bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Ambient and Pervasive Personalized Learning Ecosystem: “Smart Learning” in the Age of the Internet of Things
Authors: Betts Anastasia , Thai Khanh-Phuong , Gunderia Sunil , Hidalgo Paula , Rothschild Meagan , Hughes Diana 
Published: July 2020

Abstract
Despite recent advances in technology, personalized learning to address diverse needs of students remains difficult to achieve at scale. With the availability and affordability of smart devices in the era of the Internet of Things, learners, parents, and educators are more “connected” than ever before. Education stakeholders and technology developers can leverage these advances to collect data about, inform, deliver, and improve education for all learners. In this paper, we review the core components of a Smart Learning framework and describe a personalized mastery-based learning system that leverages the framework to deliver personalized learning at scale. In the context of Smart Learning in the Internet of Things, we propose an Ambient and Pervasive Personalized Learning Ecosystem (APPLE), a learner-centered approach that uses Bloom’s Four Agents of Change in the Internet of Things ecosystem to provide learners a comprehensive and personalized learning experience. This ecosystem uses people, processes, data, things, and networked connections to create new capabilities, richer learning experiences, and unprecedented educational opportunities for learners, educators, and families. We further discuss the challenges surrounding the implementation of such an ecosystem, specifically calling for applications of learning engineering approaches, the need of interoperability across systems and components, and the importance of ethical considerations.
An approach for optimizing multi-objective problems using hybrid genetic algorithms
Authors: Maghawry Ahmed , Hodhod Rania , Omar Yasser , Kholief Mohamed 
Published: July 2020

AbstractOptimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence.
The effects of mutant Ras proteins on the cell signalome
Authors: Takács Tamás , Kudlik Gyöngyi , Kurilla Anita , Szeder Bálint , Buday László , Vas Virag 
Published: July 2020

AbstractThe genetic alterations in cancer cells are tightly linked to signaling pathway dysregulation. Ras is a key molecule that controls several tumorigenesis-related processes, and mutations in RAS genes often lead to unbiased intensification of signaling networks that fuel cancer progression. In this article, we review recent studies that describe mutant Ras-regulated signaling routes and their cross-talk. In addition to the two main Ras-driven signaling pathways, i.e., the RAF/MEK/ERK and PI3K/AKT/mTOR pathways, we have also collected emerging data showing the importance of Ras in other signaling pathways, including the RAC/PAK, RalGDS/Ral, and PKC/PLC signaling pathways. Moreover, microRNA-regulated Ras-associated signaling pathways are also discussed to highlight the importance of Ras regulation in cancer. Finally, emerging data show that the signal alterations in specific cell types, such as cancer stem cells, could promote cancer development. Therefore, we also cover the up-to-date findings related to Ras-regulated signal transduction in cancer stem cells.
The Role of Mental Effort in Fostering Self-Regulated Learning with Problem-Solving Tasks
Authors: van Gog Tamara , Hoogerheide Vincent , van Harsel Milou 
Published: July 2020

AbstractProblem-solving tasks form the backbone of STEM (science, technology, engineering, and mathematics) curricula. Yet, how to improve self-monitoring and self-regulation when learning to solve problems has received relatively little attention in the self-regulated learning literature (as compared with, for instance, learning lists of items or learning from expository texts). Here, we review research on fostering self-regulated learning of problem-solving tasks, in which mental effort plays an important role. First, we review research showing that having students engage in effortful, generative learning activities while learning to solve problems can provide them with cues that help them improve self-monitoring and self-regulation at an item level (i.e., determining whether or not a certain type of problem needs further study/practice). Second, we turn to self-monitoring and self-regulation at the task sequence level (i.e., determining what an appropriate next problem-solving task would be given the current level of understanding/performance). We review research showing that teaching students to regulate their learning process by taking into account not only their performance but also their invested mental effort on a prior task when selecting a new task improves self-regulated learning outcomes (i.e., performance on a knowledge test in the domain of the study). Important directions for future research on the role of mental effort in (improving) self-monitoring and self-regulation at the item and task selection levels are discussed after the respective sections.
The transformation of professors’ careers: standardization, hybridization, and acceleration?
Authors: Benz Pierre , Bühlmann Felix , Mach André 
Published: July 2020

