Stages Of Process Research Articles

Способи зниження складності обчислень при розрахунках потокорозподілу в мережах нестисливої рідини при фіксованих коефіцієнтах гідравлічного опору

Based on the analysis of the process of solving the problem of flow distribution calcula-tion in compressible fluid systems, which boils down to finding the root of a nonlinear system of equations using the Newton — Raphson method, methods of reducing the complexity of calculations at the stages of the iterative process are proposed. The studies belong to the class of hydraulic systems for which the total pressure change on the branch elements is a monotonic smooth function of the flow rate G. For the iterations of the Newton-Raphson method, the stages are analyzed: linearization of the nonlinear system of equations, determination of the discrepancy and error estimation, determination of cost growth and obtaining a new iterative cost value. A method and algorithm for determining the discrepancy and estimating the error with linear complexity relative to the number of branches of graph E is proposed. A method of reducing the number of unknowns in the system of flow distribution equations at the stage of forming a system of linear equations based on the use of the nodal convolution method is described. A number of variants of heuristic algorithms for determining the parameter that takes into account the rate of in-crease in costs when forming a new iterative value of costs and their analysis are present-ed.

Integrated Curriculum Model: Reformulation and Actualization in the Basics of Islamic Science Course

Reformulation and actualization of the integrated curriculum has become one of the characteristics of every State Islamic University. However, conceptually the terminology for integrating science and curriculum has not yet reached an agreement among experts. Even now, it is still a discourse that has not ended. This research tries to reveal two main problems: first, how the integrated curriculum is applied in the learning process of the basics of Islamic science in the chemistry education study program. Second, the extent to which the application of the integrative curriculum is appropriate in relation to learning the basics of Islamic science in the chemistry education study program. This research uses a qualitative descriptive-analysis approach with purposive sampling consisting of head of the study program, 2 lecturers, 5 students from unit 01 and 5 students from unit 02 in semester 2 (two) who took the basics of Islamic science course in the Chemistry Education Study Program. Data were collected using direct observation techniques, in-depth interviews, and document studies and questionnaires. The data analysis technique goes through the stages of data reduction, data presentation, data processing and interpretation and drawing conclusions (verification). The research results show that the implementation of the integrated curriculum in the basics of Islamic science courses has been realized at the planning and presentation stage of integrated content in line with CPL and the material taught in class is in sync with CPMK and RPS. Meanwhile, the model used is an integration model based on monotheism and fiqh. At the implementation stage in unit 01 the lecturer did not carry out apperception. Meanwhile, in unit 02 the lecturer begins learning by praying together, and after that the lecturer conducts an apperception and explores the students' learning experiences. The suitability of the curriculum implemented in the Chemistry Education Study Program with the integrated curriculum in the Basics of Islamic Science course has been realized adequately although it still needs improvement. It is hoped that the results of this research will become an initial reference for lecturers, observers and educational policy makers.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production.

We evaluated the feasibility of using untargeted metagenomic sequencing of raw milk for detecting anomalous food ingredient content with artificial intelligence methods in a study specifically designed to test this hypothesis. We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of processing. The approach could potentially be applied in the food industry for safety and quality control.

Double Enzyme Active Hydrogel Program Regulates the Microenvironment of Staphylococcus aureus-Infected Pressure Ulcers.

The treatment of infected pressure ulcers (IUPs) requires addressing diverse microenvironments. A pressing challenge is to effectively enhance the regenerative microenvironment at different stages of the healing process, tailoring interventions as needed. Here, a dual enzyme mimetic and bacterial responsive self-activating antimicrobial hydrogel designed to enhance IPUs healing is introduced. This hydrogel incorporates pH-responsive dual enzyme-active nanoplatforms (HNTs-Fe-Ag) encapsulated within a methacrylate-modified silk fibroin (SFMA) and dopamine methacrylamide (DMA) matrix. This composite hydrogel exhibits adaptive microenvironment regulation capabilities. Under the low pH microenvironment of bacterial infection, it has excellent antimicrobial activity by self-activating the •OH generation in conjunction with photothermal effects. Under the neutral and alkaline microenvironment of chronic inflammation, it catalyzes the decomposition of hydrogen peroxide (H2O2) to produce oxygen (O2), thereby alleviating hypoxia and scavenging reactive oxygen species (ROS), which in turn remodulates the phenotype of macrophages. The composite hydrogel demonstrates on-demand therapeutic effects in the microenvironment of infected wounds, significantly enhancing the regenerative microenvironment of IUPs by promoting wound closure, inflammation regulation, and collagen deposition through self-activated antimicrobial action during infection and adaptive hypoxia relief during recovery. This approach offers a novel strategy for developing smart wound dressings.

