Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Mindfulness Yoga vs Stretching and Resistance Training Exercises on Anxiety and Depression for People With Parkinson Disease: A Randomized Clinical Trial.

Importance Clinical practice guidelines support exercise for patients with Parkinson disease (PD), but to our knowledge, no randomized clinical trials have tested whether yoga is superior to conventional physical exercises for stress and symptom management. Objective To compare the effects of a mindfulness yoga program vs stretching and resistance training exercise (SRTE) on psychological distress, physical health, spiritual well-being, and health-related quality of life (HRQOL) in patients with mild-to-moderate PD. Design, Setting, and Participants An assessor-masked, randomized clinical trial using the intention-to-treat principle was conducted at 4 community rehabilitation centers in Hong Kong between December 1, 2016, and May 31, 2017. A total of 187 adults (aged ≥18 years) with a clinical diagnosis of idiopathic PD who were able to stand unaided and walk with or without an assistive device were enrolled via convenience sampling. Eligible participants were randomized 1:1 to mindfulness yoga or SRTE. Interventions Mindfulness yoga was delivered in 90-minute groups and SRTE were delivered in 60-minute groups for 8 weeks. Main Outcomes and Measures Primary outcomes included anxiety and depressive symptoms assessed using the Hospital Anxiety and Depression Scale. Secondary outcomes included severity of motor symptoms (Movement Disorder Society Unified Parkinson's Disease Rating Scale [MDS-UPDRS], Part III motor score), mobility, spiritual well-being in terms of perceived hardship and equanimity, and HRQOL. Assessments were done at baseline, 8 weeks (T1), and 20 weeks (T2). Results The 138 participants included 65 men (47.1%) with a mean (SD) age of 63.7 (8.7) years and a mean (SD) MDS-UPDRS score of 33.3 (15.3). Generalized estimating equation analyses revealed that the yoga group had significantly better improvement in outcomes than the SRTE group, particularly for anxiety (time-by-group interaction, T1: β, -1.79 [95% CI, -2.85 to -0.69; P = .001]; T2: β, -2.05 [95% CI, -3.02 to -1.08; P < .001]), depression (T1: β, -2.75 [95% CI, -3.17 to -1.35; P < .001]); T2: β, -2.75 [95% CI, -3.71 to -1.79; P < . 001]), perceived hardship (T1: β, -0.92 [95% CI, -1.25 to -0.61; P < .001]; T2: β, -0.76 [95% CI, -1.12 to -0.40; P < .001]), perceived equanimity (T1: β, 1.11 [95% CI, 0.79-1.42; P < .001]; T2: β, 1.19 [95% CI, 0.82-1.56; P < .001]), and disease-specific HRQOL (T1: β, -7.77 [95% CI, -11.61 to -4.38; P < .001]; T2: β, -7.99 [95% CI, -11.61 to -4.38; P < .001]). Conclusions and Relevance Among patients with mild-to-moderate PD, the mindfulness yoga program was found to be as effective as SRTE in improving motor dysfunction and mobility, with the additional benefits of a reduction in anxiety and depressive symptoms and an increase in spiritual well-being and HRQOL. Trial Registration Centre for Clinical Research and Biostatistics identifier: CUHK_CCRB00522.
MARIO Project: Validation and Evidence of Service Robots for Older People with Dementia.

