Open Research Oklahoma (Oklahoma State Univ.)
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Evidence-based approaches to treatment intensity in children with developmental language disorders and autism spectrum disorder
No full textEffective intervention for children with language disorders requires an evidence-based approach to treatment intensity, focusing on critical factors such as dose, frequency, and duration of therapy. This systematic review explores the impact of treatment intensity on language outcomes for children with Developmental Language Disorder (DLD) and Autism Spectrum Disorder (ASD). DLD affects approximately 7.58% of children and is characterized by persistent difficulties in acquiring and using language, while ASD impacts 1 in 36 children in the United States, presenting challenges in social communication and repetitive behaviors.
To conduct this review, a systematic search was performed using Google Scholar, incorporating 9 key terms: treatment intensity, treatment intensity in child language disorders, dose intensity, intervention duration, total intervention, dose intensity in child language disorders, cumulative intervention, child language disorders, and language deficits. The initial search yielded 8,880 articles. Using predefined inclusion criteria, 267 articles were selected for detailed analysis.
Key components of treatment intensity were examined, including session frequency, session duration, and total cumulative intervention time. The review identified critical differences in treatment approaches for children with DLD and ASD. For DLD, effective interventions included phonological training, structured language therapy, and repetition-based learning, highlighting the need for consistent, high-frequency sessions to support language acquisition.
For children with ASD, effective strategies included behavioral and naturalistic approaches, such as Applied Behavior Analysis (ABA), Positive Behavioral Interventions and Supports (PBIS), and Augmentative and Alternative Communication (AAC). The findings emphasize the role of treatment intensity in achieving meaningful progress, noting that higher dose frequency and longer intervention duration were associated with better language and communication outcomes.
This review underscores the necessity of tailoring treatment intensity to meet the specific needs of each child, incorporating evidence-based practices into clinical and educational settings. By understanding the optimal balance of dose, frequency, and duration, clinicians can enhance the effectiveness of interventions for children with DLD and ASD. Future research should focus on refining treatment protocols and exploring innovative approaches to maximize language development in diverse pediatric populations
Artificial intelligence approaches for multi-object pavement condition and safety evaluation
No full textEnsuring the safety and durability of roadway infrastructure relies heavily on accurate pavement condition assessment and effective crash prediction. Conventional techniques for detecting pavement distress—such as manual inspections and contact-based friction tests—are resource-intensive, time-consuming, and susceptible to subjectivity. Additionally, traditional Safety Performance Functions (SPFs), which are generally derived from basic roadway and traffic characteristics, often fail to account for the influence of surface conditions on crash likelihood.
Recent advancements in computer vision and deep learning have enabled the automation of pavement assessment through image-based analysis. Notably, the integration of multimodal data sources, including 2D grayscale images and 3D depth data, has shown promise in enhancing the detection of subtle surface irregularities under various environmental conditions. Deep learning (DL) architectures, including convolutional neural networks (CNNs) and mixed transformers (MiT), provide robust capabilities for pixel-level segmentation and feature extraction from complex pavement surfaces. Two different deep learning models have been developed in this study for multimodal multi-object pavement feature detection, including a U-Net-based model and a SegFormer-B5 model with MiT backbones. Both models demonstrate effective capabilities for identifying various pavement features, achieving overall accuracy of 97.10% and 98.24% for the U-Net-based model and the SegFormer-B5 model, respectively. Simultaneously, crash modeling can benefit from incorporating detailed pavement surface condition indicators, especially texture parameters that affect tire–road interaction and vehicle control. This study introduces a comprehensive framework that leverages high-resolution 3D imaging and DL to automate pavement condition assessment and safety evaluation at highway speeds. A mobile sensing platform equipped with a 0.1-mm resolution 3D laser safety sensor and a 1-mm resolution 3D condition survey system was deployed to collect both high-resolution data at low speed and lower-resolution data during highway-speed operation. A super-resolution model, PT-SRGAN based on a recursive generative adversarial network (GANs), was employed to reconstruct 0.1-mm texture surface data from 1-mm highway-speed imagery, enabling both macro- and micro-texture features to be captured for safety evaluation. Subsequently, texture metrics at both levels and seven 3D parameters (provide details) were calculated. Data from 27 representative pavement sections, along with historical crash data, were used to develop texture enhanced SPFs based on the reconstructed texture datasets.