МҰНАЙ ЖӘНЕ ГАЗ КЕНОРЫНДАРЫНЫҢ ӨНІМДЕРІН ӨНДІРУДЕ ҚАЗІРГІ ЗАМАНҒЫ ЦИФРЛЫ ЖӘНЕ ЗИЯТКЕРЛІ ТЕХНОЛОГИЯЛАРДЫ ҚОЛДАНУ АСПЕКТІЛЕРІ

Most oil fields cover thousands of square kilometers, and some have reserves in difficult local conditions (heat, permafrost, desert, deep water) or difficult to recover. Due to these factors and a number of other problems related to technological safety, there are difficulties in the development of oil fields (with a steady increase in demand for oil and gas). Therefore, oil companies still need a highly reliable digital infrastructure that provides high productivity and security and manages many of the devices used in the oil and gas business throughout the field. Therefore, in recent years, oil companies around the world are increasingly managing their oil fields using "smart" technologies. They have proven to be a powerful tool for ensuring the safety of oil fields and increasing labor productivity, as well as economic efficiency. The technologies used include the improvement of all stages of the work process through the rapid acquisition of production data associated with advanced analytics, which contributes to a more complete and efficient development of the oil field. With experience in the implementation of "intelligent" technologies, it already shows high efficiency and very reliable results (both in terms of the oil production process itself and in terms of reducing the cost of individual operations). The use of modern digital and intelligent technologies in the production process in the oil and gas sector plays an important role in order to increase efficiency, ensure safety and reduce environmental impact. Application of digital technologies-data collection and analysis: IoT sensors (Internet of Things) allow you to collect data from deposits in real time and send it to analytical platforms. Field modeling: Using digital Twin technology, virtual field models are created that allow you to predict and optimize production processes. Automation: The automated systems and robotics used help to increase productivity in the management of the oil and gas production process.

Proposed framework for medication delivery system in the Jordanian public health sector

BackgroundIn Jordan, the confluence of traffic congestion and overcrowding in public hospitals poses a significant challenge for patients to collect their medications timely. This challenge was further intensified during the COVID-19 pandemic. Recognizing this issue, the Ministry of Health (MOH) and Electronic Health Solutions (EHS) intend to establish a Medication Delivery System (MDS), designed to provide patients with home delivery of medications and ensure proper treatment. This paper outlines a comprehensive framework to guide requirements engineers in devising an effective MDS framework, with a focus on expediting the development and testing processes and mitigating the risks associated with constructing such a system.MethodThe proposed methodology entails a robust, structured approach to requirements development for an MDS that integrates an electronic health record system, billing system, pharmacy application, the patient-oriented My Hakeem app, and a delivery tracking system. The requirements elicitation and analysis processes were undertaken by a multidisciplinary committee from MOH and EHS teams, ensuring a diverse understanding of stakeholder needs and expectations. The requirement specifications were meticulously documented via a data dictionary, unified modeling language (UML), and context diagrams. The quality and accuracy of the requirements were verified through an extensive validation process, involving thorough review by various EHS teams and the MOH committee.ResultsThe MDS was implemented across numerous MOH facilities within a timeline that was a third of the original projection, leveraging the same level of resources and expertise. Post the requirements development phase, there were no changes requested by any stakeholders, indicating a high level of requirement accuracy and satisfaction.ConclusionThe study illustrates that our proposed methodology significantly results in a comprehensive, well-documented, and validated set of requirements, which streamlines the development and testing phases of the project and effectively eliminates requirement errors at an early stage of the requirements development process.

