Artificial intelligence has been integral to daily societal systems, including modern education through tools like OpenAI’s ChatGPT. While past studies have assessed ChatGPT’s performance in various domains, such as law and medicine, a gap in research on the analysis of its efficacy in secondary school-level subjects persists. Therefore, this study assessed the performance of ChatGPT in high school level linguistics and mathematics questions, in correlation to the perceptions of students and professors. By extension, it provides a more detailed analysis of ChatGPT’s potential as a learning tool. To achieve this, SAT questions are administered to ChatGPT. Through this investigation, it was observed that ChatGPT generally demonstrates greater consistency in linguistics compared to mathematics, with different levels of reliability across distinct SAT subareas. Additionally, ChatGPT was also observed to perform better than at least 50% of high school SAT student test takers, with accuracy rates of 59.59% in linguistics and 56.41% in mathematics. Through survey and interview, the study also reveals that there is a gap between student perception on ChatGPT’s performance than its simulation accuracy rate. In linguistics, there was a significant gap between the mean survey with interview results and the simulation accuracy, while in mathematics, the gap was smaller.

Alzheimer’s disease (AD) is the prevalent form of dementia caused by the accumulation of neurotoxic amyloid-beta (Aβ) plaques produced by the cleavage of the human Aβ precursor protein (HAPP). Clearance of Aβ can be promoted via the apoptosis of peripheral cells where HAPP is overexpressed. This can be achieved by the induction of an immune response via exposure to CD8+ T-cell epitopes from HAPP. Immunoinformatics was used to map these epitopes and the human leukocyte antigens (HLAs) that pair with them. Six immunogenic epitopes were obtained, with one epitope exhibiting “promiscuity”,” binding to two different HLAs. A total of seven epitope-HLA pairs were identified with their respective binding free energies and dissociation constants: DTKEGILQY- HLA-A*26:01 (−13.3, 4.20E-10), TPDAVDKY- HLA-B*35:01 (−10.9, 2.10E-08), EVHHQKLVFF- HLA-A*26:01 (−11.1, 1.40E-08), KADKKAVIQHF- HLA-B*58:01 (−10.0, 8.40E-08), AEPQIAMF- HLA-B*44:02 (−9.90, 1.10E-07), AEPQIAMF- HLA-B*44:03 (−8.90, 5.70E-07), and LLPVNGEF- HLA-B*15:01 (−7.00, 1.20E-05). While most of the epitope-HLA pairs exhibited good binding evidenced by their low binding free energies and dissociation constants, AEPQIAMF- HLA-B*44:03 and LLPVNGEF- HLA-B*15:01 did not meet the required threshold. The epitopes are nonallergenic and nontoxic and exhibit significant sequence conservation across different isoforms. They also do not demonstrate significant cross-reactivity with other proteins in the human proteome. The identified epitope-HLA pairs showed highest population coverage in the East Asian region, specifically in South Korea and Japan. These results indicate that these CD8+ T-cell epitopes can be used for  potential peptide vaccine candidate especially in the identified regions.

The Heisenberg group over Z, HH(Z), is the set of all 3 × 3 upper triangular matrices with integer entries above the diagonal and ones on the diagonal together with matrix multiplication. For a positive integer nn ≥ 2, its finite counterpart, HH!, is defined similarly with entries in Z! under modular multiplication. Equivalently, these groups can be described as sets of triples (aa, bb; cc) with a nonabelian group operation involving the standard commutator structure.

This paper investigates the word length, the minimal number of generators required to express a group element in HH(Z) and HH”, where pp is prime, relative to their standard generating sets. We define and analyze two specific automorphisms, σσ and φφ, that preserve word length and enable us to limit our analysis to a smaller representative subset of group elements without loss of generality. These automorphisms allow for more efficient classification and the derivation of explicit formulas for computing word length. We use these results to categorize elements in HH” into three types based on their algebraic structure and their behavior under these automorphisms. In addition, we explore identities related to word length.

