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Background and Objectives: Asthma is associated with several comorbidities, one of which is obesity. The worldwide increase in obesity has been accompanied by a parallel rise in asthma prevalence, with obesity recognized as a significant risk factor for both the development and severity of asthma. Obesity is often linked to various comorbidities, which can complicate asthma management and lead to poorer clinical outcomes. This study aims to investigate the prevalence of obesity and its comorbidities in adults with asthma in a single center in Saudi Arabia, providing an overview of the associated health implications. Materials and Methods: This single-center, retrospective study aimed to assess the prevalence of obesity and other comorbidities in asthma patients. Data were collected from King Khalid University Hospital in Saudi Arabia between July 2023 and December 2023. Results: This study revealed that 72.1% of asthma patients were either obese or overweight. Female patients had significantly higher BMI values compared to males. Our study revealed that 38.21% of female asthma patients (mean age = 57 ± 13.85 years) had comorbidities compared to 24.14% of male asthma patients (mean age = 59 ± 14.02 years). Furthermore, the proportion of obese asthmatic patients with comorbidities was significantly greater than those without comorbidities. Conclusions: This study investigates obesity prevalence and associated comorbidities in adult asthmatics in a single center in Saudi Arabia. The findings reveal a 72.1% rate of obesity and overweight among asthmatic patients, with higher BMI and comorbidity prevalence in females. These results underscore the need for targeted interventions addressing obesity and comorbidities, especially in female asthmatics.

The utilization of recycled materials in concrete technology has gained significant attention in recent years, promoting sustainability and resource conservation. This paper investigates the behavior of lightweight self-compacting concrete (LWSCC) with recycled tire steel fibers (RTSFs). The effects of RTSFs on the flowability of the composite material and its density were assessed. The mechanical properties of the developed material were examined and beam tests were performed, aiming to assess its feasibility for structural applications. The compressive and tensile strengths were determined to evaluate the mechanical properties of the developed concrete mixtures. The beam tests were conducted to assess the flexural behavior of the beam specimens. Three different steel fiber contents of 0, 0.5, and 1% volumetric fractions of concrete were used in this study. The test results indicate that incorporating the fibers did not negatively impact the flowability and density of the LWSCC mixtures. In addition, the use of RTSFs enhanced the tensile strength of the developed concrete mixtures, where fibrous concrete showed increases in the splitting tensile strength in the range of 38 to 76% over that of non-fibrous concrete. On the other hand, the compressive strength of the mixtures was not affected. The test beams with RTSFs exhibited improved flexural performance in terms of delaying and controlling cracking, enhancing ultimate load, and increasing ductility. Compared with the control non-fibrous beam, the increases in the cracking load, ultimate load, and ductility index were up to 63.8, 9.3, and 16%, respectively. The test results of the beams were compared with theoretical predictions, and good agreement was found.

Aim: This study aimed to compare the torque loss, fracture load, compressive strength, and failure types of selective-laser-sintered cobalt chromium (SLM-Co-Cr), computer-aided design and computer-aided manufacturing zirconium oxide (CAD-CAM-ZrO), and machined titanium (Ti) implant abutments. Methods: Thirty endosseous dental implants were vertically embedded with machined Ti (control group), CAD-CAM-ZrO, and SLM-Co-Cr abutments. Abutment fabrication involved CAD-CAM milling and SLM technology. The de-torque assessment included preload reverse torque values (RTVs), cyclic loading, and post-RTVs using a customized protocol. Fracture load assessment employed ISO-14801 standards, and statistical analysis was conducted using ANOVA and Tukey Post hoc tests (p < 0.05). Results: In pre-load RTVs, SLM-Co-Cr showed the lowest mean torque loss (24.30 ± 2.13), followed by machined Ti (27.33 ± 2.74) and CAD-CAM-ZrO (22.07 ± 2.20). Post-load RTVs decreased for all groups. Fracture load and compressive strength were highest for SLM-Co-Cr, with significant differences among groups (p < 0.001). Fracture types included abutment failures in SLM-Co-Cr and machined Ti, while CAD-CAM-ZrO exhibited crown separation with deformation. Conclusion: SLM-Co-Cr-fabricated implant abutments exhibited superior stability and resistance to rotational forces, higher fracture loads, and greater compressive strength compared to CAD-CAM-ZrO and machined Ti.

Oral cancer (OC) is a serious health concern that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. The publicly available repository the Gene Expression Omnibus (GEO) was to the source for the collection of OC-related datasets. GSE74530, GSE23558, and GSE3524 microarray datasets were collected for analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted p < 0.05 were applied to calculate the upregulated and downregulated differential expression genes (DEGs) from the three datasets. After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein–protein interaction (PPI) networks were built to identify the most active genes, and a clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study OC-associated DEGs in-depth. Finally, top gene performers from PPI networks were used to apply drug signature analysis. After applying filtration and cut-off criteria, 2508, 3377, and 670 DEGs were found for GSE74530, GSE23558, and GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the cell cycle and influenza A. The PPI network holds 88 nodes and 492 edges, and CDC6 had the highest number of connections. Four clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of OC pathophysiology and the development of new therapies for OC.

Background: The logistics industry is keen to meet customer expectations, which in turn supports trade by enhancing business competitiveness and promoting economic development. Logistics companies realized that fulfillment of customer needs has a significant effect on their business survival, their sales and ultimately their profits. This research analyzes the variables that influence customer satisfaction with parcel delivery services in the Kingdom of Saudi Arabia (KSA), particularly the SERVQUAL dimensions: tangibility, reliability, responsiveness, assurance, and empathy. The research also examined whether national culture affects customer perceptions of parcel delivery services in KSA or not. Methods: The research collected data using a self-administered survey, which was directed at a sample of customers in the KSA, who had recent experience with parcel delivery services. Results: The results of statistical analysis showed that the best predicators of customer satisfaction are reliability, followed by the dimensions of empathy, then tangibility and finally responsiveness. In addition, the results showed significant differences in customers’ overall satisfaction between Saudis and non-Saudis. Saudis were more satisfied with the service than non-Saudis were. Conclusions: This research showed the key role of national culture in perceiving parcel delivery service quality in KSA. The findings provide important messages to the administrators of the logistics services in KSA, which have become crucial for the Saudi Vision 2030. They need to customize their service according to customer needs and provide regular training for their staff to be supportive, reliable and have the credibility to deliver services as promised.