When generative artificial intelligence meets multimodal composition: Rethinking the composition process through an AI-assisted design project

This study explores the integration of generative artificial intelligence (GenAI) design technologies, including Adobe Firefly and DALL·E, into the teaching and learning of multimodal composition. Through focus group discussions and case studies, this paper demonstrates the potential of GenAI in reshaping the various stages of the composition process, including invention, designing, and revising. The findings reveal that GenAI technologies have the potential to enhance students’ multimodal composition practices and offer alternative solutions to the wicked problems encountered during the design process. Specifically, GenAI facilitates invention by offering design inspirations and enriches designing by expanding, removing, and editing the student-produced design contents. The students in this study also shared their critical stance on the revision process by modifying and iterating their designs after their uses of GenAI. Through showcasing both the opportunities and challenges of GenAI technologies, this paper contributes to the ongoing scholarly conversations on multimodal composition and pedagogy. Moreover, the paper offers implications for the future research and teaching of GenAI-assisted multimodal composition projects, with the aim of encouraging thoughtful integration of GenAI technologies to foster critical AI literacy among college composition students.

Проблемні питання забезпечення якості дорожньо-будівельних матеріалів та виробів в контексті особливостей їх сертифікації в Україні відповідно до ДСТУ EN ISO/IEC 17065:2019 та закону України «Про надання будівельної продукції на ринку»

Introduction. The article examines the importance of certification of road construction materials as one of the main tools for ensuring their quality. Problems. The authors emphasize that the certificate of conformity is a key document that confirms that the products meet the established regulatory requirements. Product certification, which includes laboratory tests and control of the production process, ensures the objectivity of quality assessment. The article describes in detail the stages of the certification process, from submitting an application to technical supervision of certified products. The role of certification bodies, which must be accredited by the National Accreditation Agency of Ukraine (NAAU), is emphasized separately, and their responsibility for the safety and quality of certified road construction materials is also noted. The authors also draw attention to the problem of non-compliance with the quality standards of road construction products in Ukraine, which leads to road defects, such as cracks and subsidence, which can appear already a year after the road is put into operation. In this context, it is emphasized the need to strengthen the quality control of both produced in Ukraine and imported road construction materials. The article also considers the issue of voluntary certification of road construction products, which is carried out at the initiative of the manufacturer or supplier, which is becoming more and more important in the conditions of tender procurement. At the same time, the lack of quality control of products sold through retail networks and the lack of effective quality control of imported road construction materials are emphasized.

Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview

This study examines Retrieval-Augmented Generation (RAG) in large language models (LLMs) and their significant application for undertaking systematic literature reviews (SLRs). RAG-based LLMs can potentially automate tasks like data extraction, summarization, and trend identification. However, while LLMs are exceptionally proficient in generating human-like text and interpreting complex linguistic nuances, their dependence on static, pre-trained knowledge can result in inaccuracies and hallucinations. RAG mitigates these limitations by integrating LLMs’ generative capabilities with the precision of real-time information retrieval. We review in detail the three key processes of the RAG framework—retrieval, augmentation, and generation. We then discuss applications of RAG-based LLMs to SLR automation and highlight future research topics, including integration of domain-specific LLMs, multimodal data processing and generation, and utilization of multiple retrieval sources. We propose a framework of RAG-based LLMs for automating SRLs, which covers four stages of SLR process: literature search, literature screening, data extraction, and information synthesis. Future research aims to optimize the interaction between LLM selection, training strategies, RAG techniques, and prompt engineering to implement the proposed framework, with particular emphasis on the retrieval of information from individual scientific papers and the integration of these data to produce outputs addressing various aspects such as current status, existing gaps, and emerging trends.

Application of MgO based‐nanofluid for controlling the growth of asphaltene flocs under static and micromodel dynamic conditions

AbstractIn this study, the impact of magnesium oxide (MgO) nanoparticles on the control of asphaltene aggregates growth was examined. The investigation began with static testing, followed by dynamic testing, where nanofluid was injected into a constructed glass micromodel simulating a porous medium. The results obtained from light microscopy and asphaltene dispersant tests demonstrated that the MgO nanoparticles with an average diameter of 50 nm postpone the asphaltene onset point (AOP) and delay the growth of asphaltene aggregates in crude oil. Also, the results obtained from these experiments illustrated the performance of synthesized nanoparticles in various concentrations on inhibition of asphaltene deposit in the crude oil medium, in the order of 750 > 1500 > 100 > 1000 > 500 ppm. The results from both microscopy and ADT experiments strongly validate the effectiveness of MgO nanoparticles across varying concentrations, highlighting the optimal dosage of 750 ppm. Images of nanofluid flooding at the optimal concentration in the glass micromodel demonstrate effective nanoparticle inhibition and enhanced oil recovery from the porous medium. These findings corroborate the results obtained from ADT and microscopy tests. The results of FT‐IR analysis show the adsorption of asphaltene particles on the surfaces of MgO nanoparticles in wavelengths of 2900–3000 cm−1. Moreover, dynamic light scattering (DLS) analysis results indicated that the average diameter of suspended particles was 3580 nm before adsorption and 6230 nm after adsorption, indicating the controlled adsorption of asphaltene onto the surface of MgO nanoparticles. The findings from this study can be applied to manage asphaltene formation across all stages of oil processing and production.

