nils-herrmann/clean_scirepeval huggingface dataset:
Silicon atom and silicon oxide molecule, within the metallic and semiconducting carbon nanotubes as promising centers candidates for hydrogen adsorption: A DFT theoretical study
Year: 2018

Hydrogen adsorption on a single Si and SiO2 molecule, doped within C(6,6) and C(10,0) carbon nanotubes (CNTs) is studied using first principles calculations based on density functional theory. Two orientations of the H2 molecule, inside the nanotubes, are compared. Our calculations revealed a rather weak hydrogen binding energy inside both types of pristine CNTs namely, 0.51 eV/H2 and 0.38 eV/H2 for C(6,6) and C(10,0) nanotubes, respectively. When a single Si atom is doped in the interior surface of either type of CNTs, it tends to decouple from the wall and to drift towards the nanotube's axis. A Si atom can bind two hydrogen atoms more strongly 1.4 eV/H2 and 1.13 eV/H2 on Si within metallic and semiconducting CNTs, respectively) than just a pristine CNT would do. A SiO2 molecule binds the hydrogen atoms even stronger, along with formation of water molecule within the metallic CNT. The corresponding binding energy of 1.73 eV/H2 for the C(6,6) is found to be the highest one among the configurations considered. Based on our resuls, we believe that intrinsically Si and SiO2 doped CNTs can be considered as plausible candidates for enhancing the hydrogen adsorption properties.
Improved DOA estimation algorithm for co prime linear arrays using root MUSIC algorithm
Year: 2017

The problem of direction of arrival (DOA) estimation with co prime linear arrays is researched. An improved DOA estimation algorithm based on root multiple signal classification (MUSIC) is proposed. The method can solve the matching error problem in Sun's algorithm. In addition, it can make full use of signal space and noise space to improve the estimation accuracy with low complexity. Experimental results verify the validity of the proposed algorithm.
Planning for Effective School Leadership: Teachers' Perceptions of the Leadership Skills and Ethical Behaviors of School Principals.
Year: 2020

Schools need principals who have experienced the most current and compelling theories and practices of school leadership in terms of leading schools to success for each student. There is a considerable amount of research that proposes a definite link between the leadership abilities of a school leader and the impact such leadership has on student success (Darling Hammond, LaPointe, Meyerson, Orr, 2007; Heck Hallinger, 2014; Marzano, Waters, McNulty, 2005) School principals, like leaders in other fields, encounter differing levels of success, which can be influenced by the leadership abilities and ethical behaviors of the school principal. This quantitative study, which took place in a large metropolitan area of the Mid Atlantic region of the United States, examines the relationship between the overall leadership abilities of school principals with their ethical leadership behaviors, as perceived by teachers. This study found a strong (and expected) relationship between the overall leadership abilities of school principals and ethical behaviors, as perceived by teachers. However, statistically significant differences were found among the perceptions in the areas of gender, ethnicity, experience, types of school (elementary, middle, high) and jurisdiction (public, private, religious) INTRODUCTION Planning for the preparation for aspiring school principals and support for current school principals is of critical importance to policymakers and institutions of higher learning that have educational leadership programs. In particular, school districts need principals who have experienced the most current and compelling theories and practices of school leadership for both the current and future context of improving student achievement and the teaching/learning environment of schools. In addition, there is a considerable amount of research that proposes a definite link between the leadership abilities of a school leader and the impact such leadership has on student achievement (Darling Hammond, LaPointe, Meyerson, Orr, 2007; Heck Hallinger, 2014; Marzano, Waters, McNulty, 2005) In most cases, the preparation and support for school principals originates from colleges and universities that offer graduate level programs and degrees in educational leadership, management, and administration. Currently, such accredited institutions are guided by national and state educational leadership standards, such as the Professional Standards for Educational Leaders (PSEL) (NPBEA, 2015) The state of Maryland, where this study occurred, replaced its own educational leadership framework with the PSEL to guide administrator preparation, licensure, and evaluation in Maryland.  Thus, it is quite obvious that planning, problem solving, and decision making are some of the most important parts of the contemporary school leader. And with this, school leaders are often faced with ethical and moral dilemmas. Fullan (2011) suggests that school leaders facing such dilemmas are
Does Climate Change Affect Real Estate Prices? Only If You Believe in It
Year: 2019

