nils-herrmann/clean_scirepeval huggingface dataset:
Nursing Theory in the Quest for the Sustainable Development Goals
Venue: Nursing science quarterly
Year: 2020

The 17 United Nations Sustainable Development Goals (SDG) are intended to promote a safe, healthy, and equitable world by the year 2030. Nurses are at the forefront of realizing the 2030 agenda through concerned citizenship and professional leadership. Nursing theory informs knowledge development and theory guided practice essential for nurses working in all domains and in all nations. Although all extant nursing theories are relevant, a select few are discussed in detail to make explicit the links between theory and SDG realization. Middle range theories are also valuable in helping to contextualize nursing practice through the lens of the SDGs. The SDGs address five themes People, Planet, Peace, Prosperity, and Partnership and theory remains vital to ensure nurses working in all settings are equipped to meet the needs of humanity and the world, now and in the future.
Online Teaching Learning Readiness of Dela Paz National High School
Year: 2020

The present world is calling for an immediate large scale transformation in educational process impacted by the pandemic COVID 19. Many institutions concluded to online learning as an instant remedy to keep educating learners outside school. Despite the advantages learners can gain from this method, it cannot be denied that numerous studies have revealed problems associated with low levels of preparedness and capabilities of learners exposed to online learning, resulting to high drop outs. Relatively, this study explored on learners' readiness to Online Teaching and Learning (OTL) of Dela Paz National High School in Antipolo City, Philippines. The survey determines level of readiness in terms of technology accessibility, technological confidence, support, and training, and attitude of learners towards success of OTL as adapted from a survey questionnaire, which was further developed electronically, validated and tested for usability by IT and content experts. Seventy four respondents were randomly selected from the population of Junior and Senior High School learners of Dela Paz National High School enrolled during school year 2019 2020. Data was collected and subjected to statistical analysis for interpretation. The responses imply that both levels of learners are ready to be engaged in online teaching learning as they have access to gadgets with internet connection. In terms of confidence, skills, and social support, the results give an impression of positive readiness level to online education, however, it was noted that in terms of trainings, they have to be provided with intervention strategies to prepare them for using LMS. For assessing learners' attitude, the two group respondents manifested confidence towards successful OTL sharing the same perception on study habits, abilities, motivation and usefulness, except for time management. This can be attributed to the difference on the number of tasks needed to be accomplished by the learners at home, school, etc. The researcher recommends that the specifics pointed in the results obtained be used by school administrators and teachers for planning and management of OTL.
Optimal parameter choices via precise black box analysis
Venue: Theor. Comput. Sci.
Year: 2020

It has been observed that some working principles of evolutionary algorithms, in particular, the influence of the parameters, cannot be understood from results on the asymptotic order of the runtime, but only from more precise results. In this work, we complement the emerging topic of precise runtime analysis with a first precise complexity theoretic result. Our vision is that the interplay between algorithm analysis and complexity theory becomes a fruitful tool also for analyses more precise than asymptotic orders of magnitude. As particular result, we prove that the unary unbiased black box complexity of the OneMax benchmark function class is n ln n c n o n for a constant c which is between 0.2539 and 0.2665. This runtime can be achieved with a simple 1 1 type algorithm using a fitness dependent mutation strength. When translated into the fixed budget perspective, our algorithm finds solutions which are roughly 13% closer to the optimum than those of the best previously known algorithms. To prove our results, we formulate several new versions of the variable drift theorems, which also might be of independent interest.
A Study on a Nonlinear Control Algorithm for the Automatic Berthing of Ships
Year: 2008

his study discusses the design of an automatic berthing control algorithm for ships with a haw thruster and a stern thruster, as well as a rudder. A nonlinear mathematical model for the law speed maneuvering of ships was used to design a MIMO (multi input multi output) nonlinear control algorithm. The algorithm consists of two parts, the forward velocity control and heading angle control. The control algorithm was designed based on the longitudinal and yaw dynamic models of ships. The desired heading angle was obtained by the so called "Line of Sight" method. An optimal control force allocation method forthe rudder and the thrusters is suggested. The nonlinear control algorithm was tested by numerical simulations using MATLAB, and showed good tracking performance.
Characterization of the deformation and phase transformation behavior of VC free and VC containing FeMnSi based shape memory alloys by in situ neutron diffraction
Year: 2017

