Miracle-dz/UniFAR-FTUs huggingface dataset:
Purchase intention in social commerce: An empirical examination of perceived value and social awareness

Purpose With the advent of the digital era, the internet has gradually become an essential factor in people’s lives. Its increasing popularity has also caused e-commerce to thrive, and has increased the power of online marketing. In recent years, social commerce has emerged as a new mode of operation which can be distinguished from traditional e-commerce. The paper aims to discuss these issues. Design/methodology/approach The main purpose of this study is to determine which factors influence a consumer’s behavioral intention in the context of social commerce websites, and to explore overall purchase intentions based on the consumer’s cognitive evaluation. The examination reveals that consumers’ multidimensional perceptions influence both their perceptions of value and their purchase intentions. Other related social awareness factors explored by this study are also shown to influence customers’ purchase intentions. Findings In summary, the results show that perceived value and social awareness factors influence customers’ purchasing decisions and behavioral intentions for different gender. These findings have significant theoretical and managerial implications. Originality/value Social commerce is a subset of e-commerce brought about by the development of social networking. Activities conducted on social networking websites can be considered as a form of social commerce. In Taiwan, social commerce is still in the early stages of development.
OsDRE2 contributes to chitin-triggered response through its interaction with OsRLCK185

ABSTRACT The rice receptor-like cytoplasmic kinase 185 (OsRLCK185) interacts with the chitin receptor complex OsCERK1/CEBiP and positively regulates chitin-induced immune responses including MAP kinase activation, ROS production and defense gene expression. To elucidate the regulatory mechanisms of OsRLCK185-mediated immunity, we searched for interactors of OsRLCK185. OsDRE2a, rice homologs of the yeast Dre2 protein, were identified as novel interactors of OsRLCK185. OsDRE2a interacted with OsRLCK185 at plasma membrane. The conserved cysteine residues in CIAPIN1 domain of OsDRE2a were essential for tight interaction of OsRLCK185. OsDRE2a was phosphorylated by OsRLCK185. The expression of OsDRE2a and OsDRE2b was induced after chitin treatment. Reduction of OsDRE2a and OsDRE2b mRNA levels by RNA interference resulted in the decreased chitin-induced ROS production. Thus, it is likely that OsDRE2 regulates OsRLCK185-mediated immune responses. Graphical Abstract OsDRE2 interacts with OsRLCK185 at plasma membrane and is phosphorylated by OsRLCK185. Silencing of OsDRE2 causes the reduction of chitin-induced ROS production.
Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

Background: The discovery of early, non-invasive biomarkers for the identification of “preclinical” or “pre-symptomatic” Alzheimer's disease and other dementias is a key issue in the field, especially for research purposes, the design of preventive clinical trials, and drafting population-based health care policies. Complex behaviors are natural candidates for this. In particular, recent studies have suggested that speech alterations might be one of the earliest signs of cognitive decline, frequently noticeable years before other cognitive deficits become apparent. Traditional neuropsychological language tests provide ambiguous results in this context. In contrast, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can pinpoint language modifications in potential patients. This interdisciplinary study aimed at using NLP to identify early linguistic signs of cognitive decline in a population of elderly individuals. Methods: We enrolled 96 participants (age range 50–75): 48 healthy controls (CG) and 48 cognitively impaired participants: 16 participants with single domain amnestic Mild Cognitive Impairment (aMCI), 16 with multiple domain MCI (mdMCI) and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening composed by MMSE, MoCA, GPCog, CDT, and verbal fluency (phonemic and semantic). The spontaneous speech during three tasks (describing a complex picture, a typical working day and recalling a last remembered dream) was then recorded, transcribed and annotated at various linguistic levels. A multidimensional parameter computation was performed by a quantitative analysis of spoken texts, computing rhythmic, acoustic, lexical, morpho-syntactic, and syntactic features. Results: Neuropsychological tests showed significant differences between controls and mdMCI, and between controls and eD participants; GPCog, MoCA, PF, and SF also discriminated between controls and aMCI. In the linguistic experiments, a number of features regarding lexical, acoustic and syntactic aspects were significant in differentiating between mdMCI, eD, and CG (non-parametric statistical analysis). Some features, mainly in the acoustic domain also discriminated between CG and aMCI. Conclusions: Linguistic features of spontaneous speech transcribed and analyzed by NLP techniques show significant differences between controls and pathological states (not only eD but also MCI) and seems to be a promising approach for the identification of preclinical stages of dementia. Long duration follow-up studies are needed to confirm this assumption.
Sleep efficiency and neurophysiological patterns in middle‐aged men are associated with cognitive change over their adult life course