The failure to treat infectious diseases due to the continual emergence of drug-resistant microbes poses a huge and serious challenge for human health globally. Currently, the discovery and development of natural therapeutic compounds are attracting considerable attention from researchers worldwide. In this project, two types of pollen grains (maize and clover) were evaluated for potential antimicrobial activities. Extracts of both pollen grains were purified using HPLC, which has been shown to have numerous phenolic and flavonoid compounds. Pyro catechol and methyl gallate were detected in high concentrations (1145.56 and 1056.57 µg/mL, respectively) in the maize extract, while caffeic acid, quercetin, and kaempferol (464.73, 393.05, and 390.93 µg/mL, respectively) were among the compounds observed at high concentrations in the clover pollen grains extract. Staphylococcus aureus, Escherichia coli, Salmonella typhi, and Candida albicans were more sensitive to the clover pollen grains extract with inhibition zones of 22 ± 0.2, 18 ± 0.1, 29 ± 0.3, and 42 ± 0.4 mm compared to the size of the inhibitory zones caused by the maize pollen grains extract (19 ± 0.3, 15 ± 0.4, 27 ± 0.1, and 22 ± 0.4 mm, respectively). Moreover, lower MIC values for the clover pollen grains extract were recorded against C. albicans (1.97 ± 0.04 µg/mL), S. aureus (62.5 ± 1.00 µg/mL), and E. coli (62.5 ± 0.07 µg/mL) than the MICs caused by the maize pollen grains extract. The use of a transmission electron microscope revealed that the E. coli that had been treated with the clover pollen grains extract showed changes in its cell walls compared to that treated with the maize pollen grains extract. The clover pollen grains extract exhibited a stronger antioxidant potential, with an IC50 value of 22.18 µg/mL, compared to an IC50 value of 54.85 µg/mL for the maize pollen grains extract, via a DPPH scavenging assay. Regarding anticancer activity, the maize pollen grains extract was revealed to be more effective in terms of inhibiting the human colon cancer cell line HCT-116, with an IC50 value of 67.02 ± 1.37 µg/mL, compared with the observed toxicity caused by the clover extract, with an IC50 value of 75.03 ± 1.02 µg/mL. Overall, the clover pollen grains extract demonstrated potent antibacterial and antioxidant activities, but not anticancer activity, when compared to the maize grains extract. Thus, the current findings related to both types of pollen grains (clover and maize) highlight their potential therapeutic applications for the treatment of certain infectious diseases and malignancies.

Background: Congenital heart diseases (CHD) are the most common congenital malformations in newborns and remain the leading cause of mortality among infants under one year old. Molecular diagnosis is crucial to evaluate the recurrence risk and to address future prenatal diagnosis. Here, we describe two families with various forms of inherited non-syndromic CHD and the genetic work-up and resultant findings. Methods: Next-generation sequencing (NGS) was employed in both families to uncover the genetic cause. In addition, we performed functional analysis to investigate the consequences of the identified variants in vitro. Results: NGS identified possible causative variants in both families in the protein kinase domain of the TGFBR1 gene. These variants occurred on the same amino acid, but resulted in differently substituted amino acids (p.R398C/p.R398H). Both variants co-segregate with the disease, are extremely rare or unique, and occur in an evolutionary highly conserved domain of the protein. Furthermore, both variants demonstrated a significantly altered TGFBR1-smad signaling activity. Clinical investigation revealed that none of the carriers had (signs of) aortopathy. Conclusion: In conclusion, we describe two families, with various forms of inherited non-syndromic CHD without aortopathies, associated with unique/rare variants in TGFBR1 that display altered TGF-beta signaling. These findings highlight involvement of TGFBR1 in CHD, and warrant consideration of potential causative TGFBR1 variants also in CHD patients without aortopathies.

Infected burned skin is a life-threatening condition, which may lead to sepsis. The aims of this work are to formulate a biofilm composed of silver sulfadiazine (SSD), chitosan (CS), and sodium alginate (SA), and to evaluate its wound-healing effectiveness. A full factorial design was used to formulate different matrix formulations. The prepared biofilm was tested for physicochemical, and in vitro release. The optimized formulation is composed of 0.833% of CS and 0.75% of SA. The release of SSD almost reached 100% after 6 h. The mechanical properties of the optimized formula were reasonable. The antibacterial activity for the optimized biofilm was significantly higher than that of blank biofilm, which is composed of CS and SA, p = 1.53922 × 10−12. Moreover, the in vivo study showed a 75% reduction in wound width when using the formulated SSD biofilm compared to standard marketed cream (57%) and the untreated group (0%).

Profit maximization is critical in the control of power system networks for both power providers and users. Electrical energy is freely accessible in the electrical grid during off-peak hours, with storage units helping to store excess energy and assist the electrical grid during high-demand situations. Such techniques promote grid stability and ensure safe operation. Because renewable resources are intermittent, energy storage technologies are especially significant in renewable-associated power systems. Vehicle-to-grid (V2G) technology has recently acquired popularity in preserving power grid stability in the presence of renewable resources.V2G technology employs automobiles as mobile storage devices and focuses on the efficient utilization of extra power available during off-peak hours. The goal of this work is to improve the functioning of a V2G system in a power network to reduce energy production costs while increasing system profitability. This study for deregulated power environments also depicts the influence of V2G mixing on system voltage profile and locational marginal pricing (LMP), as well as the performance of the Unified Power Flow Controller (UPFC) on system economics. The MiPower simulation program is used in the study to find the best placement of the power storage unit for the modified IEEE 14-bus system.

Predictive maintenance is a system’s competency in distinguishing future scenarios where the machine is likely to fail and schedule repairs just prior to this happening. A heuristic technology to enable efficient predictive maintenance is digital twin technology. The development of a twin system between real-time machinery and the virtual world is made possible by digital twin technology, which is ideal for predictive maintenance. Induction motors, which are the core of industrial machinery, are sparsely represented in the digital twin domain. Therefore, this study created a digital twin of a squirrel cage induction motor, utilizing data-driven modeling and multiple physics, and integrated it with a custom predictive maintenance system. The purpose of this study is to implement digital twin technology for induction motors for fault diagnosis and predictive maintenance. This framework can extrapolate running parameters to presciently detect motor remaining useful lifetime as well as erratic fault diagnosis. The experimental setup for the 2.2 kW squirrel cage induction motor has been integrated into the digital workspace via the dSPACE MicroLabBox controller to allow frequent calibration and reference signal setup. The resultant digital framework deployed on MATLAB Simulink provided high accuracy without placing a great computational load on the processor. The proposed model’s commercial application may open the way for computational intelligence in Industry 4.0 adoption of induction motors.

