Miracle-dz/UniFAR-FTUs huggingface dataset:
Leadership for rural school community partnerships

This paper presents a model for examining effective leadership for rural school community partnerships, derived from Australian research supported by the Rural Industries Research and Development Corporation. The research team investigated effective school community partnerships in five different Australian rural locations. Four government and one independent school featured in the study. Partnership effectiveness was confirmed by seeking advice from a range of experts including State, Commonwealth, independent school and Catholic education authorities, as well as rural education professionals. The particular focus of the study was on the community outcomes of such partnerships. The model is consistent with, but further develops, earlier partnership models. It uses the leadership process, rather than the leader, as the unit of analysis. The model outlines a five-stage process of partnership development: trigger, initiation, development, maintenance and sustainability. While the stages of the process appear to be consistent across study sites, the way in which the model is implemented differs according to context, with factors such as the level of maturity of the school community partnership influencing the process. The flexibility of the model, in terms of better understanding the contextualised nature of educational leadership, suggests it has broader application beyond rural school community partnerships.
Joint Channel Estimation and Data Detection for Space-Time Block Coded System via EM Algorithm

Detection and decoding of the space-time coded signals over unknown fading channels is a challenging task. In the paper the expectation-maximization (EM) algorithm according to maximum likelihood (ML) criterion is applied to the problem of joint channel estimation and data detection of space-time block coded system over time-selective fading channels. In order to ensure fast converge of the algorithm, the initial of parameters is carefully chosen by combining the pilot symbols matrix assisted estimation and linear interpolation. At the basis of initialization, a series of iterations of the EM algorithm, in which consists of an estimation phase followed by detection/decoding phase, are utilized to realize the joint estimation and detection/decoding scheme. In decoding phase, linear decoding method is used due to the orthogonality of the space-time block code, thus its computation complexity is greatly reduced. Simulation results show that the scheme works with novel performance
From Roles Modeled by Using the MESSAGE Methodology to their Implementation with the ASF Framework

Introduction In multi-agent systems (MAS) roles are an important issue since they define responsibilities and obligations to the agents, i.e. they guide and restrict the behaviors of agents in a social context [Silva et al., 2004b]. Several agent oriented software methodologies such as MESSAGE [Caire et al., 2002], GAIA [Wooldridge et al., 2000] and TROPOS [Mylopoulos et al., 2001], modeling languages such as MAS-ML [Silva and Lucena, 2004a] and AUML [Odell et al., 2000], and software architecture such as JADE [Bellifemine et al., 2001] and ASF [Silva et al., 2004b] concern with agent roles. Although roles are being used in developed MAS, there is still a need for case studies that demonstrate how to define roles in the context of organizations and how to assign agents to roles. This paper presents a case study of the urban traffic domain that illustrates the use of roles during the analysis phase as well as the mapping of the generated role models into code. While using the urban traffic domain, we are interested in provide a semantic support to the governance of laws. In the attempt to enable law conscious among software agents, an ontology was used to describe the semantics of the laws that regulate MAS. The case study was modeled by using the MESSAGE methodology and was implemented with the ASF framework.
Improved Method for Isolation of Neonatal Rat Cardiomyocytes with Increased Yield of C-Kit+ Cardiac Progenitor Cells

Cell therapy represents a promising new paradigm for treatment of heart disease, a major cause of death in the industrialized world. The recent discovery of tissue resident c-Kit+ cardiac progenitor cells (CPCs) has fueled scientific efforts to exploit these cells therapeutically for regenerative interventions, and primary culture of cardiomyocytes is a common in-vitro model to investigate basic molecular mechanisms underlying cardiac degeneration and regeneration. Current protocols for cardiomyocyte isolation frequently result in low cell yield and insufficient depletion of fibroblasts, which then overgrow the cardiomyocytes in culture. In this protocol we describe an improved method for the isolation of neonatal rat cardiomyocytes that also enables enhanced yields of CPCs. Gentle techniques of enzymatic and mechanical tissue processing ensure high cell numbers and viability, while subsequent Percoll density gradient centrifugation minimizes fibroblasts. We compared the advantages of different enzymes and found that Collagenase 2 alone leads to very high yields of cardiomyocytes, whereas the application of Matrase™ enzyme blend increases the relative yield of c-Kit+ CPCs to up to 35%. Cardiomyocytes and CPCs isolated with this protocol may constitute an important cell source for investigating heart disease as well as cell based therapeutic approaches.
Off-Line Programming in the Shipbuilding Industry : Open Architecture and Semi-Automatic Approach

