Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of a Therapeutic Tai Ji Quan Intervention vs a Multimodal Exercise Intervention to Prevent Falls Among Older Adults at High Risk of Falling

Importance Falls in older adults are a serious public health problem associated with irreversible health consequences and responsible for a substantial economic burden on health care systems. However, identifying optimal choices from among evidence-based fall prevention interventions is challenging as few comparative data for effectiveness are available. Objective To determine the effectiveness of a therapeutically tailored tai ji quan intervention, Tai Ji Quan: Moving for Better Balance (TJQMBB), developed on the classic concept of tai ji (also known as tai chi), and a multimodal exercise (MME) program relative to stretching exercise in reducing falls among older adults at high risk of falling. Design, Setting, and Participants A single-blind, 3-arm, parallel design, randomized clinical trial (February 20, 2015, to January 30, 2018), in 7 urban and suburban cities in Oregon. From 1147 community-dwelling adults 70 years or older screened for eligibility, 670 who had fallen in the preceding year or had impaired mobility consented and were enrolled. All analyses used intention-to-treat assignment. Interventions One of 3 exercise interventions: two 60-minute classes weekly for 24 weeks of TJQMBB, entailing modified forms and therapeutic movement exercises; MME, integrating balance, aerobics, strength, and flexibility activities; or stretching exercises. Main Outcomes and Measures The primary measure at 6 months was incidence of falls. Results Among 670 participants randomized, mean (SD) age was 77.7 (5.6) years, 436 (65%) were women, 617 (92.1%) were white, 31 (4.6%) were African American. During the trial, there were 152 falls (85 individuals) in the TJQMBB group, 218 (112 individuals) in the MME group, and 363 (127 individuals) in the stretching exercise group. At 6 months, the incidence rate ratio (IRR) was significantly lower in the TJQMBB (IRR, 0.42; 95% CI, 0.31-0.56; P < .001) and MME groups (IRR, 0.60; 95% CI, 0.45-0.80; P = .001) compared with the stretching group. Falls were reduced by 31% for the TJQMBB group compared with the MME group (IRR, 0.69; 95% CI, 0.52-0.94; P = .01). Conclusions and Relevance Among community-dwelling older adults at high risk for falls, a therapeutically tailored tai ji quan balance training intervention was more effective than conventional exercise approaches for reducing the incidence of falls. Trial Registration ClinicalTrials.gov identifier: NCT02287740
Estimation of Mini-UAVs Network Parameters for Search and Rescue Operation Scenario with Gauss-Markov Mobility Model

Flying Ad-hoc Networks (FANETs) are Mobile Ad Hoc Networks (MANETs) subtypes where Unmanned Aerial Vehicles (UAV) collection communicates with each other for sharing information. The mobility models are developed for representing the nodes movement. The mobility model principal part is to develop realistic simulation environment and to evaluate the performance of different routing protocols. The article considers the Gauss-Markov mobility model describing UAVs movement in a monitoring scenario. All findings were obtained while the experimental study. Simulation modelling was as a research method and it was based on NS-2 network simulator. There were specified characteristics of the network developed according to the simulation model using the AODV and OLSR routing protocols. The connectivity dependence on network parameters (the number of nodes and the transmission range) has been examined.
On the Influence of Fog Colonies Partitioning in Fog Application Makespan

This paper presents a study of the use of network centrality indices as suitable indicators to determine the partitioning of fog colonies. Fog colonies have been used previously to enable a two level (inter and intra colony) resource management for the fog service placement problem. We propose selecting the nodes with highest values, which indicate the node importance, as the colony controllers. The remaining devices are subordinated to the closest controllers. We studied six centrality indices in three network topologies and two architecture sizes. The fog applications are designed as a set of interoperated services which makespan is increased when services are allocated in different colonies, or colonies have highest intra or inter distances. Consequently, we considered the network distance as indicator of the application makespan. The results showed that the smaller network distances was obtained with the Betweenness centrality index and a Barabasi-Albert network topology. It was also observed that the network distance only has significant differences when the colony size is varied between 1 and 20.
Technological, Organisational and Environmental (TOE) Factors that Influence the Adoption of Cloud Based Service SMEs in India

Due to its several benefits, Cloud based services are effectively used by several businesses especially Small and Medium Enterprises (SMEs). SMEs perceived an important role in economic growth of any country. They are responsible for the reduction of poverty, development of rural areas, generation of job opportunities, entrepreneurship and increasing export of nation. SMEs do not have sufficient resources to adopt new technologies to enhance their business performance. Though some factors which induce these organisations to adopt Cloud based services. These paper have focused on Technological, Organisational and Environmental (TOE) factors that influence the adoption of Cloud based services in different organisations. Considering the importance of SMEs towards Cloud based technology, the paper attempts to find the factors that influence the adoption of Cloud based services in Indian SMEs.
Temporal Topic Correlation and Evolution

