Miracle-dz/UniFAR-FTUs huggingface dataset:
A data envelopment analysis (DEA)-based model for power interruption cost estimation for industrial companies

In this paper, a new model based on Data Envelopment Analysis (DEA) and Inverse Data Envelopment Analysis (IDEA) is presented for estimating the effect of electricity on the output of industrial companies. To this end, the effect of electricity deviation, which serves as one input that can influence a manufacturing company's final product, is evaluated. Intuitively, it is known that a direct relationship exists between electricity consumption and the output of the manufacturing company. However, finding a function that accurately represents this relationship is not easy. To check the applicability of the proposed method, it is tested on data from eight major vehicle manufacturing companies. In this model, labor hours, electrical energy consumption, and the value of raw materials are used as inputs, and the sales value is used as the proxy for the output of the company. These input and output data are used to find the efficiency of each company. Then, by changing the electricity consumption level, the output changes are derived. To calculate the outage cost, the deviation of the output is divided by the deviation of the electricity consumption, and the outage cost is estimated.
The New Expats : Economic Determinants of Bilateral Expatriate, FDI, and International Trade Flows

Globalization has led to dramatic growth in two-way flows of highly skilled professionals – what TIME magazine recently termed the “New Expatriates. ” However, no study has attempted to explain empirically these migrations of highly skilled expats motivated by profit-maximizing multinational enterprises (MNEs), much less one based upon a formal theoretical foundation. We provide three potential contributions. First, we develop a general-equilibrium-based theoretical framework to rationalize the existence of two-way expat flows as complements to (two-way) foreign direct investment (FDI) and trade flows. Second, we motivate a theoretical rationale for estimating “gravity equations” of two-way expat flows, economic size, and economic similarity – and in a manner consistent with estimating gravity equations of bilateral trade, foreign affiliate sales, and FDI flows. Third, we show that – due to relative factor endowment differences – both horizontal and vertical MNE motives explain patterns of highly skilled migration flows; however, the bulk of such flows are explained by two-way expat flows among horizontal MNEs.
Association study on tardive dyskinesia and polymorphisms in COMT and MAOA in Chinese population.

Center for Biological Psychiatry, Beijing Hui-Long-Guan Hospital, Beijing, China and Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA Correspondence to Yunlong Tan, MD, PhD, Center for Biological Psychiatry, Beijing Hui-Long-Guan Hospital, Changping District, Beijing 100096, China Tel: + 86 10 627 15511 x6477; fax: + 86 10 510 74591; e-mail: yltan21@126.com
The power of reflective relational machines

A model of database programming with reflection, called reflective relational machine, is introduced and studied. The reflection consists here of dynamic generation of queries in a host programming language. The main results characterize the power of the machine in terms of known complexity classes. In particular, the polynomial-time restriction of the machine is shown to express PSPACE, and to correspond precisely to uniform circuits of polynomial depth and exponential size. This provides an alternative, logic-based formulation of the uniform circuit model, more convenient for problems naturally formulated in logic terms. Since time in the polynomially-bounded machine coincides with time in the uniform circuit model, this also shows that reflection allows for more "intense" parallelism, which is not attainable otherwise (unless P=PSPACE). Other results concern the power of the reflective relational machine subject to restrictions on the number of variables used. <<ETX>>
Automated docking for novel drug discovery

Introduction: The volume of three-dimensional structural information of macromolecules and the number of computational tools to predict binding modes and affinities of molecular complexes are increasing daily. Molecular docking is a rational structural approach employed to predict thermodynamic parameters based on molecular recognition between two or more molecules. In addition, docking studies have become very important for therapeutic applications in modern structure-based drug design because this computational tool uses few economic resources. However, they omit many biological conditions that critically influence small and macromolecular structural motions. To mimic physiological conditions, it is necessary to consider other environmental factors, such as the presence of water molecules and the flexibility of ligands and side chain residues of proteins. Furthermore, molecular dynamics simulations have been coupled with docking procedures to expand the boundaries and obtain more reliable information. Areas covered: In this article, we review current advances in protein-small molecule docking and possible future directions. Expert opinion: Docking studies include many conformations to predict binding free energies (scoring functions) and to search (scoring sampling) for the most representative binding conformations. Therefore, several biological properties, from side chain residues to complete protein motions, have been included in docking studies to improve theoretical predictions.
Dexterous Tethered Space Robot: Design, Measurement, Control, and Experiment

