Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Health Behaviors and Personal Values of Childless Women, Pregnant Women and Women Who Recently Delivered

Preconception lifestyle modifications and reduction of several known risk factors may have an influence on future pregnancy outcomes. The aim of the study was to analyze health behaviors and personal values as well as to assess the relationship between these factors in women without children, in pregnant women and in women who had already delivered babies. The questionnaire survey included the Health Behavior Inventory (HBI), the Personal Value List (PVL) and sociodemographic data and was conducted in 538 women. These women were divided into three groups: women who had recently delivered (n = 235), pregnant women (n = 121) and childless women (n = 182). Pregnant women demonstrated a significantly higher level of declared health behaviors, and also, they rated higher on the subscales values “positive mental attitude” and “health practices”, in comparison to women who had recently delivered and to childless women. In all tested groups, the highest rated personal value was “a successful family life”, while the most appreciated symbol of happiness was “love and friendship”. Our results suggest that the system of values and the perception of happiness symbols may influence women’s health behaviors. Positioning “health” in the hierarchy of personal values as the most important one may facilitate the introduction of healthy behaviors. This, in turn, could reduce several adverse pregnancy outcomes that are potentially modifiable with changing preconception health attitudes. Our results also identify several unanswered questions and highlight areas where new research is needed.
Ionotropic Gelation: A Promising Cross Linking Technique for Hydrogels.

Microencapsulation is by far the most commonly exploited technique for the generation of multipartuculate drug delivery system. But serious drawbacks of this technique like non-uniform coating, non-reproducible release kinetics and more importantly, the use of more or less harsh conditions in the formulation process limits the encapsulation of the many substances such as proteins, enzymes and live cells etc. Besides this, the regulatory authorities, such as U.S.FDA are restricting to greater degrees the amount of additional components allowed such as organic solvents. The solution of this problem is presented by a technique, which involved neither the use of harsh chemicals nor elevated temperature and based on principle of ionotropic gelation (polyelectrolyte complexation). This review emphasizes the importance of ionotropic gelation and its cross linking potential to formulate hydrogel beads using different natural polymers.
Adequacy considerations in concentrated solar thermal integrated electric power system

Production of electric power utilizing fossil fuels results in environmentally hazardous emissions and a reduction of fossil fuel reserves. The high variation in fuel prices is another area of concern when discussing sustainable energy use that considers future requirements. Concentrated Solar Power (CSP) is an emerging technology in the renewable energy field and is a promising addition to electric power systems. The output power of CSP is highly variable due to the intermittent nature of solar energy resources and the availability of CSP components. A probabilistic model of CSP has been developed to determine the effects of variation of direct solar irradiation and air temperature on power systems reliability. The model is applied to the Roy Billinton Test system (RBTS) to evaluate the reliability contribution of CSP. The impact of a range of factors, such as the system load, installed CSP capacity, and installation site parameters, on the capacity credit and effective load carrying capability of CSP are evaluated and presented in this paper.
Significance of Notch and Wnt signaling for chemoresistance of colorectal cancer cells HCT116

5‐fluorouracil (5‐FU) and oxaliplatin (OxaPt) are the main chemotherapeutics for colorectal cancer (CRC). Chemotherapy response rates for advanced CRC remain low, primarily due to intrinsic or acquired chemoresistance. The importance of Notch and Wnt signaling for carcinogenesis of CRC as well as crosstalk of Notch and Wnt signaling with many oncogenic signaling pathways suggest that Notch and Wnt pathways could be responsible for chemoresistance. In this study, we compared changes in Notch and Wnt signaling after 5‐FU and OxaPt treatment in CRC cells HCT116 and its chemoresistant sublines HCT116/FU and HCT116/OXA. The levels of Notch1 receptor intracellular domain NICD1 and non‐phosphorylated β‐catenin, the reporters of Notch and Wnt signaling, were upregulated in untreated chemoresistant HCT116/FU and HCT116/OXA cells. Our data suggest that Notch inhibitor RO4929097 (RO) and Wnt inhibitor XAV939 (XAV) enhance the survival potential of OxaPt‐treated cells. The protein level of Notch target gene HES1 was significantly upregulated in chemoresistant HCT116/FU and HCT116/OXA cells, compared to HCT116. HES1 silencing increased viability of HCT116 and its chemoresistant sublines after 5‐FU or OxaPt treatment. The results of HES1 downregulation coincide with RO and XAV effects on cell viability of OxaPt‐treated cells.
EquiMoves: A Wireless Networked Inertial Measurement System for Objective Examination of Horse Gait

