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This paper analyses the effect of bank lending and fee income on Islamic and conventional bank's performance. The paper builds a theoretical model and provides empirical evidence to show that Islamic banks as compared to conventional banks can have a greater reliance on fee-based income than returns from loans to increase their profitability. Using data from a sample of 20 countries for the period from 2000 to 2015 for Islamic and conventional banks, we find that the bank fee is an important determinant of the profitability of an Islamic bank. Interestingly, many commonly used measures such as loan to deposit ratio do not affect the Islamic banks' profitability as much as they do for conventional banks. Our findings imply that Islamic banks' lower sensitivity to loan to deposit ratio may contribute to lower credit risk. However, an over-reliance on fee-based income may affect their growth, profitability and sustainability in the long run.

The organizational design of subcontracting in the construction industry has received limited attention. This article integrates transaction cost economics and resource-based view to investigate how the governance capabilities of the general contractor impact the organizational arrangement of subcontracting and how the goal orientation of project moderates these relationships. Regression analyses were conducted on information collected by a questionnaire survey administered to a sample of 279 respondents who were employed by Chinese general contractors and who were privy to strategy information in construction projects. The results showes that the general contractor with strong governance capabilities tends to increase the extent of the work to be subcontracted and the dispersion of subcontractors used in projects. Moreover, these relationships change when the goal orientation in projects differs. In projects that display an exploitative goal orientation, the positive impact of governance capabilities on the extent of subcontracting will be weakened while the impact on subcontractor dispersion will be strengthened. In contrast, the relationship between governance capabilities and the extent of subcontracting will be stronger, and the relationship between governance capabilities and the subcontractor dispersion will be weaker in projects that exhibit an explorative goal orientation. The findings contribute to the subcontracting literature by distinguishing between the extent of subcontracting and subcontractor dispersion and by highlighting the importance of the general contractor's governance capabilities and project goal orientation when setting up a subcontracting arrangement.

Faith, as a way of how people understand God and the world and make or discover meaning in their life, is characterized by individual differences and changes over the lifetime. Our research investigates these changes over the life span in terms of hierarchically ordered types that are the elements in our developmental model, which is a critical advancement and modification of Fowler’s theory of faith development. This study is the first to investigate whether there is development in faith over the adult life span and to identify predictors of developmental change. Our mixed-method design used three-wave longitudinal data (mean lag time 10.47 years from Wave 1 to Wave 3). A sample of N = 75 participants were interviewed with the faith development interview three times and each time answered comprehensive questionnaires. Results evidenced faith development over the life span: a majority in our data moved to a higher type, while change to a lower type did occur. Openness to experience and the religious schema truth of text and teachings were potential antecedents for faith development. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Many studies have found that combining density forecasts improves predictive accuracy for macroeconomic variables. A prevalent approach known as the Linear Opinion Pool (LOP) combines forecast densities from “experts”; see, among others, Stone (1961), Geweke and Amisano (2011), Kascha and Ravazzolo (2011), Ranjan and Gneiting (2010) and Gneiting and Ranjan (2013). Since the LOP approach averages the experts’ probabilistic assessments, the distribution of the combination generally differs from the marginal distributions of the experts. As a result, the LOP combination forecasts sometimes fail to match salient features of the sample data, including asymmetries in risk. In this paper, we propose a computationally convenient transformation for a target macroeconomic variable with an asymmetric marginal distribution. Our methodology involves a Smirnov transform to reshape the LOP combination forecasts using a nonparametric kernel-smoothed empirical cumulative distribution function. We illustrate our methodology with an application examining quarterly real-time forecasts for US inflation based on multiple output gap measures over an evaluation sample from 1990:1 to 2017:2. Our proposed methodology improves combination forecast performance by approximately 10% in terms of both the root mean squared forecast error and the continuous ranked probability score. We find that our methodology delivers a similar performance gain for the Logarithmic Opinion Pool (LogOP), a commonly-used alternative to the LOP.

Intercollegiate student-athletes appear to be a high-risk population with respect to mental health. Student athletic therapists are one of the groups with whom these athletes may be comfortable disclosing concerns. The current study investigated the relationship between mental health literacy and mental health referral efficacy in a sample of intercollegiate student therapists. One hundred and eleven student athletic therapists (81 female, 29 male, 1 nondiscloure) competed a revised version of the multicomponent mental health literacy measure and a four-item measure of mental health referral efficacy. T tests revealed statistically significant differences in mental health literacy by gender and personal history, and a multiple linear regression revealed a significant model predicting referral efficacy from mental health literacy. There are several implications of these results, particularly when working with a high-risk population of student-athletes.

U.S. foster care policy prioritizes keeping siblings together while in foster care. However, prior research on the effects of sibling placement is limited in sample, measures, and research design. In this study, we use data on 2,297 children from an urban county in years 2015–2019 and assess how sibling separation is associated with placement instability. We use multilevel parametric hazard modeling with adjustments for child, sibling, and placement characteristics. Findings indicate that children placed with at least one sibling are less likely to experience a placement move and are specifically less likely to experience a non-progress move (e.g., moves due to problems or negative experiences in their foster home). For larger sibling groups, sibling separation was not consistently associated with placement instability and there was little difference in placement instability for children placed with some versus all siblings. Results were robust to differences in measurement and model specification. Black or Hispanic race/ethnicity was also associated with increased risk of instability, and associations between sibling separation and instability were stronger for Black children, implying enhanced efforts to maintain sibling groups may be especially beneficial for Black children. Overall, findings provide support for the continuation and expansion of policies promoting sibling placement.

