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Local online marketplaces are being launched in many cities in order to better digitally position inner-city retailers against the backdrop of structural change. This article sheds light on the structures and actors involved in the implementation of eBay Deine Stadt, an initiative for local online marketplaces by eBay that has been established in more than 30 cities and regions in Germany since 2020. Based on expert interviews with a consulting firm commissioned by eBay for the implementation, as well as with municipal actors, the initial experiences, expectations, and criticisms are discussed. The goal was to conduct an initial evaluation of the local online marketplace program eBay Deine Stadt. The article thereby focuses on the role of eBay as an infrastructure provider and cooperation partner, as well as the associated processes, problems, and expectations. Based on the findings, further questions concerning future dynamics of city centres will be identified. Vielerorts werden lokale Onlinemarktplätze ins Lebens gerufen, um innerstädtische Einzelhändler*innen vor dem Hintergrund des Strukturwandels, der coronabedingt verstärkt wurde, digital besser aufzustellen. Anhand eBay Deine Stadt, einer Initiative für lokale Onlinemarktplätze von eBay, die seit 2020 in Deutschland in bislang über 30 Städten und Regionen etabliert wurde, beleuchtet der Artikel die Strukturen und Akteur*innen, die an der Umsetzung der Initiative beteiligt sind. Auf Grundlage von Expert*inneninterviews mit einem von eBay für die Umsetzung beauftragten Beratungsunternehmen sowie kommunalen Akteur*innen werden erste Erfahrungen, Erwartungen und Kritikpunkte erhoben und diskutiert. Ziel ist es, eine anfängliche Evaluation des sich noch im Aufbau befindlichen lokalen Onlinemarktplatz-Programms eBay Deine Stadt durchzuführen. Dabei stehen insbesondere die Rolle eBays als Infrastrukturgeber und Kooperationspartner sowie die damit einhergehenden Prozesse, Probleme und Erwartungen im Vordergrund. Darauf basierend werden weiterführende Fragestellungen zu zukünftigen Dynamiken von Innenstädten identifiziert.

Online auction is a cornerstone of e-commerce, and a key challenge is designing incentive-compatible mechanisms that maximize expected revenue. Existing approaches often assume known bidder value distributions and fixed sets of bidders and items, but these assumptions rarely hold in real-world settings where bidder values are unknown, and the number of future participants is uncertain. In this article, we introduce the Conformal Online Auction Design (COAD), a novel mechanism that maximizes revenue by quantifying uncertainty in bidder values without relying on known distributions. COAD incorporates both bidder and item features, using historical data to design an incentive-compatible mechanism for online auctions. Unlike traditional methods, COAD leverages distribution-free uncertainty quantification techniques and integrates machine learning methods, such as random forests, kernel methods, and deep neural networks, to predict bidder values while ensuring revenue guarantees. Moreover, COAD introduces bidder-specific reserve prices, based on the lower confidence bounds of bidder valuations, contrasting with the single reserve price commonly used in the literature. We demonstrate the practical effectiveness of COAD through an application to real-world eBay auction data. Theoretical results and extensive simulation studies further validate the properties of our approach. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Since its inception in North America in 1999, the Pokémon trading card game has become a global phenomenon, combining strategic gameplay with collectible culture. The economic value of Pokémon trading cards, particularly as collector's items, underscores their significance beyond mere playthings, making them a subject of both academic and commercial interest. In the present study, a convenience sample of 300 Pokémon trading cards were offered on the e-commerce platform eBay between 28 May 2024 and 27 May 2025, with a minimum follow-up period of 3 months per card. All cards were sold nationally in Germany to avoid high shipping costs for international transactions. The aim of the study was to comprehensively analyze sales characteristics of Pokémon trading cards, using both descriptive and inferential statistical techniques including "survival" analyses and Cox regression models. 73.3% (220/300) of the trading cards were sold during the study period, generating a total revenue of 923.60 €. Sales prices showed a markedly skewed distribution, with a median of 1.95 € per trading card. Rare and holofoil cards contributed overproportionately to the total revenue. Uncommon cards exhibited an interesting economic potential due to rapid sales kinetics. Cards from the expansions Team Rocket and Gym Challenge were considerably more popular than Base Set 2 cards, as reflected by shorter "survival" times. The vast majority of buyers were male, but female buyers spent more money per trading card on average. The German federal states Thuringia and Hamburg emerged as Pokémon trading card hubs, with numbers of cards sold and cumulated revenues exceeding expectations based on population sizes.

