Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of Debt Financing Structure and Debt Maturity - (Empirical Studies of Manufacturing Company on Indonesia Stock Exchange)

This study aims to predict and explain the determinants of debt financing structure and debt maturity from the perspective of contracting /agency cost theory and Signaling theory that explains that determines the structure of debt depends on the characteristics of companies that need funding as the company's growth opportunities, firm size , managerial ownership structure as well as the consideration of a change in environment or a desire to signal to the market about the quality of the company. Therefore, before determining the structure of corporate debt or debt maturity should be decided in advances that the financing of the debt taken, the study also examines the effect of leverage on the term structure of debt. The uniqueness of this study was to examine the effect of environmental change on the structure of funding and debt maturity, because previous research no one has studied the effect, although Thies and Clock (1992 in Simerly RL 2000) states that environmental changes increased the use of debt and long-term debt will decrease. The population of the research is the entire manufacturing companies listed in Indonesia Stock Exchange (IDX) of 145 companies with the observation period 2005-2011. Based on the criteria defined population, the number of samples analyzed was 32 companies. Methods of data analysis using Structural Equation Modeling (SEM) using AMOS Ver application program 21. The study findings suggest that contracting theoretical perspective that supports manufacturing companies that the larger companies use long-term debt, and the higher the dynamic changes in the environment will take a short-term debt, but the results of this study do not support the signaling theory, that the company is not trying to show the quality of the company in determining the maturity structure of the debt as well as managerial stock ownership and growth options has no effect on debt maturity. While the effect on the structure of the debt financing (leverage) is the size of the company and managerial stock ownership, while, corporate growth, corporate quality, and environmental changes found to have no effect on leverage. Leverage found to affect the maturity structure of the debt.
Signatures of the Induced Θ vacuum state in Heavy Ion Collisions

It was argued recently[1] that, in general, an arbitrary induced |θ〉indvacuum state would be created in the heavy ion collisions, similar to the creation of the disoriented chiral condensate with an arbitrary isospin direction. It should be a large domain with a wrong θ orientation which will mimic the physics of the world when the fundamental θ 6= 0. I suggest a few simple observables which can (hopefully) be measured on an event by event basis at RHIC, and which uniquely determine whether the induced |θ〉ind vacuum state is created.
GENERAL LINEAR MIXED MODELS OF LONGITUDINAL STUDIES : SMALL SAMPLES , VARIED DISTRIBUTIONAL CONDITIONS , AND MISSING DATA

General linear mixed models are commonly used in the analysis of unbalanced repeated measures designs (Verbeke & Molenberghs, 2000). Though the relative performance of linear mixed models in unbalanced designs has been widely contrasted with general linear modeling of data using varied approaches toward the imputation of missing data values, these studies have focussed largely on modeling under large sample sizes and standard distributional conditions. In the current paper, the use of general linear mixed modeling under unbalanced repeated measures conditions under small sample size, diverse distributional conditions, and a variety of missing data patterns is contrasted with modeling using missing value imputation methods, with the focus of documenting the characteristics of the model parameter estimates, their standard errors, and the rejection probabilities of the corresponding test statistic.
Differential Privacy for Collaborative Filtering Recommender Algorithm

Collaborative filtering plays an essential role in a recommender system, which recommends a list of items to a user by learning behavior patterns from user rating matrix. However, if an attacker has some auxiliary knowledge about a user purchase history, he/she can infer more information about this user. This brings great threats to user privacy. Some methods adopt differential privacy algorithms in collaborative filtering by adding noises to a rating matrix. Although they provide theoretically private results, the influence on recommendation accuracy are not discussed. In this paper, we solve the privacy problem in recommender system in a different way by applying the differential privacy method into the procedure of recommendation. We design two differentially private recommender algorithms with sampling, named Differentially Private Item Based Recommendation with sampling (DP-IR for short) and Differentially Private User Based Recommendation with sampling(DP-UR for short). Both algorithms are based on the exponential mechanism with a carefully designed quality function. Theoretical analyses on privacy of these algorithms are presented. We also investigate the accuracy of the proposed method and give theoretical results. Experiments are performed on real datasets to verify our methods.
Closed-loop evolutionary multiobjective optimization

