nils-herrmann/clean_scirepeval huggingface dataset:
Biodesalination-On harnessing the potential of nature's desalination processes.

Water scarcity is now one of the major global crises, which has affected many aspects of human health, industrial development and ecosystem stability. To overcome this issue, water desalination has been employed. It is a process to remove salt and other minerals from saline water, and it covers a variety of approaches from traditional distillation to the well-established reverse osmosis. Although current water desalination methods can effectively provide fresh water, they are becoming increasingly controversial due to their adverse environmental impacts including high energy intensity and highly concentrated brine waste. For millions of years, microorganisms, the masters of adaptation, have survived on Earth without the excessive use of energy and resources or compromising their ambient environment. This has encouraged scientists to study the possibility of using biological processes for seawater desalination and the field has been exponentially growing ever since. Here, the term biodesalination is offered to cover all of the techniques which have their roots in biology for producing fresh water from saline solution. In addition to reviewing and categorizing biodesalination processes for the first time, this review also reveals unexplored research areas in biodesalination having potential to be used in water treatment.
Relationship between attachment styles and happiness in medical students

Attachment theory is one of the most important achievements of contemporary psychology. Role of medical students in the community health is important, so we need to know about the situation of happiness and attachment style in these students.



This study was aimed to assess the relationship between medical students' attachment styles and demographic characteristics.



This cross-sectional study was conducted on randomly selected students of Medical Sciences in Kurdistan University, in 2012. To collect data, Hazan and Shaver's attachment style measure and the Oxford Happiness Questionnaire were used. The results were analyzed using the SPSS software version 16 (IBM, Chicago IL, USA) and statistical analysis was performed via t-test, Chi-square test, and multiple regression tests.



Secure attachment style was the most common attachment style and the least common was ambivalent attachment style. Avoidant attachment style was more common among single persons than married people (P = 0.03). No significant relationship was observed between attachment style and gender and grade point average of the studied people. The mean happiness score of students was 62.71. In multivariate analysis, the variables of secure attachment style (P = 0.001), male gender (P = 0.005), and scholar achievement (P = 0.047) were associated with higher happiness score.



The most common attachment style was secure attachment style, which can be a positive prognostic factor in medical students, helping them to manage stress. Higher frequency of avoidant attachment style among single persons, compared with married people, is mainly due to their negative attitude toward others and failure to establish and maintain relationships with others.
The top five reasons for lurking: improving community experiences for everyone

Even in busy online communities usually only a small fraction of members post messages. Why do so many people prefer not to contribute publicly? From an online survey that generated 1188 responses from posters and lurkers from 375 MSN bulletin board communities, 219 lurkers spoke out about their reasons for not posting. While lurkers did not participate publicly, they did seek answers to questions. However, lurkers’ satisfaction with their community experience was lower than those who post. Data from 19 checkbox items and over 490 open-ended responses were analyzed. From this analysis the main reasons why lurkers lurk were concerned with: not needing to post; needing to find out more about the group before participating; thinking that they were being helpful by not posting; not being able to make the software work (i.e., poor usability); and not liking the group dynamics or the community was a poor fit for them. Two key conclusions can be drawn from this analysis. First, there are many reasons why people lurk in online discussion communities. Second, and most important, most lurkers are not selfish free-riders. From these findings it is clear that there are many ways to 1 University of Maryland, Baltimore County, USA. (410) 455 3795/1217 (fax) preece@umbc.edu 2 University of Guelph, Canada. nonnecke@cis.uguelph.edu 3 University of Baltimore, USA.  Preece, J., Nonnecke, B., Andrews, D. (2004) The top 5 reasons for lurking: Improving community experiences for everyone. Computers in Human Behavior, 2, 1 (in press) 2 improve online community experiences for both posters and lurkers. Some solutions require improved software and better tools, but moderation and better interaction support will produce dramatic improvements.
CHESS—Calibrating the Hand-Eye Matrix With Screw Constraints and Synchronization