This paper studies whether house prices reflect belief differences about climate change. We show that in an equilibrium model of housing choice in which agents derive utility from ownership in a neighborhood of similar agents, prices exhibit different elasticities to climate risk. We use comprehensive transaction data to relate prices to inundation projections of individual homes and measures of beliefs about climate change. We find that houses projected to be underwater in believer neighborhoods sell at a discount compared to houses in denier neighborhoods. Our results suggest that house prices reflect heterogeneity in beliefs about long run climate change risks.
Fundamental limits on the accuracy of demographic inference based on the sample frequency spectrum
Venue: Proceedings of the National Academy of Sciences
Year: 2015

Numerous empirical studies in population genetics have used a summary statistic called the sample frequency spectrum (SFS) which summarizes the information in a sample of DNA sequences. Despite their popularity, the accuracy of inference methods based on the SFS is difficult to characterize theoretically, and it is currently unknown how the estimation accuracy improves as more sites in the genome are used. Here, we establish information theoretic limits on the accuracy of all estimators that use the SFS to infer population size histories. We study the rate of convergence to the true answer as the amount of data increases, and obtain the surprising result that it is exponentially worse than known convergence rates for many classical estimation problems in statistics. The sample frequency spectrum (SFS) of DNA sequences from a collection of individuals is a summary statistic that is commonly used for parametric inference in population genetics. Despite the popularity of SFS based inference methods, little is currently known about the information theoretic limit on the estimation accuracy as a function of sample size. Here, we show that using the SFS to estimate the size history of a population has a minimax error of at least O(1/log s) where s is the number of independent segregating sites used in the analysis. This rate is exponentially worse than known convergence rates for many classical estimation problems in statistics. Another surprising aspect of our theoretical bound is that it does not depend on the dimension of the SFS, which is related to the number of sampled individuals. This means that, for a fixed number s of segregating sites considered, using more individuals does not help to reduce the minimax error bound. Our result pertains to populations that have experienced a bottleneck, and we argue that it can be expected to apply to many populations in nature.
Development of a SAGAT Query and Simulator Experiment to Measure Situation Awareness in Maritime Navigation
Venue: AHFE
Year: 2020

Many ship collisions and groundings occur due to navigators' erroneous situation awareness (SA) The objective of this study is to develop a method to measure SA for maritime navigation and collision avoidance (SA MA) This study uses the Situation Awareness Global Assessment Technique (SAGAT) as a basis and tool to assess SA. Both interviews with experts and simulator experiments are used. Ten participants, five navigators with extensive experience, and five second year students at a nautical science program participate in the simulator experiment. Hierarchical Task Analysis (HTA) is used to map the navigation and collision avoidance tasks as input to the SA queries. The objective measurements collected from the simulator and subject matter experts are used for the SAGAT score. A well developed SAGAT query and simulator experiment results in a difference in the SA MA between the experienced navigators and the students with less experience. The study found it is difficult to measure SA MA, especially for level 2 and 3 SA.
An FPGA Accelerator for Bayesian Network Structure Learning with Iterative Use of Processing Elements
Venue: 2020 International Conference on Field Programmable Technology (ICFPT)
Year: 2020

A Bayesian network is one of the graphical models that represent the causality or correlation relationship among multiple observed phenomena. The structure learning of this network is generally NP difficult, and the computational time to obtain an approximate solution becomes huge. This paper proposes an FPGA accelerator for structure learning of Bayesian networks. The proposed method employs a dataflow type architecture and executes processes without dependency in dynamic programming in parallel. By iteratively using processing elements at each processing stage, we can efficiently use limited resources while taking advantage of the parallel performance of FPGAs. We implemented the proposed method for Xilinx Alveo U200 using high level synthesis. Evaluation results showed that we achieved up to 12.6 times faster than single core execution of software and up to 2.98 times faster than on multi core execution. We further applied the proposed method to the Local to Global algorithm and achieves 8.6 times faster than the software execution in the structure learning of a practical network with 37 nodes.
Strategic Entrepreneurship: Issues and Challenges
Year: 2013

There is a wide variety of types of entrepreneurs, but no one formula for success. However, there are many successful entrepreneurs. Understanding why some entrepreneurs succeed while others fail is important to help future entrepreneurs in their efforts to be ssuccessful. Entrepreneurship is the economic engine driving many nations" economies in the global competitive landscape. Entrepreneurship and innovation have become important for young and old and for large and small firms in all types of industries. Research conducted has shown that in receant years almost 100 percent of the new jobs in the United States were created by entrepreneurial firms of less than two years age" As result, this chapter focuses on strategic entrepreneurship.  10.5901/mjss.2013.v4n7p155
Renewable energy in South Africa's minerals energy complex: a 'low carbon' transition?
Year: 2015

