King Saud University

This article investigates the impact of the semiconformal curvature tensor's symmetry on the base and fiber manifolds of a warped product manifold. It establishes that the fiber manifold of a warped product manifold has a constant sectional curvature, whereas the base manifold is semiconformally symmetric. Furthermore, the article derives the specific forms of the semiconformal curvature tensor for both the base and fiber manifolds. Also, it is demonstrated that a semiconformally symmetric (flat) GRW space-time is a perfect fluid space-time and exhibits an irrotational velocity vector field.

BACKGROUND This study aimed to examine the impact of the COVID-19 lockdown on adherence to continuous positive airway pressure (CPAP) therapy among Saudi patients with obstructive sleep apnea (OSA). It also sought to assess the influence of demographic variables and comorbidities on CPAP adherence. METHODS A prospective cohort study was conducted at the University Sleep Disorders Center at King Saud University Medical City. The study included 67 OSA patients who exclusively used CPAP for treatment across three distinct intervals prelockdown, during lockdown, and postlockdown. Adherence to CPAP therapy was objectively measured using “mask-on on-time monitoring” data from the CPAP machines. RESULTS The study found a significant decrease in the number of days with CPAP usage during the lockdown period, which persisted postlockdown. Approximately half of the patients adhered to CPAP treatment throughout the three study intervals. The decrease in CPAP usage days during lockdown was particularly noticeable among patients younger than 50 and older than 65 years of age. The presence of comorbidities, body mass index, and sex did not significantly influence CPAP treatment adherence. CONCLUSION The COVID-19 lockdown significantly impacted CPAP treatment adherence among OSA patients, with a decrease in adherence persisting postlockdown. This highlights the need for interventions to support CPAP adherence during challenging times such as a pandemic. Further research is needed to understand the long-term effects of the pandemic on CPAP therapy adherence.

In the present study, a statistical tool called the simplex lattice mixture design method was used to create a new formulation of Natural Deep Eutectic Solvent (NADES), which is derived from a combination of three compounds (citric acid, glycerol, and water) to extract bioactive compounds from chickpea (Cicer arietinum L.) sprouts. The mixture (natural deep eutectic solvent) was formulated by combining three solvents including citric acid, glycerol, and water. The extraction was performed in a sonication bath for 30 min. The simultaneous optimization was performed to obtain the highest total polyphenol content (TPC), total flavonoid content (TFC) and antioxidants activity. The highest values of total polyphenol content (TPC), total flavonoid content (TFC) and antioxidant activity were 128.0 ± 0.2 mg GAE/100 g, 38.61 ± 0.03 mg CE/100 g and 2117 ± 1.8 µmol TE/100 g respectively. HPLC-DAD of the optimized extract was utilized for quantification of polyphenol compounds showing catechin as the main compound followed by chlorogenic acid, epicatechin, syringic acid, rutin, gallic acid, kaempferol 3-glucoside, ferulic acid, and coumaric acid. These findings may represent a significant advancement in the management of phenolic compound extraction for targeted uses, such as serving as alternatives to traditional antioxidants primarily employed in the food industry to improve nutritional quality. Furthermore, our research has shown that mixture designs are an efficient and useful method for structuring and optimizing experimental parameters to achieve the most accurate results with the minimum number of experiments.

Identifying the baseline status and the timing of ecosystem disturbances are essential for restoration programs. The historical bioaccumulation of heavy metals was assessed from an 80-cm-long core from the Manzala Lagoon (Nile Delta). The heavy metal concentrations increased slightly upward and peaked around 1964, after the completion of Aswan High Dam. The metal concentrations of shells are 2-3 times less than those of bulk sediment. The topmost sediments are enriched in Cd, Cu, and Pb above USEPA. Sediment type and sediment grain size have a minor effect on the heavy metal concentration in mollusk shells, suggesting a priority over bulk sediments. Although correlated, the shells of the grazer gastropod Melanoides tuberculata have the highest concentration of all metals relative to the suspension-feeder bivalves Cerastoderma glaucum and Saccostrea cuculata. This was attributed to the influences of the eco-physiological traits, which exert a similar influence on the bioaccumulation process of all metals.

Raw polysomnography (PSG) preprocessing is one of the first steps in any sleep disorder detection using artificial intelligence (AI) and data science (DS). This chapter mainly discusses the process of transforming raw PSG at the very beginning in a way that can be fed into a machine learning (ML) or deep learning (DL) model. This includes essential steps that come before building the actual model: starting from defining the problem, collecting raw PSG, then data exploration, and finally, preparing the data. PSG preprocessing is often highly specific to a particular dataset at hand, the main expected result of the learning model, and the equipment used for signal acquisition. For this reason, it is common in the literature to overlook raw PSG preprocessing or to mention it briefly without specifying details. Hence, giving a set of universally applicable steps is not easy. This chapter discusses the possible preprocessing steps that could be applied to the raw PSG data, which were tested empirically or proven theoretically.

