Alzheimer’s disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer’s disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer’s disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA–CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD–CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures.

With the aim of identifying a probability model that not only correctly describes the stochastic behavior of extreme environmental factors such as excess rain, acid rain pH level, and concentrations of ozone, but also measures concentrations of NO${}_2$ and leads deliberations, etc., for a specific site or multiple site forms as well as for life testing experiments, we introduced a novel class of distributions known as the Sine Burr $\mathcal{X}-\mathcal{G}$ family. Some exceptional prototypes of this class are proposed. Statistical assets of the presented class, such as density function, complete and incomplete moments, average deviation, and Lorenz and Bonferroni graphs, are proposed. Parameter estimation is made via the likelihood method. Moreover, the application is explained by using four real data sets. We have also illustrated the significance and elasticity of the proposed class in the above-mentioned stochastic phenomenon.

The dedication to enhancing the inclusion of students with disabilities has led to the growing prevalence of assistive technology (AT) in education. The present paper aims to systematically review studies on the effects of AT on the inclusion of students with impairments. A review of pertinent empirical studies published from 2009 to 2022 was conducted across four databases: Web of Science, Scopus, ERIC, and PsycINFO. The sample comprises 32 papers that fulfilled the inclusion criteria of this review, from a total of 218 papers identified. The results highlight that advanced AT markedly enhances accessibility and active involvement for students with mobility impairments, improving their academic engagement and social integration. Nonetheless, the analysis also highlights obstacles to efficient AT adoption, including inadequate teacher training, restricted awareness of AT possibilities, and difficulties related to availability and cost. The study closes with recommendations for governmental support, enhanced financing, and specialized teacher training to use advanced AT to create more inclusive educational settings for kids with significant mobility impairments.

China’s rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions.

The lithological dichotomy in the Hammam Faraun Member (Gulf of Suez, Egypt) reveals a stable western flank with Nullipore carbonate deposits, contrasting with the clastic-prone eastern margin influenced by tectonic activity. This study aims to decipher multifactorial controls on spatial lithological variability and reservoir implications through (1) foraminiferal-based paleoenvironmental reconstruction; (2) integrated sequence stratigraphic–petrophysical analysis for sweet spot identification; and (3) synthesis of lateral facies controls. This study uniquely integrates foraminiferal paleoenvironmental proxies, sequence stratigraphy, and petrophysical analyses to understand the multifactorial controls on spatial variability and its implications for reservoir characterization. Middle Miocene sea surface temperatures, reconstructed between 19.2 and 21.2 °C, align with warm conditions favorable for carbonate production across the basin. Foraminiferal data indicate consistent bathyal depths (611–1238 m) in the eastern region, further inhibited in photic depths by clastic influx from the nearby Nubian Shield, increasing turbidity and limiting carbonate factory growth. Conversely, the western shelf, at depths of less than 100 m, supports thriving carbonate platforms. In the sequence stratigraphy analysis, we identify two primary sequences: LA.SQ1 (15.12–14.99 Ma), characterized by evaporitic Feiran Member deposits, and LA.SQ2 (14.99–14.78 Ma), dominated by clastic deposits. The primary reservoir comprises highstand systems tract (HST) sandstones with effective porosity ranging from 17% to 22% (calculated via shale-corrected neutron density cross-plots) and hydrocarbon saturation of 33%–55% (computed using Archie’s equation). These values, validated in Wells 112-58 (ϕe = 19%, S_hc = 55%) and 113M-81 (ϕe = 17%, S_hc = 33%), demonstrate the primary reservoir potential. Authigenic dolomite cement and clay content reduce permeability in argillaceous intervals, while quartz dissolution in clean sands enhances porosity. This research emphasizes that bathymetry, sediment availability, and syn-sedimentary tectonics, rather than climate, govern carbonate depletion in the eastern region, providing predictive parameters for identifying reservoir sweet spots in clastic-dominated rift basins.

A less expensive and time-consuming option to simple random sampling (SRS) is the ranked set (RSS). Frequently, the RSS method produces more accurate estimators than those employed by SRS. The Chris-Jerry distribution (C-JD) is a new heavy-tailed distribution that helps model many data in human endeavors. The flexibility of the C-JD in capturing various hazard rate shapes ensures its applicability to a wide range of real-world data. This article's primary objective is to examine how well fifteen different estimation techniques, such as maximum and minimum spacing distances methods, Kolmogorov method, and variations of the method of the minimum distances, perform when compared to the maximum likelihood method for parameter estimation of the C-JD based on RSS. We conduct comprehensive simulation research and assess the efficacy of various estimates based on RSS and SRS using many criterion measures. To identify the optimal estimating strategy, partial and overall ranks of the mean estimates, mean squared errors, maximum absolute differences, mean absolute relative errors, average absolute biases, average absolute differences and average squared absolute error based on both designs are provided. The results of our study indicate that the maximum likelihood technique consistently outperforms other strategies, as evidenced by the overall rankings. Because RSS is more efficient than SRS, it is a more effective sampling method with lower accuracy measurements. To explain more, an actual data set about the fatigue life of a certain kind of Kevlar epoxy strand subjected to a constant continuous load at a pressure level of 90 % till the strand fails was examined.