This paper questions the extent to which the introduction of utility scale, privately generated renewable energy into South Africa's coal dominated electricity supply can be considered a 'low carbon transition' Rather, the renewable energy projects in question are embedded within and contribute to South Africa's high carbon, electricity intensive 'minerals energy complex' An empirical consideration is provided of some of the stakeholders involved in the implementation of the wind industry in South Africa, and the possibilities and pitfalls for its long term sustainability.
Is information risk priced for NASDAQ listed stocks?
Year: 2010

Easley et al. ( 2002) building upon the asset pricing model of Fama and French ( 1992) show that the probability of informed trading (PIN) is a determinant of asset returns for NYSE listed securities. We extend this work by examining whether the PIN is a predictive factor for NASDAQ stocks, as many studies document significant differences between NYSE and NASDAQ listed securities. In the process we examine whether the use of PIN is appropriate for NASDAQ listed securities. We find that PIN and certain stock characteristics correlate differently for our sample of NASDAQ stocks than that of Easley et al. sample of NYSE stocks. We also determine that the risk of informed trading is only weakly priced for NASDAQ stocks. Contrary to Easley et al. we do not find evidence that excess returns increases as PIN increases.
La procedure de data room dans la vente d'immeubles affectes a une activite commerciale.
Year: 2014

Depuis plusieurs annees, la procedure de data room s'est imposee aupres des professionnels de l'investissement comme le mode privilegie de vente d'immeuble leur permettant d'organiser efficacement la rotation de leur patrimoine. Juridiquement, cette procedure se deroule en deux phases. La premiere, precontractuelle, imposera aux candidats acquereurs, de determiner la valeur de l'immeuble suite a un audit de chacune de ses composantes (juridique, financiere, environnementale, technique, fiscale) puis de formuler a une date convenue entre les parties une offre ferme d'achat. La seconde, contractuelle, permettra au candidat acquereur retenu par le vendeur de finaliser, par la redaction d'une promesse puis de l'acte authentique, les conditions de realisation de la vente.Le recours a une telle procedure n'est pas exempt de risques. D'une part, dans le silence de la loi, elle n'est soumise a aucun regime specifique et releve donc du droit commun de la responsabilite civile (delictuelle et contractuelle) et plus exceptionnellement de la responsabilite penale. D'autre part le foisonnement de normes juridiques que doivent maitriser les candidats acquereurs lors de la valorisation de l'immeuble peut induire un risque d'erreur dont la consequence economique est une surevaluation du prix d'acquisition.Pour parvenir a maitriser ce double niveau de risque que nous qualifierons de juridique et d'operationnel, il a fallu dans un premier temps determiner le regime de responsabilite dont releve cette procedure et ce a chacune de ces phases, puis dans un second temps analyser le deroulement de la phase d'audit en vue de proposer un modele le plus exhaustif possible.
Robotic Process Automation: Contemporary themes and challenges
Venue: Comput. Ind.
Year: 2020

Through the application of Robotic Process Automation (RPA) organisations aim to increase their operational efficiency. In RPA, robots, or 'bots' for short, represent software agents capable of interacting with software systems by mimicking user actions, thus alleviating the workload of the human workforce. RPA has already seen significant uptake in practice; solution technologies are offered by multiple vendors. Contrasting with this early practical adoption is the hitherto relative lack of attention to RPA in the academic literature. As a consequence, RPA lacks the sound theoretical foundations that allow for objective reasoning around its application and development. This, in turn, hinders initiatives for achieving meaningful advances in the field. This paper presents a structured literature review that identifies a number of contemporary, RPA related themes and challenges for future research.
Balancing Profit and People: Corporate Social Responsibility in Business Education
Year: 2017

This qualitative study's purpose was to evaluate how undergraduate business students' perspectives, skills, and behaviors evolved through corporate social responsibility (CSR) education taught with a focus on critical thinking and sustainable problem solving. Business schools are struggling to incorporate CSR into their curriculum despite interest from students and pressure from accreditation agencies. This article primarily contributes practical tools for business schools teaching students to apply critical thinking skills and concepts gained through their business education to develop solutions to economic, social, and environmental problems. Instructors taught the following topics: definitions of CSR, the triple bottom line, stakeholder theory, exposure to social problems, businesses' roles in exacerbating or mitigating social problems, specific sustainable solutions companies and nonprofit organizations have implemented, and analysis of public companies' sustainability reports. Topics were taught using critical thinking tools, such as a decision making model, a funneling exercise, a root problems activity, and reflection and metareflection. The instruction followed a specific teaching model to promote critical thinking skills development, which can be implemented by other faculty. We found CSR concepts motivated students by giving them the tools and confidence in their abilities to solve meaningful problems and learning outcomes for both CSR and critical thinking were achieved.
A novel turn on fluorescent probe for the determination of Al3+ and Zn2+ ions and its cells applications.
Venue: Talanta
Year: 2020

