LLukas22/scidocs huggingface dataset:
A systematic review of suicide prevention interventions targeting indigenous peoples in Australia, United States, Canada and New Zealand

BACKGROUND
Indigenous peoples of Australia, Canada, United States and New Zealand experience disproportionately high rates of suicide. As such, the methodological quality of evaluations of suicide prevention interventions targeting these Indigenous populations should be rigorously examined, in order to determine the extent to which they are effective for reducing rates of Indigenous suicide and suicidal behaviours. This systematic review aims to: 1) identify published evaluations of suicide prevention interventions targeting Indigenous peoples in Australia, Canada, United States and New Zealand; 2) critique their methodological quality; and 3) describe their main characteristics.


METHODS
A systematic search of 17 electronic databases and 13 websites for the period 1981-2012 (inclusive) was undertaken. The reference lists of reviews of suicide prevention interventions were hand-searched for additional relevant studies not identified by the electronic and web search. The methodological quality of evaluations of suicide prevention interventions was assessed using a standardised assessment tool.


RESULTS
Nine evaluations of suicide prevention interventions were identified: five targeting Native Americans; three targeting Aboriginal Australians; and one First Nation Canadians. The main intervention strategies employed included: Community Prevention, Gatekeeper Training, and Education. Only three of the nine evaluations measured changes in rates of suicide or suicidal behaviour, all of which reported significant improvements. The methodological quality of evaluations was variable. Particular problems included weak study designs, reliance on self-report measures, highly variable consent and follow-up rates, and the absence of economic or cost analyses.


CONCLUSIONS
There is an urgent need for an increase in the number of evaluations of preventive interventions targeting reductions in Indigenous suicide using methodologically rigorous study designs across geographically and culturally diverse Indigenous populations. Combining and tailoring best evidence and culturally-specific individual strategies into one coherent suicide prevention program for delivery to whole Indigenous communities and/or population groups at high risk of suicide offers considerable promise.
A Unitary Approach to Lexical Pragmatics : Relevance , Inference and Ad Hoc Concepts

According to recent work in the new field of lexical pragmatics, the meanings of words are frequently pragmatically adjusted and fine-tuned in context, so that their contribution to the proposition expressed is different from their lexically encoded sense. Well-known examples include lexical narrowing (e.g. ‘drink’ used to mean ALCOHOLIC DRINK), approximation (or loosening) (e.g. ‘flat’ used to mean RELATIVELY FLAT) and metaphorical extension (e.g. ‘bulldozer’ used to mean FORCEFUL PERSON). These three phenomena are often studied in isolation from each other and given quite distinct kinds of explanation. In this chapter, we will propose a more unified account. We will try to show that narrowing, loosening and metaphorical extension are simply different outcomes of a single interpretive process which creates an ad hoc concept, or occasion-specific sense, based on interaction among encoded concepts, contextual information and pragmatic expectations or principles. We will outline an inferential account of the lexical adjustment process using the framework of relevance theory, and compare it with some alternative accounts. * This work is part of an AHRC-funded project ‘A Unified Theory of Lexical Pragmatics’ (AR16356). We are grateful to our research assistants, Patricia Kolaiti, Tim Wharton and, in particular, Rosa Vega Moreno, whose PhD work on metaphor we draw on in this paper, and to Vladimir Žegarac, François Recanati, Nausicaa Pouscoulous, Paula Rubio Fernandez and Hanna Stoever, for helpful discussions. We would also like to thank Dan Sperber for sharing with us many valuable insights on metaphor and on lexical pragmatics more generally.
A Randomized Clinical Trial Comparing Breakfast and Bedtime Administration of Insulin Glargine in Children and Adolescents with Type 1 Diabetes

BACKGROUND
Insulin glargine provides effective glycemic control when administered at bedtime in adults.


OBJECTIVE
This study aims to investigate whether insulin glargine is equally effective if administered in the morning or at bedtime in combination with preprandial anologue insulin.


METHODS
Twenty-eight patients that have been treated with an intensified insulin regimen for at least one year were randomized to insulin glargine injection at breakfast (06:00-09:00) (12 patients) or bedtime (21:00-24:00) (16 patients), plus meal-time anologue insulin in the two groups. Glucose data from each day were analyzed at four different times: between 9:00 and 21:00 (t1), between 21:00 and 24:00 (t2), between 24:00 and 04:00 (t3),04:00 and 09:00 (t4) by the Minimed continuous glucose monitoring system.