Purpose: Smartphone addiction is prevalent among medical students, and there is a concern that the coronavirus disease 2019 (COVID-19) pandemic fueled a rise in smartphone addiction. Earlier studies suggest a link between excessive smartphone usage and negative outcomes such as depression, stress, and reduced academic achievement. However, there is a dearth of both local studies in Saudi Arabia and studies conducted during the COVID-19 pandemic exploring the prevalence of smartphone addiction and its association with academic performance, depression, and perceived stress, which is the purpose of the current study. Methods: In 2021, a cross-sectional research project took place among medical students at King Saud University and the Vision Colleges located in Riyadh, Saudi Arabia. An online self-administered questionnaire consisting of demographic variables, grade point average (GPA), the Patient Health Questionnaire-9 (PHQ-9), the Perceived Stress Scale-4 (PSS-4), and the Smartphone Addiction Scale—Short Version (SAS-SV) was deployed. Results: Three hundred and fifteen students participated. Around 47.9% of students reported smartphone addiction, and the mean SAS-SV score was 32.31 ± 12.01 points. Both PHQ-9 and PSS-4 scores showed a significant positive correlation with the SAS-SV score (r = 0.216, p < 0.001 and r = 247, p < 0.001, respectively), while GPA did not (r = −0.027, p = 0.639). An adjusted analysis showed that the PSS-4 score was positively associated with the SAS-SV score (odds ratio (OR) = 1.206, p < 0.001), while the PHQ-9 score was not (OR = 102, p = 0.285). Conclusions: Smartphone addiction is prevalent among medical students and associated with perceived stress. Additional research is required to gain a deeper comprehension of this issue and to assess the success of intervention initiatives aimed at encouraging healthy smartphone usage, particularly in times of crisis like the COVID-19 pandemic.

Energy-efficient buildings are a new demand in the current era. In this paper, we present a novel metamaterial design aimed at achieving efficient solar energy absorption through a periodic MMA structure composed of a W-GaAs-W. The proposed structure can be implemented as the window coating and in turn it can absorb the incident solar energy and, then, this energy can be used to fulfill the energy demand of the building. Our results reveal significant improvements, achieving an average absorptance of 96.94% in the spectral range. Furthermore, we explore the influence of the angle of incidence on the absorber’s response, demonstrating its angle-insensitive behavior with high absorption levels (above 90%) for incidence angles up to 60° for TE polarization and 40° for TM polarization. The proposed structure presents a significant advancement in metamaterial-based solar energy absorption. By exploring the effects of structural parameters and incident angles, we have demonstrated the optimized version of our proposed absorber. The potential applications of this metamaterial absorber in self-sufficient futuristic building technologies and self-sustaining systems offer new opportunities for harnessing solar energy and are a valuable contribution to future developments in the fields of metamaterials and renewable energy.

This paper presents the design and analysis of a modified double-ended forward converter (DEFC) for stepper motor-based robotic applications. The proposed converter topology provides galvanic isolation between the input and output while also higher efficiency with a smooth operative system, making it suitable for use in robotic systems that require both power and control signals to be transmitted. The paper also discusses the control strategy for the converter, which uses Proportional Integral (PI) to regulate the output voltage and current. The control strategy is implemented using a microcontroller-based system, which provides precise control of the output parameters. The converter is tested using a stepper motor-based load, and the results demonstrate the effectiveness of the proposed topology and control strategy. In addition to the experimental results, the paper also presents a detailed analysis of the converter’s performance. The analysis includes the input voltage and current, capacitor voltage, MOSFET parameters, output voltage and current, and calculation of efficiency. The analysis results show that the proposed converter topology and control strategy offer high efficiency comparing to existing converting approaches. Overall, the proposed double-ended forward converter offers a suitable solution for stepper motor-based robotic applications, providing efficient and reliable power and control signals. The results demonstrate the effectiveness of the proposed converter topology and control strategy, making it a promising option for use in future robotic systems.

Using the transfer matrix method, the electromagnetic responses of 1D deformed and non-deformed quasi-periodic photonic crystals arranged in accordance with the Fibonacci sequence are theoretically studied. The gallium selenide (GeSe) and plasma materials (that is, electron density) are used to construct the multilayer Fibonacci structures. If this study is experimentally validated in the future, we intend to insert two transparent polymer film materials at the top and bottom of the structure, which are intended to protect the plasma material and prevent it from escaping and spreading outside the structure. The effect of the order of the Fibonacci sequence, the deformation of the thickness of the layers using a mathematical rule and the role of the plasma material in the reflectance response are discussed. We notice that the position and the width of photonic band gaps are sensitive to the Fibonacci sequence, the thickness and the density of the plasma material layers. In addition, the intensity of the resonance peaks can be controlled by adjusting the plasma material properties. The width of the photonic band gaps can be notably enlarged by applying a structural deformation along the stacks. The proposed structures have potential applications in tunable filters, micro-cavities for LASER equipment, which allow us to obtain an intense laser, and they are very useful in the communication field.

Electric vehicles (EVs) have emerged as a promising solution for sustainable transportation. The high energy density, long cycle life, and low self-discharge rate of lithium-ion batteries make them an ideal choice for EVs. Recently, these batteries have been prone to faster decay in life span, leading to sudden failure of the battery. To avoid uncertainty among EV users with sudden battery failures, a robust health monitoring and prediction scheme is required for the EV battery management system. In this regard, the Unscented Kalman Filter (UKF)-based technique has been developed for accurate and reliable prediction of battery health status. The UKF approximates nonlinearity using a set of sigma points and propagates them via the nonlinear function to enhance battery health estimation accuracy. Furthermore, the UKF-based health estimation scheme considers the state of charge (SOC) and internal resistance of the battery. Here, the UKF-based health prediction technique is compared with the Extended Kalman filter (EKF) scheme. The robustness of the UKF and EKF-based health prognostic techniques were studied under varying initial SOC values. Under these abrupt changing conditions, the proposed UKF technique performed effectively in terms of state of health (SOH) prediction. Accurate SOH determination can help EV users to decide when the battery needs to be replaced or if adjustments need to be made to extend its life. Ultimately, accurate and reliable battery health estimation is essential in vehicular applications and plays a pivotal role in ensuring lithium-ion battery sustainability and minimizing environmental impacts.