This paper presents a technique to alternatively discover the temporal research topics correlation by using a topic model, Latent Dirichlet Allocation (LDA). LDA model assumes the documents as a mixture of topics that group the co-occurrence words with the certain probabilities. Hence the model is popularly used to extract the latent topics from document collections. However, LDA gives an independence assumption between topics and is unable to model the correlation between the topics. Motivated by above limitation, this study introduces a method for improving the topic correlation. The correlation of two topics from different time periods can occur when there exists a publication tagged by the two topics and these two topics are said to be co-occurred by this publication. LDA weights of these co-occurred topics are used in our model to calculate gross-correlation values. The number of publications in a topic co-occurrence is also used in the model. Therefore, we split dataset into groups with some common sub-dataset ordered by temporal timestamp of published year. The experiment results show the correlation between topics in different time periods and results can further support the research collaboration in future.
Mapping Language to Vision in a Real-World Robotic Scenario

Language has evolved over centuries and was gradually enriched and improved. The question, how people find assignment between meanings and referents, remains unanswered. There are many of computational models based on the statistical co-occurrence of meaning-reference pairs. Unfortunately, these mapping strategies show poor performance in an environment with a higher number of objects or noise. Therefore, we propose a more robust noise-resistant algorithm. We tested the performance of this novel algorithm with simulated and physical iCub robots. We developed a testing scenario consisting of objects with varying visual properties presented to the robot accompanied by utterances describing the given object. The results suggest that the proposed mapping procedure is robust, resistant against noise and shows better performance than one-step mapping for all levels of noise in the linguistic input, as well as slower performance degradation with increasing noise. Furthermore, the proposed procedure increases the clustering accuracy of both modalities.
Distributed Stream Rebalance for Stateful Operator Under Workload Variance

Key-based workload partitioning is now commonly used in parallel stream processing, enabling effective key-value tuple distribution over worker threads in a logical operator. While randomized hashing on the keys is capable of balancing the workload for key-based partitioning when the keys generally follow a static distribution, it is likely to generate poor balancing performance when workload variance occurs on the incoming data stream. This paper presents a new key-based workload partitioning framework, with practical algorithms to support dynamic workload assignment for stateful operators. The framework combines hash-based and explicit key-based routing strategies for workload distribution, which specifies the destination worker threads for a handful of keys and assigns the other keys with the hashing function. We formulate the rebalance operation as an optimization problem, with multiple objectives on minimizing state migration costs, controlling the size of the routing table and breaking workload imbalance among the worker threads. Despite of the NP-hardness nature behind the optimization formulation, we carefully investigate and justify the heuristics behind key (re)routing and state migration, to facilitate fast response to workload variance with ignorable cost to the normal processing in the distributed system. Empirical studies on synthetic data and real-world stream applications validate the usefulness of our proposals and prove the huge advantage of our approaches over state-of-the-art solutions in the literature.
A Comparison of Heterogeneous/Homogeneous Crystallization for Phosphate Recovery from Biosolids

In this study, a fluidized bed reactor was used to compare heterogeneous and homogeneous crystallization for recovering magnesium ammonia phosphate (MAP) from the dewatering filtrate at a municipal water resource recovery facility. Investigating the factors affecting crystallization revealed that pH exerted a greater effect than the Mg/P molar ratio. The results of a heterogeneous crystallization experiment showed that removal efficiency of phosphate (RP%) and crystal efficiency of phosphate (CP%) were 68% and 66%, respectively, at a PO4‐P concentration of 200 mg/L, pH of 8.0, and Mg/P molar ratio of 1.0 for 3 h. The reaction rate of heterogeneous crystallization was markedly higher than that of homogeneous crystallization. Those pellets produced through the heterogeneous crystallization exhibited a dense surface (particle size 0.5‐1.0 mm; water content 8.7%). Through scanning electron microscopy (SEM), energy dispersive x‐ray spectroscopy (EDS), and X‐ray diffraction (XRD) examination, the crystallized patterns of the needle‐shaped pellets were identified as MgNH4PO4·6H2O.
MedThaiSAGE: Decision Support System to Suggest Healthcare Policies using Rule Findings Technique