This study combines deep learning and high-resolution texture analysis to improve pavement evaluation and crash prediction. The U-Net and SegFormer-B5 models precisely identify surface features, while the PT-SRGAN model reconstructs detailed textures from highway-speed data. These metrics were then incorporated into the Safety Performance Functions, providing a scalable, data-driven framework for enhanced roadway safety management
Speedfest: Critical design review, Black Team 2025
No full textThis report presents the Critical Design Review (CDR) for the Black Team’s 2025 Speedfest competition aircraft, submitted to fulfill of the honors thesis requirement. The aircraft is a high-performance, internal combustion-powered pylon racer and aerobatic aircraft, designed around an unmodified Desert Aircraft DA35 engine. Aerodynamic optimization led to the selection of a 60 in wing span with a NACA 2412 airfoil and a 20x13 two-blade propeller, which predicts that the aircraft can collect 22 flags during the pylon racing mission. Propulsion testing included power output measurements, fuel system sizing, and development of a lightweight custom muffler. The primary structures were fabricated using fully molded composite construction, with material testing guiding the design for minimum weight and sufficient stiffness. The final weight is estimated at 9 pounds, with the center of gravity located 0.31 inches aft of quarter chord. This report documents the multidisciplinary design process culminating in a cost-effective, competition-ready aircraft
Characterization of an anechoic chamber utilizing the SVSWR method
No full textAnechoic chambers are important for various types of electromagnetic testing. To make sure the testing performed inside an anechoic chamber is valid a characterization of the chamber must be performed. The procedures regarding the SVSWR method used to characterize EMC anechoic chambers is detailed in this document. The results of the SVSWR method will indicate if the chamber is valid to use for EMC measurements or not
Assessment of Bacillus spore antimicrobial activity against Listeria monocytogenes
No full textThe study investigated the efficacy of Bacillus subtilis spore probiotics as a novel biological control agent against Listeria monocytogenes in hummus, while comparing its performance to conventional probiotic approaches. B. subtilis spores exhibit remarkable environmental resilience and targeted antimicrobial activity. In controlled broth systems, the probiotic cocktail achieved complete suppression of L. monocytogenes (<1.48 log CFU/mL) under nutrient-limited conditions, while showing minimal effect against Gram-negative pathogens (Escherichia coli O157:H7 and Salmonella Typhimurium), highlighting its selective inhibition mechanism. The spores maintained rapid germination kinetics (<6 hours) across all tested conditions, ensuring timely activation when needed. This performance substantially exceeds results typically obtained with conventional probiotics like Lactobacillus spp., which often demonstrate poor viability and inconsistent antimicrobial effects in processed foods. The findings provide compelling evidence that B. subtilis spores represent a technologically advanced solution for clean-label food preservation, offering key advantages: (1) exceptional thermal and environmental stability, maintaining viability through processing and storage; (2) targeted suppression of high-risk pathogens while preserving beneficial microbiota. The results suggest that B. subtilis spores could significantly enhance the safety of refrigerated, ready-to-eat foods without compromising product quality or consumer acceptance
Open educational resources research case studies
No full textThis collection of case studies explores how scholars, practitioners and administrators design and implement research on open education. The book highlights the ways faculty, staff and institutional leaders across higher education in the United States and Canada investigate the impact of open educational resources and open educational practices on student success.Peer reviewedLibrar
Recreational agritourism: Implications for regional development in the Southern Great Plains
No full textAgritourism has emerged as a potential driver of regional development, particularly in rural areas where economic challenges persist. This study examines the scope and impact of recreational agritourism in the Southern Great Plains (SGP) region, comprising Kansas, Oklahoma, and Texas. Currently, the SGP has more than 6,000 agritourism operations with $220 million in related revenue. Using USDA Census of Agriculture data and spatial modeling techniques, the research identifies key factors influencing agritourism revenue and highlights spatial dependence and heterogeneity across counties.
