Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of autonomy and supportive leadership on employee vitality

Most public management studies focus on passive employee outcomes such as job satisfaction and commitment. However, there is much less known about active employee outcomes. Therefore, this study focuses on active employee outcomes within the public sector: employee vitality. Using self-determination theory (SDT), which also has received relatively limited attention in public management, this study investigated two vitality-fostering aspects: autonomy and supportive leadership. According to SDT, situations that support autonomy will enhance a person’s enjoyment of activities and the autonomous self-regulation of behavior. Furthermore, leaders who are supportive show concern for their subordinates and their feelings and enable the intrinsic motivation of their employees. Through their communication and information sharing, supportive leaders increase feelings of self-efficacy and increase the feeling that people are at the origin of their actions. It is hypothesized that employees’ vitality is positively influenced when they have autonomy and have a leader with a supportive leadership style. This was investigated using a multi-method design: a survey amongst 1.502 healthcare sector employees and an experiment using 102 students from a Dutch university. By using a multimethod design, this study answers to calls for 1) more use of multi-method designs and 2) more use of experiments in the public sector. This increases the usable knowledge for public professionals. Results show that that a) autonomy positively influences employee vitality, b) supportive leadership positively influences employee vitality and c) autonomy and supportive leadership do not reinforce their effects on vitality, contrary to expectation. Implications for management scholars and practitioners are discussed.
Monotone Forecasts

In this paper we provide necessary and sufficient conditions for the distribution of demand in the future to be stochastically increasing in the demand that has been observed in the past. We base our analysis on the multiperiod inventory model examined by Eppen and Iyer (1997). In the process of establishing the necessary and sufficient conditions we develop a new property called the sequential monotone likelihood ratio property.
Social Clicks: What and Who Gets Read on Twitter?

Online news domains increasingly rely on social media to drive traffic to their websites. Yet we know surprisingly little about how a social media conversation mentioning an online article actually generates clicks. Sharing behaviors, in contrast, have been fully or partially available and scrutinized over the years. While this has led to multiple assumptions on the diffusion of information, each assumption was designed or validated while ignoring actual clicks. We present a large scale, unbiased study of social clicks---that is also the first data of its kind---gathering a month of web visits to online resources that are located in 5 leading news domains and that are mentioned in the third largest social media by web referral (Twitter). Our dataset amounts to 2.8 million shares, together responsible for 75 billion potential views on this social media, and 9.6 million actual clicks to 59,088 unique resources. We design a reproducible methodology and carefully correct its biases. As we prove, properties of clicks impact multiple aspects of information diffusion, all previously unknown:(i) Secondary resources, that are not promoted through headlines and are responsible for the long tail of content popularity, generate more clicks both in absolute and relative terms; (ii) Social media attention is actually long-lived, in contrast with temporal evolution estimated from shares or receptions; (iii) The actual influence of an intermediary or a resource is poorly predicted by their share count, but we show how that prediction can be made more precise.
Improving alignment of faces for recognition

Face recognition systems for uncontrolled environments often work through an alignment, feature extraction, and recognition pipeline. Effective alignment of faces is thus crucial as can be an entry point in the process and poor alignments can greatly affect recognition performance. The task of alignment is particularly difficult when a face comes from highly unconstrained environments or so called faces in the wild. A lot of recent research activity has focused on faces in the wild and even simple similarity or affine transformations have proven both effective and essential to achieving state of the art performance. In this paper we explore a straightforward, fast and effective approach to aligning faces based on detecting facial landmarks using Haar-like image features and a cascade of boosted classifiers. Our approach is reminiscent of widely used face detection approaches, but focused on much more detailed features of a face such eye centres, the nose tip and corners of the mouth. This process generates multiple candidates for each landmark and we present a fast and effective filtering strategy allowing us to find sets of landmarks that are consistent. Our experiments show that this approach can outperform contemporary methods and easily fits into popular processing pipelines for faces in the wild.
Emerging Trends in “Smart Probiotics”: Functional Consideration for the Development of Novel Health and Industrial Applications

