Miracle-dz/UniFAR-FTUs huggingface dataset:
Lina Bo Bardi in Dialogue with Frida Escobedo: A Spontaneous Entanglement

ABSTRACT In 2020, Mexican architect Frida Escobedo (1979–) designed the exhibition “Lina Bo Bardi: Habitat” at Museo Jumex in Mexico City. Lina Bo Bardi (1914–92) was an Italian émigré who became one of Brazil’s most important — and few female — modernist architects. Escobedo, too, is one of few Latin American women architects whose work is recognised internationally. Using the exhibition as touchpoint, this paper recontextualises the work and archives of Lina Bo Bardi through the lens of Frida Escobedo’s current practice, revealing the socially engaged and spontaneously playful artistry of each. Whereas Bo Bardi is prescient and forward-looking, Escobedo sensitively and inventively references her predecessor. By way of interview analysis, each architect’s praxis will be discussed as a transnational entanglement of objects, curation, persona, text, user, city, and building. Drawing on Olivia De Oliveira’s 1991 interview with Bo Bardi and the author’s 2021 interview with Escobedo, the former’s pioneering of adaptive reuse and urban revitalisation, as well as the latter’s continuation of these methods and preoccupation with ruins, will be addressed. Such topics are salient as Bo Bardi’s buildings today face material decline due to bureaucracy and neglect. A central aim of this paper is therefore to salvage and engage with the residues of Bo Bardi’s departed yet potent voice whilst simultaneously highlighting the contemporary importance of her legacy. The foregrounding of not only Bo Bardi and Escobedo’s voices but also, by default, the voices of their interviewers — Olivia De Oliveira and the author of this paper — is motivated by an enthusiasm to contribute to the collective transformation of the broader gendered communication terrain.
Improving the reproducibility of proton magnetic resonance spectroscopy brain thermometry: Theoretical and empirical approaches

In proton magnetic resonance spectroscopy (1H MRS)‐based thermometry of brain, averaging temperatures measured from more than one reference peak offers several advantages, including improving the reproducibility (i.e., precision) of the measurement. This paper proposes theoretically and empirically optimal weighting factors to improve the weighted average of temperatures measured from three references. We first proposed concepts of equivalent noise and equivalent signal‐to‐noise ratio in terms of frequency measurement and a concept of relative frequency that allows the combination of different peaks in a spectrum for improving the precision of frequency measurement. Based on these, we then derived a theoretically optimal weighting factor and proposed an empirical weighting factor, both involving equivalent noise levels, for a weighted average of temperatures measured from three references (i.e., the singlets of NAA, Cr, and Ch in the 1H MR spectrum). We assessed these two weighting factors by comparing their errors in measurement of temperatures with the errors of temperatures measured from individual references; we also compared these two new weighting factors with two previously proposed weighting factors. These errors were defined as the standard deviations in repeated measurements or in Monte Carlo studies. Both the proposed theoretical and empirical weighting factors outperformed the two previously proposed weighting factors as well as the three individual references in all phantom and in vivo experiments. In phantom experiments with 4‐ or 10‐Hz line broadening, the theoretical weighting factor outperformed the empirical one, but the latter was superior in all other repeated and Monte Carlo tests performed on phantom and in vivo data. The proposed weighting factors are superior to the two previously proposed weighting factors and can improve the reproducibility of temperature measurement using 1H MRS‐based thermometry.
Bi‐level optimization of the integrated energy systems in the deregulated energy markets considering the prediction of uncertain parameters and price‐based demand response program

The proliferation of multienergy systems (MESs) in recent years has led to the higher efficiency of energy consumption in different sectors. MESs are generally studied in the context of energy hubs (EHs). Most of the previous works in the field of EH operation and scheduling are at the scale of residential households or communities. There is a research gap in proposing a framework in which the EH actively interacts with different energy networks. This article, however, is focused on the optimal operation and scheduling of the EH and two integrated energy grids such as power and heat distribution systems. The EH consists of combined heat and power (CHP), electric heat pump (EHP), thermal and electric energy storage systems, which are operated by an independent entity. The proposed optimization problem is based on the bi‐level optimization method in which the EH is the leader and two networks are the followers. Nonlinear variables are also linearized using binary expansion and Big M methods. As another research novelty, uncertainties of load demand and photovoltaic (PV) systems' output power are taken into account using a mixed machine learning (ML)‐based forecasting method. To enhance the flexibility of the system, a price‐based demand response (DR) program is proposed based on the predicted load pattern of the mixed ML method. Simulation results show the efficiency of the optimization model. Also, the DR program has a significant impact on the system's cost by 40% improvement in the profit of the system.
Improvement of RocksDB Performance via Large-Scale Parameter Analysis and Optimization

