The hidden homeownership welfare state: an international long-term perspective on the tax treatment of homeowners

Welfare is traditionally understood as social security decommodifying labour markets or as social investment policies. In the domain of housing, however, welfare for homeowners is largely hidden in the tax codes’ fiscal exemptions. Based on a content analysis of legislation, this article introduces a novel yearly database of 37 countries between 1901 and 2020 to uncover the “hidden welfare state” of taxes on imputed rent, deductibility of mortgage payments, housing capital gains tax, and value-added tax on newly built dwellings. Summary indices of homeownership attractiveness and neutrality of the tax code show that fiscal homeownership policies have been in decline until the 1980s and risen ever since. They are in place where finance is liberally and labour restrictively regulated. Contrary to the classical welfare state, they are not associated with an economic logic of industrialism or left-wing governments. They rather are an alternative to rent regulation used by Common-law jurisdictions or smaller countries. As welfare for property owners, the logic of fiscal homeownership welfare diverges from the classical welfare for the labouring classes

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN)

Design and development of automatic recommendation generation module of prescriptive maintenance model (AutoPriMa)

Mit Industry 4.0 wurde eine neue Ära in der Instandhaltung eingeleitet. Mit dem Aufkommen von Cyber-Physical Production Systems (CPPS) und der ständigen Verfügbarkeit von Sensordaten änderte sich die Wartung von der vorausschauenden zur präskriptiven Instandhaltung. Internet of Things (IoT), Data Science und Artifical Intelligence (AI) spielen daher eine wichtige Rolle bei der Entwicklung von Fertigungstechnologien. Es wird erwartet, dass die vorschreibende und präskriptive Instandhaltung bis 2022 um etwa 39% auf jährlich 10,96$Milliarden wächst. Es wird auch von der Smart Manufacturing Leadership Coalition (SMLC) dargelegt, dass die folgenden Ziele durch datengesteuerte Analysen in der intelligenten Fertigung erreicht werden können (1) 30$B by 2022. Smart Manufacturing Leadership Coalition (SMLC) has also predicted that the following targets can be achieved by data driven analytics in smart manufacturing (1), 30% reduction in capital intensity, (2) up to 40% reduction in product cycle times, and (3) overarching positive impact across energy and productivity. Lueth K. et al. (2016) stated in their report that 79% of all decision makers of Original Equipment Manufacturers will see predictive and prescriptive maintenance as one of the most important applications in the next 1-3 years. In the area of prescriptive analytics the goal is to find the best course of action for a given problem, by using techniques like recommendation engines and neural networks for solving a problem. Those techniques can then be converted for use in maintenance. A rising demand for prescriptive maintenance, which offers decision support can be anticipated, while currently predictive maintenance mostly consists of inappropriate maintenance strategies and conditions. According to Cheng et al. (2018) and R. Ranjan (2014) state of the art decision-making processes combine different data sources with data science methods to either improve the system intelligence or establish an automated big data pipeline Cheng et al. (2018) and R. Ranjan (2014). The concept Knowledge Based Maintenance (KBM)9101112 is a key enabler for digital transformation to prescriptive maintenance. As stated by Ansari, Glawar, et al. (2019), the PriMa model and its four-step methodology have been introduced and an applied as part of a proof-of-concept study, however while the paper specifies the methodology and approach in detail, it does not go into detail on how to achieve problem 1 (P1) the data input into the data warehouse, problem 2 (P2) how to build aggregator functions and most importantly, how to handle the feedback loop between the Knowledge-Base and the Decision Support Dashboard problem 3 (P3). This works aims to design an automated PriMa model, specifically focusing on the knowledge pipeline from the textual data from maintenance reports to the recommendation of a solution for the problem identified in the report. These questions have been answered by looking into the requirements given by ODonovan et al. (2015) for the data ingest process and proposing an own requirement list for a data warehouse solution (P1). In the next step three machine learning (ML) algorithms, namely Hamilton Monte-Carlo (HMC), Random Forest (RF) and Neural Networks (NN) reasoning have been generated and minimum working examples provided. Their outputs later on have been aggregated by a weighted hybrid function (P2). For the knowledge pipeline a Natural Language Processing (NLP) algorithm was applied which uses maintenance reports and extracts nouns and verbs. Those than can be matched against an ontology by using case-based reasoning (CBR) with the help of SPARQL. To sum up, the present thesis contributes on design and technical realization of the knowledge pipeline in the context of maintenance by analyzing technical requirements and developing a proof of concept demonstrator.11

Schmankerl Time Machine: Eine kulinarische Zeitreise durch die Speisekarten traditionsreicher Münchner Gaststätten

Kommt bei Ihnen zu Hause immer nur das Gleiche auf den Tisch? Sind Sie auf der Suche nach kulinarischer Vielfalt in Ihrem Alltag? Noch ist nicht Hopfen und Malz verloren! Die "Schmankerl Time Machine" lädt Sie zu einem lukullischen Streifzug durch die traditionsreiche Münchner Wirtshausgeschichte der vergangenen 150 Jahre ein. Verschaffen Sie sich einen Überblick über die Legenden am Münchner Gastrohimmel, über verglühte Sterne und nie verblühende Evergreens. Stellen Sie sich aus einem Portfolio von über 380 Speisekarten und den damit verlinkten Rezepturen Ihr unvergessliches Menü von Morgen zusammen. Wie wäre es mit einem Hummercocktail, gefolgt vom Hasen in der Terrine und Rehnüsschen, Fürst Pückler als krönendem Abschluss? Lassen Sie sich bei Ihrer Menükreation von den Vorschlägen anderer Nutzer inspirieren. Laden Sie die Speisekarte Ihres favorisierten Münchner Genusstempels hoch, um das Angebot noch zu erweitern. Let’s SchmankerlAlways the same dishes on your dining table? Are you desperately looking for some inspiration for your culinary everyday life? So not all hope is lost yet! "Schmankerl Time Machine" invites you to a culinary time travel through the traditional history of Munich-based restaurants during the past 150 years. First get an overview of Munich's gastronomic legends. Next compose your unforgettable menu from a portfolio consisting of more than 380 menus. Recipes are already linked to them. How about a lobster cocktail, followed by a rabbit in the terrine and some venison nuts, finaly crowned by Prince Pückler's ice cream variation? Let yourself be inspired by the suggestions of other users when creating your menu. Upload the menu of your favourite Munich restaurant to expand the data collection even further. Let's Schmankerl

Combining Process Monitoring with Text Mining for Anomaly Detection in Discrete Manufacturing

One of the major challenges of today's manufacturing industry is the reliable detection of process anomalies and failures in order to reduce unplanned downtimes and avoid quality issues. Process Monitoring (PM) requires the existence of a Normal Operating Condition (NOC) dataset that is used to train the respective algorithm. Obtaining such a NOC dataset involves extensive test runs aside from the actual production. Machine operators often collect a variety of unstructured process specific data in form of protocols, that contain valuable information about the process condition. We propose an approach that utilizes such text data to efficiently create the NOC dataset for a machining process in one of our learning factories. Using the NOC high-frequency machine sensor readings, we train a principal component analysis (PCA)-based model, which can identify anomalous process behavior. The model is consequently evaluated on a holdout test data set and shows promising results. Estimations of the process condition are visualized with two control charts allowing intuitive insights for the machine operator