OBJECTIVE: This study is a cross-validation of the Cognitive Bias Scale (CBS) from the Personality Assessment Inventory (PAI), a ten-item scale designed to assess symptom endorsement associated with performance validity test failure in neuropsychological samples. The study utilized a mixed neuropsychological sample of consecutively referred patients at a large academic medical center in the Midwest. PARTICIPANTS AND METHODS: Participants were 332 patients who completed embedded and free-standing performance validity tests (PVTs) and the PAI. Pass and fail groups were created based on PVT performance to evaluate classification accuracy of the CBS. RESULTS: The results were generally consistent with the initial study for overall classification accuracy, sensitivity, and cut-off score. Consistent with the validation study, CBS had better classification accuracy than the original PAI validity scales and a comparable effect size to that obtained in the original validation publication; however, the Somatic Complaints scale (SOM) and the Conversion subscale (SOM-C) also demonstrated good classification accuracy. The CBS had incremental predictive ability compared to existing PAI scales. CONCLUSIONS: The results supported the CBS, but further research is needed on specific populations. Findings from this present study also suggest the relationship between conversion tendencies and PVT failure may be stronger in some geographic locations or population types (forensic versus clinical patients).

In a sample of 28 individuals cohabiting with a partner in NYC, Boston, or Chicago, this study tested whether implementation of stay-home orders to combat the spread of COVID-19 disrupted physical activity and whether high-quality romantic relationships buffered adverse effects. Participants provided FitBit data between February and October, 2020. Stay-home orders were associated with a reduction in daily step counts, B = −1595.72, p = 0.018, increased sedentary minutes, B = 33.75, p = 0.002, and reduced daily minutes of light and moderate physical activity, B = –25.01, p = 0.011; B = –0.72, p = 0.021. No moderation effects emerged.

Several crucial reasons exist to identify whether an adult has had reading disorder (RD) and to predict a child’s likelihood of developing RD, which is known to be primarily genetically transmitted. The Adult Reading History Questionnaire (ARHQ) is among the most commonly used self-reported questionnaires. High ARHQ scores indicate an increased likelihood that an adult had RD as a child, and that their children may develop RD. This study focused on whether using a subset of ARHQ items (ARHQ-brief) could be equally effective and efficient in assessing adults’ reading history. We used a machine learning approach, lasso (known as L1 regularization), and identified 6 of 23 items that resulted in the ARHQ-brief. Data from 97 adults and 47 children were included. With the ARHQ-brief, we report a threshold of 0.323 as suitable to identify past likelihood of RD in adults with a sensitivity of 72.4% and a specificity of 81.5%. Comparison of predictive performances between ARHQ-brief and the full ARHQ showed that ARHQ-brief explained an additional 10-35.2% of the variance in adult and child reading. Further, we validated ARHQ-brief’s superior ability to predict reading ability using an independent sample of 28 children. We close by discussing limitations and future directions.

OBJECTIVE: We examined the predictive relation between childhood-diagnosed ADHD and trajectories of body mass index (BMI) from childhood to adulthood in an all-female sample, accounting for socioeconomic status (SES), childhood comorbidities (e.g., depression/anxiety), and stimulant usage. Childhood executive functioning (i.e., planning, sustained attention, and response inhibition) was also evaluated as a possible predictor of BMI trajectories. METHOD: We utilized longitudinal data from a full sample of 140 girls diagnosed with ADHD in childhood and 88 comparison girls matched on age and ethnicity. Girls were 6–12 years old at the first assessment and followed prospectively for 16 years. Data were collected on their BMI and stimulant medication usage across four evaluation waves. Using latent growth curve modeling, we evaluated the BMI trajectories of girls with ADHD and the comparison sample from childhood to adulthood. RESULTS: Although there was no significant difference in initial childhood BMI, girls with ADHD increased in BMI at a significantly faster rate than comparison girls across development, even when adjusting for covariates. Significant differences in BMI first emerged in adolescence; by adulthood, 40.2% of the ADHD sample met criteria for obesity versus 15.4% of the comparison sample. When covarying ADHD diagnosis, executive functioning measures were not significantly predictive of BMI increase. Adjusting for stimulant medication usage within the ADHD sample did not alter core findings. CONCLUSIONS: We discuss health-related implications for girls with ADHD, potential underlying mechanisms, and how our findings may inform both ADHD and obesity interventions.

The study employs a sample of US S&P 100 firms to explore the driving factors affecting the level of board commitment. We modified Clark’s (1998) CANE model and developed a corporate CANE model that is applicable for board commitment. Supporting our model, we find that primary drivers of board commitment (through board personal agency) are non-financial factors including board independence, size, gender diversity, professional experience/skills, industrial and financial experiences, senior executives' compensation, and strictly board independence. These board compositions significantly enhance the level of board commitment, except the board size. On the other hand, whilst we find significant impact for the market-based factors, i.e. board commitment level tends to increase with market value (Tobin's Q), we find weak or no evidence on the effects of accounting-based factors. This suggests that the board task value is influenced by the actual perception of the market participants rather than ‘documented’ figures.

