Miracle-dz/UniFAR-FTUs huggingface dataset:
Bringing Flexible SDN Routing and Network Functions into the Interdomain Context

Software Defined Networks and Network Function Virtualization offer new opportunities to control increasingly flexible networks using software. These technologies have already been successfully applied within networks to enable a range of useful applications that enhance network performance, security, programmability, and more. However, the power that SDN and NFV provide have not been thoroughly taken advantage of in interdomain settings. Applying these technologies in an interdomain context raises a host of challenges that stem from a lack of central controller that can dictate policy to different autonomous systems. But this also creates many new opportunities to solve interesting and challenging use cases. This paper explores two models for how networks can expose their software programmability for other remote networks to take advantage of. The first focuses on interdomain routing, and the proposal of a model that would allow a provider network to expose its FIB, and have clients select specific routes and have them written into the provider RIB. This approach enables potentially interesting use cases for security and inbound traffic engineering, but presents challenges to BGP stability. The second model we explore would allow client networks to select network functions and have them run on provider networks. This would essentially allow a network to place its own software middleboxes outside its network boundary. Once again, this approach may facilitate useful applications that improve network performance, security, and more, but creates challenges of coordination between clients and providers. This paper details these two models for interdomain cooperation, and offers potential solutions to some of the challenges that arise.
Assessment of Self-Medication and Health Pursuing Behaviour among General Public in Narasaraopet, Andhra Pradesh

In present days Patients’ behavior in making decisions regarding health is currently changing from passive recipients to recipients who play an active role in taking action to control their health and taking self-care initiatives. This study was conducted to evaluate the people’s health seeking behavior among general public and its associated factors and to evaluate the medicine taking behavior in public and the practice of self-medication. A cross-sectional study was undertaken among general public in, Andhra Pradesh, India. A convenience sampling of 300 participants successfully completed the survey. Self-administered questionnaires were distributed among the residents in the Narasaraopet. This study showed that most of the participants choose to consult the physician when they experience any health problems (47.6%), followed by self-medication (18.4%). The first action for consulting the physician was significantly predicted by respondents and prevalence of self-medication was 61%. The practice of self medication was significantly associated with educated people, people with alone living status and people with more selfcare orientation. From our study we concluded that, increasing the awareness of the public about the rational choice of getting medical assistance is a very important issue to control their health. A health education program is needed to increase the awareness about the use of medicines among the general public and to enable them to make the right decisions relating to health problems.
Bayesian Structural Equation Models via Parameter Expansion

This article describes blavaan, an R package for estimating Bayesian structural equation models (SEMs) via JAGS and for summarizing the results. It also describes a novel parameter expansion approach for estimating specific types of models with residual covariances, which facilitates estimation of these models in JAGS. The methodology and software are intended to provide users with a general means of estimating Bayesian SEMs, both classical and novel, in a straightforward fashion. Users can estimate Bayesian versions of classical SEMs with lavaan syntax, they can obtain state-of-the-art Bayesian fit measures associated with the models, and they can export JAGS code to modify the SEMs as desired. These features and more are illustrated by example, and the parameter expansion approach is explained in detail.
Improving Neural Machine Translation Using Noisy Parallel Data through Distillation

Due to the scarcity of parallel training data for many language pairs, quasi-parallel or comparable training data provides an important alternative resource for training machine translation systems for such language pairs. Since comparable corpora are not of as high quality as manually annotated parallel data, using them for training can have a negative effect on the translation performance of an NMT model. We propose distillation as a remedy to effectively leverage comparable data where the training of a student model on combined clean and comparable data is guided by a teacher model trained on the high-quality, clean data only. Our experiments for Arabic-English, Chinese-English, and German-English translation demonstrate that distillation yields significant improvements compared to off-the-shelf use of comparable data and performs comparable to state-of-the-art methods for noise ﬁltering.
Large Scale 2D Laser SLAM using Truncated Signed Distance Functions

For deployment in previously unknown, unstructured and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in the environment and create a map of it using a simultaneous localization and mapping (SLAM) approach. While most existing lidar-based methods use occupancy grids to represent a map, the use of truncated signed distance functions (TSDFs) is investigated in this paper to improve accuracy and robustness. In contrast to occupancy grids, TSDFs represent the distance to the nearest surface in every grid cell. This enables sub-pixel precision during localization and increases the basin of convergence of scan matching. To enable consistent mapping of large spaces, an efficient branch-and-bound based loop closure detection is applied. The evaluation of the proposed approach with publicly available benchmark data shows that the proposed approach yields improved accuracy in comparison to occupancy grid based methods, while requiring similar runtime. Furthermore, it is demonstrated that the proposed approach is able to map a large scale environment with urban search and rescue elements in real-time.
Impacts of transboundary air pollution and local emissions on PM 2.5 pollution in the Pearl River Delta region of China and the public health, and the policy implications

