Miracle-dz/UniFAR-FTUs huggingface dataset:
Creating a foundation for implementing an electronic health records (EHR)-integrated Social Knowledge Networking (SKN) system on medication reconciliation.

Background In fall 2016, Augusta University received a two-year grant from AHRQ, to implement a Social Knowledge Networking (SKN) system for enabling its health system, AU-Health, to progress from "limited use" of EHR Medication Reconciliation (MedRec) Technology, to "meaningful use. " Phase 1 sought to identify a comprehensive set of issues related to EHR MedRec encountered by practitioners at AU-Health. These efforts helped develop a Reporting Tool, which, along with a Discussion Tool, was incorporated into the AU-Health EHR, at the end of Phase 1. Phase 2 (currently underway), comprises a 52-week pilot of the EHR-integrated SKN system in outpatient and inpatient medicine units. The purpose of this paper is to describe the methods and results of Phase 1. Methods Phase 1 utilized an exploratory mixed-method approach, involving two rounds of data collection. This included 15 individual interviews followed by a survey of 200 practitioners, i.e., physicians, nurses, and pharmacists, based in the outpatient and inpatient medicine service at AU Health. Results Thematic analysis of interviews identified 55 issue-items related to EHR MedRec under 9 issue-categories. The survey sought practitioners' importance-rating of all issue-items identified from interviews. A total of 127 (63%) survey responses were received. Factor analysis served to validate the following 6 of the 9 issue-categories, all of which, were rated "Important" or higher (on average), by over 70% of all respondents: 1) Care-Coordination (CCI); 2) Patient-Education (PEI); 3) Ownership-and-Accountability (OAI); 4) Processes-of-Care (PCI); 5) IT-Related (ITRI); and 6) Workforce-Training (WTI). Significance-testing of importance-rating by professional affiliation revealed no statistically significant differences for CCI and PEI; and some statistically significant differences for OAI, PCI, ITRI, and WTI. Conclusion There were two key gleanings from the issues related to EHR MedRec unearthed by this study: 1) there was an absence of shared understanding among practitioners, of the value of EHR MedRec in promoting patient safety, which contributed to workarounds, and suboptimal use of the EHR MedRec system; and 2) there was a socio-technical dimension to many of the issues, creating an added layer of complexity. These gleanings in turn, provide insights into best practices for managing both clinical transitions-of-care in the EHR MedRec process; and socio-technical challenges encountered in EHR MedRec implementation.
Advanced routing on AODV using link prediction in Mobile Ad-Hoc Network

A main advantage of Mobile Ad-hoc Network(MANET) is providing mobility to the users. The erratic mobility of the nodes may cause route failures very continuously. The continuous routes square measures to interrupt cause Quality of Service have an effect on within the mobile wireless network and therefore bearing the challenge. In this paper, we've got planned a way of best signal strength primarily based link prediction utilized in AODV routing and finding the shortest path and searches for all possible paths of source to destination, and the path having best signal strength will be considered as best path to routing. Since the routing is performed through the path having best signal strength so Packet Delivery Ratio (PDR) in this case will drastically increase. As we know very well that the major concern in communication network are to deliver packets of minimum losses. So minimization in losses will drastically improve the Quality of Service (QoS). Link failure occurs due to mobility of nodes which are pure probabilistic in nature. Therefore, it is very difficult to route a packet in such a way to minimize the link failures because when we route a packet then our prime objective is to minimize packet losses in the network. That's why no research is interested in routing a packet in such a way of minimize the link failures. Then our prime objective is to minimize packet losses in the network. That's why no research is interested in routing a packet in such a way of minimize the link failures.
Thermal-Depth Fusion for Occluded Body Skeletal Posture Estimation

