational aspects of teaching. The exchange of opinions between students and lecturers should be encouraged in order to continuously improve the teaching and learning processes.
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