Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Sitting Time and Sitting Fragmentation after a Workplace Sedentary Behaviour Intervention

Prolonged sedentary behaviour (SB) has shown to be detrimental to health. Nevertheless, population levels of SB are high and interventions to decrease SB are needed. This study aimed to explore the effect of a personalized intervention aimed at reducing SB and increasing breaks in SB among college employees. A pre-experimental study design was used. Participants (n = 36) were recruited at a college in Massachusetts, USA. SB was measured over 7 consecutive days using an activPAL3 accelerometer. Following baseline measures, all participants received a personalized SB consultation which focused on limiting bouts of SB >30 min, participants also received weekly follow-up e-mails. Post-intervention measures were taken after 16 weeks. Primary outcome variables were sedentary minutes/day and SB bouts >30 min. Differences between baseline and follow-up were analyzed using paired t-tests. The intervention did not change daily sedentary time (−0.48%; p > 0.05). The number of sedentary bouts >30 min decreased significantly by 0.52 bouts/day (p = 0.010). In this study, a personalized SB intervention was successful in reducing number of bouts >30 min of SB. However, daily sedentary time did not reduce significantly. These results indicate that personalized, consultation-based interventions may be effective if focused on a specific component of SB.
Simulation of the active Brownian motion of a microswimmer

Unlike passive Brownian particles, active Brownian particles, also known as microswimmers, propel themselves with directed motion and thus drive themselves out of equilibrium. Understanding their motion can provide insight into out-of-equilibrium phenomena associated with biological examples such as bacteria, as well as with artificial microswimmers. We discuss how to mathematically model their motion using a set of stochastic differential equations and how to numerically simulate it using the corresponding set of finite difference equations both in homogenous and complex environments. In particular, we show how active Brownian particles do not follow the Maxwell-Boltzmann distribution—a clear signature of their out-of-equilibrium nature—and how, unlike passive Brownian particles, microswimmers can be funneled, trapped, and sorted.
Hardware Cost Design Optimization for Functional Safety-Critical Parallel Applications on Heterogeneous Distributed Embedded Systems

Industrial embedded systems are cost sensitive, and hardware cost of industrial production should be reduced for high profit. The functional safety requirement must be satisfied according to industrial functional safety standards. This study proposes three hardware cost optimization algorithms for functional safety-critical parallel applications on heterogeneous distributed embedded systems during the design phase. The explorative hardware cost optimization (EHCO), enhanced EHCO (EEHCO), and simplified EEHCO (SEEHCO) algorithms are proposed step by step. Experimental results reveal that EEHCO can obtain minimum hardware cost, whereas SEEHCO is efficient for large-scale parallel applications compared with the existing algorithms.
Modulated Model Predictive Control for a Z-Source-Based Permanent Magnet Synchronous Motor Drive System

This paper presents a modulated model predictive control (MMPC) based control system for a Z-source inverter based permanent magnet synchronous motor (PMSM) drive system. The proposed method uses two separate MMPC loops for the Z-source network and PMSM control. For the Z-source network, a cascaded MMPC control scheme has been proposed that provides fast and stable response. For the PMSM control, an MMPC controller is proposed that uses the discretized equations of the PMSM to predict the future value of PMSM current vectors, selects specific current vectors that minimize a certain cost function the most, and performs modulation between them during a sampling time. Experimental results are provided to validate the theoretical outcomes.
Illumination and Noise-Based Scene Classification - Application to SAR Sea Ice Imagery

Spatial intensity variation introduced by illumination changes is a challenging problem for image segmentation and classification. Many techniques have been proposed which focus on removing this illumination variation by estimating or modelling it. There is limited research on developing an illumination invariant classification technique which does not use any preprocessing. A major focus of this research is on automatically classifying synthetic aperture radar (SAR) images. These are large satellite images which pose many challenges for image classification including the incidence angle effect which is a strong illumination variation across the image. Mapping of full scene satellite images of sea-ice is important for navigational purposes for ships and also for climate research. The images obtained from the RADARSAT-2 satellite are dual band, high quality images. Currently, sea ice chart are produced manually by ice analysts at the Canadian Ice Service. However, this process can be automated to reduce processing time and obtain more detailed pixel-level ice maps. An automated classification algorithm to achieve sea ice and open water separation will greatly help the ice analyst by providing sufficient guidance in the initial stages of creating an ice map. It would also help the analyst to improve the accuracy while finding ice concentrations and remove subjective bias. The existing Iterative Region Growing by Semantics (IRGS) algorithm is not effective for full scene segmentation because of the incidence angle effect. This research proposes a ”glocal” (global as well as local) approach to solve this problem. The image is divided in a rectangular grid and each rectangle is segmented using IRGS. This is viewed as an oversegmentation of the original image. Finally, IRGS is used globally to glue together the oversegmented regions. This method yields acceptable results with the denoised images. The proposed technique can also be used for general image classification purposes. Extensive testing was done to investigate the best set of parameters for the proposed approach. Images were simulated with the SAR illumination variation and multiplicative speckle noise. The technique was effective for general classification and attained accurate results for full scene SAR segmentation.
Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction

A widely used method for prediction of complex traits in animal and plant breeding is “genomic best linear unbiased prediction” (GBLUP). In a quantitative genetics setting, BLUP is a linear regression of phenotypes on a pedigree or on a genomic relationship matrix, depending on the type of input information available. Normality of the distributions of random effects and of model residuals is not required for BLUP but a Gaussian assumption is made implicitly. A potential downside is that Gaussian linear regressions are sensitive to outliers, genetic or environmental in origin. We present simple (relative to a fully Bayesian analysis) to implement robust alternatives to BLUP using a linear model with residual t or Laplace distributions instead of a Gaussian one, and evaluate the methods with milk yield records on Italian Brown Swiss cattle, grain yield data in inbred wheat lines, and using three traits measured on accessions of Arabidopsis thaliana. The methods do not use Markov chain Monte Carlo sampling and model hyper-parameters, viewed here as regularization “knobs,” are tuned via some cross-validation. Uncertainty of predictions are evaluated by employing bootstrapping or by random reconstruction of training and testing sets. It was found (e.g., test-day milk yield in cows, flowering time and FRIGIDA expression in Arabidopsis) that the best predictions were often those obtained with the robust methods. The results obtained are encouraging and stimulate further investigation and generalization.
Interactive Shape-Aware Deformation of 3D Furniture Models

Resizing of 3D models can be very useful when creating new models or when reusing old ones. However, naive resizing can create serious visual artifacts which destroy the characteristics of an object. In this work an algorithm that protects the features of 3D models during resizing is introduced. It is specialized for furniture models because it should be applied to a furniture configurator. We observed that the distortion that occurs during scaling is not distributed uniformly across the object. Our algorithm automatically detects the vulnerable parts of a model and then stretches only the non-vulnerable ones. Furthermore, the algorithm takes into account that when scaling a mesh in a specific direction, the texture has to be adapted as well in order to prevent representation errors.
Class-E Rectifiers and Power Converters: The Operation of the Class-E Topology as a Power Amplifier and a Rectifier with Very High Conversion Efficiencies

In the late 1970s, interest in reducing the value and size of reactive components motivated power supply specialists to operate dc-to-dc converters at hundreds of kilohertz or even megahertz frequencies. Passive energy storage (mainly magnetics) is the dominant factor in the size of power electronics and also limits its cost, reliability, and dynamic response. With the goals of miniaturization and improved control bandwidth, researchers had to face the frequency-dependent turn-on and turn-off losses associated with the use of rectangular waveforms in the hard-switched topologies of that time. Similar to approaches for RF/microwave power amplifiers (PAs), the introduction of resonant circuits allowed shaping either a sinusoidal voltage or current, with parasitic reactive elements absorbed by the topology in the neighborhood of the switching frequency. The resulting resonant power converters, obtained by cascading a dc-to-ac resonant inverter with a high-frequency rectifier, first transform the dc input power into controlled ac power and then convert it back into the desired dc output [1].
Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World

The investigation of plant organisms by means of data-derived systems biology approaches based on network modeling is mainly characterized by genomic data, while the potential of proteomics is largely unexplored. This delay is mainly caused by the paucity of plant genomic/proteomic sequences and annotations which are fundamental to perform mass-spectrometry (MS) data interpretation. However, Next Generation Sequencing (NGS) techniques are contributing to filling this gap and an increasing number of studies are focusing on plant proteome profiling and protein-protein interactions (PPIs) identification. Interesting results were obtained by evaluating the topology of PPI networks in the context of organ-associated biological processes as well as plant-pathogen relationships. These examples foreshadow well the benefits that these approaches may provide to plant research. Thus, in addition to providing an overview of the main-omic technologies recently used on plant organisms, we will focus on studies that rely on concepts of module, hub and shortest path, and how they can contribute to the plant discovery processes. In this scenario, we will also consider gene co-expression networks, and some examples of integration with metabolomic data and genome-wide association studies (GWAS) to select candidate genes will be mentioned.
Feeding mechanics of teleost fishes (Labridae; Perciformes): A test of four‐bar linkage models