In this paper, we systematically introduce a novel geostationary orbit (GEO) space debris removal system called dexterous tethered space robot (DTSR). The DTSR has three notable characteristics: dexterity, lightweight, and cost effectiveness. We first describe the system's design and a typical mission scenario, and then present its two core technologies (vision-based pose measurement and coordinated controller design) in detail. Finally, we present the extensive simulations and ground semiphysical experiments to verify that the DTSR is a feasible solution to effectively remove the GEO space debris.
Power Routing for Cascaded H-Bridge Converters

Modular power converters are expected to play a major role in medium- and high-voltage/power applications. Normally, each module processes the same amount of power; however, this does not take into consideration that different modules can have a different remaining useful lifetime. This paper proposes the concept of power routing for cascaded H-bridge (CHB) converters, with the purpose of delaying the failure of the system. A third-harmonic injection into the duty cycles allows extending the imbalance capability of the structure, keeping the CHB operational even if some power paths are completely unloaded. Analytic investigation in conjunction with simulation and experimental measurements demonstrate the power routing by means of the proposed method.
Explorations in Intelligible Classification

Machine learning (ML) methods are becoming increasingly popular in the identification of biomarkers in medical imaging data. While these methods provide superior predictive accuracy, their models can be hard to interpret. More conventional statistical methods lack the predictive accuracy of the ML models, but are able to identify regions in which the difference between a patient group and healthy controls is statistically significant. We attempted to strike a middle ground between these two extremes by employing supervised dimensionality reduction (SDR) methods in the identification of useful discriminative patterns for the differential diagnosis of various parkinsonisms based on FDG-PET scans. Additionally, a new SDR method based on margin maximization in a lower-dimensional space has been developed. Results indicate that our method performs on-par with existing methods in terms of accuracy, while at the same time providing an intelligible model through the generation of features that improve the performance of a radial kernel support vector machine. In addition, we are able to demonstrate that our method is able to learn an efficient low-dimensional representation of high-dimensional data.
Multi-objective evolutionary algorithms embedded with machine learning — A survey

Multi-objective evolutionary algorithms (MOEAs) have been widely used in solving multi-objective optimization problems. A great number of the-state-of-art MOEAs have been proposed. These MOEAs can be classified into the following categories: decomposition-based, domination-based, indicator-based, and probability-based methods. Among them, the first four categories belong to non-model based methods, while the fifth one is considered to be model-based method, in which machine learning techniques are often used to build the models. Recently, embedding machine learning mechanisms into MOEAs is becoming popular and promising. In this paper, a relatively thorough review on both traditional MOEAs and those equipped with machine learning mechanisms are made, with the aim of shedding light on the future development of this emerging research field.
Discriminant Component Analysis and Self-Organized Manifold Mapping for Exploring and Understanding Image Face Spaces

Face recognition is a multidisciplinary field that involves subjects in neuroscience, computer science and statistical learning. Some recent research in neuroscience has indicated that the ability of our memory relies on the capability of orthogonalizing (pattern separation) and completing (pattern prototyping) partial patterns in order to encode, store and recall information. From a computational viewpoint, pattern separation can be cast in the subspace learning area while pattern prototyping is closer to manifold learning methods. So, subspace (or manifold) learning techniques have a close biological inspiration and reasonability in terms of computational methods to possibly exploring and understanding the human behavior of recognizing faces. Therefore, the aim of this paper is threefold. Firstly, we review some theoretical aspects about perceptual and cognitive processes related to the mechanisms of pattern separation and pattern prototyping. Then, the paper presents the basic idea of manifold learning and its relationship with subspace learning with focus on the dimensionality reduction problem. Finally, we present the Discriminant Principal Component Analysis (DPCA) and the Self-Organized Manifold Mapping (SOMM) algorithm to exemplify respectively pattern separation and completion techniques. We show experimental results to demonstrate the effectiveness of DPCA and SOMM algorithms on well-framed face image analysis.
Internal branding as a learning process: How employees adapt their behaviour after a brand change in service organisations

In service marketing, the behaviour of personnel is a crucial element in the brand building process. When a brand changes, employees have to adapt their behaviour in order to deliver the new promises of the brand to the customer. This paper examines the learning process that leads to that new behaviour. Drawing on a competence based definition of internal branding, we find the three traditional elements of the internal branding process, (i.e. the cognitive, affective and conative elements), although in a reversed order. Facilitating and prohibiting factors are identified. Suggestions for further research and managerial implications are discussed.
Remote switching of multiple smart meters and steps to check the effect on the grid's power quality

