Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Bone Mineral Density in Lumbar Spine and Fracture Rate among Eight Drugs in Treatments of Osteoporosis in Men: A Network Meta-Analysis

Context The preferred treatment for osteoporosis in men is debated, and pairwise meta-analysis cannot obtain hierarchies of these treatments. Objective The objective of this study was to integrate the evidence and provide hierarchies of eight drugs based on their effect on the bone mineral density in the lumbar spine (BMD in LS) and the fracture rate. Data Sources Eligible studies were identified by searching Amed, British Nursing Index, EMBASE, PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Google Scholar, SIGLE, the National Technical Information Service, the National Research Register (UK), and the Current Controlled Trials databases. Study Selection RCTs or quasi-RCTs reporting at least two drugs (two active drugs or one active drug and a placebo) used to treat osteoporosis in men were selected by two authors. Data Extraction Two authors independently extracted the data. Data Synthesis Thirteen studies involving 3647 patients were included. Compared with placebo therapy, zoledronate (SMDs 13.48, 95% credible intervals 11.88-15.08) yielded the most significant effect on increasing the BMD in LS, followed by alendronate (11.04, 9.68-12.41), teriparatide (20mcg) + risedronate (10.98, 8.55-13.48), risedronate (10.33, 8.68-12.01), teriparatide (20mcg) (9.33, 6.87-11.76), strontium ranelate (8.88, 7.51-10.24), ibandronate (5.49, 3.82-7.16), parathyroid hormone (1-84) (4.89, 3.12-6.62) and alfacalcidol (3.42, 1.7-5.2). Placebo therapy had a significantly higher fracture rate in contrast to risedronate (OR 2.51, 95% CrI 1.23-4.24) or zoledronate (2.92, 1.29-5.62) or teriparatide (20mcg) (4.04, 1.36-8.49) or teriparatide (40mcg) (3.5, 1.14-8.34). Zoledronate ranked first for increasing the BMD in LS, and teriparatide (20mg) was ranked first for decreasing the fracture rate. Conclusions Zoledronate might be the best choice to increase the BMD in LS and teriparatide (20mg) might lead to the lowest fracture rate.
Adaptive fuzzy modelling and control for discrete-time nonlinear uncertain systems

This paper presents an adaptive fuzzy modelling and control scheme for discrete-time nonlinear uncertain systems. The proposed adaptive scheme includes two parts: on-line fuzzy modelling using Takagi-Sugeno (T-S) fuzzy systems and model reference adaptive control design. The T-S fuzzy model has a self-organizing structure, i.e. the fuzzy rules can be added, replaced or deleted automatically via on-line clustering and the consequence parameters of the T-S model can be recursively updated by recursive least square estimation (RLSE) method, which allows it to identify complex nonlinear uncertain systems on-line. The adaptive controller is based on the T-S fuzzy model employed as a dynamic model of the plant. The controller can adaptively generate control signals while the structure and parameters of the T-S model are-updated on-line. The effectiveness of our approach is verified by its applications in the identification of a second-order nonlinear uncertain system and the tracking control of a single robot arm.
Estimating the Potential Output and Output Gap of Ghana

This paper focuses on the estimation of the potential output of Ghana. Potential output or its derivative the output gap are not observable. However, “potential output” is a powerful conceptual tool that guides analysts and policymakers in gauging whether the current observed economic activity is sustainable and how much of it is greater than or less than potential. Based on Ghanaian GDP annual data from 1960 – 2017, the paper estimates potential output and output gaps using the following methodology: linear time trends, Hodrick-Prescott (HP) filter trends, multivariate HP filter trends, and a production function model. The results show that estimates of the potential output and output gaps are model-dependent as estimates vary from one methodology to the other. The paper recommends that policymakers should not mechanically choose a model to estimate output gap. For the avoidance of costly policy mistakes, the choice of the model should be complemented with sound judgement based on a set of pertinent economic information.
Supervisory Control of Multiple Robots

