Current understanding of point defects and diffusion processes in silicon

The effects of oxidation of Si which established that vacancies (V) and Si self interstitials (I) coexist in Si at high temperatures under thermal equilibrium and oxidizing conditions are discussed. Some essential points associated with Au diffusion in Si are then discussed. Analysis of Au diffusion results allowed a determination of the I component and an estimate of the V component of the Si self diffusion coefficient. A discussion of theories on high concentration P diffusion into Si is then presented. Although presently there still is no theory that is completely satisfactory, significant progresses are recently made in treating some essential aspects of this subject

Health Building Information Modeling (HBIM)-based Facility Management: A Conceptual Framework

The outbreak of the COVID-19 epidemic has brought significant challenges to building operation and occupant health. In practice, building operators have begun to use various Internet of Things (IoT) technologies, intelligent sensing devices, and manual registration methods to update occupant information and behaviour in different building areas. Building spaces are classified according to their health, such as the distinction between safe areas and infected areas. Using the health data of occupants and spaces to help buildings operate efficiently and safely is a problem that needs to be solved urgently. This research proposed a conceptual framework for facility management driven by a Health Building Information Model (HBIM). The framework aims to incorporate the emerging data types to enrich the health information of the BIM model and provide decision support for facility operation and maintenance

Dynamic Adaptation on Non-Stationary Visual Domains

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results

On the symbolic manipulation and code generation for elasto-plastic material matrices

A computerized procedure for symbolic manipulations and FORTRAN code generation of an elasto-plastic material matrix for finite element applications is presented. Special emphasis is placed on expression simplifications during intermediate derivations, optimal code generation, and interface with the main program. A systematic procedure is outlined to avoid redundant algebraic manipulations. Symbolic expressions of the derived material stiffness matrix are automatically converted to RATFOR code which is then translated into FORTRAN statements through a preprocessor. To minimize the interface problem with the main program, a template file is prepared so that the translated FORTRAN statements can be merged into the file to form a subroutine (or a submodule). Three constitutive models; namely, von Mises plasticity, Drucker-Prager model, and a concrete plasticity model, are used as illustrative examples

Capacity reservation and utilization for a manufacturer with uncertain capacity and demand

We consider an OEM (Original Equipment Manufacturer) that has outsourced the production activities to a CM (Contract Manufacturer). The CM produces on a non-dedicated capacitated production line, i.e., the CM produces for multiple OEMs on the same production line. The CM requires that all OEMs reserve capacity slots before ordering and responds to these reservations by acceptance or partial rejection, based on allocation rules that are unknown to the OEM. Therefore, the allocated capacity for the OEM is not known in advance, also because the OEM has no information about the reservations of the other OEMs. We study this problem from the OEM's perspective who faces stochastic demand and stochastic capacity allocation from the contract manufacturer. A single-item periodic review inventory system is considered and we assume linear inventory holding, backorder, and reservation costs. We develop a stochastic dynamic programming model for this problem and characterize the optimal policy. We conduct a numerical study where we also consider the case that the capacity allocation is dependent on the demand distribution. For this case, we show the structure of the optimal policy based on a numerical study. Further, the numerical results reveal several interesting managerial insights, such as the optimal reservation policy is being little sensitive to the uncertainty of capacity allocation. In that case, the optimal reservation quantities hardly increase, but the optimal policy suggests increasing the utilization of the allocated capacity. Moreover, we show that for the contract manufacturer, to achieve the desired behavior, charging small reservation costs is sufficient