Miracle-dz/UniFAR-FTUs huggingface dataset:
Usability Evaluation of Touch Screen Interface with Various User Groups

Currently, touch screen interfaces are adopted in many consumer products in our daily lives. However, there is still little empirical knowledge studied about their interaction, especially in food serving outlets. Therefore, in this paper we investigated how interact with using touch screen interface for self ordering some beverage and dessert. The method of this system consisted of three main parts: 1. ) establishing hardware, 2. ) developing software, and 3. ) evaluating interaction. We used usability method to collect empirical data and measure how users interact. In the experiment, we used factorial design. Factors of user that we concerned are age, gender, and experience with touch screen interface. We found that only two factors that affect the time spending to ordering: 1. ) user's experience with touch screen and 2. ) interaction between users' experience with touch screen and users' age. From interviewing, most users stated that they liked this UI because it provides users convenient, friendly, and attractive with many images.
Toll-like receptor 8 agonists improve NK-cell function primarily targeting CD56brightCD16− subset

Background Toll-like receptors (TLRs) are pattern-recognition sensors mainly expressed in innate immune cells that directly recognize conserved pathogen structures (pathogen-associated molecular patterns-PAMPs). Natural killer (NK) cells have been described to express different endosomal TLRs triggered by RNA and DNA sequences derived from both viruses and bacteria. This study was addressed to establish which endosomal TLR could directly mediate NK activation and function after proper stimuli. It was also important to establish the most suitable TLR agonist to be used as adjuvant in tumor vaccines or in combined cancer immunotherapies. Methods We assessed endosomal TLR expression in total NK cells by using RT-qPCR and western blotting technique. In some experiments, we purified CD56brightCD16− and CD56dimCD16+ cells subsets by using NK Cell Isolation Kit Activation marker, cytokine production, CD107a expression and cytotoxicity assay were evaluated by flow cytometry. Cytokine release was quantified by ELISA. NK cells obtained from ovarian ascites underwent the same analyses. Results Although the four endosomal TLRs (TLR3, TLR7/8, and TLR9) were uniformly expressed on CD56brightCD16− and CD56dimCD16+ cell subsets, the TLR7/8 (R848), TLR3 (polyinosinic-polycytidylic acid, Poly I:C) and TLR9 (ODN2395) ligands promoted NK-cell function only in the presence of suboptimal doses of cytokines, including interleukin (IL)-2, IL-12, IL-15, and IL-18, produced in vivo by other environmental cells. We showed that R848 rather than TLR3 and TLR9 agonists primarily activated CD56brightCD16− NK cells by increasing their proliferation, cytokine production and cytotoxic activity. Moreover, we demonstrated that R848, which usually triggers TLR7 and TLR8 on dendritic cells, macrophages and neutrophils cells, activated CD56brightCD16− NK-cell subset only via TLR8. Indeed, specific TLR8 but not TLR7 agonists increased cytokine production and cytotoxic activity of CD56brightCD16− NK cells. Importantly, these activities were also observed in peritoneal NK cells from patients with metastatic ovarian carcinoma, prevalently belonging to the CD56brightCD16− subset. Conclusion These data highlight the potential value of TLR8 in NK cells as a new target for immunotherapy in patients with cancer.
THE EFFECTS OF WHOLE BODY VIBRATION ON POSTURAL CONTROL: THE FREQUENCY OF STIMULATION

This study investigated the efficacy of high and low-frequency acute whole-body vibration (WBV) on postural control ability. WBV stimulus were applied the following determinants: (1) type (vertical) (2) frequency (30 and 40 Hz), (3) stance position (static squat position), (4) amplitude (4mm mm), (5) knee flexion angle (120°) and time (60s). Motion of the point of application of the vertical component [medio-lateral (ML) and anterio-posterior (AP)] of the ground reaction force (center of pressure, COP). The study results showed that static WBV stimuli given at low and high frequencies and 4 mm amplitude resulted in a different postural adaptation (p<0.05). The somatosensory stimulation more rapidly adapted to the vibration at 40 Hz-4 mm.
COVID-19 preventive interventions for high-risk pregnant women and preschool children: a rehearsal for the baby boomers’ old-age pandemic?

