Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding telecare construction work: An ethnography of nursing practices

Telecare, a broad set of technologies used in home-based care, is expected to reduce the rising costs of care in an aging society on both national and European levels. The increasing use of telecare will profoundly change nursing care, in both expected and unexpected ways. The obvious difference is that care professionals will not physically be with patients when care is given at a distance. But there are also more subtle changes, like patients who use the technology on their own terms, and nurses’ improvised solutions to work around hindrances. All of these adjustments turn telecare practices into construction sites, where nurses and patients work to achieve good care. Annemarie van Hout conducted ethnographic research in two field sites: at-home palliative nursing and at-home long-term mental health care. Nurses care at a distance by starting to draw from their daily repertoire, basing their actions on their regular working methods. When nurses encounter hindrances that they have to deal with, they extend their repertoires. This research finds that lessons learned tend to stay within the practice where they are learned. To avoid this pitfall, Annemarie calls for nurses and scholars to work together. When nurses and researchers work together, detecting and articulating changes, sharing and theorizing them, they can tell a new story, a nursing story of telecare, one that can counter the policy stories this thesis begins with. Hopes for and fears of telecare are bad predictors. Let nurses, researchers and policymakers instead listen to and learn from actual practice.
Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

The lack of measurements in distribution grids poses a severe challenge for their monitoring: Since there may not be enough sensors to achieve numerical observability, so load forecasts (pseudo-measurements) are typically used, and thus an accurate state estimation is not guaranteed. However, estimation is required to control distribution grids, given the increasing amount of distributed generation. Therefore, we consider the problem of optimal sensor placement to improve the state-estimation accuracy in large-scale, 3-phase, coupled, unbalanced distribution grids. This is a combinatorial optimization problem whose optimal solution is unpractical to obtain for large networks. We explore the properties, such as convexity and modularity, of different metrics in the context of optimal experimental design, to propose and compare several tight lower and upper bounds on the performance of the optimal solution. Moreover, we show how to use these bounds to choose near-optimal solutions. We test the method on two IEEE benchmark test feeders, the 123-bus, and the 8500-node feeders, to show the effectiveness of the approach.
Spatial Distribution of Cholesterol in Lipid Bilayers

We have performed molecular simulations to study spatial distribution and orientation of cholesterol molecules within three symmetric lipid bilayer systems: two binary lipid mixtures, namely, DOPC/CHOL (1, 2-dioleoyl-sn-glycero-3-phosphocholine/cholesterol) and SM/CHOL (d20:1/20:0 sphingomyelin /Cholesterol), and a tertiary mixture of DOPC/SM/CHOL. We have studied the behavior of these bilayers at temperatures varying from 400 K to 210 K and cholesterol molar concentration from 0% to 60%. We observe that the spatial distribution of cholesterol is strongly correlated with the phase of the bilayer. In the disordered phase, cholesterol molecules are predominantly present near the center of the bilayer. In the ordered phase, cholesterol molecules are mainly present in the leaflets. At the order - disorder transition temperature, the fraction of cholesterol molecules is equal in the two leaflets and the center. In the leaflets, cholesterol molecules are oriented parallel to the bilayer normal, while near the center, cholesterol molecules are randomly oriented. In agreement with previous experimental studies, we find that increasing the cholesterol concentration favors ordered phase of the bilayers. The preference of cholesterol molecules to be present in the leaflets in the ordered phase is attributed to their favorable hydrophobic interactions with the lipid tails. 2. Statement of Significance Cholesterol has an important role in governing the physical properties of lipid bilayers, including their structural integrity, phase behavior and permeability. The spatial distribution of cholesterol in lipid bilayers is not well-understood because of the challenges associated with performing experiments for such a measurement. We show, via molecular simulations, that the spatial distribution of cholesterol molecules is strongly correlated with the phase behavior of the lipid bilayers. In the ordered phase, cholesterol molecules are predominantly present in the leaflets, whereas in the disordered phase, cholesterol molecules are in the center region of the bilayer. These results are important for understanding the relationship between lipid bilayer composition and their biological function and response.
UPLC–MS/MS simultaneous determination of methamphetamine, amphetamine, morphine, monoacetylmorphine, ketamine, norketamine, MDMA, and MDA in hair