The feeding mechanisms of two labrid fishes (Cheilinus chlorurus and C. diagrammus: Labridae: Perciformes) are modeled using four‐bar linkage theory from mechanical engineering. The actions of the feeding mechanisms are simulated by a computer program that uses morphometric data to calculate the geometry of mechanism structure. The predictions of three different four‐bar linkages regarding the kinematics of feeding are compared to the movements observed through hign speed (200 fps) cinematography. A previously unidentified four‐bar chain was found to be an accurate model of the mechanism by which upper jaw protrusion, maxillary rotation, and gape increase occur in Cheilinus. This mechanism involves the anterior jaws including the mandible, maxilla, premaxilla, palatine, and suspensorium. The accuracy of two previously described four‐bar linkages was also tested by comparison of model predictions and film results. The opercular linkage proposed by Anker ('74) as a mechanism of jaw depression via opercular levation was found to be a poor predictor of feeding movements. This four‐bar chain involves the opercle, suspensorium, interopercle, and mandible. Muller ('87) proposed a mechanism of hyoid depression involving cranial elevation due to epaxial muscle contraction as input motion The links in this mechanism include the neurocranium and hyomandibula, hyoid, sternohyoideus muscle, and pectoral girdle. This model was an accurate predictor of hyoid depression in Cheilinus when simultaneous cranial elevation and sternohyoideus contraction were simulated. Quantitative kinematic models involve simplifying assumptions when applied to complex musculoskeletal systems, but such models have a wide range of applications to vertebrate functional morphology.
Rejoinder: Time‐dynamic profiling with application to hospital readmission among patients on dialysis

Jason P. Estes1, Danh V. Nguyen2, Yanjun Chen3, Lorien S. Dalrymple4, Connie M. Rhee2, Kamyar Kalantar-Zadeh2, and Damla Şentürk5 1Research, Pratt & Whitney, East Hartford, CT 06118, U.S.A. 2Department of Medicine, University of California Irvine, Orange, CA 92868, U.S.A. 3Institute for Clinical and Translational Science, Irvine, CA 92697, U.S.A. 4Fresenius Medical Care North America, Waltham, MA 02451, U.S.A. 5Department of Biostatistics, University of California, Los Angeles, CA 90095, U.S.A.
Design and implementation of static and dynamic objects store systems using line follower robots

This paper introduces static and dynamic methods for objects store system using the line follower robot. The static means the robot moves on static lines to reach to any store location while the dynamic means that the arranged of the following lines is changed according to the location of the storage box. The static objects store system is represented by the Digital differential algorithm DDA and the dynamic objects store system is represented by the Bezier curve algorithm. In both environments the propose store system consists of several boxes that arranged in several columns. These boxes can be used to store and return small objects like mobile phones according to a secret code that entered from a user interface unit. The principle of store and restore of these objects is dependent on using line follower mobile robots. Both the methods are designed and software implemented in an environment with thirty boxes arranged in four columns. The comparisons between these algorithms are shown with respect to the length of the paths and time of arrival.
Batch Processing of Top-k Spatial-Textual Queries

Since the mid-2000s, everal indexing techniques have been proposed to efficiently answer top-k spatial-textual queries. However, all of these approaches focus on answering one query at a time. In contrast, how to design efficient algorithms that can exploit similarities between incoming queries to improve performance has received little attention. In this article, we study a series of efficient approaches to batch process multiple top-k spatial-textual queries concurrently. We carefully design a variety of indexing structures for the problem space by exploring the effect of prioritizing spatial and textual properties on system performance. Specifically, we present an efficient traversal method, SF-Sep, over an existing space-prioritized index structure. Then, we propose a new space-prioritized index structure, the MIR-Tree to support a filter-and-refine based technique, SF-Grp. To support the processing of text-intensive data, we propose an augmented, inverted indexing structure that can easily be added into existing text search engine architectures and a novel traversal method for batch processing of the queries. In all of these approaches, the goal is to improve the overall performance by sharing the I/O costs of similar queries. Finally, we demonstrate significant I/O savings in our algorithms over traditional approaches by extensive experiments on three real datasets and compare how properties of different datasets affect the performance. Many applications in streaming, micro-batching of continuous queries, and privacy-aware search can benefit from this line of work.
Temporal Reference, Attentional Modulation, and Crossmodal Assimilation