OBJECTIVE The primary aims of this study were to identify latent profiles of acute stress disorder (ASD) symptoms and to evaluate postconcussive symptom differences across the identified profiles as measured by the Acute Stress Disorder Scale and the Military Acute Concussion Evaluation, respectively. METHOD Participants (N = 315) in the current study were predominantly active-duty (75.0%), enlisted (97.8%) males (97.4%) serving in the U.S. Army (87.8%). Approximately, half of the sample reported being married or engaged (51.1%) and was on average 25.94 (SD = 6.31) years old. Participants were referred to the Air Force Theater Hospital, 332nd Air Expeditionary Wing, Joint Base Balad, Iraq, to be evaluated as part of routine clinical assessment for neurocognitive and psychological symptoms following exposure to a blast. RESULTS A 3-profile solution was identified as the most parsimonious and best-fitting model based on statistical model fit indices. Blast injured service members in Profile 3 had greater ASD total and subscale severity compared to the other 2 subgroups, with effect size estimates largely differing by hyperarousal and reexperiencing symptoms. Furthermore, Profiles 2 and 3 were more likely to demonstrate postconcussive symptoms compared to Profile 1. CONCLUSIONS Findings provide novel information on heterogenous ASD symptom profiles during the acute phase following a blast injury and highlight the relationship between psychological and physical symptoms. Classification of blast-injured service members may help identify at-risk individuals who would benefit from further clinical care and mitigate long-term psychological and neurocognitive issues. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The Internet provides the most extensive platforms for information and knowledge seeking. Accordingly, investigating antecedents for online knowledge seeking becomes an important topic for managing knowledge-based work and innovation. Taking an integrative approach drawing on social cognitive theory, expectancy theory, and theory of reasoned action, this article examines the antecedents of online knowledge seeking with a sample of 246 professionals in 43 technical RD and 2) norms of knowledge sharing positively moderate the relationship between extrinsic motivation and online knowledge seeking. This article is one of the few that develop and empirically test an integrative model of antecedents for online knowledge seeking. The findings not only contribute to the knowledge seeking and knowledge management literatures, but also shed light on managing employee knowledge behaviors and learning via the means of the Internet.

Limit distribution theory in the econometric literature for functional coefficient cointegrating regression is incorrect in important ways, influencing rates of convergence, distributional properties, and practical work. The correct limit theory reveals that components from both bias and variance terms contribute to variability in the asymptotics. The errors in the literature arise because random variability in the bias term has been neglected in earlier research. In stationary regression this random variability is of smaller order and can be ignored in asymptotic analysis but not without consequences for finite sample performance. Implications of the findings for rate efficient estimation are discussed. Simulations in the Online Supplement provide further evidence supporting the new limit theory in nonstationary functional coefficient regressions.

Berry et al. (2020) noted that predictive bias is a function of three factors: subgroup mean difference on the predictor (dx), subgroup mean difference on the criterion (dy), and test validity (rxy). They used meta-analytic estimates of each of these three to examine predictive bias against Hispanic test takers when cognitive tests are used in personnel selection. They found that tests underpredict Hispanic job performance by an average of .21 SDs, which would call into question the fairness of cognitive test use in personnel selection. We located 119 studies in which all three parameters-dy, dx, and rxy-could be obtained, thus holding sample, setting, and operationalization constant in estimating the three parameters within each study. This produced a substantially different conclusion: We find that tests overpredict Hispanic performance by .04-.20 SDs, depending on assumptions made about artifact corrections. Factors contributing to differences between the two studies include differences in range restriction corrections, sample incomparability, and Berry et al.'s use of rxy estimated from the total sample rather than within the majority subgroup. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Studies of insect herbivory on fossilized leaves tend to focus on a few, relatively simple metrics that are agnostic to the distribution of insect damage types among host plants. More complex metrics that link particular damage types to particular host plants have the potential to address additional ecological questions, but such metrics can be biased by sampling incompleteness due to the difficulty of distinguishing the true absence of a particular interaction from the failure to detect it—a challenge that has been raised in the ecological literature. We evaluate a range of methods for characterizing the relationships between damage types and host plants by performing resampling and subsampling exercises on a variety of datasets. We found that the components of beta diversity provide a more valid, reliable, and interpretable method for comparing component communities than do bipartite network metrics and that the rarefaction of interactions represent a valid, reliable, and interpretable method for comparing compound communities. Both beta diversity and rarefaction of interactions avoid the potential pitfalls of multiple comparisons. Finally, we found that the host specificity of individual damage types is challenging to assess. Whereas bipartite network metrics are sufficiently biased by sampling incompleteness to be inappropriate for fossil herbivory data, alternatives exist that are perfectly suitable for fossil datasets with sufficient sample coverage.

OBJECTIVE In nationally funded research in the U.S., ethnicity and race are measured with checkbox questions, the limited categories of which may yield significant missing data and low salience to available categories, particularly among adults of color. How adolescent-generated data compare to these limited categories, and how adolescents, not just those of color, describe their culture are understudied. In our sample, we asked the following: (a) how do adolescents' ethnicity/race and culture identifications compare?, (b) how do adolescents' open-ended ethnicity/race and checkbox ethnicity/race labels compare?, and (c) how do adolescents' ethnicity/race labels compare to available categories for identification on the National Institutes of Health's (NIH's) demographic form? METHOD Data from 76 adolescents (64.5% female, Mage = 15.78 years) were qualitatively and quantitatively analyzed. RESULTS White adolescents were as likely as adolescents of color to identify with cultural labels, and most adolescents described culture differently than race/ethnicity. White adolescents' open-ended and checkbox labels overlapped more (38.8%) than adolescents of colors' (22.5%). Only 17% of adolescents' open-ended race/ethnicity identification labels and 54% of their checkbox identifications were available on the NIH's demographic form. CONCLUSIONS As measurement of demographic data is reduced to few options that favor privileged identities, innovative methods to measure individuals' identities are warranted. Using tools that do not reflect individuals' identity labels may increase error and reflects structural problems of research inequity. Our findings provide initial evidence of this methodological issue in a sample of adolescents. Building inclusive demographic tools from individuals' self-descriptors that remain feasible and practical for use can diminish inequity associated with describing "diverse" populations. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

COVID-19 is a worldwide public health threat. Diagnosis by RT-PCR has been employed as the standard method to confirm viral infection. Sample pooling testing can optimize the resources by reducing the workload and reagents shortage, and be useful in laboratories and countries with limited resources. This study aims to evaluate SARS-CoV-2 detection by sample pooling testing in comparison with individual sample testing.We created 210 pools out of 245 samples, varying from 4 to 10 samples per pool, each containing a positive sample. We conducted detection of SARS-CoV-2-specific RdRp/E target sites.Pooling of three samples for SARS-CoV-2 detection might be an efficient strategy to perform without losing RT-PCR sensitivity.Considering the positivity rate in Dominican Republic and that larger sample pools have higher probabilities of obtaining false negative results, the optimal sample size to perform a pooling strategy shall be three samples.La COVID-19 es una amenaza de salud pública mundial. La RT-PCR es el método estándar para confirmar la infección. La estrategia de pruebas de muestras agrupadas puede reducir la carga de trabajo y la escasez de reactivos, y ser útil en países con escasos recursos. Evaluamos la detección del SARS-CoV-2 mediante esta estrategia en comparación con pruebas individuales.Creamos 210 grupos de 245 muestras, de 4 a 10 muestras por grupo, cada uno con una muestra positiva. Realizamos extracción de ARN y qRT-PCR para detectar la presencia de la diana RdRp/E.La combinación de hasta 3 muestras para la detección del SARS-CoV-2 podría ser una estrategia eficaz sin perder la sensibilidad.Considerando la tasa de positividad en República Dominicana y que los grupos con más muestras tienen mayor probabilidad de obtener resultados falsos negativos, el tamaño óptimo para realizar esta estrategia es de 3 muestras.

The research aimed to know the effect of parental leadership represented by (benevolent leadership, moral leadership, and authoritarian leadership) found in the research sample, in the organizational familiarity (employee morale, empowerment, and objective merit), the research relied on the questionnaire as a key instrument to collect the necessary data to meet its goal. As (60) forms were distributed to find the level of availability of parental leadership and organizational harmony, while (56) forms were retrieved. A set of statistical methods were used, represented by normal distribution, stability factor (Alpha Kronbach), reliability, arithmetic mean, standard deviation, and coefficient Simple correlation Pearson, multiple regression coefficient. The results showed that there is a positive correlation and effect relationship with statistically significant between parental leadership with its dimensions (benevolent leadership, moral leadership, authoritarian leadership) and organizational affiliation with its dimensions (employee morale, empowerment, and merit's Objectivity), and the research showed a direct impact relationship between parental leadership and the organizational affiliation of the studied sample. Accordingly, the research concluded that the study sample should pay attention to the nature and type of empowering workers in order to give them freedom and independence in making decisions regarding the tasks assigned to them.

“Teaching competency has different dimensions such as mastery of subject matter, enhancement of motivation of students, planning, presentation and evaluation skills and classroom managerial skills. Teacher possessing all these skills to reasonable extent can be a competent teacher”. The success of teaching mostly depends on some psychological and demographical factors of teachers. The relationship between teacher efficiency and intelligence was also the subject of study of many researchers. Many researchers indicated highly positive correlation between teaching success and intelligence. On the other the investigators concluded that the correlation between intelligence and teaching success in certain cases were positive, in some others negative, while in the rest non-significant. Therefore, it reveals to the need for further investigation and research, to find out the co-relationship between teacher efficiency and intelligence. In the present study, multi-stage stratified random sampling technique was adopted to draw the sample. The study was conducted on a sample of 589 teachers, selected from the 46 secondary schools of Bareilly region in U.P. For the purpose of measuring teaching efficiency and intelligence, the Teaching Efficiency Scale (TES)), were used. The results of the study indicate a significant and positive co-relation between intelligence and teacher efficiency.

Objective The Inventory of Interpersonal Problems–Personality Disorders (IIP-PD-47) has a controversial factor structure, as some studies have provided support for 5 correlated factors, and others have suggested the existence of a general second-order dimension. One approach of data modelling that reconciles multidimensionality and the existence of a general factor is the bifactor analysis. We used unrestricted exploratory-confirmatory bifactor modelling to validate the Brazilian version of the IIP-PD-47. Methods The sample consisted of 1,091 subjects aged 18-64 years who answered the IIP-PD-47 and a collateral measure of pathological traits, the Dimensional Clinical Personality Inventory 2 (IDCP-2). Results After testing many candidate models, our data were best represented by a bifactor model with one general factor and five specific uncorrelated factors. Nevertheless, a closer inspection of the discriminant validity of each IIP-PD-47 factor revealed strong support for the general factor and a factor capturing aggressive behaviours, but less support for the additional four specific factors. Conclusions The theoretical and practical implications of these findings are discussed, and some recommendations are offered about the need for controlling response styles when assessing PD traits via self-report inventories. Our findings indicate that the Brazilian version of IIP-PD has promising psychometric properties.

Item does not contain fulltext The purpose of this study was to describe the development and preliminary testing of a new scale assessing knowledge about melanoma risk factors and early detection/secondary prevention. Data was drawn from a longitudinal study assessing barriers and facilitators of skin self- examination among patients diagnosed with melanoma. For the current analysis, 191 patients who completed the new 9-item Knowledge About Melanoma Early Detection Scale and other study measures were included. Exploratory factor analysis with were conducted, which identified a robust scale comprised of 6 items with factor loadings ranging from .56 to .81. Higher scores on the Knowledge About Melanoma Early Detection Scale were associated with younger age and more positive attitudes about melanoma prevention, but not with biological sex, education, melanoma stage, or past self-administered and physician-provided skin checks. Further studies are needed to replicate these findings and to further establish the predictive validity of this scale and its usefulness for health research. 9 p.

Machine learning (ML) models are increasingly trained with distributed workers possessing heterogeneous resources. In such scenarios, model training efficiency may be negatively affected by \emph{stragglers}---workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy, called \emph{semi-dynamic load balancing}, to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at \emph{iteration boundaries}, being non-intrusive to intra-iteration execution. Based on it we further develop LB-BSP, an integrated worker coordination mechanism that adapts workers' load to their instantaneous processing capabilities---by right-sizing the sample batches at the synchronization barriers. We have designed distinct load tuning algorithms for ML in CPU clusters, in GPU clusters as well as in federated learning setups, based on their respective characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to $54\%$ .

The 26-item Southampton Hand Assessment Protocol (SHAP) is a test of prosthetic hand function that generates an Index of Functionality (IOF), and prehensile pattern (PP) scores. Prior researchers identified potential issues in SHAP scoring, proposing alternative scoring methods (LIF and W-LIF).Cross-sectional study.Evaluate the psychometric properties of the SHAP IOF, LIF, and W-LIF and PP scores and develop the Prosthesis Index of Functionality (P-IOF).We examined item completion, floor andceiling effects, concurrent, discriminant, construct and structural validity. The P-IOF used increased boundary limits and information from item completion and completion time. Calibration used a nonlinear mixed model. Scores were estimated using maximum a posteriori Bayesian estimation. Mixed integer linear programing (MILP) informed development of a shorter measure. Validity analyses were repeated using the P-IOF.126 persons, mean age 57 (sd 15.8), 69% with transradial amputation were included. Floors effects were observed in 18.3%-19.1% for the IOF, LIF, and W-LIF. Ten items were not completed by15% of participants. Boundary limits were problematic for all but 1 item. Correlations with dexterity measures were strong (r = 0.54-0.73). Scores differed by amputation level (p.0001). Factor analysis did not support use of PP scores. The P-IOF used expanded boundary limits to decrease floor effects. MILP identified 10 items that could be dropped. The 26-item P-IOF and 16-item P-IOF had reduced floor effects (7.5%), strong evidence of concurrent and discriminant validity, and construct validity. P-IOF reduced administrative burden by 9.5 (sd 5.6) minutes.Floor effects limit a measure's ability to distinguish between persons with low function.Analyses supported the validity of the SHAP IOF, LIF, and W-LIF, but identified large floor effects, as well as issues with structural validity of the PP scores. The 16-item P-IOF minimizes floor effects and reduces administrative burden.

Unlike previous studies, the current study uses oil price and inflationary shocks to assess the feasibility of actualizing the ECOWAS Vision 2020, which is aimed at creating a monetary union. With the help of the Blanchard and Quah (BQ) decomposition for a sample from 1975:05 to 2018:08, two sets of models are estimated: models for inflationary shocks and models for oil price shocks. It is found that although the vision is a mirage, the creation of a common currency can serve as a shock absorber against the negative spillovers of global and regional inflationary shocks. The study also finds that oil price shocks lead to appreciation of the currency for the oil exporting country Nigeria, while Nigeria, The Gambia and Ghana stand out in their responses to oil price shocks. The study recommends that these countries cannot be part of the Vision and that more coordination among ECOWAS members is needed before this Vision can be actualised.Keywords: Monetary union, Optimal currency area, ECOWAS, WAEMU, SVAR, BQ decomposition.JEL: C13, E31, E52, E58, F33, F42.

This article investigates the predictability of dry bulk shipments' physical shipping costs while testing the predictive significance of derivative products. Accordingly, a comprehensive grid search procedure is needed to simulate combinations of model structures subject to a cross-validation process. In this regard, the intelligent model search engine (IMSE) is implemented as the search algorithm. The IMSE algorithm is selected due to its broad model space naturally consisting most of the traditional econometric model structures and advanced features, such as sample size optimization (also detects structural breaks), lag structure optimization (also reflects seasonality), among others. In contrast to the previous studies in the shipping market research, IMSE executes a relaxation of statistical significance criteria to target the out of sample predictive accuracy (instead of the goodness of fit). Sparsification is retained by utilizing a dropout procedure in line with the cross-validation process. The empirical results indicate the fact that predictive accuracy can be achieved or improved with a shorter sample period. More than half of the entire dataset has not been utilized in the final selections for most simulations. One-month and two-month maturity derivative contracts are tested for predictive features, while none of them is selected for the sparse model selections.

Recent research applies patent autoclassification using machine learning to map the technological landscape of an industry value chain. However, when these methods are applied to emerging industries, the available patent sample data are small-scale and unevenly distributed, which cause overfitting and reduce the accuracy of patent classification. Therefore, this article proposes a framework to map the technological landscape of an emerging industry value chain through patent analysis with deep learning, which integrates a generative adversarial network as a data-augmentation method to overcome the problem of low-quality emerging-industry patent samples, and a deep neural network as a patent classifier. Based on this framework, this article conducts an application case of the 3-D printing industry. The evaluation results show that the integrated framework can effectively classify the patents with small-scale and unevenly distributed sample data, and depict the technological landscape of an emerging industry value chain. This article develops an efficient, reliable framework for patent autoclassification of emerging industries to overcome the lack of high-quality training samples, and it sheds light on the emerging industry value chain analysis with deep learning.

This article proposes a methodology to calculate the effect of moral hazard on short term credit (working capital) to small and medium-sized enterprises (SMEs). The methodology incorporates four categories of moral hazard ratio defined in a previous study, which are employed to determine probability of default based on a logit model. To this end, a novel Colombian database is used to calculate a moral hazard index that considers the percentages of the odds ratios of the moral hazard variables for positive coefficients on the probabilities of default. The empirical analysis result in an index measuring the impact of moral hazard on odds ratio mainly based on underinvestment moral hazard category in the sample of analyzed companies for the period ranged from 2007 to 2014.