In the modern era, various engineering applications utilize renewable solar energy, and recent prospects aim to enhance solar thermal collector efficiency through nanotechnology found to enhance solar performance. While using the parabolic trough collector, it found excellent solar conversion efficiency and attained the maximum temperature of the working fluid. Besides the intermittency due to weather conditions, the output performance will be reduced. This study aims to enhance the performance of parabolic trough solar collector by implementing magnesium oxide (MgO) coating over the tubes as 30, 20, and 10 µm particles blended with industrial black matt paint to prepare MgO-enhanced coating through the spray pyrolysis process for varying the nanoparticle size with constant thickness coating in the thermal performance of parabolic trough solar collector (PTC). The findings of this research demonstrate that particles with coating material significantly affect the thermal performance of PTC compared with non-coating. The 10 µm MgO coating featured solar collector exploited maximum heat transfer fluid temperature (81.2 °C), increased heat absorption behaviour (662.5 W), optimum thermal and exergy efficiency values of 78.9 and 69.5%, respectively, which is the optimum value rather than all others.

Healthcare systems, empowered by the integration of Artificial Intelligence (AI) and Internet of Things networks, are undergoing significant advancements, ushering in a new era of enhanced treatment experiences and improved quality of life. Edge computing plays a pivotal role as an architectural enabler; however, it also presents numerous energy-related challenges spanning sensors, communication, and edge devices. One of the most formidable challenges is the proliferation of complex communication protocols across various devices, including sensors, reconfigurable intelligent surfaces, smart devices, and edge servers, leading to substantial carbon emissions and energy consumption. To address this challenge, this paper introduces a low-carbon, sustainable edge architecture leveraging AI techniques. Specifically, we develop a deep learning-based radio frequency fingerprint access protocol to facilitate real-time and energy-efficient device access between smart devices and edge gateways. Building upon this foundation, we propose a hybrid quantum-classical optimization algorithm to achieve green data transmission at lower layers for artificial intelligence of things healthcare systems. Simulation results demonstrate that our optimized architecture achieves over 99% identification accuracy using a signal dataset of 50GB obtained from real-world smart devices and practical gateways in a real-world environment, all while maintaining energy-efficient data delivery.

In the n-tier framework, data generated by sensors requires immediate execution. The processing elements need powerful resources to entertain incoming requests. Fog computing, unlike cloud computing, provides low latency for real-time applications. However, data generated by real-time Internet of Things (IoT) devices significantly impacts fog devices. The data generated must be processed by fog devices with quick response time, minimum delay, and energy consumption and send it back to the end-users with high reliability and success rate. However, devices fail due to damage or internal state of a fog device which measures incorrectly or causes destruction which badly affects the overall system performance. The end-to-end transmission requests from IoT devices require immediate response with minimal delay, execution cost, and energy consumption in spite the occurrence of fog devices failure. In this article, we propose a novel energy efficient task scheduling algorithm based on reactive fault tolerance in an n-tier fog computing framework for IoT applications to enhance the overall fog computing performance. In case of fog device failure, the assigned task is rescheduled to other executable fog nodes without further delay. The proposed framework is based on modified particle swarm optimization and is designed and evaluated in iFogSim. The main objective of the proposed technique is to reduce energy consumption, latency, network bandwidth utilization and increase system reliability and success rate. Several experiments have been carried out by taking a maximum of 10 iterations based on which it is concluded that the proposed technique reduces energy consumption by 3%, latency by 5%, network bandwidth utilization by 3% and increases the system reliability by 2% and success rate by 8%.

Artificial Intelligence of Vehicles (AIV) is poised to revolutionize transportation by promoting low-carbon alternatives such as Electric Vehicles (EVs). However, the deployment of Fixed Charging Stations (FCSs) lags behind the growing demand, particularly in rural areas, causing range anxiety among potential EV owners. This paper proposes a smart transportation solution within the Artificial Intelligence of Things (AIoT) framework to establish a sustainable, low-carbon system. AIoT systems enable real-time data acquisition and analysis through extensive embedded IoT EV sensors and communication networks for pattern recognition and decision making on the cloud. The proposed solution integrates sensor information from Vehicle-to-Vehicle (V2V) charging, smart Home Charging Stations (HCS), and Mobile Charging Services (MCS), coordinated by cloud-fog nodes in geographically distributed zones. This paper employs the Hungarian matching algorithm for optimal decision-making of matching EVs with charging services. Our approach incorporates AIV and AIoT technologies to enhance decision making by using an ensemble-based Machine Learning (ML) model for precise EV range estimation. The comprehensive details and specifications of these proposed models are elaborated in this paper.

