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Stochastic approaches for production-inventory planning:Applications to high-tech supply chains
No full textGrowing uncertainty in both demand and supply, fast-paced technologicaldevelopments, and increasingly stringent customer requirements progressivelycomplicate supply chain management across numerous industries. To improveefficiency and foster resilience, companies focus on optimizing their productioninventory planning processes, addressing the fundamental trade-off between inventory investments and customer service levels under uncertain conditions. This thesis develops stochastic mathematical models and solution approaches to optimize production-inventory systems with diverse characteristics. It examines centralized and decentralized decision-making structures, single- and multiperiod planning, and various supply chain configurations, including singleechelon and capacitated multi-echelon systems. The models address key sources of uncertainty, such as demand, lead times and new product introduction dynamics, while exploring both risk-neutral and risk-averse decision-making perspectives. In collaboration with ASML, a leader in the high-tech sector, the first part of the thesis introduces methods tailored to complex industry-driven settings. These include a rolling horizon decision framework for integrated production and buffer planning, multi-stage stochastic programming models for risk-averse service level management and product rollover planning, and a deep reinforcement learning-based inventory policy for handling non-stationary demand and capacity constraints. The second part emphasizes theoretical contributions, offering insights into decision making under demand correlation, risk aversion and contractual agreements through analytical models. By integrating practical and theoretical advancements, this thesis aims to contribute to the broader field of productioninventory planning and supply chain management, providing structural insights to guide and complement the development of general approaches to practical problems
Beter Geven II:Verbetervoorstellen voor Vereenvoudiging en Versterking van de Giftenaftrek
No full textThe short-run effects of unexpected job loss on health
No full textThis paper provides new evidence on the effect of job loss on health. Using unique micro level panel data from the Netherlands with detailed information on health measures, employment, and job loss expectations, we estimate the immediate effect of unexpected job loss on health. We find no evidence for decreases in health, either physical or mental, upon job loss, but clear evidence for immediate reductions in headaches and fatigue. Our results suggest that the immediate effects of reduced work stress are larger than the immediate increase in financial stress from job loss
Accurate Estimates of Ultimate 100-Meter Records
No full textWe employ the novel theory of heterogeneous extreme value statistics to accurately estimate the ultimate world records for the 100-m running race, for men and for women. For this aim we collected data from 1991 through 2023 from thousands of top athletes, using multiple fast times per athlete. We consider the left endpoint of the probability distribution of the running times of a top athlete and define the ultimate world record as the minimum, over all top athletes, of all these endpoints. For men we estimate the ultimate world record to be 9.56 seconds. More prudently, employing this heterogeneous extreme value theory we construct an accurate asymptotic 95% lower confidence bound on the ultimate world record of 9.49 seconds, still quite close to the present world record of 9.58. For the women’s 100-meter dash our point estimate of the ultimate world record is 10.34 seconds, somewhat lower than the world record of 10.49. The more prudent 95% lower confidence bound on the women’s ultimate world record is 10.20
Time to say goodbye? The role of SBIR funding, VC rounds, and initial alliance for director exit in new ventures
No full textDespite the significant interest in the composition and dynamics of new venture boards, our understanding of when directors exit the boards of new ventures is limited. Drawing on the organizational life cycles framework and resource dependence arguments, we posit that key life cycle events alter a venture's resource needs and dependencies on the board, occasioning director exit. Specifically, we argue that SBIR funding, Venture Capital rounds of funding, and first alliance act as markers of new venture evolution that render existing dependencies obsolete, increasing the likelihood of director exit. Interviews with board members in the semiconductor industry informed and substantiated our theoretical claims. The results show that SBIR funding and subsequent rounds of VC funding are linked to an increased likelihood of director exit, whereas a venture's first alliance is not. The paper sheds light on the interdependencies between the board's life cycle and the life cycle of the new venture
Dispositional greed scale (DGS)
No full textGreed is the insatiable desire for more, stemming from being dissatisfied with one currently possesses. Dispositional greed or trait greed refers to an individual’s tendency to be greedy. The Dispositional Greed Scale (DGS) captures the individual differences in greediness via 7 items (or 3 items in the short scale). The DGS is available in at least eight languages. The scale was originally developed in English and Dutch, and has been translated into Chinese, German, Japanese, Portuguese, Russian, and Spanish. The scale has also been adapted for usage with young children (filled in by their parents). Across multiple studies with many different samples the DGS showed unidimensionality, high internal consistency, and good test-retest reliability