Firm survival in dynamic software markets depends on collaboration with ecosystem complementors and exploitative and/or explorative innovation. Such activities have been widely accepted as crucial antecedents for collaboration success, but few studies have analyzed the configurational effects of collaborative modes effect on firm performance. Based on a sample of 224 software firms and fuzzy set qualitative comparative analysis, results show that four causal configurations equivalently explain high innovation performance, in which exploitation, exploration, and collaboration with academic and governmental institutes are proven to be core causal conditions. Theoretically, this article contributes to the literature on collaborative innovation in terms of causality research and collaborative pathways but also enriches the literature on software ecosystems regarding partnerships and collaborative processes. Practical guidance and flexible choices for software firms to achieve high innovation performance are provided by offering four equivalent configurations.

In this article, we propose an active kriging-based learning method for hybrid reliability analysis (HRA) with random and interval variables. An improved sampling strategy is proposed to target the sampling areas. Samples with maximum responses greater than 0 and minimum responses less than 0 are selected and regarded as the candidate samples; then, a U-based learning function is developed in which multiple samples of the interval are considered instead of one particular sample. To terminate the proposed method, a hybrid convergence criterion is proposed. Finally, an improved optimization strategy based on the DIRECT algorithm is developed for the Monte Carlo simulation conducted for the HRA. The performance of the proposed method is demonstrated by four numerical cases. The results illustrate that the proposed method is accurate and efficient for HRA.

Formar profesionales capaces de incorporarse al mercado laboral y crecer integralmente con posibilidades ciertas de éxito plantea a las universidades retos que no deben soslayarse y que involucra el equilibrio en el fomento de competencias cognitivas y socioemocionales; a partir de lo anterior, en esta investigación se indaga la percepción que estudiantes universitarios han construido respecto al dominio de competencias sociocognitivas, con el propósito de diagnosticar el perfil de este tipo de habilidades para inferir áreas de intervención en el diseño de estrategias orientadas a favorecer la formación académica, profesional y personal; para ello, desde la tradición cuantitativa, con alcance descriptivo y empleando la encuesta como medio para la colecta de datos, se aplicó cuestionario construido en escala Likert, integrado por 80 ítems, con cuatro opciones de respuesta, a muestra de 243 estudiantes de cinco programas de licenciatura de la Facultad de Contaduría y Administración de la Universidad Autónoma de Chiapas; de manera general los resultados señalan que los estudiantes se perciben con alto dominio en las nueve competencias analizadas, destacando la resolución de problemas como la mejor ponderada; sin embargo, se presentan áreas de mejora en las relacionadas con creatividad e innovación, adaptabilidad y flexibilidad, manejo de conflictos y autoevaluación.

Objective: Child abuse and neglect which are the leading problems all over the world can cause major physical and mental effects. This study was conducted to investigate the effects of childhood abuse and neglect on anxiety symptoms, anxiety sensitivity and academic success in young adults.Material and Methods: This descriptive study was conducted with 301 volunteer participants aged between 17 and 24 and studying in the first year of university. ISPCAN Child Abuse Screening Tools (ICAST-R), Beck Anxiety Scale and Anxiety Sensitivity Index-3 forms were answered by the students who agreed to participate in the study and the students were asked to indicate the University Entrance Exams score. The data were statistically evaluated in theSPSS 22.0 database.Results: Of the 301 students 39.20% who participated in the study were exposed to abuse at least once before the age of 18 years. The most common types of exploitation was emotional abuse. The mean of the total score of the students taken from the Beck Anxiety Scale was 15.55±11.58 and it was found that there was a statistically significant relationship between anxiety levels and abuse status (p

Hydraulic systems are a class of typical complex nonlinear systems, which have been widely used in manufacturing, metallurgy, energy, and other industries. Nowadays, the intelligent fault diagnosis problem of hydraulic systems has received increasing attention for it can increase operational safety and reliability, reduce maintenance cost, and improve productivity. However, because of the high nonlinear and strong fault concealment, the fault diagnosis of hydraulic systems is still a challenging task. Besides, the data samples collected from the hydraulic system are always in different sampling rates, and the coupling relationship between the components brings difficulties to accurate data acquisition. To solve the above issues, a deep learning model with multirate data samples is proposed in this article, which can extract features from the multirate sampling data automatically without expertise, thus it is more suitable in the industrial situation. Experiment results demonstrate that the proposed method achieves high diagnostic and fault pattern recognition accuracy even when the imbalance degree of sample data is as large as 1:100. Moreover, the proposed method can increase about 10% diagnosis accuracy when compared with some state-of-the-art methods.

Crowdsourcing services provide a fast, efficient, and cost-effective way to obtain large labeled data for supervised learning. Unfortunately, the quality of crowdsourced labels cannot satisfy the standards of practical applications. Ground-truth inference, simply called label integration, designs proper aggregation methods to infer the unknown true label of each instance (sample) from the multiple noisy label set provided by ordinary crowd labelers (workers). However, nearly all existing label integration methods focus solely on the multiple noisy label set per individual instance while totally ignoring the intercorrelation among multiple noisy label sets of different instances. To solve this problem, a multiple noisy label distribution propagation (MNLDP) method is proposed in this article. MNLDP at first estimates the multiple noisy label distribution of each instance from its multiple noisy label set and then propagates its multiple noisy label distribution to its nearest neighbors. Consequently, each instance absorbs a fraction of the multiple noisy label distributions from its nearest neighbors and yet simultaneously maintains a fraction of its own original multiple noisy label distribution. Empirical studies on a collection of an artificial dataset, six simulated UCI datasets, and three real-world crowdsourced datasets show that MNLDP outperforms all other existing state-of-the-art label integration methods in terms of the integration accuracy and classification accuracy.