In Situ Raman Spectroscopy as a Valuable Tool for Monitoring Crystallization Kinetics in Molecular Glasses

The performance of in situ Raman microscopy (IRM) in monitoring the crystallization kinetics of amorphous drugs (griseofulvin and indomethacin) was evaluated using a comparison with the data obtained via differential scanning calorimetry (DSC). IRM was found to accurately and sensitively detect the initial stages of the crystal growth processes, including the rapid glass–crystal surface growth or recrystallization between polymorphic phases, with the reliable localized identification of the particular polymorphs being the main advantage of IRM over DSC. However, from the quantitative point of view, the reproducibility of the IRM measurements was found to be potentially significantly hindered due to inaccurate temperature recording and calibration, variability in the Raman spectra corresponding to the fully amorphous and crystalline phases, and an overly limited number of spectra possible to collect during acceptable experimental timescales because of the applied heating rates. Since theoretical simulations showed that, from the kinetics point of view, the constant density of collected data points per kinetic effect results in the smallest distortions, only the employment of the fast Raman mapping functions could advance the performance of IRM above that of calorimetric measurements.

Accurate pneumoconiosis staging via deep texture encoding and discriminative representation learning.

Accurate pneumoconiosis staging is key to early intervention and treatment planning for pneumoconiosis patients. The staging process relies on assessing the profusion level of small opacities, which are dispersed throughout the entire lung field and manifest as fine textures. While conventional convolutional neural networks (CNNs) have achieved significant success in tasks such as image classification and object recognition, they are less effective for classifying fine-grained medical images due to the need for global, orderless feature representation. This limitation often results in inaccurate staging outcomes for pneumoconiosis. In this study, we propose a deep texture encoding scheme with a suppression strategy designed to capture the global, orderless characteristics of pneumoconiosis lesions while suppressing prominent regions such as the ribs and clavicles within the lung field. To further enhance staging accuracy, we incorporate an ordinal label distribution to capture the ordinal information among profusion levels of opacities. Additionally, we employ supervised contrastive learning to develop a more discriminative feature space for downstream classification tasks. Finally, in accordance with standard practices, we evaluate the profusion levels of opacities in each subregion of the lung, rather than relying on the entire chest X-ray image. Experimental results on the pneumoconiosis dataset demonstrate the superior performance of the proposed method confirming its effectiveness for accurate pneumoconiosis staging.

Timecourse of bottom-up and top-down language processing during a picture-based semantic priming task

ABSTRACT Understanding spoken language requires rapid analysis of incoming information at multiple levels. Information at lower levels (e.g. acoustic/phonetic) cascades forward to affect processing at higher levels (e.g. lexical/semantic), and higher-level information may feed back to influence lower-level processing. Most studies have sought to examine a single stage of processing in isolation. Consequently, there is a poor understanding of how different stages relate temporally. In the present study, we characterise multiple stages of linguistic processing simultaneously as they unfold. Listeners (N = 30) completed a priming task while we collected their EEG, where a picture (e.g. of a peach) biased them to expect a target word from a minimal pair (e.g. beach/peach). We examine the processes of perceptual gradiency, semantic integration, and top-down feedback, to yield a more complete understanding of how these processes relate in time. Then, we discuss how the results from simplified priming paradigms may compare to more naturalistic settings.

