Self-employment and workplace wellbeing

By introducing psychological theories into entrepreneurship research field, this thesis aims to investigate the relationship between self-employment and workplace wellbeing.The thesis consists of three empirical studies, which set out to answer the followingquestions: 1) What are the differences of workplace wellbeing between the self-employed and employees? 2) What factors contribute to workplace wellbeing in both direct and indirect ways? 3) What is the relationship between negative workplace wellbeing and positive wellbeing, 4) How does coping mechanism reduce negative workplace wellbeing and enhance positive wellbeing? Moreover, this thesis also examines the specific issues of self-employment, such as workplace wellbeing of the self-employed under the poverty line and the differences between the self-employed with hiring employees and the self-employed without hiring any employee. This quantitative and comparative thesis has employed the matching approach to overcome selection bias and combined with other statistical methods such as CFA, SEM and moderating hierarchy regression to test the conceptual models empirically. The data used for this research is sourced from the Understanding Society, the largest household panel data in the UK. This thesis found that the self-employed experience higher positive workplace wellbeing than employees. The self-employed with hiring employees experience a significantly higher level of negative workplace wellbeing than employees. However, the self-employed without hiring any employee experience significant lower negative workplace wellbeing. Moreover, this thesis found that job demand and job control contribute to negative workplace wellbeing directly, and the relationship can be partly moderated by social support. In addition, the thesis has tested the relationship between the positive workplace wellbeing and negative workplace wellbeing, which has been verified as negative correlations. Lastly, the results showed self-efficacy is an effective coping factor to reduce negative wellbeing and enhance positive wellbeing

A fresh look at self-employment, stress and health:accounting for self-selection, time and gender

Purpose: Past research on self-employment and health yielded conflicting findings. Integrating predictions from the Stressor-Strain Outcome model, research on challenge stressors and allostatic load, we predict that physical and mental health are affected by self-employment in distinct ways which play out over different time horizons. We also test whether the health impacts of self-employment are due to enhanced stress (work-related strain) and differ for man and women. Design/methodology/approach: We apply non-parametric propensity score matching in combination with a difference-in-difference approach and longitudinal cohort data to examine self-selection and the causal relationship between self-employment and health. We focus on those that transit into self-employment from paid employment (opportunity self-employment) and analyze strain and health over four years relative to individuals in paid employment. Findings: Those with poorer mental health are more likely to self-select into self-employment. After entering self-employment, individuals experience a short-term uplift in mental health due to lower work-related strain, especially for self-employed men. In the longer-term (four years) the mental health of the self-employed drops back to pre-self-employment levels. We find no effect of self-employment on physical health. Practical implications: Our research helps to understand the nonpecuniary benefits of self-employment and suggests that we should not advocate self-employment as a “healthy” career. Originality/value: This article advances research on self-employment and health. Grounded in stress theories it offers new insights relating to self-selection, the temporality of effects, the mediating role of work-related strain, and gender that collectively help to explain why past research yielded conflicting findings

Asymptotic values and analytic sets

Asymptotic values and analytic set

Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images

Photos serve as a way for humans to record what they experience in their daily lives, and they are often regarded as trustworthy sources of information. However, there is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos, which can create confusion and diminish trust in photographs. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content. Our study benchmarks both human capability and cutting-edge fake image detection AI algorithms, using a newly collected large-scale fake image dataset Fake2M. In our human perception evaluation, titled HPBench, we discovered that humans struggle significantly to distinguish real photos from AI-generated ones, with a misclassification rate of 38.7%. Along with this, we conduct the model capability of AI-Generated images detection evaluation MPBench and the top-performing model from MPBench achieves a 13% failure rate under the same setting used in the human evaluation. We hope that our study can raise awareness of the potential risks of AI-generated images and facilitate further research to prevent the spread of false information. More information can refer to https://github.com/Inf-imagine/Sentry

Topology analysis and parameter design of three-level multi-input DC/DC converter based on multi-source access

A three-level multi-input DC/DC converter is proposed to solve the problems of complex interface circuit structure and high economic cost for multi-source access to the joint power supply distribution system. In this structure, multiple dc sources are integrated into a three-level DC/DC converter. In comparison with the two-stage counterpart, two active switches and boost diodes are eliminated, while two blocking diodes are added to block the reverse current from the dc-link capacitors. In addition, when the input inductors work in the discontinuous conduction mode, power sharing among different input sources can be achieved by properly selecting the inductance value. The working principle of the converter is analyzed by introducing nine working modes in detail and deriving the steady-state relationship expressions. The parameter range of the element is determined and the design process of a group of dynamic parameter values is shown. Finally, the power electronics real-time simulation platform is built based on StarSim HIL and the corresponding experimental waveforms are given to verify the topology and analysis

Notch1 is required for hypoxia-induced proliferation, invasion and chemoresistance of T-cell acute lymphoblastic leukemia cells

Background Notch1 is a potent regulator known to play an oncogenic role in many malignancies including T-cell acute lymphoblastic leukemia (T-ALL). Tumor hypoxia and increased hypoxia-inducible factor-1α (HIF-1α) activity can act as major stimuli for tumor aggressiveness and progression. Although hypoxia-mediated activation of the Notch1 pathway plays an important role in tumor cell survival and invasiveness, the interaction between HIF-1α and Notch1 has not yet been identified in T-ALL. This study was designed to investigate whether hypoxia activates Notch1 signalling through HIF-1α stabilization and to determine the contribution of hypoxia and HIF-1α to proliferation, invasion and chemoresistance in T-ALL. Methods T-ALL cell lines (Jurkat, Sup-T1) transfected with HIF-1α or Notch1 small interference RNA (siRNA) were incubated in normoxic or hypoxic conditions. Their potential for proliferation and invasion was measured by WST-8 and transwell assays. Flow cytometry was used to detect apoptosis and assess cell cycle regulation. Expression and regulation of components of the HIF-1α and Notch1 pathways and of genes related to proliferation, invasion and apoptosis were assessed by quantitative real-time PCR or Western blot. Results Hypoxia potentiated Notch1 signalling via stabilization and activation of the transcription factor HIF-1α. Hypoxia/HIF-1α-activated Notch1 signalling altered expression of cell cycle regulatory proteins and accelerated cell proliferation. Hypoxia-induced Notch1 activation increased the expression of matrix metalloproteinase-2 (MMP2) and MMP9, which increased invasiveness. Of greater clinical significance, knockdown of Notch1 prevented the protective effect of hypoxia/HIF-1α against dexamethasone-induced apoptosis. This sensitization correlated with losing the effect of hypoxia/HIF-1α on Bcl-2 and Bcl-xL expression. Conclusions Notch1 signalling is required for hypoxia/HIF-1α-induced proliferation, invasion and chemoresistance in T-ALL. Pharmacological inhibitors of HIF-1α or Notch1 signalling may be attractive interventions for T-ALL treatment

CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.htmlComment: 11 pages, 13 figure