Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Comprehensive Patient-Centric Care by Integrating Digital Health Technology With Direct Clinical Contact in Australia

Background There is an escalating crisis in health care, locally and internationally. The current health care model is unable to meet the increasing health care demands. Objective The aim of this study was to reconceptualize the provision of health care to produce better outcomes at no greater cost, by placing individuals in the position of authority to direct their own care, in a personalized, integrated health care system. Methods In this study, we used the Australian health care system as a model. We reviewed the current landscape of digital health in Australia and discussed how electronic medical records (EMRs) can be further developed into a personalized, integrated health care system. Results Some components of an EMR and digital health system are already being used in Australia, but the systems are not linked. A personalized, integrated health care model that is responsive to consumer needs requires not just a passive repository of medical information; it would require a team approach, including the government, health care funders, industries, consumers and advocacy groups, health care professionals, community groups, and universities. Conclusions Implementation of a personalized, integrated health care system can result in reduced pressure on the current health care system, and it can result in the delivery of best-practice health care, regardless of location. Importantly, a personalized, integrated health care system could serve as an education platform, “upskilling” not only clinicians but also, more importantly, patients and carers by providing them with accurate information about their condition, treatment options, medications, and management strategies. By proposing personalized, integrated health care, we offer an intelligent model of health care that is ubiquitous, efficient, and continuously improving.
Molecular Subtypes in Stage II-III Colon Cancer Defined by Genomic Instability: Early Recurrence-Risk Associated with a High Copy-Number Variation and Loss of RUNX3 and CDKN2A

Objective We sought to investigate various molecular subtypes defined by genomic instability that may be related to early death and recurrence in colon cancer. Methods We sought to investigate various molecular subtypes defined by instability at microsatellites (MSI), changes in methylation patterns (CpG island methylator phenotype, CIMP) or copy number variation (CNV) in 8 genes. Stage II-III colon cancers (n = 64) were investigated by methylation-specific multiplex ligated probe amplification (MS-MLPA). Correlation of CNV, CIMP and MSI, with mutations in KRAS and BRAFV600E were assessed for overlap in molecular subtypes and early recurrence risk by uni- and multivariate regression. Results The CIMP phenotype occurred in 34% (22/64) and MSI in 27% (16/60) of the tumors, with noted CIMP/MSI overlap. Among the molecular subtypes, a high CNV phenotype had an associated odds ratio (OR) for recurrence of 3.2 (95% CI 1.1-9.3; P = 0.026). Losses of CACNA1G (OR of 2.9, 95% CI 1.4-6.0; P = 0.001), IGF2 (OR of 4.3, 95% CI 1.1-15.8; P = 0.007), CDKN2A (p16) (OR of 2.0, 95% CI 1.1-3.6; P = 0.024), and RUNX3 (OR of 3.4, 95% CI 1.3-8.7; P = 0.002) were associated with early recurrence, while MSI, CIMP, KRAS or BRAF V600E mutations were not. The CNV was significantly higher in deceased patients (CNV in 6 of 8) compared to survivors (CNV in 3 of 8). Only stage and loss of RUNX3 and CDKN2A were significant in the multivariable risk-model for early recurrence. Conclusions A high copy number variation phenotype is a strong predictor of early recurrence and death, and may indicate a dose-dependent relationship between genetic instability and outcome. Loss of tumor suppressors RUNX3 and CDKN2A were related to recurrence-risk and warrants further investigation.
Static and Dynamic Contract Verifiers For Java

Design By Contract (DBC) is a systematic approach to specifying and implementing object-oriented software systems. DBC has been proved to greatly benefit software development. However, Java does not natively support DBC. We have developed a comprehensive solution to bring DBC into Java. The static and dynamic contract verifier is the most crucial part of the solution. We have developed a toolset support DBC using these two verifiers. This paper presents details of their design and implimentation. The tool used for dynamic contract verifier is ContractChecker, which generates test code into original Java code, thus enables runtime validation. Static contract verification is done by Static Contract Verifier, which uses an automated theorem prover to verify contract.
Use of zero-frequency resonator for automatically detecting systolic peaks of photoplethysmogram signal

This work investigates the application of zero-frequency resonator (ZFR) for detecting systolic peaks of photoplethysmogram (PPG) signals. Based on the authors’ studies, they propose an automated noise-robust method, which consists of the central difference operation, the ZFR, the mean subtraction and averaging, the peak determination, and the peak rejection/acceptance rule. The method is evaluated using different kinds of PPG signals taken from the standard MIT-BIH polysomnographic database and Complex Systems Laboratory database and the recorded PPG signals at their Biomedical System Lab. The method achieves an average sensitivity (Se) of 99.95%, positive predictivity (Pp) of 99.89%, and overall accuracy (OA) of 99.84% on a total number of 116,673 true peaks. Evaluation results further demonstrate the robustness of the ZFR-based method for noisy PPG signals with a signal-to-noise ratio (SNR) ranging from 30 to 5 dB. The method achieves an average Se = 99.76%, Pp = 99.84%, and OA = 99.60% for noisy PPG signals with a SNR of 5 dB. Various results show that the method yields better detection rates for both noise-free and noisy PPG signals. The method is simple and reliable as compared with the complexity of signal processing techniques and detection performance of the existing detection methods.
Post-routing analytical models for homogeneous fpga architectures

