The League System, Competitive Balance, and the Future of European Football

Research question: Which combination of league system and competitive balance in European men’s club football maximises welfare, and which policies can improve the combination?Research methods: Qualitative analysis is applied that uses the sport economics literature and other sources. An encompassing number of factors, insights and empirical results are considered to analyse systematically the options to reform European professional football.Results and findings: In the present league system, competitive balance is decreasing which reduces welfare. In a system with a Super League, competitive balance can be higher. The last system also has other advantages, as well as disadvantages, in relation to issues such as the quality of play, the role of star players, chances to lift important trophies, regional and national sentiments and rivalries, and sporting meritocracy. After discussing these issues, it is concluded that a Super League system may improve future welfare. If, however, the present system regains a higher level of competitive balance, it may well generate a higher welfare level than a Super League. Two measures for improving competitive balance are a more equal distribution of broadcasting revenues and a progressive luxury tax.Practical implications: At present, the measures above may seem to be infeasible. However, this can change if the competition authorities accept our interpretation of EU competition law and act accordingly. More generally, the government should become better aware of its potential to improve welfare in the football sector.Research contribution: The paper provides a systematic analysis of the welfare effects of different options to reform Europe’s professional men football. Because of its broad approach it sheds new light on pressing issues and it provides insights for policymaking

A Systematic Literature Review of Modelling Approaches to Evaluate the Cost Effectiveness of PET/CT for Therapy Response Monitoring in Oncology

Background and Objective: This systematic literature review addresses model-based cost-effectiveness studies for therapy response monitoring with positron emission tomography (PET) generally combined with low-dose computed tomography (CT) for various cancer types. Given the known heterogeneity in therapy response events, studies should consider patient-level modelling rather than cohort-based modelling because of its flexibility in handling these events and the time to events. This review aims to identify the modelling methods used and includes a systematic assessment of the assumptions made in the current literature.Methods: This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Information sources included electronic bibliographic databases, reference lists of review articles and contact with experts in the fields of nuclear medicine, health technology assessment and health economics. Eligibility criteria included peer-reviewed scientific publications and published grey literature. Literature searches, screening and critical appraisal were conducted by two reviewers independently. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) were used to assess the methodological quality. The Bias in Economic Evaluation (ECOBIAS) checklist was used to determine the risk of bias in the included publications.Results: The search results included 2959 publications. The number of publications included for data extraction and synthesis was ten, representing eight unique studies. These studies addressed patients with lymphoma, advanced head and neck cancers, brain tumours, non-small cell lung cancer and cervical cancer. All studies addressed response to chemotherapy. No study evaluated response to immunotherapy. Most studies positioned PET/CT as an add-on modality and one study positioned PET/CT as a replacement for conventional imaging (X-ray and contrast-enhanced CT). Three studies reported decision-tree structures, four studies reported cohort-level state-transition models and one study reported a partitioned survival model. No patient-level models were reported. The simulation horizons adopted ranged from 1 year to lifetime. Most studies reported a probabilistic analysis, whereas two studies reported a deterministic analysis only. Two studies conducted a value of information analysis. Multiple studies did not adequately discuss model-specific aspects of bias. Most importantly and regularly observed were a high risk of structural assumptions bias, limited simulation horizon bias and wrong model bias.Conclusions: Model-based cost-effectiveness analysis for therapy response monitoring with PET/CT was based on cohorts of patients instead of individual patients in the current literature. Therefore, the heterogeneity in therapy response events was commonly not addressed appropriately. Further research should include more advanced and patient-level modelling approaches to accurately represent the complex context of clinical practice and, therefore, to be meaningful to support decision making. Registration: This review is registered in PROSPERO, the international prospective register of systematic reviews funded by the National Institute for Health Research, with CRD42023402581.</p

Temperature-driven wetting transition and localized oil entrapment in stimuli-responsive Poly-octadecylmethacrylate brushes

