Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental validation of analytical wake and downstream turbine performance modelling

Wake effects in wind farms can cause significant power losses. In order to reduce these losses layout and control optimization can be applied. For this purpose, simple and fast prediction tools for the wake flow are needed. In the first part of this work, five analytical wind turbine wake models are compared to small-scale turbine wind tunnel measurements. The measurements are conducted at several downstream distances, varying the ambient turbulence intensity and upstream turbine blade pitch angle. Furthermore, an adjustment of a recently developed wake model is proposed. Subsequently, the adjusted model is found to perform best throughout all test cases. In the second part, the performance of an aligned downstream turbine is modelled based on the predicted wake flow using a Blade Element Momentum method with guaranteed convergence. In order to consider the non-uniform inflow velocity a mean-blade-element-velocity method is developed. Moreover, a blockage effect correction is applied. A comparison to wind tunnel measurement data shows that the wake velocity as well as the combined power of two aligned turbines are fairly well predicted. Additionally, the presented analytical framework of wake and downstream turbine performance modelling proposes several model improvements for state-of-the art wind farm simulation tools.
Review of Electromagnetic Vibration in Electrical Machines

Electrical machines are important devices that convert electric energy into mechanical work and are widely used in industry and people’s life. Undesired vibrations are harmful to their safe operation. Reviews from the viewpoint of fault diagnosis have been conducted, while summaries from the perspective of dynamics is rare. This review provides systematic research outlines of this field, which can help a majority of scholars grasp the ongoing progress and conduct further investigations. This review mainly generalizes publications in the past decades about the dynamics and vibration of electrical machines. First the sources of electromagnetic vibration in electrical machines are presented, which include mechanical and electromagnetic factors. Different types of air gap eccentricity are introduced and modeled. The analytical methods and numerical methods for calculating the electromagnetic force are summarized and explained in detail. The exact subdomain analysis, magnetic equivalent circuit, Maxwell stress tensor, winding function approach, conformal mapping method, virtual work principle and finite element analysis are presented. The effects of magnetic saturation, slot and pole combination and load are discussed. Then typical characteristics of electromagnetic vibration are illustrated. Finally, the experimental studies are summarized and the authors give their thoughts about the research trends.
Exploring the Impact of Different Cut-off Times on Isochrone Measurements of Accessibility

Isochrone analysis and assessments of cumulative opportunities are a common way to quantify accessibility. However, different time cut-offs have been used by different researchers, with little investigation into what is the ‘best’ cut-off time. Outstanding questions remain concerning the most effective or predictive cut-off time and the potential implications of choosing one time limit over another. The primary objective of this paper is to explore how different cut-off times affect the calculation of isochrone-based accessibility measurements and their potential to predict travel-mode choice. Fifty dissemination areas (DAs) within the Greater Toronto and Hamilton Area (GTHA) are selected to test the impact of different isochrone cut-off times in 5-minute intervals for public transit, automobile, and walking accessibility. The relative predictive power of 30- and 45-minute isochrones in modeling mode choice is also examined. This paper finds that different cut-off times do impact the interpretability of accessibility measurements in the isochrone approach, but a defined cut-off time for general use cannot be determined based on the analysis.
Nonlinear Temperature-Dependent State Model of Cylindrical LiFePO4 Battery for Open-Circuit Voltage, Terminal Voltage and State-of-Charge Estimation with Extended Kalman Filter

Ambient temperature affects the performance of a battery power system and its accuracy in state-of-charge (SOC) estimation for electric vehicles and smart grid systems. This paper proposes a battery model that considered ambient temperature, cell temperature, hysteresis voltage and thermal aging on capacity due to multiple charging and discharging. The SOC is then estimated using an extended Kalman filter. Several forms of validation were tested on an actual cell battery under specific ambient temperatures to verify the battery cell model, terminal voltage and SOC estimation performance. The SOC estimation results show an improvement in root-mean-squared error as compared to Extended Kalman Filter (EKF) without considering the temperature dependency. The proposed battery temperature-dependent model gave a smaller root-mean square error in SOC and terminal voltage at 5 °C, 15 °C and 45 °C.
Study on Differences in Turbulence Statistics between Compressible and Incompressible Low-Reynolds Number Turbulent Channel Flows Using Semi-Local Scaling

