nils-herrmann/clean_scirepeval huggingface dataset:
Rap1 GTP interacting Adaptor Molecule (RIAM) Protein Controls Invasion and Growth of Melanoma Cells*
Venue: The Journal of Biological Chemistry
Year: 2011

The Mig 10/RIAM/lamellipodin (MRL) family member Rap1 GTP interacting adaptor molecule (RIAM) interacts with active Rap1, a small GTPase that is frequently activated in tumors such as melanoma and prostate cancer. We show here that RIAM is expressed in metastatic human melanoma cells and that both RIAM and Rap1 are required for BLM melanoma cell invasion. RIAM silencing in melanoma cells led to inhibition of tumor growth and to delayed metastasis in a severe combined immunodeficiency xenograft model. Defective invasion of RIAM silenced melanoma cells arose from impairment in persistent cell migration directionality, which was associated with deficient activation of a Vav2 RhoA ROCK myosin light chain pathway. Expression of constitutively active Vav2 and RhoA in cells depleted for RIAM partially rescued their invasion, indicating that Vav2 and RhoA mediate RIAM function. These results suggest that inhibition of cell invasion in RIAM silenced melanoma cells is likely based on altered cell contractility and cell polarization. Furthermore, we show that RIAM depletion reduces b1 integrin dependent melanoma cell adhesion, which correlates with decreased activation of both Erk1/2 MAPK and phosphatidylinositol 3 kinase, two central molecules controlling cell growth and cell survival. In addition to causing inhibition of cell proliferation, RIAM silencing led to higher susceptibility to cell apoptosis. Together, these data suggest that defective activation of these kinases in RIAM silenced cells could account for inhibition of melanoma cell growth and that RIAM might contribute to the dissemination of melanoma cells.
Tilted Fiber Bragg Grating Sensors
Year: 2018

By inducing a tilted angle between the grating and the normal direction of optical fibers, tilted fiber Bragg grating (TFBG) provides an effective means to lead the forward propagating input light to the backward propagating cladding modes and the core mode. Taking advantage of novel configurations and functional material coating, TFBG can be used as a powerful lab on fiber component for various measurements of mechanical, electrical, magnetic, biological, medical, and chemical parameters. In this paper, we present a brief review of TFBG sensing technology, including TFBG fabrication, mode coupling theory, sensing principle properties (especially the surface plasmon sensing technology) Recent progresses in various applications are discussed. These applications include mechanical sensing (bending, vibration, micro displacement) electromagnetic sensing (electric field, magnetic field) biological sensing (cell density, protein, glucose) and chemical sensing (gas, electroactive microbe) Thanks to the multidisciplinary cooperation, especially the great progress of functional materials and nanotechnologies, the future of TFBG is promising in advanced and practical sensing.
Incorporating Children and Young People's Voices in Child and Adolescent Mental Health Services Using The Family Model
Year: 2017

Mental illness in children and young people is increasing in frequency and complexity, is emerging earlier and is persisting into adulthood. This is a global issue with implications for research, policy and practice. Children and young people require the experience of safe, nurturing relationships for optimal lifelong outcomes. Despite awareness of this in Child and Adolescent Mental Health services, a focus on the relational context in which children and young people present is not universal. A challenge in family focused practice is to ensure that no individual's voice is 'too loud' and that children and young people's voices are heard. This article illustrates how a balance between individual and systems understanding can be achieved in therapeutic work by incorporating the voices of children and young people and concerns of other family members. This article describes an approach to improving family focused practice in a public Child and Adolescent Mental Health service. Use of The Family Model, as a family focused practice tool, is presented across three service settings. The Family Model intervention is briefly described, outlining the way in which it supports collaborative practice and assists clinicians to achieve the balance described above. Vignettes will demonstrate how children and young people's voices are explicitly incorporated in formulating mental health issues with two generations to generate developmentally informed care plans.
FINDING INITIAL PARAMETERS OF NEURAL NETWORK FOR DATA CLUSTERING
Year: 2013

K means fast learning artificial neural network (K FLANN) algorithm begins with the initialization of two parameters vigilance and tolerance which are the key to get optimal clustering outcome. The optimization task is to change these parameters so a desired mapping between inputs and outputs (clusters) of the KFLANN is achieved. This study presents finding the behavioral parameters of K FLANN that yield good clustering performance using an optimization method known as Differential Evolution. DE algorithm is a simple efficient meta heuristic for global optimization over continuous spaces. The K FLANN algorithm is modified to select winning neuron (centroid) for a data member in order to improve the matching rate from input to output. The experiments were performed to evaluate the proposed work using machine learning artificial data sets for classification problems and synthetic data sets. The simulation results have revealed that optimization of K FLANN has given quite promising results in terms of convergence rate and accuracy when compared with other algorithms. Also the comparisons are made between K FLANN and modified KFLANN.
Ageing shocks and short run regional labour market dynamics in a spatial panel VAR approach
Year: 2018

