Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Influencing Demand for International Tourism: Tourism Demand Analysis Using Structural Equation Modelling, Revisited

Previous work on the application of structural equation modelling to tourism demand analysis is extended by examining more varied origin–destination pairs, in particular those involving longer travel distances and fewer direct economic ties. The main potential economic determinants of international tourist flows disaggregated into holidays, business visits and visits to friends and relatives (VFR) are examined simultaneously using structural equation modelling. The tourist flows studied are inbound tourism to New Zealand from Australia, Japan, the UK and USA. The empirical results show that international trade plays the major role in influencing business tourism demand, retail sales are the major influence on the demand for foreign holidays, and new private car registrations are the major determinant of VFR tourism demand. Comparison of the previous and current studies demonstrates the consistent importance of international trade in influencing business tourism, and origin income and destination price in influencing VFR tourism.
Enabling Cooperative Inference of Deep Learning on Wearables and Smartphones

Deep Learning (DL) algorithm is the state-of-the-art algorithm of many computer science fields and applied on many intelligent mobile applications. In this paper, we propose a system called CoINF, a practical, adaptive, and flexible deep learning framework that enables cooperative inference between wearable devices (e.g., smartwatches and smart glasses) and handhelds. Our framework accelerates the processing and saves the energy consumption of generic deep learning models inference on wearables via judiciously offloading the workloads to paired handhelds at fine granularity in considering of the system environment, the application requirements, and user preference. Deployed as a user-space library, CoINF offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow, with all complicated offloading details hidden. We have implemented a prototype of CoINF on Android OS, and used real deep learning models to evaluate its performance on commercial off-the-shelf smartphone and smartwatches. The experimental results show that our framework can achieve substantial execution speedup and energy saving compared to wearable-only and handheld-only strategies.
Decentralized Control of Low-Voltage Islanded DC Microgrid Using Power Management Strategies

This paper intended to control a DC microgrid in islanded operation mode using decentralized power management strategies. The DC microgrid under study included a wind turbine generator (WTG), photovoltaic (PV), battery energy storage system (BESS) and dc constant power load. According to the newly proposed strategy, each of distributed generation sources of energy and battery energy storage system can be deployed independently within any controlled microgrid through the droop method. Proposed I/V characteristic curve could be regulated locally and in real-time based on the available power of DGs and the battery state of charge (SOC), to synchronize the module performances independently and establish the power balance in the DC microgrid. Proposed strategy for the battery enables the system to supply independently the power required for the load demand when the DGs are not capable of supplying the required power to the load. This can maintain the common bus voltage within the allowable range and establish the power balance in the DC microgrid. The proposed control strategy was applied locally and without dependency on telecommunication links or any centralized energy management system on each of the distributed generation modules and battery independently. The newly proposed power management strategy was simulated through the implementation of a low voltage DC microgrid in MATLAB/SIMULINK where its performance was evaluated.
Estimation of sleep status in sleep apnea patients using a novel head actigraphy technique

Polysomnography is a comprehensive modality for diagnosing sleep apnea (SA), but it is expensive and not widely available. Several technologies have been developed for portable diagnosis of SA in the home, most of which lack the ability to detect sleep status. Wrist actigraphy (accelerometry) has been adopted to cover this limitation. However, head actigraphy has not been systematically evaluated for this purpose. Therefore, the aim of this study was to evaluate the ability of head actigraphy to detect sleep/wake status. We obtained full overnight 3-axis head accelerometry data from 75 sleep apnea patient recordings. These were split into training and validation groups (2:1). Data were preprocessed and 5 features were extracted. Different feature combinations were fed into 3 different classifiers, namely support vector machine, logistic regression, and random forests, each of which was trained and validated on a different subgroup. The random forest algorithm yielded the highest performance, with an area under the receiver operating characteristic (ROC) curve of 0.81 for detection of sleep status. This shows that this technique has a very good performance in detecting sleep status in SA patients despite the specificities in this population, such as respiration related movements.
Evolution of water management in coastal Bangladesh: from temporary earthen embankments to depoliticized community-managed polders

This article examines the historical evolution of participatory water management in coastal Bangladesh. Three major shifts are identified: first, from indigenous local systems managed by landlords to centralized government agencies in the 1960s; second, from top-down engineering solutions to small-scale projects and people’s participation in the 1970s and 1980s; and third, towards depoliticized community-based water management since the 1990s. While donor requirements for community participation in water projects have resulted in the creation of ‘depoliticized’ water management organizations, there are now increasing demands for involvement of politically elected local government institutions in water management by local communities.
The State of Standards in Genomic Sciences

