Dresden University of Applied Sciences

Closed-form solutions of non-linear least squares problems avoid the risk of divergence or drifting to a local minimum as in the iterative methods. The unique solution is obtained with low numerical effort. In this contribution, a closed-form solution of the non-symmetric planar three-parameter transformation problem is derived in detail. The presented solution considers a fully populated weight matrix and is not restricted to infinitesimal rotation angles. The analysis of the objective function demonstrates that there may also be a secondary local minimum. Applying iterative approaches have the risk of getting stuck in the local minimum of the objective function.

Objective. Mathematical modeling can offer valuable insights into the behavior of biological systems upon treatment. Different mathematical models (empirical, semi-empirical, and mechanistic) have been designed to predict the efficacy of either hyperthermia (HT), radiotherapy (RT), or their combination. However, mathematical approaches capable of modeling cell survival from shared general principles for both mono-treatments alone and their co-application are rare. Moreover, some cell cultures show dose-dependent saturation in response to HT or RT, manifesting in survival curve flattenings. An advanced survival model must, therefore, appropriately reflect such behavior.Approach. We propose a mathematical approach to model the effect of both treatments based on the general principle of sublethal damage (SLD) accumulation for the induction of cell death and irreversible proliferation arrest. Our approach extends Jung's model on heat-induced cellular inactivation by incorporating dose-dependent recovery rates that delineate changes in SLD restoration.Main results. The resulting unified model (Umodel) accurately describes HT and RT survival outcomes, applies to simultaneous thermoradiotherapy modeling, and is particularly suited to reproduce survival curve flattening phenomena. We demonstrate the Umodel's robust performance (R2 0.95) based on numerous clonogenic cell survival data sets from the literature and our experimental studies.Significance. The proposed Umodel allows using a single unified mathematical function based on generalized principles of accumulation of SLD with implemented radiosensitization, regardless of the type of energy deposited and the mechanism of action. It can reproduce various patterns of clonogenic survival curves, including any flattening, thus encompassing the variability of cell reactions to therapy, thereby potentially better reflecting overall tumor responses. Our approach opens a range of options for further model developments and strategic therapy outcome predictions of sequential treatments applied in different orders and varying recovery intervals between them.

Abstract Sealing and degradation still characterise risky land management today. Despite intensive research, even the assessment of key components such as soil organic matter has been unsatisfactory, limiting the sustainable development of foundations of civilisation. A comparison was made between natural and cultivated soils to identify the changes caused by land use. This resulted in a more precise quantification of known relationships and the discovery of new relationships between soil components that have evolved over thousands of years without human influence and that show specific changes with land use. This opens up unexpected possibilities for indirect soil analysis. It seems possible to distinguish between natural soils and soil-like carbon-containing mineral mixtures (e.g., compost and horticultural substrates). Carbon added to soils with fresh organic residues or from anthropogenic (soot, slag) or geological (coal) sources can be distinguished from soil organic matter regardless of chemical heterogeneity. The degree of carbon sequestration in soils appears to be easy to quantify. To contribute to the preservation of soil functions and the development of sustainable land use technologies, elucidation of the causal processes involved in these relationships is desirable.

