Sculpting The Floods

The following news item is taken in part from the July 27, 2011 issue of Science titled ''9 Billion?,@ by Leslie Roberts.

This paper responds to an increasing interest in how data-driven interactive artworks can support a greater understanding of the relationship between humans and data. This paper extends this work by focusing on the The Prediction Machine, an interactive and data-driven artwork that arose from collaborations between an artist, climate scientists, HCI researchers and the wider public. A study of the practice-led process reveals: (a) how the artist "performed" scientific data across multiple outputs; (b) how the artist walked a line between scientific and artistic process; (c) how the public experienced data across multiple components of the artwork; (d) how the artwork can be seen as process rather than a exhibition or artifact; and (e) how tensions between artistic strategies and scientific processes inherent in the creation of data-driven artworks might inform future work involving public engagement with scientific data.

The last decade has seen an explosion in the work done in the development of robust natural language processing systems. A common methodology used in building these systems has been to analyze a sample of the data available (either manually, or automatically for training statistical systems), build statistical/heuristical schemas based upon the analysis, and test the system on a blind sample of the data. Due to this commonly used paradigm, an important area of research that has not been given the attention it deserves is the estimation of syntactic and semantic complexity faced by these systems in the tasks they perform.The Workshop on Syntactic and Semantic Complexity in Natural Language Processing Systems, held on April 30th, 2000 at the Language Technology Joint Conference on Applied Natural Language Processing and the North American Chapter of the Association of Computational Linguistics (ANLP-NAACL2000) was organized around the goals of discussing, promoting, and presenting new research results regarding the question of complexity as it pertains to the syntax and semantics of natural language. In particular, the goal of the workshop was to focus on:•estimation of the syntactic and semantic complexity of specific NLP tasks•semantic complexity and world knowledge•role of syntactic and semantic complexity in system design and testing•syntactic and semantic complexity and its role in the evaluation of NLP systems•use of syntactic and semantic complexity as a performance predictor•relationship between syntactic and semantic complexity

The last decade has seen an explosion in the work done in the development of robust natural language processing systems. A common methodology used in building these systems has been to analyze a sample of the data available (either manually, or automatically for training statistical systems), build statistical/heuristical schemas based upon the analysis, and test the system on a blind sample of the data. Due to this commonly used paradigm, an important area of research that has not been given the attention it deserves is the estimation of syntactic and semantic complexity faced by these systems in the tasks they perform. The Workshop on Syntactic and Semantic Complexity in Natural Language Processing Systems, held on April 30th, 2000 at the Language Technology Joint Conference on Applied Natural Language Processing and the North American Chapter of the Association of Computational Linguistics (ANLP-NAACL2000) was organized around the goals of discussing, promoting, and presenting new research results regarding the question of complexity as it pertains to the syntax and semantics of natural language. In particular, the goal of the workshop was to focus on: •estimation of the syntactic and semantic complexity of specific NLP tasks •semantic complexity and world knowledge •role of syntactic and semantic complexity in system design and testing •syntactic and semantic complexity and its role in the evaluation of NLP systems •use of syntactic and semantic complexity as a performance predictor •relationship between syntactic and semantic complexity

Introduction . Amid rapid technological progress, globalisation of scientific activity, and increasing complexity of socio-technical systems, traditional ethical models for research are showing their limitations. There is an urgent need to reconsider and transform ethical norms, regulatory approaches, and institutional practices. Objective . To provide a comprehensive prognostic analysis of the transformation of research ethics and to conceptualise the contours of a new paradigm capable of addressing the challenges of the coming decade. Methodology . Based on an analysis of current trends and drivers of change — data-driven science, artificial intelligence, convergence of NBICS technologies, the shift toward open science, and globalisation — the authors formulate the principles of a new conceptual model and propose possible institutional forms for its implementation. Results . The study substantiates the inevitable transition from a reactive, retrospective ethics to a proactive, holistic, and constructive paradigm, termed the "ethics of entanglement." This model accounts for the systemic interdependence of techno logical, environmental, and social systems. Key challenges are identified, including algorithmic bias, informed consent in the era of big data, the hybrid nature of new technological objects, global inequality, and the paradoxes of open science. Specific mechanisms for transformation are proposed, such as ethical impact assessments, interdisciplinary ethics review boards, digitalisation of ethical oversight, and reform of scientific education. Conclusion . The coming decade will mark a paradigmatic shift in research ethics. Successful adaptation to new realities will require a new social contract between science and society, based on anticipatory risk management, fairness and transparency, and the demonstration of the scientific community's moral maturity.