This paper presents a novel model predictive control (MPC) approach for suppressing circulating currents in MMC-based HVDC systems. The proposed MPC eliminates the need for PI-regulators and pulse width modulators, resulting in improved dynamic response and controllability. The methodology demonstrates exceptional efficacy in controlling output current and addressing voltage ripple concerns associated with sub module (SM) capacitors. An innovative, communication-free fault ride-through (FRT) method is also introduced, eliminating the need for a DC chopper and ensuring rapid recovery following faults. To overcome the computational challenges associated with the traditional MPC algorithm, an aggregate model of the MMC is proposed, significantly reducing predicted states, hardware requirements, and calculations. Simulations validate the robustness of the proposed MPC control algorithm in tracking AC side current, suppressing circulating current, and regulating capacitor voltages under various scenarios. Future research will explore system expansion, integration with renewable energy sources, and hardware-in-loop setup testing for further validation.

Alzheimer’s disease (AD) is a progressive neurological illness that is distinguished clinically by cognitive and memory decline and adversely affects the people of old age. The treatments for this disease gained much attention and have prompted increased interest among researchers in this field. As a springboard to explore new anti-Alzheimer’s chemical prototypes, the present study was carried out for the synthesis of benzoxazole-oxadiazole analogues as effective Alzheimer’s inhibitors. In this research work, we have focused our efforts to synthesize a series of benzoxazole-oxadiazole (1–19) and evaluating their anti-Alzheimer properties. In addition, the precise structures of synthesized derivatives were confirmed with the help of various spectroscopic techniques including 1H-NMR, 13C-NMR and HREI-MS. To find the anti-Alzheimer potentials of the synthesized compounds (1–19), in vitro acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE), inhibitory activities were performed using Donepezil as the reference standard. From structure-activity (SAR) analysis, it was confirmed that any variation found in inhibitory activities of both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes were due to different substitution patterns of substituent(s) at the variable position of both acetophenone aryl and oxadiazole aryl rings. The results of the anti-Alzheimer assay were very encouraging and showed moderate to good inhibitory potentials with IC50 values ranging from 5.80 ± 2.18 to 40.80 ± 5.90 µM (against AChE) and 7.20 ± 2.30 to 42.60 ± 6.10 µM (against BuChE) as compared to standard Donepezil drug (IC50 = 33.65 ± 3.50 µM (for AChE) and 35.80 ± 4.60 µM (for BuChE), respectively. Specifically, analogues 2, 15 and 16 were identified to be significantly active, even found to be more potent than standard inhibitors with IC50 values of 6.40 ± 1.10, 5.80 ± 2.18 and 6.90 ± 1.20 (against AChE) and 7.50 ± 1.20, 7.20 ± 2.30 and 7.60 ± 2.10 (against BuChE). The results obtained were compared to standard drugs. These findings reveal that benzoxazole-oxadiazole analogues act as AChE and BuChE inhibitors to develop novel therapeutics for treating Alzheimer’s disease and can act as lead molecules in drug discovery as potential anti-Alzheimer agents.

A deregulated electricity market provides open access to all market players. In an open-access power market, the system operator is responsible for ensuring that all contracted power is dispatched. However, if this results in line flows that exceed their acceptable range, then it could threaten the system’s security. Therefore, the system operator checks for congestion as the line flow exceeds its limit. For congestion management, the system operator applies different curtailment strategies to limit the requested transaction. Therefore, in this work, an optimal power dispatch model has been presented in order to reduce the curtailment of requested power. A modified moth flame optimization technique has been implemented to frame this OPD model. The impact of congestion management on power dispatch has been analyzed, considering bilateral and multilateral dispatch in an electricity market. In addition, the effect of FACTS devices on reducing congestion and curtailing power is studied. Verification studies showed that the proposed solution reduces congestion costs by 27.14% and 29.4% in 14- and 30-bus systems, respectively. It has been verified that the MMFO approach with the FACTS device improves transaction deviations and ensures that the deregulated system provides secure energy with less cost reflected on the customers.

In recent modern power systems, the number of renewable energy systems (RESs) and nonlinear loads have become more prevalent. When these systems are connected to the electricity grid, they may face new difficulties and issues such as harmonics and non-standard voltage. The proposed study suggests the application of a whale optimization algorithm (WOA) based on a fractional-order proportional-integral controller (FOPIC) for unified power quality conditioner (UPQC) and STATCOM tools. These operate best with the help of their improved control system, to increase the system’s reliability and fast dynamic response, and to decrease the total harmonic distortion (THD) for enhancing the power quality (PQ). In this article, three different configurations are studied and assessed, namely: (C1) WOA-based FOPIC for UPQC, (C2) WOA-based FOPIC for STATCOM, and (C3) system without FACTS, i.e., base case, to mitigate the mentioned drawbacks. C3 is also considered as a base case to highlight the main benefits of C1 and C2 in improving the PQ by reducing the %THD of the voltage and current system and improving the systems’ voltage waveforms. With C2, voltage fluctuation is decreased by 98%, but it nearly disappears in C1 during normal conditions. Additionally, during the fault period, voltage distortion is reduced by 95% and 100% with C2 and C1, respectively. Furthermore, when comparing C1 to C2 and C3 under regular conditions, the percentage reduction in THD is remarkable. In addition, C1 eliminates the need for voltage sag, and harmonic and current harmonic detectors, and it helps to streamline the control approach and boost control precision. The modeling and simulation of the prepared system are performed by MATLAB/Simulink. Finally, it can be concluded that the acquired results are very interesting and helpful in the recovery to the steady state of wind systems and nonlinear loads, thereby increasing their grid connection capabilities.