Background The literature investigating the relationship between social media use, mental health, and sleep has produced inconsistent findings. Younger people spend more time on social media than other age groups, and are more likely to be impacted by social media use. This systematic review with meta-analysis aimed to synthesise the evidence on the associations between social media use, mental health, and sleep of young individuals. Methods Electronic databases PubMed, Scopus and PsycINFO were searched using an established methodology and pre-determined search terms for studies that reported the association between social media use, mental health, and sleep. Results The search yielded 6108 articles, of which 182 (n = 1,169,396) were eligible for the systematic review, and 98 (n = 102,683) could be included in the meta-analyses. The systematic review identified a high level of heterogeneity in the study results. Meta-analyses found small but significant positive associations between social media use, depression, and anxiety. In addition, problematic social media use was positively associated with depression, anxiety, and sleep problems, and negatively associated with wellbeing. Geographical location, anxiety measure type, study design, age, and gender were identified as potential moderators. Limitations Associations for specific social media platforms as well as some moderator effects were not examined due to an insufficient number of studies. Conclusions This study provides important evidence of an association between social media use/ problematic social media use, mental health, and sleep. The findings support future longitudinal research to identify the directions and underlying mechanisms of the inter-relationship between these variables.

The increasing prevalence of malicious activities in IoT-enabled healthcare and Internet of Medical Things (IoMT) systems necessitates robust intrusion detection mechanisms. This article introduces a novel approach combining meta-heuristic optimization and machine learning techniques to analyze network traffic for enhanced detection accuracy. Our proposed method utilizes eleven chaotic maps and the K-Nearest Neighbor (KNN) algorithm to identify malicious activity in IoMT and IoT network systems. Recognizing the significance of feature selection in network traffic intrusion detection, we employ the Chaotic Grey Wolf Optimizer (CGWO) to select the most relevant and impactful features for learning strategically. Our approach demonstrates superior performance through comprehensive experiments compared to well-known meta-heuristic algorithms and prior art methods, as evidenced by various evaluation metrics. This research contributes to advancing intrusion detection systems in healthcare IoMT and IoT, offering a reliable and efficient solution to safeguard against evolving cyber threats.

This study aimed to evaluate the extraction efficiency of mucilage from Cordia dichotoma fruits using various aqueous extraction methods, including microwave-assisted water extraction (MWE), hot-water extraction (HWE), and cold-water extraction (CWE). Different analytical techniques were employed to characterize the Cordia dichotoma mucilage (CDM). Additionally, the functional properties, anti-microbial, anti-inflammatory, and dye reduction potential of CDM were assessed. The results indicated a significantly (p < 0.05) higher yield of CDM (13.44 ± 0.94 %) using MWE compared to HWE (12.08 ± 0.82 %) and CWE (7.59 ± 0.73 %). The optimal extraction condition was utilized for the spray-drying process, yielding a spray-dried mucilage powder (SDMP) with a yield of 9.52 ± 1.27 %. The presence of galactose and arabinose as major sugar and functional groups such as single bondOH, COOH, single bondCH, and single bondNH from proteins, uronic acids, and sugars were identified. CDM predominantly comprises galactose and arabinose as major sugars. CDM particles exhibited an irregular morphology and demonstrated thermal stability, with maximum weight loss occurring between 221.83 and 478.66 °C. The particle size of CDM was 681.16 ± 2.18 nm with a zeta potential of −21.46 ± 1.72 mV. Rheological analysis revealed that CDM exhibits shear-thinning behavior. Furthermore, CDM displayed inherent biological activities, including antimicrobial and anti-inflammatory properties. The dye reduction potential of CDM was evidenced by an 88.67 % degradation of indigo carmine dye. In summary, this study provides insights into the cost-effective extraction methods for CDM and its potential utilization as an eco-friendly material for dye reduction.

