Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of selected factors on the immunogenicity of preventive vaccinations against hepatitis A, B and influenza in solid organ transplant recipients undergoing immunosuppressive therapy – a review

ABSTRACT Introduction Immunization is the most effective form of the primary prevention of infectious diseases. Knowledge on the efficacy and immunogenicity of vaccinations in the group of organ transplant patients taking chronic immunosuppressive treatment remains incomplete. Areas covered The aim of this paper was to analyze factors influencing the post-vaccination response in patients undergoing chronic immunosuppressive therapy based on a literature review. Only publications that evaluated the immunogenicity of influenza, HAV and HBV vaccinations in patients on immunosuppressive therapy were reviewed. Expert opinion The following methods are used to potentially increase the immunogenicity of vaccinations against HAV and HBV amongst post-transplantation patients: increasing the number of doses, increasing dose volumes, the method of administering as well as the addition of adjuvant. Immunogenicity is also impacted by the immunosuppression mechanism. Overall, vaccination has been concluded to be safe for post-transplantation patients and adverse events following immunization (AEFI) have typically been rated as mild or moderate. The instances of transplant rejections as observable in the long term have not been related to administered vaccinations. The data shows certain correlations of some factors with increased immunogenicity, however it is necessary to repeat the studies on a more representative group of patients.
The study on object recognition technology based on multispectral polarization

In the field of target detection, the technology which can identify the object from the complex background is of great significance to the development of ecology. In this paper, based on the spectral characteristics of the object reflection information, polarization and spectrum are combined to study the recognition algorithm of different objects under different background conditions. According to the polarization spectrum information of the object, an algorithm for estimating the refractive index of the object in multispectral polarization images is presented. With multispectral polarization experimental system, combined with multiwavelength information emitted by different objects, a set of algorithm suitable for multi-wavelength estimation of refractive index of objects was compiled by introducing Cauchy dispersion equation reasonably, so as to realize the identification of different object. The experimental results show that the color images can be obtained by fusing the gray images with chromatic dispersion coefficients, which represent the material properties of objects, and various objects can be recognized easily and intuitively from different colors; According to the algorithm, the refractive index of the metal material used in the experiment is complex refractive index, and the refractive index of the oil is maintained between 1.3 and 1.5 at a certain wavelength, so that the object recognition is realized.
A Methodological study to develop a ‘Nursing Checklist’ for receiving patients in ICUs

The current study was carried out to develop a 'Nursing Checklist' for receiving patients in ICUs of a tertiary care hospital.Using methodological study design, the study was carried out in total five phases. First, a blue print of the checklist was prepared after identifying the needs and an exhaustive review literature. In 2nd phase, face and content validity of the nursing checklist was established with Delphi technique. Feasibility of the checklist was checked by conducting a pilot study in 3rd phase. In 4th phase, Inter-rater agreement between two raters was checked with Cohen's Kappa. Then checklist was implemented by 30 subjects in ICUs of a tertiary care hospital and internal consistency reliability of the items was established with Cronbach's alpha in 5th phase. The data was analyzed by using SPSS version 16. Final draft of the Nursing checklist had 12 main steps with 105 items. The checklist was found feasible after conducting pilot study. The Nursing checklist was homogeneously reliable with a very good inter-rater agreement between raters. Cohen's Kappa value was 0.98-1.00. The checklist was found internally consistent. Cronbach's alpha value was 0.97. Results of the nursing checklist indicate that it is valid and reliable and it can be applied in daily practice and in the clinical research context. Abstract:
Textile Defect Classification Based on Convolutional Neural Network and SVM

Textile defect detection and classification is an important part of the textile production process, however, to detect accurately and efficiently is still difficult. In this study, we present an effective formulation for textile defect detection. Unlike traditional textile detecting methods, a conventional neural network (CNN) support vector machine (SVM) is designed to extract the depth features of textile images and to classify defects. The effectiveness of textile feature extraction is improved by optimizing Alexnet to obtain a CNN with better feature extraction and less computation. Textile defect classification is more effective using SVM instead of Softmax. Experimental results demonstrated that the improved Alexnet-SVM gave 99% accuracy in defect classification using the TILDA database. This accuracy was 5% greater than Alexnet and GoogleNet.
The effect of guanxi on organizational commitment via harming behavior: The moderated mediation role of envy

The purpose of this study is to examine the mediation role of harming behavior (HB) in the relationship of guanxi and organizational commitment (OC) by using a moderated mediation model. In addition, this study explores whether malicious envy (ME) can moderate the relationship of OC and guanxi when mediated by HB. Data were collected from 215 employees who had experience working in an organization in the United States. An integrated mediation and moderation model was evaluated. The findings contribute to understanding of the effects of guanxi on OC. Although guanxi relationships between supervisors and subordinates directly promote OC, the high level of ME in response to guanxi relationships among coworkers in the workplace leads to a high level of HBs, reducing OC.
Needs, Burden, Coping and Quality of Life in Stroke Caregivers A Pilot Survey