Reliable occluded skeletal posture estimation is a fundamentally challenging problem for vision-based monitoring techniques. This is due to several imaging related challenges introduced by existing depth-based pose estimation techniques that fail to provide accurate joint position estimates when the line of sight between the imaging device and the patient is obscured by an occluding material. In this work, we present a new method of estimating skeletal posture in occluded applications using both depth and thermal imaging through volumetric modeling and introduce a new occluded ground-truth tracking method inspired by modern motion capture solutions. Using this integrated volumetric model, we utilize Convolutional Neural Networks to characterize and identify volumetric thermal distributions that match trained skeletal posture estimates which includes disconnected skeletal definitions and allows correct posture estimation in highly ambiguous cases. We demonstrate this approach by correctly identifying common sleep postures that present challenging cases for current skeletal joint estimations, obtaining an average classification accuracy of ~94.45%.
Exploring non-volatile main memory architectures for handheld devices

As additional functionality is being added to contemporary handheld devices, the SoCs inside these devices are becoming increasingly complex. Similarly, the applications executing on these handhelds are beginning to exhibit an ever increasing memory footprint. To support these trends, main memory capacity of these SoCs has been increasing over time. Due to these developments, memory system's contribution to the overall system power has increased dramatically. Non-volatile memories have been used in server architectures to increase capacity as well as keep memory system's power consumption in check. However, in the handheld domain, where user experience and battery life are of paramount importance, the applicability of such technologies has not been widely studied. In this paper, we propose and evaluate a number of hybrid memory architectures using mobile DRAM and PCM. We show that intelligent memory architectures, cognizant of workload's memory access patterns can provide significant energy savings without compromising on user experience. Using proposed approach, we can devise architectures that exhibit significant energy savings with only a 2.8% performance loss.
Narrowband RFI Suppression for SAR System via Fast Implementation of Joint Sparsity and Low-Rank Property

The synthetic aperture radar (SAR), as a wideband radar system, operates over a large frequency band ranging from the low very high frequency to millimeter waves. It often overlaps in frequency with other radio-frequency systems including radio, television, and cellular networks. Therefore, radio-frequency interference (RFI) suppression is the severe test for SAR systems. Recently, some methods are proposed to suppress the RFI based on the sparse recovery in range-frequency domain and low-rank extraction in the azimuth dimension. However, all the previous methods exploit one property of the RFI, which may leave the room for performance improvement. Hence, in this paper, we propose three methods to jointly exploit the sparsity and low-rank property of RFI. We first include the sparse term and low-rank term of the RFI in the objective function to separate the RFI and the useful signal, which is defined as the joint sparsity and low-rank property method. It has better performance but heavier computational burden than the algorithm employing only the low-rank property. Then, we use row-sparse (RS) concept in lieu of the two properties, since the narrowband RFI has relatively stable frequencies during the synthetic aperture time. The RS method avoids the low-rank optimization and dramatically decrease the computational burden. Also for the real radar system, the sampling frequency is commonly larger than the frequency bandwidth. Therefore, there are some redundant data in the received signal. We exploit both the downsampling operation and the RS concept to speed up the convergence, which is called the fast row-sparse (FRS) method. The FRS method can further eliminate the out-of-mainband RFI. The real SAR data are provided to demonstrate the effectiveness of the three proposed methods.
Longitudinal patient-oriented outcomes in neuropathy

Objective: To evaluate longitudinal patient-oriented outcomes in peripheral neuropathy over a 14-year time period including time before and after diagnosis. Methods: The 1996–2007 Health and Retirement Study (HRS)–Medicare Claims linked database identified incident peripheral neuropathy cases (ICD-9 codes) in patients ≥65 years. Using detailed demographic information from the HRS and Medicare claims, a propensity score method identified a matched control group without neuropathy. Patient-oriented outcomes, with an emphasis on self-reported falls, pain, and self-rated health (HRS interview), were determined before and after neuropathy diagnosis. Generalized estimating equations were used to assess differences in longitudinal outcomes between cases and controls. Results: We identified 953 peripheral neuropathy cases and 953 propensity-matched controls. The mean (SD) age was 77.4 (6.7) years for cases, 76.9 (6.6) years for controls, and 42.1% had diabetes. Differences were detected in falls 3.0 years before neuropathy diagnosis (case vs control; 32% vs 25%, p = 0.008), 5.0 years for pain (36% vs 27%, p = 0.002), and 5.0 years for good to excellent self-rated health (61% vs 74%, p < 0.0001). Over time, the proportion of fallers increased more rapidly in neuropathy cases compared to controls (p = 0.002), but no differences in pain (p = 0.08) or self-rated health (p = 0.9) were observed. Conclusions: In older persons, differences in falls, pain, and self-rated health can be detected 3–5 years prior to peripheral neuropathy diagnosis, but only falls deteriorates more rapidly over time in neuropathy cases compared to controls. Interventions to improve early peripheral neuropathy detection are needed, and future clinical trials should incorporate falls as a key patient-oriented outcome.
Different Effects of p52SHC1 and p52SHC3 on the Cell Cycle of Neurons and Neural Stem Cells