European science for health research needs and priorities.

When discussing the fundamental concepts of modeling continuous-time and discrete- time real-world systems in Chap. 1 it was noted that an accurate mathematical model is necessary for the use of computer simulation, which is focused on a better and/or deeper understanding of the dynamic behavior of real-world systems. The complexity of man-made systems in engineering and science, as well as the complexity of systems in biology, medicine, and nature, mostly do not allow closed analytical solutions for all the sets of linear and/or nonlinear mathematical equations as they have been outlined in Chap. 2 to describe real-world systems. Assuming that the model has successfully been described, meaning the realworld system is represented in terms of differential equations, partial differential equations, state-space equations, difference equations, queues, Petri-nets, etc., a solution of which can be obtained based on computational simulation. Using computers for solving the equations that describe real-world systems in an effective and sufficient way, numerical integration methods are of importance. This is why, for a number of years, considerable effort has been devoted to the development of simulation software for continuous-time and discrete-time systems.

The painting on fabric (or the batik technique) became firmly established in the educational process and creative practice of artists at the turn of the 20th–21st centuries. However, in Russian art history there are no publications that reflect the specifics of the spread of this technique in Europe, the USA and Russia. This article is devoted to the peculiarities of the use of painting on fabric in textile factories and its role in the development of the Leningrad school of artistic textiles. This article is about the peculiarities of using fabric painting in textile factories and its role in the development of the Leningrad school of artistic textiles. The purpose of the article is to summarize information about the applying of batik in the textile industry and art workshops. The article not only describes the historical stages of the introduction of batik into artistic practice, but also introduces into scientific circulation new data related to previously unpublished works of Leningrad textile artists. The author shares the concepts of “fabric painting”, “direct painting on fabric”, “hot batik”. It was revealed that in the USSR the most common term was “fabric painting,” which, on the one hand, reflected the hand-made nature of the technique, and on the other, emphasized that painted curtains and panels belonged to the sphere of monumental and decorative art. It has been established that the assortment has changed from small products to monumental curtains; from household items to art exhibits. It is shown that the evolution of artistic painting on fabric influenced educational programs in universities and technical schools; batik became a part of the art exhibitions and interiors of public buildings throughout the USSR. Based on the archival sources, Soviet periodicals and interviews with artists, the author concludes that painting on fabric was one of the main directions in which Leningrad textiles developed in the 1950–1980s.

