nils-herrmann/clean_scirepeval huggingface dataset:
The role of sleep disturbances in depressive like behavior with emphasis on a ketoglutarate dehydrogenase activity in rats.
Venue: Physiology Behavior
Year: 2020

Sleep disorders may induce anxiety and depressive like behaviors. Furthermore, sleep disorders can alter the function of a KGDH (a ketoglutarate dehydrogenase) which is involved in the citric acid cycle. In this study, we evaluated the effect of two models of sleep deprivation (SD) including total SD (TSD) and partial SD (PSD) and two models of napping combined with each models of SD on rats' performance in Forced Swim Test (FST) and a KGDH activity in both hemispheres of the amygdala. 64 male Wistar rats were used in this study. A modified water box was also used to induce SD. The results showed that, immobility was increased in 48 hour PSD group, indicating a possible depressive like behavior. Swimming time was also increased following 48 hour TSD. However, climbing time was decreased in 48 hour PSD/TSD groups. Additionally, a KGDH activity was increased in the left amygdala in 48 hour TSD and PSD groups. In conclusion, PSD may increase depressive like behavior. TSD and PSD can decrease swimming time but increase climbing time, and these effects may be related to serotonergic and noradrenergic transmissions, respectively. Increase in a KGDH activity in the left amygdala may be related to the brain's need for more energy during prolonged wakefulness. a KGDH activity in the right amygdala was unaffected probably due to a decrease in alertness following SD.
Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models.
Venue: Chemical research in toxicology
Year: 2020

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs) random forest (RF) and decision trees (DTs) neural networks, regression models, naive Bayes, k nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.
Revisiting 3D van Krevelen diagrams as a tool for the visualization of volatile profile of varietal olive oils from Alentejo region, Portugal.
Venue: Talanta
Year: 2020

This paper aims to study the volatile composition of monovarietal extra virgin olive oils from different varieties from Alentejo region (Portugal) Volatile profile of 82 olive oils was performed by head space solid phase micro extraction hyphenated with gas chromatography/mass spectrometry (HS SPME GC/MS) A total of 107 volatile compounds, belonging to several class of compounds, like aldehydes, hydrocarbons, alcohols, terpenoids, ketones, sulphurous compounds, acids and esters were identified, assigned to a specific biogenic origin and total abundances were plotted in modified 3D van Krevelen diagrams. The 3D plots showed that there are compositional differences among olive oil varieties. Discriminant analysis confirmed that the pattern of volatile compounds includes enough information to group sample variety amongst discrete monovarietal olive oil. Combining statistical analysis (t Student) and graphical tools (van Krevelen diagram) seems to be a powerful tool to determine what molecular families were characteristic of each olive oil variety, contributing to the valorization of monovarietal Portuguese olive oil.
Information Sources for Food Studies Research
Year: 2006

Food studies is highly interdisciplinary across both science and social science fields. It can include anything pertaining to food and eating, from how food is grown, to when and how it is eaten, to who eats it and with whom, and the nutritional quality. Relevant publications for both current and historical work are scattered across academic fields and throughout the literature. Research in food habits published in the agricultural literature is often as useful as studies of food habits in anthropology or sociology studies. This article presents some useful and effective strategies for conducting a comprehensive search of food studies literature.
Analysis of Spurious Local Solutions of Optimal Control Problems: One Shot Optimization Versus Dynamic Programming
Venue: 2021 American Control Conference (ACC)
Year: 2021

Dynamic programming (DP) has a rich theoretical foundation and a broad range of applications, especially in the classic area of optimal control and the recent area of reinforcement learning (RL) Many optimal control problems can be solved as a single optimization problem, named one shot optimization, or via a sequence of optimization problems using DP. However, the computation of their global optima often faces the NP hardness issue due to the non linearity of the dynamics and non convexity of the cost, and thus only local optimal solutions may be obtained at best. Furthermore, in many cases arising in machine learning and model free approaches, DP is the only viable choice, and therefore it is essential to understand when DP combined with a local search solver works. In this work, we introduce the notions of spurious local minimizers for the one shot optimization and spurious local minimum policies for DP, and show that there is a deep connection between them. In particular, we prove that under mild conditions the DP method using local search can successfully solve the optimal control problem to global optimality if and only if the one shot optimization is free of spurious solutions. This result paves the way to understand the performance of local search methods in optimal control and RL.
Resilience in academic stress: Exploring the role of cognition in how students adjust to life at the University of York
Year: 2014