The results reveal new insights into the combinatorial and algebraic structure of Heisenberg groups, with implications for computational group theory, representation theory, and cryptography. Our approach offers a novel perspective that bridges structural group theory with algorithmic word computation.

A new exponentiated power Shanker distribution is proposed, and its statistical properties are thoroughly discussed. The distribution parameters are estimated using the maximum likelihood estimation method. The new distribution is then extended to a regression model by applying the logarithmic transformation. The proposed regression model is applied to a marriage dissolution data set consisting of 568 entries, where the response variable is the number of years in the marriage and the predictor variables are the husband’s education level, husband’s race (black or not), marital mixing (mixed or not), and divorce status. The results reveal that some predictors significantly influence the duration of marriage. The estimated regression model is given by year(xˆ) = 0.0809 − 1.6190(Heduc) + 0.4343(Heblack) − 1.0747(Mixed) + 1.1819(Divorce). The model selection criteria for the propose model yield the following values: Akaike information criterion = 8073.054, Bayesian information criterion = 8086.080, consistent Akaike information criterion = 8087.080, and Hannan-Quinn information criterion = 8078.137. Furthermore, a machine learning-based multiple regression model is conducted to estimate mean absolute error, mean squared error, and root mean squared error, which are 10.1138, 162.1605, and 12.7342, respectively. The results are compared with those obtained from a traditional linear regression model, and findings suggest that the machine learning-based multiple regression model outperforms the linear regression model, with smaller error values.

The performance of facial recognition models is influenced by the choice of data partitioning strategy. Appropriate data splitting techniques enable more reliable estimation of model generalization and help assess overfitting. In this study, we examine the performance of a convolutional neural network (CNN)-based facial recognition model under four commonly used data splitting approaches: random splitting, stratified splitting, bootstrap validation, and k-fold cross-validation. Experiments are conducted on two benchmark data sets, Labeled Faces in the Wild and the Olivetti Research Laboratory. Model performance is evaluated using accuracy as well as empirical estimates of model bias and variance. The results indicate that k-fold cross-validation provides a more stable performance estimate under the experimental conditions considered, suggesting its suitability as a validation procedure for CNN-based facial recognition models.

This study presents the first documented report on the successful colonization of the indigenous and underutilized fern species Diplazium esculentum by the fungi Rhizophagus irregularis in the Philippines. The beneficial role of the microorganism was evaluated through its morphological characteristics, with improved growth parameters of the plant, and biochemical characteristics revealed a significant increase in relative water content, membrane stability index, total soluble sugar, amino acids, and sucrose content. Hydrogen peroxide and malondialdehyde were recorded to have high levels under drought stress conditions. This study showed a decline in concentrations due to the inoculation of R. irregularis, highlighting its potential to alleviate stress. These results feature the potential of this microorganism to minimize the impact of drought stress on indigenous fern plants and allow a better adaptation to dry conditions.

Reactive oxygen species (ROS) play essential roles in cellular processes, including immune defense and cell signaling. However, overproduction of the p66Shc protein leads to increased ROS levels and oxidative stress, greatly contributing to the progression of Alzheimer’s disease (AD). This study investigated the inhibitory potential effects of flavanols from the Camellia sinensis plant on p66Shc to reduce ROS generation. These flavanols examined included catechin, epicatechin gallate (ECG), epigallocatechin gallate (EGCG), gallocatechin gallate (GCG), epigallocatechin (EGC), epicatechin (EC), and gallocatechin (GC). A homology model of p66Shc was constructed, and molecular docking simulations were performed to explore the flavanols and p66Shc interactions. One-way analysis of variance testing showed significant differences in binding affinities, and GCG had the strongest interaction (−7.6 kcal/mol) via van der Waals interactions. ECG and EGCG followed (−6.9 kcal/mol) and formed additional pi–anion and pi–alkyl interactions. Catechin had a binding score of −6.4 kcal/mol, while the remaining flavanols had a score of −6.2 kcal/mol with varied interactions. These findings suggested that GCG, ECG, and EGCG could be explored further for their potential role in mitigating oxidative stress in AD.