Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological Well-Being (PWB) of School Adolescents Aged 12–18 yr, its Correlation with General Levels of Physical Activity (PA) and Socio-Demographic Factors In Gilgit, Pakistan

Background: Adolescence is a critical developmental stage marked by complex transitions. The purpose of study was to assess school adolescents’ PWB, examine the relationship of PA and socio-demographic factors with PWB. Methods: A cross sectional study conducted in five randomly selected schools with 345 adolescents (aged 12–18) from grade 6th–10th. A self-administered Well-Being index was adapted to measure PWB and questionnaire for adolescents PA (PAQ-A). Socio-demographic variables determined: age, gender, household income and parental education. Bivariate and multivariate regression analyses performed to examine the association between PWB, PA and covariates. Results: Findings indicated the mean age 14.64 (SD=1.275), 55.4% were female. Without gender difference the majority (43.4%) of adolescents showed moderate, while (23.2%) revealed low level of PWB. Participants with low level likely to have depression but scores were significantly not different between low, moderate and high PWB with PA. Socio-demographic trends of adolescents’ were observed significant (P <0.005) for PWB. In multivariable analysis the mean wellbeing in females adjusted for other covariates was significant (P =0.004) than males. PWB importantly (P <0.001) decreased by 3.36 units as its covariates increased and PA score in boys found 0.05 unit more than girls. Conclusion: The study results are invaluable in addressing low, moderate and high levels of PWB. Inadequate PA and PWB decreasing with some socio-demographic covariates is crucial health issue among female adolescents in Pakistan. Further studies need to find barrier, social indicators of PWB and implication of health among adolescents.
The role of social networks in development of small-scale enterprises in the Chimanimani district of Zimbabwe

Abstract The past decade has witnessed an increased interest in the concept of social networks after the seminal theses of Coleman (1988) and Putnam (1993). An area that has attracted a great deal of interest is the value of social networks in small-scale enterprise development. This paper interrogates the role of social networks in the establishment and expansion of rural non-farm enterprises in the Chimanimani district of Zimbabwe and established that rural non-farm entrepreneurs resort to their social networks for information and other resources needed to establish and expand their enterprises, and that there are some gender differences in the use of the various social networks.
THE SURVIVAL OF DARK MATTER HALOS IN THE CLUSTER Cl 0024+16

Theories of structure formation in a cold dark matter dominated universe predict that massive clusters of galaxies assemble from the hierarchical merging of lower mass subhalos. Exploiting strong and weak gravitational lensing signals inferred from panoramic Hubble Space Telescope imaging data, we present a high-resolution reconstruction of the mass distribution in the massive, lensing cluster Cl 0024+16 at z = 0.39. Applying galaxy–galaxy lensing techniques we track the fate of dark matter subhalos as a function of projected cluster-centric radius out to 5 Mpc, well beyond the virial radius. We report the first detection of the statistical lensing signal of dark matter subhalos associated with late-type galaxies in clusters. The mass of a fiducial dark matter halo that hosts an early-type L* galaxy varies from M = 6.3+2.7−2.0 × 1011 M☉ within r < 0.6 Mpc, 1.3+0.8−0.6 × 1012 M☉ within r < 2.9 Mpc, and increases further to M = 3.7+1.4−1.1 × 1012 M☉ in the outskirts. The mass of a typical dark matter subhalo that hosts an L* galaxy increases with projected cluster-centric radius in line with expectations from the tidal stripping hypothesis. The mass of a dark matter subhalo that hosts a late-type L* galaxy is 1.06+0.52−0.41 × 1012 M☉. Early-type galaxies appear to be hosted on average in more massive dark matter subhalos compared to late-type galaxies. Early-type galaxies also trace the overall mass distribution of the cluster whereas late-type galaxies are biased tracers. We interpret our findings as evidence for the active assembly of mass via tidal stripping in galaxy clusters. The mass function of dark matter subhalos as a function of projected cluster-centric radius is compared with an equivalent mass function derived from clusters in the Millennium Run simulation populated with galaxies using semianalytic models. The shape of the observationally determined mass functions based on an I-band-selected sample of cluster members and lensing data are in agreement with the shapes of the subhalo mass functions derived from the Millennium Run simulation. However, simulated subhalos appear to be more efficiently stripped than lensing observations suggest. This is likely an artifact of comparison with a dark matter only simulation. Future simulations that simultaneously follow the detailed evolution of the baryonic component during cluster assembly will be needed for a more detailed comparison.
Differential processing of immediately repeated verbal and non‐verbal stimuli: an evoked‐potential study

