Miracle-dz/UniFAR-FTUs huggingface dataset:
Does functional diversity increase effectiveness of community care teams? The moderating role of shared vision, interaction frequency, and team reflexivity

textabstractAs interprofessional collaboration becomes more commonplace in health and social care, both scholars and practitioners are searching for ways to make the most out of functionally diverse teams. Earlier research has shown that the presence of different functional backgrounds may lead teams to perform better, because they have a larger pool of knowledge and experience to draw from. Other studies show, however, that functional diversity increases categorization, reduces team cohesion, and complicates interpersonal communication, thereby reducing performance. It remains unclear under which conditions positive or negative outcomes may occur. The present research tested the influence of functional diversity on team identity, team performance, and client satisfaction, and examined factors which may moderate these relationships. Based on earlier studies in this specific context, we focused on three team processes as possible moderators: shared vision, interaction frequency, and team reflexivity. In a survey among health and social care professionals working in community care teams in the Netherlands (n = 167), all three are shown to moderate the relationship between functional diversity and team effectiveness. In the absence of these processes, functional diversity appears to reduce team outcomes, whereas when these processes are present, the relationships are positive. In sum, in order for community care teams to reap the benefits of functional diversity, it is essential that members develop a shared vision, interact frequently, and practice team reflexivity.
Detection and tracking approach using an automotive radar to increase active pedestrian safety

Vulnerable road users, e.g. pedestrians, have a high impact on fatal accident numbers. To reduce these statistics, car manufactures are intensively developing suitable safety systems. Hereby, fast and reliable environment recognition is a major challenge. In this paper we describe a tracking approach that is only based on a 24 GHz radar sensor. While common radar signal processing loses much information, we make use of a track-before-detect filter to incorporate raw measurements. It is explained how the Range-Doppler spectrum of pedestrian can help to initiate and stabilize tracking even in occultation scenarios compared to sensors in series.
NON-FUNCTIONAL REQUIREMENT PREFERENCES FOR ‘ARDNAS’ SYSTEM

As grid resources are geographically distributed, efficient resource discovery and management has become one of the important requirements. Besides, Grid users are independent identities and negotiation is necessary for reconciling their diverse characteristics. Therefore special mechanism is required to negotiate and discover the required resource or similar resource as an alternative when discovery fails. However, the quality of the service being provided in the grid environment depends on both functional as well as the nonfunctional requirements [NFR]. But conflicts between NFRs are not yet resolved effectively. Towards this end, a system of ‘Non-Functional Requirement Preferences for ‘ARDNAS’ (Agent Based Resource Discovery with Alternate Solution) ‘NFR-ARDNAS ‘System’ is proposed to provide an expeditious and efficient resource and alternate resource when discovery fails with NFR preferences. In addition to the service provided to the grid user, the nonfunctional requirements preferences are also analyzed and the conflicts among them are resolved based on the trade-off analysis done with the help of fuzzy rule sets.
Simplest Fuzzy PI Controllers under Various Defuzzification Methods

This paper deals with the simplest fuzzy PI controllers under various defuzzification methods. The various simplest fuzzy PI controllers consist of two triangular input fuzzy sets and three triangular output fuzzy sets on the universe of discourse of input and output variables, respectively, linear control rules, intersection T-norm, Lukasiewicz and Zadeh OR T-conorms, and any valid inference methods and defuzzification methods. Properties of fuzzy PI controllers under various defuzzification methods are investigated through Simulation. Further, simulation results are included to demonstrate the influence defuzzification methods on the performance of the fuzzy PI controllers.Copyright c © 20032005 Yang’s Scientific Research Institute, LLC. All rights reserved.
A Unified Control Scheme for Track Seeking and Following of a Hard Disk Drive Servo System

This paper proposes a new nonlinear time-varying unified control scheme (UCS) for track-seeking and track-following in hard disk drives (HDDs) so as to avoid problem of mode switching. The proposed control scheme utilizes the concept of the conventional composite nonlinear feedback (CNF) scheme to achieve fast and smooth seeking. In order to be advantageous over the conventional CNF control, the UCS consists of both linear and nonlinear time-dependent components, which are independent of absolute seeking error. Simulation and implementation results show that, during track-seeking mode, the proposed scheme has a significantly better performance robustness against variations in seek length as compared to the conventional CNF control. For track-following, disturbance and noise models are involved in the problem formulation and sub-optimal H ¿ method is used to design the controller so as to achieve high head-positioning accuracy. It turns out that the unified controller removes 27.8% of the nonrepeatable disturbances and noise.
Affix-augmented stem-based language model for persian