RESULTS
Baseline characteristics were similar in the two groups. The sensor values were lower before breakfast in the bedtime group (180.5 ± 49.0 vs 223.8 ± 47.3 mg/dl, p=0.03). There were 13.7 events.patient (-1).day(-1) in the bedtime group and 6.9 events.patient (-1).day(-1) in the breakfast group in which glucose levels fell below 60 mg/dl (p=0.3). There were 121.6 events.patient (-1).day(-1) in the bedtime group and 162.4 events.patient (-1).day(-1) in the breakfast group in which glucose levels exceeded 180 mg/dl (p=0.05). Nighttime hypoglycemia only reached to a statistical significance between the two groups between 24:00 and 04:00. There were no significant correlations between the duration of nocturnal hypoglycemia, age, duration of diabetes, gender and HbA1c levels.


CONCLUSION
Breakfast group is hyperglycemic during the day and hyperglycemia starts in the morning at 04:00. There is no significant difference in the frequency or duration of hypo/hyper glycemia during the day and night irrespective of the timing of glargine injection except pre-breakfast levels are significantly better in the bedtime group and hypoglycemia occurs between midnight and 04:00 in the bedtime group.
Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior

We propose a method to examine structural and behavioral determinants of residential electricity consumption, by developing separate models for daily maximum (peak) and minimum (idle) consumption. We apply our method on a data set of 1628 households’ electricity consumption. The results show that weather, location and floor area are among the most important determinants of residential electricity consumption. In addition to these variables, number of refrigerators and entertainment devices (e.g., VCRs) are among the most important determinants of daily minimum consumption, while number of occupants and high-consumption appliances such as electric water heaters are the most significant determinants of daily maximum consumption. Installing double-pane windows and energyefficient lights helped to reduce consumption, as did the energy-conscious use of electric heater. Acknowledging climate change as a motivation to save energy showed correlation with lower electricity consumption. Households with individuals over 55 or between 19 and 35 years old recorded lower electricity consumption, while pet owners showed higher consumption. Contrary to some previous studies, we observed no significant correlation between electricity consumption and income level, home ownership, or building age. Some otherwise energy-efficient features such as energy-efficient appliances, programmable thermostats, and insulation were correlated with slight increase in electricity
Predicting the popularity of micro-reviews: A Foursquare case study

We tackle the problem of predicting the future popularity level of micro-reviews, focusing on Foursquare tips, whose high degree of informality and briefness offer extra difficulties to the design of effective popularity prediction methods. Such predictions can greatly benefit the future design of content filtering and recommendation methods. Towards our goal, we first propose a rich set of features related to the user who posted the tip, the venue where it was posted, and the tip’s content to capture factors that may impact popularity of a tip. We evaluate different regression and classification based models using this rich set of proposed features as predictors in various scenarios. As fas as we know, this is the first work to investigate the predictability of micro-review popularity (or helpfulness) exploiting spatial, temporal, topical and, social aspects that are rarely exploited conjointly in this domain. © 2015 Published by Elsevier Inc.
Enhancing tribological performance of the magnetic tape/guide interface by laser surface texturing