Hair is a stable specimen and has a longer detection window (from weeks to months) than blood and urine. Through the analysis of hair, the long-term information of the drug use of the identified person could be explored. Our work is to establish an ultra-performance liquid chromatography–tandem mass spectroscopy (UPLC–MS/MS) method for simultaneous determination of methamphetamine, amphetamine, morphine, monoacetylmorphine, ketamine, norketamine, 3,4-methylenedioxymethamphetamine (MDMA), and 3,4-methylenedioxyamphetamine (MDA) in hair. Methoxyphenamine was used as an internal standard. The chromatographic separation was performed on a UPLC ethylene bridged hybrid (BEH) C18 (2.1 mm × 50 mm, 1.7 μm) column using a mobile phase of acetonitrile–water with 10 mmol/L ammonium acetate solution which containing 0.05% ammonium hydroxide. The multiple reaction monitoring in positive electrospray ionization was used for quantitative determination. The intra-day and inter-day precisions (relative standard deviation [RSD]) were below 15%. The accuracy ranged between 85.5% and 110.4%, the average recovery rate was above 72.9%, and the matrix effect ranged between 92.7% and 109.2%. Standard curves were in the range of 0.05–5.0 ng/mg, and the correlation coefficients were greater than 0.995. The established UPLC–MS/MS method was applied to analyze the hair samples successfully.
Paper in press: 2013 IEEE RO-MAN: The 22nd IEEE International Symposium on Robot and Human Interactive Communication, August 26-29, 2013, Gyeongju, Korea

This paper explores new possibilities for social interaction between a human user and a robot with an abstract shape. The social interaction takes place by simulating behaviors such as submissiveness and dominance and analyzing the corresponding human reactions. We used an object that has no resemblance with human features in its shape or expression mode, in order to exclude the effect of these features on the human behavior. An intelligent walk-in closet was made to behave either dominantly or submissively using lighting effects. The behaviors of the closet were rated by participants using the Bem Sex Role Inventory in a pilot study, resulting in the selection of one submissive and one dominant lighting behavior for the closet. Participants’ personality was measured using the Social Dominance Orientation questionnaire. These data were then compared to measurements of user satisfaction and feelings of dominance, arousal, and valence after scenario completion. A surprising effect was revealed as participants with a dominant personality reported feeling submissive to a dominant system, while in comparison, persons with a submissive personality felt more dominant in the same condition. Furthermore, it was found that a submissive system was generally more preferred by users. We draw a careful conclusion that people interact differently with systems that show human-like attitudes, than they would in response to similar attitude expressed by other person. These findings need to be investigated further with dominant/submissive nonverbal behaviors that are then simulated on a humanoid robot.
Laryngeal and buccopharyngeal morphology of some South African Bufonidae: new data sets for anuran taxonomy

Descriptions of adult larynxes and larval buccopharynxes of Natal Bufonidae are presented. These appear to possess characters of taxonomic value and may contribute towards better definitions of species boundaries in problem taxa. The name Bufo pusillus Mertens, 1937 is used here for southern African toads currently called B. maculatus Hallowell, 1854. The author follows Channing (1991) in recognising Bufo poweri Hewitt, 1935. The Western Cape populations of Bufo pardalis Hewitt, 1935 are here called Bufo sp. A to indicate that they may be taxonomically distinct.
The Effect of Surfactant and Environment on the Toxicity of Metriflufen to Soybeans (Glycine max) and Johnsongrass (Sorghum halepense)

Metriflufen {2-[4-(4-trifluoromethylphenoxy)phenoxy] propanoic acid} was applied as the methyl ester at 0.28 and 0.56 kg/ha over-the-top to johnsongrass [Sorghum halepense (L.) Pers. ] growing from rhizomes and to soybeans [Glycine max (L.) Merr. ‘Lee 68′]. After herbicide treatment, plants were grown in the growth chamber for 14 days at 16, 24, or 32 C with relative humidity (RH) at 40 or 100% at each air temperature. Johnsongrass was not controlled at 16 C regardless of metriflufen rate, RH, or the addition of nonoxynol [α-(p-nonylphenyl)-ω-hydroxypoly (oxyethylene)] (with 9.5 moles of polyoxyethylene) surfactant at 0.25 (g/100 ml) to spray solutions. Johnsongrass control at 24 C varied from 5 to 98%, with significantly better control at 100% than at 40% RH. The presence of surfactant increased johnsongrass control at 24 C and 40% RH but not at 24 C and 100% RH. Johnsongrass control at 32 C varied from 48 to 98%, and it was not increased by the presence of the surfactant, regardless of metriflufen rate or RH level. At 16 C metriflufen was more injurious to soybeans than to johnsongrass, but at 24 and 32 C johnsongrass control was significantly greater than soybean injury. The presence of surfactant in spray solutions generally did not increase soybean injury, regardless of temperature or RH level. These results suggest that metriflufen is most selective in controlling johnsongrass in soybeans at 24 C, especially under high RH.
OPTIMIZING NUMERIC MULTI-DIMENSIONAL FUNCTIONS

Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
Development of an InDel marker set to establish hybridization between wild boar and domestic pig (Sus scrofa) breeds

Wild boar and domestic pig breeds belong to the same species (Sus scrofa), so they can easily have viable offspring. This could be a problem in preserving the genetic lines of wild boars, keeping clean the food industry from lower-grade hybrid boar meat, and „producing” ethically questionable trophies, too. The aim of our study was to develop a cost-efficient, fast, easy and accurate marker set which can separate the wild boars from hybrids and domestic pig breeds. The InDel markers were developed using 59 full pig genomes of 17 different breeds (e.g. Duroc, Large White, Landrace, Mangalica, wild boar). Sequence differences between the genomes of wild boars and domestic breeds were identified in variant call files, and verified using the IGV software. Wild boar, mangalica and duroc specific primers to amplify the chosen InDel regions were designed using Primer3. After preliminary tests five markers were chosen, three wild boar specific, one Mangalica specific and one Duroc specific one. Fluorescently labelled primers were used to make the valuation easier and more accurate with capillary electrophoresis instead of gel-electrophoresis. The markers were optimised individually and in multiplex conditions and tested in samples of 11 breeds. In conclusion, a new, faster and cheaper set was developed to separate the wild boars from the hybrids and domestic breeds. Based on the preliminary testing on wild boars, duroc and mangalica breeds zero samples resulted false negative, so it is 100% accurate. In addition, it is a much more cost- and time-effective way than testing every single sample with STR sets.
Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short‐term memory units

Fatigue driving has become one of the major causes of traffic accidents. The authors propose an effective method capable of detecting fatigue state via the spatial-temporal feature of driver's eyes. In this work, the authors consider fatigue detection as image-based sequence recognition and an end-to-end trainable convolutional neural network with long short-term memory (LSTM) units is designed. First, the authors apply a deep cascaded multi-task framework to extract eye region from infrared videos. Then the spatial features are learned by deep convolutional layers and the relationships between adjacent frames are analysed via LSTM units. Finally, through authors' model, a sequence-level prediction for driving state is produced. The proposed method achieves superior accuracy over the state-of-the-art techniques on authors' own dataset. Experimental results demonstrate the feasibility of authors' method.
Metasurface-Enabled Multifunctional Single-Frequency Sensors without External Power

IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical space, digital twins, autonomous driving, smart cities, and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of available sensing devices. Here, we provide a novel sensor design approach that employs metasurfaces to enable multifunctional sensing without requiring an external power source. Importantly, unlike existing metasurface-based sensors, our metasurfaces can sense multiple physical parameters even at a fixed frequency by breaking classic harmonic oscillations in the time domain, making the proposed sensors viable for usage with limited frequency resources. Moreover, we provide a method for predicting physical parameters using the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. Our study affords new opportunities for sensing multiple physical properties without relying on an external power source or needing multiple frequencies, which markedly simplifies and facilitates the design of next-generation wireless communication systems.
CROSA: Context‐aware cloud service ranking approach using online reviews based on sentiment analysis

The explosion of cloud services over the Internet has raised new challenges in cloud service selection and ranking. The existence of a great variety of offered cloud services made the users think deeply about the most appropriate services that meet their needs and at the same time are adaptable to their context. Nowadays, online reviews are used for the purpose of enhancing the effectiveness of finding useful product information, having impact on the consumers' decision‐making process. In this context, the current paper suggests a context‐aware cloud service ranking approach using online reviews and based on sentiment analysis (CROSA). Its main objective is to ease the cloud service selection. The CROSA approach analyzes sentiments associated with service measurement index (SMI)–based service properties for each alternative cloud service. Moreover, it enhances the cloud service decision‐making by supporting fuzzy sentiments through the intuitionistic fuzzy set theory and PROMETHEE II. The experimental results presented in this paper show that this approach is efficient and performing.
Snowball Sampling: Problems and Techniques of Chain Referral Sampling