Language modeling is used in many NLP applications like machine translation, POS tagging, speech recognition and information retrieval. It assigns a probability to a sequence of words. This task becomes a challenging problem for high inflectional languages. In this paper we investigate standard statistical language models on the Persian as an inflectional language. We propose two variations of morphological language models that rely on a morphological analyzer to manipulate the dataset before modeling. Then we discuss shortcoming of these models, and introduce a novel approach that exploits the structure of the language and produces more accurate. Experimental results are encouraging especially when we use n-gram models with small training dataset.
Hierarchical reinforcement learning: a hybrid approach

In this thesis we investigate the relationships between the symbolic and sub-symbolic methods used for controlling agents by artificial intelligence, focusing in particular on methods that learn. In light of the strengths and weaknesses of each approach, we propose a hybridisation of symbolic and subsymbolic methods to capitalise on the best features of each. We implement such a hybrid system, called RACHEL, which incorporates techniques from Teleo-Reactive Planning, Hierarchical Reinforcement Learning and Inductive Logic Programming. RACHEL uses a novel behaviour representation, as Reinforcement-Learnt Teleo-operators which define the behaviours in terms of their desired consequences but leave the implementation of the policies to be learnt by reinforcement learning. An RL-TOP is a symbolic description of the purpose of a behaviour, and is used by RACHEL as a planning operator and as the definition of a reward function. Two new hierarchical reinforcement learning algorithms are introduced, Planned Hierarchical Semi-Markov Q-Learning and Teleo-Reactive Q-Learning. The former is an extension of the Hierarchical Semi-Markov Q-Learning algorithm to use computer generated plans in place of task-hierarchies (which are commonly provided by the trainer). The latter is an elaboration of the algorithm to include more intelligent behaviour termination. The knowledge contained in the plan is used to determine when an executing behaviour is no longer appropriate, resulting in more efficient policies. Incomplete descriptions of the effects of behaviours can lead the planner to make false assumptions in building plans. As behaviours are learnt not implemented, not every effect of actions can be known in advance. RACHEL implements a “reflector” which monitors for such unwanted side-effects. Using ILP, it learns to predict when they will occur, and so repair its plans to avoid them. Together, the components of RACHEL form a system which is able to receive will achieve its goals, learn concrete policies and optimal choices of behaviour, discover and predict any unwanted side-effects that result and repair its plans to avoid them. It is a demonstration that different approaches to AI, symbolic and sub-symbolic, can be elegantly combined into a single agent architecture.
Novel failure prognostics approach with dynamic thresholds for machine degradation

Estimating remaining useful life (RUL) of critical machinery is a challenging task. It is achieved through essential steps of data acquisition, data pre-processing and prognostics modeling. To estimate RUL of a degrading machinery, prognostics modeling phase requires precise knowledge about failure threshold (FT) (or failure definition). Practically, degrading machinery can have different levels (states) of degradation before failure, and prognostics can be quite complicated or even impossible when there is absence of prior knowledge about actual states of degrading machinery or FT. In this paper a novel approach is proposed to improve failure prognostics. In brief, the proposed prognostics model integrates two new algorithms, namely, a Summation Wavelet Extreme Learning Machine (SWELM) and Subtractive-Maximum Entropy Fuzzy Clustering (S-MEFC) to predict degrading behavior, automatically identify the states of degrading machinery, and to dynamically assign FT. Indeed, for practical reasons there is no interest in assuming FT for RUL estimation. The effectiveness of the approach is judged by applying it to real dataset in order to estimate future breakdown of a real machinery.
A Superoxide Dismutase Capable of Functioning with Iron or Manganese Promotes the Resistance of Staphylococcus aureus to Calprotectin and Nutritional Immunity

Staphylococcus aureus is a devastating mammalian pathogen for which the development of new therapeutic approaches is urgently needed due to the prevalence of antibiotic resistance. During infection pathogens must overcome the dual threats of host-imposed manganese starvation, termed nutritional immunity, and the oxidative burst of immune cells. These defenses function synergistically, as host-imposed manganese starvation reduces activity of the manganese-dependent enzyme superoxide dismutase (SOD). S. aureus expresses two SODs, denoted SodA and SodM. While all staphylococci possess SodA, SodM is unique to S. aureus, but the advantage that S. aureus gains by expressing two apparently manganese-dependent SODs is unknown. Surprisingly, loss of both SODs renders S. aureus more sensitive to host-imposed manganese starvation, suggesting a role for these proteins in overcoming nutritional immunity. In this study, we have elucidated the respective contributions of SodA and SodM to resisting oxidative stress and nutritional immunity. These analyses revealed that SodA is important for resisting oxidative stress and for disease development when manganese is abundant, while SodM is important under manganese-deplete conditions. In vitro analysis demonstrated that SodA is strictly manganese-dependent whereas SodM is in fact cambialistic, possessing equal enzymatic activity when loaded with manganese or iron. Cumulatively, these studies provide a mechanistic rationale for the acquisition of a second superoxide dismutase by S. aureus and demonstrate an important contribution of cambialistic SODs to bacterial pathogenesis. Furthermore, they also suggest a new mechanism for resisting manganese starvation, namely populating manganese-utilizing enzymes with iron.
A Search for Dense Gas in Luminous Submillimeter Galaxies with the 100 m Green Bank Telescope