AbstractOver the recent decades, the turn to managerial governance has thoroughly transformed the European university systems and, by extension, the careers of university professors. Scholars postulated that professors’ careers have undergone an increasing formalization, that disciplinary careers have been hybridized, and that new modes of selection and recruitment (through assistant professorship) have been introduced. This article, based on a case study of the EPFL Lausanne, one of the leading European technical universities, aims to understand these changes by conducting a sequence analytical study of the actual trajectories of professors. By studying the careers of 351 professors who were nominated between 1969 and 2010 at the EPFL, we first develop a typology of professors’ careers. We distinguish between “direct careers,” “seniority careers,” “conversion careers,” and “parallel careers.” We then examine, based on a series of binomial logistic regression models, how career types vary according to the nomination cohort, the discipline, and the recruitment mode. Our results show that slower “seniority careers” within academia have become more important in recent cohorts, that disciplinary logics still shape professors’ careers, and that new recruitment mechanisms, such as assistant professorship, do not necessarily lead to accelerated careers. This article contributes to the literature by showing that beyond the analysis of the institutional setting of academic careers, it is also important to study their actual progression.
Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices
Authors: Togeiro de Alckmin Gustavo , Kooistra Lammert , Rawnsley Richard , Lucieer Arko 
Published: July 2020

AbstractPasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.
Lung Cancer Occurrence—Correlation with Serum Chromium Levels and Genotypes
Authors: Baszuk Piotr , Janasik Beata , Pietrzak Sandra , Marciniak Wojciech , Reszka Edyta , Białkowska Katarzyna , Jabłońska Ewa , Muszyńska Magdalena , Lesicka Monika , Derkacz Róża , Grodzki Tomasz , Wójcik Janusz , Wojtyś Małgorzata , Dębniak Tadeusz , Cybulski Cezary , Gronwald Jacek , Kubisa Bartosz , Wójcik Norbert , Pieróg Jarosław , Gajić Darko , Waloszczyk Piotr , Scott Rodney J. , Wąsowicz Wojciech , Jakubowska Anna , Lubiński Jan , Lener Marcin R. 
Published: July 2020

AbstractLung cancer is the leading cause of cancer-related death worldwide. Exposure to environmental and occupational carcinogens is an important cause of lung cancer. One of these substances is chromium, which is found ubiquitously across the planet. The International Agency for Research on Cancer has classified chromium(VI) as a human carcinogen. The aim of this study was to assess whether serum chromium levels, as well as DNA variants in selected genes involved in carcinogenesis, xenobiotic-metabolism, and oxidative stress could be helpful in the detection of lung cancer. We conducted a study using 218 lung cancer patients and 218 matched healthy controls. We measured serum chromium levels and genotyped ten genetic variants in ERCC2, XRCC1, MT1B, GSTP1, ABCB1, NQ01, CRTC3, GPX1, SOD2 and CAT. The odds ratios of being diagnosed with lung cancer were calculated using conditional logistic regression with respect to serum chromium level and genotypes. The odds ratio for the occurrence of lung cancer increased with increasing serum chromium levels. The difference between the quartiles with the lowest vs. highest chromium level was more than fourfold in the entire group (OR 4.52, CI 2.17–9.42, p < 0.01). This correlation was significantly increased by more than twice when specific genotypes were taken into consideration (ERCC–rs12181 TT, OR 12.34, CI 1.17–130.01, p = 0.04; CRTC3–rs12915189 non GG, OR 9.73, CI 1.58–60.10, p = 0.01; GSTP1–rs1695 non AA, OR 9.47, CI 2.06–43.49, p = < 0.01; CAT–rs1001179 non CC, OR 9.18, CI 1.64–51.24, p = 0.01). Total serum chromium levels > 0.1 μg/L were correlated with 73% (52/71) of lung cancers diagnosed with stage I disease. Our findings support the role of chromium and the influence of key proteins on lung cancer burden in the general population.
The risk of QTc-interval prolongation in COVID-19 patients treated with chloroquine
Authors: Sinkeler F. S. , Berger F. A. , Muntinga H. J. , Jansen M. M. P. M. 
Published: July 2020

Abstract
Background
Chloroquine, a quinolone antimalarial drug, is known to potentially inhibit pH-dependent viral replication of the SARS-CoV‑2 infection. Therefore, chloroquine is considered as a treatment option for coronavirus disease 2019 (COVID-19). Chloroquine is known for prolonging the QT interval, but limited data are available on the extent of this QT-prolonging effect.

