Miracle-dz/UniFAR-FTUs huggingface dataset:
Stem cell transplantation for chronic lymphocytic leukaemia

Early results of autologous stem cell transplantation (ASCT) in chronic lymphocytic leukaemia (CLL) suggested a significant proportion of patients remained disease‐free for years, raising the possibility of cure. More recent studies have shown no evidence of a plateau in the survival curves indicating that, at best, ASCT may only prolong disease‐free survival. Problems remain over progenitor cell mobilization and one study has raised anxieties about post‐transplant myelodysplasia. The impact of ASCT in CLL will only be properly ascertained in a randomized clinical trial and this in underway in Europe. Initial results of conventional allogeneic transplantation (allo‐SCT) were very disappointing, with an unacceptably high mortality, but did show that cure was possible in some patients. The introduction of reduced intensity conditioning has limited the early transplant‐related mortality but it remains too early to determine what proportion of patients will be cured. In view of these uncertainties, is important that reduced intensity allo‐SCT for CLL is conducted in the context of a clinical trial. Finally, CLL is very heterogeneous condition and great deal more is becoming understood about the prognostic factors. These will become important in allowing patients and their physicians a choice in balancing the risks of various treatment options.
Lexical Categorical Disambiguation using a Multi-Agent Systems Architecture

We present a distributed architecture model for lexical categorical disambiguation based on multi agent systems. This proposal aims to minimize some shortcomings from sequential approaches to natural language processing systems, through cooperation among intelligent and distributed modules. We have proposed a model for a linguistic society of agents which includes linguistic knowledge as well as a set of procedures to manipulate it, and mechanisms for social reasoning. The disambiguation is solved by a subsociety of acquaintance agents that use a communication protocol based on cooperative learning in order to reach a common goal.
Impact of Subway Stations on Commercial Land Prices

The purpose of this study is to analyze the impact of subway station on commercial land values through the case of Hong Kong.Methods of local linear regression,analysis of regression and hypothesis testing were employed.The results indicate that the subway station has significant impacts on the commercial land prices,with the first radius of influence of 350m and the second of 550m.On this basis,the model of the spatial relations between land prices and distances was established.The government should pay more attention to the planning of the commercial land around the station and appropriately increase the proportion of commercial zone.
Forecasting wind power generation patterns based on SOM clustering

Due to incontinent use of fossil fuels all over the world, it comes to be exhausted and also causes serious environmental pollutions and global warming. Therefore, people begin to find renewable energy which is clean, no limit and reproducible. Among several renewable energies, wind power is the most promising one which can be connected to the electric power system. However, it is very important to predict the wind power generation patterns in the electric power system to balance the load and generation. In this paper, we propose a framework to predict the wind power generation patterns with classification models. This framework consists of the following steps: (1) data preprocessing to handle noise data, missing values, (2) assignment of class labels to wind power generation patterns using SOM clustering, (3) classification model construction to predict the wind power generation patterns. The experiment result shows that the rules from decision tree are simple and easy to interpret. And it is possible to predict wind generation patterns.
Flavor Of Meat Meat Products And Seafoods

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Like or dislike? Adolescents’ responses to personalized social network site advertising

ABSTRACT Increasingly, personal data posted by users of social network sites (SNSs) can be used to personalize advertising. The present study investigates how adolescents respond to personalized ads in terms of attitude toward the ad, brand engagement and intention to forward, and whether privacy concerns moderate their responses. According to pretest results, a medium level of personalization was expected to be optimal in terms of advertising effectiveness. A within-subjects experiment involving 40 participants aged 14–18 years was performed. Three conditions of personalized advertising were designed with, respectively, low, medium, and high levels of personalization. The study found that the highest personalization condition generated the most positive response and that privacy concerns did not moderate the effects of personalization. The privacy paradox is discussed as an alternative explanation, along with other implications of the results.
The Canadian automotive market

The automotive sector is Canada’s largest manufacturing sector, accounting for 12% of its manufacturing GDP and 25% of its manufacturing trade. The principal objective of this study is to calculate the impact of changes in Canada’s trade policy on the automotive sector. The study is organized in five sections: the first identifies current and future trends in the industry; the second contains an econometric model to analyse the market effects of four trade policy scenarios on automobile production; the third identifies the impact of trade policy on foreign direct investment; the fourth contains an analysis of the market effects of trade policy changes on the aftermarket auto parts sector; and the last section of the study discusses the future direction of the automotive industry.
Early and Late Level Fusion of Deep Convolutional Neural Networks for Visual Concept Recognition

Visual concept recognition is an active research field in the last decade. Related to this attention, deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition in videos. In this study, we investigate various aspects of convolutional neural networks for visual concept recognition. We analyze recent studies and different network architectures both in terms of running time and accuracy. In our proposed visual concept recognition system, we first discuss various important properties of popular convolutional network architecture under consideration. Then we describe our method for feature extraction at different levels of abstraction. We present extensive empirical information along with best practices for big data practitioners. Using these best practices we propose efficient fusion mechanisms both for single and multiple network models. We present state-of-the-art results on benchmark datasets while keeping computational costs at low level. Our results show that these state-of-the-art results can be reached without using extensive data augmentation techniques.
Modeling Supply Chain Acquisitions

