University of Arkansas at Little Rock

Droughts are affecting ecosystems worldwide and are expected to become increasingly frequent and intense in the near future. While the detrimental impacts of droughts on terrestrial ecosystems are well documented, it is largely unknown whether and how drought effects on soils can alter ecosystem responses to subsequent drought. We will present several case studies which demonstrate that drought effects on soil microbial communities not only affect soil functioning in response to recurrent drought, but can also have legacy effects on grassland productivity and how it is affected by subsequent drought. Furthermore, we will showcase recent advances in testing for drought legacy effects on soil properties related to plant water availability, highlighting that scenarios of frequent and more intense drought can lead to reduced plant water access even following rain events and during subsequent dry periods. Our findings suggest that drought soil legacies induced by repeated and / or severe drought can have major implications for the functioning of grassland ecosystems and their response to subsequent drought.

To understand Black mothers' perspectives on the autism screening process with the Modified Checklist for Autism in Toddlers (M-CHAT) and preferences in provider communications and interactions around autism screening. This qualitative study thematically analyzed semi-structured interviews of Black mothers (N = 11) whose child screened positive on an autism screener administered as part of a routine well-child visit in primary care. All interviews were audio-recorded, transcribed, and entered into NVivo software for data management and analysis. Coders achieved an average of over 95% agreement in double-coded transcripts across all thematic codes. Results from a qualitative thematic analysis of Black mothers' experiences with and preferences for early autism screening in primary care produced 5 primary themes: (1) preference for the pediatrician to report positive screening results, (2) desire to discuss developmental concerns with family members, (3) online resources as key information resources on autism and child development juxtaposed to, (4) valuing face-to-face time with early childhood providers, and (5) barriers to completing the M-CHAT. Black mothers described their preferences for completing autism screening for their children and emphasized the role of the pediatrician, family members, and online resources in providing information about child development and autism concerns. Trust in the pediatrician emerged as a salient theme, which runs counter to prior narratives that describe earned mistrust between Black caregivers and medical providers. Results can inform the improvement of early autism screening processes and health care communication for underrepresented families in primary care settings.

Medicare Annual Wellness Visits (AWVs) are yearly appointments with a PCP aimed at preventing disease or disability, based on current health and risk factors. Despite the benefits of AWVs, utilization remains low, particularly in rural states like Arkansas. This study aims to better understand the low utilization by exploring the awareness and perception of AWVs among older Arkansans. The research team conducted 9 focus groups with 96 Medicare-enrolled Arkansans. The researchers utilized an inductive thematic coding process with multiple coders for analyzing the focus group transcripts. Results suggest that Arkansans were largely unaware of AWVs. Participants saw the visits as repetitive and burdensome, did not understand their benefits, and did not understand how they differed from routine physicals. Encouragement from providers helped motivate utilization. Some specific aspects of AWVs were highlighted as useful and beneficial, such as the cognitive assessments. Still, some participants had fear, anxiety, or distrust related to the cognitive assessments. We recommend the utilization of these findings in designing messaging and awareness campaigns around AWVs and encouragement of provider promotion.

This work deals with Reconfigurable Intelligent Surfaces or (also known as Intelligent Reflecting Surfaces) which provide an improvement of performance of wireless communications by utilizing software-controlled meta-surfaces for the reflected signals from the source to destination, especially when the direct path is weak and hence improving the antenna array. The performance evaluation is done by one of the techniques that utilizes the very high rates and/or large meta-surfaces to outperform the classic method of decode and forward, both in terms of the total transmit power and the energy efficiency. The channel measurements used by classifying them as a function of the frequency band, and usage of deployment scenarios such as indoor/outdoor, and system configuration. So, the 5G and beyond which is 6G use several antennas including the algorithms to make use of signal processing. This improves the antenna array technology. In the next steps, we work on machine learning-based performance prediction using a deep neural network (DNN) to evaluate the performance of the RIS-aided system in the low-frequency range. This would provide a greater influence of scatterers with a weaker signal attenuation allowing the neural network to pick up a larger number of features, e.g. accurately predicting the energy efficiency (EE), and outage probability (OP).

