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Infants are most vulnerable to child maltreatment, which may be due in part to economic instability during the perinatal period. In 2024, Rx Kids was launched in Flint, Michigan, achieving near 100% aggregate take up and providing every expectant mother with unconditional cash transfers during pregnancy and infancy. Synthetic difference-in-differences was used to compare changes in allegations of maltreatment within the first six months of life in Flint before and after implementation of Rx Kids relative to the corresponding change in control cities without the program. In the three years prior to the implementation of Rx Kids, the proportion of infants with a maltreatment allegation within the first six months of life was 21.7% in Flint and 19.5% among control cities. After implementation of Rx Kids in 2024, the maltreatment allegation rate dropped to 15.5% in Flint, falling below the maltreatment allegation rate of 20.6% among the control cities. Rx Kids was associated with a statistically significant 7.0 percentage-point decrease in the maltreatment allegation rate (p = 0.021), corresponding to a 32% decrease relative to the pre-intervention period. There was a decrease in the r

Understanding coastal geomorphic change is essential for advancing the United Nations Sustainable Development Goals (SDGs) through a multi-scale coastal management framework. In particular, characterization of coastal geomorphic change across multiple spatial and temporal scales can provide essential insights and context-specific knowledge that can inform and empower local communities. In this study, we present a multi-scale assessment of coastal geomorphic change in southwestern Lake Michigan in the Laurentian Great Lakes. Three spatial scales: county, reach, and transect and two temporal scales: long-term and short-term were examined using nine sets of historical aerial imagery spanning 1937 to 2020. The site-averaged long-term (1937-2020) change rates for the bluff crest, bluff toe, and shoreline were -0.22, -0.17, and -0.16 m/year, respectively. In the short term (1995-2020), the corresponding rates were -0.22, -0.15, and -0.32 m/year, indicating an increasing shoreline erosion in recent years. The coastal geomorphic changes at county, reach, and transect scales were further characterized, showing regional and localized distributions of coastal erosion in our study sites. The m

Wave directionality plays a critical role in shaping coastal conditions and influencing local livelihoods, underscoring the importance of conducting detailed analyses. This study examines directional wave climate along the southwestern coast of Lake Michigan from 1979 to 2023 using the Directional Wave Entropy (DWE). Directionality was characterized in terms of inter-annual trends, monthly patterns, spatial variation, and extreme wave conditions. Overall, results exhibited a strong bi-directionality, with dominant northern and southern wave systems along the coast of our study site. A few annual trends for the inter-annual wave climate were observed, and there is a clear seasonal variation such that bi-directionality increases in the summer and winter seasons. As for spatial variation of wave directionality, all locations in the study sites presented a bi-directional wave climate. The two dominant directions of wave directionality: northern and southern mean significant wave heights were also characterized in all locations of study sites as 0.566 and 0.563 meters. Furthermore, the extreme wave heights in the northern direction are significantly greater than the extreme waves in the

This paper focuses on the impact of rule representation in Michigan-style Learning Fuzzy-Classifier Systems (LFCSs) on its classification performance. A well-representation of the rules in an LFCS is crucial for improving its performance. However, conventional rule representations frequently need help addressing problems with unknown data characteristics. To address this issue, this paper proposes a supervised LFCS (i.e., Fuzzy-UCS) with a self-adaptive rule representation mechanism, entitled Adaptive-UCS. Adaptive-UCS incorporates a fuzzy indicator as a new rule parameter that sets the membership function of a rule as either rectangular (i.e., crisp) or triangular (i.e., fuzzy) shapes. The fuzzy indicator is optimized with evolutionary operators, allowing the system to search for an optimal rule representation. Results from extensive experiments conducted on continuous space problems demonstrate that Adaptive-UCS outperforms other UCSs with conventional crisp-hyperrectangular and fuzzy-hypertrapezoidal rule representations in classification accuracy. Additionally, Adaptive-UCS exhibits robustness in the case of noisy inputs and real-world problems with inherent uncertainty, such a

City council meetings are vital sites for civic participation where the public can speak directly to their local government. By addressing city officials and calling on them to take action, public commenters can potentially influence policy decisions spanning a broad range of concerns, from housing, to sustainability, to social justice. Yet studies of these meetings have often been limited by the availability of large-scale, geographically-diverse data. Relying on local governments' increasing use of YouTube and other technologies to archive their public meetings, we propose a framework that characterizes comments along two dimensions: the local concerns where concerns are situated (e.g., housing, election administration), and the societal concerns raised (e.g., functional democracy, anti-racism). Based on a large record of public comments we collect from 15 cities in Michigan, we produce data-driven taxonomies of the local concerns and societal concerns that these comments cover, and employ machine learning methods to scalably apply our taxonomies across the entire dataset. We then demonstrate how our framework allows us to examine the salient local concerns and societal concerns