Temperature-driven wetting transition and localized oil entrapment in stimuliresponsive Poly-octadecylmethacrylate brushesSmart polymer coatings with switchable wettability gained considerable attention due to their potentialin a wide range of industrial applications such as microfluidics, self – cleaning surfaces and controlleddrug delivery. Surface wettability is governed by surface chemistry and morphology and can bemodified by external stimuli such as temperature, light, and pH.In this study, we explore the switchable wettability of n-alkanes on stimulus-responsive polyoctadecylmethacrylate (P18MA) brushes and introduce a method for reversible oil entrapment usinglaser-induced heating with micrometer-scale precision.Our results reveal a two-stage temperature-driven wetting transition of n-alkanes on these brushes.Initially, a moderate temperature increase causes the brush layer to swell while maintaining a finitecontact angle of the oil. As the temperature rises further, the oil spreads across the already swollen brushlayer. Combining macroscopic wetting experiments, Atomic Force Microscopy (AFM) adhesionmeasurements, and non-linear optical Sum Frequency Generation (SFG) spectroscopy, we show thatthis two-stage wetting transition is linked to the initial melting transition of the bulk polymer and asubsequent, surface-specific melting transition that remains in an ordered state up to a few degreesabove the bulk melting temperature

Les obstacles à l'approvisionnement fondé sur la valeur en santé:le cas de la province de Québec

The concept of value-based purchasing aims to move away from the logic of price reduction in purchasing to integrate other dimensions that support the acquisition of innovations in healthcare institutions. As with other fundamental transformations in supply management, a series of obstacles hinder the implementation of this concept. A case study, conducted with a Canadian healthcare purchasing group as part of collaborative research, captures the relationships between the barriers identified in the literature. We discuss the potential efforts public policymakers should put in place to deploy this concept while identifying some relevant research questions.</p

Statistical theory for image classification using deep convolutional neural network with cross-entropy loss under the hierarchical max-pooling model

Convolutional neural networks (CNNs) trained with cross-entropy loss have proven to be extremely successful in classifying images. In recent years, much work has been done to also improve the theoretical understanding of neural networks. Nevertheless, it seems limited when these networks are trained with cross-entropy loss, mainly because of the unboundedness of the target function. In this paper, we aim to fill this gap by analysing the rate of the excess risk of a CNN classifier trained by cross-entropy loss. Under suitable assumptions on the smoothness and structure of the a posteriori probability, it is shown that these classifiers achieve a rate of convergence which is independent of the dimension of the image. These rates are in line with the practical observations about CNNs.</p

A systematic review of predictive, optimization, and smart control strategies for hydrogen-based building heating systems

The use of energy in the built environment contributes to over one-third of the world's carbon emissions. To reduce that effect, two primary solutions can be adopted, i.e. (i) renovation of old buildings and (ii) increasing the renewable energy penetration. This review paper focuses on the latter. Renewable energy sources typically have an intermittent nature. In other words, it is not guaranteed that these sources can be harnessed on demand. Thus, complement solutions should be considered to use renewable energy sources efficiently. Hydrogen is recognized as a potential solution. It can be used to store excess energy or be directly exploited to generate thermal energy. Throughout this review, various research papers focusing on hydrogen-based heating systems were reviewed, analyzed, and classified from different perspectives. Subsequently, articles related to machine learning models, optimization algorithms, and smart control systems, along with their applications in building energy management were reviewed to outline their potential contributions to reducing energy use, lowering carbon emissions, and improving thermal comfort for occupants. Furthermore, research gaps in the use of these smart strategies in residential hydrogen heating systems were thoroughly identified and discussed. The presented findings indicate that the semi-decentralized hydrogen-based heating systems hold significant potential. First, these systems can control the thermal demand of neighboring homes through local substations; second, they can reduce reliance on power and gas grids. Furthermore, the model predictive control and reinforcement learning approaches outperform other control systems ensuring energy comfort and cost-effective energy bills for residential buildings.<br/

Bridging silos through governance innovations: the role of the EU cities mission

Cities and local governments are increasingly under pressure to accelerate transformative change in energy and climate transitions. To help cities in their climate actions, the European Commission (EC) has established the EU Cities Mission, which aims for climate neutrality by 2030 for participating cities. The literature argues that one of the main obstacles to accelerating decarbonization lies in organizational divisions and other forms of structural silos. One of the possible ways to address these challenges and accelerate transformation is through governance innovations. The EU Cities Mission is a governance innovation that aims to incentivize and support climate and energy transitions in cities. In this paper, we critically assess the EU Cities Mission’s framework and implementation plan in terms of its potential and possible gaps in addressing different types of silos. To do so, we develop an analytical framework based on academic literature that outlines types of silos and strategies for addressing them. Our results show that key EU Cities Mission documents include several strategies to bridge silos, but that some silos are less frequently addressed. This is particularly the case for silos that rely on political leadership. The paper concludes by drawing out the implications of our findings for the scholarly literature and practice