BACKGROUND In the EU funded MARIO project, specific technological tools are adopted for the people living with dementia (PLWD). In the final stage of the project, a validation of the MARIO companion robot was performed from August to October 2017. OBJECTIVE The aims of the present study are: 1) to illustrate the key results and evidence obtained in the final evaluation phase of the project across the three different pilot sites; 2) to assess the engagement dimensions of the PLWD who interacted with the MARIO robot; and 3) to assess the acceptability and efficacy of the MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience, quality of life in PLWD, and burden level of the caregivers. METHODS 38 people (M = 14; F = 24) with Alzheimer's disease were screened for eligibility and all were included. The following tests were administered Pre and Post interactions with MARIO: Observational Measurement of Engagement (OME), Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT), Frontal Assessment Battery (FAB), Neuropsychiatric Inventory (NPI), Cornell Scale for Depression in Dementia (CSDD), Multidimensional Scale of Perceived Social Support (MSPSS), 14-item Resilience Scale (RS-14), Quality of Life in Alzheimer's Disease (QOL-AD), Caregiver Burden Inventory (CBI), Tinetti Balance Assessment (TBA), and Comprehensive Geriatric Assessment (CGA) was carried out. RESULTS In Post-MARIO interactions, significant improvements were observed in RS-14 (p = 0.020). Considering the age of the people, PLWD with 68-76 years perceived that they had major social support (MSPSS Total: p = 0.016) and friends to support them (MSPSS Fri: p = 0.014). Indeed, the younger people (55-67 years) were less depressed (CSDD: p = 0.033), and more resilient (RS-14: p = 0.003). The people aged 77-85 years perceived they had major family support (MSPSS Fam: p = 0.018). The participants were gender and education matched without any statistically significant difference. CONCLUSION MARIO may be a useful tool in mitigating depression and loneliness, while enhancing social connectedness, resilience, and overall quality of life for people with dementia.
Applying the Critical Power Model to a Full Body Resistance Training Movement.

PURPOSE. This study: 1) determined if the mathematical model used to derive critical power could be used to identify the critical resistance (CR) for the deadlift; 2) compared predicted and actual repetitions to failure at 50, 60, 70, and 80% one-repetition maximum (1RM); and 3) compared the CR to the estimated sustainable resistance for 30 repetitions (ESR30). METHODS. Twelve subjects completed 1RM testing for the deadlift followed by 4 visits to determine the number of repetitions to failure at 50, 60, 70, and 80% 1RM. The CR was calculated as the slope of the line of the total work completed (repetitions x weight [kg] x distance [m]) versus the total distance (m) the barbell traveled. The actual and predicted repetitions to failure were determined from the CR model and compared using paired samples t-tests and simple linear regression. The ESR30 was determined from the power curve analysis and compared to the CR using paired samples t-tests and simple linear regression. RESULTS. The weight and repetitions completed at CR was 56±11 kg and 49±14 repetitions, respectively. The actual repetitions to failure were less than predicted at 50% (p<0.001) and 80% 1RM (p<0.001) and greater at 60% 1RM (p=0.004), but there was no difference at 70% 1RM (p=0.084). The ESR30 (75±14 kg) was greater (p<0.001) than the CR. CONCLUSIONS. The total work versus distance relationship can be used to identify the CR for the deadlift, which reflected a sustainable resistance that may be useful in the design of resistance-based exercise programs.
Making the Visual Tangible: Substituting Lifting Speed Limits for Object Weight in VR

Abstract We developed a novel interaction technique that allows virtual reality (VR) users to experience “weight” when hefting virtual, weightless objects. With this technique the perception of weight is evoked via constraints on the speed with which objects can be lifted. When hefted, heavier virtual objects move slower than lighter virtual objects. If lifters move faster than the lifted object, the object will fall. This constraint causes lifters to move slowly when lifting heavy objects. In two studies we showed that the size-weight illusion (SWI) is evoked when this technique is employed. The SWI occurs when two items of identical weight and different size are lifted and the smaller item is perceived as heavier than the larger item. The persistence of this illusion in VR indicates that participants bring their real-world knowledge of the relationship between size and weight to their virtual experience, and suggests that our interaction technique succeeds in making the visible tangible.
Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.
A Simple Image Acquisition System and Its Calibration for Image-Based 3D Reconstruction

