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The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (H2S) and carbon dioxide (CO2), from natural gas before delivery to end-users. Consequently, chemical absorption-based acid gas removal units (AGRUs) are widely implemented due to their high efficiency and reliability. The most common solvent used in AGRU is monodiethanolamine (MDEA), often mixed with piperazine (PZ) as an additive to accelerate acid gas capture. The absorption performance, however, is significantly influenced by the solvent mixture composition. Despite this, solvent composition is often determined through trial and error in experiments or simulations, with limited studies focusing on predictive methods for optimizing solvent mixtures. Therefore, this paper aims to develop a predictive technique for determining optimal solvent compositions under varying sour gas conditions. An ensemble algorithm, Extreme Gradient Boosting (XGBoost), is selected to develop two predictive models. The first model predicts H2S and CO2 concentrations, while the second model predicts the MDEA and PZ compositions. The results demonstrate that XGBoost outperforms other algorithms in both models. It achieves R2 values above 0.99 in most scenarios, and the lowest RMSE and MAE values of less than 1, indicating robust and consistent predictions. The predicted acid gas concentrations and solvent compositions were further analyzed to study the effects of solvent composition on acid gas absorption across different scenarios. The proposed models offer valuable insights for optimizing solvent compositions to enhance AGRU performance in industrial applications.

With the escalating demand for Radio Frequency Identification (RFID) technology and the Internet of Things (IoT), there is a growing need for sustainable and autonomous power solutions to energize low-powered devices. Consequently, there is a critical imperative to mitigate dependency on batteries during passive operation. This paper proposes the conceptual framework of rectenna architecture-based radio frequency energy harvesters’ performance, specifically optimized for low-power device applications. The proposed prototype utilizes the surroundings’ Wi-Fi signals within the 2.4 GHz frequency band. The design integrates a seven-stage Cockroft-Walton rectifier featuring a Schottky diode HSMS286C and MA4E2054B1-1146T, a low-pass filter, and a fractal antenna. Preliminary simulations conducted using Advanced Design System (ADS) reveal that a voltage of 3.53 V can be harvested by employing a 1.57 mm thickness Rogers 5880 printed circuit board (PCB) substrate with an MA4E2054B1-1146T rectifier prototype, given a minimum power input of −10 dBm (0.1 mW). Integrating the fabricated rectifier and fractal antenna successfully yields a 1.5 V DC output from Wi-Fi signals, demonstrable by illuminating a red LED. These findings underscore the viability of deploying a fractal antenna-based radio frequency (RF) harvester for empowering small electronic devices.

Several investigations have proven the existence of monsoons in Indonesia. However, this has received little attention due to the scientific argument that the region of 10° N–10° S is not monsoonal because it receives precipitation all year round. This study used space–time SVD analysis of atmospheric and oceanic field data for 30 years (1990–2020) to detect monsoon signals and related features. The single-field SVD analysis of rainfall revealed that the first mode accounts for only 33% of the total variance, suggesting it is highly variable. Both the PC space and time series show the well-known monsoon pattern. Further, the Indonesian monsoon regimes and phases are defined based on the revealed rainfall features. The wet season lasts from November to April, accounting for more than 77% of annual precipitation. The coupled-field SVD analyses show that Indonesian monsoon rainfall strongly correlates with local SST (PC1 accounts for 70.4%), and the pattern is associated with the Asian winter monsoon. The heterogonous vector correlation map analysis revealed that the related features during the monsoon, including the strengthening and weakening of subtropical anticyclones, the intertwining of westerly wind in the Indian Ocean, and variations in the north–south dipole structure of the ocean temperature, are linked to variations in Indonesia’s monsoon rainfall. This result can serve as the dynamic basis for defining the Indonesian monsoon index in the context of the center of action.