A number of medical research is produced by healthcare organizations in Thailand from their routine work, called Routine to Research (R2R). The information from R2R is becoming more important and can be analyzed to find hidden useful knowledge, which may lead to policy of the country. To analyze the data, Association Rule Finding technique and Knowledge Discovery are applied to find relevant patterns and relationship of the related variables in R2R data. The result can be used as evidence for policy makers in the process of policy making. In addition, MedThaiSAGE--a web-based application is also developed to allow users to see the relevant set of rules and visualize related information. The results show that there are some interesting rules that likely to be used by Public Health Policy Makers to develop a policy in order to improve and provide Thai people better health care services.
Choice of the Behavioral Model of the Non-Linear Solid-State Power Amplifier

This article discusses behavioral models of nonlinear power amplifiers based on the expansion of the boundary between their input and output signals in incomplete (truncated) systems of orthogonal functions that do not depend on the realizations of the signal. In this case, the coefficients of the model can be calculated expeditiously, using the corresponding asymmetric modification of the fast Fourier transform. Comparison of these models with traditional polynomial models.
Positive correlation between cognitive impairment and renal microangiopathy in patients with type 2 diabetic nephropathy: a multicenter retrospective study

Objective This study was performed to explore the correlation between cognitive impairment and renal microangiopathy in patients with type 2 diabetic nephropathy (T2DN) by detecting changes in cognitive function and cerebral metabolism in these patients with different stages of T2DN. Methods Prospectively maintained databases were reviewed from 2006 to 2017. Blood biochemical indexes and the urinary albumin excretion rate (UAER) were measured in all participants. Cognitive function was assessed by the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA). Cognitive impairment was the primary endpoint. Renal microangiopathy was the secondary endpoint. Pearson correlation analysis was used to assess correlations. Results Two hundred sixteen patients with type 2 diabetes mellitus (T2DM) were divided into three groups according to their UAER: T2DM without nephropathy (n=72), early T2DM with nephropathy (n=74), and the clinical stage of early T2DM with nephropathy (n=70). Healthy participants were selected as the normal control group (n=70). Pearson correlation analysis demonstrated that the total MMSE and MoCA score was negatively correlated with the UAER (r=−0.327) and positively correlated with the estimated glomerular filtration rate (r=0.428) in patients with T2DN. Conclusions The present study showed a positive correlation between cognitive impairment and renal microangiopathy in patients with T2DN.
A Distributed Sensor Network for Monitoring Noise Level and Noise Sources in Urban Environments

Smart city applications for acoustic monitoring become essential to cope with the overall increasing noise pollution in urban environments. This paper gives an overview over a distributed sensor network for noise monitoring in the German city of Jena. Several acoustic sensor units allow for classifying among various acoustic scenes and events using Convolutional Neural Networks (CNN) and measuring different noise level parameters. Connected by a communication system based on MQTT (Message Queue Telemetry Transport), these sensors communicate measurement data to a central server for data postprocessing and storage. Finally, a web-based application allows for various real-time visualizations of noise exposure distributed over the city.
The continual threat of influenza virus infections at the human–animal interface

Abstract This year, in 2018, we mark 100 years since the 1918 influenza pandemic. In the last 100 years, we have expanded our knowledge of public health and increased our ability to detect and prevent influenza; however, we still face challenges resulting from these continually evolving viruses. Today, it is clear that influenza viruses have multiple animal reservoirs (domestic and wild), making infection prevention in humans especially difficult to achieve. With this report, we summarize new knowledge regarding influenza A, B, C and D viruses and their control. We also introduce how a multi-disciplinary One Health approach is necessary to mitigate these threats.
A Simple Elementary Proof of P=NP based on the Relational Model of E. F. Codd

The P versus NP problem is studied under the relational model of E. F. Codd. I found that the term "complete configuration" is unnecessary and harmful in computational complexity theory because of excessive symbol redundancy. For an input, its valid sequences of complete configurations are normalized into a relational model of shared trichoices with no redundancy. To simplify the problem, a polynomial time nondeterministic Turing machine is polynomially reduced to a periodic machine, which only reverses its tape head displacement at the tape ends. By enumerating all the O(p(n)) shared trichoices, a polynomial time p(n) periodic machine is simulated in time O((p(n))^4) under logarithmic cost. A simple elementary proof of P=NP is obtained.
Creating Smart Environments: Analysis of Improving Security on Smart Homes