A large research-based consensus was achieved over the past 30 years concerning the importance of prenatal and early childhood development: Preventive interventions are needed early in life because physical and psychological problems during pregnancy and early childhood often lead to serious physical, psychological, educational, and social problems throughout the life course. These problems are also transmitted to the next generation. The COVID-19 pandemic is likely to have increased the number of families who need these early-life preventive interventions. Without intensive support, children from high-risk families are likely to fail in school, to have serious physical and mental health problems, and to reproduce another generation of children with similar physical, cognitive, and mental health problems. We underline the need to: (1) assess the extent of the COVID-19 damage on pregnant women and on their spouses, as well as on the families with preschool children; (2) help service providers identify the state-of-the art services they should implement; (3) assess the implementation of these services; and (4) help service providers maintain highly effective interventions. For the next 20 to 30 years at least, governments will be under intense pressure to invest massively in the health and care of the baby boomers. We are thus facing abysmal health care and retirement costs for the next 3 decades. Governments should be pressured to substantially invest in the support of pregnant women and preschool children, rather than in the sustained quality of life of the aging baby boomers.
A Retrospective Study on Skellam and Related Distributions

This paper reviews works on Skellam distribution, its extensions and areas of applications. Available literature shows that this distribution is flexible for modelling integer data where they appear as count data or paired count data in the field of finance, medicine, sports, and science. Bivariate Skellam distribution, dynamic Skellam model and other extensions are also discussed and additional literature are provided.
Energy-aware and Reliable Service Placement of IoT applications on Fog Computing Platforms by Utilizing Whale Optimization Algorithm

: Fog computing is known as a new computing technology where it covers cloud computing shortcomings in term of delay. This is a potential for running IoT applications containing multiple services taking benefit of closeness to fog nodes near to devices where the data are sensed. This article formulates service placement issue into an optimization problem with total power consumption minimization inclination. It considers resource utilization and traffic transmission between different services as two prominent factors of power consumption, once they are placed on different fog nodes. On the other hand, placing all of the services on the single fog node owing to power reduction reduces system reliability because of one point of failure phenomenon. In the proposed optimization model, reliability limitations are considered as constraints of stated problem. To solve this combinatorial problem, an energy-aware reliable service placement algorithm based on whale optimization algorithm (ER-SPA-WOA) is proposed. The suggested algorithm was validated in different circumstances. The results reported from simulations prove the dominance of proposed algorithm in comparison with counterpart state-of-the-arts.
AMANDA SETTI RAIZE Comparação das taxas de implantação entre a Injeção intracitoplasmática de espermatozoide morfologicamente selecionado (IMSI) e a injeção intracitoplasmática de espermatozoide (ICSI) em casais com falha prévia de ICSI ou fator

A promising method for observing spermatozoa, motile sperm organelle morphology examination (MSOME) enables the evaluation of the nuclear morphology of motile spermatozoa in real time at high magnification and has allowed the introduction of a modified microinjection procedure, intracytoplasmic morphologically selected sperm injection (IMSI). Since its development, several studies have intensively investigated the efficacy of MSOME and IMSI. The objective of the present study is to review the current literature on the MSOME and IMSI techniques. RBMOnline a 2013, Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.
COVID-19’s impact on the perceived risk of ocean cruising: A best-worst scaling study of Australian consumers

This paper investigates aspects of cruising of most concern to Australian consumers during the COVID-19 pandemic, following the shutdown of cruising globally. Using a mixed-method approach, the study asked cruisers and non-cruisers which of the risks associated with cruising were of most concern. The study found health was the most concerning risk for respondents, regardless of cruising history and, contrary to previous research, cruisers were more concerned about health risk than non-cruisers. Results indicated cruisers were also concerned about financial risk, while safety and psychological risk were the next most concerning aspects for non-cruisers. Implications for the cruise industry are discussed and areas for further research are identified. Findings suggest the industry cannot take for granted repeat cruisers’ return post-pandemic. As non-cruisers are much less positive and less willing to cruise than before COVID-19, the continued future growth of the cruise sector is also under threat.
Newly detected open clusters in the Galactic disk using Gaia EDR3

The astrometric satellite Gaia recently released part of its third data set, which provides a good opportunity to hunt for more open clusters in the Milky Way. In this work, we conduct a blind search for open clusters in the Galactic disk using a sample-based clustering search method with high spatial resolution, which is especially suited to finding hidden targets. In addition to confirming 1930 previously known open clusters and 82 known globular clusters, 704 new stellar clusters are proposed as potential open clusters at Galactic latitudes of $|$b$|$<20 {deg}. For each of these new open clusters, we present the coordinates, detailed astrometric parameters, and ages, as well as the radial velocity, if available. Our blind search greatly increases the number of Galactic open clusters as objects of study and shows the incompleteness of the open cluster census across our Galaxy
Impact of Controller Saturation on Instability Behavior of Grid-Connected Inverters

For the power-electronics-based power system, a kind of stable operation with constant-amplitude oscillation was often witnessed in the reported instability incidents. In some cases, the root is proved to be negative damping by the theoretical analysis based on the linear model and the impedance-based stability theory. However, the negative damped resonances should diverge. This leaves a void why the analysis result is not in accord with the field measurement. This article aims to provide a potential answer for the disaccord, i.e., the controller saturation which is commonly ignored in the modeling process due to its nonlinearity. The controller saturation is linearized by the describing function and the improved linear model is developed. By applying the generalized Nyquist stability criterion, the impact of the controller saturation on the instability behavior of grid-connected inverters is identified, which reveals the underlying mechanism of the constant-amplitude oscillation. The theoretical expectations are finally validated by the experimental results.
Uncertainty Analysis of Flow Resistance Modeling In Sewer Pipes With Different Bed Conditions Using KELM Method