We report deep K-band (18-27 GHz) observations with the 100 m Green Bank Telescope of HCN(1-0) line emission toward the two submillimeter-selected galaxies (SMGs) SMM J02399-0136 (z = 2.81) and SMM J16359+6612 (z = 2.52). For both sources we have obtained spectra with channel-to-channel rms noise of σ ≤ 0.5 mJy, resulting in velocity-integrated line fluxes ≲0.1 Jy km s-1, although we do not detect either source. Such sensitive observations, aided by gravitational lensing of the sources, permit us to put upper limits of L ≲ 2 × 1010 K km s-1 pc2 on the intrinsic HCN(1-0) line luminosities of the two SMGs. The far-infrared (FIR) luminosities for all three SMGs with sensitive HCN(1-0) observations to date are found to be consistent with the tight FIR-HCN luminosity correlation observed in Galactic molecular clouds, quiescent spiral galaxies, and (ultra) luminous infrared galaxies in the local universe. Thus, the observed HCN luminosities remain in accordance with what is expected from the universal star formation efficiency per dense molecular gas mass implied by the aforementioned correlation, and more sensitive observations with today's large-aperture radio telescopes hold the promise of detecting HCN(1-0) emission in similar objects in the distant universe.
Improving view rendering quality and coding efficiency by suppressing compression artifacts in depth-image coding

We consider the effect of depth-image compression artifacts on the quality of virtual views rendered using neighboring views. Such view rendering processes are utilized in new video applications such as 3D television (3DTV) and free viewpoint video (FVV). We first analyze how compression artifacts in compressed depth-images result in distortions in rendered views. We show that the rendering position error is a monotonic function of the coding error. For the scenario in which cameras are arranged with parallel optical axes, we further demonstrate specific properties of rendering position error. Exploiting special characteristics of depth-images, namely smooth regions separated by sharp edges, we investigate a possible solution to suppress compression artifacts by encoding depth-images with a recently published sparsity-based in-loop de-artifacting filter. Simulation results show that applying such techniques not only provides significantly higher coding efficiency for depth-image coding, but, more importantly, also improves the quality of rendered views in terms of PSNR and subjective quality.
String matching query verification on cloud-hosted databases

Database query verification schemes attempt to provide authenticity, completeness, and freshness guarantees for queries executed on untrusted cloud servers. A number of such schemes currently exist in the literature, allowing query verification for queries that are based on matching whole values (such as numbers, dates, etc.) or for queries based on keyword matching. However, there is a notable gap in the research with regard to query verification schemes for pattern-matching queries. Our contribution here is to provide such a verification scheme that provides correctness guarantees for pattern-matching queries executed on the cloud. We describe a trivial scheme, âŸżand show how it does not provide completeness guarantees, and then proceed to describe our scheme based on efficient primitives such as cryptographic hashing and Merkle hash trees along with suffix arrays. We also provide experimental results based on a working prototype to show the practicality of our scheme.
Toward Engineering Synthetic Microbial Metabolism

The generation of well-characterized parts and the formulation of biological design principles in synthetic biology are laying the foundation for more complex and advanced microbial metabolic engineering. Improvements in de novo DNA synthesis and codon-optimization alone are already contributing to the manufacturing of pathway enzymes with improved or novel function. Further development of analytical and computer-aided design tools should accelerate the forward engineering of precisely regulated synthetic pathways by providing a standard framework for the predictable design of biological systems from well-characterized parts. In this review we discuss the current state of synthetic biology within a four-stage framework (design, modeling, synthesis, analysis) and highlight areas requiring further advancement to facilitate true engineering of synthetic microbial metabolism.
Sox8 is expressed at similar levels in gonads of both sexes during the sex determining period in turtles

A critical gene involved in mammalian sex determination and differentiation is the Sry‐related gene Sox9. In reptiles, Sox9 resembles that of mammals in both structure and expression pattern in the developing gonad, but a causal role in male sex determination has not been established. A closely related gene, Sox8, is conserved in human, mouse, and trout and is expressed in developing testes and not developing ovaries in mouse. In this study, we tested the possibility of Sox8 being important for sex determination or sex differentiation in the red‐eared slider turtle Trachemys scripta, in which sex is determined by egg incubation temperature between stages 15 and 20. We cloned partial turtle Sox8 and anti‐Müllerian hormone (Amh) cDNAs, and analyzed the expression patterns of these genes in developing gonads by reverse transcriptase‐polymerase chain reaction and whole‐mount in situ hybridization. While Amh is expressed more strongly in males than in females at stage 17, Sox8 is expressed at similar levels in males and females throughout the sex‐determining period. These observations suggest that differential transcription of Sox8 is not responsible for regulation of Amh, nor responsible for sex determination in turtle. Developmental Dynamics 231:387–395, 2004. © 2004 Wiley‐Liss, Inc.
Design and implementation of full bridge DC-DC converter for photovoltaic application

