The primary objective of this study is to examine the statistically homogeneous clustering in the hierarchical arrangement of the use of adverbial clauses for location, recognition, and comprehension of words presented in three-dimensional, rebus, and distorted forms. The study was conducted on a sample of 140 participants. The first subset of participants constituted the experimental group, consisting of 70 deaf students, while the second subset formed the control group, comprising 70 hearing students of the same chronological age. A battery of tests was utilized as a measurement instrument, including the “Test of Writing and Comprehending Adverbial Clauses for Location,” “Test of Reading, Writing, and Comprehending Words Presented in Three-Dimensional Form,” “Test of Reading, Writing, and Comprehending Words Presented in Rebus Form,” and “Test of Reading, Writing, and Comprehending Words Presented in Distorted Form.” In the descriptive analysis, frequencies of the total words achieved by both deaf and hearing participants were computed for the different types of measurement instruments employed. To identify the most robust homogeneity of participants concerning the applied variables, non-hierarchical and hierarchical Cluster Analyses were employed. The research results indicate a significant lag in the use of adverbial clauses for location, writing, reading, and understanding words presented in three-dimensional, rebus, and distorted forms among deaf children in comparison to their hearing peers. The Cluster Analysis revealed the most significant linkage between the variable “Number of used adverbial clauses for location” and the variable “Number of recognized words presented in three-dimensional form.” This link was clustered with the variable “Number of written words presented in rebus form” and the variable “Number of written words presented in three-dimensional form.” An analysis of variance for isolated clusters identified a statistically significant difference in the entire measurement space of adverbial clauses for location, three-dimensional, rebus, and distorted forms of words, with a level of statistical significance at p=0.00.

In deaf students, there is no contextual understanding and recognition of word types through linguistic competence testing compared to the hearing population, because 67.10% do not understand, and 10.00% of deaf children partially understand the contextual application of word types in a written text task. The aim of the study is to determine the distance of language discourse between deaf and hearing respondents and to establish a discrimination factor that can be used for practical purposes in a classification sense, with the aim of determining the priority of linguistic education and language elements of deaf children in relation to their lagging behind hearing children. The study was conducted on a sample of 140 respondents. The first subsample of respondents, the experimental group consisted of 70 deaf students, and the second subsample, the control group of 70 hearing students, of the same chronological age and gender. The measuring instrument “Test of comprehension of the written form of expression” was applied. The collected data were processed using the discriminant analysis method. The results of the study showed that the discrimination factor is in the sequence of the use of words, adjectives, exclamations and adverbs. Nouns and prepositions have a negative correlation, which points to the fact that these four types of words are in direct implication with nouns and prepositions, and represents information that these four types of words must be more represented in the educational materials of written expression of deaf children. The results of the study also open up a series of questions focused on the quality of the educational processes of deaf children, as well as the level of lag in written communication compared to hearing children. The results of the study can influence the raising of general rehabilitation procedures to a higher level of responsibility in education centers where deaf children are educated.

The study investigated the extraction yield of defatted Silybum marianum seed samples using maceration as the sole extraction technique. Different solvent types (methanol, ethanol, and water) and extraction durations were tested. Prior to extraction, the samples were ground and defatted with n-hexane. For each combinationofsolvent type, and extraction duration, the extracted mass (g of extract/g of defatted sample) was determined. The impact of each parameter on the yield was analyzed, revealing significant effects.Results showed that water-based maceration for 4 hours yielded the highest average mass of dry extract, followed by shorter durations at 2 hours. Ethanol occasionally outperformed methanol, particularly at the 2-hour mark, but methanol consistently produced lower yields across longer extraction durations. These findings emphasize the need for careful optimization of solvent type and extraction duration to maximize extraction yield.Subsequent analysis using Tukey's HSD test revealed significant differences in dry extract mass among solvents. Water yielded the highest at 2 and 4 hours, ethanol at 4 hours, and methanol at 4 hours as well. KEYWORDS:Silybum marianum;maceration;solvent types;plant extraction,yield analysis

On the threshold of a new technological era, Sixth Generation (6G) networks promise to revolutionize global connectivity, bringing mobile communications to data speeds in the terabits per second range and ultra-low latency. These networks will enhance the user experience enable a wide range of advanced applications and emerging services. Artificial Intelligence (AI)-powered network functions and services, also known as Network Intelligence Functions (NIF) and Network Intelligence Service (NIS), are essential to achieve this vision. In this study, we present the design and development of an end-to-end framework for orchestrating AI-based functions. Utilizing Kubernetes (K8s) and Prefect, we showcase its implementation through an AI-driven Traffic Classification (TC) use case. Our results confirm the feasibility of the proposed framework, offering valuable insights in the lifecycle management design, such as data collection, decision-making, and critical performance metrics, including deployment time and model performance in terms of accuracy and inference times among three different Machine Learning (ML)-based TC models.

Image-based high-throughput plant phenotyping utilises various imaging techniques to automatically and non-invasively understand the growth of different plant species. These innovative imaging infrastructures are implemented to monitor plant development over time in indoor or outdoor environments. However, understanding the relationship between genotype and phenotype interactions under different environments remains challenging. This research study demonstrates superior extraction of leaf morphological features of different Arabidopsis thaliana ecotypes by analysing leaf geometry using a sequence of RGB images. Upon successful extraction of anatomical features, leaf length and area are converted into physical coordinates. Furthermore, considering these leaf features as 1D signals, the Fourier Spectrum is analysed, and most descriptive features are selected using PCA. Finally, leaf shape classification is established by training and testing five distinct ML models. A thorough evaluation of selected models demonstrates superiority in classifying two common leaf shapes of Arabidopsis plants.

The increasing demand for high-quality and efficient Channel Estimation (CE) in 5G New Radio (5G-NR) systems has prompted the exploration of advanced Deep Learning (DL) techniques. While traditional methods, such as Linear Interpolation (LI) and Least Squares (LS), provide reasonable accuracy and are practical for real-time physical layer processing, recent DL-based CE approaches have primarily focused on accuracy, often without evidence of real-time capabilities. In this paper, we present a comprehensive evaluation of DL-based Super-resolution (SR) methods for CE, comparing models like Super Resolution Convolutional Neural Network (SRCNN), ChannelNet, and Enhanced Deep Super-Resolution (EDSR) in both 1D and 2D convolutional architectures. We optimize these models using NVIDIA TensorRT to reduce computational complexity and latency. Our results show that the optimized 1D-EDSR model achieves the best performance with a Mean Squared Error (MSE) of 0.0126, outperforming all other models in terms of accuracy. However, the optimized 1D-EDSR model fails to meet real-time constraints due to additional computational overhead (0.6798 ms/sample). In contrast, the 1D-SRCNN model offers a balanced trade-off between MSE (0.01738) and inference time (0.0866ms/sample), achieving 40% higher accuracy than LS (0.0288) while maintaining the best energy efficiency (1.48 mJ/sample).