The global value creation networks have experienced increased volatility and dynamic behavior in recent years, resulting in an acceleration of a trend already evident in the shortening of product and technology cycles. In addition, the manufacturing industry is demonstrating a trend of allowing customers to make specific adjustments to their products at the time of ordering. Not only do these changes require a high level of flexibility and adaptability from the cyber-physical systems, but also from the employees and the supervisory production planning. As a result, the development of control and monitoring mechanisms becomes more complex. It is also necessary to adjust the production process dynamically if there are unforeseen events (disrupted supply chains, machine breakdowns, or absences of staff) in order to make the most effective and efficient use of the available production resources. In recent years, reinforcement learning (RL) research has gained increasing popularity in strategic planning as a result of its ability to handle uncertainty in dynamic environments in real time. RL has been extended to include multiple agents cooperating on complex tasks as a solution to complex problems. Despite its potential, the real-world application of multi-agent reinforcement learning (MARL) to manufacturing problems, such as flexible job-shop scheduling, has been less frequently approached. The main reason for this is most of the applications in this field are frequently subject to specific requirements as well as confidentiality obligations. Due to this, it is difficult for the research community to obtain access to them, which presents substantial challenges for the implementation of these tools. This paper focuses on the application and comparison of single-agent RL as well as MARL algorithms for solving the problem of dynamic scheduling in the form of an intelligent resource allocation problem using a model factory as an example, where the objective is to reduce the makespan of given jobs. To reduce the entry barriers for other researchers and to ensure reproducibility, a simulation environment is provided to the research community, which was also used to perform the experiments of this study. By including redundant operations, variations in order compositions, product variants, setup times, and an automated material transportation tools, a realistic approach is made possible. Moreover, the (composite of) intelligent dispatcher(s) is faced with variations in operation times and breakdowns. Further, this study investigates the convergence behavior of trained RL and MARL models, as well as their performance in handling unknown and unforeseen scenarios compared to heuristic approaches. The experiments demonstrate that even under significant time constraints, RL, especially under the multi-agent setting with Proximal Policy Optimization (PPO) algorithm at the core, is able to outperform conventional heuristic methods when dealing with the complex problem of production scheduling under uncertainty.

This paper asks the right questions and comes to an important and correct logical answer: While any mass of an object means that it has an equivalent form of energy, the inverse is not true: Not every form of energy contributes to the mass of a body. Or in the words of the author: "that energy not always has mass". The author thinks for himself and is not predetermined and dictated by authorities. And indeed, the major energetic problems of modern physics result precisely from the misinterpretation of E = mc 2 as total energy. Please also allow me to briefly discuss some contents of the reviews to the paper: The reviewers who claim that it is known that electromagnetic energy E hf does "not have mass" or does not contribute to the mass of an object are shortsighted. Since Poincaré it is known and experimentally proven that a photon gas in a cavity behaves like a fluid with the dynamic mass. And free photons have dynamic mass, too. How else could one explain why photons can hit solar sails and accelerate them? Also, Planck had already known since 1904 that a warmer body (containing more photons and thus more E hf ) is heavier. Every form of energy contributes to the mass of a body, with the exception of positional energy Epot, because the mass-proportionality of E pot assumed by Einstein contradicts the conservation of energy: https://www.htwdresden.de/en/luc/research/translate-to-english-thermodynamik/thermodynamics-vs-relativity

Auf den ersten Blick scheint uns das Themengebiet Digitale Identitäten bereits gut vertraut. Wir nutzen in unserem immer stärker von Technologie durchdrungenen Alltag eine Vielzahl digitaler Identitäten, ohne länger darüber nachzudenken. Überall dort, wo wir uns -mehr oder weniger gut überprüfbar -identifizieren und authentifizieren müssen, besitzen wir auch eine digitale Identität. Eine Anmeldung zu Social Media hier, ein Bezahlvorgang im stationären Handel oder im Webshop dort und nicht zuletzt die elektronische Steuererklärung beim Finanzamt suggerieren, dass das Thema digitale Identitäten leicht zu überblicken ist. Weit gefehlt! Zwar ist eine "Digitale Identität" gemäß ISO/IEC 24760-1:2019 scheinbar leicht als Datensatz beschreibbar. Doch mit dem Verständnis dieses Datensatzes ist das Thema nicht ansatzweise erschlossen und verstanden. Im Gegenteil: Die Norm selbst beschreibt einen "kleinsten gemeinsamen Nenner" aus technischer Sicht. Zahlreiche weitere Regelungen sowie technische und fachliche Anforderungen müssen beachtet werden, um alltagstaugliche und rechtskonforme Verfahren zu realisieren. Allerdings können viele Dienste und Anwendungen nicht auf bereits bestehende digitale Identitäten zurückgreifen, sondern erfordern eine erneute Registrierung, Identifizierung und Datenverifikation. Damit entstehen viele digitale Identitäten, die jeweils nur für sehr begrenzte Zwecke einsetzbar sind. So kommt es zu einer großen Fragmentierung von Diensten, Benutzerkonten, Apps und Sicherheitsprozeduren, die wir im digitalen Alltag oft als Hindernisse empfinden.

Editorial