Companies that are considering an acquisition are generally concerned with combining businesses successfully. Acquisitions are strategic decisions, aimed at increasing the current operations as well as positioning themselves for future success. The aim of this dissertation research reflects that ambition: providing decision support models and methodologies for making network design decisions that enable successful current and future supply chain operations. These businesses operate in an uncertain world, where decisions regarding supply chain network design must be made despite the possibility of unforeseen future events that may disrupt or damage the supply chain. In an effort to aid decision makers in designing supply chain networks that can operate well in the uncertain future, we offer both a deterministic and a robust optimization model that consider both cost and network connectivity as objective functions. Decision makers are able to evaluate several solutions with different cost and connectivity values, choosing the levels that best serve the needs of their newly merged company. The proposed robust optimization model also allows decision makers to control the conservativeness of the model while making good decisions despite uncertain or incomplete data, which is not uncommon in acquisition scenarios.
Connecting Sustainable Land Use and Quality Management in Sheep Farming: Effective Stakeholder Participation or Unwelcome Obligation?

A large portion of Iceland’s soil and vegetation has been lost over the centuries, with the productivity of the land often being far below its full potential. In order to address this issue, farmers have become increasingly involved in sustainable land use and restoration efforts, for example through voluntary participation in Quality Management in Sheep Farming (QMS). This study explores the effectiveness of the scheme regarding land improvement measures and participation processes, based on farmers’ experiences, views and expectations. Data was collected through qualitative interviews with farmers within a specific study area, who all participated in the QMS scheme and then analyzed using the methods of grounded theory. There is evidence to suggest that carrying out reclamation projects influences environmental behavior to some extent. However, a gap seems to exist between the land use part of QMS and other aspects of the scheme, with the objectives of restoration and sustainable land use needing to be clarified, and subsidies better linked to restoration achievements. A consensus and consistency needs to be developed among the different actors, institutional and legal framework, when it comes to the priorities and methods used within the scheme. The scheme is more or less ‘top-down’ oriented, with farmers regarded as passive assistants for advancing a certain agenda rather than influential or valuable voices within the system. Thus, for the QMS to reach its potential, information flow and communication within and outside of the system needs to be enhanced with incentive and innovation enabled and encouraged.
Malaysian public sector employees' financial preparedness and personal financial plan implementation

A stable economic situation would minimize uncertainties in a longer time horizon. A stable economic situation and job security enable individuals to plan effectively for their future life. This study aims to determine the perceived financial preparedness of employees in the Malaysian public sector regarding their future needs based on age, differences in future financial preparedness and financial plan implementation based on each financial dimension among the identified financial adequacy groups. Multistage sampling based on states, departments and name lists was employed in this study resulting in 472 usable self-administered questionnaires. Slightly more than half of the respondents perceived their future financial preparedness was adequate based on their current personal financial preparation. Among those who perceived their financial preparation as adequate for their old age, financial preparedness tended to increase with age and all aspects of perceived future financial preparedness achieved scores above mean. This shows that the financially adequate respondents were confident that they were prepared in all aspects of financial preparedness. They were most confident about their preparation for health needs, expenses and emergency fund during their retirement or old age. Financially adequate respondents also saved more than the financially inadequate group, had better cash-flow, had more investments and subscribed to insurance for protection. Although public sector employees are supposed to be financially prepared for their old age through their present job security and pension scheme, they are still financially ill-prepared, especially the younger employees. This study suggests that early intervention programmes by employers and financial institutions would enable the potential ill-prepared to take advantage of the time value of money.
Security in Network Layer of IoT: Possible Measures to Preclude

The internet of things (IoT) is an imminent model in the field of wireless telecommunications. It is also considered as a third wave of information technology after the Internet and mobile communication. Basically, IoT is a wireless interconnected network of variety of objects such as radio frequency identification (RFID) tags, sensors, actuators, mobile phones and other types of wireless devices. It has extensible application in the areas such as public security, infrastructure development, modern agriculture, environment protection, urban management, healthcare, enhanced learning, and business service, among others. IoT is a self-configuring wireless network of sensors where the primary goal of establishing connection is to offer interconnectivity of various objects. The concept of IoT was coined by the Auto-Id center of the Massachusetts Institute of Technology (MIT) in 1999 B. Balamurugan VIT University, India Dyutimoy Biswas VIT University, India Security in Network Layer of IoT
NADPH Oxidase Polymorphisms in Cerebral Small Vessel Disease

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...And Nothing Else Matters? On the Dimensionality and Predictability of International Stock Returns