The differences in turbulence statistics between compressible and incompressible turbulent channel flows are investigated at low-Reynolds numbers using semi-local scaling. DNS of the compressible turbulent channel flow between isothermal walls at low-Mach numbers and DNS of the incompressible flow are performed. It is revealed that the reduction in the pressure-strain correlation term is due to the reductions in RMS velocity-derivative fluctuations and in the absolute correlations between pressure and velocity-derivative fluctuations in the compressible turbulent channel flow at low-Mach and Reynolds numbers.
Bridging India’s housing gap: lowering costs and CO2 emissions

ABSTRACT More than 60 million homes in India are unfit for decent living. Replacing this stock with decent housing will entail significant costs and increase energy consumption and related CO2 emissions due to both upfront and long-term energy requirements. This paper assesses the life cycle costs (LCC), life cycle energy (LCE) and CO2 emissions impacts of filling the current housing gap with different building materials and technologies, and maintaining reasonable standards of indoor temperature and humidity. These outcomes are assessed under different climatic conditions and residential behavioural patterns, using urban and rural housing archetypes, and considering conventional as well as low-cost materials and energy-savings measures. The results demonstrate that stabilized-earth blocks are a preferred solution to the prevailing norm of fired bricks. Along with filler slab and roof insulation, they offer a win–win solution to reduce both LCC by 18% and LCE by 17% compared with conventional techniques in bridging the housing gap. LCE savings can be further increased to 28% without increasing the investment cost compared with conventional solutions. The insights provided by this study on abatement costs and efficacy can be used by policy-makers for affordable housing and climate-related policies.
Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.
Recent zooarchaeological investigations at the Boarding School site (24GL302), Glacier County, Montana

This article presents an analysis of the bison assemblage recovered from excavations at the Boarding School site (24GL302), located in Glacier County, Montana. Excavations at the site took place following the inadvertent discovery of a large bone bed uncovered during foundation construction for a new school by the Bureau of Indian Affairs. The assemblage represents contexts associated with both the adjacent Late Precontact period bison kill site first excavated by Thomas Kehoe in the 1950s and the later occupation of the site during its use as a boarding school for Blackfoot children in the first half of the twentieth century. Through these remains, this work provides new insight into this continually used landscape.
FEM simulation of thermo-mechanical stress and thermal fatigue life assessment of high-speed steel work rolls during hot strip rolling process

Abstract Work rolls used for hot strip rolling undergo the cyclic sequence of heating-cooling over the roll surface due to contact with the hot strip and the following cooling systems during every rolling revolution. Severe temperature gradient and thermal stress caused by cyclic heating-cooling loading are dominant factors for thermal fatigue failure of work rolls. Therefore, investigations on temperature and thermal stress-strain are essential for understanding the thermal fatigue of work roll. In this study, a thermo-mechanical analysis with elastic-plastic material parameters via a simplified finite element model is carried out to compute the stress-strain range experienced by the work roll during rolling and idling. Then, a model with the initial crack in the roll surface considering different depths and a model with the hot strip is conducted to better understand the thermal behaviors of the work roll during hot rolling respectively. Furthermore, the thermal fatigue life of work roll is assessed based on the modified Coffin-Manson relation, considering the effect of multi-axial stress state and temperature-dependent material properties caused by temperature fluctuations in work roll during hot rolling.
Anadromy efficacy of native kokanee in Alturas Lake, Idaho

Abstract Eaton KR, Tardy KA. 2022. Anadromy efficacy of native kokanee in Alturas Lake, Idaho. Lake Reserv Manage. 38:197–208. Partial migration is a complex process that can have profound impacts on ecosystems and populations. Oncorhynchus nerka ecotypes in the Sawtooth Valley include migratory and resident populations, with each exhibiting partial migration. Kokanee can exhibit anadromous behavior and therefore are valuable for O. nerka recovery. In the Sawtooth Valley, Alturas Lake supports a native kokanee population for which the ability to produce outmigrants could influence conservation decisions. In this study, we used contemporary genetic tools to identify the life history origin of Alturas Lake O. nerka outmigrants. Additionally we use population and survival estimates to determine the extent and success of any present kokanee migration. We found that in Alturas Lake, all juvenile outmigrants are progeny of native kokanee. We also found that increased population abundance is related to a higher number of outmigrants. Furthermore, juvenile survival estimates of Alturas kokanee outmigrants were similar to juvenile Redfish Lake sockeye salmon survival estimates. These similar survival estimates and return of anadromous kokanee adults throughout program history indicate that the Alturas Lake kokanee population is successful in exhibiting anadromous behavior. While kokanee are not a focus for O. nerka recovery, this study shows the importance of partial migration in a native kokanee population in preserving the anadromous life history strategy. Our results highlight that in locations with no sockeye salmon population, like Alturas Lake, native kokanee populations are vital to O. nerka recovery and should be monitored and protected accordingly. Supplemental data for this article is available online at https://doi.org/10.1080/10402381.2022. 2026541.
ScaleStore: A Fast and Cost-Efficient Storage Engine using DRAM, NVMe, and RDMA