The stress induced fcc austenite to hcp martensite transformation in the iron based shape memory alloy (SMA) Fe 17Mn 5Si 10Cr 4Ni with and without VC precipitates is investigated by in situ neutron diffraction measurements upon uniaxial loading and unloading. Based on experimentally derived elastic moduli the critical resolved shear stress (CRSS) for the fcc to hcp phase transformation was calculated. VC precipitates promote the martensite transformation by shifting the CRSS from 152 MPa to 85 MPa. A nearly perfect plastic behavior is found for the (220) grains with a high Schmid factor of 0.47. While (220) (111) and (200) oriented grains exhibit a phase transformation, (311) grains plastically deform solely by slip. During plastic deformation a load redistribution from soft behaving (220) grains to hard behaving (200) orientated grains takes place. The presence of VC precipitates leads to a broadening of the stress interval at which a martensite transformation is induced. This is explained by spatially heterogeneously distributed martensite transformation temperatures which are caused by VC precipitates. The microstructural reason for pseudo elasticity is found to be a combination of back transformation from hcp to fcc and a reversible motion of Shockley partial dislocations.
Long Term climate change commitment and reversibility: An EMIC intercomparison
Year: 2012

AbstractThis paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) The focus is on long term climate projections designed to 1) quantify the climate change commitment of different radiative forcing trajectories and 2) explore the extent to which climate change is reversible on human time scales. All commitment simulations follow the four representative concentration pathways (RCPs) and their extensions to year 2300. Most EMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year 2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near preindustrial values in most models for RCPs 2.6 6.0. The MOC weakening is more persistent for RCP8.5. Elimination of anthropogenic CO2 emissions after 2300 resu.
NeuTM: A neural network based framework for traffic matrix prediction in SDN
Venue: NOMS 2018 2018 IEEE/IFIP Network Operations and Management Symposium
Year: 2018

This paper presents NeuTM, a framework for network Traffic Matrix (TM) prediction based on Long Short Term Memory Recurrent Neural Networks (LSTM RNNs) TM prediction is defined as the problem of estimating future network traffic matrix from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well suited to learn from data and classify or predict time series with time lags of unknown size. LSTMs have been shown to model longrange dependencies more accurately than conventional RNNs. NeuTM is a LSTM RNN based framework for predicting TM in large networks. By validating our framework on real world data from GEANT network, we show that our model converges quickly and gives state of the art TM prediction performance.
Analisa Variasi Musiman Tinggi Gelombang Laut Secara Global Selama Periode 1984 2003
Year: 2014