SHC3 is exclusively expressed in postmitotic neurons, while SHC1 is found in neural stem cells and neural precursor cells but absent in mature neurons. In this study, we discovered that suppression of p52SHC1 expression by RNA interference resulted in proliferation defects in neural stem cells, along with significantly reduced protein levels of cyclin E and cyclin A. At the same time, p52SHC3 RNAi caused cell cycle re‐entry (9.54% in S phase and 5.70% in G2‐M phase) in primary neurons with significantly up‐regulated expression of cyclin D1, cyclin E, cyclin A, CDK2, and phosphorylated CDK2. When p52SHC3 was overexpressed, the cell cycle of neural stem cells was arrested with reduced protein levels of cyclin D1, cyclin E, and cyclin A, while overexpression of p52SHC1 did not result in significant changes in postmitotic neurons. Our results indicate that p52SHC3 plays an important role in maintaining the mitotic quiescence of neurons, while p52SHC1 regulates the proliferation of neural stem cells. J. Cell. Physiol. 230: 172–180, 2016. © 2015 Wiley Periodicals, Inc.
Hidden Markov modelling of intra-snore episode behavior of acoustic characteristics of obstructive sleep apnea patients

Obstructive sleep apnea (OSA) is a breathing disorder that can cause serious medical consequences. It is caused by full (apnea) or partial (hypopnea) obstructions of the upper airway during sleep. The gold standard for diagnosis of OSA is the polysomnography (PSG). The main measure for OSA diagnosis is the apnea-hypopnea index (AHI). However, the AHI is a time averaged summary measure of vast amounts of information gathered in an overnight PSG study. It cannot capture the dynamic characteristics associated with apnea/hypopnea events and their overnight distribution. The dynamic characteristics of apnea/hypopnea events are affected by the structural and functional characteristics of the upper airway. The upper airway characteristics also affect the upper airway collapsibility. These effects are manifested in snoring sounds generated from the vibrations of upper airway structures which are then modified by the upper airway geometric and physical characteristics. Hence, it is highly likely that the acoustical behavior of snoring is affected by the upper airway structural and functional characteristics. In the current work, we propose a novel method to model the intra-snore episode behavior of the acoustic characteristics of snoring sounds which can indirectly describe the instantaneous and temporal dynamics of the upper airway. We model the intra-snore episode acoustical behavior by using hidden Markov models (HMMs) with Mel frequency cepstral coefficients. Assuming significant differences in the anatomical and physiological upper airway configurations between low-AHI and high-AHI subjects, we defined different snorer groups with respect to AHI thresholds 15 and 30 and also developed HMM-based classifiers to classify snore episodes into those groups. We also define a measure called instantaneous apneaness score (IAS) in terms of the log-likelihoods produced by respective HMMs. IAS indicates the degree of class membership of each episode to one of the predefined groups as well as the instantaneous OSA severity. We then assigned each patient to an overall AHI band based on the majority vote of each episode of snoring. The proposed method has a diagnostic sensitivity and specificity between 87–91%.
Development and validation of a brief version of the Stigmatizing Situations Inventory

The Stigmatizing Situations Inventory (SSI) is one of the most commonly used measures of weight stigma experiences but may be impractical for some researchers because of its length (50 items). The present report describes the development and validation of a brief version of the SSI that could be used as a more efficient tool for assessing experiences with weight stigma.
Improving After Action Review (AAR) Practice