Study of Aluminium, Gallium and Gallium Boron as P-Type Dopants for New-Generation n+np+ Solar Cells

Silicon n-type n+np+ solar cells offer many advantages over conventional n+pp+ cells, including better resistance to light-induced degradation and higher conversion efficiency potential. However, the formation of the p+ emitter in n+np+ cells requires high diffusion temperatures and the use of alternative boron dopants is necessary to overcome the limitations of conventional processes. This study explored aluminium, gallium and gallium/boron co-doping as p-type dopants for the fabrication of thin (140 µm) n+np+ solar cells. The results showed that aluminium is not suitable for the formation of the p+ emitter due to its low solid solubility in silicon and its high segregation towards silicon oxide. Gallium required high diffusion temperatures and suffered from a degradation of the concentration profile in later stages of the manufacturing process, leading to poor performing solar cells. Gallium/boron co-doping has proved to be a promising alternative to boron. Thin n+np+ solar cells doped with GaB achieved a maximum conversion efficiency of 13.7%, slightly lower than that of boron-doped cells (14.9%). Optimisation of the GaB diffusion process and surface passivation could further improve the performance of these cells. This study demonstrates the potential of gallium/boron co-doping for the manufacture of new-generation thin n+np+ solar cells. Further research is needed to fully exploit the advantages of this technology and contribute to improving the efficiency and cost of silicon solar cells.

Preprocessing Text Data for the Doc2Vec Model in the Automatic Classification of Tickets in Support Centers of Software Providers

Leading software providers typically implement customer technical support functions, which are crucial for promoting and enhancing the competitiveness of their products and services in global markets. The high volume and heterogeneity of support tickets (functional, temporal, linguistic, etc.) highlight the importance of efficient classification systems. Effective classification optimizes the distribution of these tickets among support center specialists and automates their processing using an established knowledge base. However, classifying these tickets is a loosely formalized task. For companies that have accumulated substantial data on customer requests, automating classification through machine learning methods and natural language processing models, such as Word2Vec, FastText, BERT, and GPT, becomes feasible. It is generally accepted that classification effectiveness primarily depends on the model employed. Nevertheless, the quality of these models is significantly influenced by the nature of the training data. Literature review of the reveals significant research interest in methods for the automatic classification of tickets specifically tailored to the operational conditions of software provider support centers. However, there is a noticeable gap in the literature regarding the impact of data preprocessing on the quality of these models. The article aims to clarify the techniques of data preprocessing and analyze their impact on the effectiveness of text classification, considering the specificity of software provider support centers. This study examines the stages of the automatic classification process for tickets, accounting for the unique characteristics of the data (customer text requests). A relevant set of specified methodological and instrumental tools was developed and tested using open data from a global software provider (DevExpress). The testing involved a database of 165,000 tickets. The study's results indicate that preprocessing can improve classification metrics such as F-measure, Precision, and Recall from 77% to 79%. Additionally, preprocessing significantly reduces the dimensionality of text data (by 48.2%) and increases model training speed (by 26.5%) without loss of accuracy, ensuring cost-efficiency and operational efficiency in the use of computational resources.

Developing a Monitoring and Evaluation (M&E) Plan for Organization's Strategic Objectives Using the Design Thinking Approach

The objective of this study was to develop a theoretical framework tailored for managers of small and medium-sized businesses, as well as project managers. Design thinking is human centered approach that involves stakeholders at different levels of its application in the development of innovations. Originally as an engineering method, it has been applied in diverse fields, including entrepreneurship. This paper presents an extended version of the double diamond framework used in design thinking. The double diamond framework was developed in 2005 as a graphical approach for achieving project design process. The author’s in-depth analysis of the model indicates that it can be applied to formulating business strategic objectives, a use case not previously discussed in the literature. Therefore, this paper aims contribute to the scientific body of knowledge by extending the double diamond model for strategic objective formulation. A normative conceptual methodology was used to develop this framework. The development of a monitoring and evaluation plan for businesses or projects, based on a traditional literature review methodology, illustrates how design thinking can offer a new perspective on these processes, helping to address key challenges. This paper demonstrates how each of the five stages in the design thinking process can be employed during the development of a monitoring and evaluation plan. Although this development process can be tedious and complicated, applying the design thinking approach can simplify and streamline it, ensuring active involvement from all stakeholders through the introduced extended framework. Recommendations for future research include applying the proposed extended framework in empirical studies involving large organizations. For practical implications, managers of small and medium businesses are encouraged to apply this model when developing strategies or incorporate it into project management to enhance the development of a project's monitoring and evaluation plan.