Air-lubricated foil bearings are used in a variety of applications such as turbomachinery, journal bearings, dentistry equipment, etc. [1–3]. One of the most intricate applications of air-lubricated foil bearings can be found in magnetic tape drives [4], where a magnetic tape moves over a read/write head and tape drive components such as guides (stationary) and rollers (rotating). Friction is an important parameter in designing a tape transport for a commercial high-performance tape drive. The overall friction force between tape and tape drive components determines the torque needed by the motor to drive the tape and affects the wear of the tape. Metal particulate (MP) tape is widely used in commercial tape recording systems since the tribological performance of the magnetically superior metal evaporated (ME) tape is inferior to that of MP tape. Lateral tape motion (LTM) is defined as the timedependent motion of the tape perpendicular to the tape transport direction. It is a friction related phenomenon that can cause track misregistration [5]. Although friction between tape and tape drive components was observed to attenuate LTM [6–8], it is well known that tape drives with pressurized air bearing guides instead of rotating guides exhibit significantly lower LTM than tape drives with rotating guides [9]. Elimination of rotating tape drive components suppresses LTM due to run-out of those components. However, pressurized air bearing guides require an external compressor, which is an obstacle for commercialization of this type of tape drive. Based on the above information, it is therefore desirable to create an efficient self-acting foil bearing between the tape and the guide, thereby eliminating the need for an external compressor, while still benefiting from a low friction air bearing. In a recent study [10], it was observed that for a tape tension of 1 N, a tape speed of at least 8 m/s was needed to achieve full fluid lubrication and hence, a low friction coefficient. This paper explores the possibility of creating an efficient low speed air bearing between a magnetic tape and a cylindrical guide, thereby expanding the speed range of low friction. We use a novel approach to tape guide design by using laser surface texturing (LST) of the guide surface. We have compared the tribological performance of the laser surface textured guide with various non-textured guides, thereby showing the benefits of textured guide surfaces.
Stock Market Prediction: Using Historical Data Analysis

The Stock market process is full of uncertainty and it’s affected by many factors. Hence the Stock market prediction is one of the important exertions in business and finance. There are two types of analysis possible for prediction, fundamental and technical. In this paper both fundamental and technical analysis are considered. fundamental analysis is done using social media data by applying sentiment analysis process. Social media data has high impact today than ever, it can helpful in predicting the trend of the stock market and Technical analysis is done using historical data of stock prices by applying machine learning algorithms. The method involves collecting news and also collect social media data and extracting sentiments expressed by individual. Then the correlation between the sentiments and stock values is analysed. The learned model can then be used to make future predictions about stock value. It’s can be shown that this method is able to predict the stock performance and sentiment and social data are also closely correlated with recent news. Keywords— prediction, Big data, social media analytics, machine learning.
Smart and Digital City : A Systematic Literature Review

Abstract The concept of Smart City embraces several definitions depending on the meanings of the word “smart”: intelligent city, knowledge city, ubiquitous city, sustainable city, digital city, etc. Many definitions of Smart City exist, but no one has been universally acknowledged yet. From literature analysis it emerges that Smart City and Digital City are the most used terminologies in literature to indicate the smartness of a city. This Chapter explores the literature about Smart City and Digital City from 1993 to the end of 2012 in order to investigate how these two concepts were born, how they have developed, which are the shared features and differences between them. To accomplish with these goals, three steps were followed: (1) to set up a search strategy for systematic literature review to collect a representative subset of papers about Smart City and Digital City using Google Scholar; (2) to store the selected subset in an ad-doc database to synthesize the literature review; (3) to organize the literature review subset to extract quantitative and qualitative data and information about Smart City and Digital City evolution. The author proposes a literature review taxonomy through five specific analysis: (1) time analysis, to explore the causes of the trend of Smart City and Digital City literature in the latest twenty years; (2) terminology analysis, to examine how and where these two ideas were born and what have been the main events influenced their development; (3) definitions analysis, to select and compare the most cited and validated definitions of Smart City and Digital City trying to identify similarities, differences or overlaps between these two concepts; (4) typology analysis, to investigate if Smart City and Digital City are included into a specific urban strategy pursued by government or if they face specific urban problems without a comprehensive framework; (5) geographic analysis, to understand where are the largest concentrations of Smart Cities and Digital Cities in the world and which are their main characteristics and best practices. Smart and Digital City: A Systematic Literature Review
A Bayesian Computer Vision System for Modeling Human Interactions

ÐWe describe a real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task [1]. The system is particularly concerned with detecting when interactions between people occur and classifying the type of interaction. Examples of interesting interaction behaviors include following another person, altering one's path to meet another, and so forth. Our system combines top-down with bottom-up information in a closed feedback loop, with both components employing a statistical Bayesian approach [2]. We propose and compare two different state-based learning architectures, namely, HMMs and CHMMs for modeling behaviors and interactions. The CHMM model is shown to work much more efficiently and accurately. Finally, to deal with the problem of limited training data, a synthetic aAlife-styleo training system is used to develop flexible prior models for recognizing human interactions. We demonstrate the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training. Index TermsÐVisual surveillance, people detection, tracking, human behavior recognition, Hidden Markov Models.
Cervical epidural abscess after cupping and acupuncture.