In this paper, we propose ScaleStore, a novel distributed storage engine that exploits DRAM caching, NVMe storage, and RDMA networking to achieve high performance, cost-efficiency, and scalability at the same time. Using low latency RDMA messages, ScaleStore implements a transparent memory abstraction that provides access to the aggregated DRAM memory and NVMe storage of all nodes. In contrast to existing distributed RDMA designs such as NAM-DB or FaRM, ScaleStore stores cold data on NVMe SSDs (flash), lowering the overall hardware cost significantly. The core of ScaleStore is a distributed caching strategy that dynamically decides which data to keep in memory (and which on SSDs) based on the workload. The caching protocol also provides strong consistency in the presence of concurrent data modifications. Our evaluation shows that ScaleStore achieves high performance for various types of workloads (read/write-dominated, uniform/skewed) even when the data size is larger than the aggregated memory of all nodes. We further show that ScaleStore can efficiently handle dynamic workload changes and supports elasticity.
Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems

A self-driving database management system (DBMS) aims to configure, deploy, and optimize almost all aspects of itself automatically without human intervention or guidance. Achieving this high level of automation relies on machine learning (ML) models that predict how a DBMS will behave in different scenarios. This behavior encompasses all DBMS runtime operations, including query execution and maintenance tasks. These ML-based behavior models for a self-driving DBMS require low-level training data about a DBMS's internals. Such training data includes (1) features that describe the workload, environment, and DBMS configuration, and (2) both DBMS- and hardware-level metrics. But it is difficult to collect training data from a DBMS while it is running because it can introduce performance and measurement degradations that hinder the ML models' ability to predict the DBMS's behavior correctly. We present the TScout (TS) framework for collecting training data from self-driving DBMSs. Our framework is an internal approach where developers annotate a DBMS's source code with hooks to monitor the system's behavior. TS then extracts these hooks and generates a kernel-level program (via Linux's BPF) that efficiently captures metrics from multiple sources (e.g., CPU performance counters, memory allocators). TS combines these metrics with internal DBMS state observations, generating training data for behavior models. We integrated TS in a PostgreSQL-compatible DBMS and measured its ability to collect training data for both OLTP and OLAP workloads. Our results show that TS generates training data for a deployed DBMS to train more accurate models than previous methods with only a 7% performance reduction.
Understanding the Experience of Code Review: Misalignments, Attention, and Units of Analysis

Code review is a common practice in software development and numerous studies have described different aspects of the process; its characteristics, the expectations on that process, issues around reviewer allocation, and more. However, one aspect that has not been studied to a large extent is the experience of the developers in the code review process. This is unfortunate given the significant amount of time that developers spend on this activity, where problems that degrade developers’ experience on a daily basis can create work environment issues. In this paper, we present an extended analysis of an exploratory mixed-method study where we focus on developers’ experience of code review. We use semi-structured interviews to gather data from two multi-national companies and conduct a follow-up survey. Our results suggest that developers are frequently bothered by misalignments in the code review tooling and process which is hindering them in carrying out their code review tasks effectively. We present an initial characterization of misalignments that may hamper the developer experience. Based on our findings, we propose directions for further exploration to improve the developer experience.
Budget-aware Index Tuning with Reinforcement Learning

Index tuning aims to find the optimal index configuration for an input workload. It is a resource-intensive task since it requires making multiple expensive "what-if" calls to the query optimizer to estimate the cost of a query given an index configuration without actually building the indexes. In this paper, we study the problem of budget-aware index tuning where the number of what-if calls allowed when searching for the optimal configuration during tuning is constrained. This problem is challenging as it requires addressing the trade-off between investing what-if calls on exploring new configurations versus exploiting a known promising configuration. We formulate budget-aware index tuning as a Markov decision process, and propose a solution based on Monte Carlo tree search, a classic reinforcement learning technology. Experimental evaluation on both standard industry benchmarks and real workloads shows that our solution can significantly outperform alternative budget-aware solutions in terms of the quality of the index configuration.
Automated Category Tree Construction in E-Commerce