Despite a downward trend in pollutant levels because of a series of emission control policies, the Pearl River Delta ( PRD ) region continues to suffer from a high number of ﬁ ne particulate matter ( PM 2.5 ) events and the resultant public health impacts. To effectively control PM 2.5 in the region, a comprehensive understanding of source contribution and PM 2.5 responses to various emission species is critical. We applied the Community Multiscale Air Quality Modeling System together with the high-order decoupled direct method, to simulate air quality and PM 2.5 sensitivity and examined PM 2.5 responses to emission species in the PRD region in the four seasons of 2010. We employed a concentration-response function to quantify the resultant number of premature mortalities attributable to outdoor PM 2.5 . We estimated that local and transboundary air pollution ( TAP ) contributed 27% and 73%, respectively, of the region ’ s PM 2.5 . In absolute terms, the largest impacts from local and TAP occurred in winter. With respect to relative contributions among the different sources, regional TAP ( between cities in the region ) ( R-TAP ) and local contributions had the largest effect in summer, whereas superregional TAP ( from outside of the region ) contributed the most in fall and winter. Outdoor PM 2.5 pollution caused 20 160 ( 95% con ﬁ dence interval: 5100 – 39 310 ) premature mortalities every year in the PRD region. Averaging among cities, 50%, 20%, and 30% of these deaths were attributable to S-TAP, R-TAP, and local contributions, respectively. Precursor gas emissions ( i.e. NH 3 , volatile organic compounds, SO 2 , and NO x ) affect PM 2.5 level in a nonlinear manner; thus, individual pollutant control strategies are less effective for improving PM 2.5 pollution than an integrated strategy. On the basis of our ﬁ ndings, we recommend that controls for multiple emission species should be implemented to control PM 2.5 pollution in the region.
DBscan Algorithm and Decision Tree to Automate Trip Purpose Detection

One of transportation research topic is detecting trip purpose. Given a collection of GPS mobility records, researchers endeavored to infer useful information such as trip, travel mode, and trip purpose. Obtaining these attributes will help researcher in transportation modelling. This work proposed an approach in defining a trip or a trip segmentation which is a part of trip purpose problem as well as inferring the trip purpose. By Utilizing Dbscan clustering algorithm, decision tree, and some useful features, we are able to detect the trips and their purposes as well as building the model to automate the trip derivation.
Sensitivity analyses of primary outcome analyses using multiple imputation (Supplement to The efficacy of a transdiagnostic emotion focused treatment for chronic pain

Methods Given that baseline anxiety was associated with propensity for missing data in the study, we reanalyzed the data using multiple imputation as a sensitivity analysis [2]. The results from the regression models were pooled across 50 imputed data sets (each saved at the 500th imputation cycle). Estimates and standard errors were averaged across the 50 imputed data sets using Rubin's [3] pooling equations. Although both multiple imputation and full information maximum likelihood (FIML) work under the same missing data assumption (MAR) and yield similar results in large samples with the same set of variables, an inclusive strategy with more variables is often preferred in order to reduce bias and increase efficiency [1]. In addition, MAR assumes that all observed variables associated with missing data are include in the model to produce accurate results. Given this and due to that multiple imputation model used all outcome variables measured at pre-treatment, including baseline anxiety, results (i.e., estimates, standard errors, p-values) from multiple imputation were compared with those obtained in FIML.
INVESTING WHEN IT COUNTS:

The Population Council confronts critical health and development issues—from stopping the spread of HIV to improving reproductive health and ensuring that young people lead full and productive lives. Through biomedical, social science, and public health research in 50 countries, we work with our partners to deliver solutions that lead to more effective policies, programs, and technologies that improve lives around the world. Established in 1952 and headquartered in New York, the Council is a nongovern-mental, nonprofit organization governed by an international board of trustees. Contents Figures, boxes, and tables iv Abbreviations iv Foreword v Acknowledgments vi Preface vii 1 Introduction 1 1.1 Investing in very young adolescent girls and boys: Why it matters 2 1.2 Social, biological, and developmental changes in early adolescence 3 2 The health and well-being of VYAs: Overview and current status 9 2.1 Disease and disability: How do young adolescent girls and boys fare? 10 2.2 Understanding the health-related behaviors of VYAs to stem the spread of noncommunicable diseases 10 2.3 Health outcomes for VYAs: The diverging world of girls and boys 11 3 Social and contextual influences on health: Evidence and insights 17 3.1 Critical domains of young adolescent girls' and boys' lives 18 3.2 Gender differences in transitions from primary to secondary school 21
Localization of a Single Sensor with Respect to a Single Beacon using Received Signal Strength (RSS) in Terrestrial Environment

