Evaluation of the Boleyn solar home, Portland, Oregon

The Boleyn home located in Portland Oregon (latitude 45°) is evaluated based on monitoring data taken from 1977 to 1982. The Boleyn home is a two-story (plus basement) frame home. It takes benefit of passive solar gains and is equipped with an active solar system. The active solar system consists of a 429 ft² (39.9 m²) collector with 3750 gallons (14400 liter) of water for thermal storage. The active solar system and the monitoring equipment were installed by PGE (Portland General Electric Company). The performance of the building and of the active solar system and the validity of models predicting parameters of the home and of the solar system are evaluated. Performance of the home: The Boleyn home is a very well insulated house with low infiltration rate and benefits from passive solar gains obtained from 96 ft2 of glass area to the south. The Boleyn home is heated by four sources: passive solar gains, internal gains, active solar system, and backup system. Reduced transmission losses through the floor area to the basement heated by losses from the solar storage tanks contributes a significant amount of the overall gain from the active solar system. Performance of the active system: The design goal of the solar system to meet 50% of the house heating requirements was exceeded. The performance of the Revere double-glazed collector did not deteriorate over time and matchs the manufacturer's data within 5%. A significant amount of heat was lost through the storage tanks because insulation was lacking on the tanks around penetrations and around the pipes. Heat loss due to thermosyphoning was present before installation of a backf low check valve in the collector loop. The overall efficiency of the system was improved by reducing the pump power in the solar collection loop and installation of an improved backup system. Installation of a domestic hot water preheat tank reduced the domestic hot water load by 73%. Validity of predictive tools: The design heat loss calculated by using the ASHRAE method matched the experimentally evaluated design heat loss within 6%. The annual heating requirements predicted by the degree-day-method by ASHRAE and Calpas3 were accurate within 12% and 21% respectively. A large cooling load caused by passive solar gains predicted by Calpas3 did not occur at the Boleyn home. The Boleyns open the windows and accept higher indoor temperatures than predicted by Calpas3. The active solar fraction predicted by F -chart was 66.3%. The experimentally evaluated value is 74.5%. The heat loss from the solar storage tanks was 3.6 times higher than the prediction performed by PGE, which did not include losses from piping and uninsulated portions of the tank

SENS: Semantic Synthetic Benchmarking Model for Integrated Supply Chain Simulation and Analysis

Supply Chain (SC) modeling is essential to understand and influence SC behavior, especially for increasingly globalized and complex SCs. Existing models address various SC notions, e.g., processes, tiers and production, in an isolated manner limiting enriched analysis granted by integrated information systems. Moreover, the scarcity of real-world data prevents the benchmarking of the overall SC performance in different circumstances, especially wrt. resilience during disruption. We present SENS, an ontology-based Knowledge-Graph (KG) equipped with SPARQL implementations of KPIs to incorporate an end-to-end perspective of the SC including standardized SCOR processes and metrics. Further, we propose SENS-GEN, a highly configurable data generator that leverages SENS to create synthetic semantic SC data under multiple scenario configurations for comprehensive analysis and benchmarking applications. The evaluation shows that the significantly improved simulation and analysis capabilities, enabled by SENS, facilitate grasping, controlling and ultimately enhancing SC behavior and increasing resilience in disruptive scenarios

Stochastic Inventory Control Systems with Consideration for the Cost Factors Based on EBIT

Semiconductor manufacturing in the current world is more competitive than ever/ is extremely competitive. Due to a short market life-span and high uncertainty in future demand, Supply chain management is a competitive advantage which plays an important role in today`s global semiconductor industry. A very important consequence of uncertain demand and having long lead time is the great risk of incurring shortages and excessive inventory. This paper con-siders the view of the second tier semiconductor supplier in automotive industries and studies, using the periodic review analysis, a single item single stage inventory system with sto-chastic demand. The values of s (reorder point) and Q (order quantity) are the two decisions required to implement the policy. The lead time is assumed known and constant. The only uncertainty is associated with demand. Assuming hold-ing, production, salvage and backorder costs, we determine the optimal numerical value of the level s (reorder point) using a simulation approach, and thus define the optimal inventory policy to minimize the total expected inventory cost while being able to achieve the desired customer service levels

Quantum-Assisted Solution Paths for the Capacitated Vehicle Routing Problem

Many relevant problems in industrial settings result in NP-hard optimization problems, such as the Capacitated Vehicle Routing Problem (CVRP) or its reduced variant, the Travelling Salesperson Problem (TSP). Even with today's most powerful classical algorithms, the CVRP is challenging to solve classically. Quantum computing may offer a way to improve the time to solution, although the question remains open as to whether Noisy Intermediate-Scale Quantum (NISQ) devices can achieve a practical advantage compared to classical heuristics. The most prominent algorithms proposed to solve combinatorial optimization problems in the NISQ era are the Quantum Approximate Optimization Algorithm (QAOA) and the more general Variational Quantum Eigensolver (VQE). However, implementing them in a way that reliably provides high-quality solutions is challenging, even for toy examples. In this work, we discuss decomposition and formulation aspects of the CVRP and propose an application-driven way to measure solution quality. Considering current hardware constraints, we reduce the CVRP to a clustering phase and a set of TSPs. For the TSP, we extensively test both QAOA and VQE and investigate the influence of various hyperparameters, such as the classical optimizer choice and strength of constraint penalization. Results of QAOA are generally of limited quality because the algorithm does not reach the energy threshold for feasible TSP solutions, even when considering various extensions such as recursive, warm-start and constraint-preserving mixer QAOA. On the other hand, the VQE reaches the energy threshold and shows a better performance. Our work outlines the obstacles to quantum-assisted solutions for real-world optimization problems and proposes perspectives on how to overcome them.Comment: Submitted to the IEEE for possible publicatio