This paper presents a simple image acquisition system composed of multiple cameras and a turntable, through which object's multiple-view images can be acquired and calibrated automatically. The main contribution of this paper is proposing a calibration algorithm for this system which using a simple calibration equipment to calibrate the cameras' intrinsic and their extrinsic relate to the center of turntable, and with these system parameters, all the acquired multi-view images can be easily calibrated. Combined with MVS, mesh generation and texture mapping algorithms, complete texture model can be automatically reconstructed from the calibrated images. The calibration and reconstruction examples based on the system are given in the end of the paper. The experimental results show the presented system with our calibration algorithm has high reconstruction accuracy, and can be used for many reconstruction applications.
Leveraging User Mobility and Mobile App Services Behavior for Optimal Edge Resource Utilization

Edge computing has seen a great progress nowadays eliminating the network latency risks by placing data and functionality in low-end devices closer to the end user is central in application domains such as the support of the backend of mobile apps. When this problem is combined with big data requirements linked to the number of mobile app users, the optimization of edge resource utilization becomes extremely important cost- and quality-wise. This work explores approaches to model resource demand based on user mobility and application characteristics. The Application and User Context Resource Predictor (AUCORP) is introduced that performs performance analysis employing machine learning methods. The evaluation is based on real data from the deployment of a real mobile app for a large music festival.
ALICE: Autonomous Link-based Cell Scheduling for TSCH

Although low-power lossy network (LLN), at its early stage, commonly used asynchronous link layer protocols for simple operation on resource-constrained nodes, development of embedded hardware and time synchronization technologies made Time-Slotted Channel Hopping (TSCH) viable in LLN (now part of IEEE 802.15.4e standard). TSCH has the potential to be a link layer solution for LLN due to its resilience to wireless interference (e.g., WiFi) and multipath fading. However, its slotted operation incurs non-trivial cell scheduling overhead: two nodes should wake up at a time-frequency cell together to exchange a packet. Efficient cell scheduling in dynamic multihop topology in wireless environments has been an open issue, preventing TSCH's wide adoption in practice. This work introduces ALICE, a novel autonomous link-based cell scheduling scheme which allocates a unique cell for each directional link (a pair of nodes and traffic direction) by closely interacting with the routing layer and using only local information, without any additional communication overhead. We implement ALICE on Contiki and evaluate its effectiveness on the IoT-LAB public testbed with 68 nodes. ALICE generally outperforms Orchestra (the state-of-the-art method) and even more so under heavy traffic and high node density, increasing throughput by 2 times with 98.3% reliability and reducing latency by 70%, route changes by 95%, and radio duty cycle by 35%. ALICE can serve as an autonomous scheduling framework, which paves the way for TSCH-based LLN to go on.
Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks

Weight and activation sparsity can be leveraged in hardware to boost the performance and energy efficiency of Deep Neural Networks during inference. Fully capitalizing on sparsity requires re-scheduling and mapping the execution stream to deliver non-zero weight/activation pairs to multiplier units for maximal utilization and reuse. However, permitting arbitrary value re-scheduling in memory space and in time places a considerable burden on hardware to perform dynamic at-runtime routing and matching of values, and incurs significant energy inefficiencies. Bit-Tactical (TCL) is a neural network accelerator where the responsibility for exploiting weight sparsity is shared between a novel static scheduling middleware, and a co-designed hardware front-end with a lightweight sparse shuffling network comprising two (2- to 8-input) multiplexers per activation input. We empirically motivate two back-end designs chosen to target bit-sparsity in activations, rather than value-sparsity, with two benefits: a) we avoid handling the dynamically sparse whole-value activation stream, and b) we uncover more ineffectual work. TCL outperforms other state-of-the-art accelerators that target sparsity for weights and activations, the dynamic precision requirements of activations, or their bit-level sparsity for a variety of neural networks.
Cotton Imports and GDP of Indonesia- Cointegration Analysis