This paper apprised the issue of finding the location of a single sensor node with a single beacon in a terrestrial wireless sensor network (WSN). Generally, the localization of a single sensor node in a terrestrial sensor network can be solved using multilateration technique with respect to three or more known beacon nodes. However, there is an area of concern, when the localization of a single sensor node (i.e. mobile station, cell phone) is to be measured with respect to only one known beacon node i.e. base transceiver station (BTS). Such a challenge is aimed to be solved with the help of received signal strength (RSS) survey data for a particular location within the desired environment. A simulated terrain and a model has been created based on RSS Survey data that defines the contours of radio frequency (RF) coverage in a particular test facility under a single beacon node. Simulation results show that our proposed model gives a solution which converges to determine the location of a single sensor node with respect to a single beacon node.
Modelling of Operating Theatres Using Simulation

This paper describes the development of a simulation model for operating theatres. Elective patient scheduling is complicated by several factors; stochastic demand for resources due to variation in the nature and severity of a patient’s illness, unexpected complications in a patient’s course of treatment and the arrival of non-scheduled emergency patients which compete for resources. Extend simulation software was used for its ability to represent highly complex systems and analyse model outputs. Patient arrivals and lengths of surgery are determined by analysis of historical data. The model was used to explore the effects increasing patient arrivals and alternative elective patient admission disciplines would have on the performance measures. The model can be used as a decision support system for hospital planners.
Joint Reaction Force and Contributions of Surrounding Muscles to Knee Joint Load during Stair Ascent in Total Knee Replacement Patients and Healthy Individuals

Total knee replacement (TKR) is commonly used to correct end stage knee osteoarthritis (OA) of the knee joint. Unfortunately, difficulty with stair climbing has been seen to exist, prolonging the challenges of TKR patents. Complete understanding of loading at the knee is of great interest in order to aid patient populations, implant manufacturers, rehabilitation, and future research. Musculoskeletal modeling provides a means to accurately approximate joint loading and the corresponding muscle contributions during a movement. The purpose of the present study was to examine if the knee joint loading are recovered to the level of healthy individuals following a TKR, and determine the contribution of the muscles to knee joint loading. Data from five healthy and five TKR patients were selected for musculoskeletal simulation. Variables of interest included knee joint reaction forces and the corresponding muscle forces. A paired samples t-test was used to detect difference between groups for each variable of interest with an alpha level set at 0.05 a priori. TKR patients showed a trend of having higher 2 nd peak compressive JRF. Some muscle compensatory strategies appear to be present in the push-off phase; however the differences in muscles do not clearly explain the trend present in compressive JRF during the 2 nd 50% of stance. Evidence from knee extension moment and muscle force contributions during the loading response phase indicates reduced muscle strength in the knee extensors of TKR patients. This result combined with greater flexor muscle force resulted in similar compressive JRF during loading response between groups.
Spectrum Sensing and Recognition in Satellite Systems

In the scenario of frequency coexistence between the geostationary (GEO) and non-geostationary (NGEO) satellite networks, the NGEO system should not incur harmful interference to the GEO system according to the policy of the Radio Regulations. Therefore, spectrum sensing as a promising solution is applied widely in this scenario. With the increasing number of NGEO satellites in the space, one NGEO system could be affected by other NGEO systems while sensing the signal from the GEO system. Given these preconditions, the cognitive radio scenario considered in this paper is that: the GEO system is regarded as the primary user, one NGEO system is regarded as the secondary user, and another NGEO system is regarded as the interfering user. Meanwhile, all the satellite systems are supposed to operate with more than one discrete transmit power levels, which is practical and fits the concept of adaptive power control. In our context, we propose a spectrum strategy using hypothesis testing as well as maximum a posteriori to differentiate the GEO signal from the interfering NGEO and noise, and then identify the specific power level utilized by the GEO system. Moreover, we derive the closed-form expressions for threshold of verifying the status of the GEO and for decision regions to determine its power level. Finally, extensive simulations are provided to verify the proposed studies.
Cluster Head Multi-hop Routing Algorithm Based on Improved Social Group Algorithm