Artificial evolution has been used for more than 50 years as a method of optimization in engineering, operations research and computational intelligence. In closed-loop evolution (a term used by the statistician, George Box) or, equivalently, evolutionary experimentation (Ingo Rechenberg's terminology), the "phenotypes" are evaluated in the real world by conducting a physical experiment, whilst selection and breeding is simulated. Well-known early work on artificial evolution-design engineering problems in fluid dynamics, and chemical plant process optimization-was carried out in this experimental mode. More recently, the closed-loop approach has been successfully used in much evolvable hardware and evolutionary robotics research, and in some microbiology and biochemistry applications. In this article, several further new targets for closed-loop evolutionary and multiobjective optimization are considered. Four case studies from my own collaborative work are described: (i) instrument optimization in analytical biochemistry; (ii) finding effective drug combinations in vitro; (iii) onchip synthetic biomolecule design; and (iv) improving chocolate production processes. Accurate simulation in these applications is not possible due to complexity or a lack of adequate analytical models. In these and other applications discussed, optimizing experimentally brings with it several challenges: noise; nuisance factors; ephemeral resource constraints; expensive evaluations, and evaluations that must be done in (large) batches. Evolutionary algorithms (EAs) are largely equal to these vagaries, whilst modern multiobjective EAs also enable tradeoffs among conflicting optimization goals to be explored. Nevertheless, principles from other disciplines, such as statistics, design of experiments, machine learning and global optimization are also relevant to aspects of the closed-loop problem, and may inspire futher development of multiobjective EAs.
Dynamical Networks ( miniporject ) Effect of Topology of the Reservoir on Performance of Echo State Networks

In this report we studied the effect of reservoir topology on the learning performance of the echo state networks. Different tasks with different properties were defined and three general models (scale-free, small-world and random) were considered for reservoirs. Some relations between the topology-related parameters of the reservoirs and performance of it (mean square error) was discovered. For memoryless and simple tasks it seems graphs with low average degree performed better for scalefree and small-world networks. In contrary, for complex tasks that need memory, networks with slightly higher average degree performed better. In average it was concluded that for same network size and same average degree (same number of links), random reservoirs and small-world reservoirs performed almost the same but better than scale-free reservoirs.
A Bending Pneumatic Rubber Actuator Realizing Soft-bodied Manta Swimming Robot

This paper shows a new design and prototyping method for a bending pneumatic rubber actuator and its application to a soft-bodied manta swimming robot. The design is based on optimal design using non-linear finite element method, in which geometrical and material non-linearity are considered and fabrication process is based on a rapid and efficient prototyping system using a CAD/CAM based rubber molding process. In this paper, the characteristics of several possible actuators are analyzed and evaluated to lead to an optimal actuator design. The actuator works very well with smooth and soft motion. The manta swimming robot in which the developed actuators are embedded is also designed based on non-linear finite element method. The developed manta swimming robot is made only of rubber and it swims in water smoothly as if it was a living fish. The experimental results of the manta robot motion show that good agreement with those of analytical results.
The TCL1A Oncoprotein Interacts Directly with the NF-κB Inhibitor IκB

The T cell leukaemia/lymphoma 1A (TCL1A) oncoprotein plays key roles in several B and T cell malignancies. Lacking enzymatic activity, TCL1A's transforming action was linked to its capacity to co-activate the protein kinase AKT via binding to its pleckstrin homology (PH) domain. However, perturbation of AKT signalling alone was recently shown insufficient to explain TCL1A oncogenesis, suggesting that TCL1A has additional cellular partners. Searching for such additional targets, we found that TCL1A binds specifically and directly to the ankyrin domain of IκB, the inhibitor of the NF-κB transcription factors. Through binding assays and a structural analysis by small angle X-ray scattering, we show that TCL1A and IκB interact in yeast-two-hybrid systems, when transiently overexpressed in 293 cells, and as recombinant proteins in vitro. We further establish that the association between TCL1A and IκB is compatible with AKT binding to TCL1A, but incompatible with IκB binding to NF-κB. By interfering with the inhibition of NF-κB by IκB, TCL1A may increase the concentration of free NF-κB molecules sufficiently to trigger expression of anti-apoptotic genes. Thus our data suggest an additional route by which TCL1A might cause cancer.
Investigating the CEO of a MAT: Examining practices and positions on ‘the street’

The emergence of Chief Executive Officers as leaders of educational service providers is positioned in multi academy trusts, the preferred structure of schooling in England. Within this structure, the Chief Executive Officer position is distinct and different from previous constructs of headteachers, since the Chief Executive operates at both street level, that is within the MAT, and beyond ‘the street’. In this article, I argue that a new conceptualisation of the headteacher is needed to explain the emerging position and practices of the Chief Executive Officer. These include the interface with the market, adopting entrepreneurial dispositions and constructing professional and business networks. I typologise these practices and positioning through the analysis of empirical data gathered from the Leadership of the Lawrence Trust Project and its Chief Executive Officer, KT Edwards.
Supporting Care by Interpretation of Expressions about Patient Experience with Machine Learning