Using a flexible spatial panel VAR model for a small scale labour market system, we investigate the dynamic interdependences between changes in the demographic structure and the labour market performance of a regional economy. With a particular focus on ageing shocks, we describe an increase in the share of elderly in regional population due to exogenous changes in the institutional context, such as pension reforms. The regional labour market implications of an ageing shock are then tested with regard to the effects on employment growth, unemployment and labour participation rate. Our results based on a sample of 71 Scandinavian regions point to negative regional labour market effects of an ageing shock implying a reduction in employment growth and a temporarily declining labour participation rate, while the unemployment rate increases. Importantly, spatial spillovers amplify these negative impacts through a marginalization of macro regional labour markets. As a robustness check, we reverse the causal ordering and analyze the responses to a positive macroeconomic shock which initially increases employment growth. We find direct and spatially indirect adjustment patterns characterized by a reduction of the unemployment rate, an increase in the labour participation rate and a decrease in the share of elderly in the population.
Mass spectrometry contamination from Tinuvin 770, a common additive in laboratory plastics.
Venue: Journal of biomolecular techniques JBT
Year: 2013

The superior sensitivity of current mass spectrometers makes them prone to contamination issues, which can have deleterious effects on sample analysis. Here, bis(2,2,6,6 tetramethyl 4 piperidyl) sebacate (marketed under the name Tinuvin 770) is identified as a major contaminant in applications using liquid chromatography coupled with mass spectrometry (LC MS) Tinuvin 770 is often added to laboratory and medical plastics as a UV stabilizer. One particular lot of microcentrifuge tubes was found to have an excess of this compound that would leach into samples and drastically interfere with LC MS data acquisition. Further analysis found that Tinuvin 770 readily leached into polar and nonpolar solvents from the contaminated tube lot. Efforts to remove Tinuvin 770 from contaminated samples were unsuccessful. A prescreening method using MALDI TOF MS is presented to prevent system contamination and sample loss.
An Analysis of Segmenting and Classifying Tumor Regions in MRI Images using CNN
Year: 2018

Brain tumor extraction from Magnetic Resonance Imaging (MRI) is an important task in medical image processing. It is one of the difficult and time consuming tasks because the structural variability among the tumor is entirely different from normal images. Manual segmentation requires expert knowledge and it has very less accuracy. So, we need intelligent machine learning technique for automatic detection of brain tumor through magnetic resonance images. These reasons motivate our exploration through machine learning solution that exploits flexible and high capacity of Deep Neural Network (DNN).The deep learning architectures are performing very accurately and they gradually outperform previous state of the art classical machine learning algorithms. MRI sequence consists of four intratumoral structures like edema, necrotic, non enhancing and enhancing regions. The purpose of this paper is to provide detailed examination of segmenting brain tumor and their regions using deep learning methods in MRI sequences.
Improved PCA Face Recognition Algorithm
Venue: ICPCSEE
Year: 2020

Face recognition technology, as a biometric recognition technology, is very mature and has many applications. It achieves in depth applications in smart campus systems, such as classroom attendance, classroom behavior analysis, and smart restaurants. Using human faces as the face data foundation, computer vision and image processing technologies are applied to research and implement face recognition. Based on the principal component analysis (PCA) theory, this paper analyzed the characteristics of face data, studied the face recognition algorithm. Considering the LBP and SVM algorithm, an improved PCA face recognition algorithm was proposed. Through comparative experiments, the results show that the proposed algorithm can improve the accuracy of face recognition.
Music and musicalities in the hip hop movement: gospel rap
Year: 2011

This article reflects on issues first raised in the doctoral thesis in Social Anthropology entitled "The Road is Long and the Ground is Slippery! the hip hop movement in Florianopolis and Lisbon" (Souza 2009) and which provides ethnographic support for the reflections raised here about this vibrant v.8 n.1 angela m. de souza, deise l. montardo musical production. Rap is music that was born in ghettos and peripheries. It has become a strong reference for youth and is inserted in a variety of social, cultural and religious contexts. Reflecting on this musical production is a complex task, mainly in an attempt to create distinctions, boundaries and limits that insist on not being maintained in defined spaces. To the contrary, shifting, movement and circulation are elements of these musicalities, one of which is gospel rap. In a dialog with ethnomusicology, this article approaches this musical style, principally through discussions about "performance" and "event," essential factors that guide the contours of what we call aesthetic musical styles within the hip hop movement in which gospel rap is inserted.
Effects of customer and cost drivers on green supply chain management practices and environmental performance
Year: 2018