Geologic carbon storage projects are maturing worldwide and the footprint of deployment in the offshore is expanding. At present, there are ten projects in operation or that have been completed, more than 50 in construction and development, and dozens of characterization studies completed or underway. Offshore geologic carbon storage offers potential benefits over onshore geologic carbon storage. These offshore projects are generally remote in location, distant from population centers, and avoid complicated pore space rights while having abundant prospective storage potential. Some offshore fields targeted for carbon storage have comparatively fewer prior borehole penetrations except for areas that have been explored for petroleum production, minimizing potential issues such as pressure interference and infrastructure impacts. Yet offshore geologic carbon storage projects face distinctive technical and economic challenges, such as seafloor geohazards (e.g., seabed instability), expensive maritime transport, and meteorological-oceanographic conditions that can damage infrastructure and impact operations. Analytical capabilities and improved computational speeds have advanced engineering, earth and energy sciences in the wake of the arrival of modern data science over the last decade. These advancements have created an opportunity for integrated, multi-systems modeling approaches utilizing artificial intelligence and machine learning that are no longer limited by computational issues. Analytical tools developed alongside this advancement in data science can be leveraged to calibrate the potential advantages and challenges of carbon storage operations in the offshore. New methods and approaches that incorporate data science to analyze multiple aspects of engineered and natural systems can provide insights that complement the characterization and onsite engineering that traditional commercial and operational software addresses. These new methods and approaches can potentially improve the outcome of energy operations and carbon storage. Providing multi-system, science-driven data analytics enhances the knowledge base that offshore developers, operators, and regulatory bodies may draw from to improve offshore site selection and operational efficiency. We provide a brief synopsis of geologic carbon storage efforts to date, an overview of the engineered and natural systems involved in offshore geologic carbon storage, and a review of publicly available, open-source, offshore and/or carbon storage related data- and science-driven tools developed by 2010 or later that are suitable for screening and assessing regions for offshore geologic carbon storage.

The need for IT specialists in radiation oncology - A position paper by the International Society for radiation oncology Informatics, endorsed by DGMP, SASRO, ÖGRO, ÖGMP, SRO and DEGRO.

Drug discovery is a complex, lengthy, and costly process with a high failure rate, especially during clinical trials. The integration of Artificial Intelligence (AI) has revolutionized various stages of drug discovery by enabling faster and more accurate analysis of biological and chemical data. However, most AI models in this field operate as “black boxes,” where their decision-making processes are opaque and difficult to interpret. This lack of transparency poses significant challenges in terms of trust, validation, and adoption of AI-generated predictions in both clinical and regulatory settings. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a promising approach to improve the interpretability of AI models without compromising their predictive power. This study aims to systematically review the opportunities, challenges, and future directions of XAI implementation in drug discovery. Using a qualitative method with a systematic literature review approach, data were collected from reputable databases including Scopus, PubMed, IEEE Xplore, SpringerLink, and ScienceDirect, focusing on publications from 2018 to 2024. The analysis identified five main themes: the role of XAI in molecular target identification, application of XAI in compound screening and molecular structure optimization, interpretation of drug toxicity predictions, challenges in XAI implementation, and future research directions. XAI techniques such as SHAP and LIME have proven useful in explaining AI model predictions, improving biological validation, and enabling more informed decision-making by scientists. However, significant challenges remain, including the trade-off between interpretability and accuracy, lack of universal standards, and the complexity of modeling biological systems. This study highlights the critical need for developing standardized interpretability frameworks, user-friendly interfaces, and collaborative environments between data scientists and healthcare professionals to foster XAI adoption in real-world drug discovery processes. Ultimately, XAI has the potential to increase transparency, trust, and efficiency, paving the way for safer and more effective therapeutic developments.

On the one hand, Industry 4.0 provides possibilities to address arising challenges such as globalisation, individualisation and shortening product lifecycles. On the other hand, it also increases changes and challenges in planning and operation processes of production systems.The paper discusses the changes in digital work in the areas of planning, operating and improving smart production systems. Current research approaches show that especially in planning processes and supportive tasks a high dynamic is evident, but also the work on the shop floor is changing. Automation technology and intelligent algorithms as a base for production planning and control up to factory-as-a-service concepts reduce operational room for manual actions, but require new digital planning, implementation and maintenance tasks. Furthermore, technologies like cobots enable new forms of flexible coexistence between human and machine in production systems. Due to the increasing complexity of products and production systems, conventional improvement approaches from the fields of Lean Management and Six Sigma are reaching their limits, as the analyses are often limited to simple relationships and correlations. Data science in the industrial environment enables new opportunities to analyse large volumes of data to identify multivariate patterns and correlations. All of this leads to new requirements for competences, roles and work organisation.