In this paper, to improve the efficiency of welding and user convenience in the shipbuilding industry, a PC-based off-line programming (OLP) technique and the development of a robot transfer unit are presented. The developed OLP system is capable of not only robot motion simulations but also automatic generations of a series of robot programs. The strength of the developed OLP system lies in its flexibility in handling the changes of the welding robot's target objects. Moreover, for a precise transfer of the robot to a desired location, an auxiliary mobile platform named a robot-origin-transfer-unit (ROTU) was developed. To enhance the cornering capability of the platform in a narrow area, the developed ROTU is equipped with 2 steering wheels and 1 driving wheel. Both the OLP and the ROTU were field- tested and their performances were proven successful.
A Normal Form for First-Order Logic over Doubly-Linked Data Structures

We use singulary vocabularies to analyze first-order definability over doubly-linked data structures. Singulary vocabularies contain only monadic predicate and monadic function symbols. A class of mathematical structures in any vocabulary can be elementarily interpreted in a singulary vocabulary, while preserving notions of total size and degree. Doubly-linked data structures are a special case of bounded-degree finite structures in which there are reciprocal connections between elements, corresponding closely with physically feasible models of information storage. They can be associated with logical models involving unary relations and bijective functions in what we call an invertible singulary vocabulary. Over classes of these models, there is a normal form for first-order logic which eliminates all quantification of dependent variables. The paper provides a syntactically based proof using counting quantifiers. It also makes precise the notion of implicit calculability for arbitrary arity first-order formulas. Linear-time evaluation of first-order logic over doubly-linked data structures becomes a direct corollary. Included is a discussion of why these special data structures are appropriate for physically realizable models of information.
A Multi-modal 2D + 3D Face Recognition Method with a Novel Local Feature Descriptor

The research on depth map is becoming a focus of image understanding and computer vision. In this paper, depth map is introduced to enhance the performance of face recognition and a novel multi-modal 2D + 3D method is proposed. First of all, we propose a new local feature descriptor called Enhanced Local Mixed Derivative Pattern (ELMDP). Then, this feature descriptor is applied on the 2D intensity image and the depth map respectively. At last the two parts of extracted feature are combined together, multiplied by corresponding confidence weights. Experiments are conducted on 3 sub-databases of Curtin Faces database which contains variations in illumination, expression, pose and disguise. Our proposed method outmatches the other methods on recognition rate and the Receiver Operating Characteristic (ROC) curve is much gentler. All the results demonstrate that the proposed method is quite outstanding and robust.
Diagnosis of common mental disorders by using PRIME-MD Patient Health Questionnaire.

BACKGROUND & OBJECTIVE Primary care physicians in their clinical settings usually fail to diagnose common mental disorders (CMDs). Primary Care Evaluation of Mental Disorders (PRIME-MD) Patient Health Questionnaire (PHQ) has been shown to facilitate diagnosis of most CMDs seen in primary health care. Its utility in Indian setting has not been evaluated. We carried out this study in Indian setting to evaluate the extent of psychiatric morbidity as measured by PRIME-MD PHQ. METHODS A total of 500 consenting patients attending Medical outpatient department were recruited. All subjects were first assessed by the physician for presence of any physical illness and psychiatric disorder and their socio-demographic data were collected. Subjects were asked to fill the PRIMEMD PHQ. Illiterate subjects were assisted by the research worker in filling up the questionnaire. RESULTS At initial evaluation physicians opined that 30.4 per cent of the subjects had psychological disorder, the most common diagnostic category was of anxiety disorders (15.8%), followed by depression (6%) and somatoform disorders (5.6%). On PHQ 42 per cent subjects had at least one psychiatric diagnosis. The most common psychiatric diagnosis was panic disorder (18.4%), followed by other anxiety disorders (16.6%). On comparing the diagnosis of physicians and PHQ, out of 152 cases (30.4%) suspected to have any psychological disorder by the physicians in the initial evaluation, 105 were found to have PHQ diagnosis. Of the 348 cases in which physicians did not suspect any psychiatric diagnosis, 243 were also negative in PHQ screening. There was a significant correlation between physicians and PHQ diagnosis. INTERPRETATION & CONCLUSION There is a high psychiatric morbidity in the general medical practice and in many cases psychiatric morbidity is either missed or is misdiagnosed and by the physicians. Our results showed that PHQ could be a valuable screening instrument for psychiatric morbidity in primary care and general medical practice.
Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech

Despite several recent proposals to achieve blind source separation (BSS) for realistic acoustic signals, separation performance is still not good enough. In particular, when the length of impulse response is long, performance is highly limited. We show it is useless to be constrained by the condition, P /spl Lt/ T, where T is the frame size of FFT and P is the length of room impulse response. From our experiments. a frame size of 256 or 512 (32 or 64 ms at a sampling frequency of 8 kHz) is best even for the long room reverberation of T/sub R/ = 150 and 300 ms. We also clarified the reason for poor performance of BSS in a long reverberant environment, finding that separation is achieved chiefly for the sound from the direction of jammers because BSS cannot calculate the inverse of the room transfer function both for the target and jammer signals.
Networking Anomaly Detection Using DSNs and Particle Swarm Optimization with Re-Clustering