— Our research aims at addressing the needs of developing data analysis about communication in respect to care seeking and primary care, discovering how health expressions evolve along the personal growth and learning process, and how to solve the needs identified in respect to developing measuring the quality of life. We provide an overview of the development of a new research methodology exploiting machine learning for analyzing patient experience expressions to support personalized care and managing in everyday life. Our research relies on an online questionnaire in which the representatives of various population groups perform interpretation tasks. Dependencies between answers about the interpretation tasks and background information are analyzed with machine learning methods. The research creates new ways to interpret and address the meanings of language usage of different groups of patients and impaired carefully and distinctively as a part of everyday life and care events.
Distributed QoS Routing Using Bounded Flooding

Quality-of-Service (QoS) routing is indispensable to real-time applications in integrated services packet-switching networks. However, existing QoS-routing schemes based on either request ooding or link-state dissemination are ineecient in terms of message overhead and/or operational cost. This paper proposes a QoS-routing algorithm with bounded ooding which not only dramatically reduces message overhead and operational cost, but also provides good performance. The overhead is reduced by limiting the search area only within which connection-request messages are ooded. Since our scheme is based on ooding, it does not require path calculation nor link-state dissemination, which can be very expensive. In order to limit the search area, we employ a distance table at each node which stores hop counts of the minimum-hop paths to all the nodes reachable via its outgoing links. Unlike link-state routing, the proposed scheme only needs network-topology information that changes much less frequently than link-load states. Extensive simulation results have demonstrated the eeectiveness of the proposed scheme in terms of performance, operational cost and message overhead. Especially, it is shown to be much better than all the other existing QoS-routing schemes in terms of message overhead and operational cost, while providing reasonably good performance.
Predicting Transcription Regulatory Mechanisms by Systematic Promoter Analysis

An important part of understanding a biological pathway or a cellular function is to delineate the transcriptional regulatory mechanisms of the genes involved. Key participants in these mechanisms are special transcription factor proteins (TFs) which recognize particular sequences, called TF binding sites in promoters. Computational approaches to identify TF binding sites have been facilitated by large scale expression profiling experiments [1] and sequence analysis of multiple genomes [2]. Although these methods have been successfully applied to simple organisms such as yeast and worm [3, 4], systematic identification of bona fide transcriptional regulators in mammals still remains a challenging problem. Two important questions which are often encountered when studying transcription regulation are: (1) Find the common transcriptional regulators of a set of correlated genes which are involved in the same biological pathway or in the same cellular process. (2) Find the genes in the genome which may be regulated by one or a set of transcriptional factors. In this study a systematic and statistical approach was taken to answer these questions by establishing an integrated model considering all the promoters and all the characterized TFs in the genome. To implement this approach, a promoter analysis pipeline (PAP) consisting of a set of analysis tools and a graphical web-based user interface was developed.
Adaptive ECG compression scheme with prior knowledge support based on compressive sensing

Mobile electrocardiogram (ECG) monitoring systems have sprung up owing to the considerable interest attracted to wireless body area networks (WBAN). The long-term acquisition process for ECG produces large amount of data, which puts forward high demand on sensor lifetime. Fortunately, compressive sensing (CS) theory has been proved useful in energy saving by compressing signal in certain degree and fulfilling transmission. However, the reconstruction error will increase with fixed compression ratio since users or the sparsity of ECG signal will change during monitoring process. This paper concerns the flexibility and reconstruction quality problem existed in traditional CS-based ECG signal processing. One adaptive ECG compression scheme inspired by closed-loop control theory is proposed, in which the compression ratio can be adjusted according to both real-time reconstruction error and prior knowledge support. Simulation results show that the proposed scheme can improve the compression performance of 10.83% compared with traditional CS-based methods.
Comparison of reactive and proactive routing protocols for different mobility conditions in WSN

