AWARENESS OF RISK FACTORS OF DKA AMONG DIABETIC ADULTS IN KSA

Background: Diabetic ketoacidosis (DKA) is a complication of Diabetes mellitus (DM) that lingers to have high rates of morbidity and mortality regardless of advances in the management of DM. DKA mainly results from insulin deficiency from new-onset diabetes, insulin noncompliance and increased insulin need because of infection Most persons with DKA have type 1 diabetes however, a subgroup of type 2 diabetes patients might as well have ketosis-prone diabetes. Awareness of Aim of the work: To assess the level of awareness of the risk factors of DKA as well as the adherence of DM patients with drugs. Methods: This is a questionnaire-based cross-sectional study enrolling a total of 100 randomly selected diabetic Saudi adults ensuring diversity in age range and educational stages. Descriptive analysis was done using Statistical Package for Social Sciences (SPSS) 23. Awareness levels for DKA were calculated as absolute frequencies and were reported as overall percentages. Results: a total of 100 randomly selected diabetic Saudi adults (81 females and 19 males), 56% were diagnosed with DM-1 while 44% had DM-2 and only 11% were active sport practitioners. Moreover, only 62% reported a robust adherence to DM medications. The majority of the respondents scored low knowledge on DKA (54%). Regarding awareness of predisposing risk factors: 9% and 29% of the participants have related DKA to infection and febrile illness respectively. While 50% suggested an association between physical stress and DKA. Conclusion: Our results revealed a compelling need to bridge the disparity in awareness of DKA among Saudi adults with both types. The current knowledge gap doesn’t only incur a significant cost burden on the patients and their sponsors because of the high cost treatment and rehabilitation but also and more severely the complications that can be life-threatening if not spotted and treated quickly. Accordingly, we recommend the launch of education and awareness programs for the public at large, in the hope that this will lead to improved quality of life particularity for DM patients and their caregivers aa well as establishing nutrition and sports programs at schools and universities that can teach children and young adults the preventive measures and appropriate management of DKA early on in life. Other public Awareness raising campaign through TV & Radio spots, culture and art activities and informational events would add a great value. Keywords: Diabetic Ketoacidosis, Dka, Type 1 Diabetes Mellitus, Type 2 Diabetes Mellitus, Cross Section, Awareness, Risk Factors

Diagnostic Imaging and Radiation Therapy in the Arab World: A New Model of Advanced Practice

This study aimed at suggesting a new model for advanced practice in the diagnostic imaging and radiation therapy in the Arab World by presenting a comparative study between the different medical imaging techniques, the concepts, benefits, risks and medical applications of these techniques has been presented with details. Attempting For building a new model of advanced practice for the diagnostic role of  imaging and radiation therapy in the Arab World; by analyzing the current status of the imaging and radiation therapy in the Arab World, and then surveying the different medical imaging techniques. Then  to suggest a model of best practices upon the outcomes of the study

A Network Intrusion Detection Approach Using Extreme Gradient Boosting with Max-Depth Optimization and Feature Selection

Network intrusion detection system (NIDS) has become a vital tool to protect information anddetect attacks in computer networks. The performance of NIDSs can be evaluated by the numberof detected attacks and false alarm rates. Machine learning (ML) methods are commonly usedfor developing intrusion detection systems and combating the rapid evolution in the pattern ofattacks. Although there are several methods proposed in the state-of-the-art, the development ofthe most effective method is still of research interest and needs to be developed. In this paper,we develop an optimized approach using an extreme gradient boosting (XGB) classifier withcorrelation-based feature selection for accurate intrusion detection systems. We adopt the XGBclassifier in the proposed approach because it can bring down both variance and bias and hasseveral advantages such as parallelization, regularization, sparsity awareness hardware optimization,and tree pruning. The XGB uses the max-depth parameter as a specified criterion toprune the trees and improve the performance significantly. The proposed approach selects thebest value of the max-depth parameter through an exhaustive search optimization algorithm.We evaluate the approach on the UNSW-NB15 dataset that imitates the modern-day attacks ofnetwork traffic. The experimental results show the ability of the proposed approach to classifyingthe type of attacks and normal traffic with high accuracy results compared with the currentstate-of-the-art work on the same dataset with the same partitioning ratio of the test set