Hand–eye calibration is a classic problem in robotics that aims to find the transformation between two rigidly attached reference frames, usually a camera and a robot end-effector or a motion tracker. Most hand–eye calibration techniques require two data streams, one containing the eye (camera) motion and the other containing the hand (robot/tracker) motion, and the classic hand–eye formulation assumes that both data streams are fully synchronized. However, different motion capturing devices and cameras often have variable capture rates and timestamps that cannot always be easily triggered in sync. Although probabilistic approaches have been proposed to solve for nonsynchronized data streams, they are not able to deal with different capture rates. We propose a new approach for unsynchronized hand–eye calibration that is able to deal with different capture rates and time delays. Our method interpolates and resamples the signal with the lowest capture rate in a way that is consistent with the twist motion constraints of the hand–eye problem. Cross-correlation techniques are then used to produce two fully synchronized data streams that can be used to solve the hand–eye problem with classic methods. In our experimental section, we show promising validation results on simulation data and also on real data obtained from a robotic arm holding a camera.
Bier spots are an under-recognized cutaneous manifestation of lower extremity lymphedema: a case series and brief review of the literature.

Bier spots represent a benign vascular mottling characterized by multiple irregular white macules along the extensor surfaces of the arms and legs. They have been reported in a variety of diverse conditions with no consistent disease association. We have identified a novel association between these physiologic anemic macules and lower extremity lymphedema. Eleven patients between 23 and 70 years of age (5 male and 6 female) were diagnosed with Bier spots as evidenced by reversible white macules ranging from 3 to 8 mm in diameter on the extensor portions of the feet, ankles, and calves. The thighs were affected as well in 2 morbidly obese subjects. We suspect that these lesions are not uncommon in lymphedema but are simply under-recognized.
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform

Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs). We propose a simpler and novel update scheme to maintain orthogonal recurrent weight matrices without using complex valued matrices. This is done by parametrizing with a skew-symmetric matrix using the Cayley transform; such a parametrization is unable to represent matrices with negative one eigenvalues, but this limitation is overcome by scaling the recurrent weight matrix by a diagonal matrix consisting of ones and negative ones. The proposed training scheme involves a straightforward gradient calculation and update step. In several experiments, the proposed scaled Cayley orthogonal recurrent neural network (scoRNN) achieves superior results with fewer trainable parameters than other unitary RNNs.
Dominance of Saccharomyces cerevisiae in alcoholic fermentation processes: role of physiological fitness and microbial interactions

Winemaking, brewing and baking are some of the oldest biotechnological processes. In all of them, alcoholic fermentation is the main biotransformation and Saccharomyces cerevisiae the primary microorganism. Although a wide variety of microbial species may participate in alcoholic fermentation and contribute to the sensory properties of end-products, the yeast S. cerevisiae invariably dominates the final stages of fermentation. The ability of S. cerevisiae to outcompete other microbial species during alcoholic fermentation processes, such as winemaking, has traditionally been ascribed to its high fermentative power and capacity to withstand the harsh environmental conditions, i.e. high levels of ethanol and organic acids, low pH values, scarce oxygen availability and depletion of certain nutrients. However, in recent years, several studies have raised evidence that S. cerevisiae, beyond its remarkable fitness for alcoholic fermentation, also uses defensive strategies mediated by different mechanisms, such as cell-to-cell contact and secretion of antimicrobial peptides, to combat other microorganisms. In this paper, we review the main physiological features underlying the special aptitude of S. cerevisiae for alcoholic fermentation and discuss the role of microbial interactions in its dominance during alcoholic fermentation, as well as its relevance for winemaking.
Implications for Educational Classification and Psychological Diagnoses Using the Wechsler Adult Intelligence Scale – Fourth Edition With Canadian Versus American Norms

Building on a recent work of Harrison, Armstrong, Harrison, Iverson and Lange which suggested that Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) scores might systematically overestimate the severity of intellectual impairments if Canadian norms are used, the present study examined differences between Canadian and American derived WAIS-IV scores from 861 postsecondary students attending school across the province of Ontario, Canada. This broader data set confirmed a trend whereby individuals’ raw scores systematically produced lower standardized scores through the use of Canadian as opposed to American norms. The differences do not appear to be due to cultural, educational, or population differences, as participants acted as their own controls. The ramifications of utilizing the different norms were examined with regard to psychoeducational assessments and educational placement decisions particularly with respect to the diagnoses of Learning Disability and Intellectual Disability.
Wide band multi-beam cylindrical lens