Disrupted sleep is a contributing factor to cognitive ageing, while also being associated with neurodegenerative disorders. Little is known, however, about the relation of sleep and the gradual cognitive changes over the adult life course. Sleep electroencephalogram (EEG) patterns are potential markers of the cognitive progress. To test this hypothesis, we assessed sleep architecture and EEG of 167 men born in the Copenhagen Metropolitan Area in 1953, who, based on individual cognitive testing from early (~18 years) to late adulthood (~58 years), were divided into 85 subjects with negative and 82 with positive cognitive change over their adult life. Participants underwent standard polysomnography, including manual sleep scoring at age ~58 years. Features of sleep macrostructure were combined with a number of EEG features to distinguish between the two groups. EEG rhythmicity was assessed by spectral power analysis in frontal, central and occipital sites. Functional connectivity was measured by inter‐hemispheric EEG coherence. Group differences were assessed by analysis of covariance (p < 0.05), including education and severity of depression as potential covariates. Subjects with cognitive decline exhibited lower sleep efficiency, reduced inter‐hemispheric connectivity during rapid eye movement (REM) sleep, and slower EEG rhythms during stage 2 non‐REM sleep. Individually, none of these tendencies remained significant after multiple test correction; however, by combining them in a machine learning approach, the groups were separated with 72% accuracy (75% sensitivity, 67% specificity). Ongoing medical screenings are required to confirm the potential of sleep efficiency and sleep EEG patterns as signs of individual cognitive progress.
Reliance on Science: Worldwide Front-Page Patent Citations to Scientific Articles

To what extent do firms rely on basic science in their R&D efforts? Several scholars have sought to answer this and related questions, but progress has been impeded by the difficulty of matching unstructured references in patents to published papers. We introduce an open-access dataset of references from the front pages of patents granted worldwide to scientific papers published since 1800. Each patent-paper linkage is assigned a confidence score, which is characterized in a random sample by false negatives vs. false positives. We outline several avenues for strategy research enabled by these new data.
Interest of Youth Living With HIV in Long-Acting Antiretrovirals

Objectives: This study's primary objective was to characterize attitudes to long-acting antiretrovirals (LAARV), among youth aged 13–24 years living with perinatally acquired HIV and nonperinatally acquired HIV. Secondary objectives included: assessing whether those with detectable HIV RNA PCR viral load had higher enthusiasm for LAARV compared to those with suppressed viral load, and examining characteristics associated with LAARV enthusiasm. Methods: A cross-sectional survey of 303 youth living with HIV (YHIV) followed at 4 pediatric/adolescent HIV clinics in the United States was performed to determine interest in LAARV, using a modified survey instrument previously used in adults. Interest in LAARV across groups was compared. Poisson regression with robust variance was used to determine the impact of various characteristics on interest in LAARV. Findings: Overall, 88% of YHIV reported probable or definite willingness to use LAARV. The enthusiasm level was similar between youth with perinatally acquired HIV and nonperinatally acquired HIV (P = 0.93). Youth with HIV viral load >1000 copies per milliliter had significantly higher interest than youth with suppressed viral load [prevalence ratio 1.12 (95% confidence interval: 1.03 to 1.20); P = 0.005]. Female youth participants who had had past experience with implantable contraceptive methods had a significantly higher interest in LAARV (100% vs. 85.5%; P = 0.002). Proportion of respondents endorsing definite willingness to use was significantly higher with decreased injection frequency compared with increased injection frequency. Interpretation: YHIV at 4 urban US pediatric/adolescent HIV clinics had high levels of enthusiasm for LAARV. LAARV should be given high priority as a potentially viable treatment option to improve clinical outcomes in YHIV.
A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems

The emerging non-wearable fall detection systems rely on processing radio waves reflected off the body of the home user who has no active interaction with the system, increasing the user privacy and acceptability. This paper proposes a non-stationary channel model that is important for the development of such systems. A 3-D stochastic trajectory model is designed to capture targeted mobility patterns of the home user. The model is featured with a forward fall mechanism, which is actuated at a random point along the path. A transmitter emits radio waves throughout an indoor propagation environment, while a receiver collects fingerprints of the scattering objects on the emitted waves. The corresponding radio channel is modeled by a process capturing the time-variant Doppler effect caused by the home occupant. The time-frequency behavior of the non-stationary channel is studied by computing the Doppler power spectral density and by performing spectrogram analysis. The instantaneous mean Doppler shift and Doppler spread are derived and simulated. The model is confirmed with experimental results performed at 5.9 GHz. The results are insightful for developing reliable fall detection algorithms, while the model is useful for studying the impact of different walking/falling patterns on the overall fall detection system performance.
Neural Networks Meet Physical Networks: Distributed Inference Between Edge Devices and the Cloud

