nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Recidivism in Adolescent Males Using the Minnesota Multiphasic Personality Inventory A and the Trauma Symptom Checklist for Children
Year: 2009

Small subsets of first time criminal offenders go on to commit additional crimes. For some, the number of crimes committed can be quite great. It is believed to be the case that a small group of "recidivists" commits a majority of crime. A number of methods to detect who is likely to recidivate have been suggested, but there is no present consensus as to a best method. As recidivism is a low base rate event, it is difficult to predict. A brief overview of these methods is provided. Then, a novel method for detecting recidivism is outlined and examined. In this method one determines cut offs based on Minnesota Multiphasic Personality Inventory A K Scale (MMPIA) and the Trauma Symptom Checklist for Children post Traumatic Stress Scale (TSCC) are used to predict recidivism. It is suggested that the MMPIA K Scale measures not only defensiveness but also ego strength and openness to treatment. It is further suggested that those with poor ego strength or who are overly defended, with the presence of unresolved trauma (as measured by the TSCC PTS scale) are more likely to recidivate. Results indicate that this method does not map well to a linear model, but that it is an effective method to detect recidivism in this sample. A comparison to other similar methods is provided and the ramifications and limitations of this study are discussed. Degree Type Dissertation Rights Terms of use for work posted in CommonKnowledge. This dissertation is available at CommonKnowledge: http:/commons.pacificu.edu/spp/104 Copyright and terms of use If you have downloaded this document directly from the web or from CommonKnowledge, see the "Rights" section on the previous page for the terms of use. If you have received this document through an interlibrary loan/document delivery service, the following terms of use apply: Copyright in this work is held by the author(s) You may download or print any portion of this document for personal use only, or for any use that is allowed by fair use (Title 17, SS107 U.S.C. Except for personal or fair use, you or your borrowing library may not reproduce, remix, republish, post, transmit, or distribute this document, or any portion thereof, without the permission of the copyright owner.  Email inquiries may be directed to: copyright@pacificu.edu This dissertation is available at CommonKnowledge: http:/commons.pacificu.edu/spp/104 PREDICTING RECIDIVISM IN ADOLESCENT MALES USING THE MINNESOTA MULTIPHASIC PERSONALITY INVENTORY A AND THE TRAUMA SYMPTOM CHECKLIST FOR CHILDREN A DISSERTATION SUBMITTED TO THE FACULTY OF SCHOOL OF PROFESSIONAL PSYCHOLOGY PACIFIC UNIVERSITY HILLSBORO, OREGON BY MIGUEL KELLER HIDALGO BARNES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PSYCHOLOGY DECEMBER 21, 2009 APPROVED BY THE COMMITTEE: Daniel S. McKitrick, Ph.D Steven L. Henry, Psy.D PROFESSOR AND DEAN: Michel Hersen, Ph.D. ABP
Artificial Intelligence, Real World Automation and the Safety of Medicines
Venue: Drug Safety
Year: 2020

Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.
SPU Net: Self Supervised Point Cloud Upsampling by Coarse to Fine Reconstruction with Self Projection Optimization
Venue: ArXiv
Year: 2020

The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets. Although significant progress has been made with deep learning models, they require ground truth dense point sets as the supervision information, which can only trained on synthetic paired training data and are not suitable for training under real scanned sparse data. However, it is expensive and tedious to obtain large scale paired sparse dense point sets for training from real scanned sparse data. To address this problem, we propose a self supervised point cloud upsampling network, named SPU Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. Specifically, we propose a coarse to fine reconstruction framework, which contains two main components: point feature extraction and point feature expansion, respectively. In the point feature extraction, we integrate self attention module with graph convolution network (GCN) to simultaneously capture context information inside and among local regions. In the point feature expansion, we introduce a hierarchically learnable folding strategy to generate the upsampled point sets with learnable 2D grids. Moreover, to further optimize the noisy points in the generated point sets, we propose a novel self projection optimization associated with uniform and reconstruction terms, as a joint loss, to facilitate the self supervised point cloud upsampling. We conduct various experiments on both synthetic and real scanned datasets, and the results demonstrate that we achieve comparable performance to the state of the art supervised methods.
Performance of Circular Reinforced Concrete Bridge Piers Subjected to Vehicular Collisions
Year: 2014

Vehicle collisions with bridge piers can result in significant damage to the support pier and potentially lead to catastrophic failure of the whole structure. The Nation's aging infrastructure suggests that many structures no longer meet current design standards, placing many bridge susceptible to failure if subjected to an extreme loading event. This research aims to study the structural response of reinforced concrete bridge piers subjected to vehicle collisions. A sensitivity analysis is conducted to observe the causes of shear and bending failures of bridge piers subjected to vehicle collision. Parameters, such as pier diameter, transverse reinforcement spacing, vehicle impact velocity, pile cap height, and multi pier configuration, are investigated in this study. The finite element code LS DYNA is utilized to simulate and analyze the vehicle collisions to obtain accurate and detailed results. The vehicle models offered by the are used to conduct this research. The finite element modeling controls and material properties are validated by conducting an impact drop hammer experiment. The bridge pier collision models are validated by comparing vehicle damage and impact forces with iv published research results. Conservation of energy is also checked to assure stability within the impact simulation. A sensitivity analysis suggests that different pier parameters have a profound effect on failure modes and distribution of impact forces. Piers with large stiffness result in high impact forces, low lateral displacements, and high resistance to shear forces and bending moments. A performance based analysis shows that bridge piers can be designed using damage ratios associated with particular damage states.
Blood Vessel Segmentation and shape analysis forquantification of Coronary Artery Stenosis inCT Angiography
Year: 2011

This thesis presents an automated framework for quantitative vascular shape analysis of the coronary arteries, which constitutes an important and fundamental component of an automated image based diagnostic system. Firstly, an automated vessel segmentation algorithm is developed to extract the coronary arteries based on the framework of active contours. Both global and local intensity statistics are utilised in the energy functional calculation, which allows for dealing with non uniform brightness conditions, while evolving the contour towards to the desired boundaries without being trapped in local minima. To suppress kissing vessel artifacts, a slice by slice correction scheme, based on multiple regions competition, is proposed to identify and track the kissing vessels throughout the transaxial images of the CTA data. Based on the resulting segmentation, we then present a dedicated algorithm to estimate the geometric parameters of the extracted arteries, with focus on vessel bifurcations. In particular, the centreline and associated reference surface of the coronary arteries, in the vicinity of arterial bifurcations, are determined by registering an elliptical cross sectional tube to the desired constituent branch. The registration problem is solved by a hybrid optimisation method, combining local greedy search and dynamic programming, which ensures the global optimality of the solution and permits the incorporation of any hard constraints posed to the tube model within a natural and direct framework. In contrast with conventional volume domain methods, this technique works directly on the mesh domain, thus alleviating the need for image upsampling. The performance of the proposed framework, in terms of efficiency and accuracy, is demonstrated on both synthetic and clinical image data. Experimental results have shown that our techniques are capable of extracting the major branches of the coronary arteries and estimating the related geometric parameters (i.e. the centreline and the reference surface) with a high degree of agreement to those obtained through manual delineation. Particularly, all of the major branches of coronary arteries are successfully detected by the proposed technique, with a voxel wise error at 0.73 voxels to the manually delineated ground truth data. Through the application of the slice by slice correction scheme, the false positive metric, for those coronary segments affected by kissing vessel artifacts, reduces from 294% to 22.5% In terms of the capability of the presented framework in defining the location of centrelines across vessel bifurcations, the mean square errors (MSE) of the resulting centreline, with respect to the ground truth data, is reduced by an average of 62.3% when compared with initial estimation obtained using a topological thinning based algorithm.
Synthesis Of Silver Nanoparticles By Chemical Reduction And Their Antimicrobial Activity
Year: 2012

In this article we have studied the antimicrobial activity of silver nanoparticles with five different types of bacteria. Silver nanoparticles were chemically synthesized using silver nitrate as a precursor material and tri sodium citrate as reducing agent. UV Vis spectroscopy and Scanning Tunnelling Microscopy (STM) were used to study the absorption spectra and surface morphology of Silver nanoparticles. The antimicrobial activity of silver nanoparticles was investigated against five different types of microorganisms such as Bacillus subtilis, Bacillus megaterium, Escherichia coli, Salmonella typhimurium, and Staphylococcus aureus.
Integrating Multi Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches
Year: 2021