In large lakes, ice cover plays an important role in shipping and navigation, coastal erosion, regional weather and climate, and aquatic ecosystem function. In this study, a novel deep learning model for ice cover concentration prediction in Lake Michigan is introduced. The model uses hindcasted meteorological variables, water depth, and shoreline proximity as inputs, and NOAA ice charts for training, validation, and testing. The proposed framework leverages Convolution Long Short-Term Memory (ConvLSTM) and Convolution Neural Network (CNN) to capture both spatial and temporal dependencies between model input and output to simulate daily ice cover at 0.1° resolution. The model performance was assessed through lake-wide average metrics and local error metrics, with detailed evaluations conducted at six distinct locations in Lake Michigan. The results demonstrated a high degree of agreement between the model's predictions and ice charts, with an average RMSE of 0.029 for the daily lake-wide average ice concentration. Local daily prediction errors were greater, with an average RMSE of 0.102. Lake-wide and local errors for weekly and monthly averaged ice concentrations were reduced by a

The Robotics Major at the University of Michigan was successfully launched in the 2022-23 academic year as an innovative step forward to better serve students, our communities, and our society. Building on our guiding principle of "Robotics with Respect" and our larger Robotics Pathways model, the Michigan Robotics Major was designed to define robotics as a true academic discipline with both equity and excellence as our highest priorities. Understanding that talent is equally distributed but opportunity is not, the Michigan Robotics Major has embraced an adaptable curriculum that is accessible through a diversity of student pathways and enables successful and sustained career-long participation in robotics, AI, and automation professions. The results after our planning efforts (2019-22) and first academic year (2022-23) have been highly encouraging: more than 100 students declared Robotics as their major, completion of the Robotics major by our first two graduates, soaring enrollments in our Robotics classes, thriving partnerships with Historically Black Colleges and Universities. This document provides our original curricular proposal for the Robotics Undergraduate Program at the

The University of Michigan Robotics program focuses on the study of embodied intelligence that must sense, reason, act, and work with people to improve quality of life and productivity equitably across society. ROB 204, part of the core curriculum towards the undergraduate degree in Robotics, introduces students to topics that enable conceptually designing a robotic system to address users' needs from a sociotechnical context. Students are introduced to human-robot interaction (HRI) concepts and the process for socially-engaged design with a Learn-Reinforce-Integrate approach. In this paper, we discuss the course topics and our teaching methodology, and provide recommendations for delivering this material. Overall, students leave the course with a new understanding and appreciation for how human capabilities can inform requirements for a robotics system, how humans can interact with a robot, and how to assess the usability of robotic systems.

This research report journal aims to investigate the feasibility of establishing a nuclear presence at Western Michigan University. The report will analyze the potential benefits and drawbacks of introducing nuclear technology to WMUs campus. The study will also examine the current state of nuclear technology and its applications in higher education. The report will conclude with a recommendation on whether WMU should pursue the establishment of a nuclear presence on its campus.

High-resolution multispectral satellite imagery was utilized to quantify shoreline recession at eleven beaches around Lake Michigan during a record-setting water level increase between 2013 and 2020. Shoreline changes during this period ranged from 20 m to 62 m, corresponding to 52-95% of the initial beach widths. Average estimated shoreline erosion across all beaches varied from 1% to 75% of the observed changes, with the remainder attributed to inundation. Significant correlations were found between shoreline erosion and wave-related factors, including offshore wave power, offshore bathymetric slope, storm energy, and potential alongshore sediment transport divergence. In contrast, parameters related to cross-shore transport, such as dimensionless fall velocity, exhibited weak correlations. Additionally, the results underscore the importance of distinguishing between immediately reversible changes (inundation) and morphological changes that could be reversible over longer timescales, when assessing the impact of rising water levels., The findings also suggest that in addition to waves playing a key role in regulating shoreline changes, alongshore sediment transport processes may

This paper reports on the design and construction of infrastructure and test stations for small-diameter monitored drift tube (sMDT) assembly and testing at the University of Michigan (UM) to prepare for the ATLAS Muon Spectrometer upgrade for the high-luminosity program of the Large Hadron Collider. Procedures of the tube assembly and quality assurance and control (QA/QC) tests are described in detail. More than 99% of the tubes meet the tube QA/QC specifications based on 2100 tubes built at UM. The UM test stations are also used for QA/QC testing on the tubes constructed at Michigan State University. These tubes are being used to construct the sMDT chambers which will replace the current MDT chambers of the barrel inner station of the Muon Spectrometer.

With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used numerical third generation wave model, SWAN is applied to predict waves in Lake Michigan. Wind data were analyzed to determine wind variation frequency over Lake Michigan. Wave predictions uncertainty due to wind local effects were compared during a period where wind had a fairly constant speed and direction over the northern and southern basins. The study shows that despite model calibration in Lake Michigan area, the model deficiency arises from ignoring wind effects in small scales. Wave prediction also emphasizes that small scale turbulence in meteorological forces can increase error in predictions up to 35%. Wave frequency and coherence analysis showed that both models are able to reveal the time scale of the wave variation with same accuracy. Insufficient number of meteorological stations can result in neglecting local wind effects and discrepancies in current predictions. The uncertainty of wave numerical models due to input uncertainties and model principals s

Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term complication in diabetic patients associated with painful neuropathy, foot ulceration and amputation. The Michigan neuropathy screening instrument (MNSI) is one of the most common screening techniques for DSPN, however, it does not provide any direct severity grading system. Method: For designing and modelling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. MNSI variables and patient outcomes were investigated using machine learning tools to identify the features having higher association in DSPN identification. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading. Results: The top-7 ranked features from MNSI: 10-gm filament, Vibration perception (R), Vibration perception (L), previous diabetic neuropathy, the appearance of deformities, appearance of callus and appearance of fissure were identified as key features for identifying DSPN using the extra tree model. The area under the curve (AUC) of the nomogram for the internal and external datasets w

Assessing the effects of anthropogenic disturbances on wildlife is a necessary conservation task. The soundscape is a critical habitat component for acoustically communicating organisms, but the use of the soundscape as a tool for assessing disturbance impacts has been relatively unexplored until recently. Here we present a broad modeling framework for assessing disturbance impacts on soundscapes, which we apply to quantify the influence of a shelterwood logging on soundscapes in northern Michigan. Our modeling approach can be broadly applied to assess anthropogenic disturbance impacts on soundscapes. The approach accommodates inherent differences in control and treatment sites to improve inference about treatment effects, while also accounting for extraneous variables (e.g., rain) that influence acoustic indices. Recordings were obtained at 13 sites before and after a shelterwood logging. Four sites were in the logging region and nine sites served as control recordings outside the logging region. We quantify the soundscapes using common acoustic indices (Normalized Difference Soundscape Index (NDSI), Acoustic Entropy (H), Acoustic Complexity Index (ACI), Acoustic Evenness Index (A

We present the design for MYSTIC, the Michigan Young STar Imager at CHARA. MYSTIC will be a K-band, cryogenic, 6-beam combiner for the Georgia State University CHARA telescope array. The design follows the image-plane combination scheme of the MIRC instrument where single-mode fibers bring starlight into a non-redundant fringe pattern to feed a spectrograph. Beams will be injected in polarization-maintaining fibers outside the cryogenic dewar and then be transported through a vacuum feedthrough into the ~220K cold volume where combination is achieved and the light is dispersed. We will use a C-RED One camera (First Light Imaging) based on the eAPD SAPHIRA detector to allow for near-photon-counting performance. We also intend to support a 4-telescope mode using a leftover integrated optics component designed for the VLTI-GRAVITY experiment, allowing better sensitivity for the faintest targets. Our primary science driver motivation is to image disks around young stars in order to better understand planet formation and how forming planets might influence disk structures.

We detail our ongoing work in Flint, Michigan to detect pipes made of lead and other hazardous metals. After elevated levels of lead were detected in residents' drinking water, followed by an increase in blood lead levels in area children, the state and federal governments directed over $125 million to replace water service lines, the pipes connecting each home to the water system. In the absence of accurate records, and with the high cost of determining buried pipe materials, we put forth a number of predictive and procedural tools to aid in the search and removal of lead infrastructure. Alongside these statistical and machine learning approaches, we describe our interactions with government officials in recommending homes for both inspection and replacement, with a focus on the statistical model that adapts to incoming information. Finally, in light of discussions about increased spending on infrastructure development by the federal government, we explore how our approach generalizes beyond Flint to other municipalities nationwide.

We describe an exercise of using Big Data to predict the Michigan Consumer Sentiment Index, a widely used indicator of the state of confidence in the US economy. We carry out the exercise from a pure ex ante perspective. We use the methodology of algorithmic text analysis of an archive of brokers' reports over the period June 2010 through June 2013. The search is directed by the social-psychological theory of agent behaviour, namely conviction narrative theory. We compare one month ahead forecasts generated this way over a 15 month period with the forecasts reported for the consensus predictions of Wall Street economists. The former give much more accurate predictions, getting the direction of change correct on 12 of the 15 occasions compared to only 7 for the consensus predictions. We show that the approach retains significant predictive power even over a four month ahead horizon.

We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al., Comp. Meth. App. Mech. Eng., 356, 44-74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.

We introduce the Michigan Infrared Test Thermal ELT N-band (MITTEN) Cryostat, a new facility for testing infrared detectors with a focus on mid-infrared (MIR) wavelengths (8-13 microns). New generations of large format, deep well, fast readout MIR detectors are now becoming available to the astronomical community. As one example, Teledyne Imaging Sensors (TIS) has introduced a long-wave Mercury-Cadmium-Telluride (MCT) array, GeoSnap, with high quantum efficiency (> 65 %) and improved noise properties compared to previous generation Si:As blocked impurity band (BIB) detectors. GeoSnap promises improved sensitivities, and efficiencies, for future background-limited MIR instruments, in particular with future extremely large telescopes (ELTs). We describe our new test facility suitable for measuring characteristics of these detectors, such as read noise, dark current, linearity, gain, pixel operability, quantum efficiency, and point source imaging performance relative to a background scene, as well as multiple point sources of differing contrast. MITTEN has an internal light source, and soon an accompanying filter wheel and aperture plate, reimaged onto the detector using an Offner