The values of sustainable hydrogen production with nano-ZrO2 + H2Oads., nano-ZrO2 + H2Oliq. nano-TiO2 + H2Oads., and nano-TiO2 + H2Oliq. systems were 7.0 × 1017 at 80 min, 140.0 × 1017 at 60 min, 2.21 × 1016 at 8 h, 80.0 × 1016 at 6 h molecules/g. The values of W(H2) in these systems were 4.44 × 1013, 2.78 × 1014, 1.38 × 1013 and 4.2 × 1013 molecules, g−1∙s−1. The values of G(H2) were 2.14, 13.5, 0.16 and 0.48 molecules/100 eV. The nano-ZrO2 structure changed from monoclinic to triclinic while no change was reported in the nano-TiO2 structure. These studies are useful in producing hydrogen and structural changes.

To address the bottleneck issue of poor carrier separation and transfer efficiency in NiCo2O4 photocatalyst, a novel 1D/2D-rod-on-rose–like NiCO2O4/BiOI nanohybrid with abundant OV’s was successfully synthesized using a single-step hydrothermal method and employed to the photocatalytic degradation of Rhodamine B (RhB). The study revealed that the optimized NiCo2O4-OV/BiOI hybrid could possess superior photocatalytic degradation efficiency towards RhB degradation under visible light with a rate constant that was 3.8 and 3.03 times greater than that of BiOI and NiCo2O4-OV. Experimental findings indicated that the formation of NiCo2CO4-OV/BiOI heterojunction significantly improved the charge separation efficiency and facilitated the formation of surface OV’s. These OVs enhanced photogenerated e⁻-h⁺ separation and increased catalytic efficiency. Quenching experiments results confirmed that both holes and superoxide radicals are playing crucial roles in the degradation process. Thus, an oxygen vacancy and engineering NiCo2CO4-OV/BiOI heterojunction-enhanced degradation mechanism was proposed, offering insights for the integration of advanced oxidation technologies and the development of catalytic materials to enhance pollutant degradation efficiency. Graphical abstract

Considering the indispensability of environmental protection, the utilization of natural ingredients as sustainable and eco‐friendly alternatives to synthetic dyes in fabric dyeing is of great significance. Betacyanins are naturally sourced pigments of red‐violet color that have the propensity to be fixed to mordanted substrates with reasonable permanency. In this study, the betacyanins sourced from beetroot (Beta vulgaris) were used to dye pre‐mordanted silk fabrics employing ultrasonication. In addition, a comparative analysis of the effects of five different mordants such as orange‐peel extracts, lemon peel extracts, tannic acid, ferrous sulfate, and alum on the color strength and fastness of beetroot‐dyed silk fabrics was presented in this research. For the characterization of the extracted dye, undyed, dyed, and mordanted silk fabric, infrared spectroscopy was performed. To observe the interaction of extracted dye and silk fabric, scanning electron microscopy was also accomplished. The outcomes of the characterization validated the successful extraction of dyes from beetroot and dyeing silk fabric with it. The values of color intensity (K/S), CIE L* a* b*, and CIE L* c* h* were measured using a reflectance spectrophotometer. At the same time, dry and wet rubbing and lightfastness ratings of dyed silk fabrics were determined according to the ISO standards. When 5% (o.w.f.) ferrous sulfate was used in pre‐mordanting, the ratings of colorfastness to wash, rub, and light ranged from 4 to 4/5, and the K/S value of the final sample was 4.88, which is higher than the K/S values of any other dyed samples. In contrast, the performance of 5% (o.w.f.) lemon peel extracts as a mordant was remarkably higher than orange peel extracts and even better than alum and tannic acid to some extent. The beetroot‐dyed silk fabric pre‐mordanted with 5% lemon peel exhibited a K/S value of 4.32, and the overall colorfastness rating ranged from 3/4 to 4/5. In conclusion, beetroot extracts hold the promise of being utilized as a sustainable replacement for environmentally harmful synthetic dyestuff in silk dyeing. Furthermore, in silk dyeing with beetroot extract, lemon peel extract is a potent alternative to ferrous sulfate as a mordant.

This study addresses the problem of attack identification in discrete event systems modeled with Petri nets, focusing specifically on sensor attacks that mislead observers to making incorrect decisions. Insertion attacks are one of the sensor attacks that are considered in this work. First, we formulate a novel observation structure to systematically model insertion attacks within the Petri net framework. Second, by generating an extended reachability graph that incorporates the observation structure, we can find a special class of markings whose components can have negative markings. Third, an observation place is computed by formulating an integer linear programming problem, enabling precise detection of attack occurrences. The occurrence of an attack can be identified by the number of tokens in the designed observation place. Finally, examples are provided to verify the proposed approach. Comparative analysis with existing techniques demonstrates that the reported approach offers enhanced detection accuracy and robustness, making it a significant advancement in the field of secure discrete event systems.