To describe differences in regionalization of hip fracture care and the volume-outcome relationship in five countries. We conducted a population-based cross-sectional cohort study in Canada, Israel, the Netherlands, Taiwan, and the United States. Within each country, we stratified patients into quintiles based upon the volume of hip fractures in the hospital where they were treated. We measured regionalization by the proportion of acute-care hospitals that treated patients with hip fractures and summarized the hospital volume distribution by the ratio of hip fracture volumes for high-volume hospitals versus low-volume hospitals. We then examined age- and sex-standardized outcomes and treatment for patients treated at high-volume and low-volume hospitals. We used nationally representative administrative data on adults aged ≥ 66 years hospitalized with hip fracture from 2011 to 2019. We followed them until death or 365 days after the discharge date. Across countries, the percentage of all acute-care hospitals that treated hip fractures differed widely (from 37.0% in Canada to 82.8% in Israel), with high-volume hospitals treating 4-14 times as many hip fractures as low-volume hospitals. The absolute risk-adjusted difference in 30-day mortality for high-volume compared to low-volume hospitals ranged between (-1.9% [95% CI, -2.2 to -1.7] in Canada and +1.1% [95% CI, 0.4-1.8] in the Netherlands). The proportion of patients receiving non-operative fracture treatment was lower in high-volume hospitals than low-volume hospitals in all countries (-5.4% [95% CI, -6.5 to -4.3] in Israel to -0.1% [95% CI, -0.5 to 0.3] in the Netherlands). Hip fracture regionalization differed substantially across countries. The direction and the magnitude of association between greater regionalization and improved patient outcomes were inconsistent across countries.

Now in its sixth edition, this textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. Each chapter features hands-on exercises that showcase applications across various fields of multivariate data analysis. These exercises utilize high-dimensional to ultra-high-dimensional data, reflecting real-world challenges in big data analysis. For this new edition, the book has been updated and revised and now includes new chapters on modern machine learning techniques for dimension reduction and data visualization, namely locally linear embedding, t-distributed stochastic neighborhood embedding, and uniform manifold approximation and projection, which overcome the shortcomings of traditional visualization and dimension reduction techniques. Solutions to the book’s exercises are supplemented by R and MATLAB or SAS computer code and are available online on the Quantlet and Quantinar platforms. Practical exercises from this book and their solutions can also be found in the accompanying Springer book by W.K. Härdle and Z. Hlávka: Multivariate Statistics - Exercises and Solutions.

This white paper explores the concept of "Location Profiling" as a critical instrument for enhancing logistics efficiency. In an era of increasing digitalisation and interconnectedness, the availability of precise and reliable location data is critical for the optimisation of logistics processes, the reduction of costs, and the facilitation of strategic decision-making. The paper sets out to investigate the challenges that businesses encounter when managing location data. Such challenges include data inconsistencies, outdated information, and integration issues. It introduces innovative solutions and technologies that enable real-time processing and dynamic updates of location information. The concept of Location Profiling, which extends beyond basic geographical data to encompass detailed infrastructural and operational insights, is presented as a strategic resource for enhancing logistics operations. The use cases provided offer a practical illustration of the ways in which Location Profiling can support network planning, route optimisation, asset management and autonomous driving. The paper's central theme is the importance of integrating robust location data systems into business operations to foster innovation, efficiency, and long-term competitiveness in logistics.

In recent years, process mining has emerged as the leading Big Data technology for business process analysis. By extracting knowledge from event logs readily available in information systems, process mining provides new ways to discover, monitor, and improve processes while being agnostic to the source system. Despite its undisputed practical relevance, we have a limited understanding of how organizations realize value potentials from applying process mining in different organizational contexts. Addressing this gap, we conduct an assessing literature review by analyzing 58 papers from the literature on process mining to synthesize the existing knowledge on business value realization from process mining. Our analysis is guided by adopting the perspective of process mining embedded within its organizational context. By analyzing the dimensions of the nomological net around process mining, we contribute to the broader research field of Big Data value realization twofold. First, we uncover which benefits organizations gain by applying process mining. Second, we analyze the organizational capabilities and practices that influence how organizations use and implement process mining. In addition, we reveal how process mining leads to business value realization. Based on these results, we suggest directions for future research on process mining in the organizational context.

Although German companies have a strong innovative tradition, they must use intellectual property creatively if they are to be successful in meeting the economic challenges of the 21st century.