The rapid growth in Field Programmable Gate Array (FPGA) architecture design space has led to an explosion in architectural choices that exceed well over 1,000,000 configurations. This makes searching for pareto-optimal solutions using a CAD-based incremental design process near impossible for hardware architects and application engineers. Designers need fast and accurate analytical models in order to evaluate the impact of their design choices on performance. Despite the proliferation of FPGA models, todays state-of the art modeling tools suffer from two drawbacks. First, they rely on circuit characteristics extracted from various stages of the FPGA CAD flow making them CAD dependent. Second, they lack ability to take routing architecture parameters into account. These two factors pose as a barrier for converging to the desired implementation rapidly. In this research, we address these two challenges and propose the first static power and post-routing wirelength models in academia. Our models are unique as they are CAD-independent, and they take both logic and routing architecture parameters into account. Using the static power model we are able to estimate the active and idle leakage power dissipation in homogeneous FPGAs with average correlation factor of 95% and mean percentage error of 17% over experimental results based on MCNC benchmarks. Using our wirelength model, we are able to obtain a low mean percentage error of 4.2% and an average correlation factor of 84% using MCNC and VTR benchmarks. We also show that utilizing wirelength model for architecture optimization process reduces the design space exploration time by 53% compared to the CAD-based process. We finally propose an algorithmic approach to estimate the logic density (i.e., number of LUTs) of multiplexer-based circuits, and address the problem of discrete effects in FPGA analytical models. We show that a model that generates logic density of a fundamental circuit element, such as a multiplexer, can be used to estimate performance metrics, such as critical path delay and power.
Feature extraction for the author name disambiguation problem in a bibliographic database

Author name disambiguation in bibliographic databases has been, and still is, a challenging research task due to the high uncertainty there is when matching a publication author with a concrete researcher. Common approaches normally either resort to clustering to group author's publications, or use a binary classifier to decide whether a given publication is written by a specific author. Both approaches benefit from authors publishing similar works (e.g. subject areas and venues), from the previous publication history of an author (the higher, the better), and validated publication-author associations for model creation. However, whenever such an algorithm is confronted with different works from an author, or an author without publication history, often it makes wrong identifications. In this paper, we describe a feature extraction method that aims to avoid the previous problems. Instead of generally characterizing an author, it selectively uses features that associate the author to a certain publication. We build a Random Forest model to assess the quality of our set of features. Its goal is to predict whether a given author is the true author of a certain publication. We use a bibliographic database named Authenticus with more than 250,000 validated author-publication associations to test model quality. Our model achieved a top result of 95.37+ accuracy in predicting matches and 91.92+ in a real test scenario. Furthermore, in the last case the model was able to correctly predict 61.86+ of the cases where authors had no previous publication history.
Detecting jute plant disease using image processing and machine learning

Detecting stem diseases of plants by image analysis are still in an inchoate state in the research field. This research has been conducted on detecting the stem diseases of jute plants which is one of the most important cash crops in some of the Asian countries. An automated system based on an Android application has been implemented to take pictures of the disease affected stems of jute plants and send them to the dedicated server for assaying. On the server side, the affected portion from the image will be segmented using customized thresholding formula based on hue-based segmentation. The consequential feature values will be extracted from the segmented portion for texture analysis using color co-occurrence methodology. The extracted values will be compared with the sample values stored in the pre-defined database which will lead the disease to be identified and classified using Multi-SVM classifier. At the final step, the classification result along with the necessary control measures will be sent back to the farmer within three seconds through the application on their phone.
Length‐Tension Relationship of Smooth Muscle of the Hog Carotid Artery

The length-tension relationships for arterial smooth muscle were determined using vascular strips teased from the media of hog carotid arteries. Histological examination revealed that the strips were (1) free of adventitia and (2) composed of smooth muscle cells oriented parallel to the long axis of the strips. Measurements from electron micrographs indicated that 60% of the cross-sectional area consisted of smooth muscle. At L0 (the optimal length for tension development) the total cross-sectional area of the strips varied from 0.3 to 0.8 mm2. Potassium-induced depolarization was the most effective stimulus for force development. Norepinephrine and electric field stimulation elicited responses which were 70% and 30%, respectively, of that produced by potassium-induced depolarization. At L0 the intrinsic load-bearing capacity (P0) of the contractile system was 2.22 × 106 dynes/cm2. Tension development fell off gradually at lengths shorter than L0 and more rapidly at lengths greater than L0. Passive length-tension curves were determined after quick releases from higher to lower lengths to avoid complications arising from tone. Passive tension was negligible at lengths below 0.9 L0. At L0 the passive tension was 10% of P0.
Subdivision methods for geometric design

