Miracle-dz/UniFAR-FTUs huggingface dataset:
Johrei Family Healing: A Pilot Study

Johrei is a form of spiritual healing comprising “energy channelling” and light massage given either by a trained healer or, after some basic training, by anyone. This pilot trial aimed to identify any potential benefits of family-based Johrei practice in childhood eczema and for general health and to establish the feasibility of a subsequent randomised controlled trial. Volunteer families of 3-5 individuals, including at least one child with eczema were recruited to an uncontrolled pilot trial lasting 12 months. Parents were trained in Johrei healing and then practised at home with their family. Participants kept diaries and provided questionnaire data at baseline, 3,6 and 12 months. Eczema symptoms were scored at the same intervals. Scepticism about Johrei is presently an obstacle to recruitment and retention of a representative sample in a clinical trial, and to its potential use in general practice. The frequency and quality of practise at home by families may be insufficient to bring about the putative health benefits. Initial improvements in eczema symptoms and diary recorded illness, could not be separated from seasonal factors and other potential confounders. There were no improvements on other outcomes measuring general health and psychological wellbeing of family members.
Transcriptional Signature of an Altered Purine Metabolism in the Skeletal Muscle of a Huntington's Disease Mouse Model

Huntington's disease (HD) is a fatal neurodegenerative disorder, caused by a polyglutamine expansion in the huntingtin protein (HTT). HD has a peripheral component to its pathology: skeletal muscles are severely affected, leading to atrophy, and malfunction in both pre-clinical and clinical settings. We previously used two symptomatic HD mouse models to demonstrate the impairment of the contractile characteristics of the hind limb muscles, which was accompanied by a significant loss of function of motor units. The mice displayed a significant reduction in muscle force, likely because of deteriorations in energy metabolism, decreased oxidation, and altered purine metabolism. There is growing evidence suggesting that HD-related skeletal muscle malfunction might be partially or completely independent of CNS degeneration. The pathology might arise from mutant HTT within muscle (loss or gain of function). Hence, it is vital to identify novel peripheral biomarkers that will reflect HD skeletal muscle atrophy. These will be important for upcoming clinical trials that may target HD peripherally. In order to identify potential biomarkers that might reflect muscle metabolic changes, we used qPCR to validate key gene transcripts in different skeletal muscle types. Consequently, we report a number of transcript alterations that are linked to HD muscle pathology.
DMT of Wireless Networks: An overview

The e�cient operation of single-source, single-sink wireless network is considered with the diversity-multiplexing gain tradeo� (DMT) as the measure of performance. Whereas in the case of a point-to-point MIMO channel the DMT is determined by the fading statistics, in the case of a network, the DMT is additionally, a function of the time schedule according to which the network is operated, as well as the protocol that dictates the mode of operation of the intermediate relays.In general, it is only possible at present, to provide upper bounds on the DMT of the network in terms of the DMT of the MIMO channel appearing across cuts in the network. This paper presents a tutorial overview on the DMT of half-duplex multi-hop wireless networks that also attempts to identify where possible, codes that achieve the DMT.For example, it is shown how one can construct codes that achieve the DMT of a network under a given schedule and either an amplify-and-forward or decode-and-forward protocol. Also contained in the paper,are discussions on the DMT of the multiple-access channel as well as the impact of feedback on the DMT of a MIMO channel.
Comparison of Negative Pressure Wound Therapy (NPWT) Using Polyvinyl Alcohol (PVA) Sponge with Advanced Wound Dressings

Objective: The primary purpose of this study is to compare appearance of granulation tissue, rate of wound infection, period of hospital stay and duration for complete wound healing using two different methods of wound dressing at secondary healthcare centres. Material and Methods: 127 patients between the age group 14-68 years with an average age of 42 years were included in this study. Out of these 127 cases, 1 died due to urosepsis with severe lung infection and 6 cases did not turn up for follow up treatment. The remaining 120 cases were divided into two groups i.e. A and B. Group A was subjected to modified negative pressure wound therapy (NPWT) using sterilized polyvinyl alcohol sponge as an alternative to standard Granu Foam. A Ryle’s tube with additional pores were created and interposed between the PVA sponges. The Ryle’s tube was attached to the suction drain and another end of the suction drain connected directly to wall suction which is programmed to produce a pressure of 125 mmHg. At the end of 72 hours, a new cycle was repeated. Group B was subjected to daily dressing with advanced wound dressings (dermacyn and solcoseryl gel). Results: Statistically better result was obtained in favour of NPWT for wound dressing. The appearance of granulation tissue was more rapid in group A patients. The infection rate of group A patients was very low when compared to group B patients. The mean time (duration) to complete healing was faster in Group A, however the mean length of hospital stay was longer as compared to group B patients. Conclusion: Healing is more rapid with less infection rate in NPWT group compared to advanced wound dressing group. NPWT using wall suction and PVA sponge can be performed in secondary healthcare centres with low resources as well as for patients who could not have afforded charges of commercially available standard VAC device or advance wound dressings.
Model-checking and control of self-assembly