The more we immerse ourselves in the 'Digital Transformation', social and business, the more the new technologies that are launched to the market arise. Thinking about the concept of 'housing of the future', 'smart environments' or 'autonomous cars' may seem somewhat distant or perhaps very few people enjoy, but it is not. Above all, it is a phenomenon that is happening thanks to the development of the Internet of Things (IoT). The development of intelligent devices has been changing the behavioral patterns of users, such as using smart devices for everything to collect information that we rarely knew. However, with the introduction of IoT, we will be experiencing giant challenges to secure and protect its advanced information services due to the significant increase of the cyber-attacks, and number of interconnected resources that can be a double-edged sword. The purpose of this paper is to present a solution to this problem implementing a smart hub, complemented with an analytical review of security in all the devices interconnected focused in smart homes. It also presents how vulnerable and exposed we are with the interconnection of all our devices, identifying limitations and research challenges.
G3 PhyloChip Analysis Confirms the Promise of Plant-Based Culture Media for Unlocking the Composition and Diversity of the Maize Root Microbiome and for Recovering Unculturable Candidate Divisions/Phyla

The rapid development of high-throughput techniques and expansion of bacterial databases have accelerated efforts to bring plant microbiomes into cultivation. We introduced plant-only-based culture media as a successful candidate to mimic the nutritional matrices of plant roots. We herein employed a G3 PhyloChip microarray to meticulously characterize the culture-dependent and -independent bacterial communities of the maize root compartments, the endo- and ecto-rhizospheres. An emphasis was placed on the preference of the growth of unculturable candidate divisions/phyla on plant-only-based culture media over standard culture media (nutrient agar). A total of 1,818 different operational taxonomic units (OTUs) were resolved representing 67 bacterial phyla. Plant-only-based culture media displayed particular affinity towards recovering endophytic over ectophytic rhizobacteria. This was shown by the slightly higher recovery of CFUs for endophytes on plant-only-based culture media (26%) than on standard culture media (10%) as well as the higher taxa richness and numbers of exclusive families of unculturable divisions/phyla. Out of 30 bacterial phyla (comprising >95% of the whole population), 13 were of a significantly higher incidence on plant-only-based culture media, 6 phyla of which were not-yet-cultured (Atribacteria, OP9; Dependentiae, TM6; Latescibacteria, WS3; Marinimicrobia, SAR406; Omnitrophica, OP3; BRC1). Furthermore, plant-only-based culture media significantly enriched less abundant and/or hard-to-culture bacterial phyla (Acidobacteria, Gemmatimonadetes, and Tenericutes). These results present conclusive evidence of the ability of plant-only-based culture media to bring the plant-fed in situ microbiome into the status of plant-fed in vitro cultures, and to widen the scope of cultivation of heretofore-unculturable bacterial divisions/phyla.
An empirical learning-based validation procedure for simulation workflow

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models.
Language-Based Expression of Reliability and Parallelism for Low-Power Computing

Improving the energy-efficiency of computing systems while ensuring reliability is a challenge in all domains, ranging from low-power embedded devices to large-scale servers. In this context, a key issue is that many techniques aiming to reduce power consumption negatively affect reliability, while fault tolerance techniques require computation or state redundancy that increases power consumption, thereby leading to systematic tradeoffs. Managing these tradeoffs requires a combination of techniques involving both the hardware and the software, as it is impractical to focus on a single component or level of the system to reach adequate power consumption and reliability. In this paper, we adopt a language-based approach to express reliability and parallelism, in which programs remain adaptable after compilation and may be executed with different strategies concerning reliability and energy consumption. We implement the proposed programming model, which is named <inline-formula><tex-math notation="LaTeX">$\mathtt {MISO}$</tex-math> <alternatives><inline-graphic xlink:href="fonseca-ieq1-2771376.gif"/></alternatives></inline-formula>, and perform an experimental analysis aiming to improve the reliability of programs, through fault injection experiments conducted at compile-time, as well as an experimental measurement of power consumption. The results obtained indicate that it is feasible to write programs that remain adaptable after compilation in order to improve the ability to balance reliability, power, and performance.
Effect of a Thin Fluid Layer on Surface Wave Speed Measurements: A Lung Phantom Study

Lung ultrasound (US) surface wave elastography (SWE) is a novel technique that measures superficial lung tissue elastic properties. A thin pleural fluid layer covers a lung, but its effect on lung measurements in SWE is unknown. We modeled a lung and pleural fluid with sponges and a thin layer of US transmission gel. Sponge surface wave speeds measured from SWE were compared for sponges without and with the thin US gel layer at 3 wave excitation frequencies. The comparison showed that the sponge surface wave speed measurements were not affected by the thin gel layer.