An Archive-Guided Equilibrium Optimizer Based on Epsilon Dominance for Multi-Objective Optimization Problems

In real-world applications, many problems involve two or more conflicting objectives that need to be optimized at the same time. These are called multi-objective optimization problems (MOPs). To solve these problems, we introduced a guided multi-objective equilibrium optimizer (GMOEO) algorithm based on the equilibrium optimizer (EO), which was inspired by control–volume–mass balance models that use particles (solutions) and their respective concentrations (positions) as search agents in the search space. The GMOEO algorithm involves the integration of an external archive that acts as a guide and stores the optimal Pareto set during the exploration and exploitation of the search space. The key candidate population also acted as a guide, and Pareto dominance was employed to obtain the non-dominated solutions. The principal of ϵ-dominance was employed to update the archive solutions, such that they could then guide the particles to ensure better exploration and diversity during the optimization process. Furthermore, we utilized the fast non-dominated sort (FNS) and crowding distance methods for updating the position of the particles efficiently in order to guarantee fast convergence in the direction of the Pareto optimal set and to maintain diversity. The GMOEO algorithm obtained a set of solutions that achieved the best compromise among the competing objectives. GMOEO was tested and validated against various benchmarks, namely the ZDT and DTLZ test functions. Furthermore, a benchmarking study was conducted using cone-ϵ-dominance as an update strategy for the archive solutions. In addition, several well-known multi-objective algorithms, such as the multi-objective particle-swarm optimization (MOPSO) and the multi-objective grey-wolf optimization (MOGWO), were compared to the proposed algorithm. The experimental results proved definitively that the proposed GMOEO algorithm is a powerful tool for solving MOPs

Environment-Aware Energy Efficient and Reliable Routing in Real-Time Multi-Sink Wireless Sensor Networks for Smart Cities Applications

Internet of things (IoT) is one of the leading technologies that have been used in many fields, such as environmental monitoring, healthcare, and smart cities. The core of IoT technologies is sensors; sensors in IoT form an autonomous network that is able to route messages from one place to another to the base station or the sink. Recently, due to the rapid technological development of sensors, wireless sensor networks (WSNs) have become an important part of IoT. However, in applications such as smart cities, WSNs with one sink might not be suitable due to the limited communication range of sensors and the wide area to be covered. Therefore, multi-sink WSN solutions seem to be suitable for such applications. The multi-sink WSNs are gaining popularity because they increase network throughput, network lifetime, and energy usage. At the same time, multi-hop routing is essential for the WSNS to collect data from sensor nodes and route it to the sink node for decision-making. Many routing algorithms developed for multi-sink WSNs focus on being energy efficient to extend the network lifetime, but the delay was not the main concern. However, these algorithms are unable to deal with such applications in which the data packets have to reach sink nodes within predefined real-time information. On the other hand, in the most existing routing schemes, the effects of the external environmental factors such as temperature and humidity and the reliability of real-time data delivery have largely been ignored. These issues can dramatically influence the network performance. Therefore, this paper designs a routing algorithm that satisfies three critical conditions: energy-efficient, real-time, environment-aware, and reliable routing. Therefore, the routing decisions are made according to different parameters. Such parameters include environmental impact metrics, energy balance metrics to balance the energy consumption among sensor nodes and sink nodes, desired deadline time (required delivery time), and wireless link quality. The problem is formed in integer linear programming (ILP) for optimal solution. The problem formulation is designed to fully understand the problem with its major constraints by the sensor networks research community. In addition, the optimal solution for small-scale problems could be used to measure the quality of any given heuristic that might be used to solve the same problem. Then, the paper proposes swarm intelligence to solve the optimization problem for large-scale multi-sink WSNs as a heuristic algorithm. The proposed algorithm is evaluated and analyzed compared with two recent algorithms, which are the most related to our proposal, SMRP and EERP protocols using an extensive set of experiments. The obtained results prove the superiority of the proposed algorithm over the compared algorithms in terms of packet delivery ratio, deadline miss ratio, average end-to-end delay, network lifetime, and energy imbalance factor under different aspects. In particular, the proposed algorithm requires more computational energy compared to comparison algorithms