Author the study, in data collection, analysis, tabulation and the research Author the of the advisory committee involved in planning, constant monitoring throughout the study, analyzing and interpreting the results. Both the final ABSTRACT The study examines the impact of cotton imports on the real GDP (Gross Domestic Product) of Indonesia for a period from 1992 to 2018 using ARDL approach and Granger causality analysis. Results of the study indicated that cotton imports have negative effect on economic growth. For every 1% increase in cotton imports the real GDP decreased by 0.107% in the long run. Any disequilibrium in the model is adjusted with a high speed of adjustment of 107.7% in less than a year. Shocks and the trend are adjusted in less than one year. There is no causality between imports of cotton and the real GDP. The study suggested effort should be taken by the government to increase yield of cotton by the use of technology and also a need to initiate farmers to take up cotton farming.
Enhanced Osteoblastic Differentiation Using Graphene Oxide Coating on Anodized Titanium

The aim of this study is to improve bone ingrowth and efficiency of orthopedic implants using anodized titanium and graphene oxide coating. Calcium mineralization from MC3T3-E1 pre-osteoblasts (bone-forming cells) on a modified surface of titanium by anodization and electrodeposition of graphene oxide was investigated. Not only graphene oxide coating improves biocompatibility and corrosion resistance, but also anodization of titanium eliminates residuals and reduces the releasing of metal ions. The effects of both surface modifications to calcium deposition and osteopontin expression by osteoblasts were evaluated in this study. Calcium deposited during osteoblast differentiation were observed with alizarin red S (ARS) staining after MC3T3-E1 cells were cultured on polystyrene (PS), graphene oxide coating on polystyrene (GO), anodized titanium (ATi), and graphene oxide coating on anodized titanium (ATiGO) for 14 and 21 days. The concentration of ARS dye was significantly increased when cells were cultured for 21 days onto ATiGO when compared with ATi. Moreover, osteopontin expression was detected with an indirect immunofluorescence staining using flow cytometer after MC3T3-E1 cells were cultured on the samples for 14 days. A relative fluorescence intensity (RFI) of osteopontin in MC3T3-E1 cells on ATiGO was significantly more than that on PS and GO. The results in this study showed that ATiGO increased calcium deposition by and osteopontin expression in osteoblasts, indicating bone formation on the samples.
Automatic Segmentation of the Prostate on 3D CT Images by Using Multiple Deep Learning Networks

Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate segmentation from CT images is a very challenging task due to the low contrast of soft tissue and the large variations of the prostate shape. In this paper, we propose an automatic segmentation method by using a two dimension (2-D) and a three dimension (3-D) convolutional neural network (CNN). First, instead of segmenting the whole image, we extract the volumes of interest (VOI) accurately to remove irrelevant regions by using a CNN based VOI extraction method (CBVEM). Then, we use the 3-D CNN to learn the holistic three-dimension deep features for distinguishing the prostate voxels from the non-prostate voxels in order to obtain the segmentation results. Deep learning networks can automatically learn the deep features based on the data, which are different from the handcrafted features. The proposed method has been evaluated on a dataset of 150 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 89.74% compared to the manual segmentation. Our deep learning based method is faster and returns similar results compared to those atlas-based, deformable model-based and feature-based classification methods. Due to the CBVEM and 3-D CNN, our method also achieves better performances on the same data in a minimum processing time compared to other deep CNN based methods. This proposed method for automatic segmentation of the prostate on 3-D CT images can have a variety of clinical applications.
Demo Abstract: Fast Feedback Control and Coordination with Mode Changes for Wireless Cyber-Physical Systems

This abstract describes the first public demonstration of feedback control and coordination of multiple physical systems over a dynamic multi-hop low-power wireless network with update intervals of tens of milliseconds. Our running system can dynamically change between different sets of application tasks (e.g., sensing, actuation, control) executing on the spatially distributed embedded devices, while closed-loop stability is provably guaranteed even across those so-called mode changes. Moreover, any subset of the devices can move freely, which does not affect closed-loop stability and control performance as long as the wireless network remains connected.
Efficacy of Aloe Vera Hydrogel in Inhibition The Growth of Campylobacter rectus and Provetella intermedia Bacteria