Database systems usually have many parameters that must be configured by database administrators and users. RocksDB achieves fast data writing performance using a log-structured merged tree. This database has many parameters associated with write and space amplifications. Write amplification degrades the database performance, and space amplification leads to an increased storage space owing to the storage of unwanted data. Previously, it was proven that significant performance improvements can be achieved by tuning the database parameters. However, tuning the multiple parameters of a database is a laborious task owing to the large number of potential configuration combinations. To address this problem, we selected the important parameters that affect the performance of RocksDB using random forest. We then analyzed the effects of the selected parameters on write and space amplifications using analysis of variance. We used a genetic algorithm to obtain optimized values of the major parameters. The experimental results indicate an insignificant reduction (-5.64%) in the execution time when using these optimized values; however, write amplification, space amplification, and data processing rates improved considerably by 20.65%, 54.50%, and 89.68%, respectively, as compared to the performance when using the default settings.
Complementing Büchi Automata with Ranker (Technical Report)

We present the tool Ranker for complementing B\"uchi automata (BAs). Ranker builds on our previous optimizations of rank-based BA complementation and pushes them even further using numerous heuristics to produce even smaller automata. Moreover, it contains novel optimizations of specialized constructions for complementing (i) inherently weak automata and (ii) semi-deterministic automata, all delivered in a robust tool. The optimizations significantly improve the usability of Ranker, as shown in an extensive experimental evaluation with real-world benchmarks, where Ranker produced in the majority of cases a strictly smaller complement than other state-of-the-art tools.
Smart Diet Management through Food Image and Cooking Recipe Analysis

Food monitoring has become an indispensable practice for personal health management in increasingly growing populations. To facilitate this process, advanced image processing and AI technologies have been applied to empower automated recognition of food item and nutrient using food images taken by smart mobile devices. However, existing tools suffered from unsatisfactory precision and compromised convenience, which has hindered broad application of food logging tools. In this study we explore new solutions to image-based food recognition for improved performance, with a particular focus on domestic cooking applications. Particularly, we leverage advanced machine learning and nature language processing techniques, in conjunction with comprehensive food nutrient profiles in the knowledge base and the contextual ingredient information parsed from publicly available recipes, in developing a new food recognition system. Our optimized models were proved to be effective in ingredient recognition based on food images and is under the integration into an Android app named FoodInsight, available at https://github.con zhuxinyishcn/FoodInSight.
APPROXIMATE IMPLICITIZATION BASED ON RBF NETWORKS AND MQ QUASI-INTERPOLATION 1)

Renhong Wang Jinming Wu(Institute of Mathematical Sciences, Dalian University of Technology, Dalian 116024, ChinaEmail: renhong@dlut.edu.cn, wujm97@yahoo.com.cn)AbstractIn this paper, we propose a new approach to solve the approximate implicitizationproblem based on RBF networks and MQ quasi-interpolation. This approach possessesthe advantages of shape preserving, better smoothness, good approximation behavior andrelatively less data etc. Several numerical examples are provided to demonstrate the eﬀec-tiveness and ﬂexibility of the proposed method.Mathematics subject classiﬁcation: 41A05, 65D17, 92B20.Key words: RBF networks, MQ quasi-interpolation, Approximate implicitization, Rationalcurves.
UNDERESTIMATING THE GENDER GAP? AN EXPLORATORY TWO-STEP CLUSTER ANALYSIS OF STEM LABOR SEGMENTATION AND ITS IMPACT ON WOMEN

Recommended Citation Torres-Olave, Blanca Minerva. Underestimating the Gender Gap? An Exploratory Two-Step Cluster Analysis of STEM Labor Segmentation and Its Impact on Women. Journal of Women and Minorities in Science and Engineering, 25, 1: 53-74, 2019. Retrieved from Loyola eCommons, School of Education: Faculty Publications and Other Works, http://dx.doi. org/10.1615/JWomenMinorScienEng.2019021133
On the n-Dimensional Phase Portraits

The phase portrait for dynamic systems is a tool used to graphically determine the instantaneous behavior of its trajectories for a set of initial conditions. Classic phase portraits are limited to two dimensions and occasionally snapshots of 3D phase portraits are presented; unfortunately, a single point of view of a third or higher order system usually implies information losses. To solve that limitation, some authors used an additional degree of freedom to represent phase portraits in three dimensions, for example color graphics. Other authors perform states combinations, empirically, to represent higher dimensions, but the question remains whether it is possible to extend the two-dimensional phase portraits to higher order and their mathematical basis. In this paper, it is reported that the combinations of states to generate a set of phase portraits is enough to determine without loss of information the complete behavior of the immediate system dynamics for a set of initial conditions in an n-dimensional state space. Further, new graphical tools are provided capable to represent methodically the phase portrait for higher order systems.
Investigation of the Dynamic Characteristics and Machining Stability of a Bi-rotary Milling Tool