Objective: A military multitasking environment was simulated to examine the effects of an intelligent agent, RoboLeader, on the performance of robotics operators. Background: The participants’ task was to manage a team of ground robots with the assistance of RoboLeader, an intelligent agent capable of coordinating the robots and changing their routes on the basis of battlefield developments. Method: In the first experiment, RoboLeader was perfectly reliable; in the second experiment, RoboLeader’s recommendations were manipulated to be either false-alarm prone or miss prone, with a reliability level of either 60% or 90%. The visual density of the targeting environment was manipulated by the presence or absence of friendly soldiers. Results: RoboLeader, when perfectly reliable, was helpful in reducing the overall mission times. The type of RoboLeader imperfection (false-alarm vs. miss prone) affected operators’ performance of tasks involving visual scanning (target detection, route editing, and situation awareness). There was a consistent effect of visual density (clutter of the visual scene) for multiple performance measures. Participants’ attentional control and video gaming experience affected their overall multitasking performance. In both experiments, participants with greater spatial ability consistently outperformed their low-spatial-ability counterparts in tasks that required effective visual scanning. Conclusion: Intelligent agents, such as RoboLeader, can benefit the overall human-robot teaming performance. However, the effects of type of agent unreliability, tasking requirements, and individual differences have complex effects on human-agent interaction. Application: The current results will facilitate the implementation of robots in military settings and will provide useful data to designs of systems for multirobot control.
Dominance Alone Is Not Enough

It has often been noted that the correlation between dizygotic (DZ) twins is smaller than predicted from the monozygotic (MZ) correlation under a simple additive genetic model. Possible genetic explanations of this finding are considered. It is shown that duplicate gene interactions between pairs of moderately frequent alleles at polygenic loci are sufficient to produce surprisingly small (approximately 0.12) genetic correlations between siblings.
Image fusion based on a sparse linear system

This paper proposes an image fusion algorithm based on a sparse linear equation system, which uses local extreme of high resolution image and intensity of multi-spectral image to construct the system. Based on this sparse system, the multi-scale image decomposition algorithm can be implemented. This algorithm extracts the details of the high-resolution image and integrates with the multi-spectral information to derive the fused image.
A Learning Approach for Adaptive Image Segmentation

As mentioned in many papers, a lot of key parameters of image segmentation algorithms are manually tuned by de- signers. This induces a lack of flexibility of the segmentation step in many vision systems. By a dynamic control of these parameters, results of this crucial step could be drastically improved. We propose a scheme to automatically select segmentation algorithm and tune theirs key parameters thanks to a preliminary supervised learning stage. This paper details this learning approach which is composed by three steps: (1) optimal parameters extraction, (2) algorithm selection learning, and (3) generalization of parametrization learning. The major contribution is twofold: segmentation is adapted to the image to segment, and in the same time, this scheme can be used as a generic framework, independant of any application domain.
Optimal Broadcasting in Mesh-Connected Architectures

In this paper, we disprove the common assumption that the time for broadcasting in a mesh is at best proportional to the square root of the number of processors, at least in the presence of worm-hole routing. We present an optimal algorithm for broadcasting in mesh-connected distributed-memory architectures with worm-hole routing. By organizing the processing nodes in a logical spanning tree, the algorithm executes in time proportional to the logarithm of the number of nodes without inducing contention in the communication network. We restrict the number of nodes in each dimension of the processor mesh to be a power of two. Our method provides insight into how to avoid and/or reduce network contention on meshes for other communication operations. Experimental results on the Intel Touchstone Delta system are included.
Analysis of SIR-based Triple SC System over Correlated Alpha-Mu Fading Channels

In this paper the performance analysis of signal-to-interference ratio (SIR) based Selection Combining (SC) diversity system over α-μ fading distributed and correlated channels is presented. Formulae for the output SIR’s probability density function (pdf) and cumulative distribution function (cdf) are derived. Also, the results presented in the paper include all important scenarios of triple SC diversity systems, which is main contribution of this paper. Analytical and numerical results are obtained for triple SC pointing out the effects of fading severity and correlation on the system performances.
Innovation in Service Quality Measurement: a Case of Nigerian Healthcare Sector

