Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbital Stabilization of Inverted-Pendulum Systems via Coupled Sliding-Mode Control

In this paper, we propose a coupled sliding-mode control (SMC) method for the periodic orbit generation and the robust exponential orbital stabilization of inverted-pendulum systems. We first design an SMC law to force a coupled sliding surface to be reached in finite time, such that the zero dynamics are generated in the form of a second-order undamped and forced nonlinear differential equation. Through the stability analysis, it is shown that there exist exponentially stable periodic solutions of the resulting zero dynamics (i.e., limit cycles around either the upright or downward equilibrium), even in the presence of the matched disturbance. Second, we design a target orbit stabilization control law by further introducing an auxiliary control law to the designed SMC law. This auxiliary control law utilizes the general integral of the autonomous zero dynamics, which preserves its zero value along the given target orbit, and thus, it can contribute to the exponential stabilization of the general integral. To demonstrate the validity of the proposed method, both the periodic orbit generation and target orbit stabilization control of the cart-pendulum, as an example among inverted-pendulum systems, are performed in numerical simulations.
Control Architecture Design and Localization for a Gas Cutting Robot

Conventional control architecture which has been employed in mobile robot control software is divided into two categories such as knowledge-based and behavior-based control architecture. Early implementation of control architecture was mainly focused on building for sensing the environment, modeling it, planning based on this perceived model and executing the planned action to achieve a certain task. This design approach is called sense-model-plan-act (SMPA) or knowledge-based. A mobile robot making use of a knowledge-based controller tries to achieve its goal by following closely the sense-modelplan-act procedure. SMPA controller also needs initial knowledge, required to model its task environment prior for the robots to executing the planned task. Hence, if the initial knowledge is suited to its working environment, the resulting tasks guarantee success. Although the resulting overall behavior is predictable, the controller often suffers from being slow and becomes complex as it deals with a dynamic environment because most of the controller processing time is consumed in building a model, doing general perception and planning. Therefore, it is suitable controller for robots to require high-level intelligence and work in static and predictable environment. Brooks proposed a radically different approach in the design of mobile robot control architecture to address the drawback of knowledge-based control architecture. This control architecture functions a horizontal computation scheme so that each behavior is a fixed action pattern with respect to the sensory information. When the mobile robot is confronted by a dynamic environment, a behavior-based robot can react fast because of the direct coupling between its behaviors and the sensed states. The robot controller can be built incrementally, thus making it highly modular and easy to construct. However, this reactive approach still suffers from planning the productive and efficient actions in an unstructured environment because they are only confined to reactions to sensors and the changing states of other modules. In this chapter, we address the functional safety problems potentially embedded in the control system of the developed mobile robot and introduce a concept of Autonomous Poor Strip Cutting Robot (APSCR) control architecture with a focus on safety in order to design the walkthrough procedure for each behavior. In section 2, we explain the working environment where the robot will be manually or autonomously operated. Section 3 explains the control architecture of APSCR. In section 4, we explain the localization system of APSCR. Finally, section 5 shows some experimental result and in section 6 conclusions will be lastly addressed.
Two-sensor track association based on probabilistic relaxation labeling

Using multi-sensor to detect and track targets has been always a research focus in recent years. And the track-to-track association is an important part of the distributed multi-sensor tracking system. Presently, most of the association algorithms need to do the temporal alignment at the first step, which may bring more errors and degrade the algorithms' performance due to the impact of noise or inaccurate motion model. Therefore, a novel track-to-track association algorithm between two sensors is proposed in this paper. It synthetically takes account of the target statements and the weighted statistical distances to define the compatibility coefficients of the probabilistic relaxation algorithm. By calculating the compatibility of track pairs it avoids directly measuring the similarities of the tracks from different sensors and implements the association of tracks without temporal alignment. Simulation results show that the proposed method performs better than some classical track association algorithms in many scenarios such as parallel and crossed motion.
Career and Talent Development Self-Efficacy of Filipino Students: The Role of Self-Compassion and Hope

Correspondence concerning this article can be addressed to Ma. Jenina N. Nalipay, Educational Policy Research and Development Center, Philippine Normal University, Taft Avenue, Manila, Philippines. Email: nalipay.mjn@pnu.edu. ph Recent reforms in the Philippine educational system (i.e., Enhanced Basic Education Act of 2013, the implementation of the K to 12 Program) aim to prepare students for lifelong learning, enhance The recent reforms in the Philippine educational system aim to develop lifelong learners who are competent in their chosen career and capable of contributing to society. Career and talent development self-efficacy is an enabling resource that could facilitate the realization of these goals. The present study examines a model of career and talent development self-efficacy predicted by self-compassion through hope in a sample of Filipino college students (N = 620). Results of structural equation modeling revealed that the model has good fit to the data, and that self-compassion has a significant indirect effect on career and talent development self-efficacy through hope. These findings highlight the importance of developing students’ personal resources in order to maximize their skills and abilities in developing their talents and being successful in their chosen career.
A Novel Semi Supervised Algorithm for Text Classification Using BPNN by Active Search

