Motivational Drivers to Develop Apps for Social Software-Platforms: The Example of Facebook

Online social networks like Facebook or MySpace have enjoyed a formidable success in recent times which is partly due to the provision of software-platforms for the development of social applications through third-party complementors. Consequently, the study of aspects on the role and motivations of these complementors becomes increasingly important. Our empirical study contributes by revealing how the different motivations of social application developers are interrelated and how these motivations influence application developers’ effort intensity on the platform. Drawing on established motivation theories, we develop a theoretical model and test it using empirical data from Facebook application developers. PLS-based structural equation modeling demonstrated that “external rewards” and “status and job opportunity” motives were the dominating motivational drivers. Moreover, we found that external rewards undermine intrinsic motivation, while internalized motives strengthen it. Based on our findings, we discuss practical implications regarding incentive schemes and theoretical implications as starting point for further research

Perceived Software Platform Openness: The Scale and its Impact on Developer Satisfaction

Application developers are of growing importance to ensure that software platforms (e.g. Facebook, Android) gain or maintain a competitive edge. However, despite calls for research to investigate developers’ perspective on platform-centric ecosystems, no research study has been dedicated to identifying the facets that constitute developers’ perception of platform openness. In this paper, we develop a scale of platform openness as perceived by third-party application developers. Using both qualitative and quantitative methods, we conceptualize perceived platform openness as a second-order construct. Empirical evidence from a survey of Android application developers (N=254) support this construct’s validity. Furthermore, we identify perceived platform openness as a major driver of complementors’ overall satisfaction with the platform. Our study thus contributes to a better understanding of platform openness in particular and the management of platform-centric ecosystems in general

Accelerated evidence synthesis in orthopaedics—the roles of natural language processing, expert annotation and large language models

peer reviewedIn an era of electronical medical records, rapidly expanding publication rates of medical knowledge, and large-scale registries, orthopaedics is in a dire need of innovative approaches to facilitate the adoption of the latest knowledge in clinical practice. While machine learning (ML) has been heralded as one solution to many research tasks hampered by previous technological limitations [12], there is an increasing need to direct our attention towards subdomains of ML that are convenient for the extraction of meaningful clinical information stored in medical records. We believe natural language processing (NLP) to be one such domain of ML, with an immense future potential to catalyse rate-limiting steps in orthopaedic research

El primer genoma mitocondrial completo de Diadema antillarum (Diadematoida, Diadematidae)

The mitochondrial genome of the long-spined black sea urchin, Diadema antillarum, was sequenced using Illumina next-generation sequencing technology. The complete mitogenome is 15,708 bp in length, containing two rRNA, 22 tRNA and 13 protein-coding genes, plus a noncoding control region of 133 bp. The nucleotide composition is 18.37% G, 23.79% C, 26.84% A and 30.99% T. The A + T bias is 57.84%. Phylogenetic analysis based on 12 complete mitochondrial genomes of sea urchins, including four species of the family Diadematidae, supported familial monophyly; however, the two Diadema species, D. antillarum and D. setosum were not recovered as sister taxa.El genoma mitocondrial del erizo de mar negro de espinas largas, Diadema antillarum, se secuenció utilizando la tecnología de secuenciación de nueva generación de Illumina. El mitogenoma completo tiene un tamaño de 15,708 pb, que contiene dos ARNr, 22 ARNt y 13 genes codificadores de proteínas, además de una región de control no codificante de 133 pb. La composición de nucleótidos es 18.37% G, 23.79% C, 26.84% A y 30.99% T. El sesgo A+T es del 57.84%. El análisis filogenético basado en 12 genomas mitocondriales completos de erizos de mar, incluyendo cuatro especies de la familia Diadematidae, apoya la monofilia familiar. Sin embargo,  las dos especies de Diadema en este estudio,  D. antillarum y D. setosum no fueron identificadas como taxones hermanos

Abrupt climatic events during the last glacial-interglacial transition in Alaska

Evidence is mounting that abrupt climatic shifts occurred during the last glacial-interglacial transition (LGIT) in the North Atlantic and other regions. However, few high-resolution climatic records of the LGIT exist from the high latitudes of the North Pacific rim. We analyzed lake sediments from southwestern Alaska for biogenic silica, organic carbon, organic nitrogen, diatom assemblages, and compound-specific hydrogen isotopes. Results reveal climatic changes coincident with the Younger Dryas, Intra-Allerod Cold Period, and Pre-Boreal Oscillation. However, major discrepancies exist in the paleoclimate patterns of the Bolling-Allerod interstadial between our data and the GISP2 18O record from Greenland, and causes are uncertain. These data suggest that the North Pacific and North Atlantic experienced similar reversals during climatic warming of the LGIT but that the Bolling-Allerod cooling trend in the GISP2 18O record is probably not a hemispheric or global pattern

A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges.

peer reviewedArtificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV