Southern Arkansas University
  • Magnolia, United States
Recent publications
Laboratory activities are essential and important components of learning chemistry at the undergraduate level. The COVID-19 pandemic led to disruption of traditional modes of teaching and learning over the whole education spectrum including laboratory courses in chemistry. Although unfortunate, the COVID-19 lockdown period and following years challenged well accepted norms leading to new opportunities for higher education. The purpose of this work is to synthesize useful lessons from student experiences during COVID-19 pandemic and post-pandemic era aiming for improved future chemistry laboratories at the college and university level. Previously published studies addressed advantages and disadvantages of face-to-face vs. remote and online teaching of chemistry laboratory courses. However, there are only a few student-centered studies which analyze students’ perceptions of undergraduate chemistry laboratories in the post-pandemic era. Although the study was conducted at the university in the United States, we believe the lessons learned could be used globally. The present study contributes to the existing body of knowledge in this field of research. It is unique because surveyed students experienced different modalities of the laboratory and were given an opportunity to compare both modalities side by side. Therefore, student experiences provide stronger foundations of their preferences and perceptions described in this work. Based on our findings, it appears that post-pandemic undergraduate students taking a non-major course prefer hands-on experiments and a hybrid modality of chemistry laboratory.
A person's Mental Health (MH) dramatically influences their complete evolution in life, including their cognitive, emotional, and psychomotor components. A person with good MH is content with life and can be creative, learn new things, and take risks to accomplish more significant objectives. Currently, college students are dealing with MH concerns for various causes, which affect their academic performance and significantly contribute to poor academic results. Therefore, encouraging MH in college students presents a significant problem for educators, parents, teacher educators, and governments. Adolescence is a crucial and delicate time characterized by considerable physical, emotional, social, and religious changes. The physical, social, and psychological facets of an individual's growth are laid out in this period, with mental health as a crucial factor in promoting these gains. Therefore, it becomes crucial for researchers to use Deep Learning (DL) algorithms to study the association between MH and vital psychological characteristics, such as emotional intelligence, personality traits, and intelligence. The personal aspects, namely personality, emotional intelligence, and MH are all related ideas that influence one another. Individuals must have mental well-being and emotional harmony to have a good personality. The current study uses DL techniques to investigate the relationship between college students' MH, emotional intelligence, and personality features. To perform a thorough study on emotion identification and Mental Health Prediction (MHP) among college students, this project investigates the integration of edge computing enabled by the Internet of Things (IoT) in the context of intelligent systems. Innovative treatments are urgently needed due to this population's rising prevalence of MH issues. This paper aims to continuously monitor and predict college students' MH using Edge Computing (EC) and IoT technology.
This study explores the relationship between scientific and technological innovations and the financial performance of startups, focusing on a representative Artificial Intelligence (AI) healthcare startup. Utilizing Principal Component Analysis (PCA), the research aims to dissect the complex interplay between innovation-related metrics—such as R&D spending, patent counts, and technology adoption rates—and financial outcomes like revenue growth, profitability, and market share. The PCA methodology enabled the reduction of high-dimensional data into PCA, which clearly illustrates how several dimensions of innovation impact financial metrics. The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments. This paper aims to assist the ecosystem of startup consumers (including investors, business owners, and policymakers) in making better decisions that balance technological progress with economic objectives.
Epilepsy is a neurological condition that is found in most people all over the world, and the ability to accurately anticipate seizures in epileptic patients has a significant impact on both their level of protection and their overall quality of life. This research proposes a unique patient specific seizure prediction approach based on Deep Learning (DL) using long-term scalp electroencephalogram (EEG) recordings to predict seizure onset. Preictal brain states should be adequately detected and differentiated from the prevalent interictal brain states as early as possible to make this technology acceptable for real-time use. A single automated system has been designed for the Features Extraction (FE) and classification processes. The raw EEG signal that has not been pre-processed is considered the input to the system, and the signal is further reduced using subsequent computations. An innovative reconstruction approach using Variational Auto-Encoder Generative Adversarial Networks (VAE+C+GAN) with the Cramer Distance (CD) and a Temporal-Spatial-Frequency (TSF) loss function is presented in this research work. The machine that discriminates receives instructions to differentiate between created tests and actual samples, while the generator is verified to produce false samples that the discriminator does not recognize as fake. The proposed VAE+C+GAN’s experimental results have been examined, and a classification accuracy of 95% has been achieved. According to the experiment's findings, the VAE-C-GAN performs better than the current EEG classification system and has excellent potential for real-time applications.