The INFLUENCE 3.0 model: updated predictions of locoregional recurrence and contralateral breast cancer, now also suitable for patients treated with neoadjuvant systemic therapy

Background: Individual risk prediction of 5-year locoregional recurrence (LRR) and contralateral breast cancer (CBC) supports decisions regarding personalised surveillance. The previously developed INFLUENCE tool was rebuild, including a recent population and patients who received neoadjuvant systemic therapy (NST). Methods: Women, surgically treated for nonmetastatic breast cancer, diagnosed between 2012 and 2016, were selected from the Netherlands Cancer Registry. Cox regression with restricted cubic splines was compared to Random Survival Forest (RSF) to predict five-year LRR and CBC risks. Separate models were developed for NST patients. Discrimination and calibration were assessed by 100x bootstrap resampling. Results: In the non-NST and NST group, 49,631 and 10,154 patients were included, respectively. Age, mode of detection, histology, sublocalisation, grade, pT, pN, hormonal receptor status ± endocrine treatment, HER2 status ± targeted treatment, surgery ± immediate reconstruction ± radiation therapy, and chemotherapy were significant predictors for LRR and/or CBC in non-NST patients. For NST patients this was similar, but excluding (y)pT and (y)pN status, and including presence of ductal carcinoma in situ, axillary lymph node dissection and pathologic complete response. For non-NST patients, the Cox and RSF models were integrated in the online tool with 5-year AUCs of 0.77 (95%CI:0.77–0.77) and 0.68 (95%CI:0.67–0.68)] for LRR and CBC prediction, respectively. For NST patients, the RSF model performed best (AUCs 0.77 (95%CI:0.76–0.78) and 0.73 (95%CI:0.69–0.76) for LRR and CBC, respectively). Regarding calibration, observed-predicted differences were all &lt;1 %. Conclusion: This INFLUENCE 3.0 models showed moderate performance in LRR and CBC prediction. The models have been made available as online tool to enable clinical decision support regarding personalised follow-up.</p

Thermal Data from Málaga's historical centre:Surface and Air Temperature Measurements Captured via Mobile Station and Thermal Imaging

This dataset contains air and surface temperature measurements taken twice daily at 11:00 and 23:00 GMT+2 from 24th June to 5th July 2024 (a total of 10 days) in the historical centre of Malaga, Spain. It includes detailed thermal readings from various street materials and the facades of historical buildings, offering insights into the thermal properties and responses of these elements at different times of day. This dataset provides valuable information on localized temperature variations within the historical centre, influenced by different materials and architectural styles. It can be used to model microclimate variations, evaluate the thermal behavior of both historical and contemporary materials, and inform urban planning and heritage conservation efforts

No impact of story context and avatar power on performance in a stop-signal game

This study investigates the impact of gamification on response inhibition in a Stop-Signal Task (SST) and examines participants' gamification experience. The findings reveal that, after accounting for approach- and avoidance-motivation as well as impulsiveness, higher immersion is associated with impaired response inhibition. This effect could be attributed to a substantial decline in immersion between the first and second SST sessions. Despite intrinsic motivation and avatar identification not significantly predicting performance, both factors exhibited a decline across sessions, suggesting an overall diminished gaming experience in the second session. Alternatively, motivational variables as immersion and avatar identification might be detrimental to response inhibition, by shifting attention away from relevant task elements. Contrary to expectations, approach and avoidance narratives did not influence outcome variables or participant experience, while different avatars led to altered avatar identification, particularly favouring strong avatars. The declining motivation over time might stem from a lack of tangible goals within the gamified task, where narrative elements alone failed to induce sufficient goal-oriented motivation. These findings underscore the nuanced interplay between gamification elements, task complexity, and participants' expectations, emphasizing the need for carefully tailored gamification strategies in experimental designs.</p