A Cross sectional community based pilot study was conducted to assess the needs, burden, coping and health related quality of life in the caregivers of survivors of stroke. Using Purposive sampling techniques, 22 caregivers of survivors of stroke were interviewed. The various tools used were;semi-structured Family Need Questionnaire (FNQ), Zarit Burden Interview Schedule (ZBIS), Coping Checklist (CCL) and World Health Organization Quality of Life-BREF (WHOQoL-BREF) interview Schedule.Results shows that Health Information (HI) related needs were most important needs followed by seeking need of Professional Support (PS), and need of having a Community Support Network (CSN). Caregivers reported higher level of burden in financial area followed by burden in term of disturbed relationship. Acceptance was most frequently used coping strategies reported by caregivers followed by problem solving and distraction positive. Study reported good quality of social life in caregivers.A negative correlation coefficient was reported between quality of life and burden among caregivers. Though, small sample size was the major limitation of the study, yet it offers useful implications in planning nursing interventions to improve the quality of life of caregivers of stroke patients. Abstract : Nursing and Midwifery
Peer caring Behaviour among nursing students

: Caring is instrumental to professional nursing practice. Nurses are expected to integrate unconditional caring in their personal as well as professional conduct. While some individuals have this trait within inherently while others integrate it during years of formal education. The present study aimed to investigate peer caring behaviour among the nursing students. a cross sectional investigation using self administered questionnaire. Study sample included 306 nursing students. The ratings on each item of the peer caring behaviour scale and mean rating on each item is presented in results. Almost 65% of study participants had average levels of peer caring. Further, the peer caring was compared across various groups revealing statistically significant difference between groups. The present study highlights the need to capitalize on promotion of peer caring during years of training.
Wheels Know Why You Travel

Trip purpose - i.e., why people travel - is an important yet challenging research topic in travel behavior analysis. Generally, the key to this problem is understanding the activity semantics from trip contexts. However, most existing methods rely on passengers' sensitive information - e.g., daily travel log or home address from surveys - to achieve accurate results, and could thus be hardly applied in real-life scenarios. In this paper, we aim to predict the passenger's trip purpose in the scenarios of door-to-door ride services (e.g., taxi trips) by only using the vehicle's GPS trajectory on roads, for which "wheels" is used as a metaphor. Specifically, we propose a novel dual-attention graph embedding model based on the vehicle's trajectory and public POI check-in data. Firstly, both data are aggregated to augment the activity semantics of trip contexts, including the spatiotemporal context and POI contexts at the origin and destination, which are important clues. Based on that, graph attention networks and soft-attention are employed to model the dependency of different contexts on the trip purpose, so as to obtain the trip's comprehensive activity semantics for the final prediction. Extensive experiments are conducted based on the large-scale labeled datasets in Beijing. The prediction results show a considerable improvement compared to state-of-the-arts. A case study demonstrates the feasibility of our study.
Fake News Propagation: A Review of Epidemic Models, Datasets, and Insights

Fake news propagation is a complex phenomenon influenced by a multitude of factors whose identification and impact assessment is challenging. Although many models have been proposed in the literature, the one capturing all the properties of a real fake-news propagation phenomenon is inevitably still missing. Modern propagation models, mainly inspired by old epidemiological models, attempt to approximate the fake-news propagation phenomena by blending psychological factors, social relations, and user behavior. This work provides an in-depth analysis of the current state of fake-news propagation models supported by real-world datasets. We highlighted similarities and differences in the modeling approaches, wrapping up the main research trends. Propagation models, transitions, network topologies, and performance metrics have been identified and discussed in detail. The thorough analysis we provided in this article, coupled with the highlighted research hints, have a high potential to pave the way for future research in the area.
Layer by Layer, Patterned Valves Enable Programmable Soft Surfaces

Programmable surfaces, which can be instructed to alter their shape or texture, may one day serve as a platform for tangible interfaces and adaptive environments. But so far, these structures have been constrained in scale by a challenging fabrication process, as the numerous constituent actuators must be built and assembled individually. We look towards emerging trends in mechanical engineering and consider an alternate framework --- layer-driven design, which enables the production of dynamic, discretely-actuated surfaces at multiple scales. By centering the construction around patterning and stacking, forgoing individual assembly in favor of bulk processes such as photo-etching and laser cutting, we avoid the need for multiple manufacturing steps that are repeated for each of the many actuators that compose the surface. As an instance of this layer-driven model, we build an array of electrostatic valves, and use this composite material (which we refer to as Stoma-Board) to drive four types of pneumatic transducers. We also show how this technique may be readily industrialized, through integration with the highly mature and automated manufacturing processes of modern electronics.
Design and Evaluation of a Clippable and Personalizable Pneumatic-haptic Feedback Device for Breathing Guidance