Dynamic neural network is an emerging research topic in deep learning. Compared to static models which have fixed computational graphs and parameters at the inference stage, dynamic networks can adapt their structures or parameters to different inputs, leading to notable advantages in terms of accuracy, computational efficiency, adaptiveness, etc. In this survey, we comprehensively review this rapidly developing area by dividing dynamic networks into three main categories: 1) instance-wise dynamic models that process each instance with data-dependent architectures or parameters; 2) spatial-wise dynamic networks that conduct adaptive computation with respect to different spatial locations of image data and 3) temporal-wise dynamic models that perform adaptive inference along the temporal dimension for sequential data such as videos and texts. The important research problems of dynamic networks, e.g., architecture design, decision making scheme, optimization technique and applications, are reviewed systematically. Finally, we discuss the open problems in this field together with interesting future research directions.

To improve drinking water accessibility and safety in rural China, the Chinese government launched the rural drinking water program in the 1980s. As part of the program, tap water infrastructure has been constructed in rural areas to supply tap water to rural residents. This policy intervention provides a unique opportunity to examine the impact of early life exposure to tap water on children’s cognitive achievement in later life. Using data extracted from the China Family Panel Studies (CFPS), we find that one additional year of exposure to tap water in early life increases average cognitive test score by 0.109 standard deviations for a sample of rural children aged 10-15 in 2010. The effect is larger for children whose fathers are less-educated. Event study estimates confirm that the beneficial impacts are concentrated in early life with limited additional impact after the time window.

Purpose: This study investigated the differences between binary and nonbinary transgender individuals' experiences of transgender identity through the Transgender Identity Survey (TIS). Method: Bin...

Objectives This study aimed to describe existing knowledge of crisis supporters (volunteers responding to crisis calls/texts/messages) regarding self-harm and suicide in older adults and investigate knowledge translation through use of a novel online adaptive learning tool. Methods The online educational tool was tested in an Australian national sample of crisis supporters (trained volunteers) aged 18+. Knowledge Transfer was evaluated utilizing a pre/post intervention methodology for data collection. The collaboratively developed online educational tool comprised a pre-test (10 questions), middle learning module (individualised for participant's incorrect pre-test responses) and post-test (10 questions) on suicidal behaviours in older adults. Data analysed included the demographic characteristics of the participants, individual question scores, and summed pre- and post-tests scores. Group differences in change scores were assessed with either one-way between subjects ANOVA or independent samples t-test, depending on the number of groups within each variable. Pre-post education comparisons on individual change in scores were made using a paired samples t-test. Statistical significance was defined as p Results 104 crisis supporters completed the tool (pre-test, middle lesson and post-test). There was significant improvement in knowledge of crisis supporters after the intervention (pre-test scores Mean (M) = 4.56, SD = 1.62 and post-test scores M = 7.61, SD = 1.60; t (103) = 17.242, p Conclusion Dedicated training about suicidal behaviors in older adults is needed given their high rates of suicide and differing underlying reasons and needs compared to younger adults.

Cannabis use is prevalent and concerns about cannabis misuse are increasing. A reinforcer pathology approach emphasizes the roles of drug reinforcing value (demand) and overvaluation of immediate rewards (delay discounting [DD]) in drug use but has been applied to a lesser extent to cannabis. The present study investigated the independent and interactive roles of these processes in relation to cannabis misuse in a community sample of adult cannabis users (N = 324; 44.8% female; Mage = 33.25). Participants completed a Marijuana Purchase Task (MPT), the Monetary Choice Questionnaire (MCQ), and the Cannabis Use Disorder Identification Test-Revised (CUDIT-R) to assess demand, DD, and cannabis misuse, respectively. Zero-order correlations revealed significant associations between CUDIT-R scores and both the demand indices (|rs | = .21-.56, p < .01-.001) and DD (r = .21, p < .01). In multivariate analyses, lower elasticity (i.e., price insensitivity) was robustly associated with higher CUDIT-R scores, while other demand indicators did not explain additional unique variance. However, as elasticity, intensity, and Omax exhibited robust zero-order intercorrelations, shared variance appeared to drive the association. An interactive relationship between elasticity and DD was not significant. These findings indicate that cannabis misuse is associated with both cannabis demand, particularly as measured by insensitivity to escalating costs, and immediate reward orientation, but the relationship was not synergistic. These results support a reinforcer pathology approach to cannabis misuse and, although causality cannot be inferred cross-sectionally, suggest that evaluating the longitudinal significance of these indicators is warranted. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding vacuity in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying the given constraints is a key enabler for the viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution, and can be very inefficient to produce legal scenarios of interest. We show how, given a set of constraints on the input scenarios succinctly defined by finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification., Comment: 14 pages. IEEE Transactions on Software Engineering, 2021

In this paper, we have proposed a completely fair energy scheduling mechanism for a smart distributed multi-microgrid system which guarantees that the energy is scheduled fairly among all the buyer microgrids (MGs). It is a non-priority based starvation free mechanism as all the buyers are treated equal and every buyer MG gets at least some portion of its energy requirement fulfilled through the internal trading. The energy scheduling is done in two phases: during phase I, energy is equally distributed among all the buyer MGs. Because of this equal distribution of energy, the buyer MGs are benefited as they are not required to purchase their entire energy requirement from utility grid at higher price. During phase II, Vickrey auction scheme is used to schedule the remaining energy amongst the buyer MGs whose energy requirements were not fulfilled during the first phase. We have evaluated the proposed mechanism for a sample instance of microgrid network by simulating the environment. The simulation demonstrates that using the proposed mechanism the total energy buying cost for all buyer MGs is reduced greatly compared with the schemes where only auctioning is used for internal trading.