The link between gut microbiota and human health is well-recognized and described. This ultimate impact on the host has contributed to explain the mutual dependence between humans and their gut bacteria. Gut microbiota can be manipulated through passive or active strategies. The former includes diet, lifestyle, and environment, while the latter comprise antibiotics, pre- and probiotics. Historically, conventional probiotic strategies included a phylogenetically limited diversity of bacteria and some yeast strains. However, biotherapeutic strategies evolved in the last years with the advent of fecal microbiota transplant (FMT), successfully applied for treating CDI, IBD, and other diseases. Despite the positive outcomes, long-term effects resulting from the uncharacterized nature of FMT are not sufficiently studied. Thus, developing strategies to simulate the FMT, using characterized gut colonizers with identified phylogenetic diversity, may be a promising alternative. As the definition of probiotics states that the microorganism should have beneficial effects on the host, several bacterial species with proven efficacy have been considered next generation probiotics. Non-conventional candidate strains include Akkermansia muciniphila, Faecalibacterium prausnitzii, Bacteroides fragilis, and members of the Clostridia clusters IV, XIVa, and XVIII. However, viable intestinal delivery is one of the current challenges, due to their stringent survival conditions. In this review, we will cover current perspectives on the development and assessment of next generation probiotics and the approaches that industry and stakeholders must consider for a successful outcome.
Compact signatures for high-speed interest point description and matching

Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statistical classification to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementation on low-memory devices. In this paper, we show that we can exploit the sparseness of these signatures to compact them, speed up the computation, and drastically reduce memory usage. We base our approach on Compressive Sensing theory. We also highlight its effectiveness by incorporating it into two very different SLAM packages and demonstrating substantial performance increases.
Cold Flow Simulations for a Pulse Detonation Rocket Ejector

This paper reports an exploratory experimental cold flow test of the first mode in a multi-mode propulsion concept. A small model of a wallmounted pulse detonation rocket (PDR) ejecting into a duct was fabricated and integrated into the supersonic wind tunnel at the University of Texas at Arlington (UTA). Although supersonic testing proved to be inconclusive, the static tests showed that pulsed flows entrain more mass than steady state flows. It also indicated that thrust augmentation is more favorable at low frequencies for this model. Background Pulse detonation engines (PDE) have been researched extensively as an alternate form for high-speed propulsion. A concept has been proposed for the adaptation of a PDE into a type of scramjet in which the PDE ejects into a subsonic or supersonic secondary flow to provide thrust. A model of such a configuration was developed and tested in the supersonic blow-down tunnel at the Aerodynamics Research Center (ARC) at the University of Texas at Arlington (UTA). This was done as a means of determining if this is a viable option for future propulsion. The first step is to conduct “cold-flow” simulations in which the gas cycles in the PDE chamber are left undetonated to provide a baseline for future experiments. As a means of safety, atmospheric air is used to replace the fuel and the oxidizer since there is no combustion. That is the basis for this research and thesis. Multi-mode Propulsion Concept In the National Aerospace Plane program, as well as other hypersonic vehicle proposals, one of the major obstacles that must be overcome is the development of a propulsion system that can transition between all flight regimes. A propulsion system for this type of application is needed to start at rest, accelerate through the transonic region, and continue to accelerate to a hypersonic cruise or even escape the sensible atmosphere. One novel approach to solving this problem is a multi-mode propulsion concept proposed in Munipalli et al. . This system proposed takes advantage of ejectors and detonation physics as a means of providing thrust. This system operates with four distinct modes (Fig 1) as shown below. (1) An ejector augmented pulsed detonation rocket for take off to moderate supersonic Mach numbers (2) A pulsed normal detonation wave mode at combustion chamber Mach numbers less than the Chapman-Jouguet Mach number, (3) An oblique detonation wave mode for Mach numbers in the air-breathing regime that are higher than the Chapman-Jouguet Mach number, and (4) A pure pulsed detonation rocket (PDE) mode of operation at high altitude. The obvious advantage to this is all modes can make use of the same internal geometry, eliminating the need for additional flow paths. Another benefit is the atmospheric air entering the duct does not have to be slowed to subsonic speeds at higher Mach numbers. This greatly reduces the losses in total pressure associated with shock waves and, in turn, increases efficiency. Shock waves also increase the static temperature, and without them, the gas Figure 1: Schematic of proposed
An Optimization Approach for Job-shop with Financial Constraints - In the Context of Supply Chain Scheduling Considering Payment Delay between Members