The main aim of tertiary education is to prepare students to make a positive contribution to society by developing within them the ability to function efficiently and effectively within the workplace. However, research is instead showing that during university life, more than half of all students report a high prevalence of mental health problems (Stallman, 2010) leading to a disruption of studies (Jackson, Pancer, Pratt Hunsberger, 2000) and a subsequent failure to develop as anticipated (Stallman, 2011) This suggests that there is a need to invest in programmes which aid students in adjusting to being at university. Researchers recommend that such programmes focus on building students' levels of resilience; their capacity to adapt and grow in response to university life (Stallman, 2011) This thesis aims to clarify how universities can promote student adjustment by exploring the cognitive processes that influence the levels of resilience of students high on negative trait emotion. Negative trait emotion refers to the predisposition to experience intense and frequent negative emotion (Rosenberg, 1998) and has been highlighted as exerting a harmful impact on individuals' abilities to adapt to their experiences. Therefore, in this way, the thesis aims to uncover the cognitive mechanisms that should be targeted in order to improve students' capacity to adjust and grow while at university, enabling them to subsequently perform at a high standard post graduation.
Pengaruh faktor Hidro Oseanografi terhadap pertumbuhan dan tingkat kelangsungan hidup (Survival Rate) hasil Transplantasi Terumbu Karang jenis Acropora sp. di Perairan Paiton Probolinggo
Year: 2019

Perairan paiton termasuk dalam Selat Madura yang terdapat ekosistem terumbu karang. Adanya aktivitas kapal pengangkut batubara dan limpasan air hangat untuk pendingin mesin secara terus menerus dapat mengancam kehidupan terumbu karang Penelitian ini bertujuan untuk mengetahui laju pertumbuhan dan tingkat kelangsungan hidup terumbu karang terhadap kondisi fisika kimia perairan Paiton. Parameter perairan yang diukur adalah suhu, arus, kecerahan, salinitas, derajat keasaman (pH) oksigen terlarut (DO) nitrat (NO3) fosfat (PO4) Metode transplantasi yang dilakukan dengan menanam fragmen yang telah dipotong pada substrat keras (kanstin FABA) pada kedalaman 8m dengan jenis terumbu karang yang di transplantasi adalah Acropora formosa, Acropora pulchra, Acropora intermedia. laju pertumbuhan selama 6 bulan didapatkan pada Acropora formosa 0,1674 cm/minggu 1,55, Acropora pulchra 0,2021 cm/minggu 1,96, Acropora intermedia 0,1382 cm/minggu 1,28. Tingkat kelangsungan hidup didapatkan pada setiap jenis yaitu pada !Acropora formosa 93,33% Acropora pulchra 93,33% Acropora intermedia 96,67% Hasil rata rata pengukuran parameter hidro oseanografi yaitu, suhu 29,1 0C, kecerahan 6,81 m, arus 5,59 cm/s, salinitas berkisar 30,39 ppt, derajat keasaman (pH) berkisar 7,5 oksigen terlarut berkisar 5,4 mg/l, kandungan nitrat berkisar 0,105 mg/l, kandungan fosfat berkisar antara 0,012 mg/l. Uji korelasi statistik menunjukkan bahwa parameter perairan mendapatkan korelasi sangat kuat dan sangat lemah terhadap laju pertumbuhan Acropora formosa, Acropora pulchra Acropora intermedia dengan nilai korelasi positif dan negatif. Korelasi parameter perairan dengan tingkat kelangsungan hidup mendapatkan korelasi sangat kuat dan sangat lemah dengan nilai positif dan negatif.
Environmental perspective of COVID 19
Venue: Science of The Total Environment
Year: 2020

The outbreak of COVID 19 has caused concerns globally. On 30 January WHO has declared it as a global health emergency. The easy spread of this virus made people to wear a mask as precautionary route, use gloves and hand sanitizer on a daily basis that resulted in generation of a massive amount of medical wastes in the environment. Millions of people have been put on lockdown in order to reduce the transmission of the virus. This epidemic has also changed the people's life style; caused extensive job losses and threatened the sustenance of millions of people, as businesses have shut down to control the spread of virus. All over the world, flights have been canceled and transport systems have been closed. Overall, the economic activities have been stopped and stock markets dropped along with the falling carbon emission. However, the lock down of the COVID 19 pandemic caused the air quality in many cities across the globe to improve and drop in water pollutions in some parts of the world.
OpenSim: Open Source Software to Create and Analyze Dynamic Simulations of Movement
Venue: IEEE Transactions on Biomedical Engineering
Year: 2007

Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.
Gimnasio Prenatal GYM BABY AND MOM preparando embarazos sanos
Year: 2020

Nuestro gimnasio prenatal GYM BABY AND MOM es una idea cuyo fin es implementar servicios de preparacion fisicos y emocionales de la mujer durante el embarazo, logrando multiples beneficios durante el tiempo de gestacion, entre esos la prevencion de cualquier problema que pudieran presentar a la hora del parto, y manteniendo un estilo de vida saludable tanto fisica como emocional. Con esto se logran importantes beneficios como lo son el afecto, la seguridad y la estabilidad emocional entre la madre y el hijo(a) A parte de ser un gimnasio donde podra asistir cualquier mujer en estado de embarazo de la ciudad de Cartagena. Se esta estudiando la posibilidad de realizar convenios con las EPS, para adquirir una amplia cobertura y que mas mujeres se beneficien de nuestros servicios.
Frequency Domain Causality Relationship Analysis Between Poverty, Economic Growth and Financial Development in Algeria
Year: 2019

Since the previous periods, poverty reduction has been a big concern for many countries especially in developing countries like Algeria; in this paper, we shall explore the causal relationship between poverty reduction, economic growth and financial development in Algeria during the period of 1970 2017, the aim of this research is to answer the question which sector causes the poverty reduction: real sector or financial sector? Therefore, we employed the modern frequency domain causality presented by Breitung and Candelon (2006) with a comparison with the time domain causality under Lutkepohl (2006) procedure, the results suggest that there is unidirectional causality running from the real sector (economic growth) to poverty rates in the short and long run terms, also, we found that there is an unidirectional causality running from the financial sector to poverty rates only in the long run term, while another causality running from poverty rates to the financial sector but in the short run term. This article aims at contributing to enlarge the literature review by utilizing the frequency domain causality in the field of poverty studies because of its effectiveness to test the causalities in different frequencies.
Keywords Short fiber suspensions fiber orientation parallel computing microstructure generation fiber interaction numerical scale transition
Year: 2018

The microstructure of a short fiber thermoplastic composite greatly affects its mechanical properties. We wish to simulate the flow behavior of concentrated fiber suspensions containing a significant number of fibers in order to understand the evolution of fiber orientation during the forming process. We aim to ensure a scale transition in numerical rheometry by simulating a whole injected part or a thickness of a part with a complex geometry. This paper focuses first on a numerical method to generate an initial isotropic state of concentrated fiber suspensions. Then, kinematic of fiber suspensions under Newtonian shear flow is studied through a direct numerical simulation (DNS) taking into account the different existing interaction forces
Efficiently Finding Top K Items from Evolving Distributed Data Streams
Venue: 2014 10th International Conference on Semantics, Knowledge and Grids
Year: 2014

The problem of efficiently finding top k frequent items has attracted much attention in recent years. Storage constraints in the processing node and intrinsic evolving feature of the data streams are two main challenges. In this paper, we propose a method to tackle these two challenges based on space saving and gossip based algorithms respectively. Our method is implemented on SAMOA, a scalable advanced massive online analysis machine learning framework. The experimental results show its effectiveness and scalability.
Environmental atitudes of urban dwellers in Kuala Lumpur, Malaysia
Year: 2017

Environmental concern on climate change have greater impact to urban health around the globe. Temperature risk, water quality status and air pollution are major threat for living environment including urban biodiversity. Thus, the aim of the paper are to investigate environmental attitude among people in Kuala Lumpur, Malaysia. There are five urban parks around Kuala Lumpur have been selected as case studies and 380 respondents have been participated in the survey questionnaire. Data gathered under the demographic section range of age, gender, ethnicity, levels of education, and income. New Ecological Paradigm (NEP) have been adopted as theory underpinning to identify public concerns. The result have presented that there are significant different among people's between three ethnicity consist of Malay, Chinese and Indian, six different levels of education and six different income groups. Malays are majorities ethnic and have more attached to nature since childhood and play a vital rules in environmental education. Meanwhile, young generation have greater space in exposure them to importance of protecting earth every in daily routine. Finally, high income level could contribute social, environment and economic benefit opportunities to the societies. Therefore, urban biodiversity policies should focus of these three major elements in promoting environmental concern with in urban dwellers in Kuala Lumpur.  New Ecological Paradigm, Environmental Attitude, climate change and Urban biodiversity
NLP Cube: End to End Raw Text Processing With Neural Networks
Venue: CoNLL
Year: 2018

