Miracle-dz/UniFAR-FTUs huggingface dataset:
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF THESIS APPROVAL

Balasubramanian Linknath Surya. M.S.E.C. E. , Purdue University, May 2020. ASICImplemented MicroBlaze-based Coprocessor for Data Stream Management Systems. Major Professor: John J. Lee. The drastic increase in Internet usage demands the need for processing data in real time with higher efficiency than ever before. Symbiote Coprocessor Unit (SCU), developed by Dr. Pranav Vaidya, is a hardware accelerator which has potential of providing data processing speedup of up to 150x compared with traditional data stream processors. However, SCU implementation is very complex, fixed, and uses an outdated host interface, which limits future improvement. Mr. Tareq S. Alqaisi, an MSECE graduate from IUPUI worked on curbing these limitations. In his architecture, he used a Xilinx MicroBlaze microcontroller to reduce the complexity of SCU along with few other modifications. The objective of this study is to make SCU suitable for mass production while reducing its power consumption and delay. To accomplish this, the execution unit of SCU has been implemented in application specific integrated circuit and modules such as ACG/OCG, sequential comparator, and D-word multiplier/divider are integrated into the design. Furthermore, techniques such as operand isolation, buffer insertion, cell swapping, and cell resizing are also integrated into the system. As a result, the new design attains 67.9435 μW of dynamic power as compared to 74.0012 μW before power optimization along with a small increase in static power, 39.47 ns of clock period as opposed to 52.26 ns before time optimization.
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF COMMITTE APPROVAL

Zhou, Shuyi. M.S.M.E. , Purdue University, December 2017. Experimental and Numerical Study on Microbubble Coalescence Dynamics. Major Professor: Likun Zhu. This thesis work aims to make a better knowledge on the insights of physics on microbubble coalescence process, using experimental and numerical approaches. The neck growth and bubble surface wave propagation at the early stage of coalescence, merging preference, as well as a reaction-channel modified microfluidic gas generator are presented in the thesis. Coalescence of unequal-sized microbubbles captive on solid substrate was observed from cross-section view using synchrotron high speed imaging technique and a microfluidic gas generation device. The bridging neck growth and surface wave propagation at the early stage of coalescence were investigated by experimental and numerical methods. The results show that theoretical half power law of neck growth rate is still valid when viscous effect is neglected. However, the inertial-capillary time scale is based on the radius of smaller parent microbubble. The surface wave propagation rate is proportional to the inertial-capillary time scale, which is based on the radius of larger parent microbubble of a coalescence pair. Meanwhile, the relationship of preference distance and size inequality microbubbles were studied using the same micrfluidic gas generator and observation facilities. The size inequality of parent microbubbles would affect the preference distance of merged bubble in between. The merged bubble gets less closer to the larger parent bubble with an exponent of 1.82 as a reference, which largely affected by shear stress begotten on the solid interface. To express this phenomenon distinguished with free merging bubble pair, we propose the wall shear stress hinders the process of that
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF THESIS APPROVAL

Kattakuri, Vikranth R. MSME, Purdue University, August 2019. Failures in Spacecraft Systems: An Analysis from the Perspective of Decision Making. Major Professor: Jitesh H. Panchal. Space mission-related projects are demanding and risky undertakings because of their complexity and cost. Many missions have failed over the years due to anomalies in either the launch vehicle or the spacecraft. Projects of such magnitude with undetected flaws due to ineffective process controls run into unwarranted cost, schedule overruns and account for huge losses. Such failures continue to occur despite the studies on systems engineering process deficiencies and the best systems engineering practices in place. To understand the reasons behind such failures, this work analyses some of the major contributing factors behind majority of space mission technical failures. To achieve this objective, we analyzed the failure data of space missions that happened over the last decade. Based on that information, we analyzed the launch-related failure events from a design decision-making perspective by employing failure event chain-based framework. By analyzing the failure events with this framework, we identify some dominant cognitive biases that might have impacted the overall system performance leading to unintended catastrophes. The ability of any design team to achieve optimal performance is limited by communication and knowledge deficiencies between highly dissimilar subsystems. These inefficiencies work to bias each subsystem engineer to prioritize the utility provided by the subsystem they are responsible for. In order to understand how engineering design decisions are influenced by the presence of cognitive biases, the second part of this study establishes a mathematical framework for utility-based selection based on
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF THESIS APPROVAL