Ad hoc networks prove to be challenging networks for communication due to its resource constrained nature and frequent changes in the network topology. Though a lot of research has been going on in the various parts of the world for optimization of communications in the ad hoc networks. The application spectrum of ad hoc networks includes commercial, public safety applications and military sector. The erratic size of the network and its exotic topology pose a magnificent challenge to the routing algorithms used in such networks. The present paper offers an analysis of two or the reactive as well as proactive protocols used in the ad hoc networks. The analytical study has been performed for four different scenario conditions. All the four conditions are implemented on the battle field monitoring using the ad hoc networks. In this paper we present a comparison of the performance of reactive and proactive protocols for different mobility conditions in wireless sensor networks. The sensor network simulator architecture used is for battle field monitoring and provides support for sensing capabilities in network nodes. The mobile nodes collect data from battle field and send it to the base station. The performance of the routing protocols varies with the different mobility patterns of the nodes in the network. Power consumption model has been used to faithfully represent an actual sensor hardware and sensor network oriented traffic model.
Visualizing Gene Co-Expression as Google Maps

We visualize gene co-regulation patterns by creating 2D embeddings from microarray data corresponding to complete gene sets from the mouse genome, across large numbers of cell types. We use google maps and client-side graphics to dissiminate pre-rendered such visualizations with a small but intuitive set of interactions. We conduct an anecdotal evaluation with domain specialists and demonstrate that biologists appreciate this approach because it facilitates low-overhead access to readily analyzable perspectives of unfamiliar datasets and because it offers a convenient way of disseminating large datasets in visual form.
Report of the Supersymmetry Theory Subgroup

The authors provide a mini-guide to some of the possible manifestations of weak-scale supersymmetry. For each of six scenarios they provide: (1) a brief description of the theoretical underpinnings; (2) the adjustable parameters; (3) a qualitative description of the associated phenomenology at future colliders; (4) comments on how to simulate each scenario with existing event generators.
Implementation and Validation of Different Reconfiguration Strategies Between HSA and PSO for Loss Reduction

: In electric distribution system, the problem of finding the optimal configuration of radial network may be classified as the Travelling Salesman Problem. When a medium voltage system (20 kV) has a number of connected feeders whose circuit breakers (CBs) can be opened or closed, the combinatorial number of possible system configurations created by switching CBs become very large. In order to determine the minimum loss configuration of medium voltage system, a new computation algorithm is proposed. Harmony Search Algorithm (HSA) is proposed to solve the network re-configuration problem to get optimal switching combination in the network. The HSA is a recently developed algorithm which is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search which eliminates the need for derivative information. Simulations are carried out on 33-bus radial network of IEEE test system to validate the proposed algorithm. The results are compared with PSO ( Particle Swarm Optimization ) as a popular approaches available in the literature. It is observed that the proposed method performed well compared to the PSO in terms of the quality of solutions.
Building adaptive systems a component based approach to building neural networks

Adaptive systems based on neural-networks have abilities to efficiently recognise, classify or model data which can be used in interaction with different environments. Still such systems are not widely used in industrial and commercial applications due to the difficulties in their implementations and lack of usable tools for their development. In this paper we present a Rapid Application Development (R, 4 D) environment which enables easy construction, training and deployment of adaptive system. Due to its component-based concept the tool can be used for building different types of adaptive components and systems without requiring expertise of neural networks.
Measurements of Planar Metal–Dielectric Structures Using Split-Post Dielectric Resonators

Split-post dielectric resonators were used for the measurement of the complex permittivity and the complex permeability of planar, uniaxialy anisotropic, low- and medium-loss metamaterials at microwave frequencies. Several metamaterials with different metal patterns deposited on dielectric substrates by plasma deposition were investigated, including edge-coupled and broadside-coupled split-rings and circles. It has been shown that, depending on the type of metal pattern and their surface resistance, the metamaterials may exhibit various magnetic and dielectric properties. Several small metamaterial samples were scanned in the cavity electromagnetic field by moving them inside the split-post dielectric resonator. This allowed us to identify loss mechanisms in the samples and to distinguish between their electric and magnetic properties. Our measurements were confirmed by modeling the transmission/reflection characteristics of virtual waveguides containing real metamaterial samples and homogenous samples with the same effective electromagnetic properties as those obtained from the split-post dielectric resonator method using a finite-difference time-domain electromagnetic simulator software package.
JWIG: Yet Another Framework for Maintainable and Secure Web Applications

Although numerous frameworks for web application programming have been developed in recent years, writing web applications remains a challenging task. Guided by a collection of classical design principles, we propose yet another framework. It is based on a simple but flexible server-oriented architecture that coherently supports general aspects of modern web applications, including dynamic XML construction, session management, data persistence, caching, and authentication, but it also simplifies programming of server-push communication and integration of XHTML-based applications and XML-based web services. The resulting framework provides a novel foundation for developing maintainable and secure web applications.