Data-driven fault diagnosis primarily involves the identification of different fault locations and fault severities. Focusing on a chal-lenging task for which the target fault severities do not exist in the training samples, this study proposes a generalized cross-severity bearing fault diagnosis scheme based on a novel hierarchical cross-category inference framework. The proposed method uses an outlier detection scheme based on unsupervised feature mapping and local outlier probability calculation to identify the unseen samples. A neural network embedded with a tree-structured decision layer acts as a backbone to execute fault diagnosis at different hierarchies for different sample types, seen or unseen. Additionally, the metric learning method is used to support the approximate severity inference of the unseen samples after the fault locations are identified in the hierarchical model. Experiments performed on an aeronautical bearing test rig revealed that the proposed scheme is both feasible and superior to existing methods.

Mobile operators collect and store the network generated traffic data for analysis. Time Series Prediction (TSP) has been used in mobile network traffic data analysis to produce predictive results for network planning and resource allocation. We propose a novel method of predicting mobile network traffic using neural networks based on conditional probability modeling between adjacent data windows. Firstly, we develop a pre-processing method to aggregate the raw traffic log data and sample the aggregated time series to adjacent data windows, as training samples. Secondly, we use neural networks to parameterize the conditional probability between adjacent data windows and estimate the probability by training the neural networks with sampled data. The estimated conditional probability is then used to ensemble the prediction. Thirdly, we show theoretically that the prediction based on all historical data is equivalent to the prediction based on just previous data window, given the estimation of conditional probability between adjacent data windows. We also analyze computation complexity and show that seasonality will reduce the computational complexity. In the experiment, we compare the prediction performance among the models with different seasonality, sample size and number of hidden layers, and show that the proposed schemes achieve better prediction accuracy than state-of-the-art.

This research is focused on the question of deciding what to say in a (health) communication campaign. The goal is to search for ways of selecting/tailoring effective campaign messages which are persuasive for the targeted audiences. Accordingly, by considering Social Judgement Theory in the foundation of Integrated Behavior Model; two variables, belief strength and involvement are proposed with a method to predict the persuasiveness of campaign messages. When the people have strong beliefs and high involvements, a message that does not match with the most important behavioral determinant should be used in the campaign. However, when the people have weak beliefs, a message matching with the most important behavioral determinant should be used without considering the involvement level. These hypotheses are tested in the field of health communication on (cervical cancer) HPV vaccine acceptance and all accepted. CATI is used to collect data on surveys. The study sample, which is derived through a method close to probability sampling, is consisted of mothers who live in Istanbul/Turkey and have at least one 11-26-year-old daughter(N=145). Multiple linear regressions, decision trees (CHAID) and ANOVA are used for the analysis.

Imagining narratives involving sudden economic scarcity has been shown to increase delay discounting rates. However, previous studies have only compared active and control narratives between groups. Moreover, an investigation of the quantitative effects of different narrative scenario types has not been conducted. In this study, active and control scarcity narratives were administered within-subject in a sample of individuals meeting criteria for alcohol use disorder (AUD). Individuals with AUD (N = 81; 26.9% female) were recruited via Amazon Mechanical Turk. After assignment to the job (N = 42) or storm (N = 39) narrative groups, participants completed delay discounting tasks while imagining an active (job loss/hurricane) and control (job neutral/mild storm) condition. Both active narratives increased delay discounting relative to the corresponding control condition (p < .001), with no effect of order of presentation (p = .202). Additionally, both narrative types exerted similar effect sizes on discounting rates (job: d = 0.54; storm: d = 0.45). This study replicates and extends previous research on the manipulability of delay discounting rates with scarcity narratives. We demonstrated that the active narrative significantly increased delay discounting relative to a control narrative within-subject, regardless of presentation order. Moreover, both the job loss and hurricane narratives exerted a similar effect on the delay discounting rate. These results highlight the robust ability of the Narrative Theory framework to shift delay discounting rates and suggest that in AUD, imagining economic scarcity due to job loss or hurricane exerts comparable effects on behavioral economic decision-making. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

This study investigates the associations between school principals’ job crafting and well-being. Drawing on the job demands-resources approach of the job crafting model and self-determination theory, we hypothesized that need satisfaction and need frustration act as likely mechanisms between the positive and negative effects of job crafting behaviors and work-related well-being (work engagement and burnout). Using cross-sectional data collected from a sample of Finnish school principals (n = 518), our structural equation model revealed that crafting structural job resources was positively associated with need satisfaction and negatively with need frustration. Increasing challenge job demands was positively associated with need satisfaction, which, in turn, was associated with higher work-related well-being. In addition, crafting structural job resources was directly positively linked to burnout as well as work engagement. Moreover, we found that decreasing hindrance demands was positively associated with burnout and negatively associated with work engagement via need frustration. No associations were found between crafting social resources, indicators of basic psychological needs, or work-related well-being. These findings provide insights into how principals determine their own basic psychological needs via job crafting, and how this is reflected in their work-related well-being. The study advances job crafting theory and self-determination theory, with important implications for practice and future research.

Organizational capabilities, such as technological capabilities and marketing capabilities are key determinants of the success of technology commercialization (TC). However, literature has rarely explored how firms combine and balance these two potentially interrelated yet competing capabilities in pursuing successful TC. Adopting insights from the ambidexterity perspective, this article examines the tensions and interplay between technological capabilities and marketing capabilities, and conceptualizes the simultaneous pursuit of both capabilities as capability ambidexterity. We propose that, having a balancing capability (properly matching technological capabilities and marketing capabilities to avoid conflicts), a combining capability (effectively utilizing both capabilities), and simultaneously applying the balancing capability and the combining capability positively affect TC success. We also identify human resource-based conditions (high-commitment work systems) as a contingency under which the balancing capability and the combining capability are more likely to promote TC success. We report results from a sample of 262 Chinese manufacturing firms (based on a total of 524 respondents) that provide general support for the proposed model. Our results contribute to a better understanding of capability-based explanations of TC.