In this paper, we describe and validate the EquiMoves system, which aims to support equine veterinarians in assessing lameness and gait performance in horses. The system works by capturing horse motion from up to eight synchronized wireless inertial measurement units. It can be used in various equine gait modes, and analyzes both upper-body and limb movements. The validation against an optical motion capture system is based on a Bland–Altman analysis that illustrates the agreement between the two systems. The sagittal kinematic results (protraction, retraction, and sagittal range of motion) show limits of agreement of ±2.3 degrees and an absolute bias of 0.3 degrees in the worst case. The coronal kinematic results (adduction, abduction, and coronal range of motion) show limits of agreement of −8.8 and 8.1 degrees, and an absolute bias of 0.4 degrees in the worst case. The worse coronal kinematic results are most likely caused by the optical system setup (depth perception difficulty and suboptimal marker placement). The upper-body symmetry results show no significant bias in the agreement between the two systems; in most cases, the agreement is within ±5 mm. On a trial-level basis, the limits of agreement for withers and sacrum are within ±2 mm, meaning that the system can properly quantify motion asymmetry. Overall, the bias for all symmetry-related results is less than 1 mm, which is important for reproducibility and further comparison to other systems.
User Credential Cloning Attacks in Android Applications: Exploiting Automatic Login on Android Apps and Mitigating Strategies

Automatic login is a commonly used feature of smartphones, because their small keyboards make it difficult to key in user credential information. However, this feature may pose a serious risk to smartphone users? privacy. The stored data for automatic login could be stolen by an attacker, resulting in identity theft. In this article, we demonstrate an execution of this attack in a systematic manner through two real-world Android application case studies by implementing a prototype. We also discuss five possible defense strategies to mitigate the risk of user credential data being stolen from the application files.
A brain-computer interface for long-term independent home use

Abstract Our objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% (r = −.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this first user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute significantly to quality of life and productivity.
A Novel Equivalent Baseband Channel of Hybrid Beamforming in Massive Multiuser MIMO Systems

The conventional full radio frequency (RF) configuration appears to be unfeasible in massive multiple-input-multiple-output (MIMO) systems due to the still-exorbitant energy consumption of RF chains at millimeter wave frequencies. In this letter, we address this challenge by proposing a novel two step hybrid beamforming scheme for the downlink massive multiuser MIMO systems. Our purpose is to maximize the overall spectral efficiency with limited RF chains. In the first step, we aim to find an equivalent baseband channel by maximizing its capacity through the analog RF processing. In the second step, the inter-user interference is eliminated by the block diagonalization (BD) technique. Simulation results demonstrate that the proposed scheme can achieve a higher capacity than the traditional BD scheme with much fewer number of RF chains.
A Deep Convolutional Neural Network for Location Recognition and Geometry based Information

: In this paper we propose a new approach to Deep Neural Networks (DNNs) based on the particular needs of navigation tasks. To investigate these needs we created a labeled image dataset of a test environment and we compare classical computer vision approaches with the state of the art in image classiﬁcation. Based on these results we have developed a new DNN architecture that outperforms previous architectures in recognizing locations, relying on the geometrical features of the images. In particular we show the negative effects of scale, rotation, and position invariance properties of the current state of the art DNNs on the task. We ﬁnally show the results of our proposed architecture that preserves the geometrical properties. Our experiments show that our method outperforms the state of the art image classiﬁcation networks in recognizing locations.
A Channel State Information Feedback and Prediction Scheme for Time-Varying Underwater Acoustic Channels

We consider a Channel State Information feedback and prediction scheme for time-varying underwater acoustic channels.Given the time-variant of underwater channels,it is desirable to set a feedback and prediction scheme to address the problem.With a goal of reaching the actual delayed signal-to-noise ratio(SNR) and replacing it by the estimated channel SNR,Holt-Winters (HW)prediction algorithm is developed to estimate the channel SNR.The proposed algorithm are evaluated in simulated channels.Simulation results demonstrate that the proposed algorithms can be used as a suitable method for CSI prediction.
Flexible, Stretchable Skin Sensors for Two-Dimensional Position Tracking in Medical Simulators