This paper presents an anomaly detection method using Digital Signature of Network Segment (DSNS) and Par- ticle Swarm Optimization-based clustering (PSO-Cls). The PSO algorithm is an evolutionary computation technique whose main characteristics include low computational complexity, ability to escape from local optima, and small number of input parameters dependence, when compared to other evolutionary algorithms, e.g. genetic algorithms (GA). In the PSO-Cls algorithm, swarm intelligence is combined with K-means clustering, in order to achieve high convergence rates. On the other hand, DSNS consists of normal network traffic behavior profiles, generated by the application of Baseline for Automatic Backbone Management (BLGBA) model in SNMP historical network data set. The proposed approach identifies and classifies data clusters from DSNS and real traffic, using swarm intelligence. Anomalous behaviors can be easily identified by comparing real traffic and cluster centroids. Tests were performed in the network of State University of Londrina and the obtained detection and false alarm rates are promising.
The Division and Measurement of Different Growth Stages of Family Enterprises

The growth of family enterprises is a continual and gradual process of advance, evolvement and development, which can be divided into four growth stages as the primitive stage, the classical stage, the standardized stage and the autonomous stage. Sustainable development is of vital importance for family enterprises to develop and step up competitiveness. On the basis of the methods for measuring the level of clanization of the relationship among the staff in the family enterprises, this paper suggested the method of measuring the clanization on two levels of ownership and management control, and carried out quantitative determination for every growth stage of family enterprises in the hope of guiding the family enterprises to find out the best strategy for sustainable development.
Part of speech tagging with min-max modular neural networks

Part of speech (POS) tagging systems using neural networks have been proposed by Ma et al. (1999). They can tag the untrained data at a practical level of accuracy by training a small Thai corpus with ten thousand order words. The multilayer perceptron type of neural networks used, however, was found to converge slowly and took a very long time to train even the above mentioned small amount of training data. This paper presents an alternative method for solving the POS tagging problems with the min-max modular neural network proposed by Lu and Ito (1997). By using this modular neural network, the part of speech tagging problems can be broken down into a number of independent smaller and simpler sub-problems, and all of the sub-problems can be learned by small network modules in parallel.
On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.
Adaptive Neuro-fuzzy Inference System Design of Inverted Pendulum System on an Inclined Rail

The basic aim of our work was to design appropriate controller to control the angle of the pendulum and the position of the cart in order to stabilize the conventional inverted pendulum system on an inclined rail. We improved the adaptive neuro-fuzzy inference system (ANFIS) on the basis of conventional fuzzy controller. A neuro-fuzzy hybrid approach was used to design the fuzzy rule base on a basis of building a Sugeno fuzzy model in order to swing a pendulum attached to a cart from an initial downwards position to an upright position and maintain that state. The adaptive neuro-fuzzy logic controller was designed in the Matlab-Simulink environment. By training and checking of effective data, the results proved that the adaptive neuro-fuzzy controller had good performance about stability in the real-time control of the inverted pendulum on an inclined rail.
Prevalence and factors associated with food intake difficulties among residents with dementia

Background Few studies have examined the prevalence of food intake difficulties and their associated factors among residents with dementia in long-term care facilities in Taiwan. The purpose of the study was to identify the best cutoff point for the Chinese Feeding Difficulty Index (Ch-FDI), which evaluates the prevalence of food intake difficulties and recognizes factors associated with eating behaviors in residents with dementia. Methods and findings A cross-sectional design was adopted. In total, 213 residents with dementia in long-term care facilities in Taiwan were recruited and participated in this study. The prevalence rate of food intake difficulties as measured by the Chinese Feeding Difficulty Index (Ch-FDI) was 44.6%. Factors associated with food intake difficulties during lunch were the duration of institutionalization (beta = 0.176), the level of activities of daily living-feeding (ADL-Q1) (beta = -0.235), and the length of the eating time (beta = 0.416). Associated factors during dinner were the illuminance level (beta = -0.204), sound volume level (beta = 0.187), ADL-Q1 (beta = -0.177), and eating time (beta = 0.395). Conclusions Food intake difficulties may potentially be associated with multiple factors including physical function and the dining environment according to the 45% prevalence rate among dementia residents in long-term care facilities.
MANAGING AND MONITORING OF PARKING LOT BY A VIDEO CAMERA

