This study assesses the effects of different polytetrafluoroethylene (PTFE) particle sizes and concentrations on the performance of dual-layer membranes in direct contact membrane distillation (DCMD). Specifically, particle sizes of 0.5 μm, 1 μm, and 6 μm were systematically evaluated at concentrations of 0 wt%, 2 wt%, 4 wt%, and 6 wt%. Comprehensive analyses, including scanning electron microscopy (SEM), liquid entry pressure (LEP), contact angle, thermogravimetric analysis (TGA), mercury intrusion porosimetry (MIP), atomic force microscopy (AFM), permeate flux, nitrogen gas permeation, and salt rejection, were employed to characterize the membranes. Under conditions of a feed temperature of 70 °C and a salt concentration of 8000 ppm for a 24 h duration, the results clearly indicated that a 0.5 μm PTFE particle size combined with a 6 wt% concentration exhibited the highest performance. This configuration achieved a permeate flux of 11 kg·m²/h and a salt rejection rate of 99.8%. The outcomes of this research have significant implications for the optimization of membranes used in DCMD applications, with potential benefits for sustainable water treatment and energy conservation.

Background: The prevalence of type 2 diabetes mellitus (T2DM) has been increasing rapidly in Iraq over the past few decades. Identifying the most significant factors contributing to the development of T2DM is crucial to reducing its prevalence in this region. Genetics and epigenetics have been shown to play a role in the initiation of T2DM, making it essential to study the genetic variations of diabetic patients to identify common biomarkers associated with this disorder in Iraq. Methods: We conducted a systematic review and meta-analysis of published data to identify potential pathological indicators associated with T2DM in Iraq. We searched various databases and included studies that reported the association between genetic biomarkers and T2DM in Iraqi patients. We then analyzed the data using a random-effects model to estimate the overall effect size and identify the most common biomarkers associated with the risk of T2DM. Results: We identified 31 explored genes in 41 studies that exhibited the most common biomarkers with a strong association with the risk of T2DM in Iraq. Our exploration embraced genomic, demographic, and sensitivity analyses, enriching insights. Conclusions: Our study provides valuable insight into the genetic SNPs associated with T2DM in Iraq and offers a foundation for future research in this area. The identified SNPs can help in the development of personalized treatment options, leading to better disease management and improved patient outcomes. The study also highlights the need for further investigation into potential genetic SNPs and factors that may contribute to the development of T2DM in Iraq. While our study is limited by the sample sizes of some of the included studies, it provides a starting point for researchers seeking to identify common diagnostic markers for T2DM in Iraq.

Diabetes is one of the most common diseases that has had devastating effects on the general population. It is also among the most popular research trends in modern medicine. Thus, due to the complexity and desirability of this particular affliction, there is a lot of demand towards understanding this disease better, so that it can pave the way towards better solutions in combating diabetes. The aim of this review is to provide a categorization of the risk factors leading to Type II Diabetes. In order to provide a justification for the type of diabetes, an explanation is provided which covers the other types of diabetes and their relative infliction rate. Once the basics understanding of diabetes established, the various aspects that increase the risk of Type II Diabetes (T2D) can be classified. Several different facets are studied in order to come up with a novel classification of the disease. The classification divides the risk factors into sociodemographic and pathophysiological attributes. The major affecting attribute is identified to be genetics, as it is intertwined with other attributes. With the use of bioinformatics, advanced gene sequencing techniques can be applied that would enable the identification of patterns more closely affecting Type II Diabetes. These patterns can be cross-analyzed with pre-existing conditions in order to identify similarities and possible risk factors from other diseases. Ultimately, this classification provides a deeper understanding of the factors affecting Type II Diabetes.

In this numerical study, the performance of ceramic-based mullite hollow fiber (HF) membranes in a direct contact membrane distillation (DCMD) process was evaluated. Three types of membranes were tested: (i) hydrophobic membrane C8-HFM, (ii) rod-like omniphobic membrane (C8-RL/TiO₂), and (iii) flower-like omniphobic membrane (C8-FL/TiO₂). The CFD model was developed and validated with experimental results, which were performed over a 500 min period. The initial mass flux of C8-HFM was 30% and 9% higher than that of C8-FL/TiO₂ and C8-RL/TiO₂, respectively. However, the flower-like omniphobic membrane C8-FL/TiO₂ had the lowest drop in flux, around 11%, while the rod-like omniphobic membrane C8-RL/TiO₂ had a 15% reduction, both better than the 23% reduction in the hydrophobic membrane C8-HFM over the 500 min. The study also analyzed the impact of fouling by examining the variation in mass transfer coefficient (MTC) over time. The results indicated that the ceramic-based mullite HF membranes with TiO₂ flowers and rods demonstrated a high resistance to fouling compared to C8-HFM. The modified membranes could find applications in the desalination and handling of seawater samples containing organic contaminants. The CFD model’s versatility can be utilized beyond the current investigation’s scope, offering a valuable tool for efficient membrane development solutions, particularly for challenges such as the presence of organic contaminants in seawater.

There is substantial demand for high network traffic due to the emergence of new highly demanding services and applications such as the internet of things (IoT), big data, blockchains, and next-generation networks like 5G and beyond. Therefore, network resource planning and forecasting play a vital role in better resource optimization. Accordingly, forecasting accuracy has become essential for network operation and planning to maintain the minimum quality of service (QoS) for real-time applications. In this paper, a hybrid network- bandwidth slice forecasting model that combines long-short term memory (LSTM) neural network and various local smoothing techniques to enhance the network forecasting model's accuracy was proposed and analyzed. The results show that the proposed hybrid forecasting model can effectively improve the forecasting accuracy with minimal data loss.

This study investigates the effects of Copper oxide (CuO) and aluminum oxide (Al₂O₃) nanofluids with different diameter sizes and concentrations dissolved in water on a double-pipe heat exchanger (DPHE) behavior numerically. Thereby evaluating the effect of nanofluid's characteristics on heat transfer coefficient, friction factor, Reynolds number and Nusselt number. This objective was accomplished by numerically investigating (through ANSYS) and determining the effect of volume fraction and diameter of nanoparticles on heat transfer and fluid flow via a DPHE. The results showed that under ideal conditions such as 4 vol% and diameter 25 nm, Al₂O₃ has a better performance by 99.61% than water under 20000 Reynolds number, while CuO performance was 93.52% at the same conditions.