Data-driven framework for modeling deterioration of pavements in the state of Iowa

Highway networks serve the public by providing access to critical facilities such as hospitals, schools, and markets. Although maintenance and rehabilitation resemble a burden on transportation agencies, postponing required road maintenance can result in even higher direct and indirect costs (Burningham, 2005). Developing a robust and accurate pavement management system (PMS) is the key to supporting decision-makers at local and state highway agencies. One of the most important components of pavement management systems is predicting the deterioration of the network through performance models. In this research, two major objectives were investigated. In the first part, the process and outcome of deterioration modeling for three different pavement types in the state of Iowa was described. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). Typically, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the Long/Short Term Memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that while the individual regression models achieved higher prediction accuracy with respect to asphalt pavements, the LSTM model achieved a higher prediction accuracy over time for concrete and composite pavement types. In the second part, describes how the accuracy of prediction models can have an effect on the decision-making process in terms of the cost of maintenance and rehabilitation activities. The process is simulating the propagation of the error between the actual and predicted values of pavement performance indicators. Different rate of error was added into the result of prediction models. The results showed a strong correlation between the prediction models\u27 accuracy and the cost of maintenance and rehabilitation activities. Also, increasing the rate of error contribution to the prediction model resulting in a higher benefit reduction rate

Is Steering Practice Task Dependent?

A driving simulation experiment was conducted to examine the performance improvement of participants while conducting a lane keeping task and two lane changing tasks on a straight road. Forty-four participants, sixteen females and twenty-eight males, drove one of three driving conditions. The data was analyzed to test whether 1) practice is better than no practice; 2) practicing a less challenging but similar steering task is good practice for a more challenging steering task; and 3) practicing a more challenging but similar steering task is good practice for a less challenging steering task. The results indicate that practicing the more challenging lane changing task had a significant impact on the performance of the subsequent, less challenging but similar task.https://engagedscholarship.csuohio.edu/u_poster_2014/1021/thumbnail.jp

Optimal Transport-based Nonlinear Filtering in High-dimensional Settings

This paper addresses the problem of nonlinear filtering, i.e., computing the conditional distribution of the state of a stochastic dynamical system given a history of noisy partial observations. The primary focus is on scenarios involving degenerate likelihoods or high-dimensional states, where traditional sequential importance resampling (SIR) particle filters face the weight degeneracy issue. Our proposed method builds on an optimal transport interpretation of nonlinear filtering, leading to a simulation-based and likelihood-free algorithm that estimates the Brenier optimal transport map from the current distribution of the state to the distribution at the next time step. Our formulation allows us to harness the approximation power of neural networks to model complex and multi-modal distributions and employ stochastic optimization algorithms to enhance scalability. Extensive numerical experiments are presented that compare our method to the SIR particle filter and the ensemble Kalman filter, demonstrating the superior performance of our method in terms of sample efficiency, high-dimensional scalability, and the ability to capture complex and multi-modal distributions.Comment: 24 pages, 15 figure

Case Report Peripheral Edema Occurring during Treatment with Risperidone Combined with Citalopram

An 80-year-old female presented with symptoms of depression, worthlessness, hopelessness, loss of energy, insomnia, impatience, and forgetfulness associated with persecutory delusion that had begun about one year before her visit. She was diagnosed with major depression with psychotic signs and began treatment with risperidone (2 mg/night) and citalopram (20 mg/day). After 20 days, she returned and reported partial improvement in her symptoms, although she had developed severe swelling of the hands and feet. The results of liver and renal function tests and rheumatologic tests were found to be within normal limits. Risperidone was discontinued for a week, and the swelling resolved completely. Risperidone was then administered again, and the swelling returned so that the patient had to discontinue taking the drug. The reappearance of edema on rechallenge is strong evidence implicating risperidone as the cause of the swelling

Capacity of HDL to Efflux Cellular Cholesterol from Lipid-Loaded Macrophages Is Reduced in Patients with Familial Hypercholesterolemia