By bringing smart and advanced solutions, the Internet of Things (IoT) has opened up new dimensions of innovative services and processing power for contemporary living standards. IoT has a wide range of devices and communication entities as a result of the widespread applications of these services, making network management a challenging task. Therefore, it is critical to redesign the IoT network’s management. The inherent programmability and centralized capabilities of software-defined networking (SDN) make network management simpler, enable network abstraction, make network evolution easier, and have the potential to handle the IoT network. However, security issues still present the IoT dilemma. Distributed Denial of Service (DDoS) attacks are among the most significant security threats in IoT systems. This paper studies in-depth DDoS attacks in IoT and in SDN. A review of different detection and mitigation techniques based on SDN, blockchain and machine learning models is conducted. A holistic, secure IoT system is proposed on the basis of SDN with multicontrollers. Blockchain is considered to guarantee security in distributed SDN-IoT networks and ANN to improve the detection and mitigation process.

In this paper, we propose a new method for drawing a cryptographically strong substitution box using the Lorenz system and quantum genetic algorithm techniques. We used the chaotic function to generate an initial random sequence of bits and the quantum crossover to provide a new and improved substitution box with increased non-linearity. The aim of the proposed algorithm was to generate a strong and secure substitution box that can be utilized in symmetric cryptosystems. The use of chaotic Boolean functions, genetic algorithm techniques, and the quantum crossover helped achieve this goal, and statistical tests further confirmed the randomness and efficiency of the generated substitution box. The results of the statistical test suite showed that the substitution box produced by the proposed algorithm is a “pass” in terms of randomness and has strong cryptographic properties. The tests include a frequency (monobit) test, a frequency test within a block, a linear complexity test, an approximate entropy test, and a cumulative sums test among others. The p-values obtained in the tests indicate that the randomness of the generated substitution box meets the requirements of a cryptographically secure substitution box.

This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN). The goal of the proposed technique is to detect and classify Asynchronous Motor faults at their early stages, under both normal and abnormal operating conditions. The proposed technique uses a recurrent neural network in two phases to identify and label interturn insulation concerns, with the first phase being utilised to establish whether or not the motors are healthy. In the second step, it discovers and categorises potentially dangerous interturn errors. The SSO approach is used in the second phase of the recurrent neural network learning procedure, with the goal function of minimizing error in mind. The proposed CSSRN technique simplifies the system for detecting and categorizing the interturn insulation issue, resulting in increased system precision. In addition, the proposed model is implemented in the MATLAB/Simulink, where metrics such as accuracy, precision, recall, and specificity may be analysed. Similarly, existing methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), and Salp Swarm Algorithm Artificial Neural Network (SSAANN) are utilised to evaluate metrics such as Root mean squared error (RMSE), Mean bias error (MBE), Mean absolute percentage error (MAPE), consumption, and execution time for comparative analysis.

The traditional single-treatment strategy for cancer is frequently unsuccessful due to the complexity of cellular signaling. However, suppression of multiple targets is vital to defeat tumor cells. In this research, new compounds for the treatment of cancer were developed successfully as novel hybrid anticancer agents. Based on a molecular hybridization strategy, we designed hybrid agents that target multiple protein kinases to fight cancer cells. The proposed hybrid agents combined purine and isatin moieties in their structures with 4-aminobenzohydrazide and hydrazine as different linkers. Having those two moieties in one molecule enabled the capability to inhibit multiple kinases, such as human epidermal receptor (EGFR), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor receptor 2 (VEGFR2) and cyclin-dependent kinase 2 (CDK2). Anticancer activity was evaluated by performing cytotoxicity assays, kinase inhibition assays, cell cycle analysis, and BAX, Bcl-2, Caspase 3 and Caspase 9 protein level determination assays. The results showed that the designed hybrids tackled the cancer by inhibiting both cell proliferation and metastasis. A molecular docking study was performed to predict possible binding interactions in the active site of the investigated protein kinase enzymes.

Thermal plasma has emerged as a technology with tremendous promise for municipal wastes that should be disposed of sustainably. A numerical simulation of a symmetric turbulent plasma jet from a thermal air plasma torch was developed using COMSOL Multiphysics®5.4 engineering simulation software. The velocities, temperature, arc root motion, and joule heating of the plasma jet were examined under the impact of the gas mass flow rate and current. Moreover, the electrical power required for the municipal solid waste (MSW) processing was estimated. The enthalpy and the effectiveness of the plasma torch were analyzed and discussed. Subsequently an investigation was conducted into the gasification characteristics of MSW using air and steam gases. The torch’s power and efficiency could be enhanced with a higher mass flow rate and temperature. Three operating modes were identified from the current–arc flow combination. Among the plasma gas considered, the air gas plasma torch guarantees an acceptable thermal efficiency and a low anode erosion rate. Plasma gasification produces cleaner syngas with higher efficiency (84%) than the conventional process due to the elevated temperature used during the process that breaks down all the char, dioxins, and tars.

In this article, a new version of the generalized q-deformed Sinh–Gordon equation is presented, and analytical solutions are developed for specific parameter sets using those equations. There is a possibility that the new equation can be used to model physical systems that have broken symmetries and include also effects related to amplification or dissipation. In addition, we have include some illustrations that depict the varied patterns of soliton propagation.

Renewable energy is the energy for future generations as it is clean and widely available. The solar absorber is a sustainable energy source that converts solar energy into heat energy. The structural optimization is analyzed to enhance the absorption of the multilayer design. The proposed efficient solar absorber is made of a multilayer plus-shaped resonator supported by a SiO2 substrate with a graphene spacer. The multilayer approach is utilized to enhance the absorption of the overall structure. The absorption of the multilayer solar absorber design is presented with AM 1.5 response observing the amount of energy absorbed from solar radiation. The different structural parameters are optimized to obtain the efficiency plus-shaped absorber design. The results of a different angle of incidence clearly show that the absorber is giving high absorption over a wide-angle range. The design results are also being analyzed with other similar works to show the improvement. The proposed absorber with high efficiency will be a good choice for solar thermal energy conversion applications.

