CE Review

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‘I just don’t ever use that word’: investigating stakeholders’ understanding of heritage
(Taylor & Francis, 2017) Kryder-Reid, Elizabeth; Foutz, Jeremy W.; Wood, Elizabeth; Zimmerman, Larry J.; Anthropology, School of Liberal Arts
Understanding the value of heritage sites for diverse stakeholders requires both paying attention to the fields of power in which the sites operate and applying methodologies that are open to user-defined paradigms of value. In the U.S., official discourse often frames the value of heritage sites associated the deep Native American past as archaeological sites, an interpretation that is consistent with settler colonial ideologies. This narrative generally obfuscates connections between the heritage of the sites and contemporary peoples, and it effaces the history of colonialism and dispossession. A study of stakeholder-defined heritage at two contested sites in the central Midwest revealed both congruencies and conflicts among diverse constituencies’ articulations of the sites’ value. At Mounds State Park a proposed dam and reservoir ‘Mounds Lake’ project would inundate a large portion of the site. At Strawtown Koteewi, Native American tribes have made repatriation claims under the federal Native American Graves Protection and Repatriation Act (NAGPRA).The study also problematised the term ‘cultural heritage’ as it is understood and used by the different constituencies, particularly for culturally and historically affiliated Native Americans. It also highlighted the positions of the constituencies within the broader fields of power implicated in these contested sites.
A Model for Providing Free Patient Care and Integrating Student Learning and Professional Development in an Interprofessional Student-Led Clinic
(Wolters Kluwer, 2017) George, Lydia; Bemenderfer, Sara; Cappel, Maggie; Goncalves, Kathryn; Hornstein, Micaela; Savage, Chelsea; Altenburger, Peter; Bellew, James; Loghmani, Terry; Physical Therapy, School of Health and Rehabilitation Sciences
Background and Purpose. The need to reduce the barriers of access and affordability in health care is evident. The Indiana University Student Outreach Clinic (IU SOC) is a community-based, pro bono, interprofessional, student-led clinic dedicated to removing barriers to health care. The purpose of this report is to describe the implementation approach, sustainability efforts, and initial outcomes of this community-based physical therapy clinic model with the aim of making it transparent for others to replicate. Method/Model Description and Evaluation. An overview of the IU SOC, implementation and sustainability of the physical therapy clinic model, and student learning opportunities are described. Keys to successful implementation are enumerated. Learning opportunities include: clinical competency, professional values, civic engagement, interprofessional education and collaborative practice (IPECP), peer mentorship, and leadership development. Outcomes. Preliminary clinic and learning opportunity outcomes collected from patient databases and student surveys and reflections suggest the IU SOC is having a positive impact on the community it serves by providing care patients would otherwise have not received, while simultaneously supporting learning. Patient volume and student participation are expanding. Initial outcomes suggest this model is valuable for the professional growth of future physical therapists. Discussion and Conclusion. The impact of this clinic model on the community and students is just beginning to be realized and understood. Key elements of success are that it's (a) community-based, (b) interprofessional, and (c) highly collaborative. Free, student-led, interprofessional health care clinics may offer an important means for improving health care access while simultaneously preparing entrylevel professionals for practice.
We Are All Teachers: Modeling Democratic Engagement in Faculty Development
(2017) Studer, Morgan; Benton, Melissa; Rogers, Christian; Quirke, Michelle; Computer Information and Graphics Technology, School of Engineering and Technology
Model-Based Adaptive Fault Diagnosis in Lithium Ion Batteries: A Comparison of Linear and Nonlinear Approaches
(SAE, 2017) Sidhu, Amardeep; Izadian, Afshin; Anwar, Sohel; Mechanical Engineering, School of Engineering and Technology
In this paper, multiple-model adaptive estimation techniques have been successfully applied to fault detection and identification in lithium-ion batteries. The diagnostic performance of a battery depends greatly on the modeling technique used in representing the system and the associated faults under investigation. Here, both linear and non-linear battery modeling techniques are evaluated and the effects of battery model and noise estimation on the over-charge and over-discharge fault diagnosis performance are studied. Based on the experimental data obtained under the same fault scenarios for a single cell, the non-linear model based detection method is found to perform much better in accurately detecting the faults in real time when compared to those using linear model based method.