Graph grammars can be used to model highly distributed systems where local interaction rules control formation or self-assembly tasks. In this paper, we explore model-checking graph grammar systems, introducing the zero-one-many collapse as a way of reducing the usually enormous number of states and transitions produced by a graph grammar system. From this collapse, we also define a canonical initial graph, that captures some of the characteristic behavior of larger graphs with the same zero-one-many collapse. Finally, we show through examples how these results allow us to effectively reason about the behavior of a graph grammar - and also how to improve a graph grammar based on an analysis of its collapsed Kripke structures
Design and Analysis of Tubular Permanent Magnet Linear Wave Generator

Due to the lack of mature design program for the tubular permanent magnet linear wave generator (TPMLWG) and poor sinusoidal characteristics of the air gap flux density for the traditional surface-mounted TPMLWG, a design method and a new secondary structure of TPMLWG are proposed. An equivalent mathematical model of TPMLWG is established to adopt the transformation relationship between the linear velocity of permanent magnet rotary generator and the operating speed of TPMLWG, to determine the structure parameters of the TPMLWG. The new secondary structure of the TPMLWG contains surface-mounted permanent magnets and the interior permanent magnets, which form a series-parallel hybrid magnetic circuit, and their reasonable structure parameters are designed to get the optimum pole-arc coefficient. The electromagnetic field and temperature field of TPMLWG are analyzed using finite element method. It can be included that the sinusoidal characteristics of air gap flux density of the new secondary structure TPMLWG are improved, the cogging force as well as mechanical vibration is reduced in the process of operation, and the stable temperature rise of generator meets the design requirements when adopting the new secondary structure of the TPMLWG.
Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit

An Insight Toolkit (ITK) implementation of our knowledgebased segmentation algorithm applied to brain MRI scans is presented in this paper. Our algorithm is a refinement of the work of Teo, Saprio, and Wandall. The basic idea is to incorporate prior knowledge into the segmentation through Bayesrule. Image noise is removed via an affine invariant anisotropic smoothing of the posteriors as in Haker et. al. We present the results of this code on two different projects. First, we show the effect of applying this code to skull-removed brain MRI scans. Second, we show the effect of applying this code to the extraction of the DLPFC from a user-defined subregion of brain MRI data.We present our results on brain MRI scans, comparing the results of the knowledge-based segmentation to manual segmentations on datasets of schizophrenic patients.
KBS development through ontology mapping and ontology driven acquisition

The benefits of reuse have long been recognized in the knowledge engineering community where the dream of creating knowledge based systems (KBSs) on-the-fly from libraries of reusable components is still to be fully realised. In this paper we present a two stage methodology for creating KBSs: first reusing domain knowledge by mapping it, where appropriate, to the requirements of a generic problem solver; and secondly using this mapped knowledge and the requirements of the problem solver to "drive" the acquisition of the additional knowledge it needs.allFor example, suppose we have available a KBS which is composed of a propose-and-revise problem solver linked with an appropriate knowledge base/ontology from the elevator domain. Then to create a diagnostic KBS in the same domain, we require to map relevant information from the elevator knowledge base/ontology, such as component information, to a diagnostic problem solver, and then to extend it with diagnostic information such as malfunctions, symptoms and repairs for each component. We have developed MAKTab, a Protege plug-in which supports both these steps and results in a composite KBS which is executable.
Optical Character Recognition on Graphics Hardware