Bi-rotary milling head is the primary component of multiple-axis machine tool toward the multiply machining operation. The machining performance is greatly related to the structure characteristics and positioning precisions of the swivel head. This study was aimed at developing a bi-rotary milling head module, which is composed of a direct drive motor, cross roller bearings and motorized spindle unit. In order to evaluate the machining stability at the design phase, the dynamic characteristics of the rotary milling were first analyzed with finite element method. Especially, the variations of the dynamic characteristics of the spindle tool with the changing of the titling configuration of swivel axis were examined. In order to consider the accurate presentation of a spindle tool system and swivel mechanism, the bearings in the rolling components were also included in the finite element model and simulated with surface contact elements with adequate contact stiffness. The dynamic frequency response function of the spindle tool at different swinging positions were predicted for comparisons, which were further used to calculate the machining stability based on the machining mechanics. The current results show that the feeding direction and swinging positions of rotary milling head have a significant influence on the dynamic characteristics and machining ability of the spindle tool. The variations of the cutting depth with the swinging of A axis fall in the range of 11% to 40%, depending on the feeding direction and swinging angle. The analysis results are expected to clearly demonstrate the variation of the machining performance of the spindle tool under different milling configurations. The devised model and modeling approach can be applied to develop a five axis milling machine with desired dynamic and machining performance.
Antecedents of children's satisfaction with company websites and their links with brand awareness

This chapter focuses on children’s satisfaction with the use of commercial websites. The authors address two relevant gaps in extant literature: children as users of Information Technology and the concept of Information System success itself. Children’s use of IT needs focus since this age group has been largely neglected by extant IS research. The concept of success needs further research because the research results on this dependent variable have been controversial, and there is still no full agreement on the antecedents of success. In this chapter, the authors therefore contextualize the classic Information Systems success model of DeLone and McLean to IS success with children. They carried out a survey of a sample of 76 children aged 11 to 14, located in various cities in France. The results show that children get higher brand awareness if they are more satisfied with the kids website of the brand. The chapter finds the satisfaction with the site depends upon the personalization that is possible and the presence of dynamic elements on the site. Characters and animations that help navigating the site are also appreciated. A good presentation of the site’s rules could not be shown to be a significant antecedent of satisfaction. Qualitative data that was gathered suggests that children are not really aware of the link between symbols they see on the site and products that are sold. DOI: 10.4018/978-1-4666-0170-3.ch014
A non‐contact vision‐based system for multipoint displacement monitoring in a cable‐stayed footbridge

Vision‐based monitoring receives increased attention for measuring displacements of civil infrastructure such as towers and bridges. Currently, most field applications rely on artificial targets for video processing convenience, leading to high installation effort and focus on only single‐point displacement measurement, for example, at mid‐span of a bridge. This study proposes a low‐cost and non‐contact vision‐based system for multipoint displacement measurement based on a consumer‐grade camera for video acquisition and a custom‐developed package for video processing. The system has been validated on a cable‐stayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. The analysis results indicate that the system provides valuable information about bridge deformation of the order of a few centimetres induced, in this application, by pedestrian passing. The measured data enable accurate estimation of modal frequencies of either the bridge deck or the bridge cables and could be used to investigate variations of modal frequencies under varying pedestrian loads.
Ensemble modeling for American chestnut distribution: Locating potential restoration sites in Pennsylvania

The American chestnut (Castanea dentata Borkh.) was an economically, ecologically, and culturally important tree in eastern American hardwood forests. However, the American chestnut is currently functionally absent from these forests due to the introduction of an invasive fungus (Cryphonectria parasitica (Murr.) Barr) and causal agent of chestnut blight in the early 1900s. Field experiments are being carried out to develop a blight-resistant American chestnut tree, but range-wide restoration will require localized understanding of its current distribution and what factors contribute to suitable American chestnut habitat. While previous studies have researched species distribution of the American chestnut, it is important to understand how species distribution modeling (SDM) technique impacts model results. In this paper we create an ensemble model that combines multiple different SDM techniques to predict areas of suitable American chestnut habitat in Pennsylvania. Results indicate that model accuracy varied considerably by SDM technique – with artificial neural networks performing the worst (Area-Under-the-Curve, AUC = 0.705) and gradient boosting models performing the best (AUC = 0.877). Even though SDM technique accuracy varied, most models identified the same environmental variables as the most important: ratio of sand to clay in the soil, canopy cover, topographic convergence index, and topographic position index. This study offers insight into the best SDM techniques to use, as well as a method of combining SDMs for higher prediction confidence.
Exploring the Effect of Digital Economy on PM2.5 Pollution: The Role of Technological Innovation in China