To assist people in practicing mindful breathing and regulate their perceived workload while not disturbing the ongoing foreground task during daily routines, we developed a mobile and personalizable pneumatic-haptic feedback device that provides programmable subtle tactile feedback. The device consists of three soft inflatable actuators embedded with DIY stretchable sensors. We introduce their simple and cost-effective fabrication method. We conducted a technical and user-based evaluation of the device. The user-based evaluation focused on the personalization of the tactile feedback based on users' experience assessed during three pilot studies. Different personalization parameters have been tested, such as two tactile patterns, different levels of intensity and frequency. We collected the participants' self-reports and physiological data. Our results show that the device has the potential of a breathing guide under certain conditions. We provide the main findings and design insights from each study and suggest recommendations for developing an on-body personalizable pneumatic-haptic feedback interface.
Contextual Biases in Microinteraction Ecological Momentary Assessment (μEMA) Non-response

Ecological momentary assessment (EMA) is used to gather in-situ self-report on behaviors using mobile devices. Microinteraction EMA (μEMA), is a type of EMA where each survey is only one single question that can be answered with a glanceable microinteraction on a smartwatch. Prior work shows that even when μEMA interrupts far more frequently than smartphone-EMA, μEMA yields higher response rates with lower burden. We examined the contextual biases associated with non-response of μEMA prompts on a smartwatch. Based on prior work on EMA non-response and smartwatch use, we identified 10 potential contextual biases from three categories: temporal (time of the day, parts of waking day, day of the week, and days in study), device use (screen state, charging status, battery mode, and phone usage), and activity (wrist motion and location). We used data from a longitudinal study where 131 participants (Mean age 22.9 years, SD = 3.0) responded to μEMA surveys on a smartwatch for at least six months. Using mixed-effects logistic regression, we found that all temporal, activity/mobility, and device use variables had a statistically significant (p<0.001) association with momentary μEMA non-response. We discuss the implication of these results for future use of context-aware μEMA methodology.
Hogel-Free Holography

Holography is a promising avenue for high-quality displays without requiring bulky, complex optical systems. While recent work has demonstrated accurate hologram generation of 2D scenes, high-quality holographic projections of 3D scenes has been out of reach until now. Existing multiplane 3D holography approaches fail to model wavefronts in the presence of partial occlusion while holographic stereogram methods have to make a fundamental tradeoff between spatial and angular resolution. In addition, existing 3D holographic display methods rely on heuristic encoding of complex amplitude into phase-only pixels which results in holograms with severe artifacts. Fundamental limitations of the input representation, wavefront modeling, and optimization methods prohibit artifact-free 3D holographic projections in today’s displays. To lift these limitations, we introduce hogel-free holography which optimizes for true 3D holograms, supporting both depth- and view-dependent effects for the first time. Our approach overcomes the fundamental spatio-angular resolution tradeoff typical to stereogram approaches. Moreover, it avoids heuristic encoding schemes to achieve high image fidelity over a 3D volume. We validate that the proposed method achieves 10 dB PSNR improvement on simulated holographic reconstructions. We also validate our approach on an experimental prototype with accurate parallax and depth focus effects.
dSyncPS: delayed synchronization for dynamic deployment of distributed machine learning

The increasing demand of applying machine learning technologies in various domains has driven the involvement of complex machine learning models. To fulfill this demand, distributed machine learning has become the de facto standard computing paradigm for model training. Machine-Learning-as-a-Service (MLaaS) has also emerged as a solution provided by cloud service providers to address this need. With MLaaS, customers can submit their models and training datasets to the service providers, and leverage the existing cloud infrastructure for model training and inference. However, we find that, for end users who require complex and accurate machine learning models but only obtains moderate amount of data, existing solutions are insufficient. The main issue is the lack of support for dynamic deployment of distributed machine learning tasks. To address this issue, we propose a parameter server based framework, called dSyncPS, that allows worker nodes to participate in training dynamically. The key idea is that it separates parameter synchronization from aggregation function in the parameter server nodes, thus resulting in a delayed synchrony.
Recursive Sparse Representation for Identifying Multiple Concurrent Occupants Using Floor Vibration Sensing