A series of 12 compounds was designed and synthesized, based on 2-mercaptobenzoxazole derivatives containing either the substituted benzenes 4a–d, substituted isatins 5a–f, or heterocycles 6a–b. The in vitro antiproliferative activity of the compounds was evaluated against hepatocellular carcinoma (HepG2), mammary gland cancer (MCF-7), breast cancer (MDA-MB-231), and the epithelioid cervix carcinoma (HeLa) cancer cell lines. Compounds 4b, 4d, 5d, and 6b had the most potent antiproliferative activity, with IC50 values ranging from 2.14 to 19.34 µM, compared to the reference drugs, doxorubicin and sunitinib. Compound 6b revealed a remarkably broad antitumor activity pattern against HepG2 (IC50 6.83 µM), MCF-7 (IC50 3.64 µM), MDA-MB-231 (IC50 2.14 µM), and HeLa (IC50 5.18 µM). In addition, compound 6b showed potent inhibitory activities against EGFR, HER2, VEGFR2, and the CDK2 protein kinase enzymes, with IC50 values of 0.279, 0.224, 0.565, and 0.886 µM, respectively. Moreover, compound 6b induced caspase-dependent apoptosis and cell cycle arrest at the G2/M phase. Finally, a molecular docking simulation was performed for compound 6b to predict the potential ligand–protein interactions with the active sites of the EGFR, HER2, and VEGFR2 proteins.

For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be appropriate for both job seekers and employers. The reason is that the number of produced results for job seekers may be enormous. Therefore, they are required to spend time reading and reviewing their finding criteria. Reciprocally, recruitment is a crucial process for every organization. Identifying potential candidates and matching them with job offers requires a wide range of expertise and knowledge. This article proposes a reciprocal recommendation based on bi-directional correspondence as a way to support both recruiters’ and job seekers’ work. Recruiters can find the best-fit candidates for every job position in their job postings, and job seekers can find the best-match jobs to match their resumes. We show how machine learning can solve problems in natural language processing of text content and similarity scores depending on job offers in major Saudi cities scraped from Indeed. For bi-directional matching, a similarity calculation based on the integration of explicit and implicit job information from two sides (recruiters and job seekers) has been used. The proposed system is evaluated using a resume/job offer dataset. The performance of generated recommendations is evaluated using decision support measures. Obtained results confirm that the proposed system can not only solve the problem of bi-directional recommendation, but also improve the prediction accuracy.

Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases, along with steatosis and non-alcoholic steatohepatitis (NASH), and is associated with cirrhosis and hepatocellular carcinoma. Candidate gene and genome-wide association studies have validated the relationships between NAFLD, NASH, PNPLA3, TM6SF2, and HFE. The present study utilized five polymorphisms in three genes: PNPLA3 (I148M and K434E) TM6SF2 (E167K), and HFE (H63D and C282Y), based on undocumented case–control studies in the Saudi Arabian population. A total of 95 patients with NAFLD and 78 non-NAFLD subjects were recruited. Genomic DNA was isolated, and polymerase chain reaction and Sanger sequencing were performed using specific primers for the I148M, K434E, E167K, H63D, and C282Y. NAFLD subjects were older when compared to controls and showed the significant association (p = 0.0001). Non-significant association was found between gender (p = 0.26). However, both weight and BMI were found to be associated. Hardy–Weinberg equilibrium analysis confirmed that H63D, I148M, and K434E polymorphisms were associated. Genotype analysis showed only K434E variant was associated with NAFLD and non-NAFLD (OR-2.16; 95% CI: 1.08–4.31; p = 0.02). However, other polymorphisms performed with NAFLD and NASH were not associated (p > 0.05), and similar analysis was found when ANOVA was performed (p > 0.05). In conclusion, we confirmed that K434E polymorphism showed a positive association in the Saudi population.

This study investigated the translucency of 3D-printed denture base resins modified with zirconium dioxide nanoparticles (ZrO2NPs) under thermal cycling. A total of 110 specimens were fabricated and divided into 3 groups according to the materials, i.e., heat-polymerized resin, and 3D-printed resins (NextDent, and ASIGA). The 3D-printed resins were modified with 0, 0.5, 1, 3, and 5 wt.% of ZrO2NPs. All the specimens were subjected to 5000 thermal cycles. The translucency was measured using a spectrophotometer. The results showed that the heat-polymerized resin had considerably higher translucency than the 3D-printed resins. Compared to the unmodified group, the translucency decreased significantly after adding 5% ZrO2NPs to NextDent and 3% ZrO2NPs to ASIGA resins. The highest translucency was achieved for NextDent by adding 0.5% ZrO2NPs and for ASIGA without any ZrO2NPs. It was found that the average concentration level in ASIGA was significantly higher than that in NextDent. These findings revealed that 3D-printed resins have lower translucency than heat-polymerized acrylic resin, and adding ZrO2NPs at low concentrations did not affect the translucency of the 3D-printed resins. Therefore, in terms of translucency, 3D-printed nanocomposite denture base resins could be considered for clinical applications when ZrO2NPs are added at low concentrations.

Researchers are trying to work out how to make a broadband response metamaterial absorber (MMA). Electromagnetic (EM) waves that can pass through the atmosphere and reach the ground are most commonly used in the visible frequency range. In addition, they are used to detect faults, inspect tapped live-powered components, electrical failures, and thermal leaking hot spots. This research provides a numerical analysis of a compact split ring resonator (SRR) and circular ring resonator (CRR) based metamaterial absorber (MMA) using a three-layer substrate material configuration for wideband visible optical wavelength applications. The proposed metamaterial absorber has an overall unit cell size of 800 nm × 800 nm × 175 nm in both TE and TM mode simulations and it achieved above 80% absorbance in the visible spectrums from 450 nm to 650 nm wavelength. The proposed MA performed a maximum absorptivity of 99.99% at 557 nm. In addition, the steady absorption property has a broad range of oblique incidence angle stability. The polarization conversion ratio (PCR) is evaluated to ensure that the MMA is perfect. Both TM and TE modes can observe polarization insensitivity and wide-angle incidence angle stability with 18° bending effects. Moreover, a structural study using electric and magnetic fields was carried out to better understand the MMA’s absorption properties. The observable novelty of the proposed metamaterial is compact in size compared with reference paper, and it achieves an average absorbance of 91.82% for visible optical wavelength. The proposed MMA also has bendable properties. The proposed MMA validation has been done by two numerical simulation software. The MMA has diverse applications, such as color image, wide-angle stability, substantial absorption, absolute invisible layers, thermal imaging, and magnetic resonance imaging (MRI) applications.