Classroom Discussions as Distortions: Examining Discriminatory Teacher Practices
(Sage, 2017-03) Sosa, Teresa; Bhathena, Catherine D.; School of Education
Ms. Mendez, English Department chair in a large urban high school, has noticed a persistent pattern in the practices of her colleagues. These practices tend to be racially insensitive and emphasize a noncritical view that does not attend to students’ experiences and positions students from a deficit perspective. Realizing that such practices serve as social reproductions of racist and classist orientations that reproduce the existing social order, Ms. Mendez decided school leadership should be informed. However, she worries that the school’s leadership will not work to enact change and instead will take her concerns lightly.
Imagining Conformity: Consumption and Homogeneity in the Postwar African American Suburbs
(Springer, 2017-03) Mullins, Paul R.; Anthropology, School of Liberal Arts
In the wake of World War II many urbanites left cities for a suburban life that has been persistently derided for its apparent social, material, and class homogeneity. This paper examines the African American experience of post–World War II suburbanization and the attractions of suburban life for African America. The paper examines two suburban projects in Indianapolis, Indiana, one a “sweat equity” housing community and the other a subdivision, both of which placed consumption at the heart of postwar citizenship. Rather than frame such consumption simply in terms of resistance to anti-Black racism, the two suburban experiences illuminate the African American imagination of visual and material “sameness” and demonstrate the challenges of archaeological studies of ethnicity and stylistic distinction.
Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm
(SAE, 2017-03) Dheekonda, Raja Sekhar Rao; Panda, Sampad K.; Khan, Nazmuzzaman; Al-Hasan, Mohammad; Anwar, Sohel; Mechanical Engineering, School of Engineering and Technology
Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object’s description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms. In this paper, we present preliminary results using only camera images for detecting various objects using deep learning network, as a first step toward multi-sensor fusion algorithm development. The simulation experiments based on camera images show encouraging results where the proposed deep learning network based detection algorithm was able to detect various objects with certain degree of confidence. A laboratory experimental setup is being commissioned where three different types of sensors, a digital camera with 8 megapixel resolution, a LIDAR with 40m range, and ultrasonic distance transducer sensors will be used for multi-sensor fusion to identify the object in real-time.
Adolescent Gambling: An Update on Research Since 2010
(Elsevier, 2017-05) Blinn-Pike, Lynn; Sociology, School of Liberal Arts
Case studies and evidence-based approaches to addressing urban soil lead contamination
(Elsevier, 2017-08) Laidlaw, Mark A. S.; Filippelli, Gabriel M.; Brown, Sally; Paz-Ferreiro, Jorge; Reichman, Suzie M.; Netherway, Pacian; Truskewycz, Adam; Ball, Andrew S.; Mielke, Howard W.; Earth Science, School of Science
Urban soils in many communities in the United States and internationally have been contaminated by lead (Pb) from past use of lead additives in gasoline, deterioration of exterior paint, emissions from Pb smelters and battery recycling and other industries. Exposure to Pb in soil and related dust is widespread in many inner city areas. Up to 20–40% of urban children in some neighborhoods have blood lead levels (BLLs) equal to or above 5 μg per decilitre, the reference level of health concern by the U.S. Centers for Disease Control. Given the widespread nature of Pb contamination in urban soils it has proven a challenge to reduce exposure. In order to prevent this exposure, an evidence-based approach is required to isolate or remediate the soils and prevent children and adult's ongoing exposure. To date, the majority of community soil Pb remediation efforts have been focused in mining towns or in discrete neighborhoods where Pb smelters have impacted communities. These efforts have usually entailed very expensive dig and dump soil Pb remediation techniques, funded by the point source polluters. Remediating widespread non-point source urban soil contamination using this approach is neither economical nor feasible from a practical standpoint. Despite the need to remediate/isolate urban soils in inner city areas, no deliberate, large scale, cost effective Pb remediation schemes have been implemented to isolate inner city soils impacted from sources other than mines and smelters. However, a city-wide natural experiment of flooding in New Orleans by Hurricane Katrina demonstrated that declines in soil Pb resulted in major BLL reductions. Also a growing body of literature of smaller scale pilot studies and programs does exist regarding low cost efforts to isolate Pb contaminated urban soils. This paper reviews the literature regarding the effectiveness of soil Pb remediation for reducing Pb exposure and BLL in children, and suggests best practices for addressing the epidemics of low-level Pb poisoning occurring in many inner city areas.
Electrochemical Model-Based Condition Monitoring via Experimentally Identified Li-Ion Battery Model and HPPC
(MDPI, 2017-08-25) Rahman, Md Ashiqur; Anwar, Sohel; Izadian, Afshin; Mechanical Engineering, School of Engineering and Technology
Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.

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