LLukas22/scidocs huggingface dataset:
Evidence-Based Effective Practices With Older Adults

O lder adults (persons over age 65) constitute a unique population that has been underserved in the area of counseling services (Myers & Schwiebert, 1996). An estimated one third of individuals in this population have mental health problems for which professional intervention is needed, including anxiety, depression, and other diagnos-able mental disorders (Smyer & Qualls, 1999), but this estimate fails to incorporate normative developmental issues such as the transition to retirement, grandparenthood, and second careers. When developmental issues are included, clearly the incidence of mental health needs among the older population increases. Given the changes in the demographic structure of society in the United States, specifically the dramatic increase in the numbers of older persons, it is highly likely that professional counselors will encounter older persons and their families as clients (Myers, 1995). As a consequence , counselors' repertoire of knowledge and skills should include strategies for assessment and intervention to address both the common mental health concerns of persons in later life and the normative issues of aging. Although persons over the age of 60 make up almost 13% of the total United States population (AARP, 2000), they represent only 6% to 8% of persons seen in community mental health clinics and outpatient mental health settings and an even smaller percentage of clients seen by private counseling practitioners (Smyer & Qualls, 1999). Barriers to the use of these services by this population include bias toward older persons among service providers and reluctance of older persons to seek counseling, as well as a lack of adequate training and supervised clinical practice with this population that would allow counselors to be both comfortable with and effective in service to older clients (Nordhus & VandenBos, 1998). Such training requires an understanding of effective intervention practices. In this article, we provide a brief overview of the older population and describe several important differences in counseling with older and younger persons. We review and discuss outcome studies that reveal effective strategies and best practices for counseling with older persons. We accessed these studies from reviews of PsycINFO, the ERIC database, and counseling journals. We also consider implications for clinical practice, counselor training, and needed research. Professional counselors working with older persons need to understand how those in this population are like persons of other ages and how they differ. A brief review of key demographic indicators provides information on some risk factors in later …
Detection of antibodies against Leptospira serovars via microscopic agglutination tests in dogs in the United States, 2000-2007.

OBJECTIVE
To use results of microscopic agglutination tests (MATs) conducted at a commercial veterinary diagnostic laboratory to determine temporal and demographic distributions of positive serologic test results for leptospirosis in dogs and identify correlations among results for various Leptospira serovars.


DESIGN
Serosurvey.


STUDY POPULATION
MAT results for 33,119 canine serum samples submitted to a commercial veterinary diagnostic laboratory from 2000 through 2007.


PROCEDURES
Electronic records of MAT results for dogs were obtained from a veterinary diagnostic laboratory. Seropositivity for antibodies against Leptospira serovars was determined by use of a cutoff titer of >or=1:1,600 to reduce the possible impact of postvaccinal antibodies on results. Correlations between results for all possible pairs of serovars were calculated by ordinal ranking of positive (>or=1:100) antibody titer results.


RESULTS
2,680 samples (8.1%; 95% confidence interval [CI], 7.8% to 8.4%) were seropositive for antibodies against Leptospira serovars. The highest percentage of positive MAT results was for the year 2007 (10.2%; 95% CI, 9.5% to 10.9%) and for the months of November and December during the study period. Antibodies were most common against serovars Autumnalis, Grippotyphosa, Pomona, and Bratislava. Seroprevalence of leptospirosis was lowest for dogs>10 years of age but was similar across other age strata.


CONCLUSIONS AND CLINICAL RELEVANCE
Leptospirosis can affect dogs of small and large breeds and various ages. Although an increase in proportions of positive MAT results was evident in the fall, monthly and annual variations suggested potential exposure in all months. Because of the limitations of MAT results and the limited number of serovars used in the test, bacterial culture should be used to identify infective Leptospira serovars.
Women-led firms and the gender gap in top executive jobs