In this paper, we present a multiple concurrent occupant identification approach through footstep-induced floor vibration sensing. Identification of human occupants is useful in a variety of indoor smart structure scenarios, with applications in building security, space allocation, and healthcare. Existing approaches leverage sensing modalities such as vision, acoustic, RF, and wearables, but are limited due to deployment constraints such as line-of-sight requirements, sensitivity to noise, dense sensor deployment, and requiring each walker to wear/carry a device. To overcome these restrictions, we use footstep-induced structural vibration sensing. Footstep-induced signals contain information about the occupants' unique gait characteristics, and propagate through the structural medium, which enables sparse and passive identification of indoor occupants. The primary research challenge is that multiple-person footstep-induced vibration responses are a mixture of structurally-codependent overlapping individual responses with unknown timing, spectral content, and mixing ratios. As such, it is difficult to determine which part of the signal corresponds to each occupant. We overcome this challenge through a recursive sparse representation approach based on cosine distance that identifies each occupant in a footstep event in the order that their signals are generated, reconstructs their portion of the signal, and removes it from the mixed response. By leveraging sparse representation, our approach can simultaneously identify and separate mixed/overlapping responses, and the use of the cosine distance error function reduces the influence of structural codependency on the multiple walkers' signals. In this way, we isolate and identify each of the multiple occupants' footstep responses. We evaluate our approach by conducting real-world walking experiments with three concurrent walkers and achieve an average F1 score for identifying all persons of 0.89 (1.3x baseline improvement), and with a 10-person "hybrid" dataset (simulated combination of single-walker real-world data), we identify 2, 3, and 4 concurrent walkers with a trace-level accuracy of 100%, 93%, and 73%, respectively, and observe as much as a 2.9x error reduction over a naive baseline approach.
Motion of asymmetric bodies in two-dimensional shear flow

Abstract At low Reynolds numbers, axisymmetric ellipsoidal particles immersed in a shear flow undergo periodic tumbling motions known as Jeffery orbits, with the orbit determined by the initial orientation. Understanding this motion is important for predicting the overall dynamics of a suspension. While slender fibres may follow Jeffery orbits, many such particles in nature are neither straight nor rigid. Recent work exploring the dynamics of curved or elastic fibres have found Jeffery-like behaviour along with chaotic orbits, decaying orbital constants and cross-streamline drift. Most work focuses on particles with reflectional symmetry; we instead consider the behaviour of a composite asymmetric slender body made of two straight rods, suspended in a two-dimensional shear flow, to understand the effects of the shape on the dynamics. We find that for certain geometries the particle does not rotate and undergoes persistent drift across streamlines, the magnitude of which is consistent with other previously identified forms of cross-streamline drift. For this class of particles, such geometry-driven cross-streamline motion may be important in giving rise to dispersion in channel flows, thereby potentially enhancing mixing.
Unsteady interaction of crossflow instability with a forward-facing step

Abstract Experiments have been conducted on a swept wing model in a low-turbulence wind tunnel at chord Reynolds number of $2.17 \times 10^{6}$ to investigate the unsteady interaction of a forward-facing step (FFS) with incoming stationary crossflow (CF) vortices. The impact of varying the FFS height on the development and growth of primary and secondary CF disturbances and the ensuing laminar–turbulent transition is quantified through detailed hot-wire anemometry and infrared thermography measurements. The presence of the FFS results in either a critical (i.e. moderate transition advancement) or a supercritical behaviour (i.e. transition advancing abruptly to the FFS location). The arrival of the forced stationary CF vortices at the step is accompanied by their amplification. Unsteady analysis for the critical cases indicates temporal velocity fluctuations following closely the development of the baseline configuration (i.e. agreeing with the development of secondary instabilities). Consequently, laminar breakdown originates from the outer side of the upwelling region of the CF vortices. In contrast, for the supercritical FFS, the laminar breakdown unexpectedly originates from the inner side of the upwelling region. Evidence points to an unsteady mechanism possibly supported by locally enhanced spanwise-modulated shears and the recirculation region downstream of the FFS edge. This mechanism appears to govern the abrupt tripping of the flow in supercritical step cases. The findings in this work provide insight into the unsteady FFS–CF vortex interaction, which is pivotal to understanding the influence of an FFS on the laminar–turbulent boundary-layer transition in swept aerodynamic surfaces.
Computational and Structural Biotechnology Journal

The study of protein molecular surfaces enables to better understand and predict protein interactions. Diﬀerent methods have been developed in computer vision to compare surfaces that can be applied to protein molecular surfaces. The present work proposes a method using the Wave Kernel Signature: Protein LOcal Surﬁcial Similarity Screening (PLO3S). The descriptor of the PLO3S method is a local surface shape descriptor projected on a unit sphere mapped onto a 2D plane and called Surface Wave Interpolated Maps (SWIM). PLO3S allows to rapidly compare protein surface shapes through local comparisons to ﬁlter large protein surfaces datasets in protein structures virtual screening protocols.