We use the information contained in 161 cross-sectional trade signals to explore the multidimensionality and predictability of individual stock returns in five international markets (North America, Europe, Pacific, Japan, and Emerging Markets). All markets are highly dimensional, but the most important return predictors differ across regions. Exploiting these characteristics, a single globally diversified high-dimensioned long/short return forecast strategy realizes a monthly equal-weighted (value-weighted) four-factor alpha of 4.1% (1.9%), and an annualized Sharpe ratio of 7.9 (3.4). Collectively, these results add to our understanding of market integration, data mining, and the degree of informational efficiency of global stock markets.
Environmental Management Accounting Practices and Environmental Performance for Malaysian Manufacturing Industry

The aim of this study is to review the relationship between environmental management accounting practices and environmental performance for Malaysian manufacturing industry. This paper is to explore the environmental management accounting practices (environmental cost, environmental safety, continuous improvement, management commitment, and customer focus) and environmental performance (financial performance and operational performance). This study proposed relationship model between environmental management accounting practices and environmental performance for Malaysian manufacturing industry. Based on the proposed conceptual model and reviewed, research hypothesis are being developed.
A highly parallelized processor for face detection based on Haar-like features

This paper presents a hardware architecture for face detection based on the Viola and Jones object detection method. To achieve high speed detection, two major features are introduced in this paper, including rapid integral image generation and hybrid stage classifier. A register array structure is utilized for storing and generating integral image, facilitating the simultaneous access and parallel classifier evaluation. Moreover, a hybrid stage classifier is proposed which combines both serial processing and parallel processing, making the best trade-off between detection speed and resource consumption. In addition, the detection system can detect human faces in a 384×288 image at a speed of 22 fps when the specialized processor in a Stratix II FPGA works at 100 MHz.
Revealing Taxi Driver's Mobility Intelligence through His Trace

This study develops a methodology for the analysis of taxi drivers’ operation behavior in a real urban environment. The research objective is to spatially and temporally quantify, visualize, and examine taxi drivers’ operational behavior and skill (as measured by income), which the authors call ‘mobility intelligence’. For the first time, taxi drivers’ different operation strategies were systematically analyzed through their daily activity traces. Routes and economic behavior data were collected with the use of Global Positioning System (GPS) and a set of spatiotemporal analysis tools were developed. Drivers are categorized by their daily income into top drivers and ordinary drivers. A 3D clustering technique is used to quantitatively analyze the spatiotemporal patterns for top driver and ordinary driver. Also, fractal analysis is employed to quantify tortuosity of movement paths and to explore how top and ordinary drivers operate on different spatial scales at different times, where the primary focus is to reveal top driver mobility intelligence.
Hemobarogram subwave component pulse signal identification

Hemobarogram (HBG) is the direct blood pressure (BP) measurement that contains a rich set of information, where in this case, focused on the left upper limb cardiovascular system. The goal is to retrieve knowledge from the acquired HBG that can be used to monitor the current status of the cardiovascular system functionality, providing important diagnostic warnings associated with cardiovascular diseases. This research focuses on the automatic identification of the cardio periodic cycle, i.e. the individual heart beat pulses, utilizing a subwave set decoupled through the nonlinear time-series transformation from the original HBG signal. The outcome showed successful periodic identification for both the energy generative subwave, and the energy dissolutive subwave, while the two combined showed HBG interaction functionality throughout the upper limb circulation system. A trial of over 40 pulses of HBG recordings has been identified for its cycles, and the proposed procedure showed consistency in identifying all the individual pulses.
Contagion in Credit Default Swap Premiums and Spillover Effects From Bond Liquidity To Stock Returns

This dissertation investigates two distinct topics. First, I document an increase in the correlations between credit default swap (CDS) spread changes during the credit crisis and investigates the source of that increase. One possible explanation is that correlations increased because fundamental values became more correlated during the crisis. However, I find that changes in the fundamental determinants of credit risk account for only 20% of the increase in correlations. Further, I show that changes in counterparty risk did not affect correlations during the turmoil. In contrast, I find that changes in liquidity risk contributed to the increase in correlations; however, liquidity alone cannot explain the full increase. Finally, I show that a systematic repricing of credit risk, as evidenced by increased volatility in the default risk premium, was the main factor that amplified correlations. Second, I investigate the causal relationship between bond liquidity and stock returns. Bond prices increase with liquidity, which could positively affect stock returns by decreasing the cost of capital and increasing profitability. To isolate an increase in bond liquidity, I define the initiation of the Trade Reporting and Compliance Engine (TRACE) as an exogenous shock to bond liquidity and conduct an event study. I find a cumulative average abnormal return of 2.2% associated with the Phase 1 (July 1, 2002) dissemination date. Further, I find that firms with larger changes in liquidity, as measured by the imputed roundtrip cost (IRC), have larger average abnormal stock returns. Moreover, firms with a higher probability of Informed trading (PIN)
Optimization studies of weight matrix in LQR for reentry vehicle profile-following

Because of particular dynamic characteristics, the traditional method for calculating weight matrices in LQR is not suitable for reentry vehicle profile-following. This paper designs parameter varying weight matrix. In this method, on the basis of vehicle entry flight characteristics, we design the functional relationship between weight matrix parameters and flight states. The simulations show that this method can improve the performance of reentry vehicle profile following obviously, and it is more robust to different disturbances.