Human papillomavirus (HPV) genotype distribution varies according to the assessment method and the population targeted. This study aimed to assess HPV infection prevalence in women aged 23 to 82 with abnormal cytology attending King Fahad Medical City (KFMC), Riyadh, Saudi Arabia, using retrospective data collected from January 2021 to December 2022. Cytological distribution included 155 samples of atypical squamous cells of undetermined significance (ASCUS) (n = 83), low-grade squamous intraepithelial lesion (LSIL) (n = 46), high-grade squamous intraepithelial lesion (HSIL) (n = 14), atypical squamous cells cannot exclude high-grade squamous intraepithelial lesion (ASC-H) (n = 10), and squamous cell carcinoma (SCC) (n = 2). All samples were submitted to HPV detection and genotyping using Xpert HPV assay specimens. The most prevalent epithelial abnormalities were ASCUS (53.50%). Positive HPV infection results were observed in 52.9% of the samples. The highest prevalence of HPV genotypes, accounting for 31%, was attributed to the other high-risk genotypes, including 31, 33, 35, 39, 51, 52, 56, 58, 59, 66, and 68, followed by high-risk genotype 16, which counted in 11.60% of cases. Individuals who tested positive for HPV 16 were at a high risk of ASC-H, HSIL, and LSIL. Those testing positive for HPV 18–45 exhibited an elevated risk of LSIL, and those with positive results for other high-risk HPV genotypes were at an increased risk of ASCUS and LSIL, suggesting a low oncogenic potential. The results suggest that the percentage of association between samples with abnormal cervical presentation and negative high-risk HPV diagnosis is noticeably increasing. This underscores the need for effective screening programs and an understanding of the impact of specific HPV genotypes on cervical abnormalities.

Background: Computer vision syndrome (CVS) can be described as ocular-related symptoms that result from prolonged exposure and use of computers, smartphones, tablets, and other devices with digital displays. The main objective of this study was to investigate the prevalence of CVS among school-age children, the associated signs, risk factors, and the association between the disease before and during the COVID-19 pandemic in the Jazan region of Saudi Arabia. Methods: The study employed a descriptive cross-sectional research design. The targeted population was school-going children aged 6 to 18 in the Jazan region in the Southwest of Saudi Arabia. A sample of 440 participants was selected to represent the population under study. Data were collected using self-administered questionnaires. Sociodemographic characteristics were recorded, such as age, gender, education level, parents’ education, occupation, frequency, and intensity of eye symptoms if present. Results: Most of the participants were adolescents between 16 and 18 and at a high-school education level. According to the total symptoms score, the CVS prevalence was 35.4%. Prevalence of CVS significantly affects age, gender, and school level (p < 0.05 for all). A similar significant association was reported between the symptoms experienced before and during COVID-19 and the CVS (p < 0.05). Conclusion: A total of 407 adolescents aged 16–18 responded to the questionnaire (response rate of 92.5%; 407 out of 440). The study estimated the prevalence of CVS among school-going children in Jazan to be low. The main signs associated with CVS included headache, tearing, itchiness, blurred vision, eye redness, eye pain, and dryness. The attitude of children toward their health condition during the COVID-19 pandemic and the prevalence of CVS have a significant relationship.

This analysis focuses on extending and developing some previous studies of energy transport through nanofluids to include the states of combined convection flow of a Williamson hybrid nanofluid that flows around a cylinder. Mathematical models that simulate the behavior of these upgraded nanofluids are constructed by expanding the Tiwari and Das model, which are then solved numerically via Keller box approaches. The accuracy of the results is emphasized by comparing them with the previous published outcomes. Nanosolid volume fraction 0≤χ≤0.1, combined convection −1≤λ≤5, radiation factor 0.1≤R≤6, Weissenberg number 0.2≤We≤ 0.9, and magnetic factor 0.1≤M≤1 are the factors that have been taken into consideration to examine the energy transfer performance of Williamson hybrid nanofluid. Numerical and graphical outcomes are obtained using MATLAB, analyzed, and discussed in depth. According to the outcomes, the Weissenberg number reduces energy transfer and friction forces. Both the combined convective coefficient and the radiation factor improved the rate of energy transfer and increased the velocity of the host fluid. The fluid velocity and rate of energy transfer can be reduced by increasing the magnetic factor. The nanoparticle combination of silver and aluminum oxide (Ag-Al2O3) has demonstrated superiority in enhancing the energy transfer rate and velocity of the host fluid.

Carbon dioxide (CO2) is a major greenhouse gas responsible for climate change. Diatoms, a natural sink of atmospheric CO2, can be cultivated industrially in autotrophic and mixotrophic modes for the purpose of CO2 sequestration. In addition, the metabolic diversity exhibited by this group of photosynthetic organisms provides avenues to redirect the captured carbon into products of value. These include lipids, omega-3 fatty acids, pigments, antioxidants, exopolysaccharides, sulphated polysaccharides, and other valuable metabolites that can be produced in environmentally sustainable bio-manufacturing processes. To realize the potential of diatoms, expansion of our knowledge of carbon supply, CO2 uptake and fixation by these organisms, in conjunction with ways to enhance metabolic routing of the fixed carbon to products of value is required. In this review, current knowledge is explored, with an evaluation of the potential of diatoms for carbon capture and bio-based manufacturing.