In spite of the fact that chain referral sampling has been widely used in qualitative sociological research, especially in the study of deviant behavior, the problems and techniques involved in its use have not been adequately explained. The procedures of chain referral sampling are not self-evident or obvious. This article attempts to rectify this methodological neglect. The article provides a description and analysis of some of the problems that were encountered and resolved in the course of using the method in a relatively large exploratory study of ex-opiate addicts.
Static Analysis of Mobile Programs

Abstract : The goal of the STAMP (STatic Analysis of Mobile Programs) project has been to build tools for proving the absence of malware in Android applications, also known as apps. The performers focus is on guarantees, their techniques have a large static component, as static proofs are the only known method of reasoning about all possible program executions. Like most systems written in modern languages, Android applications heavily use pointer data structures, complex path conditions, and multiple layers of object-oriented abstractions. Stanford's hypothesis has been that static analysis techniques have reached the point that sound, precise and scalable static analysis for interesting security properties is entirely feasible.
Performance Improvements of Random Dopant Fluctuation-Induced Variability in Negative Capacitance MOSFETS

In this paper, we investigate the impact of random dopant fluctuation (RDF) on the statistical variations in negative capacitance MOSFETs (NCFETs) through a device simulation coupled with the Landau–Khalatnikov (LK) equation. Compact models for feedback mechanisms that are based on the internal gate voltage amplification in NCFETs are proposed. The results show that internal voltage amplification plays a decisive role in performance improvement of device variability. Further, our simulation study demonstrates that owing to the feedback mechanism, the dispersions of the performance parameters in NCFETs exhibit different statistical distribution characteristics compared to their MOSFET counterparts. Our study may provide further insight regarding device and/or circuit designs utilizing NCFETs.
Accurately Inferring Personality Traits from the Use of Mobile Technology

This paper shows that human personality can be accurately predicted by looking at the data generated by our smartphones. GPS location, calls, battery usage and charging, networking context like bluetooth devices and WiFi access points in proximity, and more give enough information about individual habits, reactions, and idiosyncrasies to make it possible to infer the psychological traits of the user. We demonstrate this by using machine learning techniques on a dataset of 55 volunteers who took a psychological test and allowed continuous collection of data from their smartphones for a time span of up to three years. Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism (the so called Big5 personality traits) can be predicted with good accuracy even by using just a handful of features. The possible applications of our findings go from network optimization, to personal advertising, and to the detection of mental instability and social hardship in cities and neighborhoods. We also discuss the ethical concerns of our work, its privacy implications, and ways to tradeoff privacy and benefits.
Strategic CSR: A Concept Building Meta‐Analysis

This study develops the concept of Strategic Corporate Social Responsibility (Strategic CSR) by meta‐analyzing the available empirical evidence on the relationship between CSR and corporate financial performance (CFP). Using meta‐analytic structural equation modeling on effect size data from 344 primary studies, our study documents four empirical mechanisms explaining how CSR positively affects CFP: by 1) enhancing firm reputation, 2) increasing stakeholder reciprocation, 3) mitigating firm risk, and 4) strengthening innovation capacity. We propose these four mechanisms to identify four causally relevant attributes that allow us to conceptually distinguish Strategic CSR from CSR more generally. Our findings indicate that the four mechanisms combined explain 20 per cent of the CSR‐CFP relationship, suggesting that considerable room remains for future empirical research. The development of an empirically informed, causal conceptualization of Strategic CSR responds to a long‐heard call for better‐specified concepts in empirical CSR research.
The Empirical Research of the Influence of Leadership Positive Emotion on Counterproductive Work Behavior

Leader’s emotion is one of the most influential factors of employee behavior in the workplace. The expression or of a leader’s positive emotion can directly or indirectly affect the employee’s work performance, especially the employee’s Counterproductive Work Behavior (CWB). This paper systematically explains the internal mechanism how the leader’s positive emotion affects employees’ CWB through experimental methods, and uses the Emotions As Social Information Modle (EASI) to analyze and explain the research results. Finally, this study exposes the inner psychological process of Leader’s positive emotion affecting employees’ CWB through their own positive emotions, proving the moderating effect of leadership justice in this process, and this study enriches the results of the research field of CWB.