Objective
To assess the QTc-prolonging potential of chloroquine in COVID-19 patients and to evaluate whether this prolongation increases with the cumulative dose of chloroquine and is associated with the peak plasma concentration of chloroquine. Furthermore, the number of patients who prematurely discontinued treatment or had an adjustment in dose due to QTc-interval prolongation was established.

Methods
A retrospective, observational study was performed in patients aged over 18 years, hospitalised for a suspected or proven infection with COVID-19, and therefore treated with chloroquine, with a baseline electrocardiogram (ECG) performed prior to the start of treatment and at least one ECG after starting the treatment.

Results
In total, 397 patients were included. The mean increase in QTc interval throughout the treatment with chloroquine was 33 ms. Nineteen out of 344 patients unnecessarily had their treatment prematurely discontinued or adjusted due to a prolonged QTc interval based on the computerised interpretation of the ECG.

Conclusion
Chloroquine treatment in COVID-19 patients gradually increased the QTc interval. Due to a significant number of overestimated QTc intervals by computer analysis, it is advisable to measure the QTc interval manually before adjusting the dose or withdrawing this potentially beneficial medication.
Optimal performance of Cu1.8S1−xTex thermoelectric materials fabricated via high-pressure process at room temperature
Authors: Zhang Rui , Pei Jun , Han Zhi-Jia , Wu Yin , Zhao Zhao , Zhang Bo-Ping 
Published: July 2020

AbstractCu1.8S has been considered as a potential thermoelectric (TE) material for its stable electrical and thermal properties, environmental benignity, and low cost. Herein, the TE properties of nanostructured Cu1.8S1−xTex (0 ⩽ x ⩽ 0.2) bulks fabricated by a facile process combining mechanical alloying (MA) and room-temperature high-pressure sintering (RT-HPS) technique were optimized via eliminating the volatilization of S element and suppressing grain growth. Experimentally, a single phase of Cu1.8S was obtained at x = 0, and a second Cu1.96S phase formed in all Cu1.8S1−xTex samples when 0.05 ⩽ x ⩽ 0.125. With further increasing x to 0.15 ⩽ x ⩽ 0.2, the Cu2−zTe phase was detected and the samples consisted of Cu1.8S, Cu1.96S, and Cu2−zTe phases. Benefiting from a modified band structure and the coexisted phases of Cu1.96S and Cu2−zTe, the power factor is enhanced in all Cu1.8S1−xTex (0.05 ⩽ x ⩽ 0.2) alloys. Combining with a drastic decrease in the thermal conductivity due to the strengthened phonon scatterings from multiscale defects introduced by Te doping and nano-grain boundaries, a maximum figure of merit (ZT) of 0.352 is reached at 623 K for Cu1.8S0.875Te0.125, which is 171% higher than that of Cu1.8S (0.130). The study demonstrates that doping Te is an effective strategy to improve the TE performance of Cu1.8S based materials and the proposed facile method combing MA and RT-HPS is a potential way to fabricate nanostructured bulks.
A kind of artificial intelligence model based on nature without statistic
Authors: Meng Jun , Yu Jie-zhou , Chen Xiao 
Published: July 2020

AbstractSimple rules can generate complexity and human can also learn fast with rules. Can machines learn in a similar way? Can artificial intelligence be independent of statistics? Machine learning is growing rapidly but models are poorly interpretable and depend on statistics. We propose a method by iteration based on causality which is the real one exists in the system. It is composed of fixed goals and basic rules called DNA rules. These DNA rules can be obtained from the definition and is not statistical rules. The causality in rules promises the process to be precise, because the potential attractor of the system is deterministic, because it is subjected to the rules although the system is complex especially under uncertain interference. Such a model not only works well in the traditional deterministic systems like the stable point and limited circle but also can work in some seemingly random and systems which are considered to be stochastic systems. The model is taken to play a game and it makes the machine learns fast and adaptively, and it is also interpretable with the causality and independent from the amount of data for it is based on causal iteration. It learns and even predicts the seemingly random interference in the game. We found such a model is adaptable, and it works well even in out-of-sample situations. The model is compared with an LSTM network in prediction a seemingly random sequence, the result shows the causality-based model also works well. We think that it may help to solve some problems hard for the traditional statistical method and become an enrichment for the current models.
The realities of scaling within evidence-based policy
Authors: SUPPLEE LAUREN H. , KANE MAGGIE C. 
Published: July 2020

