University of Detroit Mercy

Modern technologies rely heavily on a number of inorganic elements. From the lithium in your laptop battery to the helium that cools hospital MRI machines inorganic materials are crucial to many parts of life. The supply of these materials that we can extract from the environment is finite and many of them present an environmental hazard when concentrated in landfill waste. Recycling is therefore key to being able to continue to sustainably use these elements. Examining in detail the many materials that can be classified as inorganic, and for which recycling is either non-existent or only in its infancy this book will help you to understand whether such materials can be re-used in some way, and how we might make advances into the recycling, re-use, and possible repurposing of any such materials or objects. Throughout, the discussion of how or if these materials can be recycled is balanced with economic and practical considerations.

ABSTRACT Women with multi-gender attractions (i.e. bisexual+ women) experience alarmingly high rates of sexual violence (SV), which puts women at risk for posttraumatic stress disorder (PTSD). Bisexual+ women experience higher rates of PTSD compared to heterosexual and lesbian/gay people. The PTSD Checklist for DSM-5 (PCL-5) is a widely used self-report measure of PTSD symptoms. We conducted an item response theory (IRT) analysis to evaluate the item difficulty and discrimination of PCL-5 items in assessing PTSD symptoms related to adult SV among young bisexual+ women. We combined two samples of women: sample 1 participants were recruited via MTurk (N = 267; Mage = 25.9, range 18–35; 82.4% White) and sample 2 participants recruited via online ads (N = 133; Mage = 22.0, range 18–25, 85.0% White). Item difficulty and discrimination was evaluated using IRT with a graded response model. At clinically significant levels of PTSD, the most discriminating items were: flashbacks, nightmares, reckless behavior, inability to experience positive emotions, and intrusive memories, whereas the least discriminating items were: internal avoidance, hypervigilance, difficulty with sleep, external avoidance, and exaggerated startle response. The most difficult items were: reckless behavior, flashbacks, traumatic amnesia, nightmares, and irritability, whereas the least difficult items were: internal avoidance, cued emotional distress, external avoidance, hypervigilance, and strong negative emotions. Results suggest that symptoms related to flashbacks, nightmares, and reckless behavior are important for assessing clinically significant SV-related PTSD symptoms among young bisexual+ women, whereas symptoms related to internal and external avoidance and hypervigilance may be relatively less informative.

Organizational causes of burnout among mental health providers (MHPs) are extensively documented in the literature. Additionally, several studies have established a relationship between higher burnout rates and poorer patient care. However, it remains unclear whether worse patient care results from organizational causes of burnout, MHP burnout itself, or a combination of both. Therefore, an in-depth qualitative exploration of MHPs’ experiences with organizational causes of burnout and their perceptions of how these causes may affect patient care was conducted. Fifty-four MHPs (i.e., social workers, psychologists and psychiatrists) across nine Veteran Health Administration (VA) medical centers participated in semi-structured interviews. Content analysis was used to analyze the data. Findings revealed a complex relationship between organizational factors and individual behaviors influenced by burnout, and their potential impact on patient care. MHPs’ acknowledged that their burnout could impact the quality of care provided to Veterans. The behaviors associated with burnout, such as disengagement, lack of empathy, distraction, lack of preparedness, and procrastination, may inadvertently compromise the therapeutic alliance and the overall efficacy of treatment. Findings also indicated that MHP experienced moral distress from the dissonance between the desire to provide high-quality care and the reality of institutional constraints. Additionally, MHP burnout impacted Veterans’ trust and engagement with the VA mental health care system. Therefore, addressing burnout requires a multifaceted approach including organizational reforms, support and resources for MHPs, and dedicated support for MHPs to deliver high quality care. Our findings emphasize the critical need for organizational-level interventions that prioritize clinical care over bureaucratic demands.

