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Zaki Brahmi,1 Mohammad Mahyoob,2 Mohammed Al-Sarem,1 Jeehaan Algaraady,3 Khadija Bousselmi,4 Abdulaziz Alblwi1 1Department of Computer Science, Taibah University, Madina, Kingdom of Saudi Arabia; 2Department of Languages and Translation, Taibah University, Madina, Kingdom of Saudi Arabia; 3Department of English, Taiz University, Taiz, Yemen; 4Department of Computer Science, LISTIC, University of Savoie Mont Blanc, Chambéry, FranceCorrespondence: Mohammad Mahyoob, Email mqassem@taibahu.edu.saPurpose: Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies.Methods: The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms.Originality: This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014– 2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse.Findings: The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).Keywords: speech disorder, speech recognition, dysarthria, machine learning, assistive technologies

The data warehousing process requires an architectural revolution to settle big data challenges and address new data sources such as social networks, recommendation systems, smart cities, and the web to extract value from shared data. In this respect, the pipeline modeling community for the acquisition, storage, and processing of data for analysis purposes is enacting a wide range of technological solutions that present significant challenges and difficulties. More specifically, the choice of the most appropriate tool for the user's specific business needs and the interoperability between the different tools have become a primary challenges. From this perspective, we propose in this paper a new interactive framework based on machine learning techniques (ML) to assist experts in the process of modeling a customized pipeline for data warehousing. More precisely, we elaborate first (i) an analysis of the experts' requirements and the characteristics of the data to be processed, then (ii) we propose the most appropriate architecture to their requirements from a multitude of specific architectures instantiated from a generic one, by using (iii) several ML methods to predict the most suitable tool for each phase and task within the architecture. Additionally, our framework is validated through two real-world use cases and user feedback. [JJCIT 2024; 10(3.000): 306-318]

This study proposes to monitor the physical, immune and cognitive responses and adaptations of elite rugby players throughout the season based on the loads performed. Anthropometric measurements, physical fitness tests (e.g., muscle strength and power, linear and change-of-direction speed, cardiorespiratory fitness) and analyses of serum concentrations of markers of muscle damage (creatine kinase [CK] and lactate dehydrogenase [LDH]) and brain-derived neurotrophic factor (BDNF) were carried out over a sporting season (24 weeks) for 17 elite rugby players (10 forwards and 7 backs) aged 18.91 ± 0.76 years. The physical fitness test results show improvements in the performance of both forwards and backs over the season (p < 0.05), with an advantage for backs compared with forwards in most tests (p < 0.05). Muscle damage markers decreased at the end of the season compared with the baseline levels for forwards (p < 0.05). CK levels were unchanged for the backs, but there were increased LDH concentrations at the end of the season compared with baseline (p < 0.05). Serum BDNF levels decreased for the total group between the second and third sampling (p < 0.05). The muscular and physical capacities of rugby players differ according to their playing position. Immune responses and adaptations, as well as BDNF levels, vary throughout the season and depend on the physical load performed.

Mild steel (MS) coated with protective bilayer hybrid coatings of polyetherimide, and ZnO/CuO was exposed to artificial treated wastewater (ATWW) inoculated with Desulfovibrio sp. (biotic system). Proteins from the extracellular polymeric substances (EPS) matrix of Desulfovibrio sp. responded to the coatings. PEI-CuO offered the highest performance against biocorrosion. The higher PEI-CuO resistivity value of $2.17\times 10^{6}~\Omega {\cdot }\mathrm{cm}^{2}$ contrasted with the decrease to $1.5\times 10^{5}$ and $1.6\times 10^{3}~\Omega {\cdot }\mathrm{cm}^{2}$ for PEI-ZnO and PEI coatings, respectively. The one order of magnitude increased resistance of the biotic system, compared to sterilized ATWW (abiotic system), resulted from the protective nature of the bacterial biofilm.

Thermal springs harbour microorganisms, often dominated by cyanobacteria, which form biofilms and microbial mats. These phototrophic organisms release organic exudates into their immediate surroundings, attracting heterotrophic bacteria that contribute to the diversity and functioning of these ecosystems. In this study, the microbial mats from a hydrothermal pool in the Ksar Ghilane oasis in the Grand Erg Oriental of the Desert Tunisia were collected to obtain cyanobacterial cultures formed by single cyanobacterial species. High-throughput analysis showed that while the microbial mat hosted diverse cyanobacteria, laboratory cultures selectively enriched cyanobacteria from the Leptolyngbya, Nodosilinea , and Arthronema. Per each of these genera, multiple non-axenic uni-cyanobacterial cultures were established, totalling 41 cultures. Cyanobacteria taxa mediated the assembly of distinct heterotrophic bacterial communities, with members of the Proteobacteria and Actinobacteria phyla dominating. The bacterial communities of uni-cyanobacterial cultures were densely interconnected, with heterotrophic bacteria preferentially co-occurring with each other. Our study highlighted the complex structures of non-axenic uni-cyanobacterial cultures, where taxonomically distinct cyanobacteria consistently associate with specific groups of heterotrophic bacteria. The observed associations were likely driven by common selection pressures in the laboratory, such as cultivation conditions and specific hosts, and may not necessarily reflect the microbial dynamic occurring in the spring microbial mats. [ABSTRACT FROM AUTHOR]

The aim of this research work is the optimization of the coagulation/flocculation process in the treatment of surface water for maximum dissolved organic matter (DOM) removal using response surface methodology (RSM). For this purpose, several jar test experiments were performed in order to identify the most influencing factors. Afterwards, RSM was done to investigate the effects and the interactions of three chosen variables (coagulant concentration, flocculant concentration, and initial pH), whereas the responses were the DOM removal in terms of chemical oxygen demand (COD), in terms of absorbance at the wavelength 254 nm (UV-254), and the final pH. The optimal conditions were as follows: 133 mg/L of coagulant, 60 mg/L of flocculant and an initial pH equal to 6.91. Under these conditions, the efficiency removals were 56% in terms of COD and 59% in terms of UV-254 with a final pH equal to 6.78. High variance coefficient R2 values, with 0.96 for the removal in terms of COD and 0.92 in terms of UV-254, confirm the reliability and the validity of the obtained model. HIGHLIGHTS Coagulation/flocculation is a very efficient process for removing dissolved organic matter from surface waters.; Response surface methodology approach is a very effective and powerful tool for optimizing the coagulation/flocculation process.; Optimization of the coagulation/flocculation process reduces the use of chemicals and protects the environment.;