Stimuli are better retained in memory if they are repeated after a delay than if they are immediately repeated. This effect is called the spacing effect (SE). Recent electroencephalographic (EEG) studies showed that delayed repetition of meaningful designs in a continuous recognition task induces an evoked response very similar to new presentations. In contrast, immediately repeated designs induced circumscribed, stronger activation of the left medio‐temporal lobe (MTL) at 200–300 ms. In amnesic subjects, this signal was missing, indicating that it has a memory‐protective effect. Here, high‐density EEG was used in humans to explore whether meaningless verbal (non‐words) and non‐verbal (geometric designs) stimuli also have a SE associated with such lateralized, temporally limited activation of the left MTL upon immediate repetition. The results revealed a SE for both materials. Timing and localization of brain activity differed as a function of stimulus material. Specific responses to immediate repetitions occurred at 200–285 ms for non‐verbal stimuli and at 285–380 ms for verbal material. Source estimations revealed increased activity in right inferior frontal areas for immediate non‐verbal repetitions and in left fronto‐parietal areas for immediate verbal repetition in comparison to new presentations. These findings show that, while the SE is a ubiquitous phenomenon, the neural processes underlying it vary according to stimulus material.
Can corrective feedback on second language speech perception errors affect production accuracy

This study investigated whether different types of corrective feedback (CF) in second language speech perception training have differential effects on second language speech production. One hundred Korean learners of English were assigned to five different groups and participated in eight computer-assisted perception training sessions focusing on English vowels. While no CF was provided to the control group, participants in the four treatment groups received one of three types of auditory CF or a visual type of CF. A pretest, an immediate posttest, and a delayed posttest each consisted of a production measurement at a controlled-speech level. Results revealed that the extent to which the participants’ production accuracy benefited from the perception training depended on CF type. In addition, by adopting the perception accuracy data by Lee and Lyster ( 2016b ), the current study found that improvement in perception accuracy was a significant predictor of improvement in production accuracy.
Adoption of Web-based Services for Transaction Processing by Organizations: A Multilevel Contextual Analysis

The purpose of this study was to determine the affect that contextual factors such as IS maturity of the organization, organizational factors, and environmental characteristics have on perceived usefulness and adoption of web-based services for transaction processing in organizations. To achieve the purpose of the study a research model and 10 hypotheses were developed based on past literature review. To test the hypotheses, data were collected from senior managers in small business organizations using an instrument that was carefully developed and tested. Structural Equation Modeling was performed to test the goodness of fit of the model as well as the hypotheses. The model suggested moderate fit of the data. This study reveals the importance of perceived usefulness in adoption of technologies by organizations. Other important factors are IS maturity of a company, firm performance, and the budget allocated for IS by the leaders in the organization.
A general stability criterion for droplets on structured substrates

Wetting morphologies on solid substrates, which may be chemically or topographically structured, are studied theoretically by variation of the free energy which contains contributions from the substrate surface, the fluid– fluid interface and the three-phase contact line. The first variation of this free energy leads to two equations—the classical Laplace equation and a generalized contact line equation—which determine stationary wetting morphologies. From the second variation of the free energy we derive a general spectral stability criterion for stationary morphologies. In order to incorporate the constraint that the displaced contact line must lie within the substrate surface, we consider only normal interface displacements but introduce a variation of the domains of parametrization.
A HOMOTOPY DOUBLE GROUPOID OF A HAUSDORFF SPACE II: A VAN KAMPEN THEOREM

This paper is the second in a series exploring the properties of a functor which assigns a homotopy double groupoid with connections to a Hausdorff space. We show that this functor satisfies a version of the van Kampen theorem, and so is a suitable tool for nonabelian, 2-dimensional, local-to-global problems. The methods are analogous to those developed by Brown and Higgins for similar theorems for other higher homotopy groupoids. An integral part of the proof is a detailed discussion of commutative cubes in a dou- ble category with connections, and a proof of the key result that any composition of commutative cubes is commutative. These results have recently been generalised to all dimensions by Philip Higgins.
ANOMALOUS BCS EQUATION FOR A LUTTINGER SUPERCONDUCTOR

A self-consistence equation describing a superconductor whose normal state is a Luttinger liquid is computed in a rigorous way by renormalization group techniques. The gap and the critical temperatures are quite different with respect to the ones of normal superconductors (i.e. superconductors whose normal state is a Fermi liquid) and in particular, the critical temperature can be quite high for suitable values of the parameters. Such self-consistence equation can be obtained in a BCS mean field theory for a model of two Mattis models exchanging Cooper pairs. The connection of this model with recent theories of high Tc superconductivity is briefly discussed.
Parent Depressive Symptomatology Moderates the Etiology of Externalizing Behavior in Childhood: An Examination of Gene-Environment Interaction Effects

Parent depressive symptomatology is robust risk factor for externalizing behavior in childhood (Goodman et al., 2011). Although the precise mechanisms underlying this association have yet to be fully illuminated, there is some evidence that parent depression can impact externalizing behavior via both genetic and environmental pathways. In the current study, we investigated the extent to which genetic and environmental influences on externalizing behavior are moderated by parent depressive symptoms (i.e., genotype-environment interaction) in a sample of 2,060, 6- to 11-year-old twins. Results suggest that genetic influences explain more variance in externalizing behavior as maternal depressive symptoms increase, whereas shared environmental effects decrease. These findings were specific to maternal depressive symptoms, however, and did not extend to not paternal depressive symptoms. Findings are critical for understanding the role of parental depression as a risk factor for problematic child behavior, and informing programs that seek to minimize the impact of this risk factor.
Design and Implementation of a Shape Memory Alloy Actuated Reconfigurable Airfoil

The unique thermal and mechanical properties exhibited by shape memory alloys (SMAs) present exciting design possibilities in the field of aerospace engineering. When properly trained, SMA wires act as linear actuators by contracting when heated and returning to their original shape when cooled. These SMA wire actuators can be attached to points on the inside of an airfoil, and can be activated to alter the shape of the airfoil. This shape-change can effectively increase the efficiency of a wing in flight at several different flow regimes. To determine the necessary placement of the SMA wire actuators within the wing, a global optimization method that incorporates a coupled structural, thermal, and aerodynamic analysis has been utilized. A genetic algorithm(GA) has been chosen as the optimization tool to efficiently converge to a design solution. The GA used in this case is a hybrid version with global search and optimization capabilities augmented by the simplex method with selective line search as a local search technique. A cost function based on the aerodynamic properties of the airfoil has been used to optimize this design problem to maximize the lift-to-drag ratio for a reconfigured airfoil shape at subsonic flow conditions. A wind tunnel model reconfigurable wing was fabricated based on the design optimization to verify the predicted structural and aerodynamic response. Wind tunnel tests indicated an increase in lift for a given flow velocity and angle of attack by activating the SMA wire actuators. The pressure data taken during the wind tunnel tests followed the trends expected from the numerical pressure results.
The erosion of right livelihood: counter-educational aspects of the commodification of mindfulness practice

ABSTRACT The links between education, therapy, and mindfulness practice are explored as a preliminary to the analysis of the commodification of such practice as a result of the “McDonaldizing” process over recent years. Contemporary “McMindfulness” developments in education and the workplace run counter to the Buddhist precept of “right livelihood” in that they distort both the means and ends of the educational endeavor. When mindfulness practice is employed to achieve instrumentalist outcomes linked to short-term productivity or specific operational goals, the therapeutic function concerned with the transformation of negative emotions and the alleviation of suffering is submerged beneath predominantly utilitarian and materialistic concerns. In order to restore mindfulness teaching and learning – and relationship roles and transaction modes within psychotherapy – to a position of right livelihood, it is necessary to challenge this crude commodification process by emphasizing the ethical and genuinely educational aspects of mindfulness programs.
Low-Metallicity Gas Clouds in a Galaxy Protocluster at Redshift 2.38