This work merges the physical and digital worlds through a YOLO-based safety system for a drill press, designed to facilitate safe coexistence between humans and machines. The proposed system mitigates two primary safety hazards associated with drill press operation: the sudden and forceful rotation of the workpiece due to drill bit binding, and the risk of hand or finger injury from contact with the rotating spindle, chuck, or drill bit. In this work, a RealSense D415 camera is used to capture images and obtain depth information. This work utilizes a YOLO-v11n segmentation model trained to recognize hands, workpiece hold-down clamps, the spindle/chuck, and various types of drill bits. The system logic first prompts the user to update the camera position and orientation if necessary, and then checks whether two hold-down clamps are visible. Once the system verifies the presence of the hold-down clamps, it begins to capture images to detect and locate the hands, the chuck, and the drill bit. Safety clearances are computed first using spherical approximations of the hands, chuck, and drill, as needed, and then using less conservative cylindrical approximations of the chuck and drill. If a user's hand is detected within a specified threshold distance of a rotating element, the 110 vac power to the drill press is cut off, and the user is prompted to turn the physical drill press switch off before resuming work. A serially connected TTL device is used to interrupt the power to the drill press via a solid-state relay. The proof-of-concept system performed well, with a frame-to-frame cycle time of 85 ms. This concept can be applied to many other potentially dangerous workshop tools, including table saws, band saws, compound miter saws, jointers, and routers.

Abstract MATLAB Grader (hereafter, simply Grader) is the MathWorks (the makers of MATLAB) product that allows creation of interactive course assignments, grades student work automatically providing immediate feedback as they work, and integrates into the common learning management systems. Immediately after learning about Grader in the fall of 2023, the author began implementing Grader assignments in his Vehicle Dynamics course and, despite some issues related to learning and implementing Grader, feedback was very positive. This led the author to implement Grader for a course in Systems Optimization during the Winter 2023-2024 semester, and, following more positive feedback, it has since been implemented in three more courses, namely Principles of Mechanical Engineering for Electrical Engineers, a subsequent offering of Vehicle Dynamics, and Systems Optimization. The author learned many things the hard way as he progressively integrated more and more Grader into his courses. The author has generated a long list of lessons learned, presumed best practices, and workarounds for issues encountered while integrating Grader into his courses. The goal of this paper is to disseminate those learnings to accelerate the process of any other faculty wanting to make the leap into using MATLAB Grader in his/her own courses. The paper includes a brief description of each MATLAB Grader item the author has developed aimed at connecting the author to interested collaborators.

The objective of this study was to evaluate the efficacy of the periodontal endoscope (PE) as a teaching resource to improve dental hygiene (DH) students' detection and removal skills. This multi-center clinical study included participating DH students from the University of Michigan (UM), University of Minnesota (UMN), and Central Community College (CCC). A total of 111 DH students, 41 juniors, and 70 seniors. All participating students and 20 faculty were randomized into control or intervention groups, in which there were 54 and 57 students, respectively. Eight study team examiners calibrated and used the PE. The intervention group faculty received endoscopic visualization of calculus to deliver endoscope guided (EG) feedback, while the control group received the clinical standard procedure from their designated faculty. Agreement between the calculus index of students and examiners and of faculty and examiners was estimated using weighted kappa statistics. The intervention group demonstrated a statistically significant difference (0.014) for calculus removal compared to the control group. For the post calculus detection and removal, the intervention group had a higher kappa for all surfaces compared to the control group: mesial surfaces, (intervention 0.158, control 0.002), distal surfaces (intervention 0.108, control 0.034), buccal surfaces (intervention 0.169, control 0.139), and lingual surfaces (intervention 0.134, control 0.070). The intervention group detected and removed more calculus than the control group. This study represented the first known three-DH program clinical trial to implement innovative technology to enhance student performance. Students who received EG feedback demonstrated enhanced tactile detection and calculus removal. .

Cryptocurrency price prediction remains a challenging problem due to the highly volatile nature of digital asset markets. This study introduces a Hybrid Quantum-Classical Neural Network (HQCNN) for cryptocurrency price forecasting, leveraging quantum feature encoding and extraction with deep learning architectures. The proposed model utilizes Quantum Convolutional Neural Networks (QCNN) to enhance feature representations, which are then processed by classical deep learning models such as Long Short-Term Memory (LSTM) and Transformer networks. Experimental results on historical cryptocurrency data demonstrate that HQCNN outperforms state-of-the-art models. The proposed model achieves a mean absolute error (MAE) of 0.023 and a root mean squared error (RMSE) of 0.036, surpassing traditional deep learning methods such as LSTM (MAE: 0.040, RMSE: 0.056) and Transformerbased models (MAE: 0.035, RMSE: 0.050). Ablation studies confirm the importance of quantum feature encoding, as removing it degrades performance by up to $\mathbf{3 8. 2 \%}$. Additionally, HQCNN reduces inference time to 7 milliseconds, making it suitable for real-time trading applications. Despite its advantages, HQCNN faces challenges related to quantum hardware constraints, training overhead, and interpretability in financial decision-making. Future work will focus on deploying the model on real quantum processors, integrating reinforcement learning for market adaptability, and enhancing interpretability with quantum explainability techniques.