Early detection of probes is very important for limiting toxic effects of various transition metal ions for example aluminum and zinc. Monitoring of these metal ions can be challenging via conventional methods since they are high cost instrumentations, time consuming and so on. We report facile preparation of a fluorescence probe containing biphenyl groups that effectively selective and superb sensitivity towards aluminum (III) and zinc (II) in neutral solutions without interference from each other and other ions. In neutral pH value, the probe FOB displayed the OFF ON fluorescence enhancement at 464 nm and 512 nm toward aluminum (III) and zinc (II) respectively. The detection limit values of FOB for Al3+ and Zn2+ in neutral solutions were 1.27 and 1.02 nM, respectively and these values were significantly lower than permitted Al3+ and Zn2+ concentrations in drinking water determined by the World Health Organization (WHO) and European Water Quality. Also, fabricated cyano biphenyl based probe is effective for sensing for aluminum (III) and zinc (II) in living human colon cancer cells even when employed at low concentrations (1.0 mM) Overall, this work allows us to obtain a great potential to be applied to detect Al3+ and Zn2+
Feature Enhanced Nonequilibrium Bidirectional Long Short Term Memory Model for Chinese Text Classification
Venue: IEEE Access
Year: 2020

This article proposes a model for Chinese text classification based on a feature enhanced nonequilibrium bidirectional long short term memory (Bi LSTM) network that analyzes Chinese text information in depth and improves the accuracy of text classification. First, the bidirectional encoder representations from transformers model was used to vectorize the original Chinese corpus and extract preliminary semantic features. Then, a nonequilibrium Bi LSTM network was applied to increase the weight of text information containing important semantics and further improve the effects of the key features in Chinese text classification. Simultaneously, a hierarchical attention mechanism was used to widen the gap between the important and unimportant data. Finally, the softmax function was used for classification. By comparing the classification performance of the proposed scheme with those of various other models, it was observed that the model substantially improved the precision of Chinese text classification and had a strong ability to recognize Chinese text features. The model achieved 97% precision on the experimental dataset.
The fast development of sea cucumber fisheries in the Mediterranean and NE Atlantic waters: From a new marine resource to its over exploitation
Year: 2018

In the last years, sea cucumbers consumption is increasing in the Asian region, as consequence more than 70% of the sea cucumber fisheries around the world are fully/over exploited or depleted. Therefore, Asiatic markets are looking for new target species mainly from Mediterranean Sea and NE Atlantic Ocean. In this paper, we establish the state of the art of the sea cucumber fisheries in this geographical area, analyze the negative effects of over exploitation on five species (Holothuria polii, Holothuria tubulosa, Holothuria mammata, Holothuria arguinensis and Parastichopus regalis) through three study cases well documented in Turkey, Spain and Portugal, assess the Holothuria forskali fishery under regulation in NW Spain, and finally suggest some management measures to improve, and/or develop a right regulation for the exploitation of these new resources. The current fishery pressure has already provoked some important negative effects on wild populations of sea cucumbers from Mediterranean and NE Atlantic waters, such as decrease in density, abundance, and genetic diversity, loss of the biggest individuals, increase on the prevalence of some diseases, and even "local extinction" in some places. Most of the countries where these species are being caught, have not legislation for these fisheries. Therefore, an improvement in the biological knowledge of these new target species is mandatory to effectively suggest right management measures to protect stocks, and to favor the development of national, and international legislation to prevent illegal catches. Existing legislation, e.g. that from Galicia (NW Spain) or Turkey, could be used as a starting point to develop a more effective set of rules. Fishery on sea cucumbers by using scuba diving equipment should be forbidden or better regulated because of its heavy impact on wild populations. Current increasing demand on sea cucumbers should be satisfied by aquaculture production; its biotechnology on species from the Mediterranean and NE Atlantic is being already developed with increasing success, which will allow a reduction of fishery pressures on wild populations of these new target species, and the possibility to develop restocking programs.
Deep Spatio Temporal Residual Networks for Citywide Crowd Flows Prediction
Venue: AAAI
Year: 2017

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, such as inter region traffic, events, and weather. We propose a deep learning based approach, called ST ResNet, to collectively forecast the inflow and outflow of crowds in each and every region of a city. We design an end to end structure of ST ResNet based on unique properties of spatio temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. Experiments on two types of crowd flows in Beijing and New York City (NYC) demonstrate that the proposed ST ResNet outperforms six well known methods.