An accurate prediction of flow resistance in channels or pipes which is used for carrying rain water or sewage is an important issue in hydraulic engineering. In this study, the capability of two artificial intelligence methods namely; Feed Forward Neural Network (FFNN) and Kernel Extreme Learning Machine (KELM) was utilized for the Manning roughness coefficient modeling in circular sewer pipes with smooth and rough beds. During model developing process, at first the most effective variables related to hydraulic and sediment properties were selected using Factorial Analysis (FA) method, then, various models were developed based on the selected parameters. The models were tested using some available experimental datasets. The obtained results showed that there resulted high efficiency with FFNN and KELM methods in Manning coefficient modeling. It was found that both hydraulic and sediment properties were effective in modeling process. The sensitivity analysis results showed that d50/R variable (the ratio of median diameter of sediment size to the hydraulic radius) was the most significant parameter in modeling process. Finally, an uncertainty analysis was conducted to assess the dependability of the best applied ‎model, and the results revealed that the KELM model possesses an acceptable level of ‎uncertainty in roughness coefficient modeling.
Optimizing the flexible design of hybrid renewable energy systems

Abstract Engineering systems often operate for a long period of time under varying conditions. The system should be designed based on the best available information at the time of the decision. Designers should also account for future uncertainties in the initial design of the system. The initial design may or may not change as the future evolves and conditions change. The goal of this study is to optimize the design of a hybrid renewable energy system (HRES) to deliver electricity under highly uncertain demand. This research explores designing the hybrid system while taking into account uncertainties over a long period of time (i.e., 20 years in this study). The objective is to minimize the expected discounted cost of the HRES during the next 20 years. A design solution may also be flexible, which means that the design can be adapted or modified in the future to meet new scenarios. This article incorporates flexibility or capacity expansion into engineering design under long-range uncertainty when the objective function is evaluated via a Monte Carlo simulation. The value of expanding capacity is measured by comparing the cost without capacity expansion and cost with capacity expansion.
The systems architecture ontology (SAO): an ontology-based design method for cyber–physical systems

PurposeManaging components' resources plays a critical role in the success of systems' architectures designed for cyber–physical systems (CPS). Performing the selection of candidate components to pursue a specific application's needs also involves identifying the relationships among architectural components, the network and the physical process, as the system characteristics and properties are related.Design/methodology/approachUsing a Model-Driven Engineering (MDE) approach is a valuable asset therefore. Within this context, the authors present the so-called Systems Architecture Ontology (SAO), which allows the representation of a system architecture (SA), as well as the relationships, characteristics and properties of a CPS application.FindingsSAO uses a common vocabulary inspired by the Architecture Analysis and Design Language (AADL) standard. To demonstrate SAO's applicability, this paper presents its use as an MDE approach combined with ontology-based modeling through the Ontology Web Language (OWL). From OWL models based on SAO, the authors propose a model transformation tool to extract data related to architectural modeling in AADL code, allowing the creation of a components' library and a property set model. Besides saving design time by automatically generating many lines of code, such code is less error-prone, that is, without inconsistencies.Originality/valueTo illustrate the proposal, the authors present a case study in the aerospace domain with the application of SAO and its transformation tool. As result, a library containing 74 components and a related set of properties are automatically generated to support architectural design and evaluation.
Software Product Lines for Multi-Cloud Microservices-Based Applications

Multi-cloud computing is the use of resources and services from multiple independent cloud providers. It is used to avoid vendor lock-in, comply with location regulations, and optimize reliability, performance and costs. Microservices is an architectural style becoming increasingly used in cloud computing as it allows for better resources usage. However, building multi-cloud systems is a very complex and time consuming task, which calls for automation and supporting tools. Here we present an automated approach to build a multi-cloud environment for a microservices-based application , considering the concerns of cloud heterogeneity and variability.
Reliability/Performance-Aware Scheduling for Parallel Applications With Energy Constraints on Heterogeneous Computing Systems