A novel ultra-wideband multi-beam cylindrical lens antenna for operation in the frequency range from 8 GHz to 18 GHz using parallel plate techniques is designed and fabricated. The cylindrical lens consists of four excitation ports, two parallel plates that are filled with teflon and a flare in order to produce four distinct fan-beams scanning in the azimuth plane with a minimum gain of 15 dB. CST Microwave Studio is used to analyze and optimize the antenna. The 3 dB E-plane and H-plane beamwidths are 10° and 30°, respectively, which can scan 40° in the azimuth plane with four distinct beams. The minimum gain of each beam is about 15 dBi at 8 GHz and 18 dBi at 18 GHz. There is a good agreement between the simulated and measured results.
Evolutionary Multi-task Learning for Modular Knowledge Representation in Neural Networks

The brain can be viewed as a complex modular structure with features of information processing through knowledge storage and retrieval. Modularity ensures that the knowledge is stored in a manner where any complications in certain modules do not affect the overall functionality of the brain. Although artificial neural networks have been very promising in prediction and recognition tasks, they are limited in terms of learning algorithms that can provide modularity in knowledge representation that could be helpful in using knowledge modules when needed. Multi-task learning enables learning algorithms to feature knowledge in general representation from several related tasks. There has not been much work done that incorporates multi-task learning for modular knowledge representation in neural networks. In this paper, we present multi-task learning for modular knowledge representation in neural networks via modular network topologies. In the proposed method, each task is defined by the selected regions in a network topology (module). Modular knowledge representation would be effective even if some of the neurons and connections are disrupted or removed from selected modules in the network. We demonstrate the effectiveness of the method using single hidden layer feedforward networks to learn selected n-bit parity problems of varying levels of difficulty. Furthermore, we apply the method to benchmark pattern classification problems. The simulation and experimental results, in general, show that the proposed method retains performance quality although the knowledge is represented as modules.
Distributing learning over time: the spacing effect in children's acquisition and generalization of science concepts.

The spacing effect describes the robust finding that long-term learning is promoted when learning events are spaced out in time rather than presented in immediate succession. Studies of the spacing effect have focused on memory processes rather than for other types of learning, such as the acquisition and generalization of new concepts. In this study, early elementary school children (5- to 7-year-olds; N = 36) were presented with science lessons on 1 of 3 schedules: massed, clumped, and spaced. The results revealed that spacing lessons out in time resulted in higher generalization performance for both simple and complex concepts. Spaced learning schedules promote several types of learning, strengthening the implications of the spacing effect for educational practices and curriculum.
Evaluation Of Noise Cancellation Using LMS And NLMS Algorithm

This paper is focused on the adaptive noise cancellation of speech signal using the least mean square (LMS) and normalized least mean square method (NLMS). Adaptive Noise Cancellation is an alternative way of cancelling noise present in a corrupted signal. In this technique, evaluation of distorted signal by additive noise or interference achieved with no a priori estimates of signal or noise. A comparative study is carried out using LMS and NLSM methods. Result shows that these methods has potential in noise cancellation and can be used for variety of applications.
Sentence similarity detection in Malayalam language using cosine similarity

Identifying paraphrase in Malayalam language is difficult task because it is a highly agglutinative language and the linguistic structure in Malayalam language is complex compared to other languages. Here we use individual words synonyms to find the similarity between two sentences. In this paper, cosine similarity method is used to find the paraphrases in Malayalam language. In this paper we present the observations on sentence similarity between two Malayalam sentences using cosine similarity method, we used test data of 900 and 1400 sentence pairs of FIRE 2016 Malayalam corpus that used in two iterations to present and obtained an accuracy of 0.8 and 0.59.
Developmental dyspraxia by any other name: are they all just clumsy children?