The After Action Review (AAR) is a process technique that uses a review of experience to avoid recurrent mistakes and reproduce success. Initially developed by the United States Army, many organizations have adopted the AAR; and military, governmental, industrial, and not-forprofit organizations have embraced and employ the process. The AAR has gained widespread acceptance among organizations whose personnel work in high-risk environments; those in which common human error can produce unacceptable consequences. Among those organizations, U.S. wildland fire agencies first began conducting AARs in the late 1990s, with the process entering this environment through an evolving leadership training curriculum. Today, a significant part of the wildland fire workforce now understands the purpose and intent of the AAR, and many fire units conduct some type of AAR process. However, anecdotal evidence suggests that effective AAR practice has not penetrated wildland fire operations as thoroughly as might be hoped, and too few resources are optimally conducting AARs. As a vehicle for capturing and learning from experience, the AAR provides an effective tool of continuous learning for the organization. It is within this organizational learning context that this paper suggests ways to improve AAR practice within wildland fire agencies, and advocates three strategic actions necessary to systematically and comprehensively use the AAR process in wildland fire agencies.
Potential Vaccine Targets against Rabbit Coccidiosis by Immunoproteomic Analysis

The aim of this study was to identify antigens for a vaccine or drug target to control rabbit coccidiosis. A combination of 2-dimensional electrophoresis, immunoblotting, and mass spectrometric analysis were used to identify novel antigens from the sporozoites of Eimeria stiedae. Protein spots were recognized by the sera of New Zealand rabbits infected artificially with E. stiedae. The proteins were characterized by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF/TOF-MS) analysis in combination with bioinformatics. Approximately 868 protein spots were detected by silver-staining, and a total of 41 immunoreactive protein spots were recognized by anti-E. stiedae sera. Finally, 23 protein spots were successfully identified. The proteins such as heat shock protein 70 and aspartyl protease may have potential as immunodiagnostic or vaccine antigens. The immunoreactive proteins were found to possess a wide range of biological functions. This study is the first to report the proteins recognized by sera of infected rabbits with E. stiedae, which might be helpful in identifying potential targets for vaccine development to control rabbit coccidiosis.
Development of nanofibrous membranes for enzymatic food processing

This work presents the design, preparation and characterization of a novel glucose electrochemical biosensor based on the immobilization of glucose oxidase (GOX) into a nylon nanofibrous membrane (NFM) prepared by electrospinning and functionalized with multiwalled carbon nanotubes (CNT). A disc of such GOX/CNT/NFM membrane (40 μm thickness) was used for coating the surface of a glassy carbon electrode. The resulting biosensor was characterized by cyclic voltammetry and chronoamperometry, with ferrocene methanol as mediator. The binding of GOX around the CNT/NFM greatly enhances the electron transfer, which results in a biosensor with a current five times higher than without CNT. The potential usefulness of the proposed biosensor was demonstrated with the analysis of glucose in commercial beverages and along the monitoring of the brewing process for making beer, from the mashing to the fermentation steps.
The prediction of multiaxial fatigue probabilistic stress–life curve by using fuzzy theory

Abstract The fuzziness of the traditional multiaxial fatigue prediction model is discussed and the fuzzy theory is applied into fatigue reliability analysis. The fuzzy linear regression analysis method is used to determine the fuzzy coefficients in the multiaxial stress–life equation under a small sample condition, and the corresponding multiaxial fatigue probabilistic stress–life curve is calculated with different confidence levels.
From gelatinous to muscle food chain: rock cod Patagonotothen ramsayi recycles coelenterate and tunicate resources on the Patagonian Shelf.