Heterogeneous Computing Systems (HCSs) have developed rapidly due to their high performance and low cost, and have been adopted by more and more applications. Energy consumption, reliability, and schedule length are the core issues of HCSs. Due to the negative correlation between frequency and reliability, DVFS-supported HCSs requires high energy consumption and a long schedule length to obtain high reliability, which resulting in performance degradation. In this paper, we focus on the reliability and performance-aware scheduling for energy-constrained parallel applications on HCSs. First, we design an energy pre-allocation mechanism based on Energy Demand Rate (EDR) to pre-allocate energy reasonably. Second, we propose an EDR-aware Maximizing Reliability of Energy-Constrained parallel applications (EMREC) scheduling algorithm. Third, considering that maximize reliability will cause the schedule length to be too long and unacceptable, we further highlight the concept of Reliability Performance Ratio (RPR). Finally, we propose a Maximizing RPR with Energy-Constrained parallel applications (MRPEC) scheduling algorithm, which enables parallel applications have a smaller schedule length while with high reliability. Extensive experimental results in real-world and randomly generated applications show the effectiveness of the proposed algorithms under different conditions.
Learning Hierarchical Video Graph Networks for One-Stop Video Delivery

The explosive growth of video data has brought great challenges to video retrieval, which aims to find out related videos from a video collection. Most users are usually not interested in all the content of retrieved videos but have a more fine-grained need. In the meantime, most existing methods can only return a ranked list of retrieved videos lacking a proper way to present the video content. In this paper, we introduce a distinctively new task, namely One-Stop Video Delivery (OSVD) aiming to realize a comprehensive retrieval system with the following merits: it not only retrieves the relevant videos but also filters out irrelevant information and presents compact video content to users, given a natural language query and video collection. To solve this task, we propose an end-to-end Hierarchical Video Graph Reasoning framework (HVGR), which considers relations of different video levels and jointly accomplishes the one-stop delivery task. Specifically, we decompose the video into three levels, namely the video-level, moment-level, and the clip-level in a coarse-to-fine manner, and apply Graph Neural Networks (GNNs) on the hierarchical graph to model the relations. Furthermore, a pairwise ranking loss named Progressively Refined Loss is proposed based on prior knowledge that there is a relative order of the similarity of query-video, query-moment, and query-clip due to the different granularity of matched information. Extensive experimental results on benchmark datasets demonstrate that the proposed method achieves superior performance compared with baseline methods.
Volumetric Instance-Level Semantic Mapping Via Multi-View 2D-to-3D Label Diffusion

Robots operating in real-world settings often need to plan interactions with surrounding scene elements and therefore, it is crucial for them to understand their workspace at the level of individual objects. In this spirit, this work presents a novel approach to progressively build instance-level, dense 3D maps from color and depth cues acquired by either a moving RGB-D sensor or a camera-LiDAR setup, whose pose is being tracked. The proposed framework processes each input RGB image with a semantic instance segmentation neural network and uses depth information to extract a set of per-frame, semantically labeled 3D instance segments, which then get matched to object instances already identified in previous views. Following integration of these newly detected instance segments in a global volumetric map, an efficient label diffusion scheme that considers multi-view instance predictions together with the reconstructed scene geometry is used to refine 3D segmentation boundaries. Experiments on indoor benchmarking RGB-D sequences show that the proposed system achieves state-of-the-art performance in terms of 3D segmentation accuracy, while reducing the computational processing cost required at each frame. Furthermore, the applicability of the system to challenging domains outside the traditional office scenes is demonstrated by testing it on a robotic excavator equipped with a calibrated camera-LiDAR setup, with the goal of segmenting individual boulders in a highly cluttered construction scenario.
Meta-Hierarchical Reinforcement Learning (MHRL)-Based Dynamic Resource Allocation for Dynamic Vehicular Networks

With the rapid development of vehicular networks, there is an increasing demand for extensive networking, computting, and caching resources. How to allocate multiple resources effectively and efficiently for dynamic vehicular networks is extremely important. Most existing works on resource management in vehicular networks assume static network conditions. In this paper, we propose a general framework that can enable fast-adaptive resource allocation for dynamic vehicular environments. Specifically, we model the dynamics of the vehicular environment as a series of related Markov Decision Processes (MDPs), and we combine hierarchical reinforcement learning with meta-learning, which makes our proposed framework quickly adapt to a new environment by only fine-tuning the top-level master network, and meanwhile the low-level sub-networks can make the right resource allocation policy. Extensive simulation results show the effectiveness of our proposed framework, which can quickly adapt to different scenarios, and significantly improve the performance of resource management in dynamic vehicular networks.
The enhanced Folaga: a hybrid AUV with modular payloads

The chapter describes the development of a payload-carrying capability on the Folaga vehicles (eFolaga). In the eFolaga design, lightweight, small dimensions, low-cost characteristics have all been maintained, as well as high manoeuvrability and hovering capacities. A general methodology to derive lumped parameter models of eFolaga like vehicles has been described, where the identification of the eFolaga buoyancy change and mass displacement actuators has also been reported. By judicious design, it is possible to lift the eFolaga modularity also at the software level, and indeed to have intelligent payloads implementing specific autonomous behaviours setting up a dialogue with the native eFolaga GNC.