The synthetic superabsorbent polymers (SAPs) market is experiencing significant growth, with applications spanning agriculture, healthcare, and civil engineering, projected to increase from $9.0 billion USD in 2019 to $12.9 billion USD by 2024. Despite this positive trend, challenges such as fluctuating raw material costs and lower biodegradability of fossil fuel-based SAPs could impede further expansion. In contrast, cellulose and its derivatives present a sustainable alternative due to their renewable, biodegradable, and abundant characteristics. Lignocellulosic biomass (LCB), rich in cellulose and lignin, shows promise as a source for eco-friendly superabsorbent polymer (SAP) production. This review discusses the applications, challenges, and future prospects of SAPs derived from lignocellulosic resources, focusing on the cellulose extraction process through fractionation and various modification and crosslinking techniques. The review underscores the potential of cellulose-based SAPs to meet environmental and market needs, offering a viable path forward in the quest for more sustainable materials.

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

In the rapidly evolving landscape of artificial intelligence (AI), its significance and accelerated development are undeniable. AI has emerged as a cornerstone technology with profound implications across various domains, driving innovation and reshaping the way we approach complex problems. Particularly, the utilization of AI in coding tasks has garnered substantial attention, given its potential to streamline development processes and enhance the efficiency of software engineering practices. Against this backdrop, this paper presents a detailed comparative analysis of four different AI platforms, namely ChatGPT, Gemini, Blackbox, and Microsoft Copilot, in addressing key challenges within the realm of computer science, spanning natural language processing, image processing, and cybersecurity. The study focuses on leveraging the C++ programming language to develop solutions for these multifaceted problems across the aforementioned platforms. Each platform's outputs are meticulously evaluated on various parameters including accuracy, execution time, code size, and time complexity to provide a comprehensive understanding of their performance. Furthermore, an iterative optimization methodology is employed, entailing three rounds of refinement for the code produced by each platform, with the resultant outputs subjected to comparative analysis in each iteration. Through this rigorous approach, the paper not only elucidates the efficacy of different AI platforms in addressing diverse computational challenges but also underscores the iterative enhancement process on AI platforms for refining code quality and performance across multiple domains within computer science.

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

Identifying food and calculating calories are key components of encouraging good eating habits and controlling dietary intake. These days, it's easier to create smart systems that can recognize food items from images or videos and calculate their calorie content using artificial intelligence, computer vision, and machine learning. This study gives an overview of the latest methods and tools for calorie counting and food identification. It looks at the challenges of accurately identifying different foods, including complex dishes and foods from various cuisines, and addresses the variables taken into account when determining the calorie content of food items, including ingredient composition, portion size, and nutritional makeup. To enhance user experience and enable comprehensive calorie tracking, it explores real-time processing capabilities, user-friendly interfaces, and integration with other health and fitness platforms. Additionally, it provides a list of potential recipes. The interface developed in the proposed system is currently trained to identify and provide the calories of 100 food items, including basic food items, fruits, vegetables, and processed foods. The research work uses a real-time dataset for training and employs software such as YOLOv8, and TensorFlow for practical implementation.

<p>Computer vision is one of the most important areas of artificial intelligence and lip reading is one of the most important areas of computer vision. Lip-reading, which is more important in noisy environments or where there is no sound flow, is one of the working areas that can help the hearing-impaired people. There is no dataset in Turkish for lip reading, which there are different datasets at alphabet, word, and sentence level in different languages. The dataset of this study was created by the author and video data were collected from 72 people for 71 words. Audio streams were removed from the collected videos and a dataset was created using only images. Due to the small size of the dataset, the data was replicated with the Camtasia application. After the model of the research was designed and trained, the model was tested on adjectives, nouns, and verbs dataset and success rates of 71.8%, 71.88%, and 79.69% were obtained, respectively.</p>

ABSTRACT With growing interest in the role and integration of Artificial Intelligence (AI) and AI pedagogies in transformative art education this paper voices the pedagogic reflections of three art educators in Singapore who have engaged AI in their art provision. It discusses and exemplifies the advantages (intelligent), like interdisciplinary opportunity, enhanced student engagement and increased connectionism, and limitations (artificial), like ethical and moral dilemmas and positioning, of AI integration in three art education settings showcasing how educators and learners engage. Model of pedagogic reflection is used to reveal how the educators provided transformative learning with AI, whilst showing the influence of personal art interests, technique modelling and professional reflections on teaching and making involving AI. Paper contributions include insights into and examples of AI practices and pedagogies (like AI literacy and prompt engineering), that are and could be used in art education, reflections on their successes and limits and commentary on the position of AI integration in the circular economy of art education. The paper is positioned to mobilize educator and student voice as an influential driver in the transformation of art education given ongoing developments in digital education futures.