How Employee Use of Generative Artificial Intelligence Affects Self-Evaluation: Investigating Implications for Job Insecurity and Career Commitment

This research delves into the transformative influence of Generative Artificial Intelligence (GenAI) on the self-evaluation processes of employees within contemporary organizations. In an era marked by rapid technological advancements, the integration of AI into various facets of the workplace is reshaping traditional paradigms. We use Self-Evaluation Maintenance (SEM) Model, which is our theoretical lens, and combining with the concept of Core Self-Evaluation (CSE) to conduct our research model. This study seeks to elucidate whether the usage of GenAI, specifically in the context of performance compares to GenAI, and then the impact on CSE, which we plan to use in this research, has discernible effects on how employees perceive and evaluate their own contributions. In addition, we adapt various reliable scales to assess the constructs in our research model. This research employs surveys and content analysis of questionnaire data to investigate the perceptions of employees in organizations that have introduced GenAI-driven tools for performance appraisal. The objective is to determine whether these tools, by providing real-time feedback, personalized recommendations, and novel evaluation metrics, result in changed self-perceptions and attitudes towards one\u27s work

The dilemma for lipid productivity in green microalgae: importance of substrate provision in improving oil yield without sacrificing growth

10.1186/s13068-016-0671-2Biotechnology for Biofuels911-1

Extend the debt as it is not deeply out-of-the-money

In this paper, we modify the extendible debts model proposed in Longstaff (1990) to help relieve the moral hazard problem induced in the original model. In Longstaff¡¦s model, extending the maturity of the defaulted debts gives the borrower an incentive to default even if the borrower is insolvent. In this paper, we argue that the debt should not be extended if it is defaulted severely. We have shown that the extendible debt valuation can be obtained by the compound option pricing besides the PDE approach. We also have derived the fair interest rate of the extendible debts in this paper.

In-situ Monitoring of Internal Local Temperature and Voltage of Proton Exchange Membrane Fuel Cells

The distribution of temperature and voltage of a fuel cell are key factors that influence performance. Conventional sensors are normally large, and are also useful only for making external measurements of fuel cells. Centimeter-scale sensors for making invasive measurements are frequently unable to accurately measure the interior changes of a fuel cell. This work focuses mainly on fabricating flexible multi-functional microsensors (for temperature and voltage) to measure variations in the local temperature and voltage of proton exchange membrane fuel cells (PEMFC) that are based on micro-electro-mechanical systems (MEMS). The power density at 0.5 V without a sensor is 450 mW/cm2, and that with a sensor is 426 mW/cm2. Since the reaction area of a fuel cell with a sensor is approximately 12% smaller than that without a sensor, but the performance of the former is only 5% worse

In Situ Measurement of the Junction Temperature of Light Emitting Diodes Using a Flexible Micro Temperature Sensor

This investigation aimed to fabricate a flexible micro resistive temperature sensor to measure the junction temperature of a light emitting diode (LED). The junction temperature is typically measured using a thermal resistance measurement approach. This approach is limited in that no standard regulates the timing of data capture. This work presents a micro temperature sensor that can measure temperature stably and continuously, and has the advantages of being lightweight and able to monitor junction temperatures in real time. Micro-electro-mechanical-systems (MEMS) technologies are employed to minimize the size of a temperature sensor that is constructed on a stainless steel foil substrate (SS-304 with 30 μm thickness). A flexible micro resistive temperature sensor can be fixed between the LED chip and the frame. The junction temperature of the LED can be measured from the linear relationship between the temperature and the resistance. The sensitivity of the micro temperature sensor is 0.059 ± 0.004 Ω/°C. The temperature of the commercial CREE® EZ1000 chip is 119.97 °C when it is thermally stable, as measured using the micro temperature sensor; however, it was 126.9 °C, when measured by thermal resistance measurement. The micro temperature sensor can be used to replace thermal resistance measurement and performs reliably