Pemasan global dan perubahan iklim secara langsung dapat mempengaruhi keadaan lingkungan, terutama di lautan. Gelombang tinggi yang terjadi di laut dapat menyebabkan berbagai kerusakan pantai. Maka dari itu, pendekatan klimatologi perlu dilakukan dengan menganalisa tinggi gelombang yang pernah terjadi. Penelitian ini dilakukan untuk mengetahui sebaran tinggi gelombang laut secara global yang dibagi menurut musim dengan menggunakan data hasil hindcasting dari pemodelan numerik WAM selama periode 20 tahun, dari tahun 1984 sampai 2003. Hasil yang diperoleh dari penelitian ini berupa peta distribusi rata rata tinggi gelombang signifikan (Hs) yang dibagi menurut empat musim yang terjadi di dunia selama 20 tahun. Rata rata tinggi gelombang pada bulan Januari Februari Maret dan bulan Oktober November Desember cenderung tinggi di bagian bumi dengan koordinat latitude 0@ 90@ dan longitude 0@ 360@ Sedangkan di koordinat latitude 0@ 90@ dan longitude 0@ 360@ cenderung memiliki Hs yang tinggi sepanjang tahun. Rata rata tinggi gelombang di beberapa lokasi secara global mengalami perubahan selama 20 tahun. Pada laut bagian selatan bumi, trend rata rata tinggi gelombang signifikan cenderung meningkat. Sedangkan pada bagian utara bumi, seperti samudera Pasifik utara dan samudera Atlantik utara memiliki trend tinggi gelombang yang cenderung turun dari tahun 1984 hingga 2003. Trend kenaikan dan penurunan yang terjadi tidak begitu signifikan. Global warming and climate change could affect the environment, especially in the ocean. Wave height that occur in the ocean can cause a variety of damage to the beach. Therefore, the approach needs to be done to analyze the climatological wave height that ever happened. This study was conducted to determine the spread of global sea wave height divided by season, using hindcasting results data from WAM numerical modeling over a period of 20 years, from 1984 until 2003. Results obtained from this study purports to map of the distribution of average significant wave height (Hs) are divided by the four seasons that occur in the world for 20 years. The average wave height in January February March and the month of October November December likely high in the earth with coordinates of 0@ 90@ latitude and 0@ 360@ longitude. Whereas in the coordinate 0@ 90@ latitude and longitude 0o 360o tend to have high average of Hs throughout the year. The average wave height at various locations globally has changed for 20 years. In the southern part of the earth, the trend of average significant wave height tends to increase. Whereas in the northern part of the earth, such as the deep north Pacific and north Atlantic ocean has a Hs trend that tends to decrease from 1984 to 2003.
[Recommendations on neurophysiological studies during the COVID 19 pandemic]
Venue: Revista de neurologia
Year: 2020

At the end of January, the current outbreak of COVID 19 coronavirus disease was declared an important international public health emergency. In Spain, since the government declared the state of alarm on 14 March 2020, doctors responsible for carrying out neurophysiological tests have been performing them without any consensus criterion or clear safety guidelines for doctors, technicians or patients. The following recommendations, based on current knowledge of the disease and therefore liable to change in the future, are proposed when the pandemic appears to have entered a process of decreasing virulence and, with it, the strict containment measures established to date. However, in view of the possibility of a second wave of the pandemic, it seems necessary to establish basic and minimum recommendations to respect the patient's right to appropriate care, similar to that provided prior to the pandemic, and to maintain minimum safety standards for the patients themselves and for the doctors, technicians and health personnel carrying out these tests. These recommendations concern the constitution of a priority based on the reason for consultation, the establishment of calls to check the patient's clinical situation before going to the outpatient department and the rules for carrying out neurophysiological tests, which are generally based on the preservation of hospital circuits, respect for and observation of the known barriers to contagion of this disease, and the use of disposable material. These recommendations are of particular interest, especially given the uncertainty of not knowing the evolution of the SARS CoV 2 infection in the coming weeks or months.
VoteChain: A Blockchain Based E Voting System
Venue: 2019 Global Conference for Advancement in Technology (GCAT)
Year: 2019

In the past, electronic voting systems have not seen widespread adoption due to data privacy concerns. Previously proposed e voting systems make use of a central database to store data, resulting in the servers used to store these databases being a single point of failure. These systems have also been found to be vulnerable to DoS attacks, leading to concerns over their reliability.Blockchains have been used to build secure and scalable distributed systems which have shown several benefits over centralized systems. They have seen uses in sectors ranging from finance and healthcare to food and energy.In this paper, we present VoteChain, a blockchain based voting system to help bring transparency and security to polls. We report on our implementation of VoteChain, as well as the results obtained in testing the system in a real world poll which prove that such a system can be used in practice for large scale elections.
Superpixel level polarimetric feature extraction based on circular loop spatial pyramid pooling
Venue: Journal of Applied Remote Sensing
Year: 2019