Logistical and financial barriers to accessing medical equipment through registered suppliers may push people to use online peer-to-peer marketplaces. We sought to characterise the availability of standard wheelchairs sold on these platforms. From March 21-26, 2025, we searched for "manual wheelchairs" across five commonly used online peer-to-peer marketplace platforms (Facebook Marketplace, Craigslist, eBay, OfferUp and Mercari) in an urban zip code (55455). We extracted the first 20 results per platform with at least 1 photo. We extracted information, including asking price, reported retail price, reported condition, etc. We aggregated data across platforms and used descriptive analysis to characterise listings. We identified and included 67 wheelchair listings. The median (IQR) asking price was $150 ($102-$270). Sellers reported that 33 (49%) wheelchairs were in "new or like new" condition, 30 (45%) were in "used or good" condition, and 4 (6%) noted deficiencies such as missing wheels. Twelve listings featured 1-2 photos (18%), 24 (36%) featured 3-5 photos, and 31 (46%) listings featured >5 photos. Fifty-three (79%) listings offered shipping; median (IQR) shipping price $19 ($0-$41). Fifty-six (84%) were from individuals, while 11 (16%) were listed by businesses. The abundant listings for manual wheelchairs on online peer-to-peer marketplaces represent a workaround to current barriers in access through traditional, regulated sources. While these options improve access to critical medical equipment in the short term, they mask underlying systemic issues and put vulnerable individuals at risk. Future directions could explore policies to improve access and regulation of online wheelchair sales. Patients in need of mobility devices such as wheelchairs face several logistical, time, and financial barriers to accessing critical equipment through regulated suppliers.They may seek to bypass traditional supply routes and use ‘‘cash pay’’ to access wheelchairs through online peer-to-peer marketplaces.We found abundant listings for manual wheelchairs on online peer-to-peer marketplaces, both by private individuals and businesses.Without mechanisms to verify appropriateness or condition, wheelchairs purchased through online peer-to-peer marketplaces may not meet established global standards for wheelchair provision.While such workarounds may ease access to critical medical equipment in the short term, they mask underlying systemic issues and put vulnerable individuals with poor health literacy at risk.

Advances in omics technologies, such as epigenomics and metabolomics, provide novel insights into the biological mechanisms underlying Alzheimer's disease (AD). However, little is known how different omics layers interact and jointly relate to AD neuropathology. We performed a comprehensive single- and multi-omics analysis integrating genome-wide DNA methylation and high-resolution metabolomics data from 157 frontal cortex samples. We developed novel single and multi-omics profile scores (PS) for AD pathology, using a combination of machine learning, regression, and pathway analysis. For the ABC score (Amyloid, Braak, CERAD) the PS of DNAm outperformed metabolomics-based PS (median R†: 0.11 vs. 0.04). Combining both omics layers with the best-performing multi-omics PS yielded a partial R† of 0.15 for the ABC score independent of age, sex, race and socioeconomic factors. DNAm-specific pathways highlighted redox balance, immune activation, synaptic signaling, and lipid biosynthesis, whereas metabolomics-specific pathways emphasized inflammatory, hormonal, lipid, and energy metabolism. Notably, both omics layers converged on lipid metabolism and signal transduction as shared biological systems implicated in AD neuropathology. Despite limited gains in predictive accuracy, integrative pathway and network analyses of DNAm and metabolomics PS converged on lipid metabolism and signal transduction, underscoring shared biological mechanisms and the value of multi-omics approaches for biological insight rather than prediction alone.