AbstractThe economic model for scaling described by Al-Ubaydli and colleagues offers recommendations to policymakers who make decisions about whether or not to implement evidence-based programs. The core economic model does not currently acknowledge the broader context of policy decision-making and therefore may fail to achieve its objectives. The model focuses primarily on the generation and use of available research in the decision on whether to scale a program. Research studying the use of evidence in policymaking points to a complex set of factors beyond just the strength of the evidence such as leadership, relationships, timing and financial resources that contribute to decisions to scale a program. Second, there is already a robust evidence-based policy movement at the federal, state and local levels. The economic model should leverage this movement rather than providing recommendations that might stall or redirect the movement. The economic model can push the field to strengthen the available evidence while providing recommendations on selecting models to scale within the currently available evidence. This commentary finishes with suggestions for moving forward.
Fluvial incision caused irreversible environmental degradation of an ancient city in the Mu Us Desert, China
Authors: Wen Penghui , Wang Nai'ang , Wang Yixin , Huang Yinzhou , Cheng Hongyi , He Tonghui 
Published: July 2020

AbstractAncient cities are excellent spatiotemporal indicators for the study of historical human activities and environmental change. The ancient city of Sanchahe is located at the southern margin of the Mu Us Desert, China. It is an ideal location for studying the complex relationships between historical desertification and human activities. Field observations of the ancient city walls, a well, and a spring, as well as 14C dating, grain size, spatial analysis of archaeological sites, and analyses of historical seismicity, indicated that neotectonics may have contributed to crustal uplift and accelerated fluvial incision of the Wuding River. This rapid incision caused a decline in groundwater levels, which is an important reason for the irreversible environmental degradation around Sanchahe city over the past 800 yr. This study provides new evidence for such environmental degradation and may contribute to a better understanding of historical desertification in the Mu Us Desert.
That Woman from Michigan: How Gender Resentment Shapes the Efficacy of Stay-at-Home Policies
Authors: Kalaf-Hughes Nicole , Leiter Debra 
Published: July 2020

AbstractThe United States has the highest number of COVID-19 cases, yet many Americans have responded indifferently toward policies designed to combat the spread of the virus. While nearly all 50 states have implemented some type of stay-at-home policy to encourage social distancing, there has been high variation in the degree of compliance. We argue that this variance is partly driven by gender resentment. Gender resentment reduces trust in female political leaders and thus decreases compliance with government policy and recommendations. Using data from SafeGraph and the 2016 American National Election Study, we demonstrate that the effect of stay-at-home policies on social distancing is reduced when gender resentment increases in states with female leaders. However, when gender resentment is low, there is no difference in the effect of policies on behavior. This research has important implications for understanding unseen barriers that can mediate the efficacy of female political leaders.
Public Opinion, Crisis, and Vulnerable Populations: The Case of Title IX and COVID-19
Authors: Druckman James N. , Sharrow Elizabeth A. 
Published: July 2020

AbstractA central function of democratic institutions is to protect vulnerable populations. The stability and success of these institutions depends, in part, on popular support. Times of crisis can introduce novel dynamics that alter popular support for protective institutions, particularly among those who do not benefit from those protections. We explore this possibility in the context of Title IX's gender equality requirements and infrastructure to address sexual harassment in college sports. We conduct a large survey of college student-athletes to study their attitudes on these issues in response to the COVID-19 pandemic and concomitant financial challenges affecting college sports. We find that male student-athletes and those with sexist attitudes exhibit alarmingly low levels of support for ensuring the maintenance of equality and sexual harassment policy under Title IX during the COVID-19 crisis and eventual recovery. The results accentuate the vulnerability of certain populations during crises and the importance of maintaining strong institutional policy support during such times.