CECA algorithm is a very efficient clustering routing algorithm for solving WSN problems. However, in the CECA algorithm, the cluster heads send data directly to the base station in the form of single hop, this results in excessive energy consumption. To overcome this deficiency, a cluster head multi-hop routing algorithm based on an improved social group algorithm with energy consumption model and network model is proposed in the paper. Firstly, an improved SGO algorithm with adding process of learning historical information is proposed to improve the diversity of the population. Then, the improved algorithm is combined with the CECA protocol to enable the cluster heads in WSN transmit data to the base station in a multi-hop manner to prolong the lifetime of the network. To show the effectiveness of the proposed method, the algorithm is tested on a random distributed sensor network, the experimental results show that the improved algorithm can effectively prolong the life cycle of the network.
Families at the bedside: an ethnographic study of vigilance.

BACKGROUND Vigilance, the close protective involvement of family members with hospitalized relatives, is a relatively recent phenomenon in the hospital setting. Before the 1960s, hospital visiting policies restricted the presence of family members at the bedside. Policies changed during the 1960s and 1970s when health care professionals recognized that parents' staying with their hospitalized children was beneficial for both the parents and the children. Vigilance later became a phenomenon that included family members staying with adult patients. METHODS Two ethnographic studies were conducted to examine the meanings, patterns, and day-to-day experience of vigilance. Sixteen family members, described by the nursing staff as staying with the patient, participated in informal semistructured interviews. Participant observation was also used in data collection. RESULTS Data analysis yielded 5 categories of meaning that describe the experience of vigilance: commitment to care, emotional upheaval, dynamic nexus, transition, and resilience. CONCLUSIONS Managed care, shortened hospital stays, and cost containment make early involvement of the family in the patient's care imperative. An understanding of the family's needs and experiences is prerequisite to that involvement. The categories of meaning discovered in this research can help health care providers understand family members' experience of vigilance. The implications for the family physician include sensitization and awareness of family members' experiences and the developing of specific actions and interactions fostering a commitment to family-centered care that extends to the hospital setting.
Single-chip Implementation of LVDT Signal Conditioning

The aim of this work was to prove that the signal conditioning electronics for linear variable transformers (LVDTs) can be implemented in inexpensive, general-purpose 8-bit microcontrollers, making expensive dedicated signal conditioning chips redundant. A low-cost, high-resolution signal conditioning solution for LVDTs is presented. Apart from a few external passive components, the entire solution is implemented in a low-cost, analog-digital hybrid microcontroller. The excitation sinusoid is generated by filtering out the fundamental frequency of a (self-sustained) pwm-generated square wave and the secondary coils’ signals are demodulated with classic peak detector circuits implemented in the microcontroller using a combination of its embedded analog and digital building blocks. A resolution of 1 m over a range of ±6.35 mm for a commercial LVDT is reported and an uncertainty of 6 m in the absolute value is deduced. The entire solution is implemented as surface mounted components on a small printed circuit board and the LVDT core displacement is displayed on an LCD display. Due to the simplicity and low-cost components required, this signal conditioning proposal has the potential to have a significant impact on commercial LVDT signal conditioning chips in the future since it is significantly less expensive than the present state-of-the-art signal conditioning chips offered by the main commercial suppliers and other solutions previously suggested in scientific literature.
Dark tourism: towards a new post-disciplinary research agenda.

Over the past decade or so, dark tourism research – that is, the social scientific study of tourism and tourists associated with sites of death, disaster or the seemingly macabre – has witnessed a burgeoning of the literature base. Much of this research has a profundity that can and, undoubtedly, will contribute to broader social theories and to our understanding of cultural dynamics. Arguably, however, some dark tourism research has been characterised by a banality that either illustrates deficient conceptual underpinning or provides for limited disciplinary synthesis. Thus, in order to assuage any structural deficiencies in dark tourism as a coherent body of knowledge, I suggest scholars need to transgress traditional disciplinary borders and interests, and to adopt post-disciplinary research approaches that are characterised by increased reasonableness, flexibility and inclusivity. Consequently, I propose important, though not necessarily exclusive, components of a potential dark tourism research agenda that are critical to building a post-disciplinary approach. Ultimately, however, I offer this essay as a preliminary conversation and invitation to (dark) scholars to take up future dark tourism research without the restrictive dogma and parochialism of disciplinarity.