Timely and reliable yield prediction at a large scale is imperative and prerequisite to prevent climate risk and ensure food security, especially with climate change and increasing extreme climate events. In this study, integrating the publicly available data (i.e. satellite vegetation indexes, meteorological indexes, and soil properties) within the Google Earth Engine (GEE) platform, we developed one Least Absolute Shrinkage and Selection Operator (LASSO) regression, one machine learning (Random Forest, RF) and one deep learning (Long Short Term Memory Networks, LSTM) model to predict rice yield at county level across China. For satellite data, we compared the contiguous solar induced chlorophyll fluorescence (SIF) a newly emerging satellite retrieval, with a traditional vegetation index (enhanced vegetation index, EVI) The results showed that LSTM (with R2 ranging from 0.77 to 0.87, RMSE from 298.11 to 724kg/ha) and RF (with R2 ranging from 0.76 to 0.82, RMSE from 366 to 723.3 kg/ha) models outperformed LASSO (with R2 ranging from 0.33 to 0.42, RMSE from 633.46 kg/ha to 1231.39 kg/ha) in yield prediction; and LSTM was better than RF. Besides, ESI (combining EVI and SIF together) could slightly improve the model performance compared with only using EVI or SIF as the single input, primarily due to the ability of satellite based SIF in capturing extra information on drought and heat stress. Furthermore, we also explored the potential for timely rice yield prediction, and concluded that the optimal prediction could be achieved with approximately two/one month leading time before single/double rice maturity. Our findings demonstrated a scalable, simple and inexpensive methods for timely predicting rice yield over a large area with publicly available multi source data, which can potentially be applied to areas with sparsely observed data and worldwide for estimating crop yields.
Assessment of capabilities and limitations of stochastic global optimization methods for modeling mean activity coefficients of ionic liquids
Year: 2013

a b s t r a c t In this paper, we have studied and analyzed the capabilities and limitations of seven stochastic global optimization methods to model mean activity coefficients of ammonium aqueous electrolytes using the electrolyte NRTL model. These stochastic methods are: simulated annealing (SA) genetic algorithm (GA) differential evolution (DE) particle swarm optimization (PSO) DE with tabu list (DETL) harmony search (HS) and bare bones PSO (BBPSO) Convergence characteristics and performances of these optimization methods have been tested and compared using different stopping conditions. Overall, our results indicate that SA, DETL and BBPSO offer better performance and promising convergence characteristics for solving parameter estimation problems involved in the modeling of thermodynamic properties of ionic liquids
Blockchain's adoption in IoT: The challenges, and a way forward
Venue: J. Netw. Comput. Appl.
Year: 2019

The underlying technology of Bitcoin is blockchain, which was initially designed for financial value transfer only. Nonetheless, due to its decentralized architecture, fault tolerance and cryptographic security benefits such as pseudonymous identities, data integrity and authentication, researchers and security analysts around the world are focusing on the blockchain to resolve security and privacy issues of IoT. However, presently, not much work has been done to assess blockchain's viability for IoT and the associated challenges. Hence, to arrive at intelligible conclusions, this paper carries out a systematic study of the peculiarities of the IoT environment including its security and performance requirements and progression in blockchain technologies. We have identified the gaps by mapping the security and performance benefits inferred by the blockchain technologies and some of the blockchain based IoT applications against the IoT requirements. We also discovered some practical issues involved in the integration of IoT devices with the blockchain. In the end, we propose a way forward to resolve some of the significant challenges to the blockchain's adoption in IoT.
THE EFFECT OF HIGH PERFORMANCE WORKING SYSTEM AND SOCIAL WORKING CLIMATE ON EMPLOYEE WELL BEING IN A MANUFACTURE COMPANY
Year: 2019

The implementation of HPWS is believed to be able to improve organizational performance and employee well being, but in the course of the organization due to external and internal factors changes will occur such as rotation, transfer and promotion of its members. As a result, the working climate will also change. The purpose of this research is to analyze the effect of the high performance working system and social working climate to the employee well being. This research uses a survey method with two independent variables high performance working system and social working climate, and employee well being as dependent variable. Research is carried out in a manufacturing company. This research was conducted to examined the relationship between HPWS and and social working climate to employee well being. The results of this research can be used for managers as consideration in developing a work environment that supports the achievement of organizational performance.
Paciente con quemadura de segundo grado mas infeccion sobreanadida y su influencia en el estilo de vida.
Year: 2017

Una quemadura es una lesion a la piel u otro tejido organico causada principalmente por el calor o la radiacion, la radioactividad, la electricidad, la friccion o el contacto con productos quimicos. En el presente trabajo el principal objetivo es analizar los factores que intervinieron a la presencia de quemaduras y su influencia en la calidad de vida del paciente. La metodologia que se utilizo permitio encontrar informacion usando un estudio bibliografico, descriptivo y de campo en donde se relata los factores que influyeron para la aparicion de la lesion en la piel, se utilizo el modelo de atencion de enfermeria de Virginia Henderson el mismo que se basa en catorce necesidades humanas que deben ser satisfechas en cada ser humano. Los resultados de acuerdo a la informacion obtenida en la entrevista realizada al paciente, indican los diferentes problemas de salud que presento el paciente, es por ello que se realizo una guia clinica de cuidados extra hospitalarios con esto se busca que el paciente pueda alcanzar un mejor estilo de vida. En conclusion esta clase de lesiones en la piel por contacto con quimicos industriales producen serias quemaduras que deben ser tratadas de manera inmediata, para lo cual existe personal de salud especializado quien es el responsable de brindar el tratamiento y recuperacion adecuada del paciente.
Social Conformity Effects on Trust in Simulation Based Human Robot Interaction
Venue: Hum. Factors
Year: 2019