Crossmodal assimilation effect refers to the prominent phenomenon by which ensemble mean extracted from a sequence of task-irrelevant distractor events, such as auditory intervals, assimilates/biases the perception (such as visual interval) of the subsequent task-relevant target events in another sensory modality. In current experiments, using visual Ternus display, we examined the roles of temporal reference, materialized as the time information accumulated before the onset of target event, as well as the attentional modulation in crossmodal temporal interaction. Specifically, we examined how the global time interval, the mean auditory inter-intervals and the last interval in the auditory sequence assimilate and bias the subsequent percept of visual Ternus motion (element motion vs. group motion). We demonstrated that both the ensemble (geometric) mean and the last interval in the auditory sequence contribute to bias the percept of visual motion. Longer mean (or last) interval elicited more reports of group motion, whereas the shorter mean (or last) auditory intervals gave rise to more dominant percept of element motion. Importantly, observers have shown dynamic adaptation to the temporal reference of crossmodal assimilation: when the target visual Ternus stimuli were separated by a long gap interval after the preceding sound sequence, the assimilation effect by ensemble mean was reduced. Our findings suggested that crossmodal assimilation relies on a suitable temporal reference on adaptation level, and revealed a general temporal perceptual grouping principle underlying complex audio-visual interactions in everyday dynamic situations.
Theoretical analysis of doping management and its effects on power scaling

Thermal load and nonlinear effects are two contrary phenomena that make up important drawbacks in rapid progress of high-power fiber lasers. To minimize the thermal load, which limits the average power, doping concentration should be decreased, which brings about increasing length of the fiber. In contrast, the presence of nonlinear effects and their management demand the use of high-doped, shorter fibers in order to maximize the peak power. Management on doping of gain fiber and obtaining a specific doping profile function along the short gain fiber is a proposed solution for prevention of the exchange between thermal load and nonlinear effects. The study shows two different approaches for keeping the temperature levels down in addition to obtaining power scaling profiles.
Health Information Technology in Healthcare Quality and Patient Safety: Literature Review

Background The area of healthcare quality and patient safety is starting to use health information technology to prevent reportable events, identify them before they become issues, and act on events that are thought to be unavoidable. As healthcare organizations begin to explore the use of health information technology in this realm, it is often unclear where fiscal and human efforts should be focused. Objective The purpose of this study was to provide a foundation for understanding where to focus health information technology fiscal and human resources as well as expectations for the use of health information technology in healthcare quality and patient safety. Methods A literature review was conducted to identify peer-reviewed publications reporting on the actual use of health information technology in healthcare quality and patient safety. Inductive thematic analysis with open coding was used to categorize a total of 41 studies. Three pre-set categories were used: prevention, identification, and action. Three additional categories were formed through coding: challenges, outcomes, and location. Results This study identifies five main categories across seven study settings. A majority of the studies used health IT for identification and prevention of healthcare quality and patient safety issues. In this realm, alerts, clinical decision support, and customized health IT solutions were most often implemented. Implementation, interface design, and culture were most often noted as challenges. Conclusions This study provides valuable information as organizations determine where they stand to get the most “bang for their buck” relative to health IT for quality and patient safety. Knowing what implementations are being effectivity used by other organizations helps with fiscal and human resource planning as well as managing expectations relative to cost, scope, and outcomes. The findings from this scan of the literature suggest that having organizational champion leaders that can shepherd implementation, impact culture, and bridge knowledge with developers would be a valuable resource allocation to consider.
Transplantation of CCR5∆32 Homozygous Umbilical Cord Blood in a Child With Acute Lymphoblastic Leukemia and Perinatally Acquired HIV Infection

Abstract Background Allogeneic hematopoietic cell transplantation (allo-HCT) in a CCR5∆32 homozygous donor resulted in HIV cure. Understanding how allo-HCT impacts the HIV reservoir will inform cure strategies. Methods A 12-year-old with perinatally acquired, CCR5-tropic HIV and acute lymphoblastic leukemia underwent myeloablative conditioning and umbilical cord blood (UCB) transplantation from a CCR5∆32 homozygous donor. Peripheral blood mononuclear cells (PBMCs) and the rectum were sampled pre- and post-transplant. The brain, lung, lymph node (LN), stomach, duodenum, ileum, and colon were sampled 73 days after transplantation (day +73), when the patient died from graft-vs-host disease. Droplet digital polymerase chain reaction (ddPCR) and in situ hybridization (ISH) were used detect the HIV reservoir in tissues. CCR5 and CD3 expression in the LN was assessed using immunohistochemistry (IHC). Results HIV DNA (vDNA) was detected in PBMCs by ddPCR pretransplant but not post-transplant. vDNA was detected by ISH in the rectum at days –8 and +22, and in the LN, colon, lung, and brain day +73. vDNA was also detected in the lung by ddPCR. IHC revealed CCR5+CD3+ cells in the LN postmortem. Conclusions HIV was detected in multiple tissues 73 days after CCR5∆32 homozygous UCB allo-HCT despite myeloablative conditioning and complete donor marrow engraftment. These results highlight the importance of analyzing tissue during HIV cure interventions and inform the choice of assay used to detect HIV in tissue reservoirs.