Molnar, Thomas W. MS, Purdue University, May 2020. Approaches for Efficient Autonomous Exploration using Deep Reinforcement Learning. Major Professor: Eugenio Culurciello. For autonomous exploration of complex and unknown environments, existing Deep Reinforcement Learning (Deep RL) approaches struggle to generalize from computer simulations to real world instances. Deep RL methods typically exhibit low sample efficiency, requiring a large amount of data to develop an optimal policy function for governing an agent’s behavior. RL agents expect well-shaped and frequent rewards to receive feedback for updating policies. Yet in real world instances, rewards and feedback tend to be infrequent and sparse. For sparse reward environments, an intrinsic reward generator can be utilized to facilitate progression towards an optimal policy function. The proposed Augmented Curiosity Modules (ACMs) extend the Intrinsic Curiosity Module (ICM) by Pathak et al. These modules utilize depth image and optical flow predictions with intrinsic rewards to improve sample efficiency. Additionally, the proposed Capsules Exploration Module (Caps-EM) pairs a Capsule Network, rather than a Convolutional Neural Network, architecture with an A2C algorithm. This provides a more compact architecture without need for intrinsic rewards, which the ICM and ACMs require. Tested using ViZDoom for experimentation in visually rich and sparse feature scenarios, both the Depth-Augmented Curiosity Module (D-ACM) and Caps-EM improve autonomous exploration performance and sample efficiency over the ICM. The CapsEM is superior, using 44% and 83% fewer trainable network parameters than the ICM and D-ACM, respectively. On average across all “My Way Home” scenarios, the
THE PURDUE UNIVERSITY GRADUATE SCHOOL STATEMENT OF COMMITTEE APPROVAL

Xie, Linhui. M.S.E.C. E. , Purdue University, August 2018. Heritability Estimation of Reliable Connectome Features. Major Professors: Paul Salama and Li Shen. Brain imaging genetics is an emerging research field aimed at studying the underlying genetic architecture of brain structure and function by utilizing different imaging modalities. However, not all the changes in the brain are a direct result of genetic effect. Furthermore, the imaging phenotypes are promising for genetic analyses are usually unknown. In this thesis, we focus on identifying highly heritable measures of structural brain networks derived from Diffusion Weighted Magnetic Resonance imaging data. Using data for twins that is made available by the Human Connectome Project (HCP), the reliability of edge-level measures, namely fractional anisotropy, fiber length, and fiber number in the structural connectome as well as seven network-level measures, specifically assortativity coefficient, local efficiency, modularity, transitivity, cluster coefficient, global efficiency, and characteristic path length were evaluated using intraclass correlation coefficients. In addition, estimates of the heritability of the reliable measures were also obtained. It was observed that across all 64, 620 network edges between 360 brain regions in the Glasser parcellation, approximately 5% were significantly high heritability based on fractional anisotropy, fiber length, or fiber number. Moreover, all tested network level measures, that capture network integrity, segregation, or resilience, were found to be highly heritable, having a variance ranging from 59% to 77% that is attributable to an additive genetic effect.
Probiotics improve glucose and lipid metabolism in pregnant women: a meta-analysis.