Using an international survey, this study examines the drivers and consequences of green supply chain management (GSCM) practices. Building on the GSCM literature, we propose that cost and customer drivers influence internal and external green practices and enhance environmental performance. Based on a study of 246 companies in multiple countries, the results indicate that both cost drivers and customer drivers significantly influence internal and external green practices, which in turn contribute to environmental performance. Moreover, the impacts of cost and customer drivers on internal and external green practices are influenced by firm size: the impacts of cost drivers are greater for large firms than for small firms, while those of customer drivers are lower for large firms than for small firms. Our findings have both theoretical and managerial implications for the GSCM literature and practice.
African Migrants and Remittances: Impact on Growth and Development of their Home Countries
Year: 2020

This paper aims to study the impact of remittances from African migrants on their home countries' growth and development. These funds constitute a steadily increasing financing means in all these countries, even though development aid is still the preferred financing method. To highlight the role of these funds, we relied on a number of experiences as far as their targets and uses are concerned. Globally speaking, stylized facts based on case studies show that remittances have a positive impact on migrants' home countries' economies. In particular, they reduce household poverty and thereby increase their well being. In addition, they stimulate local economic activity and growth. Given the role and stability of these funds, cost reduction is essential to encourage migrants to send more money through official transfer channels. It is also necessary to increase the managerial efficiency of these funds by orienting them towards implementing profitable projects, rather than devoting them entirely to household consumption. Lastly, it is necessary to encourage the capture of related financial flows by the traditional banking system and microfinance institutions.
A SENSITIZING PROGRAM ON PREVENTION OF ROAD TRAFFIC ACCIDENTS AMONG ADOLESCENT STUDYING IN HIGHER SECONDARY COLLEGES IN AURANGABAD CITY
Venue: GLOBAL JOURNAL FOR RESEARCH ANALYSIS
Year: 2019

Road Trafc Accidents are the leading cause of unnatural deaths in the world and a major burden on the world`s economy. Better road safety measures and awareness campaign such as sensitization program will help to reduce morbidity, mortality, disability and economic impact of road trafc accidents. The study was aimed to evaluate the effectiveness of sensitizing program and to associate selected demographic variables of adolescents with the knowledge, on prevention of road trafc accidents.  Pre experimental research study with pre and post test group. A structured knowledge questionnaire used to obtain data from 60 adolescents samples.  The post test ndings shows that the calculated mean was 25.20 compared with pretest mean i.e. 18.22. The calculated paired t test value [t(59,0.05)=10.84 >2.00] is higher than the table value, which shows that the sensitizing program was very effective. The association between the level of knowledge and demographic variables were assessed by chi square test and which shows there is no signicant association between post test knowledge score and demographic variables
Experience Centered Design: Designers, Users, and Communities in Dialogue
Venue: Experience Centered Design
Year: 2010

Experience centered design, experience based design, experience design, designing for experience, user experience design. All of these terms have emerged and gained acceptance in the Human Computer Interaction (HCI) and Interaction Design relatively recently. In this book, we set out our understanding of experience centered design as a humanistic approach to designing digital technologies and media that enhance lived experience. The book is divided into three sections. In Section 1, we outline the historical origins and basic concepts that led into and flow out from our understanding of experience as the heart of people's interactions with digital technology. In Section 2, we describe three examples of experience centered projects and use them to illustrate and explain our dialogical approach. In Section 3, we recapitulate some of the main ideas and themes of the book and discuss the potential of experience centered design to continue the humanist agenda by giving a voice to those who might otherwise be excluded from design and by creating opportunities for people to enrich their lived experience with and through technology. Table of Contents: How Did We Get Here? Some Key Ideas Behind Experience Centered Design Making Sense of Experience in Experience Centered Design Experience Centered Design as Dialogue What do We Mean by Dialogue? Valuing Experience Centered Design Where Do We Go from Here?
An efficient gradient method using the Yuan steplength
Venue: Comput. Optim. Appl.
Year: 2014