The study of Artificial Neural Networks (ANNs) is a branch of Artificial Intelligence research that tries to emulate cognitive processes using computers. An example of such a process is optical character recognition (OCR). Using well-established models and algorithms, OCR can be reduced to simple matrix operations, which can be performed using today's computers. However, these algorithms and models usually have high computational and/or memory requirements. This has led to several implementations of matrix operations that take advantage of specific characteristic of various hardware configurations in order to speed up the algorithms. This paper presents a new algorithm that takes advantage of the features present in today's graphics cards in order to perform quickly and cheaply the computations required by OCR. We believe that our implementation is a viable alternative to classical hardware and software implementations because it comes at a lower price and provides higher overall efficiency by making use of the graphics card, a component that is usually idle during OCR.
Neuronal discrimination capacity

We explore neuronal mechanisms of discriminating between masked signals. It is found that when the correlation between input signals is zero, the output signals are separable if and only if input signals are separable. With positively (negatively) correlated signals, the output signals are separable (mixed) even when input signals are mixed (separable). Exact values of discrimination capacity are obtained for two most interesting cases: the exactly balanced inhibitory and excitatory input case and the uncorrelated input case. Interestingly, the discrimination capacity obtained in these cases is independent of model parameters, input distribution and is universal. Our results also suggest a functional role of inhibitory inputs and correlated inputs or, more generally, the large variability of efferent spike trains observed in in vivo experiments: the larger the variability of efferent spike trains, the easier it is to discriminate between masked input signals.
Detecting missing method calls as violations of the majority rule

When using object-oriented frameworks it is easy to overlook certain important method calls that are required at particular places in code. In this article, we provide a comprehensive set of empirical facts on this problem, starting from traces of missing method calls in a bug repository. We propose a new system that searches for missing method calls in software based on the other method calls that are observable. Our key insight is that the voting theory concept of majority rule holds for method calls: a call is likely to be missing if there is a majority of similar pieces of code where this call is present. The evaluation shows that the system predictions go further missing method calls and often reveal different kinds of code smells (e.g., violations of API best practices).
An Evolutionary Approach to Area-Time Optimization of FPGA designs

This paper presents a new methodology based on evolutionary multi-objective optimization (EMO) to synthesize multiple complex modules on programmable devices (FPGAs). It starts from a behavioral description written in a common high-level language (for instance C) to automatically produce the register-transfer level (RTL) design in a hardware description language (e.g. Verilog). Since all high-level synthesis problems (scheduling, allocation and binding) are notoriously NP-complete and interdependent, the three problems should be considered simultaneously. This drives to a wide design space, that needs to be thoroughly explored to obtain solutions able to satisfy the design constraints. Evolutionary algorithms are good candidates to tackle such complex explorations. In this paper we provide a solution based on the non-dominated sorting genetic algorithm (NSGA-II) to explore the design space in order obtain the best solutions in terms of performance given the area constraints of a target FPGA device. Moreover, it has been integrated a good cost estimation model to guarantee the quality of the solutions found without requiring a complete synthesis for the validation of each generation, an impractical and time consuming operation. We show on the JPEG case study that the proposed approach provides good results in terms of trade-off between total area occupied and execution time.
Software defined mobile sensor network for micro UAV swarm

This paper introduces novel mobile sensor networking architecture for a swarm of micro unmanned vehicles (MAVs) using software defined network (SDN) technology. The proposed architecture aims to enhance the performance of user/control plane data transmission between MAVs. SDN technique for mobile network is adopted to release the computation burden of MAV nodes and improve the wireless channel resource utilization by moving the complex network management operations to cloud-based SDN controller.
Chaotropic solutes cause water stress in Pseudomonas putida.

Low water availability is the most ubiquitous cause of stress for terrestrial plants, animals and microorganisms, and has a major impact on ecosystem function and agricultural productivity. Studies of water stress have largely focused on conditions that affect cell turgor, i.e. induce osmotic stress. We show that chaotropic solutes that do not affect turgor reduce water activity, perturb macromolecule-water interactions and thereby destabilize cellular macromolecules, inhibit growth, and are powerful mediators of water stress in a typical soil bacterium, Pseudomonas putida. Chaotropic solute-induced water stress resulted mostly in the upregulation of proteins involved in stabilization of biological macromolecules and membrane structure. Many environmental pollutants and agricultural products are chaotropic chemicals and thus constitute a previously unrecognised but common form of biological stress in water bodies and soils.
Direct Methods for Frequency Filter Performance Analysis