Background This study aims to assess the effects of probiotic supplementation on the maternal metabolism and the risk of development of gestational diabetes mellitus (GDM) in the pregnant women by a meta-analysis of relevant randomized controlled trials (RCTs). Methods The medical literature was searched from PubMed, Web of Science and the Cochrane Library since inception to October 2017. Two investigators independently performed the data extraction and quality assessment. The mean differences (MD) or standardized mean differences (SMD) or relative risk (RR) with 95% confidence intervals (CIs) were calculated with the random-effects model. Results From 648 citations, a total of ten RCTs published in 13 articles with 1,139 participants met the inclusion criteria. The meta-analysis showed that probiotics supplementation effectively reduced the fasting blood glucose (FBG) levels (MD -0.11 mmol/L, P=0.0003), serum insulin levels (MD -2.06 µU/mL, P<0.00001), insulin resistance (HOMA-IR) (MD -0.38, P<0.00001). The study found a significant effect of probiotics on decreasing the risk of GDM [risk ratio (RR) 0.52, P=0.003) in early pregnancy. Additionally, there were statistically significant reductions in the total cholesterol and triglycerides levels after probiotic interventions (SMD -0.56, P=0.03; SMD -0.66, P=0.04), respectively. Conclusions Our study shows that the probiotic use was associated with improved glucose and lipid metabolism in the pregnant women, and might also contribute to the reduced risk of GDM.
Energy Efficient Wireless Sensor Networks based on QoS Enhanced Base Station controlled Dynamic Clustering Protocol

Recent development in Micro Electro Mechanical System (MEMS) technology leads to the development of low cost and low power utilizing wireless micro sensor nodes. Wireless sensor network is a power consuming scheme, because nodes executes on restricted power batteries which reduces its lifetime. Extending the network lifetime is based on the better managing of sensing node energy resource. Energy efficient routing protocol is very essential technique in wireless sensor networks because sensor nodes are extremely energy based. As a result, several researchers have provided different routing protocol for sensor networks, particularly routing protocols depending on clusters protocols. This is because the usage of cluster based routing has several merits like minimized control messages, re-usability of bandwidth and enhanced power control. Different cluster based routing protocol is proposed by many researchers for the purpose of reducing the consumption energy in wireless sensor networks. Those technique reduces the energy consumption but with several disadvantages like lack of QoS, inefficient transmission, etc., To overcome those problems, modified QoS enhanced base station controlled dynamic clustering protocol for wireless sensor networks is proposed in this paper. Simulation results show the better energy consumption is achieved by the proposed protocol when compared to the conventional techniques. Index terms Wireless Sensor Network, Clustering protocol, QoS
Gene coexpression networks analysis of sickle stroke risk

Stroke is one of the most destructive complications of sickle cell disease (SCD), and SCD is also the most common cause of childhood stroke. Sickle cell stroke is complex and has a genetic endothelial basis. Here, we further investigated this genetic basis using weighted gene coexpression network analysis. This systems biology approach revealed the correlation between coexpressed gene modules and sickle stroke risk. The pink module was significantly correlated with stroke risk and genes in this module were mainly related to GO:0044877 (protein‐containing complex binding). In addition hub genes were identified through protein‐protein interaction enrichment analysis, including CXCR7, VCAM1, CD44, BMP2, SMAD3, BCL2L1, ITPR2, ITPR3, etc. These hub genes were significantly enriched for three Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways including “gastric acid secretion,” “pathways in cancer,” and “TGF‐ β signaling pathway. ” Altogether, our results based on this innovative method provided some novel understanding of the pathology of sickle cell stroke. Hub genes identified in this study could be potential targets for screening and prevention of stroke risk in SCD children.
Research and policy priorities for addressing prenatal exposure to opioids in Alaska