Adam Smith en ‘easy taxes’ (II)
No full textNadat we in deel 1 de hoofdlijnen hebben geschetst van het denken van de Schotse verlichtingsfilosoof Adam Smith, richten we nu de blik op zijn opvattingen over de rol van de staat en de openbare financiën (par. 5) en gaan we uitvoerig in op Smiths visie op belastingheffing en op het (belasting)recht (par. 6 en 7). De slotparagraaf (par. 8) bevat logischerwijs de samenvatting en de conclusie
Co-design of an escape room for e-mental health training of mental health care professionals:Research through design study
No full textBackground: Many efforts to increase the uptake of e-mental health (eMH) have failed due to a lack of knowledge and skills, particularly among professionals. To train health care professionals in technology, serious gaming concepts such as educational escape rooms are increasingly used, which could also possibly be used in mental health care. However, such serious-game concepts are scarcely available for eMH training for mental health care professionals. Objective: This study aims to co-design an escape room for training mental health care professionals’ eMH skills and test the escape room’s usability by exploring their experiences with this concept as a training method. Methods: This project used a research through design approach with 3 design stages. In the first stage, the purpose, expectations, and storylines for the escape room were formulated in 2 co-design sessions with mental health care professionals, game designers, innovation staff, and researchers. In the second stage, the results were translated into the first escape room, which was tested in 3 sessions, including one web version of the escape room. In the third stage, the escape room was tested with mental health care professionals outside the co-design team. First, 2 test sessions took place, followed by 3 field study sessions. In the field study sessions, a questionnaire was used in combination with focus groups to assess the usability of the escape room for eMH training in practice. Results: An escape room prototype was iteratively developed and tested by the co-design team, which delivered multiple suggestions for adaptations that were assimilated in each next version of the prototype. The field study showed that the escape room creates a positive mindset toward eMH. The suitability of the escape room to explore the possibilities of eMH was rated 4.7 out of 5 by the professionals who participated in the field study. In addition, it was found to be fun and educational at the same time, scoring 4.7 (SD 0.68) on a 5-point scale. Attention should be paid to the game’s complexity, credibility, and flexibility. This is important for the usefulness of the escape room in clinical practice, which was rated an average of 3.8 (SD 0.77) on a 5-point scale. Finally, implementation challenges should be addressed, including organizational policy and stimulation of eMH training. Conclusions: We can conclude that the perceived usability of an escape room for training mental health care professionals in eMH skills is promising. However, it requires additional effort to transfer the learnings into mental health care professionals’ clinical practice. A straightforward implementation plan and testing the effectiveness of an escape room on skill enhancement in mental health care professionals are essential next steps to reach sustainable goals
Op-ed: “On the Meaning of Indirect Discrimination in direct Taxation Cases… There is still Room for Clarity (C-18/23, F S.A. v Dyrektor Krajowej Informacji Skarbowej)”,
No full textAutomated segmentation of brain metastases in T1-weighted contrast- enhanced MR images pre and post stereotactic radiosurgery
No full textBACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. The purpose of this study is to evaluate well-known DL approaches (nnU-Net and MedNeXt) for their performance on both planning and follow-up MRI. MATERIALS AND METHODS: Pre-treatment brain MRIs were retrospectively collected for 255 patients at Elisabeth-TweeSteden Hospital (ETZ): 201 for training and 54 for testing, including follow-up MRIs for the test set. To increase heterogeneity, we added the publicly available MRI scans from the Mathematical oncology laboratory of 75 patients to the training data. The performance was compared between the two models, with and without the addition of the public data. To statistically compare the Dice Similarity Coefficient (DSC) of the two models trained on different datasets over multiple time points, we used Linear Mixed Models. RESULTS: All models obtained a good DSC (DSC > = 0.93) for planning MRI. MedNeXt trained with combined data provided the best DSC for follow-ups at 6, 15, and 21 months (DSC of 0.74, 0.74, and 0.70 respectively) and jointly the best DSC for follow-ups at three months with MedNeXt trained with ETZ data only (DSC of 0.78) and 12 months with nnU-Net trained with combined data (DSC of 0.71). On the other hand, nnU-Net trained with combined data provided the best sensitivity and FNR for most follow-ups. The statistical analysis showed that MedNeXt provides higher DSC for both datasets and the addition of public data to the training dataset results in a statistically significant increase in performance in both models. CONCLUSION: The models achieved a good performance score for planning MRI. Though the models performed less effectively for follow-ups, the addition of public data enhanced their performance, providing a viable solution to improve their efficacy for the follow-ups. These algorithms hold promise as a valuable tool for clinicians for automated segmentation of planning and follow-up MRI scans during stereotactic radiosurgery treatment planning and response evaluations, respectively. CLINICAL TRIAL NUMBER: Not applicable