Arizona State University Library, Holding: Kinh tế Việt Nam - Thăng trầm và đột phá

Arizona State University Library, Holding: Kinh tế Việt Nam - Thăng trầm và đột phá (published by Hanoi-based Political Publishing House, Vietnam).

Police Use of Force Escalation and De-escalation: The Use of Systematic Social Observation With Video Footage

As police agencies continue to incorporate body-worn cameras, it becomes increasingly important for researchers and practitioners to explore how to best use these data to better understand patterns of suspect and police behavior. Thus, drawing on a joint project between the Federal Law Enforcement Training Centers and Arizona State University, we expand on prior research demonstrating how social systematic observation (SSO) can be used with video footage to methodically detail the evolving nature of police-suspect encounters. We then illustrate how the data could be evaluated within the framework of escalation and de-escalation using an expanded version of the Resistance Force Comparative Scale (RFCS) first developed and employed in 2001. Finally, we assess the merits and challenges of using video footage to account for suspect and police behaviors in relation to escalation and de-escalation.

“I’ve Never Had to Do This on My Own”

Receiving an acceptance letter to college can be both exciting and daunting for many students. For some, attending college has been an expectation since birth, but for others they will be the first in their family to attend. This chapter identifies ways to support emerging adults, including advising, mentoring, and coaching as they transition to the college campus and remain to complete their studies. Additional information on the challenges faced by first-generation college students is provided as well. The case study describes the First-Year Success (FYS) Center at Arizona State University. Guiding questions help readers consider how they might increase student retention and success at their own institutions.

Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model

A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a MOOC. Different features and various approaches are available for the prediction of student dropout in MOOC courses. In this paper, the data derived from a self-paced math course, College Algebra and Problem Solving, offered on the MOOC platform Open edX partnering with Arizona State University (ASU) from 2016 to 2020 is considered. This paper presents a model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress. The Random Forest Model technique in Machine Learning (ML) is used in the prediction and is evaluated using validation metrics including accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) curve. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5%, respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model.

Arizona State University: A Learning Enterprise Supporting P-12 Education in the COVID-19 Pandemic

Although no one was fully prepared for a pandemic in 2020, Arizona State University was ready and able to respond. As the COVID-19 virus began to spread across the world, ASU began moving university students to remote learning environments. As K-12 schools began to close across the nation, ASU disseminated elementary- and secondary-level educational resources and created hybrid learning opportunities for students of all ages.Three distinct categories of action defined the university’s response to COVID-19: Direct provision of education to P-12 learners Provision of human and intellectual capital (people and ideas) to P-12 schools Curation and provision of free educational resources to learners, families, and schools Many of the existing long-term commitments being pursued by various ASU units had helped the university develop capabilities that could immediately be applied to help elementary and secondary learners and the education professionals serving them during the pandemic. Some of the immediate responses accelerated the university’s efforts to pursue long-term actions that could help both schools and P-12 students and families integrate remote learning and instruction into effective education models.ASU responded rapidly to the educational challenges COVID-19 presented because the university had a high degree of institutional readiness in at least three key areas of operational excellence and organizational culture: A core set of preexisting commitments and functional capabilities in the area of technology-enhanced instruction Strong existing partnerships with P-12 schools An institutional vision to universal learning that demands a university be ready and able to deliver instruction to all learners across many modalities This chapter only represents a moment in time, the beginning of the pandemic, and the actions taken to support and ameliorate consequences. Since this time, much has happened.

The Emirates Mars Mission (EMM) Emirates Mars InfraRed Spectrometer (EMIRS) Instrument

The Emirates Mars Mission Emirates Mars Infrared Spectrometer (EMIRS) will provide remote measurements of the martian surface and lower atmosphere in order to better characterize the geographic and diurnal variability of key constituents (water ice, water vapor, and dust) along with temperature profiles on sub-seasonal timescales. EMIRS is a FTIR spectrometer covering the range from 6.0-100+ μm (1666-100 cm−1) with a spectral sampling as high as 5 cm−1 and a 5.4-mrad IFOV and a 32.5×32.5 mrad FOV. The EMIRS optical path includes a flat 45° pointing mirror to enable one degree of freedom and has a +/- 60° clear aperture around the nadir position which is fed to a 17.78-cm diameter Cassegrain telescope. The collected light is then fed to a flat-plate based Michelson moving mirror mounted on a dual linear voice-coil motor assembly. An array of deuterated L-alanine doped triglycine sulfate (DLaTGS) pyroelectric detectors are used to sample the interferogram every 2 or 4 seconds (depending on the spectral sampling selected). A single 0.846 μm laser diode is used in a metrology interferometer to provide interferometer positional control, sampled at 40 kHz (controlled at 5 kHz) and infrared signal sampled at 625 Hz. The EMIRS beamsplitter is a 60-mm diameter, 1-mm thick 1-arcsecond wedged chemical vapor deposited diamond with an antireflection microstructure to minimize first surface reflection. EMIRS relies on an instrumented internal v-groove blackbody target for a full-aperture radiometric calibration. The radiometric precision of a single spectrum (in 5 cm−1 mode) is <3.0×10−8 W cm−2 sr−1/cm−1 between 300 and 1350 cm−1 over instrument operational temperatures (<∼0.5 K NE$\Delta $ Δ T @ 250 K). The absolute integrated radiance error is < 2% for scene temperatures ranging from 200-340 K. The overall EMIRS envelope size is 52.9×37.5×34.6 cm and the mass is 14.72 kg including the interface adapter plate. The average operational power consumption is 22.2 W, and the standby power consumption is 18.6 W with a 5.7 W thermostatically limited, always-on operational heater. EMIRS was developed by Arizona State University and Northern Arizona University in collaboration with the Mohammed bin Rashid Space Centre with Arizona Space Technologies developing the electronics. EMIRS was integrated, tested and radiometrically calibrated at Arizona State University, Tempe, AZ.