Women-Led Firms and the Gender Gap in Top Executive Jobs Using data on Executive Compensation from Standard and Poor's ExecuComp, this paper explores the gender gap in top executive jobs and the effect of women CEOs, Chairs, and Directors on the pay of other women executives. The results show a narrowing of the uncorrected gender pay gap from the mid-1990s. Women top executives earn between 8% to 25% less than male executives after controlling for differences in company size, occupational title, and industry. The magnitude of the gender pay gap is statistically related to the gender of the Chief Executive and Corporate Board Chair. Women CEO and Board Chairs bring more top women and at higher pay than is found in non-women-led firms. Specifically, female executives in women-led firms earn between 10-20% more than comparable executive women in male-led firms and are between 3-18% more likely to be among the highest five paid executives in these firms as well. The paper thereby provides strong empirical evidence that women leaders are associated with positive outcomes for women executives in substantive and important ways. JEL Classification: J11, J16, J33, J70, J71, J78
On Poisson-Lindley Distribution and Its Applications to Biological Sciences

The PLD has been extensively studied by Sankaran [1] and Ghitany and Mutairi [2] and they have discussed its various properties. The PLD has been generalized by many researchers. Shanker & Mishra [3] obtained a two parameter Poisson-Lindley distribution by compounding Poisson distribution with a two parameter Lindley distribution introduced by Shanker & Mishra [4]. A quasi Poisson-Lindley distribution has been introduced by Shanker & Mishra [5] by compounding Poisson distribution with a quasi Lindley distribution introduced by Shanker & Mishra [6]. Shanker et al. [7] obtained a discrete two parameter PoissonLindley distribution by mixing Poisson distribution with a two parameter Lindley distribution for modeling waiting and survival time’s data introduced by Shanker et al. [8]. Further, Shanker & Tekie [9] obtained a new quasi Poisson-Lindley distribution by compounding Poisson distribution with a new quasi Lindley distribution introduced by Shanker & Amanuel [10].
Gender differences in lower extremity kinematics, kinetics and energy absorption during landing.

OBJECTIVE
To determine whether gender differences exist in lower extremity joint motions and energy absorption landing strategies between age and skill matched recreational athletes.


DESIGN
Mixed factor, repeated measures design.


BACKGROUND
Compared to males, females execute high demand activities in a more erect posture potentially predisposing the anterior cruciate ligament to greater loads and injury. The preferred energy absorption strategy may provide insight for this performance difference.


METHODS
Inverse dynamic solutions estimated lower extremity joint kinematics, kinetics and energetic profiles for twelve males and nine females performing a 60 cm drop landing.


RESULTS
Females demonstrated a more erect landing posture and utilized greater hip and ankle joint range of motions and maximum joint angular velocities compared to males. Females also exhibited greater energy absorption and peak powers from the knee extensors and ankle plantar-flexors compared to the males. Examinations of the energy absorption contributions revealed that the knee was the primary shock absorber for both genders, whereas the ankle plantar-flexors muscles was the second largest contributor to energy absorption for the females and the hip extensors muscles for the males.


CONCLUSIONS
Females may choose to land in a more erect posture to maximize the energy absorption from the joints most proximal to ground contact.


RELEVANCE
Females may be at a greater risk to anterior cruciate ligament injury during landing due to their energy absorption strategy.
Hadronic and Spin Physics: Review

I will summarize the numerous contributions which were presented in the session, 
Hadronic and Spin Physics, largely dominated by new experimental results.
A stand-alone photovoltaic supercapacitor battery hybrid energy storage system

Most of the stand-alone photovoltaic (PV) systems require an energy storage buffer to supply continuous energy to the load when there is inadequate solar irradiation. Typically, Valve Regulated Lead Acid (VRLA) batteries are utilized for this application. However, supplying a large burst of current, such as motor startup, from the battery degrades battery plates, resulting in destruction of the battery. An alterative way of supplying large bursts of current is to combine VRLA batteries and supercapacitors to form a hybrid storage system, where the battery can supply continuous energy and the supercapacitor can supply the instant power to the load. In this paper, the role of the supercapacitor in a PV energy control unit (ECU) is investigated by using Matlab/Simulink models. The ECU monitors and optimizes the power flow from the PV to the battery-supercapacitor hybrid and the load. Three different load conditions are studied, including a peak current load, pulsating current load and a constant current load. The simulation results show that the hybrid storage system can achieve higher specific power than the battery storage system.
Free Text In User Reviews: Their Role In Recommender Systems