This communication discusses design and implementation issues of full bridge DC-DC converter developed for photovoltaic application. Here component selection criteria, design and manufacturing of transformer and inductor has beed discussed. Simulation is performed in MATLAB SimPowerSystems toolbox and experimentation is performed on the developed prototype. The converter efficiency is found to be 94%.
VERSION Title : High-resolution multi-transistor array recording of electrical field potentials in cultured brain slices Running title : Multi-transistor array recording in brain slices

We report on the recording of electrical activity in cultured hippocampal slices by a multi-transistor array (MTA) with 16384 elements. Time-resolved imaging is achieved with a resolution of 7.8 µm on an area of 1 mm 2 at 2 kHz. A read-out of fewer elements allows an enhanced time resolution. Individual transistor signals are caused by local evoked field potentials. They agree with micropipette measurements in amplitude and shape. The spatial continuity of the records provides time-resolved images of evoked field potentials and allows the detection of functional correlations over large distances. As examples, fast propagating waves of presynaptic action potentials are recorded as well as patterns of excitatory postsynaptic potentials across and along cornu ammonis.
Compensation of attacks on consensus networks

In this paper, we propose a methodology for compensation of attacks on consensus networks. We assume that the network achieves its desired consensus value under a normal operational setting i.e. when the networked system is not under attack. We propose an adaptive based technique to ensure that the networked system maintains its desired behavior when the system inputs are under some false-date injection attacks. The proposed method adaptively estimates attack signals and compensates their effects. We demonstrate the good performance of our proposed compensation scheme through simulation studies that consider both constant and time-varying attack signals.
DLD for Size Profiling of Smaller Blood Cells

This chapter examines the behaviour of the red blood cells and platelets in a deterministic lateral displacement (DLD) device with particular focus on platelets because of their complex and dynamic behaviour. Platelet function is vital yet highly variable. Rapid platelet and clotting analysis for point of care diagnostics is a growing area of interest. While commercial products exist for platelet function analysis, no product meets all clinical needs. We demonstrate the effectiveness of a new, rapid microfluidic method for platelet size and morphology measurements in whole blood that may provide more detailed information than current methods. The device described here continuously separates particles by size. Particles are displaced laterally in proportion to their size in a micro-fabricated post array, allowing particle size to be determined from exit position. Whole blood, labeled with PE-anti-CD41, was run through the device and the CD41 binds to platelet surfaces.
Online Classification with Specificity Constraints

We consider the online binary classification problem, where we are given m classifiers. At each stage, the classifiers map the input to the probability that the input belongs to the positive class. An online classification meta-algorithm is an algorithm that combines the outputs of the classifiers in order to attain a certain goal, without having prior knowledge on the form and statistics of the input, and without prior knowledge on the performance of the given classifiers. In this paper, we use sensitivity and specificity as the performance metrics of the meta-algorithm. In particular, our goal is to design an algorithm that satisfies the following two properties (asymptotically): (i) its average false positive rate (fp-rate) is under some given threshold; and (ii) its average true positive rate (tp-rate) is not worse than the tp-rate of the best convex combination of the m given classifiers that satisfies fp-rate constraint, in hindsight. We show that this problem is in fact a special case of the regret minimization problem with constraints, and therefore the above goal is not attainable. Hence, we pose a relaxed goal and propose a corresponding practical online learning meta-algorithm that attains it. In the case of two classifiers, we show that this algorithm takes a very simple form. To our best knowledge, this is the first algorithm that addresses the problem of the average tp-rate maximization under average fp-rate constraints in the online setting.
Theoretical and methodological pathways for research on elites

In this introductory essay to our special issue on elites, we outline some of the major challenges to research in this area and propose a series of theoretical and methodological pathways to address them. Theoretically we make four recommendations: (a) greater attentiveness to and specificity about the relationship between elites and power; (b) a clearer articulation of the relationships between elites and the varieties of capitalism; (c) far more attention to diversity within elites and the use of elites to understand forms of domination like white supremacy and masculine domination and (d) expanding beyond the orthodox form of Bourdieusian theoretical frameworks. Methodologically we outline how research using survey instruments, social network analysis (SNA) (and multiple correspondence analysis), interviews, ethnographic observation, experiments, archival research, administrative data and content analysis can each be deployed, built upon or redirected to help bring elites into greater focus.