Superpixel level image classification methods take advantage of contextual information of pixels and reduce the time cost in training and test processes. However, extracting a superpixel level feature is a challenging task because each superpixel has irregular size and shape. A superpixel level polarimetric feature extraction (SLPFE) based on circular loop spatial pyramid pooling (CLSPP) (SLPFE_CLSPP) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. The main idea is that the CLSPP layer divides the feature maps of each superpixel into the same number of circular loops and produces consistent feature size for each superpixel, which then feeds into the next processing step, such as classification. SLPFE_CLSPP not only makes it possible to generate the same size feature but also to utilize the spatial information of pixels and reduce the running time during SLPFE process. Compared with the existing methods, the proposed method makes a balance between overall accuracy and running time. Experimental results on real PolSAR dataset demonstrated the superiority of the proposed approach.
Strumenti di business intelligence a supporto dell'analisi e gestione dei dati sanitari georeferenziati ed applicazione allo studio di patologie nella Terra dei Fuochi.
Year: 2017

La Sanita elettronica e ormai da tempo oggetto delle policy a livello europeo, nazionale, regionale e locale; cio si e tradotto in azioni mirate alla diffusione dell'e health quale strumento comune per i vari stakeholder della sanita con l'obiettivo ultimo di aumentare l'efficienza e la produttivita del sistema salute, migliorare la comunicazione e, piu in generale, la qualita e la sicurezza delle cure. Obiettivo chiave dell'informatizzazione e anche favorire l' empowerment potenziando l' health literacy dei pazienti. L'introduzione di una nuova tecnologia quale appunto la Cartella Clinica Elettronica (CCE) e un investimento notevole in termini di risorse sia economiche sia umane che deve essere governata in maniera metodologicamente corretta. Pertanto, visto lo sviluppo tecnologico e informatico, la CCE rappresenta non solo un complesso insieme di informazioni e dati gerarchicamente organizzati e memorizzati, ma anche una preziosa fonte di informazioni di varia natura. Infatti, le CCE, oltre a contenere dati di carattere sanitario, includono una serie di informazioni accessorie, quali per esempio quelle anagrafiche utili per l'analisi dati e l'estrazione di nuove informazioni tramite l'impiego di opportune relazioni, regole, assiomi e vincoli specifici. Tali informazioni possono rappresentare una preziosa risorsa per studi di tipo epidemiologico, soprattutto se messi in relazione con i dati di tipo ambientale delle aree dove il paziente vive. A tal proposito, il dibattito circa l'effetto sulla salute umana di pressioni ambientali dovute a fenomeni di inquinamento e da decenni oggetto di studi epidemiologici che necessitano di strumenti avanzati che hanno alla base soluzioni tecnologiche, informatiche e statistiche capaci di gestire e integrare i dati sanitari e ambientali con le rispettive informazioni geo spaziali. Tra l'80 e il 90% dell'informazione gestita all'interno dei flussi informativi esistenti nella sanita possiede infatti una componente spazio temporale, il che facilita la visualizzazione dei fenomeni di interesse sanitario che si svolgono all'interno dei territori di competenza delle strutture sanitarie attraverso mappe che ne rendono estremamente esemplificata la lettura e l'interpretazione. A tale fine la tecnologia ha permesso, nell'ultimo decennio, una rapida evoluzione di strumenti che analizzano i dati in funzione della geografia, quali, ad esempio, i Sistemi Informativi Geografici (GIS) Pertanto lo scopo di questa tesi e stato lo sviluppo di uno strumento informatico prototipale in ambiente open source capace di gestire i big data di tipo sanitario mediante una piattaforma di Business Intelligence che, insieme ad un software con interfaccia grafica user friendly (GUI) sviluppato ad hoc in R e integrabile in Pentaho Business Analytics, sia capace di processare e rappresentare in maniera georeferenziata i dati presenti in un data warehouse sanitario
MS AFF: a novel semantic segmentation approach for buried object based on multi scale attentional feature fusion
Year: 2021