We introduce a flexible smart skin sensor that can conform to the surface of a mannequin, prosthetic, or medical device. It senses contact and localizes contact position continuously over its entire surface. This can allow, for example, medical mannequin simulators to assess accurate placement of fingers or tools during procedures such as localizing anatomical landmarks or correctly localizing breast tumors during exams. Prior art has also considered smart skins: flexible materials embedded with force or position sensors, which can be molded to different geometries. Several attempts at smart skins have utilized arrays of rigid sensors embedded in flexible media. Typical approaches use piezoresistive sensors [1] or capacitive force sensors [2]. Rogers et al. implement novel microfabrication techniques in silicones (e.g., Ref. [3]) for stretchable circuits. These approaches have one or more drawbacks. Most are cost prohibitive for medical training applications. All use arrays of discrete sensing elements, limiting data collection to finite locations separated by dead zones. The sensing elements are rigid, creating artifacts in the material properties of the simulated tissue. Even as sensors get smaller, sensing is limited to discrete positions in the 2D plane. We exploit a novel carbon nanotube (CNT)-doped polydimethylsiloxane (PDMS) elastomer sheet to overcome such drawbacks and implement a 2D potentiometer similar to the work initially proposed by Walz et al. as a low-cost surgical tissue simulation tool [4]. We document accurate mapping between sensor voltages and position via a polynomial surface fit.
A Performance Analysis Model of TCP over Multiple Heterogeneous Paths for 5G Mobile Services

Driven by the primary requirement of emerging 5G mobile services, the demand for concurrent multipath transfer (CMT) is still prominent. Yet, multipath transport protocols are not widely adopted and TCP-based CMT schemes will still be in dominant position in 5G. However, the performance of TCP flow transferred over multiple heterogeneous paths is prone to the link quality asymmetry, the extent of which was revealed to be significant by our field investigation. In this paper, we present a performance analysis model for TCP over multiple heterogeneous paths in 5G scenarios, where both bandwidth and delay asymmetry are taken into consideration. The evaluation adopting parameters from field investigation shows that the proposed model can achieve high accuracy in practical environments. Some interesting inferences can be drawn from the proposed model, such as the dominant factor that affect the performance of TCP over heterogeneous networks, and the criteria of determining the appropriate number of links to be used under different circumstances of path heterogeneity. Thus, the proposed model can provide a guidance to the design of TCP-based CMT solutions for 5G mobile services.
Analysis of the Game-Theoretic Modeling of Backscatter Wireless Sensor Networks under Smart Interference

In this paper, we study an interference avoidance scenario in the presence of a smart interferer which can rapidly observe the transmit power of a backscatter wireless sensor network (WSN) and effectively interrupt backscatter signals. We consider a power control with a sub-channel allocation to avoid interference attacks and a time-switching ratio for backscattering and RF energy harvesting in backscatter WSNs. We formulate the problem based on a Stackelberg game theory and compute the optimal transmit power, time-switching ratio, and sub-channel allocation parameter to maximize a utility function against the smart interference. We propose two algorithms for the utility maximization using Lagrangian dual decomposition for the backscatter WSN and the smart interference to prove the existence of the Stackelberg equilibrium. Numerical results show that the proposed algorithms effectively maximize the utility, compared to that of the algorithm based on the Nash game, so as to overcome smart interference in backscatter communications.
Eco-evolutionary dynamics, density-dependent dispersal and collective behaviour: implications for salmon metapopulation robustness

The spatial dispersal of individuals plays an important role in the dynamics of populations, and is central to metapopulation theory. Dispersal provides connections within metapopulations, promoting demographic and evolutionary rescue, but may also introduce maladapted individuals, potentially lowering the fitness of recipient populations through introgression of heritable traits. To explore this dual nature of dispersal, we modify a well-established eco-evolutionary model of two locally adapted populations and their associated mean trait values, to examine recruiting salmon populations that are connected by density-dependent dispersal, consistent with collective migratory behaviour that promotes navigation. When the strength of collective behaviour is weak such that straying is effectively constant, we show that a low level of straying is associated with the highest gains in metapopulation robustness and that high straying serves to erode robustness. Moreover, we find that as the strength of collective behaviour increases, metapopulation robustness is enhanced, but this relationship depends on the rate at which individuals stray. Specifically, strong collective behaviour increases the presence of hidden low-density basins of attraction, which may serve to trap disturbed populations, and this is exacerbated by increased habitat heterogeneity. Taken as a whole, our findings suggest that density-dependent straying and collective migratory behaviour may help metapopulations, such as in salmon, thrive in dynamic landscapes. Given the pervasive eco-evolutionary impacts of dispersal on metapopulations, these findings have important ramifications for the conservation of salmon metapopulations facing both natural and anthropogenic contemporary disturbances. This article is part of the theme issue ‘Collective movement ecology’.
Computing a Multi-location Aircraft Fleet Mix