This paper summarizes the arguments and counterarguments within the scientific discussion on the issue of service quality measurement. The main purpose of the research is to bring innovation in the operationalization conceptualization of service quality dimensions to suit the Nigerian healthcare environment. Empirical studies and extant literature show intentions from the classical five-level SERVQUAL-model. The systematization of literary sources and approaches to solving the problems of service quality measurement show a significant deviation from the pioneer five-construct SERVQUAL model. The relevance of solving this scientific problem is to bring novelty to the existing models to suit the differing service environment. The problem considered in the article was solved by developing a new construct that forms part of the service quality dimension. The study empirically confirms the use of six-construct SERVQUAL model that is structured to measure service quality in the Nigerian healthcare sector. In the first part of the article, the conceptual overview of service quality and its dimensions were analysed with a critical review of recent and previous research on service quality. Based on the results of the empirical results, a new dimension was hypothesized. The second part deals with the gap in the literature and conceptual framework. The third part of the article describes the material and method that describes the respondents and instrument adopted in the validity of the new investigated dimension. Finally, the result of the factor analysis shows the significant service quality statements that validate explanation as a reliable dimension of service quality in healthcare. A 10-item questionnaire and service statements to test the validity of explanation was administered to respondents who are the outpatients of the eight hospitals sampled in the province of Umuahia and Aba metropolis.
A Cloud-based System Framework for Storage and Analysis on Big Data of Massive BIMs

In this study, a system which utilizes Cloud technology to establish a data center to store and manage multiple BIMs simultaneously has been developed. This BIM data center can not only handle the big data of massive BIMs using multiple servers in a distributed manner, but can also be accessed by using various online devices anywhere, anytime for information sharing and visualization. Traditional BIM includes only static information, such as the geometric parameters, physical properties, and spatial relations, for modeling of the physical space. In this study, BIM was extended to dynamic BIM which also includes dynamic data, such as historical records from the monitoring of the facility environment and usage, as well as the user experience parameters from continuous observation of the interaction between users and the space. Due to such an extension, dynamic BIM became a parametric model which can be used to simulate user behaviors. Regarding the applications of dynamic BIM big data, this study proposes a Cloud-based system framework to effectively retrieve required information for various applications by big data analysis based on parallel processing of large data sets.
Graph Regularized Nonnegative Tucker Decomposition for Tensor Data Representation

Nonnegative Tucker Decomposition (NTD) is one of the most popular technique for feature extraction and representation from nonnegative tensor data with preserving internal structure information. From the perspective of geometry, highdimensional data are usually drawn in low-dimensional submanifold of the ambient space. In this paper, we propose a novel Graph reguralized Nonnegative Tucker Decomposition (GNTD) method which is able to extract the low-dimensional parts-based representation and preserve the geometrical information simultaneously from high-dimensional tensor data. We also present an effictive algorithm to solve the proposed GNTD model. Experimental results demonstrate the effectiveness and high efficiency of the proposed GNTD method.
Sparse single-index model

Let (X,Y) be a random pair taking values in Rp × R. In the so-called single-index model, one has Y = f*(θ*TX)+W, where f* is an unknown univariate measurable function, θ* is an unknown vector in Rd, and W denotes a random noise satisfying E[W|X] = 0. The single-index model is known to offer a flexible way to model a variety of high-dimensional real-world phenomena. However, despite its relative simplicity, this dimension reduction scheme is faced with severe complications as soon as the underlying dimension becomes larger than the number of observations ("p larger than n" paradigm). To circumvent this difficulty, we consider the single-index model estimation problem from a sparsity perspective using a PAC-Bayesian approach. On the theoretical side, we offer a sharp oracle inequality, which is more powerful than the best known oracle inequalities for other common procedures of single-index recovery. The proposed method is implemented by means of the reversible jump Markov chain Monte Carlo technique and its performance is compared with that of standard procedures.
SEA: Segment-enrich-annotate paradigm for adapting dialog-based content for improved accessibility