Introduction: Campylobacter rectus and Provetella intermedia bacteria as the main etiology causes of periodontal disease. One of the locally synthesized antimicrobials that is widely used for the treatment of periodontitis is Chlorhexidine, however this drug has side effects when used continuously. To overcome this, an alternative periodontitis therapy with medicinal plants is needed. This study aims to determine the effectiveness of hydrogel Aloe vera antibacterial concentrations of 2.5%, 5%, 10% and 20% in the bacteria Campylobacter rectus and Provetella intermedia. Methods: This study was a laboratory experimental study with posttest only control group design in vitro. The samples were Campylobacter rectus and Provetella intermedia. The number of groups in this study was six. The sample size was determined by Federer's formula and obtained four replications for each group. Data collection by measuring the inhibition diameter using a slide caliper. Data were analyzed using oneway ANOVA and posthoc LSD. Results: The results showed that there was a difference in the diameter of the hydrogel Aloe vera inhibition with a concentration of 2.5%, 5%, 10%, 20% and a positive control for Campylobacter rectus and Provetella intermedia bacteria. Conclusion: This study concluded that there is an antibacterial effectiveness of hydrogel Aloe vera in concentrations of 2.5%, 5%, 10% and 20% on Campylobacter rectus and Provetella intermedia bacteria.
Highly-accurate density-virial-coefficient values for helium, neon, and argon at 0.01 ○C determined by dielectric-constant gas thermometry.

The dielectric-constant gas thermometer of Physikalisch-Technische Bundesanstalt (PTB) developed for measuring the Boltzmann constant with a relative uncertainty of 1.9 parts per million was used for determining the virial coefficients of the three noble gases, helium, neon, and argon, at the triple point of water (0.01 ○C). For this purpose, isotherms were measured up to a maximum pressure of 7 MPa. The evaluation of the highly accurate data by fitting is required to derive an extended working equation for the dependence of the gas pressure on the dielectric constant. The following values have been obtained for the second B and third C virial coefficients, with the standard uncertainties given in parentheses as a multiple of the last digit, considering literature data for the dielectric virial coefficients: helium: BDCGT He0.01 ○C=11.925715 cm3/mol, CDCGT He0.01 ○C=113.4958 cm6/mol2; neon: BDCGT Ne0.01 ○C=10.994528 cm3/mol, CDCGT Ne0.01 ○C=215.815 cm6/mol2; argon: BDCGT Ar0.01 ○C=-21.233144 cm3/mol, CDCGT Ar0.01 ○C=1143.339 cm6/mol2. These values are compared with the results of the latest ab initio calculations of the second and third virial coefficients.
Secrecy outage probability analysis of energy-aware relay selection for energy-harvesting cooperative systems

The secrecy outage performance for a cooperative cognitive radio energy-harvesting network is analyzed. The cognitive network is composed of an energy-constrained cognitive source (CS), multiple energy-constrained cognitive relays (CRs) and a cognitive destination (CD) as well as an eavesdropper (E) coexists with a primary network consisting of a primary transmitter (PT) and a primary receiver (PR). The CS and CRs are equipped with energy harvesters for collecting energy from the radio frequency signal from PT and their transmit powers are limited by the interference threshold at PR. To prevent conﬁdential information leaking to E, an optimal relay selection (ORS) scheme and a suboptimal relay selection (SRS) scheme are proposed. In ORS scheme, the whole channels state information (CSI) of wireless links is available to CRs while SRS only needs to know the CSI of main channels from CRs to CD. Moreover, the closed-form expressions of secrecy outage probabilities for both ORS and SRS schemes are derived. For the purpose of comparison, the classical round-robin relay selection (RRRS) is also analyzed in terms of secrecy outage probability. Furthermore, the numerical results show that ORS achieves the best performance and RRRS performs the worst in terms of secrecy outage probability.