Efficient learning of Sparse Pauli Lindblad models for fully connected qubit topology

The challenge to achieve practical quantum computing considering current hardware size and gate fidelity is the sensitivity to errors and noise. Recent work has shown that by learning the underlying noise model capturing qubit cross-talk, error mitigation can push the boundary of practical quantum computing. This has been accomplished using Sparse Pauli-Lindblad models only on devices with a linear topology connectivity (i.e. superconducting qubit devices). In this work we extend the theoretical requirement for learning such noise models on hardware with full connectivity (i.e. ion trap devices).Comment: 6 pages, 3 figure

Quantum Computing Techniques for Multi-Knapsack Problems

Optimization problems are ubiquitous in various industrial settings, and multi-knapsack optimization is one recurrent task faced daily by several industries. The advent of quantum computing has opened a new paradigm for computationally intensive tasks, with promises of delivering better and faster solutions for specific classes of problems. This work presents a comprehensive study of quantum computing approaches for multi-knapsack problems, by investigating some of the most prominent and state-of-the-art quantum algorithms using different quantum software and hardware tools. The performance of the quantum approaches is compared for varying hyperparameters. We consider several gate-based quantum algorithms, such as QAOA and VQE, as well as quantum annealing, and present an exhaustive study of the solutions and the estimation of runtimes. Additionally, we analyze the impact of warm-starting QAOA to understand the reasons for the better performance of this approach. We discuss the implications of our results in view of utilizing quantum optimization for industrial applications in the future. In addition to the high demand for better quantum hardware, our results also emphasize the necessity of more and better quantum optimization algorithms, especially for multi-knapsack problems.Comment: 20 page

New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism.

Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood. Previous genome-wide association studies of birth weight identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes and a second variant, near CCNL1, with no obvious link to adult traits. In an expanded genome-wide association meta-analysis and follow-up study of birth weight (of up to 69,308 individuals of European descent from 43 studies), we have now extended the number of loci associated at genome-wide significance to 7, accounting for a similar proportion of variance as maternal smoking. Five of the loci are known to be associated with other phenotypes: ADCY5 and CDKAL1 with type 2 diabetes, ADRB1 with adult blood pressure and HMGA2 and LCORL with adult height. Our findings highlight genetic links between fetal growth and postnatal growth and metabolism

Modeling and Analysis of Semiconductor Supply Chains (Dagstuhl Seminar 16062)

In February 2016 the Dagstuhl Seminar 16062 explored the needs of the semiconductor industry for better planning and scheduling approaches at the supply chain level and the requirements for information systems to support the approaches. The seminar participants also spent time identifying the core elements of a conceptual reference model for planning and control of semiconductor manufacturing supply chains. This Executive Summary describes the process of the seminar and discusses key findings and areas for future research regarding these topics. Abstracts of presentations given during the seminar and the output of breakout sessions are collected in appendices

MARE: Semantic Supply Chain Disruption Management and Resilience Evaluation Framework

Supply Chains (SCs) are subject to disruptive events that potentially hinder the operational performance. Disruption Management Process (DMP) relies on the analysis of integrated heterogeneous data sources such as production scheduling, order management and logistics to evaluate the impact of disruptions on the SC. Existing approaches are limited as they address DMP process steps and corresponding data sources in a rather isolated manner which hurdles the systematic handling of a disruption originating anywhere in the SC. Thus, we propose MARE a semantic disruption management and resilience evaluation framework for integration of data sources included in all DMP steps, i.e. Monitor/Model, Assess, Recover and Evaluate. MARE, leverages semantic technologies i.e. ontologies, knowledge graphs and SPARQL queries to model and reproduce SC behavior under disruptive scenarios. Also, MARE includes an evaluation framework to examine the restoration performance of a SC applying various recovery strategies. Semantic SC DMP, put forward by MARE, allows stakeholders to potentially identify the measures to enhance SC integration, increase the resilience of supply networks and ultimately facilitate digitalization

Decision-Making Modeling and Solutions for Smart Semiconductor Manufacturing (Dagstuhl Seminar 20452)

In November 2020 the Dagstuhl Seminar 20452 explored the needs of the semiconductor industry for making smart semiconductor manufacturing decisions and the information systems to empower flexible decisions for smart production. The seminar participants also spent time identifying the core elements for a simulation testbed which allows for assessing smart planning and control decisions in the semiconductor industry. This Executive Summary describes the process of the seminar and discusses key findings and areas for future research regarding these topics. Abstracts of presentations given during the seminar and the output of breakout sessions are collected in appendices