Background:Parkinson’s disease (PD) is the second-most diagnosed age-related neurodegenerative disorder globally. PD pathology causes dysregulation of motor movement and for many, mild or minor cognitive impairment (PD-MCI). The most recommended global screening exam to detect PD-MCI is the Montreal Cognitive Assessment© (MoCA). Traditionally, the MoCA is scored according to guidelines and compared against a standardized cutoff, but clinical professionals additionally draw upon their observations of the patient’s performance to determine the score. To better understand how clinicians use the MoCA in real-world clinical settings, we employed the qualitative descriptive approach to identify performance patterns professionals utilize to assess the cognitive health of a person with PD.Methods:We curated retrospective data from nine people with PD-MCI to PD-Dementia. Each patient had one completed MoCA exam and one neuropsychological report containing health data. The assessments were organized into three groups of three and used in semi-structured interviews with six clinical professionals to gather at minimum two clinical opinions for each.Results:Three coders distilled, based on consensus, three clinically meaningful patterns from the interviews composed of features emphasized as vital by the interviewees for determining a person’s cognitive health. The derived features were from a patient’s performance on sections of the MoCA exam, sociodemographic and health data from the neuropsychological report, and dependent relationships between the assessments.Conclusions:Our study leveraged the popular MoCA exam to explore its real-world clinical use. Extracting these patterns clinicians recognized provides deeper insights into how they interpret cognitive health creating a blueprint for future efforts to tailor the exam for detecting cognitive impairment in people with PD.

In this paper, we propose a novel deep Q-reinforcement learning framework employing self-attention models to address dynamic resource allocation problems in optical wireless networks. The proposed framework overcomes the limitations of traditional recurrent neural networks, which struggle with sequential data execution and capturing dependencies within sequences. By leveraging a self-attention mechanism, the model enables users to learn channel access policies in an online, fully distributed manner without requiring coordination or global network information. The key innovation of this work is the integration of a single-agent DQL with a low-complexity self-attention mechanism. Unlike multi-agent systems, which often require complex coordination, the proposed single-agent framework simplifies the learning process while maintaining high performance. Numerical results demonstrate the superiority of the proposed framework. The model achieves a 153% improvement in cumulative rewards compared to state-of-the-art GRU and Long short-term memory based models. Additionally, it reduces collisions to less than 0.5% and achieves approximately twice the channel throughput of the slotted-Aloha protocol with optimal attempt probability.

BackgroundRepeated cognitive testing can boost performance due to practice effects (PEs). Follow‐up scores are rarely adjusted for PEs, but such correction can be highly important as it has been shown to result in earlier detection of MCI and increased validity based on concordance with Alzheimer's biomarkers. However, it remains unclear to what extent PEs persist across multiple follow‐ups and long durations, which are further complicated by normative age‐related decline. We examined PEs across 17 years from midlife to old age in a nonclinical sample.MethodMen (N = 1,608) in the longitudinal Vietnam Era Twin Study of Aging (VETSA) completed neuropsychological batteries over 4 waves: wave 1(N = 1,290, mean age=56); wave 2, mean ages=62); wave 3 (mean age=68); wave 4 (mean age=73). Waves 2 and 3 also included age‐matched attrition replacements. By comparing returnees’ performance to replacements being assessed for the first time, we estimated PEs for 30 tests at each wave using generalized estimating equations, accounting for normative age effects and differential patterns of missingness. We calculated unadjusted and PE‐adjusted cognitive scores (episodic memory, executive function, fluency, processing speed, visual memory, and visuospatial). At each wave we compared MCI prevalence based on unadjusted versus PE‐adjusted scores.ResultAmong returnees completing all 4 assessments, we found significant PEs for 11 tests at wave 2, 8 tests at wave 3, and 5 tests at wave 4. PEs were most apparent among episodic and visual memory tests. PE‐adjusted cognitive factor scores were significantly lower than unadjusted factor scores at all follow‐ups for all domains except fluency. MCI prevalence increased up to 20% after PE adjustment (from 14% to 18% at wave 4), indicating earlier detection.ConclusionPEs persist across multiple assessments and decades, even with long testing intervals. Importantly, even very small PEs can shift scores below the impairment threshold, resulting in earlier detection and diagnosis of MCI. Additionally, we previously showed that PE‐adjusted MCI diagnosis increases accuracy based on biomarker concordance and would dramatically reduce costs and length of clinical trials by reducing necessary sample size and increasing power. These results underscore the importance of accounting for PEs in longitudinal studies.