We believe that most future video uploaded over the network will be consumed by machines for sensing tasks such as automated surveillance and mapping rather than for human consumption. Today's systems typically collect raw data from distributed sensors, such as drones, with the computer vision logic implemented in the cloud using deep neural networks (DNNs). They use standard video encoding techniques, send it over the network, and then decompress it at the cloud before using the vision DNN. In other words, data encoding and distribution is decoupled from the sensing goal. This is bandwidth inefficient because video encoding schemes, such as MPEG4, might send data tailored for human perception but irrelevant for the overall sensing goal. We argue that data collection and distribution mechanisms should be co-designed with the eventual sensing objective. Specifically, we propose a distributed DNN architecture that learns end-to-end how to represent the raw sensor data and send it over the network such that it meets the eventual sensing task's needs. Such a design naturally adapts to varying network bandwidths between the sensors and the cloud, as well as automatically sends task-appropriate data features.
Study of the Tissue Distribution of TLQP-21 in Mice Using [18F]JMV5763, a Radiolabeled Analog Prepared via [18F]Aluminum Fluoride Chelation Chemistry

TLQP-21 is a neuropeptide that is involved in the control of several physiological functions, including energy homeostasis. Since TLQP-21 could oppose the early phase of diet-induced obesity, it has raised a huge interest, but very little is known about its mechanisms of action on peripheral tissues. Our aim was to investigate TLQP-21 distribution in brain and peripheral tissues after systemic administration using positron emission tomography. We report here the radiolabeling of NODA-methyl phenylacetic acid (MPAA) functionalized JMV5763, a short analog of TLQP-21, with [18F]aluminum fluoride. Labeling of JMV5763 was initially performed manually, on a small scale, and then optimized and implemented on a fully automated radiosynthesis system. In the first experiment, mice were injected in the tail vein with [18F]JMV5763, and central and peripheral tissues were collected 13, 30, and 60 min after injection. Significant uptake of [18F]JMV5763 was found in stomach, intestine, kidney, liver, and adrenal gland. In the CNS, very low uptake values were measured in all tested areas, suggesting that the tracer does not efficiently cross the blood–brain barrier. Pretreatment with non-radioactive JMV5763 caused a significant reduction of tracer uptake only in stomach and intestine. In the second experiment, PET analysis was performed in vivo 10–120 min after i.v. [18F]JMV5763 administration. Results were consistent with those of the ex vivo determinations. PET images showed a progressive increase of [18F]JMV5763 uptake in intestine and stomach reaching a peak at 30 min, and decreasing at 120 min. Our results demonstrate that 18F-labeling of TLQP-21 analogs is a suitable method to study its distribution in the body.
Life Interrupted: The Effects of Technology-Mediated Work Interruptions on Work and Nonwork Outcomes

Work interruptions have made significant inroads into the knowledge workers’ nonwork domain, in large part due to the ubiquitous nature of mobile devices that blur the work–nonwork boundaries by enabling work interruptions anywhere and at any time. We examine the effects of such technology-mediated work-related interruptions that occur during one’s time off on both work and nonwork outcomes. Leveraging theoretical perspectives from interruption, work–life interface, and conservation of resources, we conceptualize both positive and negative effects of such interruptions on behavioral and psychological outcomes. We identify three mediating mechanisms via which these effects occur: interruption overload and psychological transition via which negative effects occur and task closure via which positive effects occur. Results reveal significant effects of interruptions on work and nonwork outcomes through the three mediating mechanisms. Although positive effects are observed, the total effects of work-related interruptions are detrimental across both work and nonwork outcomes, with the strongest negative effect on work exhaustion. The results suggest that after-hours work interruptions do not necessarily benefit work performance and come at the cost of work exhaustion. Analyses also reveal that the effects of interruptions are dependent on the technology via which these occur. While phone and messaging generate negative outcomes through interruption overload, e-mail leads to both positive and negative outcomes through task closure and psychological transition respectively. The study concludes with implications for research and practice on how to mitigate negative effects and enhance positive effects.
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.
Integrating Different Levels of Automation: Lessons From Winning the Amazon Robotics Challenge 2016

This paper describes Team Delft's robot winning the Amazon Robotics Challenge 2016. The competition involves automating pick and place operations in semistructured environments, specifically the shelves in an Amazon warehouse. Team Delft's entry demonstrated that the current robot technology can already address most of the challenges in product handling: object recognition, grasping, motion, or task planning; under broad yet bounded conditions. The system combines an industrial robot arm, 3-D cameras and a custom gripper. The robot's software is based on the robot operating system to implement solutions based on deep learning and other state-of-the-art artificial intelligence techniques, and to integrate them with off-the-shelf components. From the experience developing the robotic system, it was concluded that: 1) the specific task conditions should guide the selection of the solution for each capability required; 2) understanding the characteristics of the individual solutions and the assumptions they embed is critical to integrate a performing system from them; and 3) this characterization can be based on “levels of robot automation. ” This paper proposes automation levels based on the usage of information at design or runtime to drive the robot's behavior, and uses them to discuss Team Delft's design solution and the lessons learned from this robot development experience.
Fast and Scalable Big Data Trajectory Clustering for Understanding Urban Mobility