Presbyopia is a ubiquitous age-related condition characterized by reduced near focusing ability due to lenticular stiffening. Pharmacologic agents such as pilocarpine have re-emerged as a less-invasive treatment option by inducing miosis and thereby enhancing depth of focus. However, the optimal pharmacologically induced pupil size that balances improved near vision with sufficient retinal illuminance remains undetermined. In this work, we present for the first time a direct integration of advanced theoretical modeling with a systematic synthesis of clinical trial outcomes to define the optimal target pupil size for pharmacologic presbyopia correction. We modeled visual performance using the Visual Strehl Ratio of the Optical Transfer Function (VSOTF) and convolved images of optotypes across a range of pupil diameters from 1.5 mm to 3.5 mm. This combined optical–clinical approach allowed us to quantitatively compare modeled image quality and depth of focus predictions with real-world clinical efficacy data from pilocarpine-based interventions. Simulations showed that smaller pupil sizes (1.5–2.5 mm) significantly extended depth of focus compared to standard multifocal optics while maintaining image quality within acceptable limits. These findings align with clinical trials of pilocarpine formulations, which commonly achieve post-treatment pupil diameters in the 2.0–2.5 mm range and are associated with clinically meaningful gains in near vision. Our analysis uniquely demonstrates that these clinically achieved pupil sizes closely match the theoretically optimal 2.0–3.0 mm range identified in our modeling, strengthening the evidence base for drug design and patient selection. These results reinforce the role of pharmacologically controlled pupil size as a central target in presbyopia management. By explicitly linking predictive optical modeling with aggregated clinical outcomes, we introduce a novel framework to guide future pharmacologic development strategies and refine clinical counseling in the emerging era of presbyopia therapeutics.

Background/Objectives: There are many commercial mouthrinses, used for a variety of purposes, including antiseptic activity. The objective of this study was to determine the antibacterial activity of various TheraBreath™ oral rinses against the cariogenic bacterium, Streptococcus mutans, and saliva-derived microbial communities, and their antibiofilm activity against S. mutans in vitro biofilms. Methods: Bactericidal activity against planktonic S. mutans was assessed by colony counting after 30 and 2 min exposures to mouthrinses. Ten saliva samples were exposed to mouthrinses for 30 s and plated aerobically on blood agar and Mitis Salivarius agar. Mature biofilms of S. mutans were treated with mouthrinses for 15 min followed by fluorescent vitality staining and polysaccharide measurement, followed by crystal violet staining for measurement of total biofilm remaining. Statistical analysis was performed using Kruskal–Wallis with Dunn’s multiple comparisons test comparing all mean ranks (α = 0.05). Results: TheraBreath™ Fresh Breath, Healthy Smile, and Dry Mouth exhibited no significant antibacterial activity. TheraBreath™ Healthy Gums showed antibacterial activity against S. mutans and microbes from saliva samples similar to Listerine® Naturals at all exposure times. Whitening Fresh Breath showed intermediate killing of S. mutans after 30 min in liquid but not after 2 min or against salivary microbes. Live/Dead fluorescence vitality staining showed that Healthy Gums and Whitening Fresh Breath had antibacterial activity against mature biofilms of S. mutans statistically similar to Listerine® Naturals and Colgate® Total; however, Whitening Fresh Breath did not have significant killing compared to PBS. Conclusions: TheraBreath™ Healthy Gums demonstrated similar antiseptic activity levels to other antiseptic-claiming commercial rinses. Whitening Fresh Breath was comparable but unable to kill in short exposure times.