The traditional electrical grid is transitioning into the Smart Grid (SG) and the introduction of Smart Meter (SM) is a first stage towards Smart Grid. The SM can offer new functionalities such as remote reading, automatic event reporting and the possibility of remote switching by the Distribution System Operator (DSO). The DSO can send remote signal to the breaker of the SM to disconnect or connect customers. This could be used when customers are moving or do not have a contract. DSO is currently applying this technique regularly for customers but one by one which do not affect the grid's power quality remarkably. But this technique has never been applied for multiple customers at a time in a single neighborhood and the possible effect on the grid's power quality is still unknown. This condition is studied in this paper through test scenarios which have already been planned to test. This paper presents necessary steps of remote SM switching for a large number of customers. Power quality standards and steps for measuring power quality during the tests are outlined. Possible risks of the test as well as the probability of occurring risk and also the consequences of those risks are studied in this paper.
Disease and Development in Historical Perspective

Health conditions and disease environments are important for economic outcomes. This paper argues that the main impact of disease environments on the economic development of nations is not due to the direct effect of health conditions on income, but rather because of their indirect effect via institutions. Health does affect income directly, but this can explain only a small fraction of today's differences in per capita income. In contrast, when previously isolated populations came into contact during the period of European colonial expansion, differences in disease environments had a major impact on the path of institutional development and consequently first-order consequences for economic growth. (JEL: I12, O12) Copyright (c) 2003 The European Economic Association.
LUIS—A logic for task‐oriented user interface specification

The complexity of user interface design is still steadily increasing because of an increasing variety of innovative interaction styles (e.g., pen‐based interaction) and because of an increasing orientation towards tasks as they are actually accomplished in work environments. In order to handle the resulting variety of knowledge that has to be taken into account for design, specification techniques have to rely on representation schemes for end user tasks and their organization, problem domain data, and interaction styles. Declarative knowledge representation techniques provide the capabilities to handle heterogeneous categories of knowledge as well as their integration. This article describes not only a conceptual but also a formal framework for task‐oriented, declarative user interface specification. the conceptual framework is based on a semantic network. It is formalized comprehensively through LUIS (Logical User Interface Specification). LUIS is a novel many‐sorted temporal specification logic. Using LUIS all static and dynamic relations between (1) end user tasks and problem domain data, (2) end user tasks and interaction styles (media and modalities), and (3) problem domain data and interaction styles, according to different levels of refinement can be captured. In particular, LUIS supports to stepwise refinement of knowledge to elementary data structures and basic functions. Moreover, in LUIS organizational constraints concerning the accomplishment of end user tasks can be represented by postulating axioms applying the modalities “First,” “Sometime in the future,” “Next,” and “More recently than. ” Since each knowledge category corresponds to a particular sort of the specification logic, the designer can be provided with modular knowledge sources. They can be extended or modified for further developments in a flexible way. © 1995 John Wiley & Sons, Inc.
The applicability of ultrasound dynamic receive beamformers to photoacoustic imaging

Ultrasound array transducers offer several advantages over mechanically-scanned transducers for photoacoustic imaging, including high imaging frame rates and dynamic focusing. Development of a photoacoustic array system can be accelerated by adapting existing commercial ultrasound systems and harnessing their performance-enhancing aspects such as parallel beamforming. One challenge faced when adapting commercial ultrasound systems for photoacoustic imaging is that the dynamic delay sequences required for focusing must account for one-way rather than two-way ultrasound wave propagation. Modifying the hardware may be difficult for developers and impossible for users, but some ultrasound systems provide a parameter, c: the speed of sound used to calculate these delays. A linear-array based ultrasound platform with parallel channel acquisition is used to compare experimental point-spread functions produced using an ultrasound beamformer with a scaled value of c to those produced by a photoacoustic beamformer. Scaling c by a factor of √2 provides the best image quality compared with adjustments by 1 and 2, but requires image rescaling, which can be done postacquisition or by modification of the rendering software. Although optimal focusing is achieved for linear scanning, this is not the case for sector scanning, which requires angular and depth rescaling.
The proteolytic activity of MT4‐MMP is required for its pro‐angiogenic and pro‐metastatic promoting effects