Clustering of large-scale vehicle trajectories is an important aspect for understanding urban traffic patterns, particularly for optimizing public transport routes and frequencies and improving the decisions made by authorities. Existing trajectory clustering schemes are not well suited to large numbers of trajectories in dense city road networks due to the difficulty in finding a representative distance measure between trajectories that can scale to very large datasets. In this paper, we propose a novel Dijkstra-based dynamic time warping distance measure, trajDTW between two trajectories, which is suitable for large numbers of overlapping trajectories in a dense road network as found in major cities around the world. We also propose a novel fast-clusiVAT algorithm that can suggest the number of clusters in a trajectory dataset and identify and visualize the trajectories belonging to each cluster. We conduct experiments on a large-scale taxi trajectory dataset consisting of 3.28 million trajectories obtained from the GPS traces of 15 061 taxis within Singapore over a period of one month. Our analysis finds 13 trajectory clusters spanning the major expressways of Singapore, each of which can be further divided into two sub-clusters based on the travel direction. For each cluster, we provide a time-based distribution of trajectories to yield insights into how urban mobility patterns change with the time of day. We compare the trajectory clusters obtained using our approach with those obtained using popular general and trajectory specific clustering frameworks: DBSCAN, OPTICS, NETSCAN, and NEAT. We demonstrate that the clusters obtained using our novel fast-clusiVAT framework are better than those obtained using other clustering schemes, evaluated based on two internal cluster validity measures: Dunn’s and Silhouette indices. Moreover, our fast-clusiVAT algorithm achieves significant speedup over a comparable approach without loss of cluster quality.
Influence of Physical Fitness on Special Judo Fitness Test Performance: A Multiple Linear Regression Analysis

Abstract Lopes-Silva, JP, Panissa, VLG, Julio, UF, and Franchini, E. Influence of physical fitness on Special Judo Fitness Test performance: a multiple linear regression analysis. J Strength Cond Res 35(6): 1732–1738, 2021—The purpose of this study was to identify the physical fitness variables that best predict Special Judo Fitness Test (SJFT) performance. Thirty-five male experienced judo athletes took part in this study. All athletes performed 5 tests: upper- and lower-body cycle ergometer graded exercise tests; upper- and lower-body Wingate tests; and SJFT. The peak oxygen consumption (V̇o2peak), maximum heart rate (HRmax), maximum aerobic power, and the variables corresponding to onset blood lactate accumulation (OBLA) were determined during the graded exercise tests. For the Wingate tests, peak power (PP) and mean power were used. Furthermore, the total number of throws, heart rate (HR) immediately and 1 minute after, and SJFT index were calculated. Four separated forward multiple linear regressions (forward model) were used to identify the physical fitness variables that best explained the SJFT variables. Upper-body V̇o2peak (ml·min−1·kg−1) explained 27% of variance in the total number of throws, whereas an additional 7% was explained by upper-body PP. Upper-body HRmax explained 46% of the variance in the HR immediately after the SJFT, whereas lower-body HR at OBLA accounted for an additional 8%. Lower-body HRmax explained 21% of the variance in the HR 1 minute after the SJFT, whereas peak lactate ([La−]peak) after the lower-body graded exercise test accounted for an additional 12%. Upper-body V̇o2peak (ml·min−1·kg−1) alone accounted for 15% of the variance in the SJFT index. Based on these results, the data suggest that physical fitness variables, derived from upper- and lower-body aerobic and anaerobic tests, have a small ability to predict the SJFT variables.
An Efficient Method of Generating Deterministic Small-World and Scale-Free Graphs for Simulating Real-World Networks

We propose a novel mechanism to generate a family of deterministic small-world and scale-free networks by inserting new nodes into old nodes. These models can characterize the distinguishing properties of many real-world networks because this novel class of networks incorporates some key properties characterizing a majority of real-world networked systems, namely a high clustering coefficient and a short characteristic path length. We also obtain some accurate results for their properties, including degree distribution, clustering coefficient and network diameter, exactly according to the proposed generation algorithm of the networks considered, and discuss them. The network representation approach provided here can be used to investigate the complexity of many real-world systems from the perspective of complex networks.