Environmental variables, such as resource quality, shape growth in organisms, dictating body size, an important correlate of fitness. Variation in prey characteristics among populations is frequently associated with similar variation in predator body sizes. Anthropogenic alterations to prey landscapes impose novel ecological pressures on predators that may shift predator phenotypes. Research has focused on determining the adaptability of the phenotypic response by testing its genetic heritability. Here, we asked if anthropogenic shifts in prey size across the landscape correlate with juvenile growth rates among populations of watersnakes with divergent life-history phenotypes. We sought to determine if growth rate variation is the product of genetic adaptation or a non-heritable phenotypic response. Using a common-garden design, we measured growth of neonate snakes from fish farms varying in prey size. We found juvenile growth rates are faster for snakes with larger initial body sizes and from populations with larger average prey sizes. Our data suggest variability in juvenile grow rates within and among populations are not the product of genetic adaptation, but the indirect consequence of initial offspring size variation and prey consumption. We propose larger offspring sizes may favor increased juvenile growth rates, mediated through a larger morphological capacity to consume and process energy resources relative to smaller individuals. This experiment provides evidence supporting the growing body of literature that non-heritable responses may be significant drivers of rapid phenotypic divergence among populations across a landscape. This mechanism may explain the stability and colonization of populations in response to rapid, human-mediated, landscape changes.
In recent years, light management based on localized surface plasmon resonance (LSPR) effects in perovskite solar cells (PSCs) has received significant attention. However, the use of surface plasmon polariton (SPP) excitations in PSCs has been less studied. Meanwhile, hole transport layer-free perovskite solar cells (HTL-free PSCs) have garnered interest due to their lower cost. In this study, we improve light absorption in HTL-free PSCs by simultaneously utilizing LSPR and SPP effects. Au nanotriangles are employed on the surface of the back electrode to excite SPPs. The thickness of the perovskite layer is varied from 100 nm to 400 nm. The optimal periodicity and dimensions of the triangular nanoparticles are determined for each perovskite layer thickness. In the optimal structures with perovskite layer thicknesses of 100 nm, 200 nm, 300 nm, and 400 nm, absorption enhancements of 25%, 12.4%, 13%, and 4.3% are achieved, respectively. The interaction of light with SPP and LSP modes leads to improved solar cell performance. Furthermore, the short circuit current density (JSC) in structures with layer thicknesses of 100 nm and 200 nm increased from 16.7 mA cm⁻² to 20.71 mA cm⁻² and from 19.8 mA cm⁻² to 21.86 mA cm⁻², respectively. Other photovoltaic characteristics of the solar cell were obtained through optical-electrical numerical analysis. For the improved solar cell with a perovskite thickness of 100 nm, the values of open circuit voltage, efficiency, and fill factor were 0.847 V, 0.81, and 14.24%, respectively, representing increases of 1.1%, 2.4%, and 28.7% compared to the bare device. Additionally, in the solar cell with a thickness of 200 nm, an efficiency of 17.03% was achieved, showing a 12.5% improvement compared to the bare structure. Our research results facilitate the design of high-performance, ultra-thin, semi-transparent solar cells.
MoO 3 thin film was fabricated on an indium tin oxide substrate using the physical vapor deposition technique. X‐ray diffraction and scanning electron microscopy study to investigate surface morphology, grain size, and surface structure, which are critical for absorbing solar spectra in water splitting for hydrogen energy generation. Ultraviolet–visible spectroscopy was used to confirm the absorption of solar spectra and the percentage of transmittance. Fourier‐transform infrared analysis provided the functional groups present in the deposited thin film. The Tauc plot was used to determine the thin‐film band gap, which allowed for the analysis of charge carrier transitions from the conduction band to the valence band. Electrochemical impedance spectroscopy investigations confirmed the charge transfer processes to the counter electrode and electrolyte interfaces. The observed low curve for MoO 3 indicated low resistance and allowed efficient charge transfer. Linear sweep voltammetry analysis was used to measure photocurrent and solar light to hydrogen emission when the thin film was exposed to solar spectra. The thin film's observed hydrogen emission rate was 3731.74 mol g ⁻¹ h ⁻¹ , and the STH% of MoO 3 was found to be 0.345% at 0.8 V. These findings highlight the promising potential of MoO 3 as a material for hydrogen energy generation using solar light.
