This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support the sixth-generation wireless physical platforms (6G). Due to their ability to adjust the behavior of interacting electromagnetic (EM) waves through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a lowcost, green, sustainable, and energy-efficient solution for 6G systems. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid radio frequency/visible light communication (RF-VLC) systems, health considerations, and localization. INDEX TERMS 6G technology, large intelligent surfaces (LIS), massive multiple-input multiple-output (mMIMO), millimetre waves (mmWave) communication, wireless communication.

Despite the growing interest in the interplay of machine learning and optimization, existing contributions remain scattered across the research board, and a comprehensive overview on such reciprocity still lacks at this stage. In this context, this paper visits one particular direction of interplay between learningdriven solutions and optimization, and further explicates the subject matter with a clear background and summarized theory. For instance, machine learning and its offsprings are trending because of their enhanced capabilities in automating analytical modeling. In this realm, learning-based techniques (supervised, unsupervised, and reinforcement) have grown to complement many of the optimization problems in testing and training. This paper overviews how machine learning-based techniques, namely deep neural networks, echo-state networks, reinforcement learning, and federated learning, can be used to solve complex and analytically intractable optimization problems, for which specific cases are examined in this paper. The paper particularly overviews when learning-based algorithms are useful at solving particular optimizing problems, especially those of random, dynamic, and mathematically complex nature. The paper then illustrates such applications by presenting particular use-cases in communications and signal processing including wireless scheduling, wireless offloading and resource management, power control, aerial base station placement, virtual reality, and vehicular networks. Lastly, the paper sheds light on some future research directions, where the dynamicity and randomness of the underlying optimization problems make deep learning-driven techniques a necessity, namely in sensing at the terahertz (THz) bands, cellular vehicleto-everything, 6G communication networks, underwater optical networks, distributed optimization, and applications of emerging learning-based techniques.

The superiority of optical communications in underwater mediums, in terms of higher data rate and reliability, makes underwater optical wireless communications (UOWC) more favorable to provide ultra-reliable low-latency underwater communications, as compared to other wireless technologies, e.g., acoustic and radio frequency (RF) communications. UOWC limited transmission range, however, remains a major hurdle against assessing its true deployment benefits, which motivates for the necessity of developing practical routing protocols for multihop underwater optical wireless sensor networks (UOWSNs). This paper sheds light on the existing state-of-art UOWC routing protocols, the majority of which requires centralized implementation with large end-to-end delay. The article further proposes routing algorithms which can be implemented in a distributed fashion across the multi-hop links, with a reasonable amount of information exchange. The merits of the proposed algorithms are particularly highlighted through illustrative simulations, which show how the proposed strategies outperform the classical protocols, both in terms of reliability and end-to-end latency. Finally, the paper shows how the proposed distributive routing protocols achieve ultra-reliable low-latency underwater communications. Index Terms-Underwater optical wireless communications, underwater optical wireless sensor network, routing protocols, distributed routing protocols, Ultra-reliable low-latency communication.

The prevalence of celiac disease in Saudi Arabia has been increasing for 2.2% of the Saudi population.Yet celiac disease (CD) patients are still suffering from long life gluten free diet GFD. According to literature, patients' diet can be improved by adding functional foods such as, prebiotics, probiotics and/or natural sources such as Millet to enhance the gut ecology and colon health. In the current study we have carried a descriptive study to assess the availability, prices and categories of food products suitable for celiac patients (probioitcs, prebiotics, gluten free products and millet as a natural source) in 6 main supermarkets, in Jeddah, Saudi Arabia, using a survey. Results showed that a higher costs of gluten free products seen in 6 main markets, except in "panda hypermarket". With regards to availabilities, the highest proportion of products was found in "Carrefour" and "Danube", have shown the highest, followed by "Lulu hypermarket" and "Manuel hypermarket" (P < 0.05). The lowest amounts of gluten free products were seen in Farm superstores and Al Raya compared to others (P < 0.05). With regards to categories were also looked at which shows Danube has most variations from staple, snacks, drinks, and additives. Millet has been seen to be available and lower in cost which is mostly suitable for patients at any economic level state. Findings suggest that the limited availability and high costs can influence celiac patients' quality of life and severity of symptoms among the Saudi populations. The current study has been carried for the first time in KSA, which recommends more local food production suitable for celiac patients and introducing the use of local natural sources such as millet to be used as staple food to enhance gut ecology health and reduce severity of celiac patients symptoms.

This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support next generation wireless physical platforms (6G). Due to their ability to adjust the channels through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a low cost, green, sustainable, and energy-efficient 6G solution. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid RF/VLC systems, health considerations, and localization.