A rotating chipping drum carrying a spiral row of bits is mounted in a hollow body with a portion of its periphery exposed to a chipping chamber for holding log sections to be chipped. The rotative axis of the chipping drum may be inclined to urge a log section toward one end of the chipping chamber.
Relationship of nutrition and physical activity behaviors and fitness measures to academic performance for sixth graders in a midwest city school district.

BACKGROUND To support curriculum and policy, a midwest city school district assessed the association of selected categories of nutrition and physical activity (NUTR/PA) behaviors, fitness measures, and body mass index (BMI) with academic performance (AP) for 800 sixth graders. METHODS Students completed an adapted Youth Risk Behavior Surveillance Survey (NUTR/PA behaviors), fitness assessments (mile run, curl-ups, push-ups, height, and weight) with results matched to standardized scores (Measures of Academic Progress [MAP]), meal price status, and gender. Differences in mean MAP scores (math and reading) were compared by selected categories of each variable utilizing 1-way analysis of variance. Associations were determined by stepwise multiple regression utilizing mean MAP scores (for math and for reading) as the dependent variable and NUTR/PA behaviors, fitness, and BMI categories as independent variables. Significance was set at α = 0.05. RESULTS Higher MAP math scores were associated with NUTR (more milk and breakfast; less 100% fruit juice and sweetened beverages [SB]) and PA (increased vigorous PA and sports teams; reduced television), and fitness (higher mile run performance). Higher MAP reading scores were associated with NUTR (fewer SB) and PA (increased vigorous PA, reduced television). Regression analysis indicated about 11.1% of the variation in the mean MAP math scores and 6.7% of the mean MAP reading scores could be accounted for by selected NUTR/PA behaviors, fitness, meal price status, and gender. CONCLUSION Many positive NUTR/PA behaviors and fitness measures were associated with higher MAP scores supporting the school district focus on healthy lifestyles. Additional factors, including meal price status and gender, contribute to AP.
Improvements on the k-center Problem for Uncertain Data

In real applications, there are situations where we need to model some problems based on uncertain data. This leads us to define an uncertain model for some classical geometric optimization problems and propose algorithms to solve them. The assigned version of the k-center problem for n uncertain points in a metric space is studied in this paper. The main approach is to replace each uncertain point with a clever choice of a certain point. We argue that the k-center solution for these certain replacements of our uncertain points, is a good constant approximation factor for the original uncertain k-center problem. This approach enables us to present fast and simple algorithms that give 10-approximation solution for the k-center problem in any metric space and when the ambient space is Euclidean, it can be improved to (3+ε)-approximation for any ε>0. These algorithms improve both the approximation factor and the running time of the previously known algorithms. Also, our algorithms are suitable for applying in the case of streaming and big data.
Seismic Random Noise Attenuation Using Sparse Low-Rank Estimation of the Signal in the Time–Frequency Domain

Suppression of random noise in seismic data is a challenging preprocessing task. We propose a new denoising method, which includes the following steps. First, the short-time Fourier transform of the noisy seismic signal is computed. Then, a sparse low-rank matrix is estimated based on solving a nonconvex penalty function. A threshold function associated with the penalty function based on the optimal shrinkage of the singular values is employed to solve the penalty function. The nonconvex penalty function induces sparsity to the time–frequency (TF) matrix, and the optimal shrinkage optimizes the thresholding function to extract the singular values. Finally, the seismic signal is synthesized based on the estimated sparse low-rank TF matrix. We evaluate the proposed method by using a synthetic seismic section contaminated by random noise and a prestack real seismic dataset from an oil field in the southwest of Iran. The synchrosqueezed wavelet transform-OptShrink, synchrosqueezed wavelet transform-Go Decomposition, and classical $f-x$ singular spectrum analysis methods are selected to compare the performance of the proposed method. The results indicate the superiority of the proposed method compared to the other state-of-the-art noise suppression methods.
Spray-freeze-drying of nanosuspensions: the manufacture of insulin particles for needle-free ballistic powder delivery