STUDY DESIGN
Retrospective case report.


OBJECTIVE
Report of an uncommon complication of cupping and acupuncture.


SUMMARY OF BACKGROUND DATA
Epidural abscess after cupping and acupuncture therapies is quite rare. Only a few cases of epidural abscess after acupuncture have been reported. The present report describes a case of cervical epidural abscess that developed after cupping and acupuncture.


METHODS
A 47-year-old woman presented with swelling and pain in the posterior nuchal region. Magnetic resonance imaging of the cervical spine revealed a well-enhanced epidural mass at the C1-C3 level that was determined to be an epidural abscess.


RESULTS
The symptoms related to epidural abscess resolved after treatment with antibiotics.


CONCLUSION
Although a rare complication, epidural abscess is a possibility when applying cupping and acupuncture. Therapists need to be aware of human anatomy in the vicinity of the puncture and must give continuous attention to hygiene throughout the procedure.
Bat algorithm: Recent advances

The bat algorithm (BA) is a nature-inspired algorithm, which has recently been applied in many applications. BA can deal with both continuous optimization and discrete optimization problems. The literature has expanded significantly in the past few years, this paper provides a timely review of the latest developments. We also highlight some topics for further research.
Comparison of sensitivity and specificity among 15 criteria for chronic inflammatory demyelinating polyneuropathy.

INTRODUCTION
There have been 15 formal sets of criteria published for the diagnosis of CIDP. No study to date has compared the sensitivity and specificity of all published criteria in the same patient population.


METHODS
We conducted a retrospective chart review of patients with CIDP (n = 56) and controls with diabetic polyneuropathy (n = 37) or amyotrophic lateral sclerosis (n = 39) who were followed in an academic neuromuscular practice. The sensitivity and specificity of each CIDP criterion was calculated, including clinical, laboratory, and electrodiagnostic components.


RESULTS
Sensitivities ranged from 1.8% to 87.5%; the Dyck (87.5%), Neuropathy Association (75%), and European Federation of Neurological Societies (EFNS; 73.2%) criteria ranked highest. Specificities ranged from 65.6% to 100% and, among the 3 most sensitive criteria, the EFNS (90.8%) and Neuropathy Association (82.9%) criteria were most specific.


CONCLUSIONS
In our patient population, the EFNS and Neuropathy Association criteria stand out due to high sensitivity and specificity.
Intelligent Heart Disease Prediction System Using CANFIS and Genetic Algorithm

Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease. Keywords—CANFIS, Genetic Algorithms (GA), Heart disease, Membership Function (MF).
Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features

Yarbus' pioneering work in eye tracking has been influential to methodology and in demonstrating the apparent importance of task in eliciting different fixation patterns. There has been renewed interest in Yarbus' assertions on the importance of task in recent years, driven in part by a greater capability to apply quantitative methods to fixation data analysis. A number of recent research efforts have examined the work has raised a number of interesting questions, with some investigations calling for closer examination of the validity of Yarbus' claims, and subsequent efforts revealing some of the nuances involved in carrying out this type of analysis including both methodological, and data related considerations. In this paper, we present an overview of prior efforts in task prediction, and assess different types of statistics drawn from fixation data, or images in their ability to predict task from gaze. We also examine the extent to which relatively general task definitions (free-viewing, object-search, saliency-viewing, explicit saliency) may be predicted by spatial positioning of fixations, features co-located with fixation points, fixation dynamics and scene structure. This is accomplished in considering the data of Koehler et al. (2014) [30] affording a larger scale, and qualitatively different corpus of data for task prediction relative to existing efforts. Based on this analysis, we demonstrate that both spatial position, as well as local features are of value in distinguishing general task categories. The methods proposed provide a general framework for highlighting features that distinguish behavioural differences observed across visual tasks, and we relate new task prediction results in this paper to the body of prior work in this domain. Finally, we also comment on the value of task prediction and classification models in general in understanding facets of gaze behaviour. & 2016 Published by Elsevier B.V.