Analysis methods based on determining system performance specifications by step response, as well as indirect methods: pole-zero plot, magnitude response and integral analysis methods are applied in automatic control theory for performance estimation of linear systems [1,2,3]. However, in many cases the mentioned methods result in crude performance estimation of a linear system (filter) operation. Furthermore, direct methods of linear system performance specifications (settling time, accuracy, overshoot etc.) characterization require a huge amount of calculations being performed.
integrOmics: an R package to unravel relationships between two omics datasets

Motivation: With the availability of many ‘omics’ data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated. Results: integrOmics efficiently performs integrative analyses of two types of ‘omics’ variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies. Availability: integrOmics is freely available from http://CRAN.R-project.org/ or from the web site companion (http://math.univ-toulouse.fr/biostat) that provides full documentation and tutorials. Contact: k.lecao@uq.edu. au Supplementary information: Supplementary data are available at Bioinformatics online.
A Robust and Real Time Approach for Scene Text Localisation and Recognition in Image Processing

The text localization and recognition in real time scene text images is still a big issue in current application. Mobile application and digitization in real world gives a vital and broad impact on real time scene text images. However, the efficiency of recognition rate depends upon the text localization, i.e., higher the purity of text background segmentation and decomposition, higher the rate of accuracy for the image recognition. In this paper, we present a new scene text detection algorithm based on Stroke detection and Hog Transform method. The method introduces an approach for character detection and recognition which combines the advantages of Hog Transform and Connected Component methods. Characters are detected and recognized on the basis of image regions which contain strokes of specific orientations in a specific relative position, where the strokes are efficiently detected by convolving the image gradient field with a set of oriented bar filters. The method was evaluated on a standard dataset consisting mostly real time images where it achieves state-of-the-art results in both text localization and recognition. The results clearly depict the higher bit of accuracy in terms of localization and recognition for a collected dataset.
Incremental evolution of collective network of binary classifier for polarimetric SAR image classification

In this paper, we propose a dedicated application of collective network of binary classifiers (CNBC) to address the problem of incremental learning, which occurs by introducing new SAR terrain classes. Furthermore, another major goal is to achieve a high classification performance over multiple SAR images even though the training data may not be entirely accurate. The CNBC in principle adopts a “Divide and Conquer” type approach by allocating an individual network of binary classifiers (NBCs) to discriminate each SAR terrain class among others and performing evolutionary search to find the optimal binary classifier (BC) in each NBC. Such design further allows dynamic SAR class and feature scalability in such a way that the CNBC can gradually adapt its internal topology to new features and classes with minimal effort. Experiments visually demonstrate the classification accuracy and efficiency of the proposed system over eight fully polarimetric NASA/JPL AIRSAR data sets.
Smart Energy Management of Multi-threaded Java Applications on Multi-core Processors

Multi-core processors are becoming widely deployed in computer systems. While improving the performance of applications with multi-core technology, multi-core CPUs consume more power than a single-core one. We propose a power saving technique for multi-core systems based on the observation of critical sections in multi-threaded applications. First, since only one thread on a core can enter into a critical section for a shared resource, other threads on other cores that would access the same resource would run into busy waiting state. Hence the frequency of those idle cores could be minimized to reduce energy wastages without degrading overall applications' performance. Secondly, the accurate engage/disengage timing of a critical section could be detected by matching particular byte code patterns before a thread actually enters into a critical section. Thus the frequency of busy waiting cores could be tuned in advance. Based on these findings, the power-saving technique of critical sections is proposed and implemented. With the experiments of five multi-threaded Java benchmarks, the proposed power-saving technique leads to good energy savings (11 to 15 percent) with lower values of Energy Delay Product as compared to the other power-saving techniques.
Perceived and Actual Role of Gamification Principles

Although gamification has successfully been applied in office scenarios, it remains unclear how employees really feel about the introduction of a gamified system at their workplace. In this paper, we address this issue from two directions. First, we present the outcome of an online survey where we analyze users' opinion about gamification in a workplace environment. Then, we analyze the interaction logs of a re-designed gamified enterprise book marking system to compare the employees' subjective perception of gamification with their actual behavior when using a gamified system. Results indicate that there is a strong relationship between employees' perception of gamification and their actual interaction with such system.