As short free text user-generated reviews become ubiquitous on the social web, opportunities emerge for new approaches to recommender systems that can harness users‟ reviews in open text form. In this paper we present a first experiment towards the development of a hybrid recommender system which calculates users‟ similarity based on the content of users‟ reviews. We apply this approach to the movie domain and evaluate the performance of LSA, a state-of-the-art similarity measure, at estimating users‟ reviews similarity. Our initial investigation indicates that users‟ similarity is not well reflected in traditional score-based recommender systems which solely rely on users‟ ratings. We argue that short free text reviews can be used as a complementary and effective information source. However, we also find that LSA underperforms when measuring the similarity of short, informal user-generated reviews. For this we argue that further research is needed to develop similarity measures better suited to noisy short text.
Neighbourhood sampling in bagging for imbalanced data

Various approaches to extend bagging ensembles for class imbalanced data are considered. First, we review known extensions and compare them in a comprehensive experimental study. The results show that integrating bagging with under-sampling is more powerful than over-sampling. They also allow to distinguish Roughly Balanced Bagging as the most accurate extension. Then, we point out that complex and difficult distribution of the minority class can be handled by analyzing the content of a neighbourhood of examples. In our study we show that taking into account such local characteristics of the minority class distribution can be useful both for analyzing performance of ensembles with respect to data difficulty factors and for proposing new generalizations of bagging. We demonstrate it by proposing Neighbourhood Balanced Bagging, where sampling probabilities of examples are modified according to the class distribution in their neighbourhood. Two its versions are considered: the first one keeping a larger size of bootstrap samples by hybrid over-sampling and the other reducing this size with stronger under-sampling. Experiments prove that the first version is significantly better than existing over-sampling bagging extensions while the other version is competitive to Roughly Balanced Bagging. Finally, we demonstrate that detecting types of minority examples depending on their neighbourhood may help explain why some ensembles work better for imbalanced data than others.
Condition based maintenance optimization for wind power generation systems under continuous monitoring

By utilizing condition monitoring information collected fromwind turbine components, condition based maintenance (CBM) strategy can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately, that is, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm generally consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously replace multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to the above-mentioned issues. The proposed maintenance policy is defined by two failure probability threshold values at the wind turbine level. Based on the condition monitoring and prognostics information, the failure probability values at the component and the turbine levels can be calculated, and the optimal CBM decisions can be made accordingly. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach. A comparative study based on commonly used constant-interval maintenance policy demonstrates the advantage of the proposed CBM approach in reducing the maintenance cost. 2010 Elsevier Ltd. All rights reserved.
Risk for Parkinson’s disease: twin studies for the detection of asymptomatic subjects using [18F]6-fluorodopa PET

Positron emission tomography (PET) studies were carried out with [18F]6-fluorodopa ([18F]6-FD) in monozygotic (MZ) and dizygotic (DZ) twins for the clarification of dopaminergic function. Four MZ and four DZ pairs of twins, each pair consisting of a parkinsonian index case and an asymptomatic co-twin, were collected from the Nationwide Twin Cohort. The control group comprised 14 healthy volunteers. [18F]6-FD PET examinations with a Siemens/CTI 931/08 scanner were performed dynamically over 90 min. The regions-of-interest analysis included the caudate, the putamen and the occipital reference regions. Patlak plots were calculated using occipital tissue input function. The accumulation of [18F]6-FD in the putamen of the asymptomatic co-twins was significantly lower than that in the normal subjects. This result implies that there may be a preclinical stage of Parkinson’s disease in the apparently normal co-twins at the time of the PET study.
Application of Response Surface Methodology for Methylene Blue dye removal from aqueous solution using low cost adsorbent