In recent years, machine learning and artificial intelligence approaches have been increasingly applied in the context of toxicological risk assessment. Many published overview, review, and comment papers discuss advantages, disadvantages, success stories, and open challenges for the application of machine learning models in toxicology. Machine learning methods using information from in vitro experiments can help to avoid animal experiments, thus allowing for larger numbers of experiments to be conducted. Drawbacks of machine learning models are the lack of mechanistic interpretability and the need for large amounts of high-quality data. In this work, we present a literature review of papers indexed in PubMed or published in the journal Computational Toxicology in the years 2022 to 2024, to assess the usage of machine learning methods in toxicology as well as the practices in reporting of methods and corresponding results. We do not address the suitability or the performance of methods, which is impossible to assess objectively without reanalysis on raw data, but focus on common practices and gaps in reporting. Major results are that many different machine learning methods are used in toxicology, often with appropriate internal validation. However, in only half of the cases, interpretation methods are used to address the problem that these models often make predictions as a black box. Moreover, there are very frequent gaps in reporting, in particular related to handling of missing values, and availability of data and code. Thus, this review can serve as a starting point for further tailored methodological research and guidance.

A standard assumption in game theory is that decision-makers have preplanned strategies telling them what actions to take for every contingency. In contrast, nonstrategic decisions often involve an on-the-spot comparison process, with longer response times (RT) for choices between more similarly appealing options. If strategic decisions also exhibit these patterns, then RT might betray private information and alter game theory predictions. Here, we examined bargaining behavior to determine whether RT reveals private information in strategic settings. Using preexisting and experimental data from eBay, we show that both buyers and sellers take hours longer to accept bad offers and to reject good offers. We find nearly identical patterns in the two datasets, indicating a causal effect of offer size on RT. However, this relationship is half as strong for rejections as for acceptances, reducing the amount of useful private information revealed by the sellers. Counter to our predictions, buyers are discouraged by slow rejections-they are less likely to counteroffer to slow sellers. We also show that a drift-diffusion model (DDM), traditionally limited to decisions on the order of seconds, can account for decisions on the order of hours, sometimes days. The DDM reveals that more experienced sellers are less cautious and more inclined to accept offers. In summary, strategic decisions are inconsistent with preplanned strategies. This underscores the need for game theory to incorporate RT as a strategic variable and broadens the applicability of the DDM to slow decisions.

The most effective and expedient way to improve the supply of the population with the essential nutrients is additional enrichment of food and the use of functional food, food for special dietary uses and dietary supplements in nutrition. One of the micronutrient delivery systems is liposome - microscopic phospholipid vesicle. The purpose of the review was to characterize the methods of obtaining liposomal forms of nutrients, to analyze the range of liposomal forms of nutrients of domestic and foreign production. Material and methods. Literature data were searched using library platforms PubMed, eLIBRARY, scholar.google mainly for the last 5 years, by keywords: liposomes; liposomal dietary supplements. E-commerce platforms (pharmacy aggregators Apteka.ru, Yuteka, marketplaces Amazon, ebay) have been analyzed. Results. The review describes classification, methods of obtaining liposomal forms of nutrients. Foreign manufacturers produce liposomal forms of individual vitamins (D3, B12, C) and their combinations, mineral elements (magnesium, iron), as well as coenzyme Q10, peptides. Liposomal forms of individual vitamins (A, B9, C), iron, vitamin B complex, glutathione had been registered in the Russian Federation as dietary supplements. There are evidences of a faster increase in serum calcidiol level compared to the oil form when taking vitamin D3 in liposomal form and improved correction of iron deficiency in patients when using liposomal forms of iron. Conclusion. The creation of liposomal forms of micronutrients is a promising direction for the production of foods for special dietary uses.

This chapter discusses multifractal texture estimation and characterization of brain lesions (necrosis, edema, enhanced tumor, nonenhanced tumor, etc.) in magnetic resonance (MR) images. This work formulates the complex texture of tumor in MR images using a stochastic model known as multifractional Brownian motion (mBm). Mathematical derivations of the mBm model and corresponding algorithm to extract the spatially varying multifractal texture feature are discussed. Extracted multifractal texture feature is fused with other effective features to enhance the tissue characteristics. Segmentation of the tissues is performed using a feature-based classification method. The efficacy of the mBm texture feature in segmenting different abnormal tissues is demonstrated using a large-scale publicly available clinical dataset. Experimental results and performance of the methods confirm the efficacy of the proposed technique in an automatic segmentation of abnormal tissues in multimodal (T1, T2, Flair, and T1contrast) brain MRIs.