On July 20, 2009, we began the public phase of an experiment in open access publishing with the first issue of Standards in Genomic Sciences (SIGS) [1]. The rational for the journal was to fulfill a perceived need in the community for the continued publication of “genome papers”, the once familiar companion articles that accompanied the public release of genome sequencing projects. Those papers served not only as a formal record of the accomplishment of the individuals involved in the sequencing and annotation efforts, but also provided the initial (and often the only) description of the sequence itself [2]. However, by 2007, Liolios et al. [3] had already pointed out that the publication of such papers significantly lagged behind the release of new genome sequences, leaving a gap in the public research record. Beyond genome reports, there was also a growing demand for other types of articles to meet the needs of a growing ‘omics community including detailed standard operating procedures that provide sufficient detail to not only understand the methods by which sequences were generated and annotated, but to also reproduce those results. Also needed was a reliable venue for publication of white papers and the proceedings of meetings of standards-setting bodies, such as the Genomic Standards Consortium (GSC) [4]. SIGS was conceived to fill these needs.
Absorptive Capacity Constituents in Knowledge-Intensive Industries in Serbia

The aim of this paper was to explore the determining constituents of absorptive capacity and their influence on the overall absorptive capacity level in the organizations from knowledge-intensive industries in the Republic of Serbia. The research was conducted in order to analyze how does the capability of the organizations from Serbian knowledge-intensive industries to acquire external knowledge, develop their knowledge base, communicate and exploit the absorbed knowledge affect their absorptive capacity. The methodology included quantitative and qualitative research method based on a questionnaire. The data collected were analyzed with the absorptive capacity evaluation model and by applying discriminant function analysis as a statistical method. The basic result shows that the absorptive capacity of organizations from knowledge-intensive industries in Serbia is clearly dependent on the scope of the available relevant knowledge. The fundamental conclusion as the result of this research indicates that the enrichment of the organizational knowledge bases with the relevant new content strongly enhances the absorptive capacity of organizations from knowledge-intensive industries in Serbia. Further research on this topic should be directed towards investigating the relationship between the existing knowledge base and the innovation capability in organizations from knowledge-intensive industries.
Learning predictive statistics from temporal sequences: Dynamics and strategies

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.
The Dynamics of Women Disconnected from Employment and Welfare

This study analyzes factors associated with transitions into and out of disconnectedness, which is defined as being disconnected from the labor market, welfare, and other substantial financial supports. Findings are based on discrete-time hazard models estimated with a sample of New Jersey welfare recipients followed for 5 years. Work history, human capital, policy environment, and economic conditions are found to be the factors most strongly associated with the dynamics of disconnectedness. More work history and human capital are associated with a lower probability of becoming disconnected and a higher probability of leaving disconnected status for employment. Individuals relying on unemployment insurance benefits are at high risk of becoming disconnected. This suggests that the expiration of such benefits often leads to disconnectedness. Receipt of sanctions for noncompliance with welfare’s work requirements are found to triple the risk of becoming disconnected. Finally, transitions into disconnectedness increase sharply with increases in the unemployment rate.
A methodology for specifying and controlling compliant robot motion

A methodology is developed to integrate a sixdimensional force/torque sensor into a robot programming and control system. It consists of : [1] a formalism for specifying compliant motion tasks, based on orthogonal task frames with force, velocity, and tracking directions, and provided with tools to model the task kinematics; [2] a strategy for the task execution which makes use of external control loops closed around the robot positioning system. Several experiments show the applicability of the methodology.
Effects of Biofeedback on Control and Generalization of Nasalization in Typical Speakers.