The use of cheap and eco-friendly adsorbents studied as an alternative substitution of activated carbon for removal of dyes from wastewater. Adsorbents prepared from orange peel, which is a domestic waste, successfully used to remove the methylene blue from an aqueous solution in a batch wise column. This study investigates the potential use of orange peel, pretreated with nominal treatment method, for removal of methylene blue from simulated wastewater. Treated orange peel used to adsorb methylene blue at varying dye concentration, adsorbent dosage, pH and contact time. Similar experiments conducted with some other lowcost adsorbent such as banana peel, neem leaves and commercially available powdered activated carbon (PAC). The adsorption capacity of orange peels decreased in the order of methyl orange (MO) > methylene blue (MB) > Rhodamine B (RB) > Congo red (CR) > methyl violet (MV) > amido black 10B (AB). Removal efficiency of all the adsorbents is quite effective, but orange peel found to be very effective compared to other low cost adsorbent within the short period. The sorption data were then correlated with the freundlich and the langmuir adsorption isotherm models. In both isotherms exhibited a maximum K value in which indicates that the orange peel has greater affinity for methylene blue. The maximum color removal efficiencies of orange peel at dosage of 1.0g for time duration of 45 min found to be 99% of the dye from an aqueous solution of 12.32 ppm. From the study it is understood that pH, contact time and adsorbent dosage plays a vital role in removal of dye.
Recommender Systems meet Finance: a Literature Review

The present work overviews the application of recommender systems in various financial domains. The relevant literature is investigated based on two directions. First, a domain-based categorization is discussed focusing on those recommendation problems, where the existing literature is significant. Second, the application of various recommendation algorithms and data mining techniques is summarized. The purpose of this paper is providing a basis for further scientific research and product development in this field.
An Introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graphoriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them.
Varying task difficulty in the Go/Nogo task: the effects of inhibitory control, arousal, and perceived effort on ERP components.

Similar to other executive functions, inhibitory control is thought to be a dynamic process that can be influenced by variations in task difficulty. However, little is known about how different task parameters alter inhibitory performance and processing as a task becomes more difficult. The aim of this study was to investigate the influence of varying task difficulty, via manipulation of reaction time deadline (RTD), on measures of inhibitory control, perceived effort, and task-related arousal (indexed by skin conductance level). Sixty adults completed a visual Go/Nogo task (70% Go) after being randomly assigned to one of three task difficulty conditions: High, Medium and Low, with RTDs of 300, 500 or 1000 ms, respectively. Results revealed incremental increases in Go/Nogo errors and greater perceived effort with increasing difficulty. No condition differences were found for arousal, but the amplitude of the Nogo N2 increased and peaked earlier with increasing task difficulty. In contrast, the Nogo P3 effect was reduced in the High condition compared to the Low and Medium conditions. Finally, the amplitude of N1 and P2 showed differential effects, with Nogo N1 increasing with task difficulty, while the Nogo P2 decreased. This study provides valuable baseline behavioural and ERP data for appropriately manipulating difficulty (via RTD) in Go/Nogo tasks - highlighting the potential key role of not only the N2 and P3, but also the N1 and P2 components for task performance.
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

ive Text Summarization using Sequence-to-sequence RNNs and Beyond Ramesh Nallapati IBM Watson nallapati@us.ibm.com Bowen Zhou IBM Watson zhou@us.ibm.com Cicero dos Santos IBM Watson
Optimal Channel Allocation for Data Dissemination in Mobile Computing Environments

This paper discusses wireless channel allocation problem for data dissemination in mobile computing systems. Methods for accessing data through broadcast and on-demand channels are described. We provide analytical models and cost formulae for the exclusive broadcast channels and the exclusive on-demand channels and propose a dynamic channel allocation algorithm for optimizing system performance. Our performance evaluation shows that dynamic channel allocation signi cantly improves system performance and the channel allocation algorithm gives us the optimal solution for various system parameter settings.
Clustering-based undersampling in class-imbalanced data

Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both smalland large-scale data sets. © 2017 Elsevier Inc. All rights reserved.
Forecasting foreign exchange volatility: Why is implied volatility biased and inefficient? And does it matter?

Research has consistently found that implied volatility is a conditionally biased predictor of realized volatility across asset markets. This paper evaluates explanations for this bias in the market for options on foreign exchange futures. No solution considered—including a model of priced volatility risk—explains the conditional bias found in implied volatility. Further, while implied volatility fails to subsume econometric forecasts in encompassing regressions, these forecasts do not significantly improve delta-hedging performance. Thus this paper deepens the implied volatility puzzle by rejecting popular explanations for forecast bias while demonstrating that statistical measures of bias and informational inefficiency should be treated with circumspection.