Digitalization of the Electoral Process in Russia: Legal Aspects

Introduction. Digitalization is a distinctive feature of development of the modern society, which implies intensive implementation of the new technologies into the different spheres of life, including the electoral process. In Russia, the development of measures efficiently ensuring the suffrage of the citizens, the improvement of the legal regulation of the electoral process are being carried out for more than thirty years. However, the dynamics of automation and digitalization of the electoral procedures often outrides their theoretical justification and legal comprehension. The works of Russian and foreign authors study the problems of the electoral process digitalization, the electronic forms of manifesting the civic activity and political engagement. A number of researchers consider the ongoing changes to be inevitable and call for a revision of the electoral process basic fundamentals, others propose to limit this process indicating on the impossibility to ensure the absolute information security. The paper aims at analysing the main stages of automation and digitalization of the electoral process in Russia, identify the problems associated with implementation of the new technologies and develop recommendations for the efficient transformation of the electoral system in the context of digitalization.Materials and Methods. To ensure the relevance and reliability of the obtained results, the general and specific scientific methods of cognition were used in the research: the dialectical method, the method of juridical comparativistics, as well as the content analysis of the regulatory legal acts and historical, systemic, logical and other methods. Various methods of interpreting the legal norms were used to identify the regularities in their application. The content analysis of the regulatory legal acts and the logical method made it possible to identify the electoral process digital transformation features. The method of modeling enabled formulation of the proposals on improving the legislation.Results. The main stages of automation and digitalization of the electoral process have been studied. Their features and problems have been identified. The need for transformation of the legal regulation, capable of efficient ensuring the suffrage of the citizens in the new conditions, has been substantiated.Discussion and Conclusion. Based on the results of the research, the recommendations have been developed for efficient legal regulation of the main stages of the electoral process in the context of digitalization. The theoretical conclusions formulated by the authors are proposed to be used for further research and improvement of the current electoral law of the Russian Federation.

Overview of Liquid Sample Preparation Techniques for Analysis, Using Metal-Organic Frameworks as Sorbents.

The preparation of samples for instrumental analysis is the most essential and time-consuming stage of the entire analytical process; it also has the greatest impact on the analysis results. Concentrating the sample, changing its matrix, and removing interferents are often necessary. Techniques for preparing samples for analysis are constantly being developed and modified to meet new challenges, facilitate work, and enable the determination of analytes in the most comprehensive concentration range possible. This paper focuses on using metal-organic frameworks (MOFs) as sorbents in the most popular techniques for preparing liquid samples for analysis, based on liquid-solid extraction. An increase in interest in MOFs-type materials has been observed for about 20 years, mainly due to their sorption properties, resulting, among others, from the high specific surface area, tunable pore size, and the theoretically wide possibility of their modification. This paper presents certain advantages and disadvantages of the most popular sample preparation techniques based on liquid-solid extraction, the newest trends in the application of MOFs as sorbents in those techniques, and, most importantly, presents the reader with a summary, which a specific technique and MOF for the desired application. To make a tailor-made and well-informed choice as to the extraction technique.

Multi-head Network based Students Behaviour Prediction with Feedback Generation for Enhancing Classroom Engagement and Teaching Effectiveness

Emotions are pivotal in the learning process, highlighting the importance of identifying students' emotional states within educational settings. While neural network models, particularly those rooted in deep learning, have demonstrated remarkable accuracy in detecting primary emotions like happiness, sadness, fear, disgust, and anger from facial expressions in videos, these emotions occur infrequently in learning environments. Conversely, cognitive emotions such as engagement, confusion, frustration, and boredom are significantly more prevalent, transpiring five times more frequently than basic emotions. However, unlike basic emotions which are relatively distinct, cognitive emotions present a subtler distinction, necessitating the utilization of more sophisticated models for accurate recognition. The proposed work presents an efficient Facial Expression Recognition (FER) model for monitoring the student engagement in a learning environment by considering their facial expressions like boredom, frustration, confusion and engagement. The proposed methodology includes certain pre-processing steps followed by facial expression recognition founded on Efficient-Net B3 CNN in which the learning parameters are optimized using Circle-Inspired Optimization Algorithm (CIOA). Finally, the post processing stage estimates the frame-wise group engagement level (GEL) of students based on certain expression labels. Based on the acquired results, it is noted that the suggested Efficient-Net B3 CNN-CIOA based FER model provides promising results in terms of accuracy by 99.5%, precision by 99.2%, recall by 99.5% and f1-score by 99.6%, when compared with some state-of-art facial expression recognition approaches. Also, the suggested approach computational complexity is very much less than the compared existing approaches.