PM2.5 emission causes serious harm to health and hinders the sustainable development of economy and society. Among all the factors affecting PM2.5 pollution, the role of new economic forms and information technology innovation is lacking. This study aims to explore the impact of digital economy on PM2.5 pollution and its influencing mechanism using data from 281 prefecture-level cities from 2011 to 2016. The empirical results demonstrate that digital economy is conducive to reducing PM2.5 pollution. In other words, the digital economy is conducive to alleviating PM2.5 pollution. Further analysis shows that the digital economy promotes technological innovation, which is an important mediating mechanism affecting PM2.5 pollution. Additionally, the inhibitory effect of digital economy on PM2.5 pollution is more significant in the eastern and central regions. Unfortunately, the negative impact of digital economy on PM2.5 pollution is not significant in the western region. The conclusions provide a new strategy for reducing pollution emissions and improving environmental quality and technological innovation.
The immune phenotypes and different immune escape mechanisms in colorectal cancer

The tumor microenvironment (TME) plays a crucial role in tumor progression and metastasis. However, the immune phenotypes of colorectal cancer (CRC) and the underlying immune escape mechanism have not been studied sufficiently. A total of 1802 and 619 CRC samples from the microarray and TCGA cohorts were enrolled, respectively. The ssGSEA algorithm and unsupervised clustering were used for TME cell infiltration speculation and immune phenotype recognition in the above cohorts. A total of 447 samples from Zhongshan Hospital were collected for validation. Immunohistochemistry was performed in this cohort to quantify TME cell infiltration. The single-cell RNA-seq (scRNA-seq) data of 252,940 cells from 60 CRC samples was analyzed for further mechanistic exploration. CRC samples can be classified into three distinct immune phenotypes. Subtype 1, the immune-active subtype, was characterized by high infiltration of activated adaptive immune cells. Subtype 2, the immune-desert subtype, featured high tumor purity and low infiltration of immune and stromal cells. Subtype 3, the stroma-rich subtype, had high infiltration of stromal cells. The stroma-rich subtype conferred a significantly worse prognosis. The three subtypes had different immune escape mechanisms. The immune-active subtype has the highest immune checkpoint expression level. In comparison, the immune-desert subtype had the lowest immunogenicity and defective antigen presentation. The stroma-rich subtype lacked activated immune cells. In conclusion, distinct immune phenotypes and immune escape mechanisms may provide inspiration and direction for further research on CRC immunotherapy.
An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Handwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have illnesses, injuries, or other problems. Clinical text data, like physician prescription visuals and radiology reports, should be labelled with specific information such as disease type, features, and anatomical location for more effective use. The semantic annotation of vast collections of biological and biomedical texts, like scientific papers, medical reports, and general practitioner observations, has lately been examined by doctors and scientists. By identifying and disambiguating references to biomedical concepts in texts, medical semantics annotators could generate such annotations automatically. For Medical Images (MedIMG), we provide a methodology for learning an effective holistic representation (handwritten word pictures as well as radiology reports). Deep Learning (DL) methods have recently gained much interest for their capacity to achieve expert-level accuracy in automated MedIMG analysis. We discovered that tasks requiring significant responsive fields are ideal for downscaled input images that are qualitatively verified by examining functional, responsive areas and class activating maps for training models. This article focuses on the following contributions: (a) Information Extraction from Narrative MedImages, (b) Automatic categorisation on image resolution with an impact on MedIMG, and (c) Hybrid Model to Predictions of Named Entity Recognition utilising RNN + LSTM + GRM that perform admirably in every trainee for every input purpose. At the same time, supplying understandable scale weight implies that such multi-scale structures are also crucial for extracting information from high-resolution MedIMG. A portion of the reports (30%) are manually evaluated by trained physicians, while the rest were automatically categorised using deep supervised training models based on attention mechanisms and supplied with test reports. MetaMapLite proved recall and precision, but also an F1-score equivalent for primary biomedicine text search techniques and medical text examination on many databases of MedIMG. In addition to implementing as well as getting the requirements for MedIMG, the article explores the quality of medical data by using DL techniques for reaching large-scale labelled clinical data and also the significance of their real-time efforts in the biomedical study that have played an instrumental role in its extramural diffusion and global appeal.