We present high-resolution spectroscopy of a QSO whose sight line passes through the halo of a pair of elliptical galaxies at redshift 2.38. This pair of galaxies probably lies at the center of a galaxy protocluster and is embedded in a luminous extended Lyα nebula. The QSO sight line intersects two small gas clouds within this halo. These clouds have properties similar to those of high-velocity clouds seen in the halo of the Milky Way. The gas is in a cool (<2 × 104 K) and at least 20% neutral phase, with metallicities in the range -3.0 < [Fe/H] < -1.1 and neutral hydrogen column densities of ~1019.5 cm-2. The origin of these clouds is unclear. The presence of low-metallicity gas within this possible protocluster implies either that the intracluster medium has not been enriched with metals at this redshift, or the clouds are embedded within a hot, ionized, metal-rich gas phase.
Methods for Classifying Errors on the Raven's Standard Progressive Matrices Test

Although many psychometric tests, like Raven’s Progressive Matrices, are commonly evaluated according to total score, additional variables can lend insight into the underlying cognitive processes. We examine conceptual errors on the Raven’s Standard Progressive Matrices (SPM) test. We present a complete classification of error types on the SPM using a two-kind coding scheme, yielding � 95% inter-rater reliability. We also examine how to extract error data from a computational model, and we present a method for measuring errors through systematic ablation to create a “population” of models whose performance can be examined as a group. We present a preliminary analysis of error patterns on the SPM from typically developing individuals, individuals diagnosed with autism, and a computational model called ASTI. We discuss what the error patterns suggest regarding cognition on the SPM and routes towards improving the ASTI model.
Enhancing Personal Interaction through the Web interface in Online Shopping: An Exploratory Study

Recent research has called for a need to infuse social presence into e-commerce websites, suggesting that a website low in social presence will be difficult to satisfy consumers‟ social needs (for interacting with other humans) when they are shopping online. This study finds that web interface elements of socially rich text and picture and virtual community, which provide means for personal interaction, lead to higher perception of social presence. Results, however, also show that social presence per se is not significant in predicting online purchase intention. Even so, e-vendors may also benefit from offering high level of personal interaction through their web interface as those three website features have influential impacts on trust and perceived value, two predicators of online purchase intention found in this study.
Extracting key terms from noisy and multitheme documents

We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods.
The Issues of Oppressed Groups’ Access to Collectively Managed Resources: An Empirical Analysis

Oppressed groups’ problem in collective management of resource is examined in the case of Kami’s (Blacksmith) access to charcoal in community forests in Nepal. A field survey shows that almost a half forest user groups have excluded the Kami for charcoal collection. In the government policies, rights of households with special needs are not clearly defined and protected. In an econometric analysis of the user groups, this study identified forest size, group size, intensive external support and groups’ meeting frequency are the factors determining the charcoal distribution in community forests.
Evaluation of image processing algorithms on ARM powered mobile devices

Image processing plays an important role in medical image analysis. The most popular methods for image processing and analysis are very resource hungry, which leads to some disadvantages in their applications even on a powerful desktop computers. On the other side, modern mobile devices are equipped with powerful processors with an efficient instruction architecture. This lead to better performance per watt than a desktop CPUs. This work investigates the performance of a widely used medical analysis algorithm implemented on a modern mobile devices and desktop CPU. The results obtained with ARM NEON instructions show speed improvements up to 2 times. As this research shows mobile devices cannot yet compete with powerful desktop CPUs, even with using highly optimizations or multiple threads. In the last part of the paper conclusions are drawn for acceptable image input and parameter sizes.
A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for some means of feature selection.