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Weltweit droht bis 2040 eine drastische #Wasserknappheit.
Besonders betroffen sind der #Mittelmeerraum, #Nordafrika und Teile von

Folge des Klimawandels: Millionen Menschen könnten bald von extremer Wasserknappheit betroffen sein
Bis zum Jahr 2040 droht vielerorts extreme Wasserknappheit, besonders im Mittelmeerraum, in Afrika und Nordamerika. Forscher warnen: Die Zeit zwischen Dürren wird kürzer, Wasserspeicher geraten in Gefahr.

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in A Digital World
J. Recker, R. Lukyanenko, M. A. Jabbari, B. M. Samuel, A. Castellanos
https://arxiv.org/abs/2506.18743

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in A Digital World
The role of information systems (IS) as representations of real-world systems is changing in an increasingly digitalized world, suggesting that conceptual modeling is losing its relevance to the IS field. We argue the opposite: Conceptual modeling research is more relevant to the IS field than ever, but it requires an update with current theory. We develop a new theoretical framework of conceptual modeling that delivers a fundamental shift in the assumptions that govern research in this area. T…

Canyon "Citylite:On": Stadtrad mit Bosch-Mittelmotor und Riemenantrieb
Der Koblenzer Fahrradhersteller Canyon hat mit dem Citylite:ON ein neues E-Bike für die Stadt vorgestellt. Es kommt mit Bosch-Mittelmotor und Riemenatrieb.

Autoregressive-Gaussian Mixture Models: Efficient Generative Modeling of WSS Signals
Kathrin Klein, Benedikt B\"ock, Nurettin Turan, Wolfgang Utschick
https://arxiv.org/abs/2509.17953

Autoregressive-Gaussian Mixture Models: Efficient Generative Modeling of WSS Signals
This work addresses the challenge of making generative models suitable for resource-constrained environments like mobile wireless communication systems. We propose a generative model that integrates Autoregressive (AR) parameterization into a Gaussian Mixture Model (GMM) for modeling Wide-Sense Stationary (WSS) processes. By exploiting model-based insights allowing for structural constraints, the approach significantly reduces parameters while maintaining high modeling accuracy. Channel estimat…

DoubleGen: Debiased Generative Modeling of Counterfactuals
Alex Luedtke, Kenji Fukumizu
https://arxiv.org/abs/2509.16842 https://arxiv.org/pdf/2509.16842…

DoubleGen: Debiased Generative Modeling of Counterfactuals
Generative models for counterfactual outcomes face two key sources of bias. Confounding bias arises when approaches fail to account for systematic differences between those who receive the intervention and those who do not. Misspecification bias arises when methods attempt to address confounding through estimation of an auxiliary model, but specify it incorrectly. We introduce DoubleGen, a doubly robust framework that modifies generative modeling training objectives to mitigate these biases. Th…

From OCL to JSX: declarative constraint modeling in modern SaaS tools
Antonio Bucchiarone, Juri Di Rocco, Damiano Di Vincenzo, Alfonso Pierantonio
https://arxiv.org/abs/2509.17629

From OCL to JSX: declarative constraint modeling in modern SaaS tools
The rise of Node.js in 2010, followed by frameworks like Angular, React, and Vue.js, has accelerated the growth of low code development platforms. These platforms harness modern UIX paradigms, component-based architectures, and the SaaS model to enable non-experts to build software. The widespread adoption of single-page applications (SPAs), driven by these frameworks, has shaped low-code tools to deliver responsive, client side experiences. In parallel, many modeling platforms have moved to th…

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling
Leandro Von Krannichfeldt, Kristina Orehounig, Olga Fink
https://arxiv.org/abs/2507.17526

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling
Building energy modeling is a key tool for optimizing the performance of building energy systems. Historically, a wide spectrum of methods has been explored -- ranging from conventional physics-based models to purely data-driven techniques. Recently, hybrid approaches that combine the strengths of both paradigms have gained attention. These include strategies such as learning surrogates for physics-based models, modeling residuals between simulated and observed data, fine-tuning surrogates with…

Mag je de democratie verdedigen met niet-democratische middelen, en zo ja, waar vecht je dan nog voor? | @…
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Active RISs: Modeling and Optimization
Recep Akif Tasci, Panagiotis Gavriilidis, Ertugrul Basar, George C. Alexandropoulos
https://arxiv.org/abs/2507.16499 https://

Active RISs: Modeling and Optimization
Reconfigurable Intelligent Surfaces (RIS)-empowered communication has emerged as a transformative technology for next generation wireless networks, enabling the programmable shaping of the propagation environment. However, conventional RISs are fundamentally limited by the double path loss effect, which severely attenuates the reflected signals. To overcome this, active RIS architectures, capable of amplifying impinging signals, have been proposed. This chapter investigates the modeling, perfor…

Conceptual Modelling for Life Sciences Based on Systemist Foundations
R. Lukyanenko, O. Pastor, V. C. Storey
https://arxiv.org/abs/2506.18742 https://

Conceptual Modelling for Life Sciences Based on Systemist Foundations
All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling c…