ABSTRACT The current opioid crisis in Alaska and the USA will negatively affect the health and wellbeing of future generations. The increasing number of infants born with neonatal opioid withdrawal syndrome (NOWS) has had a profound impact on families, health care providers and the child welfare system. This manuscript summarises the main themes of a Symposium held in Anchorage, Alaska with health care providers, researchers, elders and public health officials that focused on identifying emerging challenges, trends and potential solutions to address the increasing number of infants and children affected by maternal opioid use. Five areas of importance for research and policy development that would direct improvement in the care of infants with NOWS in Alaska are outlined with the goal of supporting a research agenda on opioid misuse and child health across the circumpolar north. Abbreviations: NOWS - neonatal opioid withdrawal syndrome; NAS - neonatal abstinence syndrome; MAT - medication-assisted treatment; NICU - neonatal intensive care unit; OATs - opioid agonist treatments; OCS - office of children's services; ANTHC - Alaska Native Tribal Health Consortium; OUD - opioid use disorder; SBIRT - screening, brief intervention and referral to treatment; ISPCTN - IDeA States Pediatric Clinical Trials Network; NIH - National Institutes of Health; ANMC - Alaska Native Medical Center; DHSS - Department of Health and Social Services; AAPP - All Alaska Pediatric Partnership
Designing for High-Voltage dc Grid Protection: Fault Clearing Strategies and Protection Algorithms

Protecting high-voltage (HV) DC grids requires a different approach compared with that of ac power system protection and poses one of the major challenges that must be resolved before the realization of largescale HVdc grids that use equipment from multiple vendors. HVdc grid protection, which is essential for safe and reliable HVdc grid operation, entails the appropriate detection and fault clearing of dc-side short circuit faults (i.e., dc faults). In this context, fault clearing refers to interrupting the dc fault current and isolating the faulted component.
Numerical Simulation of the Interaction between Intracranial Pressure and Cerebral Blood Flow

Numerical Simulation of the interaction between intracranial pressure (ICP) and cerebral blood flow (CBF) based on physiology and hemodynamics is presented. The circulation of the cerebral blood and the circulation of the cerebrospinal fluid are considered in this model. The model includes the main biomechanical factors assumed to affect intracranial pressure, particularly cerebrospinal fluid (CSF) dynamics, intracranial compliance, and cerebral hemodynamics, which is in accordance with the body's fact. We can list some state equations on the basis of the model, which can be solved using MATLAB. Comparison between the experimental recordings and simulated intracranial pressure signals shows that they are consistent and there is a positive correlation between the intracranial pressure and the arterial pressure, proving the validity of the model. This paper provides an assistant method for clinic noninvasive monitoring intracranial pressure.
The Effect of Specific Energy Density on Microstructure and Corrosion Resistance of CoCrMo Alloy Fabricated by Laser Metal Deposition

With the development of modern medical implants, there are significantly increasing demands for personalized prosthesis. Corrosion-resistance and dense cobalt alloy specimens have been successfully fabricated by laser metal deposition. The relationship between specific energy density, microstructure and corrosion resistance of the specimens is investigated. The results show that higher specific energy density promotes the formation of columnar grain and leads to coarse grain size. The evolution and distribution of deposited microstructure from bottom to top are summarized in a metallographic sketch. The corrosion current of deposited specimens increases from 2.071 × 10−6 A/cm2 to 6.86 × 10−5 A/cm2 and rapidly drops to 9.88 × 10−7 A/cm2 with increase of specific energy density from 318.8 J/g to 2752.3 J/g. The columnar and equiaxed structure of deposited specimens have lower corrosion current than mixed structure due to finer grain and less Mo segregation. The deposited have low level metal released because of passive film. The passive film have different formation routes in Hank’s solution and acidic saliva. The specific energy density has an important effect on the microstructure of deposited, which improves corrosion resistance and life span in implant.
Prevalence of Chronic Pain, Particularly with Neuropathic Component, and Its Effect on Overall Functioning of Elderly Patients