While navigation within complex information spaces is a problem for all users, the problem is most evident with individuals who are blind who cannot simply locate, point, and click on a link in hypertext documents with a mouse. Users who are blind have to listen searching for the link in the document using only the keyboard and a screen reader program, which may be particularly inefficient in large documents with many links or deep hierarchies that are hard to navigate. Consequently, they are especially penalized when the information being searched is hidden under multiple layers of indirections. In this article, we introduce a segment-enrich-annotate (SEA) paradigm for adapting digital content with deep structures for improved accessibility. In particular, we instantiate and evaluate this paradigm through the iCare-Assistant, an assistive system for helping students who are blind in accessing Web and electronic course materials. Our evaluations, involving the participation of students who are blind, showed that the iCare-Assistant system, built based on the SEA paradigm, reduces the navigational overhead significantly and enables user who are blind access complex online course servers effectively.
Effect of an energy-restricted, high-protein, low-fat diet relative to a conventional high-carbohydrate, low-fat diet on weight loss, body composition, nutritional status, and markers of cardiovascular health in obese women.

BACKGROUND Limited evidence suggests that a higher ratio of protein to carbohydrate during weight loss has metabolic advantages. OBJECTIVE The objective was to evaluate the effects of a diet with a high ratio of protein to carbohydrate during weight loss on body composition, cardiovascular disease risk, nutritional status, and markers of bone turnover and renal function in overweight women. DESIGN The subjects were randomly assigned to 1 of 2 isocaloric 5600-kJ dietary interventions for 12 wk according to a parallel design: a high-protein (HP) or a high-carbohydrate (HC) diet. RESULTS One hundred women with a mean (+/-SD) body mass index (in kg/m(2)) of 32 +/- 6 and age of 49 +/- 9 y completed the study. Weight loss was 7.3 +/- 0.3 kg with both diets. Subjects with high serum triacylglycerol (>1.5 mmol/L) lost more fat mass with the HP than with the HC diet (x +/- SEM: 6.4 +/- 0.7 and 3.4 +/- 0.7 kg, respectively; P = 0.035) and had a greater decrease in triacylglycerol concentrations with the HP (-0.59 +/- 0.19 mmol/L) than with the HC (-0.03 +/- 0.04 mmol/L) diet (P = 0.023 for diet x triacylglycerol interaction). Triacylglycerol concentrations decreased more with the HP (0.30 +/- 0.10 mmol/L) than with the HC (0.10 +/- 0.06 mmol/L) diet (P = 0.007). Fasting LDL-cholesterol, HDL-cholesterol, glucose, insulin, free fatty acid, and C-reactive protein concentrations decreased with weight loss. Serum vitamin B-12 increased 9% with the HP diet and decreased 13% with the HC diet (P < 0.0001 between diets). Folate and vitamin B-6 increased with both diets; homocysteine did not change significantly. Bone turnover markers increased 8-12% and calcium excretion decreased by 0.8 mmol/d (P < 0.01). Creatinine clearance decreased from 82 +/- 3.3 to 75 +/- 3.0 mL/min (P = 0.002). CONCLUSION An energy-restricted, high-protein, low-fat diet provides nutritional and metabolic benefits that are equal to and sometimes greater than those observed with a high-carbohydrate diet.
Dynamic localization of mobile robot based on triangulation centroid estimation

According to the inbuilding disaster rescue system failure in network blind spots, a self-dynamic localization system of mobile robot, which can dynamically choose beacon node and determine centroid of intersection based on circles of three beacon nodes, was proposed. This method can apply Received Signal Strength Indication (RSSI) for distance measurement. Geometric Contraints-based Triangle Centroid Estimation (GCTCE) fulfilled the localization. Kalman filter was integrated with the proposed localization to realize the error-correct. The errors caused by the interference of the environmental noises can be minimized efficiently. Especially in network blind spots, the Kalman filter provides optimal data. Simulation and experimental results showed the accuracy and adaptivity of the self-dynamic localization of mobile robots.
Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments

In this paper, we will present a novel system for learning and incrementally adapting type-2 Fuzzy Logic Controllers (FLCs) for agents embedded in Ambient Intelligent Environments (AIEs). The system learns the rules and the type-2 Membership Functions (MFs) for the type-2 FLC that models the user behavior. Over long term operations, the agent incrementally adapts the type-2 FLC rules and MFs in a life long learning mode to accommodate for the short term and long term uncertainties encountered in AIEs. We will present unique experiments carried out by different users over the course of the year in the Essex intelligent Dormitory (iDorm) which is a real AIE test bed. We will show how the type-2 agent learnt and adapted to the occupant's behavior, whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 fuzzy agents while using smaller rule bases.
LLVM in the FreeBSD Toolchain

The most obvious incentive for the FreeBSD project to switch from GCC to Clang was the decision by the Free Software Foundation to switch the license of GCC to version 3 of the GPL. This license is unacceptable to a number of large FreeBSD consumers. Given this constraint, the project had a choice of either maintaining a fork of GCC 4.2. 1 (the last GPLv2 release), staying with GCC 4.2. 1 forever, or switching to another compiler. The first option might have been feasible if other GCC users had desired the same and the cost could have been shared. The second was an adequate stopgap, but the release of the C11 and C++11 specifications— both unsupported by GCC 4.2. 1—made this an impossible approach for the longer term. The remaining alternative, to find a different compiler to replace GCC, was the only viable option. The OpenBSD project had previously investigated PCC, which performed an adequate job with C code (although generating less optimised code than even our old GCC), but had no support for C++. The TENDRA compiler had also been considered, but development had largely stopped by 2007. The remaining alternative was Clang, which was still a very young compiler in 2008, but had some significant commercial backing from companies including Apple and Google. In 2009, Roman Divacky and Pawel Worach begin trying to build FreeBSD with Clang and quickly got a working kernel, as long as optimisations were disabled. By May 2011, Clang was able to build the entire base system on both 32-bit and 64-bit x86 and so became a viable migration target. A large number of LLVM Clang bugs were found and fixed as a result of FreeBSD testing the compilation of a large body of code.
Polyglutamine-Expanded Ataxin-7 Promotes Non-Cell-Autonomous Purkinje Cell Degeneration and Displays Proteolytic Cleavage in Ataxic Transgenic Mice

Spinocerebellar ataxia (SCA) type 7 is an inherited neurodegenerative disorder caused by expansion of a polyglutamine tract within the ataxin-7 protein. To determine the molecular basis of polyglutamine neurotoxicity in this and other related disorders, we produced SCA7 transgenic mice that express ataxin-7 with 24 or 92 glutamines in all neurons of the CNS, except for Purkinje cells. Transgenic mice expressing ataxin-7 with 92 glutamines (92Q) developed a dramatic neurological phenotype presenting as a gait ataxia and culminating in premature death. Despite the absence of expression of polyglutamine-expanded ataxin-7 in Purkinje cells, we documented severe Purkinje cell degeneration in 92Q SCA7 transgenic mice. We also detected an N-terminal truncation fragment of ataxin-7 in transgenic mice and in SCA7 patient material with both anti-ataxin-7 and anti-polyglutamine specific antibodies. The appearance of truncated ataxin-7 in nuclear aggregates correlates with the onset of a disease phenotype in the SCA7 mice, suggesting that nuclear localization and proteolytic cleavage may be important features of SCA7 pathogenesis. The non-cell-autonomous nature of the Purkinje cell degeneration in our SCA7 mouse model indicates that polyglutamine-induced dysfunction in adjacent or connecting cell types contributes to the neurodegeneration.