As a crucial component of rechargeable batteries, electrolytes significantly determine interfacial characteristics and device performance. The development of advanced electrolytes relied on empirical trial‐and‐error and theoretical calculations mainly. Recently, the data‐driven methods represented by machine learning (ML) have made progress in the materials field. However, interdisciplinary barriers and the multicomponent complexity of electrolyte systems impede the advancement of artificial intelligence (AI) in the electrolyte domain. Therefore, examining the application of ML methods in liquid and solid‐state electrolytes to bridge the gap between battery science and data science is necessary. Firstly, this review summarizes the main technical routes and ion transport mechanisms of different electrolytes. Secondly, the basic concepts and suitable tasks of various ML algorithms are outlined. Following this, we investigate mainstream representation methods for electrolyte materials, which transfer material structures to machine‐readable input vectors. Subsequently, representative application cases of ML in liquid and solid‐state electrolytes are summarized. Finally, a perspective on the current challenges and future frontiers of AI‐driven electrolyte research is provided. This review offers a deep understanding of this interdisciplinary field, providing intelligent insight for advanced battery electrolyte innovation.

The “Seven Pillars” of oxidation catalysis proposed by Robert K. Grasselli represent an early example of phenomenological descriptors in the field of heterogeneous catalysis. Major advances in the theoretical description of catalytic reactions have been achieved in recent years and new catalysts are predicted today by using computational methods. To tackle the immense complexity of high-performance systems in reactions where selectivity is a major issue, analysis of scientific data by artificial intelligence and data science provides new opportunities for achieving improved understanding. Modern data analytics require data of highest quality and sufficient diversity. Existing data, however, frequently do not comply with these constraints. Therefore, new concepts of data generation and management are needed. Herein we present a basic approach in defining best practice procedures of measuring consistent data sets in heterogeneous catalysis using “handbooks”. Selective oxidation of short-chain alkanes over mixed metal oxide catalysts was selected as an example.

<p>The Pilot Lab Exascale Earth System Modelling (PL-ExaESM) is a “Helmholtz-Incubator Information & Data Science” project and explores specific concepts to enable exascale readiness of Earth System models and associated work flows in Earth System science. PL-ExaESM provides a new platform for scientists of the Helmholtz Association to develop scientific and technological concepts for future generation Earth System models and data analysis systems. Even though extreme events can lead to disruptive changes in society and the environment, current generation models have limited skills particularly with respect to the simulation of these events. Reliable quantification of extreme events requires models with unprecedentedly high resolution and timely analysis of huge volumes of observational and simulation data, which drastically increase the demand on computing power as well as data storage and analysis capacities. At the same time, the unprecedented complexity and heterogeneity of exascale systems, will require new software paradigms for next generation Earth System models as well as fundamentally new concepts for the integration of models and data. Specifically, novel solutions for the parallelisation and scheduling of model components, the handling and staging of huge data volumes and a seamless integration of information management strategies throughout the entire process-value chain from global Earth System simulations to local scale impact models are being developed in PL-ExaESM. The potential of machine learning to optimize these tasks is investigated. At the end of the project, several program libraries and workflows will be available, which provide the basis for the development of next generation Earth System models.</p><p>In the PL-ExaESM, scientists from 9 Helmholtz institutions work together to address 5 specific problems of exascale Earth system modelling:</p><ul><li>Scalability: models are being ported to next-generation GPU processor technology and the codes are modularized so that computer scientists can better help to optimize the models on new hardware.</li> <li>Load balancing: asynchronous workflows are being developed to allow for more efficient orchestration of the increasing model output while preserving the necessary flexibility to control the simulation output according to the scientific needs.</li> <li>Data staging: new emerging dense memory technologies allow new ways of optimizing I/O operations of data-intensive applications running on HPC clusters and future Exascale systems.</li> <li>System design: the results of dedicated performance tests of Earth system models and Earth system data workflows are analysed in light of potential improvements of the future exascale supercomputer system design.</li> <li>Machine learning: modern machine learning approaches are tested for their suitability to replace computationally expensive model calculations and speed up the model simulations or make better use of available observation data.</li> </ul>