Indole Derivatives Efficacy and Kinetics for Inhibiting Carbon Steel Corrosion in Sulfuric Acid Media

The global prevalence of metal corrosion is a significant challenge due to its detrimental effect. Environmentally friendly and non-hazardous alternatives for harmful and poisonous synthetic corrosion inhibitors are urgently necessary due to increasing environmental concerns and regulations prohibiting their application. In this study, indole molecules were employed as carbon steel corrosion inhibitors in acidic conditions. Gravimetrical and scanning electronic microscope (SEM) analysis was used in a preliminary investigation of indole as an organic inhibitor. The results revealed that adding indole to carbon steel before exposing it to sulfuric acid slowed and induced resistance to corrosion. The indole affixed themselves to the steel carbon surfaces, producing a barrier/protection for carbon steel. The efficacy of the indole in preventing corrosion was determined through the weight loss method. Temperature and inhibitory concentration effects on inhibition effectiveness under varying parameters were also reported. The temperatures employed were between 298 K and 328 K, while the inhibitor concentrations ranged from 1.2 × 10−3 M to 7.6 × 10−3 M, and both parameters significantly influenced corrosion inhibition effectiveness. The inhibitory mixture attained optimum efficacy in inhibiting corrosion, at 81 %, when the lowest and highest respective temperature and concentration were applied. The kinetic analysis was conducted under a range of temperatures to determine the reaction mechanisms of the inhibitor. The thermal adsorption isotherm of the inhibitor indicated that the surface adhered to Langmuir's adsorption isotherm. Additionally, investigations on corrosion and inhibition using the electrochemical impedance spectroscopy method (EIS) were conducted. This study can provide in-depth knowledge for advancing inhibitory science and engineering to enhance corrosion resistance in acidic media

Natural Eutectic Solvents and Graphene Integrated within Emulsion Liquid Membrane System for Sodium Removal from Crude Biodiesel

This study reports a new technique for sodium ion removal from biodiesel using a green emulsion liquid membrane (GELM) system based on natural deep eutectic solvents (NADESs) and graphene. The DES consists of choline chloride with glycerol and lactic acid, and tetramethylammonium chloride with glycerol and lactic acid. COSMO-RS software was used to compute the intermolecular interaction of sodium with hydrogen bond acceptors and donors of the NADES. The simulation shows that NADES was the most effective stripping phase for sodium ion removal, which follows the experimental results. The investigation on the stability of ELM and sodium (830 ppm of initially) extraction efficiency showed that the GELM-NADES technique can achieves a sodium ion extraction efficiency of 99.6 % (3.17 ppm) with high stability at a homogenization speed of 8000 rpm, homogenization time of 3 min, HBA: HBD molar ratio of 1:4, 3 wt% of span 80, 10 minutes of extraction time, 400 rpm stirring speed and 0.5 treatment ratio. The presence of graphene (0.3 g) in the system further enhanced the efficiency and shortened the required extraction time from 6 to 4 min to meet the ASTM D6752 standards. The transport mechanism of sodium ions into the ELM phases adheres to the first-order kinetics model and film theory mechanisms. The overall mass transfer coefficient K O , mass transfer coefficient of the external phase in agitated reactor K M , and interfacial reaction rate constant KF of 5.188 × 10−9, 1.373 × 10−7, and 5.392 × 10−9 m/s, respectively. ELM system with NADES and graphene can provide a cleaner route for biodiesel downstream processing

Direct application of tungstosilicic acid hydrate for thetreatment of high free fatty acid in acidic crude palmoil and for biodiesel production