Tooth eruption is defined as the axial movement of the tooth from its site of development in the alveolar bone to its functional position in the oral cavity. A delay in eruption can directly affect the accurate diagnosis, overall treatment planning, and timing of treatment. Thus, Delayed Tooth Eruption can have a significant impact on a patient’s proper health care. This study aimed to estimate the prevalence of delayed eruptions of permanent upper central incisors in Riyadh, Saudi Arabia. A retrospective study assessed panoramic radiographs of children between the ages of 6–10 years old who attended the Dental University Hospital clinics at King Saud University in Riyadh, Saudi Arabia between 2014 and 2020. The radiographs were collected and examined to detect any delayed eruption of the permanent upper central incisors. Panoramic radiographs with delayed erupted permanent upper central incisors were identified by tooth developmental stages and eruption sequence. Of the 745 radiographs, 23 (3.09%) presented at least one delayed erupted permanent upper central incisor of which boys and girls comprised 16 (69.5%) and 7 (30.4%), respectively. Retained primary teeth was the most causative factor of delayed eruption (43.4%), followed by the early loss of primary teeth (21.7%) and insufficient arch space. The present study is the first to assess the prevalence of delayed eruption of the permanent upper central incisors on a Saudi population. Boys were found to have more prevalence of delayed eruption of the permanent upper central incisors compared to girls. Clinicians should be aware of such a condition as early diagnosis of delayed eruption is essential for providing optimal dental care.

Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boards that aggregates postings from many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been implemented, most of them failed to recommend job vacancies that fit properly to the job seekers profiles when dealing with more than one job offer. They consider skills as passive entities associated with the job description, which need to be matched for finding the best job recommendation. This paper provides a recommender system to assist job seekers in finding suitable jobs based on their resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and measuring similarity between the job seeker’s skills and explicit features of job listing using content-based filtering. First-hand information was gathered by scraping jobs description from Indeed from major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job offers were analyzed and job recommendation was made by matching skills from resumes to posted jobs. To quantify recommendation success and error rates, we sought to compare the results of our system to reality using decision support measures.

Human carbonic anhydrase (CA, EC 4.2.1.1) (hCA) isoforms I, II, IX, and XII were investigated for their inhibitory activity with a series of new Schiff’s bases based on quinazoline scaffold 4–27. The hCA I isoform was efficiently inhibited by Schiff’s bases 4–6, 10–19, 22–27 and had an inhibition constant (Ki) value of 52.8–991.7 nM compared with AAZ (Ki, 250 nM). Amongst the quinazoline derivatives, the compounds 2, 3, 4, 10, 11, 16, 18, 24, 26, and 27 were proven to be effective hCA II inhibitors, with Ki values of 10.8–52.6 nM, measuring up to AAZ (Ki, 12 nM). Compounds 2–27 revealed compelling hCA IX inhibitory interest with Ki values of 10.5–99.6 nM, rivaling AAZ (Ki, 25.0 nM). Quinazoline derivatives 3, 10, 11, 13, 15–19, and 24 possessed potent hCA XII inhibitory activities with KI values of 5.4–25.5 nM vs. 5.7 nM of AAZ. Schiff’s bases 7, 8, 9, and 21 represented attractive antitumor hCA IX carbonic anhydrase inhibitors (CAIs) with KI rates (22.0, 34.8, 49.2, and 45.3 nM, respectively). Compounds 5, 7, 8, 9, 14, 18, 19, and 21 showed hCA I inhibitors on hCA IX with a selectivity index of 22.46–107, while derivatives 12, 14, and 18 showed selective hCA I inhibitors on hCA XII with a selectivity profile of 45.04–58.58, in contrast to AAZ (SI, 10.0 and 43.86). Compounds 2, 5, 7–14, 19–23, and 25 showed a selectivity profile for hCA II inhibitors over hCA IX with a selectivity index of 2.02–19.67, whereas derivatives 5, 7, 8, 13, 14, 15, 17, 20, 21, and 22 showed selective hCA II inhibitors on hCA XII with a selectivity profile of 4.84–26.60 balanced to AAZ (SI, 0.48 and 2.10).

We compared various machine learning (ML) methods, such as the K-nearest neighbor (KNN), support vector machine (SVM), and decision tree and deep learning (DL) methods, like the recurrent neural network, convolutional neural network, long short-term memory (LSTM), and gated recurrent unit (GRU), to determine the ones with the highest precision. These algorithms learn from data and are subject to different imprecisions and uncertainties. The uncertainty arises from the bad reading of data and/or inaccurate sensor acquisition. We studied how these methods may be combined in a fusion classifier to improve their performance. The Dempster–Shafer method, which uses the formalism of belief functions characterized by asymmetry to model nonprecise and uncertain data, is used for classifier fusion. Diagnosis in the medical field is an important step for the early detection of diseases. In this study, the fusion classifiers were used to diagnose diabetes with the required accuracy. The results demonstrated that the fusion classifiers outperformed the individual classifiers as well as those obtained in the literature. The combined LSTM and GRU fusion classifiers achieved the highest accuracy rate of 98%.

Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem of privacy for patient data and the security of information still a point to ponder. This research proposed a prototype model to integrate the blockchain and IoMT for providing better analysis of patient health factors. The authors suggested IoMT data to be collected over Edge Computing gateway devices and forward to Cloud Gateway. The three-layered decision making structure ensures the integrity of the data. The further analysis of information collected over sensor-based devices is done in the Cloud IoT Central Hub service. To ensure the secrecy and compliance of the patient data, Smart Contracts are integrated. After the exchange of smart contracts, a block of information is broadcast over the health blockchain. The P2P network makes it viable to retain all health statistics and further processing of information. The paper describes the scenario and experimental setup for a COVID-19 data-set analyzed in the proposed prototype mode. After the collection of information and decision making, the block of data is sent across all peer nodes. Thus, the power of IoMT and blockchain makes it easy for the healthcare worker to diagnose and handle patient data with privacy. The IoMT is integrated with artificial intelligence to enable decision making based on the classification of data. The results are saved as transactions in the blockchain hyperledger. This study demonstrates the prototype model with test data in a testing network with two peer nodes.