Background The prevalence of chronic pain among the elderly is high (estimated at 25–85%) and may adversely affect their everyday functioning, although it is often unrecognized. Material/Methods The aim of this study was to assess the prevalence of chronic pain, especially with the neuropathic component, and its effect on overall functioning of elderly patients. We enrolled 145 subjects older than 60 years (nursing home residents, or patients of outpatient geriatric clinic). Information on co-morbidities, functional and mental status, and medications was obtained using a questionnaire. Chronic pain was defined as lasting >3 months and the presence of neuropathic component was detected using the DN4 Questionnaire (Douleur Neuropathique en Questions). Results The mean age of patients was 76±9.68 years. Chronic pain was reported by 78% of participants and 32% reported neuropathic pain with neuropathic component (DN4 score ≥4 points). Patients complaining of chronic pain significantly more often presented mood disorders and lower satisfaction with life (particularly those with the highest pain intensity), with no difference in functional status according to the ADL (Activities of Daily Living) tool. Participants with chronic pain were treated with paracetamol (45%), non-steroidal anti-inflammatory drugs (25%), and opioids (24%). Conclusions The prevalence of chronic pain, particularly with neuropathic component, in the elderly population seems to be higher than expected based on previous reports, and its treatment appears to be ineffective. This problem requires further research and dissemination of knowledge on the diagnosis and treatment of chronic pain among health care workers caring for elderly patients on a daily basis.
On Learning and Learned Data Representation by Capsule Networks

Capsule networks (CapsNet) are recently proposed neural network models containing newly introduced processing layer, which are specialized in entity representation and discovery in images. CapsNet is motivated by a view of parse tree-like information processing mechanism and employs an iterative routing operation dynamically determining connections between layers composed of capsule units, in which the information ascends through different levels of interpretations, from raw sensory observation to semantically meaningful entities represented by active capsules. The CapsNet architecture is plausible and has been proven to be effective in some image data processing tasks, the newly introduced routing operation is mainly required for determining the capsules’ activation status during the forward pass. However, its influence on model fitting and the resulted representation is barely understood. In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks. Our investigation yielded the results some of which have been mentioned in the original paper of CapsNet, they are: 1) the routing operation determines the certainty with which a layer of capsules pass information to the layer above and the appropriate level of certainty is related to the model fitness; 2) in a designed experiment using data with a known 2D structure, capsule representations enable a more meaningful 2D manifold embedding than neurons do in a standard convolutional neural network (CNN), and; 3) compared with neurons of the standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.
Linear dependency between epsilon and the input noise in epsilon-support vector regression

In using the Ɛ-support vector regression (Ɛ-SVR) algorithm, one has to decide on a suitable value of the insensitivity parameter Ɛ. Smola et al. [6] determined its "optimal" choice based on maximizing the statistical efficiency of a location parameter estimator. While they successfully predicted a linear scaling between the optimal Ɛ and the noise in the data, the value of the theoretically optimal Ɛ does not have a close match with its experimentally observed counterpart. In this paper, we attempt to better explain the experimental results there, by analyzing a toy problem with a closer setting to the Ɛ-SVR. Our resultant predicted choice of Ɛ is much closer to the experimentally observed value, while still demonstrating a linear trend with the data noise.
Cell-autonomous and non-autonomous roles of daf-16 in muscle function and mitochondrial capacity in aging C. elegans

Sarcopenia, defined as the loss of skeletal muscle mass and strength, contributes to disability and health-related conditions with aging. In vitro studies indicate that age-related mitochondrial dysfunction could play a central role in the development and progression of sarcopenia, but because of limitations in the methods employed, how aging affects muscle mitochondrial function in vivo is not fully understood. We use muscle-targeted fluorescent proteins and the ratiometric ATP reporter, ATeam, to examine changes in muscle mitochondrial mass and morphology, and intracellular ATP levels in C. elegans. We find that the preserved muscle function in aging daf-2 mutants is associated with higher muscle mitochondrial mass, preserved mitochondrial morphology, and higher levels of intracellular ATP. These phenotypes require the daf-16/FOXO transcription factor. Via the tissue-specific rescue of daf-16, we find that daf-16 activity in either muscle or neurons is sufficient to enhance muscle mitochondrial mass, whereas daf-16 activity in the muscle is required for the enhanced muscle function and mobility of the daf-2 mutants. Finally, we show through the use of drugs known to enhance mitochondrial activity that augmenting mitochondrial function leads to improved mobility during aging. These results suggest an important role for mitochondrial function in muscle aging.