This study explored the use of industrial acidic crude palm oil (ACPO) for biodiesel production, facing a significant obstacle due to its high free fatty acid (FFA) content, which complicates the biodiesel production process. Typically, esterification is employed to convert FFAs into fatty acid methyl ester (FAME). Herein, the effectiveness of tungstosilicic acid hydrate (TSAH) as an unsupported heteropoly acid (HPA) catalyst for FFA esterification in ACPO was investigated. The FFA content was reduced from 8.43% to 0.95% under optimum conditions (4 wt% catalyst dosage, a methanol to oil molar ratio of 10:1, 150 min and a temperature of 60°C). Noteworthy, the TSAH catalyst showed stability over 7 cycles. The kinetic analysis revealed that the FFA esterification process closely followed pseudo first-order kinetics, with an R2 value of 0.94. Furthermore, the biodiesel produced from TSAH-treated ACPO meets the standard specifications outlined by ASTM D6751 and EN 14214. This research highlights the effectiveness of TSAH in catalyzing FFA esterification without the need for additional support materials or modifications

Ultrasonic assisted extraction of oil from argan seeds using ionic liquids as novel co‑solvent

In this study, the extraction of oil from argan seeds using ultrasonic assisted extraction was investigated using n-decane as the extraction solvent and ionic liquids as the novel co-solvents. The extraction process was assessed based on the mass ratio of solvent to seed and extraction time, alongside the exploration of different ionic liquids as co-solvents. The results showed that in the absence of ionic liquid, the optimum oil yield of 35.2% was achieved at mass ratio of solvent to seed of 6:1 at 60 °C and 30 min. The extraction process adhered to pseudo-second-order kinetic model, with a rate constant of 0.0127%−1 min−1. When ionic liquid 1-propyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([C3mim][NTf2]) was utilized as a co-solvent alongside n-decane, the oil yield showed an improvement ranging from 0.4 to 4.0%, even when the mass of n-decane used remained similar. However, ionic liquid alone cannot serve as a substitute for n-decane as the extraction solvent. The experimental results were also compared with COSMO-RS analysis. The study demonstrates that ionic liquids can slightly enhance the oil yield by working synergistically with n-decane to improve the dissolution of lignocelluloses present on the seeds. This, in turn, facilitates better penetration of solvent into the seed matrix and enhancing the oil extraction

PREVALENCE OF COMPLICATIONS AFTER ODONTOPLASTY

Background:Cosmetic dentistry, also known as enameloplasty, is what odontoplasty refers to. This cosmetic dentistry treatment that strives to enhance the function of human teeth also includes contouring and reshaping of the teeth. Enhancing the look of a persons teeth by modifying their size, length, or even shape is a popular cosmetic procedure nowadays. This research aimed to assess and understand the issues and complications that are reported to have been faced by many people who have gone through the procedure of odontoplasty. Methods:A cross-sectional study was used to understand the prevalence of complications after odontoplasty. The philosophy of positivism is appropriate for this research as it helped in the descriptive assessment of the quantitative data gathered. An inductive research approach would be implemented because this approach relies on building up new theories and developing perceptions from existing theories. This is the need of this research work, and therefore an approach of inductive nature would be the best fit.The sampling method that was implemented is stratified random sampling, which would help consider those individuals in the UK going through the odontoplasty procedure. The sample age group is within the range of 25-40 years. Results:Study included 562 participants in which all of them responded to study survey questions. The most frequent complication was weak tooth (n= 268, 47.7%). More than third of study participants didnt support the changing of natural appearance of the tooth (n= 216, 38.4%). However, 63% would like to further changing the existing shape and size of their teeth (n= 354). On the other hand, 241 participants believed that odontoplasty is a necessity (42.9%). The same percentage almost recommended others to undergo odontoplasty (n= 239, 42.5%). 312 participants felt moderate pain (55.5%) is more than half of study participants. The most frequent reason why participants underwent odontolplasty was bad shape of teeth (n= 259, 46.1%). Conclusion:The most prevalent consequence was weak teeth. More over a third of survey participants opposed altering the tooths natural look, according to the study findings. However, more than half of individuals would desire to modify their present tooth form and size. Some participants, however, thought that odontoplasty is necessary. Over fifty percent of subjects reported moderate discomfort. About half of them claimed that it would endure for extended period of tim