Compulsive behaviors are a hallmark symptom of obsessive compulsive disorder (OCD). Striatal hyperactivity has been linked to compulsive behavior generation in correlative studies in humans and causal studies in rodents. However, the contribution of the two distinct striatal output populations to the generation and treatment of compulsive behavior is unknown. These populations of direct and indirect pathway-projecting spiny projection neurons (SPNs) have classically been thought to promote or suppress actions, respectively, leading to a long-held hypothesis that increased output of direct relative to indirect pathway promotes compulsive behavior. Contrary to this hypothesis, here we find that indirect pathway hyperactivity is associated with compulsive grooming in the Sapap3-knockout mouse model of OCD-relevant behavior. Furthermore, we show that suppression of indirect pathway activity using optogenetics or treatment with the first-line OCD pharmacotherapy fluoxetine is associated with reduced grooming in Sapap3-knockouts. Together, these findings highlight the striatal indirect pathway as a potential treatment target for compulsive behavior.
The purpose of this study was to explore elementary physical education teachers’ perceptions toward prominent socializing agents (e.g. students, administrators, and parents/guardians) and related factors during the COVID-19 pandemic. A total of 15 elementary physical education teachers participated in semi-structured interviews. Using conventional qualitative content analysis techniques, these themes were identified: (a) teachers' perceptions of working through the pandemic, (b) teachers' perceptions of parental/guardian support through the pandemic, (c) teachers' perceptions of parental/guardian support through the pandemic, and (d) thinking beyond the pandemic. Participant data highlights that the pandemic's impact on elementary physical education teachers was not uniform across all experiences. Instead, teachers’ experiences through the pandemic were distilled across a continuum. This study offers additional evidence on key socializing agents’ impact on physical educators and discusses future practical and research-based considerations for the field.
The abilities of Ni-Si52, Ni-SiNT(6, 0), Ni-C52, Ni-B26N26, Ni-CNT (6, 0) and Ni-BNNT (6, 0) as catalyst of CO2-RR to CH4 are compared. The Eadoption and Eformation of Ni-Si52, Ni-SiNT(6, 0), Ni-C52, Ni-B26N26, Ni-CNT (6, 0) and Ni-BNNT (6, 0) are stable parameters. The Eadoption of CO2-RR on Ni-Si52, Ni-SiNT(6, 0), Ni-C52, Ni-B26N26, Ni-CNT (6, 0) and Ni-BNNT (6, 0) are − 3.34, -3.47, -5.42, -3.81, -3.99 and − 5.89 eV. The HCOOH, CO, HCOH, CH3OH and CH4 adsorption as important products for CO2-RR on Ni doped nanotubes an nanocages are examined. The Eformation of CO2-RR on Ni-Si52, Ni-SiNT(6, 0), Ni-C52, Ni-B26N26, Ni-CNT (6, 0) and Ni-BNNT (6, 0) are − 4.27, -4.39, -4.51, -4.87, -5.02 and − 4.92 eV. The CO2-RR mechanisms on Ni doped nanotubes and nanocages are examined. The overpotential of CO2-RR on Ni-Si52, Ni-SiNT(6, 0), Ni-C52, Ni-B26N26, Ni-CNT (6, 0) and Ni-BNNT (6, 0) are 0.338, 0.327, 0.331, 0.304, 0.295 and 0.296 V. The Overpotential of CO2-RR on Ni-C52, Ni-B26N26, Ni-CNT (6, 0) and Ni-BNNT (6, 0) are lower than metal catalysts. The Ni-Si52, Ni-SiNT(6, 0), Ni-C52, Ni-B26N26, Ni-CNT (6, 0) and Ni-BNNT (6, 0) is processed the CO2-RR.
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518 members
Mahbub Ahmed
  • Engineering and Engineering Physics
Hayder Zghair
  • Engineering and Physics
Md Islam
  • Engineering and Science
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Magnolia, United States
Head of institution
Trey Berry