Biocolonialism and Informed Consent. The Havasupai Case

"A core value in research ethics is respect for individual’s autonomy. For the researcher the way to respect this value is to guarantee informed consent to the participants. Nowadays more and more research is conducted on biological material of human origin rather than on humans. But apart from that, informed consent must be guaranteed. The case I would like to present and comment is the Havasupai case. Havasupai are an indigenous tribe lived in Grand Canyon. Due to the high percentage of diabetes type 2 among members of the tribe, the tribe decided to take part in the research conducted by the researchers from Arizona State University. In this case were a few vague aspects, which I will present during the speech, but one thing I especially notice. In the scientific community there was a great interest in obtaining Havasupai blood samples. It was caused by the fact that Havasupai are an indigenous people and they do not start a family with people out of them tribe. Because of that their DNA is scientifically more interesting than the DNA of people out of the tribe. That approach is called biocolonialism. In the past indigenous people were used because of the sources that they had on their lands. Now their DNA is a scientifically valuable source of information. Moreover, in literature it is said that researchers – the new (bio)colonizers – are conducting a “helicopter research”. They came up, took what they want and disappear. In my speech I would like to analyze problems raised in Havasupai case and present what the biocolonialism means in and for research. "

Perspective Developing Successful Collaborative Research Partnerships with AI/AN Communities

In the United States, American Indian and Alaska Native (AI/AN) people are frequently under- or misrepresented in research and health statistics. A principal reason for this disparity is the lack of collaborative partnerships between researchers and tribes. There are hesitations from both academic Western scientists and tribal communities to establish new partnerships due to differences in cultural and scientific understanding, from data ownership and privacy to dissemination and project expansion. An infamous example is the mishandling of samples collected from the Havasupai Tribe by Arizona State University (ASU) scientists, leading to a legal battle between the tribe and ASU and ending in a moratorium of research with the Havasupai people. This paper will explore three successful and positive collaborations with a large and small tribe, including how the partnerships were established and the outcomes of the collaboration. In addition, the paper will provide perspective of what needs to be addressed by Western scientists if productive collaborations with tribal groups are to be established.

Special Issue on PHM for Human Health and Performance II

This special issue was conceived during the 11th Annual Conference of Prognostic and Health Management Society’s Panel session on the September 25th at Scottsdale, AZ, USA. We would like to thank the panel members and their colleagues in their participation in this special issue focusing on engineered technologies for older adults. This work was partially funded by the NSF ERC seed grant from an interdisciplinary group of researchers from Iowa State University, Arizona State University, Georgia Tech, Florida State University, Chapman University and the University of California Irvine who are engaged in developing a large-scale grant proposal that will be focused on integrated technologies to promote resilient aging and reducing healthcare costs.The manuscripts exemplify our research focus and illustrates contributions in the fields of wearable smart sensors, sensor-data-fusion, machine learning and data mining, prediction and diagnosis, and electronic health records and databases - all in the context of prognostics and health management for human health and performance.We would like to thank the PHM Society for providing an opportunity to publish in their premier journal, and importantly, we are grateful for help of the Editor-in-Chief – Marcos Orchard, Ph.D. for his countless hours to edit and make it best possible of this special issue. Finally, we would like to express sincere appreciation to all the reviewers who have contributed their time and thoughtful feedback to making this special issue publication a success.

The Impact of dental wear on the analysis of morphological affinities based on dental non-metric traits

Dental wear is described as a limitation to dental morphological studies, as it obscures important crown trait features, resulting in significant differences on trait frequencies, an essential component for estimating biodistances. However, the actual impact of dental wear on biological distances still requires further characterization. We explore the impact of dental wear on morphological affinities for Brazilian pre-colonial series in the context of worldwide reference series. Twenty crown traits were scored using the Arizona State University Dental Anthropological System, and dental wear was quantified as an ordinal scale between 1 (no wear) and 8 (crown eroded). Seven crown trait frequencies are significantly associated with dental wear (p<0.05), demonstrating its impact on their analysis. To explore this impact on biodistances, data was divided by wear categories (all teeth, low-wear, moderate/severe wear) and morphological affinities among series was compared through Euclidean distances, Mean Measure of Divergence, and Principal Component Analysis. Results show the impact of wear is only meaningful when a sample contains many wear-biased traits with only moderate/severe wear. We conclude despite the impact of wear on individual trait frequencies, its impact on morphological affinities can be mitigated by including other variables or when comparisons focus only on large-scale biological differences.