In this work we present an automated parking lot system that is managed and monitored by a video camera. We used a web camera for monitoring and the PC with MATLAB environment for managing the parking lot model. Captured frame is first converted to the grayscale image and then to the binary image with Sobel edge detection method. Image is dilated and filled in order to get the cars in white pixels and the parking place as black pixels. To each parking place is dedicated a certain region in the image. Mean value of each region was used for indicator if the parking place is occupied or free. By using a camera as a sensor, relatively large parking area is covered with only one sensor. Proposed system is relatively cheap. It does not require major additional installations on the existing parking lot. Real-time experiment was made on a parking lot model. Experimentally observed execution time implies to a possible application on a real parking lot system.
Investigating Rational Analogy in the Spirit of John Stuart Mill: Bayesian Analysis of Confidence about Inferences across Aligned Simple Systems

What does it mean for analogy to be rational? John Stuart Mill described a probabilistic underpinning for analogical inference based on the the odds of observing systemic pairwise correspondence across otherwise independent systems by mere chance. Although proponents and critics have debated its validity, Mill’s approach has yet to be implemented computationally or studied psychologically. In this paper we examine Mill’s approach and show how it can be instantiated using Bayes theorem. Then we describe two experiments that present subjects with partially-revealed, aligned binary strings with varying degrees of intraand interstring regularity. Experimental results are compared to a formal rational analysis of the stimuli revealing conditions whereby participants exhibit confidence patterns consistent and inconsistent with Mill’s rational basis of analogy.
Heme Oxygenase-1: A Potential Antihypertensive Target?

The heme oxygenase (HO) system has received significant attention in recent years as a possible novel target for antihypertensive therapy. HO is the rate limiting enzyme in the metabolism of heme releasing bioactive molecules carbon monoxide (CO) and bilirubin each with beneficial cardiovascular actions. Induction of HO-1 has been demonstrated to lower blood pressure in several animal models of hypertension In addition to its blood pressure lowering effects, HO can also reduce target organ injury and protect against ischemic injury. Growing experimental evidence suggests that increases in either CO or bilirubin alone may also lower blood pressure and provide protection against hypertensive and ischemic end-organ damage. In this review, we will discuss the current understanding of the actions of the HO on the kidney and cardiovascular systems and how the HO system or its products may be manipulated for antihypertensive therapy.
Provably secure and efficient bounded ciphertext policy attribute based encryption

Ciphertext policy attribute based encryption (CPABE) allows a sender to distribute messages based on an access policy which can be expressed as a boolean function consisting of (OR, AND) gates between attributes. A receiver whose secret key is associated with those attributes could only decrypt a ciphertext successfully if and only if his attributes satisfy the ciphertext's access policy. Fine-grained access control, a new concept mentioned by GPSW in CCS'06 can realize a more delicate access policy which could be represented as an access tree with threshold gates connecting attributes. In ICALP'08, Goyal et al. design a bounded CPABE (denoted as GJPS) with fine-grained access policy which can be proven secure under a number-theoretic assumption. In this paper, we improve their scheme by providing faster encryption / decryption algorithm and shortened ciphertext size. Moreover, we use one-time signature technique to obtain a chosen ciphertext secure extension and give its complete security proof in the standard model under traditional Decisional Bilinear Diffie-Hellman (DBDH) assumption and strong existential unforgeability of one-time signature scheme.
Field Cancerization in Sporadic Colon Cancer

Background/Aims Aberrant DNA methylation has a specific role in field cancerization. Certain molecular markers, including secreted frizzled-related protein 2 (SFRP2), tissue factor pathway inhibitor 2 (TFPI2), N-Myc downstream-regulated gene 4 (NDRG4) and bone morphogenic protein 3 (BMP3), have previously been shown to be hypermethylated in colorectal cancer (CRC). We aim to examine field cancerization in CRC based on the presence of aberrant DNA methylation in normal-appearing tissue from CRC patients. Methods We investigated promoter methylation in 34 CRC patients and five individuals with normal colonoscopy results. CRC patients were divided into three tissue groups: tumor tissue, adjacent and nonadjacent normal-appearing tissue. The methylation status (positive: methylation level >20%) of SFRP2, TFPI2, NDRG4, and BMP3 promoters was investigated using methylation-specific PCR. Results The methylation frequencies of the SFRP2, TFPI2, NDRG4 and BMP3 promoters in tumor/adjacent/nonadjacent normal-appearing tissue were 79.4%/63.0%/70.4%, 82.4%/53.6%/60.7%, 76.5%/61.5%/69.2%, 41.2%/35.7%/50.0%, respectively. The methylation levels of the SFRP,TFPI2, NDRG4 and BMP3 promoters in tumor tissues were significantly higher than those in normal-appearing tissue (SFRP2, p=0.013; TFPI2, p<0.001; NDRG4, p=0.003; BMP3, p=0.001). No significant correlation was observed between the methylation levels of the promoters and the clinicopathological variables. Conclusions The field effect is present in CRC and affects both the adjacent and nonadjacent normal-appearing mucosa.