Membrane‐type 4 matrix metalloprotease (MT4‐MMP) expression in breast adenocarcinoma stimulates tumor growth and metastatic spreading to the lung. However, whether these pro‐tumorigenic and pro‐metastatic effects of MT4‐MMP are related to a proteolytic action is not yet known. Through site directed mutagenesis MT4‐MMP has been inactivated in cancer cells through Glutamic acid 249 substitution by Alanine in the active site. Active MT4‐MMP triggered an angiogenic switch at day 7 after tumor implantation and drastically accelerated subcutaneous tumor growth as well as lung colonization in recombination activating gene‐1‐deficient mice. All these effects were abrogated upon MT4‐MMP inactivation. In sharp contrast to most MMPs being primarily of stromal origin, we provide evidence that tumor‐derived MT4‐MMP, but not host‐derived MT4‐MMP contributes to angiogenesis. A genetic approach using MT4‐MMP‐deficient mice revealed that the status of MT4‐MMP produced by host cells did not affect the angiogenic response. Despite of this tumor intrinsic feature, to exert its tumor promoting effect, MT4‐MMP requires a permissive microenvironment. Indeed, tumor‐derived MT4‐MMP failed to circumvent the lack of an host angio‐promoting factor such as plasminogen activator inhibitor‐1. Overall, our study demonstrates the key contribution of MT4‐MMP catalytic activity in the tumor compartment, at the interface with host cells. It identifies MT4‐MMP as a key intrinsic tumor cell determinant that contributes to the elaboration of a permissive microenvironment for metastatic dissemination.
Isoflurane Hyperalgesia Is Modulated by Nicotinic Inhibition

Background The inhaled anesthetic isoflurane inhibits neuronal nicotinic acetylcholine receptors (nAChRs) at concentrations lower than those used for anesthesia. Isoflurane produces biphasic nociceptive responses, with both hyperalgesia and analgesia within this concentration range. Because nicotinic agonists act as analgesics, the authors hypothesized that inhibition of nicotinic transmission by isoflurane causes hyperalgesia. Methods The authors studied female mice at 6–8 weeks of age. They measured hind paw withdrawal latency at isoflurane concentrations from 0 to 0.98 vol% after the animals had received a nicotinic agonist (nicotine), a nicotinic antagonist (mecamylamine or chlorisondamine), or saline intraperitoneally. In addition, the authors tested the interactions between mecamylamine and isoflurane and nicotine and isoflurane in heterologously expressed &agr;4&bgr;2 nAChRs. Results Female mice had significant hyperalgesia from isoflurane. Nicotine administration prevented isoflurane-induced hyperalgesia without altering the antinociception produced by higher isoflurane concentrations. Mecamylamine treatment caused a biphasic nociceptive response similar to that caused by isoflurane. Mecamylamine and isoflurane had an additive effect, both at heterologously expressed &agr;4&bgr;2 nAChRs and on the production of hyperalgesia in vivo. Mecamylamine thus potentiated hyperalgesia but did not affect analgesia. Conclusions Since hyperalgesia occurs in vivo at isoflurane doses that antagonize nAChRs in vitro, is prevented by a nicotinic agonist, and is mimicked and potentiated by nicotinic antagonists, the authors conclude that isoflurane inhibition of nAChRs activation is involved in the pathway that causes hyperalgesia. At subanesthetic doses, isoflurane can either enhance pain responses (produce hyperalgesia) or be analgesic (antinociceptive). In rats, low volatile anesthetic concentrations (0.1–0.2 minimum alveolar concentration [MAC]) elicit hyperalgesia, while 0.4–0.6 MAC elicits antinociception.
Building Social Capital With It And Collaboration In Supply Chains: An Empirical Investigation

The importance of managing the extended enterprise is a recurrent theme, and many consider supply chains to be the next frontier of opportunities to improve profitability and competitive advantage for organizations. Building on prior work that has identified the value creation capabilities of integrated digital platforms and relational conditions, the paper draws upon social capital theory to develop an organizing framework for developing supply chain integration capabilities. We assert that both IT and relational capabilities constitute the social capital available to firms in the supply network and draw upon Nahapiet and Ghosal’s (1998) work to identify the specific facets structural, relational and cognitive social capital that are relevant in the context of supply chain integration. This framework enables us to evaluate structural relational and cognitive social capital as a platform for managing collective action and as a source of creating and sharing knowledge. A research model is developed and empirically validated that investigates the impact of different facets of social capital on supply chain integration, and subsequently supply chain integration on firm performance. Data from 110 manufacturing and retail firms was used to test the model to provide empirical support for the proposed research model.