The Canadian Armed Forces periodically examines aircraft, ship or ground vehicle fleets to determine if they need to reduce, keep the same or increase the number of platforms in their fleets. We adapt previous work on fleet estimation and multi-objective optimization to compute a Pareto-optimal set of fleets at multiple locations, taking into account mission scheduling. We apply our model, which uses a genetic algorithm based on NSGA-II, to a sample of notional scenarios to demonstrate the effectiveness of the approach.
Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.
Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning

ABSTRACT Multivariate functional data from a complex system are naturally high-dimensional and have a complex cross-correlation structure. The complexity of data structure can be observed as that (1) some functions are strongly correlated with similar features, while some others may have almost no cross-correlations with quite diverse features; and (2) the cross-correlation structure may also change over time due to the system evolution. With this regard, this article presents a dynamic subspace learning method for multivariate functional data modeling. In particular, we consider that different functions come from different subspaces, and only functions of the same subspace have cross-correlations with each other. The subspaces can be automatically formulated and learned by reformatting the problem as a sparse regression. By allowing but regularizing the regression change over time, we can describe the cross-correlation dynamics. The model can be efficiently estimated by the fast iterative shrinkage-thresholding algorithm, and the features of each subspace can be extracted using the smooth multi-channel functional principal component analysis. Some theoretical properties of the model are presented. Numerical studies, together with case studies, demonstrate the efficiency and applicability of the proposed methodology.
From the International Pediatric Sepsis Conference 2005 to the Sepsis-3 Consensus

In the last decades, sepsis, due to an immune response, has been defined as a potentially fatal organ dysfunction resulting from a dysregulated organism response to an infectious insult, in which proand anti-inflammatory responses can coexist in the early phase of the disease and which, together with nonimmunological mechanisms, decisively influence prognosis and evolution. (1-4) However, sepsis has complex pathophysiology and a varied and nonspecific clinical presentation, affecting heterogeneous groups of people; therefore, a simple and objective definition is not easy. (2,5) Despite all efforts, severe sepsis and septic shock remain major causes of death in children, especially in developing and underdeveloped countries. (6-11) In the SPROUT (Sepsis Prevalence, Outcomes, and Therapies) study in 2015, involving 7,000 children admitted to 128 pediatric intensive care units (ICUs) from 26 countries, the prevalence of severe sepsis was 8.2%, with wide variation between the continents: 6.2% in Europe and 23.1% in Africa (p < 0.001). (12) In-hospital mortality due to severe sepsis was 25%, i.e., twice as high as the mortality rates reported in other studies. (7,11-13) In another analysis of this database, there was a weak agreement (43%) between the clinical diagnosis of severe sepsis performed by the attending physician and the diagnostic criteria defined by consensus. (14) The clinical diagnosis was performed in a more liberal way, with lower laboratory confirmation, presenting lower mortality and lower multiple organ failure than the group with severe sepsis defined by the 2005 consensus. (14,15) This same discordance was described in adult patients with sepsis; however, interestingly, the divergence did not influence treatment and decision making. (16) It is imperative for sepsis and its various stages to be defined in a precise way and for these definitions to be applied in both daily care practice and the evaluation of clinical studies. (17,18) Pediatric age-specific definitions were only established in 2005 at the International Pediatric Sepsis Consensus Conference (IPSCC)(13) and were based on various existing pediatric definitions, scores on organ dysfunction, and concepts of systemic inflammatory response syndrome (SIRS) and sepsis for the adult population. The task-force stated that the definitions represented an “instrument under construction” that still needed further refinement and improvement. Although not designed to improve the early diagnosis of bedside disease and to allow immediate therapeutic intervention, the definitions of sepsis proposed by the IPSCC have been used in daily clinical practice in pediatric Daniela Carla de Souza1,2, Marcelo Barciela Brandão3,4, Jefferson Pedro Piva5,6