We investigated the co acting influences of communication and social conformity on trust in human robot interaction.  Previous work has investigated aspects of the robot, the human, and the environment as influential factors in the human robot relationship. Little work has examined the conjoint effects of social conformity and communication on this relationship. As social conformity and communication have been shown to affect human human trust, there are a priori reasons to believe that they will play an influential role in human robot trust also.  The experiment examined the influences of social conformity and robot communication on trust. A 2 x 2 (communication x social group) design was implemented with each variable having two levels (communication, no communication; positive social group, negative social group)  We created a communication manipulation which we then demonstrated to mediate the trust level between human and robot. However, this influence on trust was overcome by social information in which the subsequent trust level, attributed to the robot, was dominated by expressed social group attitudes to that robot.  The results confirm the importance of human social assessments over direct robot communication in setting human robot trust levels. When social opinions are expressed, observers appear to conform to the trust displayed by the group than relying on their own judgment.  In human robot teams, the perceptions of the group may exert a greater impact than even robot communication. This may be especially important when new human members are introduced into such teams.
Class action settlements in Australia the need for greater scrutiny
Year: 2014

Class actions in Australia are frequently resolved through a court approved settlement. Yet settlement is fraught with difficulties due to the representative nature of the class action which sees group members who are not before the court bound by the settlement. Consequently the legislature and the courts have taken steps to protect group members. This article explains and critiques those protections before suggesting reforms to ensure that class action settlements are subject to greater scrutiny. In particular, the article focuses on court appointed costs experts to assess legal fees, an independent guardian to represent group members' interests, less use of suppression orders in relation to the details of the settlement and ensuring settlement approvals are set out in publicly available judgments.
Ouroboros: A Provably Secure Proof of Stake Blockchain Protocol
Venue: CRYPTO
Year: 2016

We present "Ouroboros" the first blockchain protocol based on proof of stake with rigorous security guarantees. We establish security properties for the protocol comparable to those achieved by the bitcoin blockchain protocol. As the protocol provides a "proof of stake" blockchain discipline, it offers qualitative efficiency advantages over blockchains based on proof of physical resources (e.g. proof of work) We also present a novel reward mechanism for incentivizing Proof of Stake protocols and we prove that, given this mechanism, honest behavior is an approximate Nash equilibrium, thus neutralizing attacks such as selfish mining.
Cuadro de mando integral para la gestion del impacto social en organizaciones de empleo inclusivo
Year: 2020

Los retos que presentan las organizaciones sociales en cuanto a la medicion y visibilizacion del impacto social van en incremento. Las progresivas demandas de las comunidades, el mercado y la cooperacion publico privada, exigen mayores esfuerzos en la busqueda de eficacia y eficiencia de la accion social. Ante las multiples debilidades en la planificacion estrategica y los inexistentes o inadecuados sistemas de medicion y control, que permitan conocer y gestionar los resultados de manera efectiva, se hace necesario proponer herramientas de gestion acopladas a las caracteristicas, recursos y capacidades internas de estas organizaciones. El presente estudio busca desarrollar un instrumento para la gestion del impacto social de organizaciones o proyectos que tienen como mision fomentar el empleo inclusivo. La herramienta propuesta es una adaptacion del Mapa Estrategico y el Cuadro de Mando Integral (CMI) a la empresa social. Como resultado del proyecto, se propone un CMI de 54 indicadores, cuantitativos y cualitativos, asociados a 21 objetivos estrategicos, en su mayoria, comunes a las organizaciones que trabajan por el empleo inclusivo. Dichos indicadores se definen a partir del analisis estrategico de dos organizaciones/proyectos que trabajan por la inclusion socio laboral de colectivos vulnerables y, ademas, del analisis de diversas metodologias o enfoques ampliamente utilizados en la evaluacion de desempeno social. Despues de validar la herramienta, se concluye que su adopcion puede resultar en diversos beneficios para las entidades sociales, no solo para la planificacion y control de resultados, sino tambien para la tarea comunicativa y de rendicion de cuentas.