Infrared technology is widely used in buried object detection since it can capture the heat radiated outward from the target objects. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, making it challenging to detect the target objects, specifically in complex backgrounds. In recent years, deep learning based methods have made significant improvements in detection tasks. However, infrared images of buried objects are difficult to obtain. Less training samples, worse performance of deep learning based detection. We raise a multi scale attentional feature fusion module for infrared image semantic segmentation to solve this problem. Precisely, we integrate a series of feature maps from different levels by an atrous spatial pyramid structure. In this way, the model can obtain rich representation ability on the infrared images. Besides, a global spatial information attention module is employed to let the model focus on the target region and reduce disturbance in infrared images' background. In addition, we propose an infrared segmentation dataset based on the infrared thermal imaging system. Finally, we use state of the art object detection methods to train the buried object dataset and compare them with our proposed method. Extensive experiments on infrared segmentation dataset demonstrate the superiority of our method.
Emergence of KRAS mutations and acquired resistance to anti EGFR therapy in colorectal cancer
Venue: Nature
Year: 2012

A main limitation of therapies that selectively target kinase signalling pathways is the emergence of secondary drug resistance. Cetuximab, a monoclonal antibody that binds the extracellular domain of epidermal growth factor receptor (EGFR) is effective in a subset of KRAS wild type metastatic colorectal cancers. After an initial response, secondary resistance invariably ensues, thereby limiting the clinical benefit of this drug. The molecular bases of secondary resistance to cetuximab in colorectal cancer are poorly understood. Here we show that molecular alterations (in most instances point mutations) of KRAS are causally associated with the onset of acquired resistance to anti EGFR treatment in colorectal cancers. Expression of mutant KRAS under the control of its endogenous gene promoter was sufficient to confer cetuximab resistance, but resistant cells remained sensitive to combinatorial inhibition of EGFR and mitogen activated protein kinase kinase (MEK) Analysis of metastases from patients who developed resistance to cetuximab or panitumumab showed the emergence of KRAS amplification in one sample and acquisition of secondary KRAS mutations in 60% (6 out of 10) of the cases. KRAS mutant alleles were detectable in the blood of cetuximab treated patients as early as 10 months before radiographic documentation of disease progression. In summary, the results identify KRAS mutations as frequent drivers of acquired resistance to cetuximab in colorectal cancers, indicate that the emergence of KRAS mutant clones can be detected non invasively months before radiographic progression and suggest early initiation of a MEK inhibitor as a rational strategy for delaying or reversing drug resistance.
Survey of mushroom consumption and the possible use of gamma irradiation for sterilization of compost for its cultivation in Southern Ghana
Venue: Agriculture Food Security
Year: 2018

BackgroundMushroom cultivation is increasingly becoming a serious agribusiness in Ghana, especially at the time when entrepreneurship is being encouraged to reduce the pressure of employment in the government sector and also due to its nutritional and medicinal attributes.MethodsA survey was carried out using the rapid appraisal method to review the existing methods of sterilization, use of gamma radiation in substrate sterilization and food preservation, preference of mushrooms in Ghana by consumers and nutritional and medicinal attributes of the mushroom.ResultsThe survey demonstrated the popularity of drum (moist heat) technique of sterilization in Ghana. Majority (64% of the respondents were dissatisfied with the method of sterilization of compost and spawn substrate, while 36% indicated the method was alright by them. Majority (82% of the respondents had never heard of sterilization of substrates for cultivation and its subsequent preservation of food or mushroom by gamma irradiation technique. All consumers (100% desired to see their favorite mushroom produced all year round, and this constituted a significant (p 0.05) viewpoint. Furthermore, a significant (p 0.05) majority (90% of the respondents were all for promotion of the consumption of mushroom, while a small percentage (10% were noncommittal.ConclusionInformation and knowledge on the gamma irradiation technique for substrate production and consumption patterns of P.ostreatus mushrooms were not widely disseminated as anticipated.