Infections caused by multi-drug-resistant bacteria in patients with chronic diseases have been associated with high mortality and morbidity. While few reports have evaluated bacterial infections in multiple chronic disease patients, the focus of the current study was to investigate the prevalence of bacterial infections and the susceptibility profiles of causative strains among various groups of patients suffering from chronic diseases. Microbiological reports of patients suffering from cancer, diabetes mellitus, cardiovascular diseases, kidney diseases, and skin burns were retrospectively collected from a tertiary hospital in Saudi Arabia. Approximately 54.2% of recruited patients were males, and positive urine was the most prevalent specimen associated with kidney disease patients (25%). Escherichia coli isolates were predominant among cardiovascular, kidney, and cancer patients. Staphylococcus aureus was commonly detected in diabetics and those with burns. Although resistance patterns varied based on the type of specimens and underlying diseases, Escherichia coli showed limited resistance to colistin, carbapenems, and tigecycline, while S. aureus demonstrated susceptibility to ciprofloxacin, gentamicin, and rifampin. These observations are crucial for clinicians and policymakers to ensure effective treatment plans and improve outcomes in these patients with comorbidity.

Background and Objectives: Empathy is an important attribute of a healthy doctor–patient relationship. Although multiple studies have assessed empathy in different countries, little is known about its levels among Saudi residents and its association with perceived stress. Objectives: To assess the levels of empathy and to identify if there is an association with stress in general and across the demographic and training characteristics of residents. Materials and Methods: A cross-sectional questionnaire-based study was carried out from December 2020 to March 2021 among residents training at a tertiary academic center in Riyadh, Saudi Arabia. Empathy and perceived stress were measured using the Jefferson Scale of Empathy (JSE) and the Perceived Stress Scale (PSS). Results: A total of 229 residents participated. The mean JSE score was 105.25 ± 15.35. The mean JSE scores were significantly higher among residents training in pediatrics (mean difference (MD) = 17.35, p < 0.001), family medicine (MD = 12.24, p = 0.007), and medical specialties (MD = 11.11, p = 0.012) when compared with surgical specialties and anesthesia. In addition, residents who worked 1–4 on-calls per month had a higher mean JSE score (MD = 11.23, p = 0.028) compared with those who worked 7 or more on-calls. Lastly, no correlation between empathy and perceived stress was detected in the whole sample (r = −0.007, p = 0.913); however, there was a correlation among residents training in medical specialties (r = −0.245, p = 0.025). Conclusion: Residents in our study had empathy levels comparable with Asian but lower than Western residents. We recommend qualitative studies that explore potential factors that might affect empathy among residents and studying the association between empathy and perceived stress among medical residents. Postgraduate curricula should incorporate interventions that foster a more empathetic doctor–patient relationship.

Due to the low mechanical performances of 3D-printed denture base resins, ZrO2 nanoparticles (ZrO2NPs) were incorporated into different 3D-printed resins and their effects on the flexure strength, elastic modulus, impact strength, hardness, and surface roughness were evaluated. A total of 286 specimens were fabricated in dimensions per respective test and divided according to materials into three groups: heat-polymerized as a control group and two 3D-printed resins (NextDent and ASIGA) which were modified with 0.5 wt.%, 1 wt.%, 3 wt.%, and 5 wt.% ZrO2NPs. The flexure strength and elastic modulus, impact strength, hardness, and surface roughness (µm) were measured using the three-point bending test, Charpy’s impact test, Vickers hardness test, and a profilometer, respectively. The data were analyzed by ANOVA and Tukey’s post hoc test (α = 0.05). The results showed that, in comparison to heat-polymerized resin, the unmodified 3D-printed resins showed a significant decrease in all tested properties (p < 0.001) except surface roughness (p = 0.11). In between 3D-printed resins, the addition of ZrO2NPs to 3D-printed resins showed a significant increase in flexure strength, impact strength, and hardness (p < 0.05) while showing no significant differences in surface roughness and elastic modulus (p > 0.05). Our study demonstrated that the unmodified 3D-printed resins showed inferior mechanical behavior when compared with heat-polymerized acrylic resin while the addition of ZrO2NPs improved the properties of 3D-printed resins. Therefore, the introduced 3D-printable nanocomposite denture-base resins are suitable for clinical use.

Neonatal infections including sepsis and urinary tract infections are considered among the leading causes of mortality in neonatal intensive care units (NICU). Thus, use of empiric antibiotics is very important in infected neonates and the success of this practice is mainly reliant on the availability of an up-to-date antibiogram for currently used antibiotic drugs. In this study, we aim to determine the bacteriological profile and antibiotic susceptibility pattern of bacteria isolated from blood or/and urine cultures belonging to patients at the NICU. A total of 54 urine samples were collected in the period between January 2015 and December 2019. Data of infants with positive urine and blood bacterial isolates were gathered retrospectively. The most commonly isolated bacteria from urine observed were K. pneumoniae (44%) and E. coli (39%), while Acinetobacter baumannii (33%) and K. pneumoniae (22%) predominated in neonatal blood samples. The majority of uropathogens and blood isolates exhibited low resistance to imipenem and tigecycline, respectively. These antibiotics would be recommended for future use as empirical treatment in neonates with urinary tract infections and/or sepsis. This investigation highlights the importance of surveillance studies to manage and ensure the effectiveness of treatment plan for critically ill infants.

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.

In this study, a numerical investigation of photonic quasi-periodic Generalized Fibonacci (GF) (m, n) sequences is carried out in the visible spectrum. The transfer matrix method is employed to study the behavior of wave propagation through the photonic structures. Firstly and to highlight the importance of the GF structure, its transmittance spectrum is compared to those of periodic and ordinary Fibonacci structures. It is shown that the GF structure permits one to obtain multi-photonic band gaps (PBGs) separated by several resonance modes. The variation in the parameter m of the GF (m, 1) structure allows for the tuning of the number, the position and the width of these bands. By changing the parameter m, the wavelengths (650, 850, 1300, and 1550 nm) of the plastic and glass optical fibers can be allowed or forbidden to transmit through the structure according to the value of this parameter. In contrast, the variation in the parameter n for GF (1, n) hides all PBGs and only permits the appearance of several Kiessig fringes. The proposed structures can find application as tunable multi-band-stop filters for optical fiber wavelengths.