Demand of Text Classification is increasing with the evolution of huge amount of text data available in internet, news, institutes , To make an effective text classifier we need large amount of labeled data in the form of training samples, to get labeled data is not only expensive but also time consuming, tedious task, whereas unlabelled data is easily available & inexpensive. This paper proposes an algorithm that just makes use of some root words from expert followed by active search. Our algorithm also makes use of a very effective Term weighting method based on relevance factor that is used for feature representation, this text is train by BPNN. The proposed algorithm is compared on test data and on standard data 20 Newsgroup and mini Newsgroup on the basis of micro-average and macro-averaged F1 measure The Experimental results depicts the best micro averaged F1 measure of 0.95 at 2400 epochs for test data, 0.67 for 20 news group and is 0.95 for Mini Newsgroup which are comparable with the well known supervised Text classification.
Data mining approaches for predicting demand for healthcare services in Abu Dhabi

The application of data mining techniques provides a powerful approach to manipulate and extract useful information from existing data. It allows the learning of information from hidden data that could be used for future predictions. Accurate demand forecasting of any service has been a challenging research problem. Data mining has different techniques that could support demand prediction. Estimating the demand for healthcare services as a result of rapid population growth becomes essential for the strategic planning of Abu Dhabi Emirate. Future plans should be focused on districts that have needs, either for short- or long-term plans. The objective of this paper is to develop a data mining model that allows healthcare services demand planning. We use different data mining techniques in order to build four models to assist decision makers in predicting the demand for healthcare services in Abu Dhabi Emirate.
Stability analysis for sampled-data systems with a time-varying period

This paper proposes a novel stability analysis of linear systems with sampled-data inputs. Inspired by the input-delay approach and the stability of impulsive systems, this method provides novel sufficient stability conditions. The stability analysis concerns both constant and time-varying sampling periods. The delay-dependent conditions are expressed using computable linear matrix inequalities. Several examples show the efficiency and the limitation of such stability criteria.
Cell Surface Targeting of Heat Shock Protein gp96 Induces Dendritic Cell Maturation and Antitumor Immunity1

gp96 is a residential heat shock protein of the endoplasmic reticulum that has been implicated in the activation of dendritic cells (DCs) for the initiation of adaptive immunity. By genetic targeting of gp96 onto the cell surface, we demonstrate that direct access of gp96 to DCs induces their maturation, resulting in secretion of proinflammatory cytokines IL-1β, IL-12, and chemokine monocyte chemoattractant protein-1 and up-regulation of the expression of MHC class I, MHC class II, CD80, CD86, and CD40. Furthermore, surface expression of gp96 on tumor cells renders them regressive via a T lymphocyte-dependent mechanism. This work reinforces the notion that gp96 is an endogenous DC activator and unveils that the context in which Ag is delivered to the immune system, in this case surface expression of gp96, has profound influence on immunity. It also establishes a principle of bridging innate and adaptive immunity for cancer immunotherapy by surface targeting of an intracellular heat shock protein.
Acquisition of Violin instrumental gestures using a Commercial EMF tracking Device

This paper presents a method for the acquisition of violin instrumental gesture parameters by using a commercial two-sensor 3D tracking system based on electro-magnetic field (EMF) sensing. The methodolgy described here is suitable for acquiring instrumental gesture parameters of any bowed-string instrument, and has been devised by paying attention to intrusion, flexibility, and robustness. After reviewing relevant related work in the field, we give an overview of the application context, pointing out some basic needs to be fulfilled for our research purposes. Then, we present the steps for calibrating the system, followed by details on the computation of a number of relevant instrumental gesture parameters. The use of a number of the extracted parameters to perform score-performance alignment and database automatic annotation is also outlined. Finally, we conclude stating next steps in using acquired data, along with further developments of the methodology.
Muscarinic and nicotinic ACh receptor activation differentially mobilize Ca 2 + in rat intracardiac neurons