Category trees play a central role in many web applications, enabling browsing-style information access. Building trees that reflect users' dynamic interests is, however, a challenging task, carried out by taxonomists. This manual construction leads to outdated trees as it is hard to keep track of market trends. While taxonomists can identify candidate categories, i.e. sets of items with a shared label, most such categories cannot simultaneously exist in the tree, as platforms set a bound on the number of categories an item may belong to. To address this setting, we formalize the problem of constructing a tree where the categories are maximally similar to desirable candidate categories while satisfying combinatorial requirements and provide a model that captures practical considerations. In previous work, we proved inapproximability bounds for this model. Nevertheless, in this work we provide two heuristic algorithms, and demonstrate their effectiveness over datasets from real-life e-commerce platforms, far exceeding the worst-case bounds. We also identify a natural special case, for which we devise a solution with tight approximation guarantees. Moreover, we explain how our approach facilitates continual updates, maintaining consistency with an existing tree. Finally, we propose to include in the input candidate categories derived from result sets to recent search queries to reflect dynamic user interests and trends.
Let's Go to the Mall: Investigating the Role of User Experience in Customers’ Intention to Use Social Robots in a Shopping Mall

Aim of this study is to investigate the effects of user experience (UX) on shopping mall customers’ intention to use a social robot. Therefore, we used a Wizard of Oz approach that enabled data collection in situ. Quantitative data was obtained from a questionnaire completed by shopping mall customers who interacted with a social robot. Data was used in a regression analysis, where user experience factors served as predictors for robot use in retail. The regression model explains up to 23.2% of the variance in customers’ intention to use a social robot. In addition, we collected qualitative data on human-robot-interactions and used the data to complement the interpretation of statistical results. Our findings suggest that only hedonic qualities significantly contribute to the prediction of customers’ intention, that shopping mall customers are reluctant to grant pragmatic qualities to social robots, and that UX evaluation in HRI requires additional predictors.
SAM: Database Generation from Query Workloads with Supervised Autoregressive Models

With the prevalence of cloud databases, database users are increasingly reliant on the cloud database providers to manage their data. It becomes a challenge for cloud providers to benchmark different DBMS for a specific database instance without having access to the underlying data. One viable solution is to leverage a query workload, which contains a set of queries and the corresponding cardinalities, to generate a synthetic database with similar query performance. Existing methods for database generation with cardinality constraints, however, can only handle very small query workloads due to their high complexity and encounter challenges when handling join queries. In this work, we propose SAM, a supervised deep autoregressive model-based method for database generation from query workloads. First, SAM is able to process large-scale query workloads efficiently as its complexity is linear in the size of the query workload, the number of attributes and the attribute domain size. Second, we develop algorithms to obtain unbiased samples of base relations from the deep autoregressive model and assign join keys in a way that accurately recovers the full outer join of the target database. Comprehensive experiments on real-world datasets demonstrate that SAM is able to efficiently generate a high-fidelity database that not only satisfies the input cardinality constraints, but also is close to the target database.
Domain Adaptation for Deep Entity Resolution

Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA) results on ER are achieved by deep learning (DL) based methods, trained with a lot of labeled matching/non-matching entity pairs. This may not be a problem when using well-prepared benchmark datasets. Nevertheless, for many real-world ER applications, the situation changes dramatically, with a painful issue to collect large-scale labeled datasets. In this paper, we seek to answer: If we have a well-labeled source ER dataset, can we train a DL-based ER model for a target dataset, without any labels or with a few labels? This is known as domain adaptation (DA), which has achieved great successes in computer vision and natural language processing, but is not systematically studied for ER. Our goal is to systematically explore the benefits and limitations of a wide range of DA methods for ER. To this purpose, we develop a DADER (Domain Adaptation for Deep Entity Resolution) framework that significantly advances ER in applying DA. We define a space of design solutions for the three modules of DADER, namely Feature Extractor, Matcher, and Feature Aligner. We conduct so far the most comprehensive experimental study to explore the design space and compare different choices of DA for ER. We provide guidance for selecting appropriate design solutions based on extensive experiments.