The recent introduction of the diagnostic category developmental coordination disorder (DCD) (American Psychiatric Association [APA], 1987, 1994), has generated confusion among researchers and clinicians in many fields, including occupational therapy. Although the diagnostic criteria appear to be similar to those used to define clumsy children, children with developmental dyspraxia, or children with sensory integrative dysfunction, we are left with the question: Are children who receive the diagnosis of DCD the same as those who receive the other diagnoses, a subgroup, or an entirely distinct group of children? This article will examine the theoretical and empirical literature and use the results to support the thesis that these terms are not interchangeable and yet are not being used in the literature in a way that clearly defines each subgroup of children. Clear definitions and characteristic features need to be identified and associated with each term to guide occupational therapy assessment and intervention and clinical research.
Patriarchy and Women ’ s Subordination : A Theoretical Analysis

Patriarchy is the prime obstacle to women’s advancement and development. Despite differences in levels of domination the broad principles remain the same, i.e. men are in control. The nature of this control may differ. So it is necessary to understand the system, which keeps women dominated and subordinate, and to unravel its workings in order to work for women’s development in a systematic way. In the modern world where women go ahead by their merit, patriarchy there creates obstacles for women to go forward in society. Because patriarchal institutions and social relations are responsible for the inferior or secondary status of women. Patriarchal society gives absolute priority to men and to some extent limits women’s human rights also. Patriarchy refers to the male domination both in public and private spheres. In this way, feminists use the term ‘patriarchy’ to describe the power relationship between men and women as well as to find out the root cause of women’s subordination. This article, hence, is an attempt to analyse the concept of patriarchy and women’s subordination in a theoretical perspective.
An Integrated Micro-Fluxgate Magnetic Sensor With Front-End Circuitry

In this paper, a double-axis planar micro-fluxgate magnetic sensor and its front-end circuitry are presented. The ferromagnetic core material, i.e., the Vitrovac 6025 X, has been deposited on top of the coils with the dc-magnetron sputtering technique, which is a new type of procedure with respect to the existing solutions in the field of fluxgate sensors. This procedure allows us to obtain a core with the good magnetic properties of an amorphous ferromagnetic material, which is typical of a core with 25-mum thickness, but with a thickness of only 1 mum, which is typical of an electrodeposited core. The micro-Fluxgate has been realized in a 0.5- mum CMOS process using copper metal lines to realize the excitation coil and aluminum metal lines for the sensing coil, whereas the integrated interface circuitry for exciting and reading out the sensor has been realized in a 0.35-mum CMOS technology. Applying a triangular excitation current of 18 mA peak at 100 kHz, the magnetic sensitivity achieved is about 10 LSB/muT [using a 13-bit analog-to-digital converter (ADC)], which is suitable for detecting the Earth's magnetic field (plusmn60 muT), whereas the linearity error is 3% of the full scale. The maximum angle error of the sensor evaluating the Earth magnetic field is 2deg. The power consumption of the sensor is about 13.7 mW. The total power consumption of the system is about 90 mW.
Young Citizens and Civic Learning : Two Paradigms of Citizenship in the Digital Age

How can civic education keep pace with changing political identifications and practices of new generations of citizens? We examine research on school-based civic education in different post-industrial democracies with the aim of deriving a set of core learning categories. Most school-based approaches reflect traditional paradigms of dutiful citizenship (DC) oriented to government through parties and voting, with citizens forming attentive publics who follow events in the news. While this model may appeal to some young people, research suggests that it produces mixed learning outcomes, and may not capture the full range of learning and engagement styles of recent generations of citizens. We expand upon these conventional learning categories by identifying additional civic learning opportunities that reflect more selfactualizing (AC) styles of civic participation common among recent generations of youth who have been termed digital natives. Their AC learning styles favor interactive, networked activities often communicated with participatory media production such as videos shared across online networks. The result is an expanded set of learning categories that can be used to design, document, and compare civic learning in different environments from schools to online communities. The specter of a youth civic engagement crisis has spread through academic, government and education policy circles in many post-industrial democracies. The most prominent response is to reinvigorate school-based approaches to civic education with the aim of re-connecting young people to the very political institutions that they seem ∗ The authors wish to acknowledge the generous support of the Digital Media and Learning initiative of the John D. and Catherine T. MacArthur Foundation. The assistance of Deen Freelon and Daniel Weisberg in helping with our literature review is also gratefully acknowledged.