Results reveal significant contributions of median household income, farm size, producer demographics, and the presence of natural amenities to agritourism success. Exploratory spatial data analysis found 47 hotspot counties with high agritourism activity concentrated in south-central Texas. Notably, larger farms and counties with a higher proportion of female producers demonstrate increased agritourism revenue, reflecting the economic benefits of diversification strategies. Findings also reveal the negative relationship between higher median household income and agritourism revenues, suggesting agritourism thrives in more rural areas. The analysis is constrained by data limitations, particularly the exclusion of core agritourism activities such as direct-to-consumer sales and farmers markets. These constraints highlight the need for expanded datasets and refined definitions to capture the full spectrum of agritourism activity.
This research supports policy interventions to enhance support for agritourism development, such as the establishment of a dedicated agritourism office under the proposed AGRITOURISM Act within the USDA. By leveraging local assets and addressing regional disparities, agritourism can serve as a mechanism for rural economic development, contributing to community resilience and sustainability
Reduced mixed finite element method with a priori and a posteriori error analysis
No full textThis thesis presents a novel finite element method (FEM) for the accurate and efficient approximation of the flux variable in second-order elliptic boundary value problems. Traditional mixed FEMs used to solve these problems approximate both the flux and primary variables simultaneously by solving a coupled linear system of algebraic equations (LSAEs). However, in many applications, the flux is the main quantity of interest, and computing the primary variable is unnecessary. We introduce a new finite element scheme that directly approximates the flux without computing the primary function. With the elimination of the primary variable, the LSAEs in our method are positive definite and involve a significantly smaller number of degrees of freedom than the indefinite system in the mixed method. Moreover, any conforming finite element space can be used for flux approximation, whereas the mixed method requires a pair of approximation spaces that satisfy the discrete inf-sup stability condition, necessitating careful consideration.
The thesis establishes four main results. First, we develop a new FEM that approximates the flux independently of the primary variable. This is achieved by formulating a variational equation in the flux, eliminating the primary variable through the use of a user-defined parameter δ ∈ (0, 1]. Next, we build upon the proposed FEM by iterating the variational equation on a fixed mesh, incorporating the computed flux into the right-hand side data. This improvisation results in a considerably better flux approximation. The accuracy improvement is more pronounced for smaller values of the parameter δ ∈ (0, 1]. An analysis of the role of δ and the effect of iteration in improving accuracy is presented. Third, we develop an efficient adaptive finite element method motivated by the proposed iterative scheme, where the solution from a coarser mesh is incorporated into the right-hand side terms when solving the variational equation on a finer mesh. Finally, we establish a fully computable error bound for the computed flux. Numerical experiments are provided to confirm the theoretical findings
Impact of muscle oxidative capacity on neuromuscular recovery after eccentric damage
No full textAfter muscle damage, neuromuscular function is compromised, and exercise tolerance is reduced. Whether baseline (BL) muscle oxidative capacity (MOC), a measure of mitochondrial function, is associated with neuromuscular recovery in humans is unknown. Purpose: To test the hypothesis that MOC at BL is associated with recovery in neuromuscular efficiency (NME) and torque complexity after muscle damage. Methods: In 19 healthy adults (23 ± 2 yrs; 9M/10F), muscle oxygen consumption (mV̇O2) was estimated by near-infrared spectroscopy during repeated arterial occlusions ([heme]diff /2 slope) after intermittent isometric contractions performed at 50% of maximal voluntary contraction (MVC) until task failure. MOC at BL was determined by the recovery kinetics of mV̇O2. Muscle damage was induced by repeated isokinetic eccentric contractions until MVC torque was reduced by 40%. Participants performed MVCs and repeated the task-failure protocol 1h, 24h, 48h, and 7d post-damage. Changes in torque complexity were determined at exercise onset by detrended fluctuation analysis (DFA), approximate (ApEn). Onset NME was calculated as muscle torque of the first three contractions divided by the root mean square (RMS) of electromyography signals normalized to MVC at BL. Comparisons were made using one-way repeated measures ANOVAs and Tukey’s tests. Relationships were determined by linear regression. Results: MVC was lower 1h and 24h (both, p 0.99) compared to BL. DFA increased 24h, 48h, and 1-week (p < 0.05), compared to BL. Higher MOC at BL was associated with recovery of onset NME (p = 0.04; r² = 0.44). Conclusion: Muscle damage lowered NME at task failure within 1h but this effect was reversed by 48h after damage. The recovery of NME after damage was associated with MOC