The origin of intracellular Ca concentration ([Ca]i) transients stimulated by nicotinic (nAChR) and muscarinic (mAChR) receptor activation was investigated in fura-2 loaded neonatal rat intracardiac neurons. ACh evoked [Ca]i increases which were reduced to ~60% of control in the presence of either atropine (1 μM) or mecamylamine (3 μM) and to < 20% in the presence of both antagonists. Removal of external Ca reduced ACh-induced responses to 58% of control, which was unchanged in the presence of mecamylamine but reduced to 5% of control by atropine. The nAChR-induced [Ca]i response was reduced to 50% by 10 μM ryanodine whereas the mAChR-induced response was unaffected by ryanodine suggesting that Ca release from ryanodine-sensitive Ca stores may only contribute to the nAChR-induced [Ca]i responses. Perforated patch whole-cell recording at –60 mV shows that the rise in [Ca]i is concomitant with slow outward currents upon mAChR activation and with rapid inward currents following nAChR activation. In conclusion, different signaling pathways mediate the rise in [Ca]i and membrane currents evoked by ACh binding to nicotinic and muscarinic receptors in rat intracardiac neurons.
Adaptive Mixed Hybrid and Macro-Hybrid Finite Element Methods

In this paper, we consider efficient multilevel based iterative solvers and efficient and reliable a posteriori error estimators for mixed hybrid and macro-hybrid finite element discretizations of elliptic boundary value problems. We give an overview concerning the state-of-the-art techniques for these nonconforming approaches and illustrate the performance of the adaptivity concepts realized by some selected numerical examples.
LMI-BASED FRAMEWORK FOR THE SYNTHESIS OF SATURATING CONTROLS LAWS

Considering linear open-loop systems, the de- sign of full state feedback and observer-based output feedback control laws subject to control input saturation are addressed. The local asymptotic stability of the nonlinear closed-loop sys- tem is studied through a quadratic approach. In this context, con- ditions guaranteeing the local asymptotic stability of the nonlin- ear (saturated) closed-loop system are stated. From these condi- tions, a framework based on Linear Matrix Inequalities (LMIs) is proposed to compute control laws in order to ensure the asymp- totic stability of a given admissible set of initial states and a cer- tain degree of time-domain performance for the closed-loop sys- tem in a neighborhood of the origin.
On the management of wavelength converter allocation in WDM all-optical networks

Previous works have shown the wavelength conversion can considerably reduce the blocking probability in all-optical networks, but most of analytical models and algorithms are proposed under simplifying assumptions or restricted to specific cases. In this paper, motivated by an abstracting technique called blocking island (BI) paradigm, we propose wavelength placement algorithms both in static traffic case and dynamic traffic case. To make sure our algorithm is applicable in arbitrary topologies and any incoming traffic patterns, a simulation-based optimization approach is employed. In the simulation, we show the performance improvement obtained by full wavelength conversion can almost be achieved by using limited number of wavelength converters with careful placement.
The Formation of Organizational Reputation

In this article, we review four decades of research on the formation of organizational reputation. Our review reveals six perspectives that have informed past studies: a game-theoretic, a strategic, a macro-cognitive, a micro-cognitive, a cultural-sociological, and communicative one. We compare and contrast the different assumptions about what reputation is and how it forms that characterize these perspectives, and we discuss the implications of these differences for our theoretical understanding of stability and change, control and contestation, and the micro–macro relationship in the complex process of reputation formation.
Linear connections in non-commutative geometry

A construction is proposed for linear connections on non-commutative algebras. The construction relies on a generalization of the Leibniz rules of commutative geometry and uses the bimodule structure of . A special role is played by the extension to the framework of non-commutative geometry of the permutation of two copies of . The construction of the linear connection as well as the definition of torsion and curvature is first proposed in the setting of the derivations based differential calculus of Dubois-Violette and then a general of the Dirac operator based differential calculus of Connes and other differential calculuses is given. The covariant derivative obtained admits an extension to the tensor product of several copies of . These constructions are illustrated with the example of the algebra of matrices.
Reprin!ed From

Time and space are fundamental parameters for measuring the efficiency of algorithms, and the notion of trading off one for the other is a familiar one in the programmer's informal repertoire. Nonetheless, providing satisfactory mathematical definitions of computational time and space and establishing formal relationships between them remains a central problem in computing theory. In this chapter we examine the interplay between time and space determined by a variety of machine models and explore the connection between time and space complexity classes. We consider a number of possible inclusion relationships among these classes and discuss their consequences, along with recent results indicating that mechanical procedures may be available for reducing the space used by programs. This rosy picture is darkened somewhat by a counterexample due to Cobham, which states that minimum time and space cannot always be achieved by a single program.
Analysis of Matrix Embedding with Error Correction Capabilities

Matrix embedding with error correction capabilities is analyzed in this paper. We try to give an answer to the question, “matrix embedding (ME) first or error correction encoding (EC) first, which one is better? ” To this end, two feasible frameworks are designed and theoretically analyzed. Bounds of embedding payloads and efficiencies are presented corresponding to different distortions and error correction capabilities. Results show that the framework of “matrix embedding first” has a better performance.
A true Random-Number Encryption Method employing block cipher and PRNG