Stomach contents of 4808 fishes of 20 species caught in the eastern part of the Patagonian Shelf between 1999 and 2012 were analysed to assess dietary contributions of gelatinous plankton resources. Gelatinous plankton occurred in diets of seven species with two species, Patagonotothen ramsayi and Squalus acanthias, having >10% ctenophores in their diet. Consumption of gelatinous plankton was important in P. ramsayi and was strikingly seasonal, with maximum occurrence (up to 46% of non-empty stomachs) in late summer to autumn. Ctenophores were most abundant in P. ramsayi of 25-34 cm total length, L(T) whereas salps were more frequent in larger >35 cm L(T) individuals. In winter to spring, occurrence of gelatinous plankton in diets was minimal, reflecting their overall seasonal abundance in the ocean. The recent increase in abundance of P. ramsayi has enabled the species to recycle a significant proportion of the ecosystem production from gelatinous dead end to the main muscular food chain via seasonal reliance on ctenophores, jellyfish and tunicates. This additional influx of production that has been diverted from the gelatinous food chain favours the increase in abundance of several piscivorous top predators and affects the trophic web structure of the Patagonian Shelf ecosystem.
Evaluation of the VITOM in Digital High-Definition Video Exocolposcopy

Objective: Our aim was to present our initial clinical experience using a novel exoscopically based colposcopy system (VITOM) for the evaluation of cervical, vulvar, and vaginal diseases. Materials and Methods: Women referred to the Charite Cervix Center, Charite University, Berlin, Germany, were included. Patients with abnormal Pap smear results, vulvar lesions, or a biopsy report of neoplasia of the lower genital tract were included into the study. The VITOM was used for colposcopic evaluation and directed biopsies. Colposcopic findings were reported according to the criteria of the Committee on Nomenclature of the International Federation of Cervical Pathology and Colposcopy. Histologic diagnosis was described as normal, low-grade lesion, high-grade lesion (including cervical intraepithelial neoplasia 2,3, vulvar intraepithelial neoplasia 2,3, vaginal intraepithelial neoplasia 2,3), or cancer. Results: We recruited 76 patients (54 with cervical, 4 with vaginal, and 18 with vulvar disease) to the prospective study. Four patients were pregnant. Of patients with cervical disease, 29% had a history of previous conization and 3.7% had a history of trachelectomy. The sensitivity, specificity, negative predictive value, and positive predictive value of the VITOM for cervical intraepithelial neoplasia 2, 3 were 90%, 77%, 90% and 77%, respectively. Concordance of exocolposcopic impressions and histologic results was higher in high-grade lesions (K = 0.68, 95% CI = 0.32-0.87, p < .001) than in low-grade lesions (K = 0.41, 95% CI = 0.1-0.41, p < .05). Conclusions: Exocolposcopy with the VITOM is accurate and shows good correlation to histologic findings in high-grade disease of the lower genital tract. The potential advantages include patient and trainee involvement in examination, decision making, and documentation.
KDD Process Planning

KDD (Knowledge Discovery in Databases) process has become a new and important research area. Within the framework of KDD process and the GLS (Global Learning Scheme) system recently proposed by us, this paper concentrates on the issue of KDD process planning. In our method, the KDD process is modeled as an organized society of intelligent agents (called KDD agents), and planning is a meta-agent. We propose a formalism to describe KDD agents, in the style of OOER (Object Oriented Entity Relationship data model). Based on this representation of KDD agents as operators, we apply AI planning techniques to organize dynamically the KDD process so that the GLS system increases both autonomy and versatility.
Time-dependent, activity-based itinerary personal tour planning in multimodal transportation networks

ABSTRACT This article investigates personal, activity-based tour planning (TP) for tourists in public transportation networks. The objective is to propose an effective framework based on tour parameters, including starting days and times, duration, priority rates of cities, a list of activities and their duration, and a list of transportation means. The subsequent TP should consider the duration of trips to maximize the scores for visiting and acting in a given time frame. In this framework, two nested modules are of primary interest: first, in the main module, the order of the visited cities is determined using a genetic-based strategy; second, in the subordinate module, the shortest path in a multimodal network is obtained based on an adapted Dijkstra algorithm. Since TP is either personal or activity based, constraints of activity duration and periods of enduring activities are considered in the main framework. To evaluate the capabilities of the proposed framework, data are used from real transportation networks in 15 major cities in Iran. Then, several tour planners are employed in order to substantiate the performance of the proposed evolutionary framework. The resulting average relative error of the performed trials is 4.38%, and the simulation therefore vividly demonstrates that the proposed framework is suited for attaining optimum tour plans.