The feasibility of preparing microparticles with high insulin loading suitable for needle-free ballistic drug delivery by spray-freeze-drying (SFD) was examined in this study. The aim was to manufacture dense, robust particles with a diameter of around 50 µm, a narrow size distribution and a high content of insulin. Atomization using ultrasound atomizers showed improved handling of small liquid quantities as well as narrower droplet size distributions over conventional two-fluid nozzle atomization. Insulin nanoparticles were produced by SFD from solutions with a low solid content (<10 mg ml−1) and subsequent ultra-turrax homogenization. To prepare particles for needle-free ballistic injection, the insulin nanoparticles were suspended in matrix formulations with a high excipient content (>300 mg ml−1) consisting of trehalose, mannitol, dextran (10 kDa) and dextran (150 kDa) (abbreviated to TMDD) in order to maximize particle robustness and density after SFD. With the increase in insulin content, the viscosity of the nanosuspensions increased. Liquid atomization was possible up to a maximum of 250 mg of nano-insulin suspended in a 1.0 g matrix. However, if a narrow size distribution with a good correlation between theoretical and measurable insulin content was desired, no more than 150 mg nano-insulin could be suspended per gram of matrix formulation. Particles were examined by laser light diffraction, scanning electron microscopy and tap density testing. Insulin stability was assessed using size exclusion chromatography (SEC), reverse phase chromatography and Fourier transform infrared (FTIR) spectroscopy. Densification of the particles could be achieved during primary drying if the product temperature (Tprod) exceeded the glass transition temperature of the freeze concentrate (Tg′) of −29.4°C for TMDD (3∶3∶3∶1) formulations. Particles showed a collapsed and wrinkled morphology owing to viscous flow of the freeze concentrate. With increasing insulin loading, the d (v, 0.5) of the SFD powders increased and particle size distributions got wider. Insulin showed a good stability during the particle formation process with a maximum decrease in insulin monomer of only 0.123 per cent after SFD. In accordance with the SEC data, FTIR analysis showed only a small increase in the intermolecular β-sheet of 0.4 per cent after SFD. The good physical stability of the polydisperse particles made them suitable for ballistic injection into tissue-mimicking agar hydrogels, showing a mean penetration depth of 251.3 ± 114.7 µm.
Systematic Error-Correcting Codes for Permutations and Multi-Permutations

Multi-permutations and in particular permutations appear in various applications in an information theory. New applications, such as rank modulation for flash memories, have suggested the need to consider error-correcting codes for multi-permutations. In this paper, we study systematic error-correcting codes for multi-permutations in general and for permutations in particular. For a given number of information symbols k, and for any integer t, we present a construction of (k+r,k)systematic t-error-correcting codes, for permutations of length k+r, where the number of redundancy symbols r is relatively small. In particular, for a given t and for sufficiently large k, we obtain r=t+1, while a lower bound on the number of redundancy symbols is shown to be t. The same construction is also applied to obtain related systematic error-correcting codes for any types of multi-permutations.
Dominant Users Grouping Algorithm for Multiple RAUs-UEs Coordination in DAS System

Abstract-This paper concerns the coordinated transmission among multiple remote antenna units (RAUs) and user equipments (UEs) in distributed antenna system (DAS), where instantaneous channel state information (CSI) feedback of multiple links is a big challenge. By exploring the inherent relationship among coordinated UEs, the dominant UEs grouping algorithm is proposed only using long-term CSI in each proportional fairness (PF) scheduling decision. The so-called "dominant" refers to a subgroup of UEs as potential partners with higher probability to coordinate. In the proposed algorithm, each UE only needs to report a single value of large-scale fading (denoted as PL). At the central unit, use weighted PL instead of the resultant throughput to prioritize UEs, set the threshold according to the variance of all UEs' PL, and employ a simple scalar-wise metric for grouping. Performance evaluation shows that in contrast to the conventional methods, the proposed algorithm with twopart scheduling strategy can reduce about 20% of the feedback overhead and improve the UEs fairness. This proposal is more efficient for high-density hotspot areas where a large number of UEs exist to join the coordinated transmission.
Finding the Secret of CNN Parameter Layout under Strict Size Constraint

Although deep convolutional neural networks (CNNs) have significantly boosted the performance of many computer vision tasks, their complexities~(the size or the number of parameters) are also dramatically increased even with slight performance improvement. However, the larger network leads to more computation requirements, which are unfavorable to resource-constrained scenarios, such as the widely used embedded systems. In this paper, we tentatively explore the essential effect of CNN parameter layout, ıe, the allocation of parameters in the convolution layers, on the discriminative capability of CNN. Instead of enlarging the breadth or depth of networks, we attempt to improve the discriminative ability of CNN by changing its parameter layout under strict size constraint. Toward this end, a novel energy function is proposed to represent the CNN parameter layout, which makes it possible to model the relationship between the allocation of parameters in the convolution layers and the discriminative ability of CNN. According to extensive experimental results with plain CNN models and Residual Nets, we find that the higher the energy of a specific CNN parameter layout is, the better its discriminative ability is. Following this finding, we propose a novel approach to learn the better parameter layout. Experimental results on two public image classification datasets show that the CNN models with the learned parameter layouts achieve the better image classification results under strict size constraint.