Purpose The purpose of this study was to determine the effects of biofeedback on control of nasalization in individuals with typical speech. Method Forty-eight individuals with typical speech attempted to increase and decrease vowel nasalization. During training, stimuli consisted of consonant-vowel-consonant (CVC) tokens with the center vowels /a/ or /i/ in either a nasal or nonnasal phonemic context (e.g., /mim/ vs. /bib/), depending on the participant's training group. Half of the participants had access to augmentative visual feedback during training, which was based on a less-invasive acoustic, accelerometric measure of vowel nasalization-the Horii oral-nasal coupling (HONC) score. During pre- and posttraining assessments, acoustically based nasalance was also measured from the center vowels /a/, /i/, /æ/, and /u/ of CVCs in both nasal and nonnasal contexts. Results Linear regressions indicated that both phonemic contexts (nasal or nonnasal) and the presence of augmentative visual feedback during training were significant predictors for changes in nasalance scores from pre- to posttraining. Conclusions Participants were able to change the nasalization of their speech following a training period with HONC biofeedback. Future work is necessary to examine the effect of such training in individuals with velopharyngeal dysfunction.
The Relationships between Adverse Events, Early Antecedents, and Carbon Dioxide Reactivity as an Intermediate Phenotype of Panic Disorder

Background: Although adverse events have been consistently described to precede and potentially precipitate the onset of panic disorder, there is no information about their ability to alter the individual reactivity to inhaled carbon dioxide, a putative intermediate phenotype of susceptibility to panic disorder. Method: Seven-hundred twelve subjects belonging to the general population-based Norwegian Institute of Public Health Twin Panel underwent a 35% CO2/65% O2 inhalation challenge test and interview-based lifetime assessments of DSM-IV panic disorder, separation anxiety disorder, childhood parental separation/loss, major life events, adverse events of suffocative nature and common stressful life events. Regression models were applied to predict global subjective anxiety and DSM-IV panic symptoms after 35% CO2/65% O2 inhalation. Results: The responses to the challenge measured as semicontinuous variables were predicted by symptoms of childhood separation anxiety, childhood parental loss, common stressful events, major life events, suffocative events and the female gender. The role of most of these predictors was confirmed and held true after the exclusion of subjects with lifetime panic attacks/disorder from the analyses. Conclusions: Several factors which have been reported by previous clinical studies to influence the individual susceptibility to develop panic disorder seem to affect the individual reactivity to inhaled carbon dioxide in people from the general population. Some elements of risk may impact simultaneously upon the individual liability to panic and exaggerated sensitivity to hypercapnia.
Average cost pricing in the compound feed industry

Considers the incidence of average cost pricing in the compound feed industry in the context of strategic and marketing aspects of firms′ behaviour. The empirical analysis uses a cross‐section econometric model derived from the Koutsoyiannis model of average cost pricing and incorporating assumptions about cost and price adjustment. The model is estimated using primary data on prices and costs for a sample of firms in the North of England for main categories of compound feed products. The results indicate support for the hypothesis in association with these adjustment factors which accommodate both cost and strategic influences within product segments of the market.
The Olympus synthesis system

A description is given of the Olympus synthesis system for digital design, a vertically integrated set of tools for multilevel synthesis, technology mapping, and simulation. The system includes behavioral, structural, and logic synthesis tools, and provides technology mapping and simulation. Since it is targeted for semicustom implementations, its output is in terms of gate netlists. Instead of supporting placement and routing tools, Olympus provides an interface to standard physical design tools. The system supports the synthesis of ASICs (application specific integrated circuits) from behavioral descriptions written in a hardware description language called HardwareC. Two internal models represent the hardware at different levels of abstraction and provide a way to pass design information among different tools. Olympus has been used to design three ASIC chips, and has been tested against benchmark circuits for high-level and logic synthesis. <<ETX>>
Designing Judicial Review: Interest Groups, Congress, and Communications Policy

Members of Congress and interest groups fiercely struggle over the seemingly unimportant procedural details of legislation such as the provisions for judicial review. Charles R. Shipan, in a study based on a detailed consideration of congressional debates over communications legislation, argues that the actors realize that current procedural choices will structure future alternatives and thus are willing to expend considerable resources over these issues. Using a rational choice framework, Shipan argues that provisions for judicial review, such as the specification of which agency actions are reviewable and which courts have review authority, are among the issues over which interested parties struggle because these issues will significantly affect the outcome of important future court action.Shipan tests his theory in a detailed exploration of the development of communications legislation during the 1920s and 1930s. This is a rich period in which to study the importance of judicial review provisions, for, while most political actors accepted the courts as part of the regulatory process, the concept of assigning broad decision-making powers to agencies was new and controversial. In addition, regulation of radio was both an important issue and one fraught with uncertainty, thus inducing members of Congress and interest groups to attempt to plan ahead for future actions. Shipan examines the motivations, actions, and choices of both interest groups and members of Congress. He then looks at the impact of the choices made on later court action in the communications legislation.This book will appeal to political scientists and legal scholars interested in the politics of judicial review, the courts, legislative politics, and communication policy.Charles R. Shipan is Assistant Professor of Political Science, University of Iowa, and is a Robert Woods Johnson Fellow, University of Michigan.
Interactive 3D segmentation by tubular envelope model for the aorta treatment