In this paper the use of Job-Shop Scheduling Problem (JSSP) is addressed as a support for a supply chain scheduling considering financial exchange between different supply chain partners. The financial exchange is considered as the cash flow exchanges between different upstream and downstream partners. Moreover, several suppliers are involved in operations. The problem under study can be viewed as an extension of the classical JSSP. Machines are considered as business or logistic units with their own treasury and financial exchanges happen between the different partners. The goal then is to propose the best schedule considering initial cash flows in treasuries as given data. The problem is formulated as integer linear programming model, and then a powerful GRASPxELS algorithm is developed to solve large scale instances of the problem. The experiments on instances with financial constraints proved the methods addressed the problem efficiently in a short amount of time, which is less than a second in average.
Association between Thrombophilia and the Post-Thrombotic Syndrome

The post thrombotic syndrome (PTS) is a chronic condition that develops in 20%–40% of deep vein thrombosis (DVT) patients. While risk factors that predispose to the development of venous thromboembolism (VTE) are widely known, factors that influence the development of PTS after DVT have not been well elucidated. Over 10% of the general population is affected by one or more identifiable inherited thrombophilias which have been shown to underlie at least 1/3 of cases of VTE. The various thrombophilias are important risk factors for VTE, but it is unknown whether they also increase the risk for development of PTS. We performed a review of studies that have reported on the association between thrombophilia and the development of PTS in populations of patients with DVT and with chronic venous ulcers. Studies vary with regards to the definition of PTS, study design, follow-up period, and present conflicting results. Based on these results, the question of whether thrombophilia predisposes to the development of PTS remains unanswered.
An Improved Approach for Breast Cancer Detection in Mammogram based on Watershed Segmentation

accurate technique for breast tumor segmentation is a critical step for monitoring and quantifying breast cancer. The fully automated tumor segmentation in mammograms presents many challenges related to characteristics of an image. In this paper, a hybrid segmentation algorithm, which combines the watershed transform and level set techniques, is proposed. Since watershed segmentation is based on pixel density variation that is present in all mass tumors, it was fairly successful in locating tumors under all conditions. However it is very sensitive to small variations of the image magnitude and consequently the number of generated regions is undesirably large and the segmented boundaries are not smooth enough. Meanwhile Level set methods offer a powerful approach for the medical image segmentation since it can handle any of the cavities, concavities, convolution, splitting or merging. However, this method requires specifying initial curves and can only provide good results if these curves are placed near symmetrically with respect to the object boundary. In our proposed technique a watershed segmentation algorithm was developed to initially locate breast mass tumor candidates. In order to facilitate and improve the detection step, the segmentation results is treated as the initial localization of the desired contour, and used in the following level set method, which provides closed, smoothed and accurately localized contours or surfaces. Experimental results show the significant improvement of the final segmentation accuracy.
Endogenous natriuretic and ouabain-like factors. Their roles in body fluid volume and blood pressure regulation.

An endogenous ouabain-like sodium pump inhibitor was demonstrated originally in serum or plasma of acutely extracellular fluid volume (ECFV) expanded animals and humans. Since then numerous studies have confirmed the presence of ouabain-like factor(s) (OLF) in blood, urine, cerebrospinal fluid, and various tissues including the heart and hypothalamus. Some of these OLFs represent well-known endogenous compounds, eg, free unsaturated fatty acids, which in vitro exhibit inhibition of transepithelial sodium transport, direct inhibition of the Na-K-ATPase enzyme, displacement of 3H-ouabain from its membrane receptor, and crossreaction with a digoxin antibody. Small molecular weight (MW) OLFs of yet unknown peptidic or nonpeptidic nature, which may be of hypothalamic origin, were also detected in various animal models of hypertension and in hypertensive patients. They may play a pathophysiological role especially in salt- and volume-dependent forms of hypertension. Our results show that OLFs increase basal and vasopressin-stimulated intracellular Ca2+ release in rat vascular smooth muscle cells in culture and in human platelets similar to the newly discovered endothelin. In addition, a natriuretic factor (natriuretic hormone) was detected by bioassay in plasma and urine, whose activity changes in parallel with sodium intake. We found that this natriuretic factor is associated with small peptides with a MW of less than 1,000. It is, however, unlikely that the two biological properties, ie, the ouabain-like and natriuretic activities, reside in a single compound. A number of circulating OLFs is certainly not identical with a humoral natriuretic factor. Nevertheless, there is increasing evidence for multiple interactions between OLF and the atrial natriuretic peptide (ANP). (ABSTRACT TRUNCATED AT 250 WORDS)
Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics

Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.
Enzyme-Linked Immunosorbent Assay for Quantitation of Human Antibodies to Pneumococcal Polysaccharides

Streptococcus pneumoniae is a major human pathogen causing pneumonia, sepsis, meningitis, and otitis media ([12][1]). It causes infections most often in young children ([12][1]) and elderly adults ([1][2]) because their immune systems are either unprepared or unable to respond effectively to
MinEMail: SMS alert system for managing critical emails

Email is the primary method of digital communication for most people, but the overwhelming quantity has led to a poverty of attention. Existing manual and automatic solutions that aim to save important emails from falling through the cracks have begun to address this problem, but may increase user workload, sacrifice efficiency, or fail to identify high value communications. In response, we developed MinEMail, an alert system that uses a text message (SMS) to remind and notify users of critical emails that may have been missed or forgotten. MinEMail provides an alert infrastructure as well as accurately labeling and predicting which emails are critical, and when and how they need to be addressed. To motivate our system, we also present an up-front study with 777 participants that aims to understand the state and limitations of email and SMS in enterprise. We conduct an experience sampling study of over 3000 emails in order to construct MinEMail's predictive models. Finally, we present the results from a 15 user ecologically valid real-world deployment of MinEMail in enterprise.
Whistle-Blowing in Organizations

Preface. Introduction Who Blows the Whistle? The Prosocial Organizational Behavior Model and Personal Predictors of Whistle-blowing. Situational Predictors of Whistle-blowing, and Recent Theoretical Developments. A Model of the Predictors and Outcomes of Retaliation. What Predicts Whistle-blowing Effectiveness? We Have a Lot to Learn. The Legal Status of Whistle-blowing. Practical Implications of the Research and Legal Changes, and Conclusion. References.
Human NKG2E Is Expressed and Forms an Intracytoplasmic Complex with CD94 and DAP12

The NKG2 family of NK receptors includes activating and inhibitory members. With the exception of the homodimer-forming NKG2D, NKG2 receptors recognize the nonclassical MHC class I molecule HLA-E, and they can be subdivided into two groups: those that associate with and signal through DAP12 to activate cells, and those that contain an ITIM motif to promote inhibition. The function of NKG2 family member NKG2E is unclear in humans, and its surface expression has never been conclusively established, largely because there is no Ab that binds specifically to NKG2E. Seeking to determine a role for this molecule, we chose to investigate its expression and ability to form complexes with intracellular signaling molecules. We found that NKG2E was capable of associating with CD94 and DAP12 but that the complex was retained intracellularly at the endoplasmic reticulum instead of being expressed on cell surfaces, and that this localization was dependent on a sequence of hydrophobic amino acids in the extracellular domain of NKG2E. Because this particular sequence has emerged and been conserved selectively among higher order primates evolutionarily, this observation raises the intriguing possibility that NKG2E may function as an intracellular protein.
Evaluating state-space abstractions in extensive-form games

Efficient algorithms exist for finding optimal policies in extensive-form games. However, human-scale problems are typically so large that this computation remains infeasible with modern computing resources. State-space abstraction techniques allow for the derivation of a smaller and strategically similar abstract domain, in which an optimal strategy can be computed and then used as a suboptimal strategy in the real domain. In this paper, we consider the task of evaluating the quality of an abstraction, independent of a specific abstract strategy. In particular, we use a recent metric for abstraction quality and examine imperfect recall abstractions, in which agents "forget" previously observed information to focus the abstraction effort on more recent and relevant state information. We present experimental results in the domain of Texas hold'em poker that validate the use of distribution-aware abstractions over expectation-based approaches, demonstrate that the new metric better predicts tournament performance, and show that abstractions built using imperfect recall outperform those built using perfect recall in terms of both exploitability and one-on-one play.