In January 1999, distributed.net collaborated with the Electronic Frontier Foundation to break a DES (i.e., Data Encryption Standard) key, spending 22 hours and 15 minutes, and implying that the DES is no longer a secure encryption method. In this paper, we propose a more secure one, called the True Random Number Encryption Method (TRNEM for short), which employs current time, true random numbers and system security codes as parameters of the encryption process to increase the security level of a system. The same plaintext file encrypted by the TRNEM at different time points generates different ciphertext files. So these files are difficult to be cracked. We also analyze the security of the DES, AES (i.e., Advanced Encryption Standard) and TRNEM, and explain why the TRNEM can effectively defend some specific attacks, and why it is safer than the DES and AES.
Network Analysis of Social Changes in a Captive Chimpanzee Community Following the Successful Integration of Two Adult Groups

Chimpanzees are highly territorial and have the potential to be extremely aggressive toward unfamiliar individuals. In the wild, transfer between groups is almost exclusively completed by nulliparous females, yet in captivity there is often a need to introduce and integrate a range of individuals, including adult males. We describe the process of successfully integrating two groups of chimpanzees, each containing 11 individuals, in the Budongo Trail facility at the Royal Zoological Society of Scotland's Edinburgh Zoo. We use social network analysis to document changes in group dynamics within this population over the 16 months following integration. Aggression rates were low overall and members of the two original groups engaged in significantly fewer aggressive interactions over time. Association and grooming data indicate that relationships between members of the original groups became stronger and more affiliative with time. Despite these positive indicators the association data revealed the continued existence of two distinct subgroups, a year after integration. Our data show that when given complex space and freedom to exhibit natural fission–fusion groupings, in which the chimpanzees choose whom they wish to associate and interact with, the building of strong affiliative relationships with unfamiliar individuals is a very gradual process. Am. J. Primatol. 75:254‐266, 2013. © 2012 Wiley Periodicals, Inc.
Semiparametric Bayesian Models for Dynamic Earnings Data

This paper develops semiparametric Bayesian methods for modeling dynamic linear panel data models, and applies them to longitudinal data on labor earnings from the Panel Study of Income Dynamics. Motivated by recent structural models of optimal consumption with income risk, we focus on characterizing the entire joint distribution of earnings, rather than just estimating parameters related to conditional means and variances. Full distributional inference in semiparametric panel data models must solve a nonparametric deconvolution problem arising from the existence of unobserved additive individual-speciic terms. A computational Bayesian approach based on variable augmentation can deal with such latent-variable models eeectively, and can allow considerable exibility in distributional assumptions. We use relatively simple, low-order linear models with individual heterogeneity, but at the same time estimate the distribution of the disturbances without requiring that it belong to a restrictive parametric class, such as the class of normal distributions. A prior distribution on the space of probability distributions is used to impose smoothness on an otherwise general speciication for the disturbance terms. This nonparametric prior is constructed using a countable mixture of normals representation, where the mixing distribution is given a Dirichlet process prior. The semiparametric models for earnings dynamics are examined and compared to conventional parametric speciications by using them to generate predictive distributions|conditional distributions for future earnings based on an earnings history. The predictive distributions arising from the more general models are heavy-tailed and asymmetric, and respond quite diierently to large earnings shocks. This paper is based on Chapter 2 of my Ph.D. dissertation, Hirano (1998a), and substantially extends earlier work reported in Hirano (1998b) to a wider class of panel data models. I am grateful to
As Science Evolves, How Can Science Policy?

Getting science policy right is a core objective of government that bears on scientific advance, economic growth, health, and longevity. Yet the process of science is changing. As science advances and knowledge accumulates, ensuing generations of innovators spend longer in training and become more narrowly expert, shifting key innovations (i) later in the life cycle and (ii) from solo researchers toward teams. This paper summarizes the evidence that science has evolved—and continues to evolve—on both dimensions. The paper then considers science policy. The ongoing shift away from younger scholars and toward teamwork raises serious policy challenges. Central issues involve (a) maintaining incentives for entry into scientific careers as the training phase extends, (b) ensuring effective evaluation of ideas (including decisions on patent rights and research grants) as evaluator expertise narrows, and (c) providing appropriate effort incentives as scientists increasingly work in teams. Institutions such as government grant agencies, the patent office, the science education system, and the Nobel Prize come under a unified focus in this paper. In all cases, the question is how these institutions can change. As science evolves, science policy may become increasingly misaligned with science itself—unless science policy evolves in tandem.