We introduce NLP Cube: an end to end Natural Language Processing framework, evaluated in CoNLL's "Multilingual Parsing from Raw Text to Universal Dependencies 2018" Shared Task. It performs sentence splitting, tokenization, compound word expansion, lemmatization, tagging and parsing. Based entirely on recurrent neural networks, written in Python, this ready to use open source system is freely available on GitHub. For each task we describe and discuss its specific network architecture, closing with an overview on the results obtained in the competition.
Social Media Preference and Condom Use Behaviors: An Analysis of Digital Spaces With Young African American Males
Venue: Health education behavior the official publication of the Society for Public Health Education
Year: 2021

African American adolescents and young adults have an increased likelihood of engaging in risky sexual behaviors. With rising rates of sexually transmitted infections among this population, deeper consideration is now being focused on using social media to engage, educate, and improve sexual behavior in this group.  To determine why social media is useful and how it may affect the attitude, norms, and perceived behavioral control on condom use among young African American males (YAAMs) ages 18 to 21.  Group depth interviews (n 41, mean age 19, SD 1.2) were conducted in metro Detroit to understand and describe the who, what, where, why, and how regarding social media use and preference among YAAMs) ages 18 to 21, and their condom use behaviors.  The most popular reasons for using social media were for educational purposes, entertainment, browsing the site, self expression, seeking out or maintaining relationships with friends and family, and social justice. YouTube was stated as the easiest site to promote condom use education. YAAMs would seek out condom use education on social media sites if someone respected in the community or someone famous promoted condom use messages on the sites or if it was used to show where condoms were available for free or for purchase.  Understanding how YAAMs use social media may help researchers design better questions to address disparities within this population. More important, it can help health care providers, families, and the community promote healthy behaviors and lifestyle changes among YAAMs.
The inquiline oak gall wasp (Hymenoptera: Cynipidae) fauna from Colombia: new data and species
Year: 2020

A review of the current knowledge on the diversity of inquiline oak gall wasps (Hymenoptera: Cynipidae: Cynipinae) from Colombia is conducted for the first time. A new species, Synergus dawnus Lobato Vila and Pujade Villar, is formally described and illustrated, and an undescribed specimen from the same genus is diagnosed and discussed. New biological, morphological, and distribution data of the only two previously known species from this country Synergus colombianus Nieves Aldrey, 2005 and Synergus pedroi Pujade Villar, Lobato Vila, and Fernandez Garzon, 2017) as well as an identification key to the Colombian species of Synergus Hartig, 1840, are provided. The morphological variability of the Colombian species of Synergus especially of S. pedroi is discussed.
Abductive learning of quantized stochastic processes with probabilistic finite automata
Venue: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Year: 2013

We present an unsupervised learning algorithm (GenESeSS) to infer the causal structure of quantized stochastic processes, defined as stochastic dynamical systems evolving over discrete time, and producing quantized observations. Assuming ergodicity and stationarity, GenESeSS infers probabilistic finite state automata models from a sufficiently long observed trace. Our approach is abductive; attempting to infer a simple hypothesis, consistent with observations and modelling framework that essentially fixes the hypothesis class. The probabilistic automata we infer have no initial and terminal states, have no structural restrictions and are shown to be probably approximately correct learnable. Additionally, we establish rigorous performance guarantees and data requirements, and show that GenESeSS correctly infers long range dependencies. Modelling and prediction examples on simulated and real data establish relevance to automated inference of causal stochastic structures underlying complex physical phenomena.
Analyzing ELMo and DistilBERT on Socio political News Classification
Venue: AESPEN
Year: 2020

This study evaluates the robustness of two state of the art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification (PC) and sentiment analysis (SA) of product reviews. A "cross context" setting is enabled using test sets that are distinct from the training data. The models are fine tuned and fed into a Feed Forward Neural Network (FFNN) and a Bidirectional Long Short Term Memory network (BiLSTM) Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) are used as traditional baselines. The results suggest that DistilBERT can transfer generic semantic knowledge to other domains better than ELMo. DistilBERT is also 30% smaller and 83% faster than ELMo, which suggests superiority for smaller computational training budgets. When generalization is not the utmost preference and test domain is similar to the training domain, the traditional machine learning (ML) algorithms can still be considered as more economic alternatives to deep language representations.