We propose a new gradient method for quadratic programming, named SDC, which alternates some steepest descent (SD) iterates with some gradient iterates that use a constant steplength computed through the Yuan formula. The SDC method exploits the asymptotic spectral behaviour of the Yuan steplength to foster a selective elimination of the components of the gradient along the eigenvectors of the Hessian matrix, i.e. to push the search in subspaces of smaller and smaller dimensions. The new method has global and $R$$R linear convergence. Furthermore, numerical experiments show that it tends to outperform the Dai Yuan method, which is one of the fastest methods among the gradient ones. In particular, SDC appears superior as the Hessian condition number and the accuracy requirement increase. Finally, if the number of consecutive SD iterates is not too small, the SDC method shows a monotonic behaviour.
An Adaptive Random Path Selection Approach for Incremental Learning.
Year: 2020

In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage. This results in a fixed model that lacks the flexibility to adapt to new learning tasks. In practical settings, learning tasks often arrive in a sequence and the models must continually learn to increment their previously acquired knowledge. Existing incremental learning approaches fall well below the state of the art cumulative models that use all training classes at once. In this paper, we propose a random path selection algorithm, called Adaptive RPS Net, that progressively chooses optimal paths for the new tasks while encouraging parameter sharing between tasks. We introduce a new network capacity measure that enables us to automatically switch paths if the already used resources are saturated. Since the proposed path reuse strategy ensures forward knowledge transfer, our approach is efficient and has considerably less computation overhead. As an added novelty, the proposed model integrates knowledge distillation and retrospection along with the path selection strategy to overcome catastrophic forgetting. In order to maintain an equilibrium between previous and newly acquired knowledge, we propose a simple controller to dynamically balance the model plasticity. Through extensive experiments, we demonstrate that the Adaptive RPS Net method surpasses the state of the art performance for incremental learning and by utilizing parallel computation this method can run in constant time with nearly the same efficiency as a conventional deep convolutional neural network.
Nietzsche and Madness
Year: 2017

Evangelou addresses pressing questions revolving around Nietzsche and (his) madness and investigates madness from a twofold perspective: the madness of Nietzsche and madness for Nietzsche. While the former refers, apart from Nietzsche's own mental condition, to the ways in which his madness is understood as a potential threat to his philosophy, the latter is concerned with Nietzsche's own viewpoint and attitude toward madness. Evangelou accounts for the tension which arises from Nietzsche's dismissal of madness as philosophically indifferent on the one hand, and the fact that he has turned into a philosophical icon of the renegade from reason on the other. To respond to this tension, Evangelou proposes that one consider the 'Nietzsche event' which consists of three separate events encompassing both Nietzsche's life and philosophy.
Hierarchical routing protocols for wireless sensor network: a compressive survey
Venue: Wirel. Networks
Year: 2020

Wireless sensor networks (WSNs) are one of the key enabling technologies for the internet of things (IoT) WSNs play a major role in data communications in applications such as home, health care, environmental monitoring, smart grids, and transportation. WSNs are used in IoT applications and should be secured and energy efficient in order to provide highly reliable data communications. Because of the constraints of energy, memory and computational power of the WSN nodes, clustering algorithms are considered as energy efficient approaches for resource constrained WSNs. In this paper, we present a survey of the state of the art routing techniques in WSNs. We first present the most relevant previous work in routing protocols surveys then highlight our contribution. Next, we outline the background, robustness criteria, and constraints of WSNs. This is followed by a survey of different WSN routing techniques. Routing techniques are generally classified as flat, hierarchical, and location based routing. This survey focuses on the deep analysis of WSN hierarchical routing protocols. We further classify hierarchical protocols based on their routing techniques. We carefully choose the most relevant state of the art protocols in order to compare and highlight the advantages, disadvantage and performance issues of each routing technique. Finally, we conclude this survey by presenting a comprehensive survey of the recent improvements of low energy adaptive clustering hierarchy routing protocols and a comparison of the different versions presented in the literature.
Identity Based Deterministic Signature Scheme without Forking Lemma
Venue: IWSEC
Year: 2011

Since the discovery of identity based cryptography, a number of identity based signature schemes were reported in the literature. Although, a lot of identity based signature schemes were proposed, the only identity based deterministic signature scheme was given by Javier Herranz. This signature scheme uses Schnorr signature scheme for generating the private key of the users and uses BLS short signature scheme for generating users signature. The security of this scheme was proved in the random oracle model using forking lemma. In this paper, we introduce a new identity based deterministic signature scheme and prove the security of the scheme in the random oracle model, without the aid of forking lemma. Hence, our scheme offers tighter security reduction to the underlying hard problem than the existing identity based deterministic signature scheme.