We propose a novel interactive 3D segmentation approach and geometric model definition called tubular envelope model. It is conceived to express the shape of tubular objects. The main challenges we have achieved are the speed and interactivity of the construction. A computer program designed for this task gives the user full control of the shape and precision, with no significant computational errors. Six CT (computed tomography) aortic dissection images have been used for the tubular envelopes construction. Hence, we have proposed a generic parametric model of the aorta for its interactive construction. It leads us to rapid visualization and navigation inside the artery (rough virtual angioscopy). The low complexity of the model and the ease of interactive design makes the tubular envelope suitable for aorta segmentation in comparison to the other segmentation methods. The model accuracy is adjustable by the user according to his requirements; the time of construction is approved by clinicians. More generally, the tubular envelope could be used in other applications, e.g. to define a region of interest for more precise segmentation or feature extraction inside, to develop a parametric model with deformation capabilities.
Biogenesis of a Respiratory Complex Is Orchestrated by a Single Accessory Protein*

The biogenesis of respiratory complexes is a multistep process that requires finely tuned coordination of subunit assembly, metal cofactor insertion, and membrane-anchoring events. The dissimilatory nitrate reductase of the bacterial anaerobic respiratory chain is a membrane-bound heterotrimeric complex nitrate reductase A (NarGHI) carrying no less than eight redox centers. Here, we identified different stable folding assembly intermediates of the nitrate reductase complex and analyzed their redox cofactor contents using electron paramagnetic resonance spectroscopy. Upon the absence of the accessory protein NarJ, a global defect in metal incorporation was revealed. In addition to the molybdenum cofactor, we show that NarJ is required for specific insertion of the proximal iron-sulfur cluster (FS0) within the soluble nitrate reductase (NarGH) catalytic dimer. Further, we establish that NarJ ensures complete maturation of the b-type cytochrome subunit NarI by a proper timing for membrane anchoring of the NarGH complex. Our findings demonstrate that NarJ has a multifunctional role by orchestrating both the maturation and the assembly steps.
A Study on Big Data Techniques and Applications

We are living in on-Demand Digital Universe with data spread by users and organizations at a very high rate. This data is categorized as Big Data because of its Variety, Velocity, Veracity and Volume. This data is again classified into unstructured, semi-structured and structured. Large datasets require special processing systems; it is a unique challenge for academicians and researchers. Map Reduce jobs use efficient data processing techniques which are applied in every phases of Map Reduce such as Mapping, Combining, Shuffling, Indexing, Grouping and Reducing. Big Data has essential characteristics as follows Variety, Volume and Velocity, Viscosity, Virality. Big Data is one of the current and future research frontiers. In many areas Big Data is changed such as public administration, scientific research, business, The Financial Services Industry, Automotive Industry, Supply Chain, Logistics, and Industrial Engineering, Retail, Entertainment, etc. Other Big Data applications are exist in atmospheric science, astronomy, medicine, biologic, biogeochemistry, genomics and interdisciplinary and complex researches. This paper is presents the Essential Characteristics of Big Data Applications and State of-the-art tools and techniques to handle data-intensive applications and also building index for web pages available online and see how Map and Reduce functions can be executed by considering input as a set of documents.
Global output-feedback asymptotic stabilization for a class of uncertain nonlinear systems with unknown growth rate

In this paper, the global asymptotic stabilization by output-feedback is investigated for a class of uncertain nonlinear systems with unmeasured states dependent growth. Compared with the closely related works [9, 10, 12, 13], the remarkableness of the paper is that either the growth rate is an unknown constant or the dimension of the closed-loop system is significantly reduced, mainly due to the introduction of a distinct dynamic high-gain observer based on new updating law. Motivated by the related stabilization results, and by skillfully using the methods of universal control and backstepping, we obtain the design scheme to adaptive output-feedback stabilizing controller to guarantee the global asymptotic stability of the resulting closed-loop system. Additionally, a numerical example is considered to demonstrate the effectiveness of the proposed method.