: This study aimed to evaluate the high-density lipoprotein (HDL) capacity to efflux cellular cholesterol from lipid-loaded macrophages to find a reliable and low-cost biomarker with the purpose of better evaluating the risk of premature cardiovascular (CV) events in FH patients. This case-controlled study comprised 16 homozygous (HOFH) and 18 heterozygous (HEFH) FH patients, as well as 20 healthy subjects recruited as controls. Two main subfractions of HDL (HDL2 (d = 1.063-1.125 g/mL) and HDL3 (d = 1.125-1.210 g/mL)) were isolated from the patients' serum samples using sequential ultracentrifugation. After compositional characterization, the capacity of HDL to efflux cholesterol (CEC%) from lipid-laden macrophages was measured. The HDL2 and HDL3 subfractions showed some differences in lipid and protein composition between the studied groups. In addition, both HDL subfractions (p < 0.001) revealed significantly reduced CEC% in HOFH patients (HDL2: 2.5 ± 0.1 and HDL3: 3.2 ± 0.2) in comparison with the HEFH (HDL2: 3.2 ± 0.1% and HDL3: 4.1 ± 0.2%) and healthy (HDL2: 3.3 ± 0.2% and HDL3: 4.5 ± 0.3%) subjects. Additionally, multinomial logistic regression results indicated that the CEC% of both HDL2 (OR: 0.091; 95% CI: 0.018-0.452, p < 0.01) and HDL3 (OR: 0.118; 95% CI: 0.035-0.399, p < 0.01) subfractions are strongly and inversely associated with the homozygous form of FH. A decreased capacity of HDL particles to efflux cholesterol from macrophages might identify homozygous FH patients who are at elevated risk for premature CVDs. Prospective studies with a large sample size are warranted to evaluate this hypothesis

INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings

Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. However, existing INRs face limitations in capturing fine-grained details, handling noise, and adapting to diverse signal types. To address these challenges, we introduce INCODE, a novel approach that enhances the control of the sinusoidal-based activation function in INRs using deep prior knowledge. INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function. Through a task-specific pre-trained model, INCODE adapts the task-specific parameters to optimize the representation process. Our approach not only excels in representation, but also extends its prowess to tackle complex tasks such as audio, image, and 3D shape reconstructions, as well as intricate challenges such as neural radiance fields (NeRFs), and inverse problems, including denoising, super-resolution, inpainting, and CT reconstruction. Through comprehensive experiments, INCODE demonstrates its superiority in terms of robustness, accuracy, quality, and convergence rate, broadening the scope of signal representation. Please visit the project's website for details on the proposed method and access to the code.Comment: Accepted at WACV 2024 conferenc

COVID-19 pneumonia in a child with hepatic encephalopathy: A case study

Coronavirus disease 2019 (COVID-19) is caused by the seventh coronavirus, known as the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Children often have milder diseases than adults with very rare mortality. Gastrointestinal manifestations and a mild increase in liver enzymes have been reported in 8.8% to 53% of COVID-19 cases. However, liver failure is extremely rare and has not been reported so far in the literature. The prevalence of comorbidities is not clear in children with COVID-19. Here, we reported a fatal case of simultaneous pneumonia secondary to SARS-CoV-2and acute liver failure in a 14-year-old boy with liver cirrhosis. &nbsp

Autoimmune pancreatitis as a very rare cause of recurrent pancreatitis in children; a case report and review of literature

Autoimmune pancreatitis as chronic inflammation of the pancreas due to an autoimmune mechanism is a rare type of pancreatitis. A 14 years old girl presented with multiple episodes of abdominal pain, nausea with elevation of amylase and lipase suspicions of acute recurrent pancreatitis since 3 years of age. After through evaluation about secondary causes of recurrent and familial pancreatitis finally she responded to corticosteroid treatment. Although very rare but autoimmune processes should be considered in teenagers with recurrent pancreatitis